Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/App.config
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/App.config	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/App.config	(revision 15840)
@@ -0,0 +1,6 @@
+﻿<?xml version="1.0" encoding="utf-8" ?>
+<configuration>
+    <startup> 
+        <supportedRuntime version="v4.0" sku=".NETFramework,Version=v4.6.1" />
+    </startup>
+</configuration>
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/ExpressionClustering.csproj
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/ExpressionClustering.csproj	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/ExpressionClustering.csproj	(revision 15840)
@@ -0,0 +1,82 @@
+﻿<?xml version="1.0" encoding="utf-8"?>
+<Project ToolsVersion="14.0" DefaultTargets="Build" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
+  <Import Project="$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props" Condition="Exists('$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props')" />
+  <PropertyGroup>
+    <Configuration Condition=" '$(Configuration)' == '' ">Debug</Configuration>
+    <Platform Condition=" '$(Platform)' == '' ">AnyCPU</Platform>
+    <ProjectGuid>{956C4ADE-F86D-4DCC-BE2B-BA8C5BE4851B}</ProjectGuid>
+    <OutputType>Exe</OutputType>
+    <AppDesignerFolder>Properties</AppDesignerFolder>
+    <RootNamespace>ExpressionClustering</RootNamespace>
+    <AssemblyName>ExpressionClustering</AssemblyName>
+    <TargetFrameworkVersion>v4.6.1</TargetFrameworkVersion>
+    <FileAlignment>512</FileAlignment>
+    <AutoGenerateBindingRedirects>true</AutoGenerateBindingRedirects>
+  </PropertyGroup>
+  <PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Debug|AnyCPU' ">
+    <PlatformTarget>x64</PlatformTarget>
+    <DebugSymbols>true</DebugSymbols>
+    <DebugType>full</DebugType>
+    <Optimize>false</Optimize>
+    <OutputPath>bin\Debug\</OutputPath>
+    <DefineConstants>DEBUG;TRACE</DefineConstants>
+    <ErrorReport>prompt</ErrorReport>
+    <WarningLevel>4</WarningLevel>
+  </PropertyGroup>
+  <PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Release|AnyCPU' ">
+    <PlatformTarget>AnyCPU</PlatformTarget>
+    <DebugType>pdbonly</DebugType>
+    <Optimize>true</Optimize>
+    <OutputPath>bin\Release\</OutputPath>
+    <DefineConstants>TRACE</DefineConstants>
+    <ErrorReport>prompt</ErrorReport>
+    <WarningLevel>4</WarningLevel>
+  </PropertyGroup>
+  <ItemGroup>
+    <Reference Include="ALGLIB-3.7.0, Version=3.7.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
+      <SpecificVersion>False</SpecificVersion>
+      <HintPath>..\..\..\trunk\bin\ALGLIB-3.7.0.dll</HintPath>
+    </Reference>
+    <Reference Include="HeuristicLab.Common-3.3">
+      <HintPath>..\..\..\trunk\bin\HeuristicLab.Common-3.3.dll</HintPath>
+    </Reference>
+    <Reference Include="HeuristicLab.Problems.DataAnalysis-3.4">
+      <HintPath>..\..\..\trunk\bin\HeuristicLab.Problems.DataAnalysis-3.4.dll</HintPath>
+    </Reference>
+    <Reference Include="System" />
+    <Reference Include="System.Core" />
+    <Reference Include="System.Xml.Linq" />
+    <Reference Include="System.Data.DataSetExtensions" />
+    <Reference Include="Microsoft.CSharp" />
+    <Reference Include="System.Data" />
+    <Reference Include="System.Net.Http" />
+    <Reference Include="System.Xml" />
+  </ItemGroup>
+  <ItemGroup>
+    <Compile Include="Flann.cs" />
+    <Compile Include="Program.cs" />
+    <Compile Include="Properties\AssemblyInfo.cs" />
+  </ItemGroup>
+  <ItemGroup>
+    <None Include="App.config" />
+  </ItemGroup>
+  <ItemGroup>
+    <Content Include="flann-1.7.1.dll">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </Content>
+    <Content Include="msvcp100.dll">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </Content>
+    <Content Include="msvcr100.dll">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </Content>
+  </ItemGroup>
+  <Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />
+  <!-- To modify your build process, add your task inside one of the targets below and uncomment it. 
+       Other similar extension points exist, see Microsoft.Common.targets.
+  <Target Name="BeforeBuild">
+  </Target>
+  <Target Name="AfterBuild">
+  </Target>
+  -->
+</Project>
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/Flann.cs
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/Flann.cs	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/Flann.cs	(revision 15840)
@@ -0,0 +1,199 @@
+﻿using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Runtime.InteropServices;
+using System.Text;
+using System.Threading.Tasks;
+
+namespace ExpressionClustering {
+  // /* file flann_example.c */
+  // # include "flann.h"
+  // # include <stdio.h>
+  // # include <assert.h>
+  //   /* Function that reads a dataset */
+  //   float* read_points(char* filename, int* rows, int* cols);
+  //   int main(int argc, char** argv) {
+  //     int rows, cols;
+  //     int t_rows, t_cols;
+  //     float speedup;
+  //     /* read dataset points from file dataset.dat */
+  //     float* dataset = read_points("dataset.dat", &rows, &cols);
+  //     float* testset = read_points("testset.dat", &t_rows, &t_cols);
+  //     /* points in dataset and testset should have the same dimensionality */
+  //     assert(cols == t_cols);
+  //     /* number of nearest neighbors to search */
+  //     int nn = 3;
+  //     /* allocate memory for the nearest-neighbors indices */
+  //     int* result = (int*)malloc(t_rows * nn * sizeof(int));
+  //     /* allocate memory for the distances */
+  //     float* dists = (float*)malloc(t_rows * nn * sizeof(float));
+  // /* index parameters are stored here */
+  // struct FLANNParameters p = DEFAULT_FLANN_PARAMETERS;
+  // p.algorithm = FLANN_INDEX_AUTOTUNED; /* or FLANN_INDEX_KDTREE, FLANN_INDEX_KMEANS, ... /*
+  // p.target_precision = 0.9; /* want 90% target precision */
+  // /* compute the 3 nearest-neighbors of each point in the testset */
+  // flann_find_nearest_neighbors(dataset, rows, cols, testset, t_rows,
+  // result, dists, nn, &p);
+  // ...
+  // free(dataset);
+  // free(testset);
+  // free(result);
+  // free(dists);
+  // return 0;
+  // }
+
+  public static class Flann {
+
+    public enum flann_algorithm_t {
+      FLANN_INDEX_LINEAR = 0,
+      FLANN_INDEX_KDTREE = 1,
+      FLANN_INDEX_KMEANS = 2,
+      FLANN_INDEX_COMPOSITE = 3,
+      FLANN_INDEX_KDTREE_SINGLE = 3,
+      FLANN_INDEX_SAVED = 254,
+      FLANN_INDEX_AUTOTUNED = 255
+    };
+
+
+    public enum flann_centers_init_t {
+      FLANN_CENTERS_RANDOM = 0,
+      FLANN_CENTERS_GONZALES = 1,
+      FLANN_CENTERS_KMEANSPP = 2
+    };
+
+    public enum flann_log_level_t {
+      FLANN_LOG_NONE = 0,
+      FLANN_LOG_FATAL = 1,
+      FLANN_LOG_ERROR = 2,
+      FLANN_LOG_WARN = 3,
+      FLANN_LOG_INFO = 4
+    };
+
+    public enum flann_distance_t {
+      FLANN_DIST_EUCLIDEAN = 1, // squared euclidean distance
+      FLANN_DIST_MANHATTAN = 2,
+      FLANN_DIST_MINKOWSKI = 3,
+      FLANN_DIST_HIST_INTERSECT = 5,
+      FlANN_DIST_HELLINGER = 6,
+      FLANN_DIST_CHI_SQUARE = 7, // chi-square
+      FLANN_DIST_KULLBACK_LEIBLER = 8, // kullback-leibler divergence
+    };
+
+    public struct FLANNParameters {
+      public flann_algorithm_t algorithm; /* the algorithm to use */
+
+      /* search time parameters */
+      public int checks;                /* how many leafs (features) to check in one search */
+      public float cb_index;            /* cluster boundary index. Used when searching the kmeans tree */
+
+      /*  kdtree index parameters */
+      public int trees;                 /* number of randomized trees to use (for kdtree) */
+
+      /* kmeans index parameters */
+      public int branching;             /* branching factor (for kmeans tree) */
+      public int iterations;            /* max iterations to perform in one kmeans cluetering (kmeans tree) */
+      public flann_centers_init_t centers_init;  /* algorithm used for picking the initial cluster centers for kmeans tree */
+
+      /* autotuned index parameters */
+      public float target_precision;    /* precision desired (used for autotuning, -1 otherwise) */
+      public float build_weight;        /* build tree time weighting factor */
+      public float memory_weight;       /* index memory weigthing factor */
+      public float sample_fraction;     /* what fraction of the dataset to use for autotuning */
+
+      /* other parameters */
+      public flann_log_level_t log_level;    /* determines the verbosity of each flann function */
+      public long random_seed;            /* random seed to use */
+    };
+
+    // struct FLANNParameters DEFAULT_FLANN_PARAMETERS = {
+    // FLANN_INDEX_KDTREE,
+    // 32, 0.2f, 0.0f,
+    // 4, 4,
+    // 32, 11, FLANN_CENTERS_RANDOM,
+    // 0.9f, 0.01f, 0, 0.1f,
+    // FLANN_LOG_NONE, 0
+    // };
+
+
+    public static FLANNParameters DEFAULT_FLANN_PARAMETERS = new FLANNParameters() {
+      algorithm = flann_algorithm_t.FLANN_INDEX_KDTREE,
+      checks = 32,
+      cb_index = 0.2f,
+      trees = 4,
+      branching = 32,
+      iterations = 11,
+      centers_init = flann_centers_init_t.FLANN_CENTERS_RANDOM,
+      target_precision = 0.9f,
+      build_weight = 0.01f,
+      memory_weight = 0,
+      sample_fraction = 0.1f,
+      log_level = flann_log_level_t.FLANN_LOG_NONE,
+      random_seed = 0,
+    };
+
+
+    [DllImport("flann-1.7.1.dll")]
+    public static extern int flann_find_nearest_neighbors(float[] dataset, int rows, int cols, float[] testset, int t_rows, int[] result, float[] dist, int nn, ref FLANNParameters flann_params);
+
+    [DllImport("flann-1.7.1.dll")]
+    public static extern int flann_build_index(float[] dataset, int rows, int cols, ref float speedup, ref FLANNParameters flann_params);
+
+    [DllImport("flann-1.7.1.dll")]
+    public static extern void flann_set_distance_type(flann_distance_t distance_type, int order);
+
+    [DllImport("flann-1.7.1.dll")]
+    public static extern int flann_compute_cluster_centers(float[] dataset, int rows, int cols, int clusters, float[] result, ref FLANNParameters flann_params);
+
+    public static int FindNearestNeighbours(List<double[]> dataset, List<double[]> queryset, out List<int> results, out List<double> distances, int nearestNeighbours = 3) {
+      var _nn = nearestNeighbours;
+      var _tRows = queryset.Count;
+      var _dists = new float[_nn * _tRows];
+      var _result = new int[_nn * _tRows];
+      var _dim = dataset.First().Length;
+      FLANNParameters p = DEFAULT_FLANN_PARAMETERS;
+      p.algorithm = flann_algorithm_t.FLANN_INDEX_AUTOTUNED;
+      p.centers_init = flann_centers_init_t.FLANN_CENTERS_RANDOM;
+      p.target_precision = 0.9f;
+      p.log_level = flann_log_level_t.FLANN_LOG_INFO;
+      // copy training set
+      var _ds = new float[dataset.Count * _dim];
+      var i = 0;
+      for (int d = 0; d < _dim; d++) {
+        foreach (var e in dataset) {
+          _ds[i++] = (float)e[d];
+        }
+      }
+
+      flann_set_distance_type(flann_distance_t.FLANN_DIST_EUCLIDEAN, 0);
+
+      int nClusters = 100;
+      float[] centers = new float[nClusters * _dim];
+      flann_compute_cluster_centers(_ds, rows: dataset.Count, cols: _dim, clusters: nClusters, result: centers, flann_params: ref p);
+
+      float speedup = -1.0f;
+      // _ds must be a rows × cols matrix stored in row-major order (one feature on each row) 
+      var index = flann_build_index(_ds, rows: dataset.Count, cols: _dim, speedup: ref speedup, flann_params: ref p);
+
+      // copy testset
+      var _testset = new float[_tRows * _dim];
+      i = 0;
+      foreach (var e in queryset) {
+        foreach (var ei in e) {
+          _testset[i++] = (float)ei;
+        }
+      }
+      var res =
+        flann_find_nearest_neighbors(
+          _ds, dataset.Count, _dim,
+        _testset, _tRows,
+        _result, _dists, _nn, ref p);
+
+
+      distances = _dists.Select(fi => (double)fi).ToList();
+      results = _result.ToList();
+      return res;
+    }
+
+
+  }
+}
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/Program.cs
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/Program.cs	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/Program.cs	(revision 15840)
@@ -0,0 +1,248 @@
+﻿using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Linq;
+
+namespace ExpressionClustering {
+  class Program {
+    private static readonly string folder = Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory);
+    private static readonly string distinctSentencesFileName = Path.Combine(folder, @"distinctSentences.csv");
+    private static readonly string allSentencesFileName = Path.Combine(folder, "allSentences.csv");
+    private static readonly string outputFileName = Path.Combine(folder, "evaluations.csv.gz");
+    private static readonly string columnDelimiter = ";";
+    private static int N = 100;
+    private static double[] evalBuf = new double[N];
+
+    // pagie-1 (univariate)
+    private static double min = -5.0;
+    private static double max = +5.0;
+    private static double[] xs = Enumerable.Range(1, N).Select(xi => ((double)xi / N) * (max - min) + min).ToArray(); // input
+    private static double[] ys = xs.Select(xi => 1.0 / (1 + Math.Pow(xi, -4))).ToArray(); // target (necessary for scaling and clustering 
+
+
+    public static int MAX_STACK = 20;
+    public static double[][] stack = new double[MAX_STACK][];
+    static Program() {
+      for (int i = 0; i < MAX_STACK; i++)
+        stack[i] = new double[N];
+    }
+
+    // loads symbolic expressions in postfix notation from a stream and identifies clusters of expressions
+    static void Main(string[] args) {
+
+      TestFLANN();
+
+      var hash2Sentences = new Dictionary<string, List<string>>();
+
+
+
+      // read all sentences and determine shortest sentences
+      using (var reader = new StreamReader(allSentencesFileName)) {
+        // read header
+        reader.ReadLine();
+        int nSentences = 0;
+        while (!reader.EndOfStream) {
+          var line = reader.ReadLine();
+          var toks = line.Split(';');
+          var hash = toks[2];
+          List<string> ls;
+          if (!hash2Sentences.TryGetValue(hash, out ls)) {
+            ls = new List<string>(1);
+            hash2Sentences.Add(hash, ls);
+          }
+          ls.Add(toks[1]);
+          nSentences++;
+        }
+
+        Console.WriteLine("{0} {1}", nSentences, hash2Sentences.Count);
+        //Evaluate(toks[1], xs, evalBuf);
+      }
+
+      using (var writer = new StreamWriter(new System.IO.Compression.GZipStream(new FileStream(outputFileName, FileMode.OpenOrCreate), System.IO.Compression.CompressionMode.Compress)))
+        foreach (var kvp in hash2Sentences) {
+          var ls = kvp.Value;
+          var sentence = FindShortest(ls);
+          Evaluate(sentence, xs, evalBuf);
+          Add(writer, sentence, evalBuf);
+        }
+    }
+
+    private static void TestFLANN() {
+      var rand = new Random(1234);
+      var dim = 100;
+      var N = 10000;
+      var dataset = new List<double[]>();
+      for(int i=0;i<N;i++) {
+        var x = new double[dim];
+        for (int j = 0; j < dim; j++) x[j] = rand.NextDouble();
+        dataset.Add(x);
+      }
+      List<int> nnResults;
+      List<double> nnDists;
+      Flann.FindNearestNeighbours(dataset, dataset, out nnResults, out nnDists);
+
+    }
+
+    private static string FindShortest(List<string> ls) {
+      var minElem = ls.First();
+      for (int i = 1; i < ls.Count; i++) {
+        if (ls[i].Length < minElem.Length) minElem = ls[i];
+      }
+      return minElem;
+    }
+
+
+    #region evaluation
+    // linear scaling
+    private static void Scale(double[] evalBuf, double[] ys) {
+      double alpha;
+      double beta;
+      HeuristicLab.Problems.DataAnalysis.OnlineCalculatorError error;
+
+      HeuristicLab.Problems.DataAnalysis.OnlineLinearScalingParameterCalculator.Calculate(evalBuf, ys, out alpha, out beta, out error);
+      if (error != HeuristicLab.Problems.DataAnalysis.OnlineCalculatorError.None) {
+        alpha = 0.0;
+        beta = 1.0;
+        Console.WriteLine("WARNING: error in scaling");
+      }
+
+      for (int i = 0; i < evalBuf.Length; i++) {
+        evalBuf[i] = beta * evalBuf[i] + alpha;
+      }
+
+
+      // 
+      //   var meanE = 0.0;
+      //   var meanE2 = 0.0;
+      //   var meanY = 0.0;
+      //   var meanY2 = 0.0;
+      //   for(int i=0;i<evalBuf.Length;i++) {
+      //     var deltaE = evalBuf[i] - meanE;
+      //     meanE +=  deltaE / (i+1);
+      //     var deltaE2 = evalBuf[i] - meanE;
+      //     meanE2 += deltaE * deltaE2;
+      // 
+      // 
+      //     var deltaY = ys[i] - meanY;
+      //     meanY += deltaY / (i + 1);
+      //     var deltaY2 = ys[i] - meanY;
+      //     meanY2 += deltaY * deltaY2;
+      //     TODO COVARIANCE
+      //     Linear Scaling: b = cov(y,e) / var(e); a = meanY - b*meanE; 
+      //   }
+      // 
+      //   var varE = meanE2 / evalBuf.Length;
+      //   var varY = meanY2 / evalBuf.Length;
+      //   Console.WriteLine("{0} {1} {2} {3}", meanE, evalBuf.Average(), meanY, ys.Average());
+      //   Console.WriteLine("{0} {1}", varE, varY);
+      // 
+      //   var factor = varY / varE;
+      //   for(int i=0;i<evalBuf.Length;i++) {
+      //     evalBuf[i] = (evalBuf[i] - meanE) * factor + meanY;
+      //   }
+    }
+
+    // evaluates postfix expressions (only for a very specific format)
+    private static void Evaluate(string postfixExpr, double[] xs, double[] evalBuf) {
+      int topOfStack = -1;
+      Evaluate(postfixExpr, 0, xs, ref topOfStack);
+      Array.Copy(stack[topOfStack], evalBuf, evalBuf.Length);
+    }
+
+    private static void Evaluate(string postfixExpr, int exprPos, double[] xs, ref int topOfStack) {
+      while (exprPos < postfixExpr.Length) {
+        switch (postfixExpr[exprPos]) {
+          case '+': {
+              exprPos += 2;
+              var a = stack[topOfStack];
+              var b = stack[topOfStack - 1];
+              for (int i = 0; i < N; i++) {
+                a[i] += b[i];
+              }
+              break;
+            }
+          case '*': {
+              exprPos += 2;
+              var a = stack[topOfStack];
+              var b = stack[topOfStack - 1];
+              for (int i = 0; i < N; i++) {
+                a[i] *= b[i];
+              }
+              break;
+            }
+          case 'X': {
+              exprPos += 2;
+              topOfStack++;
+              Array.Copy(xs, stack[topOfStack], N);
+              break;
+            }
+          case 'c': {
+              // cos
+              exprPos += 4;
+              var a = stack[topOfStack];
+              for (int i = 0; i < N; i++) {
+                a[i] = Math.Cos(a[i]);
+              }
+              break;
+            }
+          case 's': {
+              // sin
+              exprPos += 4;
+              var a = stack[topOfStack];
+              for (int i = 0; i < N; i++) {
+                a[i] = Math.Sin(a[i]);
+              }
+              break;
+            }
+          case 'l': {
+              // log
+              exprPos += 4;
+              var a = stack[topOfStack];
+              for (int i = 0; i < N; i++) {
+                a[i] = Math.Log(a[i]);
+              }
+
+              break;
+            }
+          case 'e': {
+              // exp
+              exprPos += 4;
+              var a = stack[topOfStack];
+              for (int i = 0; i < N; i++) {
+                a[i] = Math.Exp(a[i]);
+              }
+
+              break;
+            }
+          case 'i': {
+              // inv
+              exprPos += 4;
+              var a = stack[topOfStack];
+              for (int i = 0; i < N; i++) {
+                a[i] = 1.0 / a[i];
+              }
+              break;
+            }
+          default: {
+              throw new InvalidOperationException(string.Format("Cannot handle {0} in {1}", postfixExpr[exprPos], postfixExpr));
+            }
+        }
+      }
+    }
+    #endregion
+
+
+
+    // add the line with it's evaluation result to a data structure for clustering
+    private static void Add(StreamWriter writer, string line, double[] evalBuf) {
+      var avg = evalBuf.Average();
+      if (double.IsNaN(avg) || double.IsInfinity(avg)) {
+        Console.WriteLine("skipping {0} {1}", evalBuf.Average(), line);
+      } else {
+        Scale(evalBuf, ys);
+
+        writer.WriteLine(string.Join("\t", evalBuf.Select(ei => ei.ToString(System.Globalization.CultureInfo.InvariantCulture))));
+      }
+    }
+  }
+}
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/Properties/AssemblyInfo.cs
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/Properties/AssemblyInfo.cs	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/Properties/AssemblyInfo.cs	(revision 15840)
@@ -0,0 +1,36 @@
+﻿using System.Reflection;
+using System.Runtime.CompilerServices;
+using System.Runtime.InteropServices;
+
+// General Information about an assembly is controlled through the following 
+// set of attributes. Change these attribute values to modify the information
+// associated with an assembly.
+[assembly: AssemblyTitle("ExpressionClustering")]
+[assembly: AssemblyDescription("")]
+[assembly: AssemblyConfiguration("")]
+[assembly: AssemblyCompany("HP Inc.")]
+[assembly: AssemblyProduct("ExpressionClustering")]
+[assembly: AssemblyCopyright("Copyright © HP Inc. 2018")]
+[assembly: AssemblyTrademark("")]
+[assembly: AssemblyCulture("")]
+
+// Setting ComVisible to false makes the types in this assembly not visible 
+// to COM components.  If you need to access a type in this assembly from 
+// COM, set the ComVisible attribute to true on that type.
+[assembly: ComVisible(false)]
+
+// The following GUID is for the ID of the typelib if this project is exposed to COM
+[assembly: Guid("956c4ade-f86d-4dcc-be2b-ba8c5be4851b")]
+
+// Version information for an assembly consists of the following four values:
+//
+//      Major Version
+//      Minor Version 
+//      Build Number
+//      Revision
+//
+// You can specify all the values or you can default the Build and Revision Numbers 
+// by using the '*' as shown below:
+// [assembly: AssemblyVersion("1.0.*")]
+[assembly: AssemblyVersion("1.0.0.0")]
+[assembly: AssemblyFileVersion("1.0.0.0")]
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/README.txt
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/README.txt	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/README.txt	(revision 15840)
@@ -0,0 +1,15 @@
+Goal is to cluster 10 Mio. functions with ~ 100 samples.
+
+Hierarchical clustering (agglomative clustering) seems useful.
+
+Approximate hierarchical clustering methods:
+  - Happieclust: terminates with runtime exceptions
+  - Twistertries: need to implement a small Java program to test this.
+
+
+We could implement our own clustering if we have a fast method for finding nearest neighbours.
+Approximate nearest neighbours:
+ - Benchmarks with many current techniques: https://github.com/erikbern/ann-benchmarks
+ - Annoy (Spotify) https://github.com/spotify/annoy
+ - https://github.com/FALCONN-LIB/FALCONN
+ - Fastest in benchmarks: https://github.com/searchivarius/nmslib
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/all_indices.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/all_indices.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/all_indices.h	(revision 15840)
@@ -0,0 +1,180 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+
+#ifndef FLANN_ALL_INDICES_H_
+#define FLANN_ALL_INDICES_H_
+
+#include "flann/general.h"
+
+#include "flann/algorithms/nn_index.h"
+#include "flann/algorithms/kdtree_index.h"
+#include "flann/algorithms/kdtree_single_index.h"
+#include "flann/algorithms/kdtree_cuda_3d_index.h"
+#include "flann/algorithms/kmeans_index.h"
+#include "flann/algorithms/composite_index.h"
+#include "flann/algorithms/linear_index.h"
+#include "flann/algorithms/hierarchical_clustering_index.h"
+#include "flann/algorithms/lsh_index.h"
+#include "flann/algorithms/autotuned_index.h"
+
+
+namespace flann
+{
+
+
+/**
+ * enable_if sfinae helper
+ */
+template<bool, typename T = void> struct enable_if{};
+template<typename T> struct enable_if<true,T> { typedef T type; };
+
+/**
+ * disable_if sfinae helper
+ */
+template<bool, typename T> struct disable_if{ typedef T type; };
+template<typename T> struct disable_if<true,T> { };
+
+/**
+ * Check if two type are the same
+ */
+template <typename T, typename U>
+struct same_type
+{
+    enum {value = false};
+};
+
+template<typename T>
+struct same_type<T,T>
+{
+    enum {value = true};
+};
+
+
+/**
+ * Checks if an index and a distance can be used together
+ */
+template<template <typename> class Index, typename Distance, typename ElementType>
+struct valid_combination
+{
+
+#define HAS_MEMBER(member) \
+    template<typename T> \
+    struct member { \
+        typedef char No; \
+        typedef long Yes; \
+        template<typename C> static Yes test( typename C::member* ); \
+        template<typename C> static No test( ... ); \
+        enum { value = sizeof (test<T>(0))==sizeof(Yes) }; \
+    };
+
+	HAS_MEMBER(needs_kdtree_distance)
+	HAS_MEMBER(needs_vector_space_distance)
+	HAS_MEMBER(is_kdtree_distance)
+	HAS_MEMBER(is_vector_space_distance)
+
+    static const bool value = same_type<ElementType,typename Distance::ElementType>::value &&
+    				(!needs_kdtree_distance<Index<Distance> >::value || is_kdtree_distance<Distance>::value) &&
+    				(!needs_vector_space_distance<Index<Distance> >::value || is_kdtree_distance<Distance>::value || is_vector_space_distance<Distance>::value);
+
+};
+
+
+/*********************************************************
+ * Create index
+ **********************************************************/
+template <template<typename> class Index, typename Distance, typename T>
+NNIndex<Distance>* create_index_(flann::Matrix<T> data, const flann::IndexParams& params, const Distance& distance,
+		typename enable_if<valid_combination<Index,Distance,T>::value,void>::type* = 0)
+{
+    return new Index<Distance>(data, params,distance);
+}
+
+template <template<typename> class Index, typename Distance, typename T>
+NNIndex<Distance>* create_index_(flann::Matrix<T> data, const flann::IndexParams& params, const Distance& distance,
+		typename disable_if<valid_combination<Index,Distance,T>::value,void>::type* = 0)
+{
+    return NULL;
+}
+
+
+template<typename Distance>
+NNIndex<Distance>* create_index_by_type(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance)
+{
+	typedef typename Distance::ElementType ElementType;
+	flann_algorithm_t index_type = get_param<flann_algorithm_t>(params, "algorithm");
+
+    NNIndex<Distance>* nnIndex;
+    switch (index_type) {
+    case FLANN_INDEX_LINEAR:
+        nnIndex = create_index_<LinearIndex,Distance,ElementType>(dataset, params, distance);
+        break;
+    case FLANN_INDEX_KDTREE_SINGLE:
+        nnIndex = create_index_<KDTreeSingleIndex,Distance,ElementType>(dataset, params, distance);
+        break;
+    case FLANN_INDEX_KDTREE:
+        nnIndex = create_index_<KDTreeIndex,Distance,ElementType>(dataset, params, distance);
+        break;
+		//! #define this symbol before including flann.h to enable GPU search algorithms. But you have
+		//! to link libflann_cuda then!
+	#ifdef FLANN_USE_CUDA
+	case FLANN_INDEX_KDTREE_CUDA:
+        nnIndex = create_index_<KDTreeCuda3dIndex,Distance,ElementType>(dataset, params, distance);
+        break;
+	#endif
+
+    case FLANN_INDEX_KMEANS:
+        nnIndex = create_index_<KMeansIndex,Distance,ElementType>(dataset, params, distance);
+        break;
+    case FLANN_INDEX_COMPOSITE:
+        nnIndex = create_index_<CompositeIndex,Distance,ElementType>(dataset, params, distance);
+        break;
+    case FLANN_INDEX_AUTOTUNED:
+        nnIndex = create_index_<AutotunedIndex,Distance,ElementType>(dataset, params, distance);
+        break;
+    case FLANN_INDEX_HIERARCHICAL:
+        nnIndex = create_index_<HierarchicalClusteringIndex,Distance,ElementType>(dataset, params, distance);
+        break;
+    case FLANN_INDEX_LSH:
+        nnIndex = create_index_<LshIndex,Distance,ElementType>(dataset, params, distance);
+        break;
+    default:
+        throw FLANNException("Unknown index type");
+    }
+
+    if (nnIndex==NULL) {
+    	throw FLANNException("Invalid index/distance combination");
+    }
+    return nnIndex;
+
+
+}
+
+}
+
+#endif /* FLANN_ALL_INDICES_H_ */
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/autotuned_index.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/autotuned_index.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/autotuned_index.h	(revision 15840)
@@ -0,0 +1,582 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+#ifndef FLANN_AUTOTUNED_INDEX_H_
+#define FLANN_AUTOTUNED_INDEX_H_
+
+#include "flann/general.h"
+#include "flann/algorithms/nn_index.h"
+#include "flann/nn/ground_truth.h"
+#include "flann/nn/index_testing.h"
+#include "flann/util/sampling.h"
+#include "flann/algorithms/kdtree_index.h"
+#include "flann/algorithms/kdtree_single_index.h"
+#include "flann/algorithms/kmeans_index.h"
+#include "flann/algorithms/composite_index.h"
+#include "flann/algorithms/linear_index.h"
+#include "flann/util/logger.h"
+
+namespace flann
+{
+
+template<typename Distance>
+NNIndex<Distance>* create_index_by_type(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance);
+
+
+struct AutotunedIndexParams : public IndexParams
+{
+    AutotunedIndexParams(float target_precision = 0.8, float build_weight = 0.01, float memory_weight = 0, float sample_fraction = 0.1)
+    {
+        (*this)["algorithm"] = FLANN_INDEX_AUTOTUNED;
+        // precision desired (used for autotuning, -1 otherwise)
+        (*this)["target_precision"] = target_precision;
+        // build tree time weighting factor
+        (*this)["build_weight"] = build_weight;
+        // index memory weighting factor
+        (*this)["memory_weight"] = memory_weight;
+        // what fraction of the dataset to use for autotuning
+        (*this)["sample_fraction"] = sample_fraction;
+    }
+};
+
+
+template <typename Distance>
+class AutotunedIndex : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+    typedef bool needs_kdtree_distance;
+
+    AutotunedIndex(const Matrix<ElementType>& inputData, const IndexParams& params = AutotunedIndexParams(), Distance d = Distance()) :
+        dataset_(inputData), distance_(d)
+    {
+        target_precision_ = get_param(params, "target_precision",0.8f);
+        build_weight_ =  get_param(params,"build_weight", 0.01f);
+        memory_weight_ = get_param(params, "memory_weight", 0.0f);
+        sample_fraction_ = get_param(params,"sample_fraction", 0.1f);
+        bestIndex_ = NULL;
+    }
+
+    AutotunedIndex(const AutotunedIndex&);
+    AutotunedIndex& operator=(const AutotunedIndex&);
+
+    virtual ~AutotunedIndex()
+    {
+        if (bestIndex_ != NULL) {
+            delete bestIndex_;
+            bestIndex_ = NULL;
+        }
+    }
+
+    /**
+     *          Method responsible with building the index.
+     */
+    virtual void buildIndex()
+    {
+        bestParams_ = estimateBuildParams();
+        Logger::info("----------------------------------------------------\n");
+        Logger::info("Autotuned parameters:\n");
+        print_params(bestParams_);
+        Logger::info("----------------------------------------------------\n");
+
+        bestIndex_ = create_index_by_type(dataset_, bestParams_, distance_);
+        bestIndex_->buildIndex();
+        speedup_ = estimateSearchParams(bestSearchParams_);
+        Logger::info("----------------------------------------------------\n");
+        Logger::info("Search parameters:\n");
+        print_params(bestSearchParams_);
+        Logger::info("----------------------------------------------------\n");
+    }
+
+    /**
+     *  Saves the index to a stream
+     */
+    virtual void saveIndex(FILE* stream)
+    {
+        save_value(stream, (int)bestIndex_->getType());
+        bestIndex_->saveIndex(stream);
+        save_value(stream, bestSearchParams_.checks);
+    }
+
+    /**
+     *  Loads the index from a stream
+     */
+    virtual void loadIndex(FILE* stream)
+    {
+        int index_type;
+
+        load_value(stream, index_type);
+        IndexParams params;
+        params["algorithm"] = (flann_algorithm_t)index_type;
+        bestIndex_ = create_index_by_type<Distance>(dataset_, params, distance_);
+        bestIndex_->loadIndex(stream);
+        load_value(stream, bestSearchParams_.checks);
+    }
+
+    /**
+     *      Method that searches for nearest-neighbors
+     */
+    virtual void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
+    {
+        if (searchParams.checks == FLANN_CHECKS_AUTOTUNED) {
+            bestIndex_->findNeighbors(result, vec, bestSearchParams_);
+        }
+        else {
+            bestIndex_->findNeighbors(result, vec, searchParams);
+        }
+    }
+
+
+    IndexParams getParameters() const
+    {
+        return bestIndex_->getParameters();
+    }
+
+    SearchParams getSearchParameters() const
+    {
+        return bestSearchParams_;
+    }
+
+    float getSpeedup() const
+    {
+        return speedup_;
+    }
+
+
+    /**
+     *      Number of features in this index.
+     */
+    virtual size_t size() const
+    {
+        return bestIndex_->size();
+    }
+
+    /**
+     *  The length of each vector in this index.
+     */
+    virtual size_t veclen() const
+    {
+        return bestIndex_->veclen();
+    }
+
+    /**
+     * The amount of memory (in bytes) this index uses.
+     */
+    virtual int usedMemory() const
+    {
+        return bestIndex_->usedMemory();
+    }
+
+    /**
+     * Algorithm name
+     */
+    virtual flann_algorithm_t getType() const
+    {
+        return FLANN_INDEX_AUTOTUNED;
+    }
+
+private:
+
+    struct CostData
+    {
+        float searchTimeCost;
+        float buildTimeCost;
+        float memoryCost;
+        float totalCost;
+        IndexParams params;
+    };
+
+    void evaluate_kmeans(CostData& cost)
+    {
+        StartStopTimer t;
+        int checks;
+        const int nn = 1;
+
+        Logger::info("KMeansTree using params: max_iterations=%d, branching=%d\n",
+                     get_param<int>(cost.params,"iterations"),
+                     get_param<int>(cost.params,"branching"));
+        KMeansIndex<Distance> kmeans(sampledDataset_, cost.params, distance_);
+        // measure index build time
+        t.start();
+        kmeans.buildIndex();
+        t.stop();
+        float buildTime = (float)t.value;
+
+        // measure search time
+        float searchTime = test_index_precision(kmeans, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn);
+
+        float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float));
+        cost.memoryCost = (kmeans.usedMemory() + datasetMemory) / datasetMemory;
+        cost.searchTimeCost = searchTime;
+        cost.buildTimeCost = buildTime;
+        Logger::info("KMeansTree buildTime=%g, searchTime=%g, build_weight=%g\n", buildTime, searchTime, build_weight_);
+    }
+
+
+    void evaluate_kdtree(CostData& cost)
+    {
+        StartStopTimer t;
+        int checks;
+        const int nn = 1;
+
+        Logger::info("KDTree using params: trees=%d\n", get_param<int>(cost.params,"trees"));
+        KDTreeIndex<Distance> kdtree(sampledDataset_, cost.params, distance_);
+
+        t.start();
+        kdtree.buildIndex();
+        t.stop();
+        float buildTime = (float)t.value;
+
+        //measure search time
+        float searchTime = test_index_precision(kdtree, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn);
+
+        float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float));
+        cost.memoryCost = (kdtree.usedMemory() + datasetMemory) / datasetMemory;
+        cost.searchTimeCost = searchTime;
+        cost.buildTimeCost = buildTime;
+        Logger::info("KDTree buildTime=%g, searchTime=%g\n", buildTime, searchTime);
+    }
+
+
+    //    struct KMeansSimpleDownhillFunctor {
+    //
+    //        Autotune& autotuner;
+    //        KMeansSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {};
+    //
+    //        float operator()(int* params) {
+    //
+    //            float maxFloat = numeric_limits<float>::max();
+    //
+    //            if (params[0]<2) return maxFloat;
+    //            if (params[1]<0) return maxFloat;
+    //
+    //            CostData c;
+    //            c.params["algorithm"] = KMEANS;
+    //            c.params["centers-init"] = CENTERS_RANDOM;
+    //            c.params["branching"] = params[0];
+    //            c.params["max-iterations"] = params[1];
+    //
+    //            autotuner.evaluate_kmeans(c);
+    //
+    //            return c.timeCost;
+    //
+    //        }
+    //    };
+    //
+    //    struct KDTreeSimpleDownhillFunctor {
+    //
+    //        Autotune& autotuner;
+    //        KDTreeSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {};
+    //
+    //        float operator()(int* params) {
+    //            float maxFloat = numeric_limits<float>::max();
+    //
+    //            if (params[0]<1) return maxFloat;
+    //
+    //            CostData c;
+    //            c.params["algorithm"] = KDTREE;
+    //            c.params["trees"] = params[0];
+    //
+    //            autotuner.evaluate_kdtree(c);
+    //
+    //            return c.timeCost;
+    //
+    //        }
+    //    };
+
+
+
+    void optimizeKMeans(std::vector<CostData>& costs)
+    {
+        Logger::info("KMEANS, Step 1: Exploring parameter space\n");
+
+        // explore kmeans parameters space using combinations of the parameters below
+        int maxIterations[] = { 1, 5, 10, 15 };
+        int branchingFactors[] = { 16, 32, 64, 128, 256 };
+
+        int kmeansParamSpaceSize = FLANN_ARRAY_LEN(maxIterations) * FLANN_ARRAY_LEN(branchingFactors);
+        costs.reserve(costs.size() + kmeansParamSpaceSize);
+
+        // evaluate kmeans for all parameter combinations
+        for (size_t i = 0; i < FLANN_ARRAY_LEN(maxIterations); ++i) {
+            for (size_t j = 0; j < FLANN_ARRAY_LEN(branchingFactors); ++j) {
+                CostData cost;
+                cost.params["algorithm"] = FLANN_INDEX_KMEANS;
+                cost.params["centers_init"] = FLANN_CENTERS_RANDOM;
+                cost.params["iterations"] = maxIterations[i];
+                cost.params["branching"] = branchingFactors[j];
+
+                evaluate_kmeans(cost);
+                costs.push_back(cost);
+            }
+        }
+
+        //         Logger::info("KMEANS, Step 2: simplex-downhill optimization\n");
+        //
+        //         const int n = 2;
+        //         // choose initial simplex points as the best parameters so far
+        //         int kmeansNMPoints[n*(n+1)];
+        //         float kmeansVals[n+1];
+        //         for (int i=0;i<n+1;++i) {
+        //             kmeansNMPoints[i*n] = (int)kmeansCosts[i].params["branching"];
+        //             kmeansNMPoints[i*n+1] = (int)kmeansCosts[i].params["max-iterations"];
+        //             kmeansVals[i] = kmeansCosts[i].timeCost;
+        //         }
+        //         KMeansSimpleDownhillFunctor kmeans_cost_func(*this);
+        //         // run optimization
+        //         optimizeSimplexDownhill(kmeansNMPoints,n,kmeans_cost_func,kmeansVals);
+        //         // store results
+        //         for (int i=0;i<n+1;++i) {
+        //             kmeansCosts[i].params["branching"] = kmeansNMPoints[i*2];
+        //             kmeansCosts[i].params["max-iterations"] = kmeansNMPoints[i*2+1];
+        //             kmeansCosts[i].timeCost = kmeansVals[i];
+        //         }
+    }
+
+
+    void optimizeKDTree(std::vector<CostData>& costs)
+    {
+        Logger::info("KD-TREE, Step 1: Exploring parameter space\n");
+
+        // explore kd-tree parameters space using the parameters below
+        int testTrees[] = { 1, 4, 8, 16, 32 };
+
+        // evaluate kdtree for all parameter combinations
+        for (size_t i = 0; i < FLANN_ARRAY_LEN(testTrees); ++i) {
+            CostData cost;
+            cost.params["trees"] = testTrees[i];
+
+            evaluate_kdtree(cost);
+            costs.push_back(cost);
+        }
+
+        //         Logger::info("KD-TREE, Step 2: simplex-downhill optimization\n");
+        //
+        //         const int n = 1;
+        //         // choose initial simplex points as the best parameters so far
+        //         int kdtreeNMPoints[n*(n+1)];
+        //         float kdtreeVals[n+1];
+        //         for (int i=0;i<n+1;++i) {
+        //             kdtreeNMPoints[i] = (int)kdtreeCosts[i].params["trees"];
+        //             kdtreeVals[i] = kdtreeCosts[i].timeCost;
+        //         }
+        //         KDTreeSimpleDownhillFunctor kdtree_cost_func(*this);
+        //         // run optimization
+        //         optimizeSimplexDownhill(kdtreeNMPoints,n,kdtree_cost_func,kdtreeVals);
+        //         // store results
+        //         for (int i=0;i<n+1;++i) {
+        //             kdtreeCosts[i].params["trees"] = kdtreeNMPoints[i];
+        //             kdtreeCosts[i].timeCost = kdtreeVals[i];
+        //         }
+    }
+
+    /**
+     *  Chooses the best nearest-neighbor algorithm and estimates the optimal
+     *  parameters to use when building the index (for a given precision).
+     *  Returns a dictionary with the optimal parameters.
+     */
+    IndexParams estimateBuildParams()
+    {
+        std::vector<CostData> costs;
+
+        int sampleSize = int(sample_fraction_ * dataset_.rows);
+        int testSampleSize = std::min(sampleSize / 10, 1000);
+
+        Logger::info("Entering autotuning, dataset size: %d, sampleSize: %d, testSampleSize: %d, target precision: %g\n", dataset_.rows, sampleSize, testSampleSize, target_precision_);
+
+        // For a very small dataset, it makes no sense to build any fancy index, just
+        // use linear search
+        if (testSampleSize < 10) {
+            Logger::info("Choosing linear, dataset too small\n");
+            return LinearIndexParams();
+        }
+
+        // We use a fraction of the original dataset to speedup the autotune algorithm
+        sampledDataset_ = random_sample(dataset_, sampleSize);
+        // We use a cross-validation approach, first we sample a testset from the dataset
+        testDataset_ = random_sample(sampledDataset_, testSampleSize, true);
+
+        // We compute the ground truth using linear search
+        Logger::info("Computing ground truth... \n");
+        gt_matches_ = Matrix<int>(new int[testDataset_.rows], testDataset_.rows, 1);
+        StartStopTimer t;
+        t.start();
+        compute_ground_truth<Distance>(sampledDataset_, testDataset_, gt_matches_, 0, distance_);
+        t.stop();
+
+        CostData linear_cost;
+        linear_cost.searchTimeCost = (float)t.value;
+        linear_cost.buildTimeCost = 0;
+        linear_cost.memoryCost = 0;
+        linear_cost.params["algorithm"] = FLANN_INDEX_LINEAR;
+
+        costs.push_back(linear_cost);
+
+        // Start parameter autotune process
+        Logger::info("Autotuning parameters...\n");
+
+        optimizeKMeans(costs);
+        optimizeKDTree(costs);
+
+        float bestTimeCost = costs[0].searchTimeCost;
+        for (size_t i = 0; i < costs.size(); ++i) {
+            float timeCost = costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost;
+            if (timeCost < bestTimeCost) {
+                bestTimeCost = timeCost;
+            }
+        }
+
+        float bestCost = costs[0].searchTimeCost / bestTimeCost;
+        IndexParams bestParams = costs[0].params;
+        if (bestTimeCost > 0) {
+            for (size_t i = 0; i < costs.size(); ++i) {
+                float crtCost = (costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost) / bestTimeCost +
+                                memory_weight_ * costs[i].memoryCost;
+                if (crtCost < bestCost) {
+                    bestCost = crtCost;
+                    bestParams = costs[i].params;
+                }
+            }
+        }
+
+        delete[] gt_matches_.ptr();
+        delete[] testDataset_.ptr();
+        delete[] sampledDataset_.ptr();
+
+        return bestParams;
+    }
+
+
+
+    /**
+     *  Estimates the search time parameters needed to get the desired precision.
+     *  Precondition: the index is built
+     *  Postcondition: the searchParams will have the optimum params set, also the speedup obtained over linear search.
+     */
+    float estimateSearchParams(SearchParams& searchParams)
+    {
+        const int nn = 1;
+        const size_t SAMPLE_COUNT = 1000;
+
+        assert(bestIndex_ != NULL); // must have a valid index
+
+        float speedup = 0;
+
+        int samples = (int)std::min(dataset_.rows / 10, SAMPLE_COUNT);
+        if (samples > 0) {
+            Matrix<ElementType> testDataset = random_sample(dataset_, samples);
+
+            Logger::info("Computing ground truth\n");
+
+            // we need to compute the ground truth first
+            Matrix<int> gt_matches(new int[testDataset.rows], testDataset.rows, 1);
+            StartStopTimer t;
+            t.start();
+            compute_ground_truth<Distance>(dataset_, testDataset, gt_matches, 1, distance_);
+            t.stop();
+            float linear = (float)t.value;
+
+            int checks;
+            Logger::info("Estimating number of checks\n");
+
+            float searchTime;
+            float cb_index;
+            if (bestIndex_->getType() == FLANN_INDEX_KMEANS) {
+                Logger::info("KMeans algorithm, estimating cluster border factor\n");
+                KMeansIndex<Distance>* kmeans = (KMeansIndex<Distance>*)bestIndex_;
+                float bestSearchTime = -1;
+                float best_cb_index = -1;
+                int best_checks = -1;
+                for (cb_index = 0; cb_index < 1.1f; cb_index += 0.2f) {
+                    kmeans->set_cb_index(cb_index);
+                    searchTime = test_index_precision(*kmeans, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1);
+                    if ((searchTime < bestSearchTime) || (bestSearchTime == -1)) {
+                        bestSearchTime = searchTime;
+                        best_cb_index = cb_index;
+                        best_checks = checks;
+                    }
+                }
+                searchTime = bestSearchTime;
+                cb_index = best_cb_index;
+                checks = best_checks;
+
+                kmeans->set_cb_index(best_cb_index);
+                Logger::info("Optimum cb_index: %g\n", cb_index);
+                bestParams_["cb_index"] = cb_index;
+            }
+            else {
+                searchTime = test_index_precision(*bestIndex_, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1);
+            }
+
+            Logger::info("Required number of checks: %d \n", checks);
+            searchParams.checks = checks;
+
+            speedup = linear / searchTime;
+
+            delete[] gt_matches.ptr();
+            delete[] testDataset.ptr();
+        }
+
+        return speedup;
+    }
+
+private:
+    NNIndex<Distance>* bestIndex_;
+
+    IndexParams bestParams_;
+    SearchParams bestSearchParams_;
+
+    Matrix<ElementType> sampledDataset_;
+    Matrix<ElementType> testDataset_;
+    Matrix<int> gt_matches_;
+
+    float speedup_;
+
+    /**
+     * The dataset used by this index
+     */
+    const Matrix<ElementType> dataset_;
+
+    /**
+     * Index parameters
+     */
+    float target_precision_;
+    float build_weight_;
+    float memory_weight_;
+    float sample_fraction_;
+
+    Distance distance_;
+
+
+};
+}
+
+#endif /* FLANN_AUTOTUNED_INDEX_H_ */
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/composite_index.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/composite_index.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/composite_index.h	(revision 15840)
@@ -0,0 +1,196 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_COMPOSITE_INDEX_H_
+#define FLANN_COMPOSITE_INDEX_H_
+
+#include "flann/general.h"
+#include "flann/algorithms/nn_index.h"
+#include "flann/algorithms/kdtree_index.h"
+#include "flann/algorithms/kmeans_index.h"
+
+namespace flann
+{
+
+/**
+ * Index parameters for the CompositeIndex.
+ */
+struct CompositeIndexParams : public IndexParams
+{
+    CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11,
+                         flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 )
+    {
+        (*this)["algorithm"] = FLANN_INDEX_KMEANS;
+        // number of randomized trees to use (for kdtree)
+        (*this)["trees"] = trees;
+        // branching factor
+        (*this)["branching"] = branching;
+        // max iterations to perform in one kmeans clustering (kmeans tree)
+        (*this)["iterations"] = iterations;
+        // algorithm used for picking the initial cluster centers for kmeans tree
+        (*this)["centers_init"] = centers_init;
+        // cluster boundary index. Used when searching the kmeans tree
+        (*this)["cb_index"] = cb_index;
+    }
+};
+
+
+/**
+ * This index builds a kd-tree index and a k-means index and performs nearest
+ * neighbour search both indexes. This gives a slight boost in search performance
+ * as some of the neighbours that are missed by one index are found by the other.
+ */
+template <typename Distance>
+class CompositeIndex : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+    typedef bool needs_kdtree_distance;
+
+    /**
+     * Index constructor
+     * @param inputData dataset containing the points to index
+     * @param params Index parameters
+     * @param d Distance functor
+     * @return
+     */
+    CompositeIndex(const Matrix<ElementType>& inputData, const IndexParams& params = CompositeIndexParams(),
+                   Distance d = Distance()) : index_params_(params)
+    {
+        kdtree_index_ = new KDTreeIndex<Distance>(inputData, params, d);
+        kmeans_index_ = new KMeansIndex<Distance>(inputData, params, d);
+
+    }
+
+    CompositeIndex(const CompositeIndex&);
+    CompositeIndex& operator=(const CompositeIndex&);
+
+    virtual ~CompositeIndex()
+    {
+        delete kdtree_index_;
+        delete kmeans_index_;
+    }
+
+    /**
+     * @return The index type
+     */
+    flann_algorithm_t getType() const
+    {
+        return FLANN_INDEX_COMPOSITE;
+    }
+
+    /**
+     * @return Size of the index
+     */
+    size_t size() const
+    {
+        return kdtree_index_->size();
+    }
+
+    /**
+     * \returns The dimensionality of the features in this index.
+     */
+    size_t veclen() const
+    {
+        return kdtree_index_->veclen();
+    }
+
+    /**
+     * \returns The amount of memory (in bytes) used by the index.
+     */
+    int usedMemory() const
+    {
+        return kmeans_index_->usedMemory() + kdtree_index_->usedMemory();
+    }
+
+    /**
+     * \brief Builds the index
+     */
+    void buildIndex()
+    {
+        Logger::info("Building kmeans tree...\n");
+        kmeans_index_->buildIndex();
+        Logger::info("Building kdtree tree...\n");
+        kdtree_index_->buildIndex();
+    }
+
+    /**
+     * \brief Saves the index to a stream
+     * \param stream The stream to save the index to
+     */
+    void saveIndex(FILE* stream)
+    {
+        kmeans_index_->saveIndex(stream);
+        kdtree_index_->saveIndex(stream);
+    }
+
+    /**
+     * \brief Loads the index from a stream
+     * \param stream The stream from which the index is loaded
+     */
+    void loadIndex(FILE* stream)
+    {
+        kmeans_index_->loadIndex(stream);
+        kdtree_index_->loadIndex(stream);
+    }
+
+    /**
+     * \returns The index parameters
+     */
+    IndexParams getParameters() const
+    {
+        return index_params_;
+    }
+
+    /**
+     * \brief Method that searches for nearest-neighbours
+     */
+    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
+    {
+        kmeans_index_->findNeighbors(result, vec, searchParams);
+        kdtree_index_->findNeighbors(result, vec, searchParams);
+    }
+
+private:
+    /** The k-means index */
+    KMeansIndex<Distance>* kmeans_index_;
+
+    /** The kd-tree index */
+    KDTreeIndex<Distance>* kdtree_index_;
+
+    /** The index parameters */
+    const IndexParams index_params_;
+};
+
+}
+
+#endif //FLANN_COMPOSITE_INDEX_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/dist.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/dist.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/dist.h	(revision 15840)
@@ -0,0 +1,816 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_DIST_H_
+#define FLANN_DIST_H_
+
+#include <cmath>
+#include <cstdlib>
+#include <string.h>
+#ifdef _MSC_VER
+typedef unsigned __int32 uint32_t;
+typedef unsigned __int64 uint64_t;
+#else
+#include <stdint.h>
+#endif
+
+#include "flann/defines.h"
+
+
+namespace flann
+{
+
+template<typename T>
+inline T abs(T x) { return (x<0) ? -x : x; }
+
+template<>
+inline int abs<int>(int x) { return ::abs(x); }
+
+template<>
+inline float abs<float>(float x) { return fabsf(x); }
+
+template<>
+inline double abs<double>(double x) { return fabs(x); }
+
+template<>
+inline long double abs<long double>(long double x) { return fabsl(x); }
+
+
+template<typename T>
+struct Accumulator { typedef T Type; };
+template<>
+struct Accumulator<unsigned char>  { typedef float Type; };
+template<>
+struct Accumulator<unsigned short> { typedef float Type; };
+template<>
+struct Accumulator<unsigned int> { typedef float Type; };
+template<>
+struct Accumulator<char>   { typedef float Type; };
+template<>
+struct Accumulator<short>  { typedef float Type; };
+template<>
+struct Accumulator<int> { typedef float Type; };
+
+
+
+/**
+ * Squared Euclidean distance functor.
+ *
+ * This is the simpler, unrolled version. This is preferable for
+ * very low dimensionality data (eg 3D points)
+ */
+template<class T>
+struct L2_Simple
+{
+    typedef bool is_kdtree_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
+    {
+        ResultType result = ResultType();
+        ResultType diff;
+        for(size_t i = 0; i < size; ++i ) {
+            diff = *a++ - *b++;
+            result += diff*diff;
+        }
+        return result;
+    }
+
+    template <typename U, typename V>
+    inline ResultType accum_dist(const U& a, const V& b, int) const
+    {
+        return (a-b)*(a-b);
+    }
+};
+
+
+
+/**
+ * Squared Euclidean distance functor, optimized version
+ */
+template<class T>
+struct L2
+{
+    typedef bool is_kdtree_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+
+    /**
+     *  Compute the squared Euclidean distance between two vectors.
+     *
+     *	This is highly optimised, with loop unrolling, as it is one
+     *	of the most expensive inner loops.
+     *
+     *	The computation of squared root at the end is omitted for
+     *	efficiency.
+     */
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
+    {
+        ResultType result = ResultType();
+        ResultType diff0, diff1, diff2, diff3;
+        Iterator1 last = a + size;
+        Iterator1 lastgroup = last - 3;
+
+        /* Process 4 items with each loop for efficiency. */
+        while (a < lastgroup) {
+            diff0 = (ResultType)(a[0] - b[0]);
+            diff1 = (ResultType)(a[1] - b[1]);
+            diff2 = (ResultType)(a[2] - b[2]);
+            diff3 = (ResultType)(a[3] - b[3]);
+            result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
+            a += 4;
+            b += 4;
+
+            if ((worst_dist>0)&&(result>worst_dist)) {
+                return result;
+            }
+        }
+        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
+        while (a < last) {
+            diff0 = (ResultType)(*a++ - *b++);
+            result += diff0 * diff0;
+        }
+        return result;
+    }
+
+    /**
+     *	Partial euclidean distance, using just one dimension. This is used by the
+     *	kd-tree when computing partial distances while traversing the tree.
+     *
+     *	Squared root is omitted for efficiency.
+     */
+    template <typename U, typename V>
+    inline ResultType accum_dist(const U& a, const V& b, int) const
+    {
+        return (a-b)*(a-b);
+    }
+};
+
+
+/*
+ * Manhattan distance functor, optimized version
+ */
+template<class T>
+struct L1
+{
+    typedef bool is_kdtree_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+
+    /**
+     *  Compute the Manhattan (L_1) distance between two vectors.
+     *
+     *	This is highly optimised, with loop unrolling, as it is one
+     *	of the most expensive inner loops.
+     */
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
+    {
+        ResultType result = ResultType();
+        ResultType diff0, diff1, diff2, diff3;
+        Iterator1 last = a + size;
+        Iterator1 lastgroup = last - 3;
+
+        /* Process 4 items with each loop for efficiency. */
+        while (a < lastgroup) {
+            diff0 = (ResultType)abs(a[0] - b[0]);
+            diff1 = (ResultType)abs(a[1] - b[1]);
+            diff2 = (ResultType)abs(a[2] - b[2]);
+            diff3 = (ResultType)abs(a[3] - b[3]);
+            result += diff0 + diff1 + diff2 + diff3;
+            a += 4;
+            b += 4;
+
+            if ((worst_dist>0)&&(result>worst_dist)) {
+                return result;
+            }
+        }
+        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
+        while (a < last) {
+            diff0 = (ResultType)abs(*a++ - *b++);
+            result += diff0;
+        }
+        return result;
+    }
+
+    /**
+     * Partial distance, used by the kd-tree.
+     */
+    template <typename U, typename V>
+    inline ResultType accum_dist(const U& a, const V& b, int) const
+    {
+        return abs(a-b);
+    }
+};
+
+
+
+template<class T>
+struct MinkowskiDistance
+{
+    typedef bool is_kdtree_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+
+    int order;
+
+    MinkowskiDistance(int order_) : order(order_) {}
+
+    /**
+     *  Compute the Minkowsky (L_p) distance between two vectors.
+     *
+     *	This is highly optimised, with loop unrolling, as it is one
+     *	of the most expensive inner loops.
+     *
+     *	The computation of squared root at the end is omitted for
+     *	efficiency.
+     */
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
+    {
+        ResultType result = ResultType();
+        ResultType diff0, diff1, diff2, diff3;
+        Iterator1 last = a + size;
+        Iterator1 lastgroup = last - 3;
+
+        /* Process 4 items with each loop for efficiency. */
+        while (a < lastgroup) {
+            diff0 = (ResultType)abs(a[0] - b[0]);
+            diff1 = (ResultType)abs(a[1] - b[1]);
+            diff2 = (ResultType)abs(a[2] - b[2]);
+            diff3 = (ResultType)abs(a[3] - b[3]);
+            result += pow(diff0,order) + pow(diff1,order) + pow(diff2,order) + pow(diff3,order);
+            a += 4;
+            b += 4;
+
+            if ((worst_dist>0)&&(result>worst_dist)) {
+                return result;
+            }
+        }
+        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
+        while (a < last) {
+            diff0 = (ResultType)abs(*a++ - *b++);
+            result += pow(diff0,order);
+        }
+        return result;
+    }
+
+    /**
+     * Partial distance, used by the kd-tree.
+     */
+    template <typename U, typename V>
+    inline ResultType accum_dist(const U& a, const V& b, int) const
+    {
+        return pow(static_cast<ResultType>(abs(a-b)),order);
+    }
+};
+
+
+
+template<class T>
+struct MaxDistance
+{
+    typedef bool is_vector_space_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+
+    /**
+     *  Compute the max distance (L_infinity) between two vectors.
+     *
+     *  This distance is not a valid kdtree distance, it's not dimensionwise additive.
+     */
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
+    {
+        ResultType result = ResultType();
+        ResultType diff0, diff1, diff2, diff3;
+        Iterator1 last = a + size;
+        Iterator1 lastgroup = last - 3;
+
+        /* Process 4 items with each loop for efficiency. */
+        while (a < lastgroup) {
+            diff0 = abs(a[0] - b[0]);
+            diff1 = abs(a[1] - b[1]);
+            diff2 = abs(a[2] - b[2]);
+            diff3 = abs(a[3] - b[3]);
+            if (diff0>result) {result = diff0; }
+            if (diff1>result) {result = diff1; }
+            if (diff2>result) {result = diff2; }
+            if (diff3>result) {result = diff3; }
+            a += 4;
+            b += 4;
+
+            if ((worst_dist>0)&&(result>worst_dist)) {
+                return result;
+            }
+        }
+        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
+        while (a < last) {
+            diff0 = abs(*a++ - *b++);
+            result = (diff0>result) ? diff0 : result;
+        }
+        return result;
+    }
+
+    /* This distance functor is not dimension-wise additive, which
+     * makes it an invalid kd-tree distance, not implementing the accum_dist method */
+
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/**
+ * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
+ * bit count of A exclusive XOR'ed with B
+ */
+struct HammingLUT
+{
+    typedef unsigned char ElementType;
+    typedef int ResultType;
+
+    /** this will count the bits in a ^ b
+     */
+    ResultType operator()(const unsigned char* a, const unsigned char* b, int size) const
+    {
+        ResultType result = 0;
+        for (int i = 0; i < size; i++) {
+            result += byteBitsLookUp(a[i] ^ b[i]);
+        }
+        return result;
+    }
+
+
+    /** \brief given a byte, count the bits using a look up table
+     *  \param b the byte to count bits.  The look up table has an entry for all
+     *  values of b, where that entry is the number of bits.
+     *  \return the number of bits in byte b
+     */
+    static unsigned char byteBitsLookUp(unsigned char b)
+    {
+        static const unsigned char table[256]  = {
+            /* 0 */ 0, /* 1 */ 1, /* 2 */ 1, /* 3 */ 2,
+            /* 4 */ 1, /* 5 */ 2, /* 6 */ 2, /* 7 */ 3,
+            /* 8 */ 1, /* 9 */ 2, /* a */ 2, /* b */ 3,
+            /* c */ 2, /* d */ 3, /* e */ 3, /* f */ 4,
+            /* 10 */ 1, /* 11 */ 2, /* 12 */ 2, /* 13 */ 3,
+            /* 14 */ 2, /* 15 */ 3, /* 16 */ 3, /* 17 */ 4,
+            /* 18 */ 2, /* 19 */ 3, /* 1a */ 3, /* 1b */ 4,
+            /* 1c */ 3, /* 1d */ 4, /* 1e */ 4, /* 1f */ 5,
+            /* 20 */ 1, /* 21 */ 2, /* 22 */ 2, /* 23 */ 3,
+            /* 24 */ 2, /* 25 */ 3, /* 26 */ 3, /* 27 */ 4,
+            /* 28 */ 2, /* 29 */ 3, /* 2a */ 3, /* 2b */ 4,
+            /* 2c */ 3, /* 2d */ 4, /* 2e */ 4, /* 2f */ 5,
+            /* 30 */ 2, /* 31 */ 3, /* 32 */ 3, /* 33 */ 4,
+            /* 34 */ 3, /* 35 */ 4, /* 36 */ 4, /* 37 */ 5,
+            /* 38 */ 3, /* 39 */ 4, /* 3a */ 4, /* 3b */ 5,
+            /* 3c */ 4, /* 3d */ 5, /* 3e */ 5, /* 3f */ 6,
+            /* 40 */ 1, /* 41 */ 2, /* 42 */ 2, /* 43 */ 3,
+            /* 44 */ 2, /* 45 */ 3, /* 46 */ 3, /* 47 */ 4,
+            /* 48 */ 2, /* 49 */ 3, /* 4a */ 3, /* 4b */ 4,
+            /* 4c */ 3, /* 4d */ 4, /* 4e */ 4, /* 4f */ 5,
+            /* 50 */ 2, /* 51 */ 3, /* 52 */ 3, /* 53 */ 4,
+            /* 54 */ 3, /* 55 */ 4, /* 56 */ 4, /* 57 */ 5,
+            /* 58 */ 3, /* 59 */ 4, /* 5a */ 4, /* 5b */ 5,
+            /* 5c */ 4, /* 5d */ 5, /* 5e */ 5, /* 5f */ 6,
+            /* 60 */ 2, /* 61 */ 3, /* 62 */ 3, /* 63 */ 4,
+            /* 64 */ 3, /* 65 */ 4, /* 66 */ 4, /* 67 */ 5,
+            /* 68 */ 3, /* 69 */ 4, /* 6a */ 4, /* 6b */ 5,
+            /* 6c */ 4, /* 6d */ 5, /* 6e */ 5, /* 6f */ 6,
+            /* 70 */ 3, /* 71 */ 4, /* 72 */ 4, /* 73 */ 5,
+            /* 74 */ 4, /* 75 */ 5, /* 76 */ 5, /* 77 */ 6,
+            /* 78 */ 4, /* 79 */ 5, /* 7a */ 5, /* 7b */ 6,
+            /* 7c */ 5, /* 7d */ 6, /* 7e */ 6, /* 7f */ 7,
+            /* 80 */ 1, /* 81 */ 2, /* 82 */ 2, /* 83 */ 3,
+            /* 84 */ 2, /* 85 */ 3, /* 86 */ 3, /* 87 */ 4,
+            /* 88 */ 2, /* 89 */ 3, /* 8a */ 3, /* 8b */ 4,
+            /* 8c */ 3, /* 8d */ 4, /* 8e */ 4, /* 8f */ 5,
+            /* 90 */ 2, /* 91 */ 3, /* 92 */ 3, /* 93 */ 4,
+            /* 94 */ 3, /* 95 */ 4, /* 96 */ 4, /* 97 */ 5,
+            /* 98 */ 3, /* 99 */ 4, /* 9a */ 4, /* 9b */ 5,
+            /* 9c */ 4, /* 9d */ 5, /* 9e */ 5, /* 9f */ 6,
+            /* a0 */ 2, /* a1 */ 3, /* a2 */ 3, /* a3 */ 4,
+            /* a4 */ 3, /* a5 */ 4, /* a6 */ 4, /* a7 */ 5,
+            /* a8 */ 3, /* a9 */ 4, /* aa */ 4, /* ab */ 5,
+            /* ac */ 4, /* ad */ 5, /* ae */ 5, /* af */ 6,
+            /* b0 */ 3, /* b1 */ 4, /* b2 */ 4, /* b3 */ 5,
+            /* b4 */ 4, /* b5 */ 5, /* b6 */ 5, /* b7 */ 6,
+            /* b8 */ 4, /* b9 */ 5, /* ba */ 5, /* bb */ 6,
+            /* bc */ 5, /* bd */ 6, /* be */ 6, /* bf */ 7,
+            /* c0 */ 2, /* c1 */ 3, /* c2 */ 3, /* c3 */ 4,
+            /* c4 */ 3, /* c5 */ 4, /* c6 */ 4, /* c7 */ 5,
+            /* c8 */ 3, /* c9 */ 4, /* ca */ 4, /* cb */ 5,
+            /* cc */ 4, /* cd */ 5, /* ce */ 5, /* cf */ 6,
+            /* d0 */ 3, /* d1 */ 4, /* d2 */ 4, /* d3 */ 5,
+            /* d4 */ 4, /* d5 */ 5, /* d6 */ 5, /* d7 */ 6,
+            /* d8 */ 4, /* d9 */ 5, /* da */ 5, /* db */ 6,
+            /* dc */ 5, /* dd */ 6, /* de */ 6, /* df */ 7,
+            /* e0 */ 3, /* e1 */ 4, /* e2 */ 4, /* e3 */ 5,
+            /* e4 */ 4, /* e5 */ 5, /* e6 */ 5, /* e7 */ 6,
+            /* e8 */ 4, /* e9 */ 5, /* ea */ 5, /* eb */ 6,
+            /* ec */ 5, /* ed */ 6, /* ee */ 6, /* ef */ 7,
+            /* f0 */ 4, /* f1 */ 5, /* f2 */ 5, /* f3 */ 6,
+            /* f4 */ 5, /* f5 */ 6, /* f6 */ 6, /* f7 */ 7,
+            /* f8 */ 5, /* f9 */ 6, /* fa */ 6, /* fb */ 7,
+            /* fc */ 6, /* fd */ 7, /* fe */ 7, /* ff */ 8
+        };
+        return table[b];
+    }
+};
+
+/**
+ * Hamming distance functor (pop count between two binary vectors, i.e. xor them and count the number of bits set)
+ * That code was taken from brief.cpp in OpenCV
+ */
+template<class T>
+struct HammingPopcnt
+{
+    typedef T ElementType;
+    typedef int ResultType;
+
+    template<typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
+    {
+        ResultType result = 0;
+#if __GNUC__
+#if ANDROID && HAVE_NEON
+        static uint64_t features = android_getCpuFeatures();
+        if ((features& ANDROID_CPU_ARM_FEATURE_NEON)) {
+            for (size_t i = 0; i < size; i += 16) {
+                uint8x16_t A_vec = vld1q_u8 (a + i);
+                uint8x16_t B_vec = vld1q_u8 (b + i);
+                //uint8x16_t veorq_u8 (uint8x16_t, uint8x16_t)
+                uint8x16_t AxorB = veorq_u8 (A_vec, B_vec);
+
+                uint8x16_t bitsSet += vcntq_u8 (AxorB);
+                //uint16x8_t vpadalq_u8 (uint16x8_t, uint8x16_t)
+                uint16x8_t bitSet8 = vpaddlq_u8 (bitsSet);
+                uint32x4_t bitSet4 = vpaddlq_u16 (bitSet8);
+
+                uint64x2_t bitSet2 = vpaddlq_u32 (bitSet4);
+                result += vgetq_lane_u64 (bitSet2,0);
+                result += vgetq_lane_u64 (bitSet2,1);
+            }
+        }
+        else
+#endif
+        //for portability just use unsigned long -- and use the __builtin_popcountll (see docs for __builtin_popcountll)
+        typedef unsigned long long pop_t;
+        const size_t modulo = size % sizeof(pop_t);
+        const pop_t* a2 = reinterpret_cast<const pop_t*> (a);
+        const pop_t* b2 = reinterpret_cast<const pop_t*> (b);
+        const pop_t* a2_end = a2 + (size / sizeof(pop_t));
+
+        for (; a2 != a2_end; ++a2, ++b2) result += __builtin_popcountll((*a2) ^ (*b2));
+
+        if (modulo) {
+            //in the case where size is not dividable by sizeof(size_t)
+            //need to mask off the bits at the end
+            pop_t a_final = 0, b_final = 0;
+            memcpy(&a_final, a2, modulo);
+            memcpy(&b_final, b2, modulo);
+            result += __builtin_popcountll(a_final ^ b_final);
+        }
+#else
+        HammingLUT lut;
+        result = lut(reinterpret_cast<const unsigned char*> (a),
+                     reinterpret_cast<const unsigned char*> (b), size * sizeof(pop_t));
+#endif
+        return result;
+    }
+};
+
+template<typename T>
+struct Hamming
+{
+    typedef T ElementType;
+    typedef unsigned int ResultType;
+
+    /** This is popcount_3() from:
+     * http://en.wikipedia.org/wiki/Hamming_weight */
+    unsigned int popcnt32(uint32_t n) const
+    {
+        n -= ((n >> 1) & 0x55555555);
+        n = (n & 0x33333333) + ((n >> 2) & 0x33333333);
+        return (((n + (n >> 4))& 0xF0F0F0F)* 0x1010101) >> 24;
+    }
+
+    unsigned int popcnt64(uint64_t n) const
+    {
+        n -= ((n >> 1) & 0x5555555555555555LL);
+        n = (n & 0x3333333333333333LL) + ((n >> 2) & 0x3333333333333333LL);
+        return (((n + (n >> 4))& 0x0f0f0f0f0f0f0f0fLL)* 0x0101010101010101LL) >> 56;
+    }
+
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
+    {
+#ifdef FLANN_PLATFORM_64_BIT
+        const uint64_t* pa = reinterpret_cast<const uint64_t*>(a);
+        const uint64_t* pb = reinterpret_cast<const uint64_t*>(b);
+        ResultType result = 0;
+        size /= (sizeof(uint64_t)/sizeof(unsigned char));
+        for(size_t i = 0; i < size; ++i ) {
+            result += popcnt64(*pa ^ *pb);
+            ++pa;
+            ++pb;
+        }
+#else
+        const uint32_t* pa = reinterpret_cast<const uint32_t*>(a);
+        const uint32_t* pb = reinterpret_cast<const uint32_t*>(b);
+        ResultType result = 0;
+        size /= (sizeof(uint32_t)/sizeof(unsigned char));
+        for(size_t i = 0; i < size; ++i ) {
+        	result += popcnt32(*pa ^ *pb);
+        	++pa;
+        	++pb;
+        }
+#endif
+        return result;
+    }
+};
+
+
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+template<class T>
+struct HistIntersectionDistance
+{
+    typedef bool is_kdtree_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+
+    /**
+     *  Compute the histogram intersection distance
+     */
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
+    {
+        ResultType result = ResultType();
+        ResultType min0, min1, min2, min3;
+        Iterator1 last = a + size;
+        Iterator1 lastgroup = last - 3;
+
+        /* Process 4 items with each loop for efficiency. */
+        while (a < lastgroup) {
+            min0 = (ResultType)(a[0] < b[0] ? a[0] : b[0]);
+            min1 = (ResultType)(a[1] < b[1] ? a[1] : b[1]);
+            min2 = (ResultType)(a[2] < b[2] ? a[2] : b[2]);
+            min3 = (ResultType)(a[3] < b[3] ? a[3] : b[3]);
+            result += min0 + min1 + min2 + min3;
+            a += 4;
+            b += 4;
+            if ((worst_dist>0)&&(result>worst_dist)) {
+                return result;
+            }
+        }
+        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
+        while (a < last) {
+            min0 = (ResultType)(*a < *b ? *a : *b);
+            result += min0;
+            ++a;
+            ++b;
+        }
+        return result;
+    }
+
+    /**
+     * Partial distance, used by the kd-tree.
+     */
+    template <typename U, typename V>
+    inline ResultType accum_dist(const U& a, const V& b, int) const
+    {
+        return a<b ? a : b;
+    }
+};
+
+
+
+template<class T>
+struct HellingerDistance
+{
+    typedef bool is_kdtree_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+
+    /**
+     *  Compute the histogram intersection distance
+     */
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
+    {
+        ResultType result = ResultType();
+        ResultType diff0, diff1, diff2, diff3;
+        Iterator1 last = a + size;
+        Iterator1 lastgroup = last - 3;
+
+        /* Process 4 items with each loop for efficiency. */
+        while (a < lastgroup) {
+            diff0 = sqrt(static_cast<ResultType>(a[0])) - sqrt(static_cast<ResultType>(b[0]));
+            diff1 = sqrt(static_cast<ResultType>(a[1])) - sqrt(static_cast<ResultType>(b[1]));
+            diff2 = sqrt(static_cast<ResultType>(a[2])) - sqrt(static_cast<ResultType>(b[2]));
+            diff3 = sqrt(static_cast<ResultType>(a[3])) - sqrt(static_cast<ResultType>(b[3]));
+            result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
+            a += 4;
+            b += 4;
+        }
+        while (a < last) {
+            diff0 = sqrt(static_cast<ResultType>(*a++)) - sqrt(static_cast<ResultType>(*b++));
+            result += diff0 * diff0;
+        }
+        return result;
+    }
+
+    /**
+     * Partial distance, used by the kd-tree.
+     */
+    template <typename U, typename V>
+    inline ResultType accum_dist(const U& a, const V& b, int) const
+    {
+        return sqrt(static_cast<ResultType>(a)) - sqrt(static_cast<ResultType>(b));
+    }
+};
+
+
+template<class T>
+struct ChiSquareDistance
+{
+    typedef bool is_kdtree_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+
+    /**
+     *  Compute the chi-square distance
+     */
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
+    {
+        ResultType result = ResultType();
+        ResultType sum, diff;
+        Iterator1 last = a + size;
+
+        while (a < last) {
+            sum = (ResultType)(*a + *b);
+            if (sum>0) {
+                diff = (ResultType)(*a - *b);
+                result += diff*diff/sum;
+            }
+            ++a;
+            ++b;
+
+            if ((worst_dist>0)&&(result>worst_dist)) {
+                return result;
+            }
+        }
+        return result;
+    }
+
+    /**
+     * Partial distance, used by the kd-tree.
+     */
+    template <typename U, typename V>
+    inline ResultType accum_dist(const U& a, const V& b, int) const
+    {
+        ResultType result = ResultType();
+        ResultType sum, diff;
+
+        sum = (ResultType)(a+b);
+        if (sum>0) {
+            diff = (ResultType)(a-b);
+            result = diff*diff/sum;
+        }
+        return result;
+    }
+};
+
+
+template<class T>
+struct KL_Divergence
+{
+    typedef bool is_kdtree_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+
+    /**
+     *  Compute the KullbackâLeibler divergence
+     */
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
+    {
+        ResultType result = ResultType();
+        Iterator1 last = a + size;
+
+        while (a < last) {
+            if (* a != 0) {
+                ResultType ratio = (ResultType)(*a / *b);
+                if (ratio>0) {
+                    result += *a * log(ratio);
+                }
+            }
+            ++a;
+            ++b;
+
+            if ((worst_dist>0)&&(result>worst_dist)) {
+                return result;
+            }
+        }
+        return result;
+    }
+
+    /**
+     * Partial distance, used by the kd-tree.
+     */
+    template <typename U, typename V>
+    inline ResultType accum_dist(const U& a, const V& b, int) const
+    {
+        ResultType result = ResultType();
+        ResultType ratio = (ResultType)(a / b);
+        if (ratio>0) {
+            result = a * log(ratio);
+        }
+        return result;
+    }
+};
+
+
+
+/*
+ * This is a "zero iterator". It basically behaves like a zero filled
+ * array to all algorithms that use arrays as iterators (STL style).
+ * It's useful when there's a need to compute the distance between feature
+ * and origin it and allows for better compiler optimisation than using a
+ * zero-filled array.
+ */
+template <typename T>
+struct ZeroIterator
+{
+
+    T operator*()
+    {
+        return 0;
+    }
+
+    T operator[](int)
+    {
+        return 0;
+    }
+
+    const ZeroIterator<T>& operator ++()
+    {
+        return *this;
+    }
+
+    ZeroIterator<T> operator ++(int)
+    {
+        return *this;
+    }
+
+    ZeroIterator<T>& operator+=(int)
+    {
+        return *this;
+    }
+
+};
+
+}
+
+#endif //FLANN_DIST_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/hierarchical_clustering_index.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/hierarchical_clustering_index.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/hierarchical_clustering_index.h	(revision 15840)
@@ -0,0 +1,716 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2011  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2011  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_
+#define FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_
+
+#include <algorithm>
+#include <string>
+#include <map>
+#include <cassert>
+#include <limits>
+#include <cmath>
+
+#include "flann/general.h"
+#include "flann/algorithms/nn_index.h"
+#include "flann/algorithms/dist.h"
+#include "flann/util/matrix.h"
+#include "flann/util/result_set.h"
+#include "flann/util/heap.h"
+#include "flann/util/allocator.h"
+#include "flann/util/random.h"
+#include "flann/util/saving.h"
+
+
+namespace flann
+{
+
+struct HierarchicalClusteringIndexParams : public IndexParams
+{
+    HierarchicalClusteringIndexParams(int branching = 32,
+                                      flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM,
+                                      int trees = 4, int leaf_size = 100)
+    {
+        (*this)["algorithm"] = FLANN_INDEX_HIERARCHICAL;
+        // The branching factor used in the hierarchical clustering
+        (*this)["branching"] = branching;
+        // Algorithm used for picking the initial cluster centers
+        (*this)["centers_init"] = centers_init;
+        // number of parallel trees to build
+        (*this)["trees"] = trees;
+        // maximum leaf size
+        (*this)["leaf_size"] = leaf_size;
+    }
+};
+
+
+/**
+ * Hierarchical index
+ *
+ * Contains a tree constructed through a hierarchical clustering
+ * and other information for indexing a set of points for nearest-neighbour matching.
+ */
+template <typename Distance>
+class HierarchicalClusteringIndex : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+private:
+
+
+    typedef void (HierarchicalClusteringIndex::* centersAlgFunction)(int, int*, int, int*, int&);
+
+    /**
+     * The function used for choosing the cluster centers.
+     */
+    centersAlgFunction chooseCenters;
+
+
+
+    /**
+     * Chooses the initial centers in the k-means clustering in a random manner.
+     *
+     * Params:
+     *     k = number of centers
+     *     vecs = the dataset of points
+     *     indices = indices in the dataset
+     *     indices_length = length of indices vector
+     *
+     */
+    void chooseCentersRandom(int k, int* indices, int indices_length, int* centers, int& centers_length)
+    {
+        UniqueRandom r(indices_length);
+
+        int index;
+        for (index=0; index<k; ++index) {
+            bool duplicate = true;
+            int rnd;
+            while (duplicate) {
+                duplicate = false;
+                rnd = r.next();
+                if (rnd<0) {
+                    centers_length = index;
+                    return;
+                }
+
+                centers[index] = indices[rnd];
+
+                for (int j=0; j<index; ++j) {
+                    DistanceType sq = distance(dataset[centers[index]], dataset[centers[j]], dataset.cols);
+                    if (sq<1e-16) {
+                        duplicate = true;
+                    }
+                }
+            }
+        }
+
+        centers_length = index;
+    }
+
+
+    /**
+     * Chooses the initial centers in the k-means using Gonzales' algorithm
+     * so that the centers are spaced apart from each other.
+     *
+     * Params:
+     *     k = number of centers
+     *     vecs = the dataset of points
+     *     indices = indices in the dataset
+     * Returns:
+     */
+    void chooseCentersGonzales(int k, int* indices, int indices_length, int* centers, int& centers_length)
+    {
+        int n = indices_length;
+
+        int rnd = rand_int(n);
+        assert(rnd >=0 && rnd < n);
+
+        centers[0] = indices[rnd];
+
+        int index;
+        for (index=1; index<k; ++index) {
+
+            int best_index = -1;
+            DistanceType best_val = 0;
+            for (int j=0; j<n; ++j) {
+            	DistanceType dist = distance(dataset[centers[0]],dataset[indices[j]],dataset.cols);
+                for (int i=1; i<index; ++i) {
+                    DistanceType tmp_dist = distance(dataset[centers[i]],dataset[indices[j]],dataset.cols);
+                    if (tmp_dist<dist) {
+                        dist = tmp_dist;
+                    }
+                }
+                if (dist>best_val) {
+                    best_val = dist;
+                    best_index = j;
+                }
+            }
+            if (best_index!=-1) {
+                centers[index] = indices[best_index];
+            }
+            else {
+                break;
+            }
+        }
+        centers_length = index;
+    }
+
+
+    /**
+     * Chooses the initial centers in the k-means using the algorithm
+     * proposed in the KMeans++ paper:
+     * Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding
+     *
+     * Implementation of this function was converted from the one provided in Arthur's code.
+     *
+     * Params:
+     *     k = number of centers
+     *     vecs = the dataset of points
+     *     indices = indices in the dataset
+     * Returns:
+     */
+    void chooseCentersKMeanspp(int k, int* indices, int indices_length, int* centers, int& centers_length)
+    {
+        int n = indices_length;
+
+        double currentPot = 0;
+        DistanceType* closestDistSq = new DistanceType[n];
+
+        // Choose one random center and set the closestDistSq values
+        int index = rand_int(n);
+        assert(index >=0 && index < n);
+        centers[0] = indices[index];
+
+        for (int i = 0; i < n; i++) {
+            closestDistSq[i] = distance(dataset[indices[i]], dataset[indices[index]], dataset.cols);
+            currentPot += closestDistSq[i];
+        }
+
+
+        const int numLocalTries = 1;
+
+        // Choose each center
+        int centerCount;
+        for (centerCount = 1; centerCount < k; centerCount++) {
+
+            // Repeat several trials
+            double bestNewPot = -1;
+            int bestNewIndex = 0;
+            for (int localTrial = 0; localTrial < numLocalTries; localTrial++) {
+
+                // Choose our center - have to be slightly careful to return a valid answer even accounting
+                // for possible rounding errors
+                double randVal = rand_double(currentPot);
+                for (index = 0; index < n-1; index++) {
+                    if (randVal <= closestDistSq[index]) break;
+                    else randVal -= closestDistSq[index];
+                }
+
+                // Compute the new potential
+                double newPot = 0;
+                for (int i = 0; i < n; i++) newPot += std::min( distance(dataset[indices[i]], dataset[indices[index]], dataset.cols), closestDistSq[i] );
+
+                // Store the best result
+                if ((bestNewPot < 0)||(newPot < bestNewPot)) {
+                    bestNewPot = newPot;
+                    bestNewIndex = index;
+                }
+            }
+
+            // Add the appropriate center
+            centers[centerCount] = indices[bestNewIndex];
+            currentPot = bestNewPot;
+            for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance(dataset[indices[i]], dataset[indices[bestNewIndex]], dataset.cols), closestDistSq[i] );
+        }
+
+        centers_length = centerCount;
+
+        delete[] closestDistSq;
+    }
+
+
+public:
+
+
+    /**
+     * Index constructor
+     *
+     * Params:
+     *          inputData = dataset with the input features
+     *          params = parameters passed to the hierarchical k-means algorithm
+     */
+    HierarchicalClusteringIndex(const Matrix<ElementType>& inputData, const IndexParams& index_params = HierarchicalClusteringIndexParams(),
+                                Distance d = Distance())
+        : dataset(inputData), params(index_params), root(NULL), indices(NULL), distance(d)
+    {
+        memoryCounter = 0;
+
+        size_ = dataset.rows;
+        veclen_ = dataset.cols;
+
+        branching_ = get_param(params,"branching",32);
+        centers_init_ = get_param(params,"centers_init", FLANN_CENTERS_RANDOM);
+        trees_ = get_param(params,"trees",4);
+        leaf_size_ = get_param(params,"leaf_size",100);
+
+        if (centers_init_==FLANN_CENTERS_RANDOM) {
+            chooseCenters = &HierarchicalClusteringIndex::chooseCentersRandom;
+        }
+        else if (centers_init_==FLANN_CENTERS_GONZALES) {
+            chooseCenters = &HierarchicalClusteringIndex::chooseCentersGonzales;
+        }
+        else if (centers_init_==FLANN_CENTERS_KMEANSPP) {
+            chooseCenters = &HierarchicalClusteringIndex::chooseCentersKMeanspp;
+        }
+        else {
+            throw FLANNException("Unknown algorithm for choosing initial centers.");
+        }
+
+        trees_ = get_param(params,"trees",4);
+        root = new NodePtr[trees_];
+        indices = new int*[trees_];
+    }
+
+    HierarchicalClusteringIndex(const HierarchicalClusteringIndex&);
+    HierarchicalClusteringIndex& operator=(const HierarchicalClusteringIndex&);
+
+    /**
+     * Index destructor.
+     *
+     * Release the memory used by the index.
+     */
+    virtual ~HierarchicalClusteringIndex()
+    {
+        if (indices!=NULL) {
+            delete[] indices;
+        }
+    }
+
+    /**
+     *  Returns size of index.
+     */
+    size_t size() const
+    {
+        return size_;
+    }
+
+    /**
+     * Returns the length of an index feature.
+     */
+    size_t veclen() const
+    {
+        return veclen_;
+    }
+
+
+    /**
+     * Computes the inde memory usage
+     * Returns: memory used by the index
+     */
+    int usedMemory() const
+    {
+        return pool.usedMemory+pool.wastedMemory+memoryCounter;
+    }
+
+    /**
+     * Builds the index
+     */
+    void buildIndex()
+    {
+        if (branching_<2) {
+            throw FLANNException("Branching factor must be at least 2");
+        }
+        for (int i=0; i<trees_; ++i) {
+            indices[i] = new int[size_];
+            for (size_t j=0; j<size_; ++j) {
+                indices[i][j] = j;
+            }
+            root[i] = pool.allocate<Node>();
+            computeClustering(root[i], indices[i], size_, branching_,0);
+        }
+    }
+
+
+    flann_algorithm_t getType() const
+    {
+        return FLANN_INDEX_HIERARCHICAL;
+    }
+
+
+    void saveIndex(FILE* stream)
+    {
+        save_value(stream, branching_);
+        save_value(stream, trees_);
+        save_value(stream, centers_init_);
+        save_value(stream, leaf_size_);
+        save_value(stream, memoryCounter);
+        for (int i=0; i<trees_; ++i) {
+            save_value(stream, *indices[i], size_);
+            save_tree(stream, root[i], i);
+        }
+
+    }
+
+
+    void loadIndex(FILE* stream)
+    {
+        load_value(stream, branching_);
+        load_value(stream, trees_);
+        load_value(stream, centers_init_);
+        load_value(stream, leaf_size_);
+        load_value(stream, memoryCounter);
+        indices = new int*[trees_];
+        root = new NodePtr[trees_];
+        for (int i=0; i<trees_; ++i) {
+            indices[i] = new int[size_];
+            load_value(stream, *indices[i], size_);
+            load_tree(stream, root[i], i);
+        }
+
+        params["algorithm"] = getType();
+        params["branching"] = branching_;
+        params["trees"] = trees_;
+        params["centers_init"] = centers_init_;
+        params["leaf_size"] = leaf_size_;
+    }
+
+
+    /**
+     * Find set of nearest neighbors to vec. Their indices are stored inside
+     * the result object.
+     *
+     * Params:
+     *     result = the result object in which the indices of the nearest-neighbors are stored
+     *     vec = the vector for which to search the nearest neighbors
+     *     searchParams = parameters that influence the search algorithm (checks)
+     */
+    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
+    {
+
+        int maxChecks = searchParams.checks;
+
+        // Priority queue storing intermediate branches in the best-bin-first search
+        Heap<BranchSt>* heap = new Heap<BranchSt>(size_);
+
+        std::vector<bool> checked(size_,false);
+        int checks = 0;
+        for (int i=0; i<trees_; ++i) {
+            findNN(root[i], result, vec, checks, maxChecks, heap, checked);
+        }
+
+        BranchSt branch;
+        while (heap->popMin(branch) && (checks<maxChecks || !result.full())) {
+            NodePtr node = branch.node;
+            findNN(node, result, vec, checks, maxChecks, heap, checked);
+        }
+
+        delete heap;
+
+    }
+
+    IndexParams getParameters() const
+    {
+        return params;
+    }
+
+
+private:
+
+    /**
+     * Struture representing a node in the hierarchical k-means tree.
+     */
+    struct Node
+    {
+        /**
+         * The cluster center index
+         */
+        int pivot;
+        /**
+         * The cluster size (number of points in the cluster)
+         */
+        int size;
+        /**
+         * Child nodes (only for non-terminal nodes)
+         */
+        Node** childs;
+        /**
+         * Node points (only for terminal nodes)
+         */
+        int* indices;
+        /**
+         * Level
+         */
+        int level;
+    };
+    typedef Node* NodePtr;
+
+
+
+    /**
+     * Alias definition for a nicer syntax.
+     */
+    typedef BranchStruct<NodePtr, DistanceType> BranchSt;
+
+
+
+    void save_tree(FILE* stream, NodePtr node, int num)
+    {
+        save_value(stream, *node);
+        if (node->childs==NULL) {
+            int indices_offset = node->indices - indices[num];
+            save_value(stream, indices_offset);
+        }
+        else {
+            for(int i=0; i<branching_; ++i) {
+                save_tree(stream, node->childs[i], num);
+            }
+        }
+    }
+
+
+    void load_tree(FILE* stream, NodePtr& node, int num)
+    {
+        node = pool.allocate<Node>();
+        load_value(stream, *node);
+        if (node->childs==NULL) {
+            int indices_offset;
+            load_value(stream, indices_offset);
+            node->indices = indices[num] + indices_offset;
+        }
+        else {
+            node->childs = pool.allocate<NodePtr>(branching_);
+            for(int i=0; i<branching_; ++i) {
+                load_tree(stream, node->childs[i], num);
+            }
+        }
+    }
+
+
+
+
+    void computeLabels(int* indices, int indices_length,  int* centers, int centers_length, int* labels, DistanceType& cost)
+    {
+        cost = 0;
+        for (int i=0; i<indices_length; ++i) {
+            ElementType* point = dataset[indices[i]];
+            DistanceType dist = distance(point, dataset[centers[0]], veclen_);
+            labels[i] = 0;
+            for (int j=1; j<centers_length; ++j) {
+                DistanceType new_dist = distance(point, dataset[centers[j]], veclen_);
+                if (dist>new_dist) {
+                    labels[i] = j;
+                    dist = new_dist;
+                }
+            }
+            cost += dist;
+        }
+    }
+
+    /**
+     * The method responsible with actually doing the recursive hierarchical
+     * clustering
+     *
+     * Params:
+     *     node = the node to cluster
+     *     indices = indices of the points belonging to the current node
+     *     branching = the branching factor to use in the clustering
+     *
+     * TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point)
+     */
+    void computeClustering(NodePtr node, int* indices, int indices_length, int branching, int level)
+    {
+        node->size = indices_length;
+        node->level = level;
+
+        if (indices_length < leaf_size_) { // leaf node
+            node->indices = indices;
+            std::sort(node->indices,node->indices+indices_length);
+            node->childs = NULL;
+            return;
+        }
+
+        std::vector<int> centers(branching);
+        std::vector<int> labels(indices_length);
+
+        int centers_length;
+        (this->*chooseCenters)(branching, indices, indices_length, &centers[0], centers_length);
+
+        if (centers_length<branching) {
+            node->indices = indices;
+            std::sort(node->indices,node->indices+indices_length);
+            node->childs = NULL;
+            return;
+        }
+
+
+        //	assign points to clusters
+        DistanceType cost;
+        computeLabels(indices, indices_length, &centers[0], centers_length, &labels[0], cost);
+
+        node->childs = pool.allocate<NodePtr>(branching);
+        int start = 0;
+        int end = start;
+        for (int i=0; i<branching; ++i) {
+            for (int j=0; j<indices_length; ++j) {
+                if (labels[j]==i) {
+                    std::swap(indices[j],indices[end]);
+                    std::swap(labels[j],labels[end]);
+                    end++;
+                }
+            }
+
+            node->childs[i] = pool.allocate<Node>();
+            node->childs[i]->pivot = centers[i];
+            node->childs[i]->indices = NULL;
+            computeClustering(node->childs[i],indices+start, end-start, branching, level+1);
+            start=end;
+        }
+    }
+
+
+
+    /**
+     * Performs one descent in the hierarchical k-means tree. The branches not
+     * visited are stored in a priority queue.
+     *
+     * Params:
+     *      node = node to explore
+     *      result = container for the k-nearest neighbors found
+     *      vec = query points
+     *      checks = how many points in the dataset have been checked so far
+     *      maxChecks = maximum dataset points to checks
+     */
+
+
+    void findNN(NodePtr node, ResultSet<DistanceType>& result, const ElementType* vec, int& checks, int maxChecks,
+                Heap<BranchSt>* heap, std::vector<bool>& checked)
+    {
+        if (node->childs==NULL) {
+            if (checks>=maxChecks) {
+                if (result.full()) return;
+            }
+            checks += node->size;
+            for (int i=0; i<node->size; ++i) {
+                int index = node->indices[i];
+                if (!checked[index]) {
+                    DistanceType dist = distance(dataset[index], vec, veclen_);
+                    result.addPoint(dist, index);
+                    checked[index] = true;
+                }
+            }
+        }
+        else {
+            DistanceType* domain_distances = new DistanceType[branching_];
+            int best_index = 0;
+            domain_distances[best_index] = distance(vec, dataset[node->childs[best_index]->pivot], veclen_);
+            for (int i=1; i<branching_; ++i) {
+                domain_distances[i] = distance(vec, dataset[node->childs[i]->pivot], veclen_);
+                if (domain_distances[i]<domain_distances[best_index]) {
+                    best_index = i;
+                }
+            }
+            for (int i=0; i<branching_; ++i) {
+                if (i!=best_index) {
+                    heap->insert(BranchSt(node->childs[i],domain_distances[i]));
+                }
+            }
+            delete[] domain_distances;
+            findNN(node->childs[best_index],result,vec, checks, maxChecks, heap, checked);
+        }
+    }
+
+private:
+
+
+    /**
+     * The dataset used by this index
+     */
+    const Matrix<ElementType> dataset;
+
+    /**
+     * Parameters used by this index
+     */
+    IndexParams params;
+
+
+    /**
+     * Number of features in the dataset.
+     */
+    size_t size_;
+
+    /**
+     * Length of each feature.
+     */
+    size_t veclen_;
+
+    /**
+     * The root node in the tree.
+     */
+    NodePtr* root;
+
+    /**
+     *  Array of indices to vectors in the dataset.
+     */
+    int** indices;
+
+
+    /**
+     * The distance
+     */
+    Distance distance;
+
+    /**
+     * Pooled memory allocator.
+     *
+     * Using a pooled memory allocator is more efficient
+     * than allocating memory directly when there is a large
+     * number small of memory allocations.
+     */
+    PooledAllocator pool;
+
+    /**
+     * Memory occupied by the index.
+     */
+    int memoryCounter;
+
+    /** index parameters */
+    int branching_;
+    int trees_;
+    flann_centers_init_t centers_init_;
+    int leaf_size_;
+
+
+};
+
+}
+
+#endif /* FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_ */
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/kdtree_cuda_3d_index.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/kdtree_cuda_3d_index.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/kdtree_cuda_3d_index.h	(revision 15840)
@@ -0,0 +1,335 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ * Copyright 2011       Andreas Muetzel (amuetzel@uni-koblenz.de). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_KDTREE_CUDA_3D_INDEX_H_
+#define FLANN_KDTREE_CUDA_3D_INDEX_H_
+
+#include <algorithm>
+#include <map>
+#include <cassert>
+#include <cstring>
+// #include "flann/"
+#include "flann/general.h"
+#include "flann/algorithms/nn_index.h"
+#include "flann/util/matrix.h"
+#include "flann/util/result_set.h"
+#include "flann/util/heap.h"
+#include "flann/util/allocator.h"
+#include "flann/util/random.h"
+#include "flann/util/saving.h"
+#include "flann/util/params.h"
+
+namespace flann
+{
+
+struct KDTreeCuda3dIndexParams : public IndexParams
+{
+    KDTreeCuda3dIndexParams( int leaf_max_size = 64 )
+    {
+        (*this)["algorithm"] = FLANN_INDEX_KDTREE_CUDA;
+        (*this)["leaf_max_size"] = leaf_max_size;
+        (*this)["dim"] = 3;
+    }
+};
+
+/**
+ * Cuda KD Tree.
+ * Tree is built with GPU assistance and search is performed on the GPU, too.
+ *
+ * Usually faster than the CPU search for data (and query) sets larger than 250000-300000 points, depending
+ * on your CPU and GPU.
+ */
+template <typename Distance>
+class KDTreeCuda3dIndex : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+    int visited_leafs;
+
+    typedef bool needs_kdtree_distance;
+
+    /**
+     * KDTree constructor
+     *
+     * Params:
+     *          inputData = dataset with the input features
+     *          params = parameters passed to the kdtree algorithm
+     */
+    KDTreeCuda3dIndex(const Matrix<ElementType>& inputData, const IndexParams& params = KDTreeCuda3dIndexParams(),
+                      Distance d = Distance() ) :
+        dataset_(inputData), index_params_(params), distance_(d)
+    {
+        size_ = dataset_.rows;
+        dim_ = dataset_.cols;
+        int dim_param = get_param(params,"dim",-1);
+        if (dim_param>0) dim_ = dim_param;
+        leaf_max_size_ = get_param(params,"leaf_max_size",10);
+        assert( dim_ == 3 );
+        gpu_helper_=0;
+
+        // Create a permutable array of indices to the input vectors.
+        vind_.resize(size_);
+        for (size_t i = 0; i < size_; i++) {
+            vind_[i] = i;
+        }
+    }
+
+    /**
+     * Standard destructor
+     */
+    ~KDTreeCuda3dIndex()
+    {
+        delete[] data_.ptr();
+        clearGpuBuffers();
+    }
+
+    /**
+     * Builds the index
+     */
+    void buildIndex()
+    {
+        leaf_count_=0;
+        node_count_=0;
+        //         computeBoundingBox(root_bbox_);
+        //                              tree_.reserve(log2((double)size_/leaf_max_size_));
+        //         divideTree(0, size_, root_bbox_,-1 );   // construct the tree
+
+        delete[] data_.ptr();
+
+        uploadTreeToGpu();
+    }
+
+    flann_algorithm_t getType() const
+    {
+        return FLANN_INDEX_KDTREE_SINGLE;
+    }
+
+
+    void saveIndex(FILE* stream)
+    {
+        throw FLANNException( "Index saving not implemented!" );
+        //         save_value(stream, size_);
+        //         save_value(stream, dim_);
+        //         save_value(stream, leaf_max_size_);
+        //         save_value(stream, vind_);
+        //         save_value(stream, data_);
+
+    }
+
+
+    void loadIndex(FILE* stream)
+    {
+        throw FLANNException( "Index loading not implemented!" );
+        //         load_value(stream, size_);
+        //         load_value(stream, dim_);
+        //         load_value(stream, leaf_max_size_);
+        //         load_value(stream, vind_);
+        //         load_value(stream, data_);
+
+
+        //         index_params_["algorithm"] = getType();
+        //         index_params_["leaf_max_size"] = leaf_max_size_;
+    }
+
+    /**
+     *  Returns size of index.
+     */
+    size_t size() const
+    {
+        return size_;
+    }
+
+    /**
+     * Returns the length of an index feature.
+     */
+    size_t veclen() const
+    {
+        return dim_;
+    }
+
+    /**
+     * Computes the inde memory usage
+     * Returns: memory used by the index
+     * TODO: return system or gpu RAM or both?
+     */
+    int usedMemory() const
+    {
+        //         return tree_.size()*sizeof(Node)+dataset_.rows*sizeof(int);  // pool memory and vind array memory
+        return 0;
+    }
+
+
+    /**
+     * \brief Perform k-nearest neighbor search
+     * \param[in] queries The query points for which to find the nearest neighbors
+     * \param[out] indices The indices of the nearest neighbors found
+     * \param[out] dists Distances to the nearest neighbors found
+     * \param[in] knn Number of nearest neighbors to return
+     * \param[in] params Search parameters
+     */
+    virtual int knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, size_t knn, const SearchParams& params)
+    {
+    	knnSearchGpu(queries,indices, dists, knn, params);
+        return knn*queries.rows; // hack...
+    }
+
+    /**
+     * \brief Perform k-nearest neighbor search
+     * \param[in] queries The query points for which to find the nearest neighbors
+     * \param[out] indices The indices of the nearest neighbors found
+     * \param[out] dists Distances to the nearest neighbors found
+     * \param[in] knn Number of nearest neighbors to return
+     * \param[in] params Search parameters
+     */
+    virtual int knnSearch(const Matrix<ElementType>& queries,
+                          std::vector< std::vector<int> >& indices,
+                          std::vector<std::vector<DistanceType> >& dists,
+                          size_t knn,
+                          const SearchParams& params)
+    {
+    	knnSearchGpu(queries,indices, dists, knn, params);
+        return knn*queries.rows; // hack...
+    }
+
+    /**
+     * \brief Perform k-nearest neighbor search
+     * \param[in] queries The query points for which to find the nearest neighbors
+     * \param[out] indices The indices of the nearest neighbors found
+     * \param[out] dists Distances to the nearest neighbors found
+     * \param[in] knn Number of nearest neighbors to return
+     * \param[in] params Search parameters
+     */
+    void knnSearchGpu(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, size_t knn, const SearchParams& params);
+
+    int knnSearchGpu(const Matrix<ElementType>& queries,
+                     std::vector< std::vector<int> >& indices,
+                     std::vector<std::vector<DistanceType> >& dists,
+                     size_t knn,
+                     const SearchParams& params)
+    {
+        flann::Matrix<int> ind( new int[knn*queries.rows], queries.rows,knn);
+        flann::Matrix<DistanceType> dist( new DistanceType[knn*queries.rows], queries.rows,knn);
+        knnSearchGpu(queries,ind,dist,knn,params);
+        for( size_t i = 0; i<queries.rows; i++ ) {
+            indices[i].resize(knn);
+            dists[i].resize(knn);
+            for( size_t j=0; j<knn; j++ ) {
+                indices[i][j]=ind[i][j];
+                dists[i][j]=dist[i][j];
+            }
+        }
+        delete [] ind.ptr();
+        delete [] dist.ptr();
+        return knn*queries.rows; // hack...
+    }
+
+    virtual int radiusSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists,
+                             float radius, const SearchParams& params)
+    {
+    	return radiusSearchGpu(queries,indices, dists, radius, params);
+    }
+
+    virtual int radiusSearch(const Matrix<ElementType>& queries, std::vector< std::vector<int> >& indices,
+                             std::vector<std::vector<DistanceType> >& dists, float radius, const SearchParams& params)
+    {
+    	return radiusSearchGpu(queries,indices, dists, radius, params);
+    }
+
+    int radiusSearchGpu(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists,
+                        float radius, const SearchParams& params);
+
+    int radiusSearchGpu(const Matrix<ElementType>& queries, std::vector< std::vector<int> >& indices,
+                        std::vector<std::vector<DistanceType> >& dists, float radius, const SearchParams& params);
+
+
+    IndexParams getParameters() const
+    {
+        return index_params_;
+    }
+
+    /**
+     * Not implemented, since it is only used by single-element searches.
+     * (but is needed b/c it is abstract in the base class)
+     */
+    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
+    {
+    }
+
+
+private:
+
+    void uploadTreeToGpu( );
+
+    void clearGpuBuffers( );
+
+
+
+
+private:
+
+    struct GpuHelper;
+
+    GpuHelper* gpu_helper_;
+
+    /**
+     * The dataset used by this index
+     */
+    const Matrix<ElementType> dataset_;
+
+    IndexParams index_params_;
+
+    int leaf_max_size_;
+
+    int leaf_count_;
+    int node_count_;
+    //! used by convertTreeToGpuFormat
+    int current_node_count_;
+
+
+    /**
+     *  Array of indices to vectors in the dataset.
+     */
+    std::vector<int> vind_;
+
+    Matrix<ElementType> data_;
+
+    size_t size_;
+    size_t dim_;
+
+    Distance distance_;
+};   // class KDTree
+
+
+}
+
+#endif //FLANN_KDTREE_SINGLE_INDEX_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/kdtree_cuda_builder.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/kdtree_cuda_builder.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/kdtree_cuda_builder.h	(revision 15840)
@@ -0,0 +1,729 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2011       Andreas Muetzel (amuetzel@uni-koblenz.de). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_CUDA_KD_TREE_BUILDER_H_
+#define FLANN_CUDA_KD_TREE_BUILDER_H_
+#include <thrust/host_vector.h>
+#include <thrust/device_vector.h>
+#include <thrust/sort.h>
+#include <thrust/partition.h>
+#include <thrust/unique.h>
+#include <thrust/scan.h>
+#include <flann/util/cutil_math.h>
+#include <stdlib.h>
+
+// #define PRINT_DEBUG_TIMING
+
+namespace flann
+{
+//      template< typename T >
+//      void print_vector(  const thrust::device_vector<T>& v )
+//      {
+//              for( int i=0; i< v.size(); i++ )
+//              {
+//                      std::cout<<v[i]<<std::endl;
+//              }
+//      }
+//
+//      template< typename T1, typename T2 >
+//      void print_vector(  const thrust::device_vector<T1>& v1, const thrust::device_vector<T2>& v2 )
+//      {
+//              for( int i=0; i< v1.size(); i++ )
+//              {
+//                      std::cout<<i<<": "<<v1[i]<<" "<<v2[i]<<std::endl;
+//              }
+//      }
+//
+//      template< typename T1, typename T2, typename T3 >
+//      void print_vector(  const thrust::device_vector<T1>& v1, const thrust::device_vector<T2>& v2, const thrust::device_vector<T3>& v3 )
+//      {
+//              for( int i=0; i< v1.size(); i++ )
+//              {
+//                      std::cout<<i<<": "<<v1[i]<<" "<<v2[i]<<" "<<v3[i]<<std::endl;
+//              }
+//      }
+//
+//      template< typename T >
+//      void print_vector_by_index(  const thrust::device_vector<T>& v,const thrust::device_vector<int>& ind )
+//      {
+//              for( int i=0; i< v.size(); i++ )
+//              {
+//                      std::cout<<v[ind[i]]<<std::endl;
+//              }
+//      }
+
+//      std::ostream& operator <<(std::ostream& stream, const cuda::kd_tree_builder_detail::SplitInfo& s) {
+//      stream<<"(split l/r: "<< s.left <<" "<< s.right<< "  split:"<<s.split_dim<<" "<<s.split_val<<")";
+//      return stream;
+// }
+//
+//
+// std::ostream& operator <<(std::ostream& stream, const cuda::kd_tree_builder_detail::NodeInfo& s) {
+//      stream<<"(node: "<<s.child1()<<" "<<s.parent()<<" "<<s.child2()<<")";
+//      return stream;
+// }
+//
+// std::ostream& operator <<(std::ostream& stream, const float4& s) {
+//      stream<<"("<<s.x<<","<<s.y<<","<<s.z<<","<<s.w<<")";
+//      return stream;
+// }
+namespace cuda
+{
+namespace kd_tree_builder_detail
+{
+//! normal node: contains the split dimension and value
+//! leaf node: left == index of first points, right==index of last point +1
+struct SplitInfo
+{
+    union {
+        struct
+        {
+            // begin of child nodes
+            int left;
+            // end of child nodes
+            int right;
+        };
+        struct
+        {
+            int split_dim;
+            float split_val;
+        };
+    };
+
+};
+
+struct IsEven
+{
+    typedef int result_type;
+    __device__
+    int operator()(int i )
+    {
+        return (i& 1)==0;
+    }
+};
+
+struct SecondElementIsEven
+{
+    __host__ __device__
+    bool operator()( const thrust::tuple<int,int>& i )
+    {
+        return (thrust::get<1>(i)& 1)==0;
+    }
+};
+
+//! just for convenience: access a float4 by an index in [0,1,2]
+//! (casting it to a float* and accessing it by the index is way slower...)
+__host__ __device__
+float get_value_by_index( const float4& f, int i )
+{
+    switch(i) {
+    case 0:
+        return f.x;
+    case 1:
+        return f.y;
+    default:
+        return f.z;
+    }
+
+}
+
+//! mark a point as belonging to the left or right child of its current parent
+//! called after parents are split
+struct MovePointsToChildNodes
+{
+    MovePointsToChildNodes( int* child1, SplitInfo* splits, float* x, float* y, float* z, int* ox, int* oy, int* oz, int* lrx, int* lry, int* lrz )
+        : child1_(child1), splits_(splits), x_(x), y_(y), z_(z), ox_(ox), oy_(oy), oz_(oz), lrx_(lrx), lry_(lry), lrz_(lrz){}
+    //  int dim;
+    //  float threshold;
+    int* child1_;
+    SplitInfo* splits_;
+
+    // coordinate values
+    float* x_, * y_, * z_;
+    // owner indices -> which node does the point belong to?
+    int* ox_, * oy_, * oz_;
+    // temp info: will be set to 1 of a point is moved to the right child node, 0 otherwise
+    // (used later in the scan op to separate the points of the children into continuous ranges)
+    int* lrx_, * lry_, * lrz_;
+    __device__
+    void operator()( const thrust::tuple<int, int, int, int>& data )
+    {
+        int index = thrust::get<0>(data);
+        int owner = ox_[index]; // before a split, all points at the same position in the index array have the same owner
+        int point_ind1=thrust::get<1>(data);
+        int point_ind2=thrust::get<2>(data);
+        int point_ind3=thrust::get<3>(data);
+        int leftChild=child1_[owner];
+        int split_dim;
+        float dim_val1, dim_val2, dim_val3;
+        SplitInfo split;
+        lrx_[index]=0;
+        lry_[index]=0;
+        lrz_[index]=0;
+        // this element already belongs to a leaf node -> everything alright, no need to change anything
+        if( leftChild==-1 ) {
+            return;
+        }
+        // otherwise: load split data, and assign this index to the new owner
+        split = splits_[owner];
+        split_dim=split.split_dim;
+        switch( split_dim ) {
+        case 0:
+            dim_val1=x_[point_ind1];
+            dim_val2=x_[point_ind2];
+            dim_val3=x_[point_ind3];
+            break;
+        case 1:
+            dim_val1=y_[point_ind1];
+            dim_val2=y_[point_ind2];
+            dim_val3=y_[point_ind3];
+            break;
+        default:
+            dim_val1=z_[point_ind1];
+            dim_val2=z_[point_ind2];
+            dim_val3=z_[point_ind3];
+            break;
+
+        }
+
+
+        int r1=leftChild +(dim_val1 > split.split_val);
+        ox_[index]=r1;
+        int r2=leftChild+(dim_val2 > split.split_val);
+        oy_[index]=r2;
+        oz_[index]=leftChild+(dim_val3 > split.split_val);
+
+        lrx_[index] = (dim_val1 > split.split_val);
+        lry_[index] = (dim_val2 > split.split_val);
+        lrz_[index] = (dim_val3 > split.split_val);
+        //                      return thrust::make_tuple( r1, r2, leftChild+(dim_val > split.split_val) );
+    }
+};
+
+//! used to update the left/right pointers and aabb infos after the node splits
+struct SetLeftAndRightAndAABB
+{
+    int maxPoints;
+    int nElements;
+
+    SplitInfo* nodes;
+    int* counts;
+    int* labels;
+    float4* aabbMin;
+    float4* aabbMax;
+    const float* x,* y,* z;
+    const int* ix, * iy, * iz;
+
+    __host__ __device__
+    void operator()( int i )
+    {
+        int index=labels[i];
+        int right;
+        int left = counts[i];
+        nodes[index].left=left;
+        if( i < nElements-1 ) {
+            right=counts[i+1];
+        }
+        else { // index==nNodes
+            right=maxPoints;
+        }
+        nodes[index].right=right;
+        aabbMin[index].x=x[ix[left]];
+        aabbMin[index].y=y[iy[left]];
+        aabbMin[index].z=z[iz[left]];
+        aabbMax[index].x=x[ix[right-1]];
+        aabbMax[index].y=y[iy[right-1]];
+        aabbMax[index].z=z[iz[right-1]];
+    }
+};
+
+
+//! - decide whether a node has to be split
+//! if yes:
+//! - allocate child nodes
+//! - set split axis as axis of maximum aabb length
+struct SplitNodes
+{
+    int maxPointsPerNode;
+    int* node_count;
+    int* nodes_allocated;
+    int* out_of_space;
+    int* child1_;
+    int* parent_;
+    SplitInfo* splits;
+
+    __device__
+    void operator()( thrust::tuple<int&, int&,SplitInfo&,float4&,float4&, int> node ) // float4: aabbMin, aabbMax
+    {
+        int& parent=thrust::get<0>(node);
+        int& child1=thrust::get<1>(node);
+        SplitInfo& s=thrust::get<2>(node);
+        const float4& aabbMin=thrust::get<3>(node);
+        const float4& aabbMax=thrust::get<4>(node);
+        int my_index = thrust::get<5>(node);
+        bool split_node=false;
+        // first, each thread block counts the number of nodes that it needs to allocate...
+        __shared__ int block_nodes_to_allocate;
+        if( threadIdx.x== 0 ) block_nodes_to_allocate=0;
+        __syncthreads();
+
+        // don't split if all points are equal
+        // (could lead to an infinite loop, and doesn't make any sense anyway)
+        bool all_points_in_node_are_equal=aabbMin.x == aabbMax.x && aabbMin.y==aabbMax.y && aabbMin.z==aabbMax.z;
+
+        int offset_to_global=0;
+
+        // maybe this could be replaced with a reduction...
+        if(( child1==-1) &&( s.right-s.left > maxPointsPerNode) && !all_points_in_node_are_equal ) { // leaf node
+            split_node=true;
+            offset_to_global = atomicAdd( &block_nodes_to_allocate,2 );
+        }
+
+        __syncthreads();
+        __shared__ int block_left;
+        __shared__ bool enough_space;
+        // ... then the first thread tries to allocate this many nodes...
+        if( threadIdx.x==0) {
+            block_left = atomicAdd( node_count, block_nodes_to_allocate );
+            enough_space = block_left+block_nodes_to_allocate < *nodes_allocated;
+            // if it doesn't succeed, no nodes will be created by this block
+            if( !enough_space ) {
+                atomicAdd( node_count, -block_nodes_to_allocate );
+                *out_of_space=1;
+            }
+        }
+
+        __syncthreads();
+        // this thread needs to split it's node && there was enough space for all the nodes
+        // in this block.
+        //(The whole "allocate-per-block-thing" is much faster than letting each element allocate
+        // its space on its own, because shared memory atomics are A LOT faster than
+        // global mem atomics!)
+        if( split_node && enough_space ) {
+            int left = block_left + offset_to_global;
+
+            splits[left].left=s.left;
+            splits[left].right=s.right;
+            splits[left+1].left=0;
+            splits[left+1].right=0;
+
+            // split axis/position: middle of longest aabb extent
+            float4 aabbDim=aabbMax-aabbMin;
+            int maxDim=0;
+            float maxDimLength=aabbDim.x;
+            float4 splitVal=(aabbMax+aabbMin);
+            splitVal*=0.5f;
+            for( int i=1; i<=2; i++ ) {
+                float val = get_value_by_index(aabbDim,i);
+                if( val > maxDimLength ) {
+                    maxDim=i;
+                    maxDimLength=val;
+                }
+            }
+            s.split_dim=maxDim;
+            s.split_val=get_value_by_index(splitVal,maxDim);
+
+            child1_[my_index]=left;
+            splits[my_index]=s;
+
+            parent_[left]=my_index;
+            parent_[left+1]=my_index;
+            child1_[left]=-1;
+            child1_[left+1]=-1;
+        }
+    }
+};
+
+
+//! computes the scatter target address for the split operation, see Sengupta,Harris,Zhang,Owen: Scan Primitives for GPU Computing
+//! in my use case, this is about 2x as fast as thrust::partition
+struct set_addr3
+{
+    const int* val_, * f_;
+
+    int npoints_;
+    __device__
+    int operator()( int id )
+    {
+        int nf = f_[npoints_-1] + (val_[npoints_-1]);
+        int f=f_[id];
+        int t = id -f+nf;
+        return val_[id] ? f : t;
+    }
+};
+
+//! converts a float4 point (xyz) to a tuple of three float vals (used to separate the
+//! float4 input buffer into three arrays in the beginning of the tree build)
+struct pointxyz_to_px_py_pz
+{
+    __device__
+    thrust::tuple<float,float,float> operator()( const float4& val )
+    {
+        return thrust::make_tuple(val.x, val.y, val.z);
+    }
+};
+} // namespace kd_tree_builder_detail
+
+} // namespace cuda
+
+
+std::ostream& operator <<(std::ostream& stream, const cuda::kd_tree_builder_detail::SplitInfo& s)
+{
+    stream<<"(split l/r: "<< s.left <<" "<< s.right<< "  split:"<<s.split_dim<<" "<<s.split_val<<")";
+    return stream;
+}
+class CudaKdTreeBuilder
+{
+public:
+    CudaKdTreeBuilder( const thrust::device_vector<float4>& points, int max_leaf_size ) : /*out_of_space_(1,0),node_count_(1,1),*/ max_leaf_size_(max_leaf_size)
+    {
+        points_=&points;
+        int prealloc = points.size()/max_leaf_size_*16;
+        allocation_info_.resize(3);
+        allocation_info_[NodeCount]=1;
+        allocation_info_[NodesAllocated]=prealloc;
+        allocation_info_[OutOfSpace]=0;
+
+        //              std::cout<<points_->size()<<std::endl;
+
+        child1_=new thrust::device_vector<int>(prealloc,-1);
+        parent_=new thrust::device_vector<int>(prealloc,-1);
+        cuda::kd_tree_builder_detail::SplitInfo s;
+        s.left=0;
+        s.right=0;
+        splits_=new thrust::device_vector<cuda::kd_tree_builder_detail::SplitInfo>(prealloc,s);
+        s.right=points.size();
+        (*splits_)[0]=s;
+
+        aabb_min_=new thrust::device_vector<float4>(prealloc);
+        aabb_max_=new thrust::device_vector<float4>(prealloc);
+
+        index_x_=new thrust::device_vector<int>(points_->size());
+        index_y_=new thrust::device_vector<int>(points_->size());
+        index_z_=new thrust::device_vector<int>(points_->size());
+
+        owners_x_=new thrust::device_vector<int>(points_->size(),0);
+        owners_y_=new thrust::device_vector<int>(points_->size(),0);
+        owners_z_=new thrust::device_vector<int>(points_->size(),0);
+
+        leftright_x_ = new thrust::device_vector<int>(points_->size(),0);
+        leftright_y_ = new thrust::device_vector<int>(points_->size(),0);
+        leftright_z_ = new thrust::device_vector<int>(points_->size(),0);
+
+        tmp_index_=new thrust::device_vector<int>(points_->size());
+        tmp_owners_=new thrust::device_vector<int>(points_->size());
+        tmp_misc_=new thrust::device_vector<int>(points_->size());
+
+        points_x_=new thrust::device_vector<float>(points_->size());
+        points_y_=new thrust::device_vector<float>(points_->size());
+        points_z_=new thrust::device_vector<float>(points_->size());
+        delete_node_info_=false;
+    }
+
+    ~CudaKdTreeBuilder()
+    {
+        if( delete_node_info_ ) {
+            delete child1_;
+            delete parent_;
+            delete splits_;
+            delete aabb_min_;
+            delete aabb_max_;
+            delete index_x_;
+        }
+
+        delete index_y_;
+        delete index_z_;
+        delete owners_x_;
+        delete owners_y_;
+        delete owners_z_;
+        delete points_x_;
+        delete points_y_;
+        delete points_z_;
+        delete leftright_x_;
+        delete leftright_y_;
+        delete leftright_z_;
+        delete tmp_index_;
+        delete tmp_owners_;
+        delete tmp_misc_;
+    }
+
+	//! build the tree
+	//! general idea:
+	//! - build sorted lists of the points in x y and z order (to be able to compute tight AABBs in O(1) )
+	//! - while( nodes to split exist )
+	//!    - split non-child nodes along longest axis if the number of points is > max_points_per_node
+	//!    - for each point: determine whether it is in a node that was split. If yes, mark it as belonging to the left or right child node of its current parent node
+	//!    - reorder the points so that the points of a single node are continuous in the node array
+	//!    - update the left/right pointers and AABBs of all nodes
+    void buildTree()
+    {
+        //              std::cout<<"buildTree()"<<std::endl;
+        //              sleep(1);
+        //              Util::Timer stepTimer;
+        thrust::transform( points_->begin(), points_->end(), thrust::make_zip_iterator(thrust::make_tuple(points_x_->begin(), points_y_->begin(),points_z_->begin()) ), cuda::kd_tree_builder_detail::pointxyz_to_px_py_pz() );
+
+        thrust::counting_iterator<int> it(0);
+        thrust::copy( it, it+points_->size(), index_x_->begin() );
+
+        thrust::copy( index_x_->begin(), index_x_->end(), index_y_->begin() );
+        thrust::copy( index_x_->begin(), index_x_->end(), index_z_->begin() );
+
+        thrust::device_vector<float> tmpv(points_->size());
+
+        // create sorted index list -> can be used to compute AABBs in O(1)
+        thrust::copy(points_x_->begin(), points_x_->end(), tmpv.begin());
+        thrust::sort_by_key( tmpv.begin(), tmpv.end(), index_x_->begin() );
+        thrust::copy(points_y_->begin(), points_y_->end(), tmpv.begin());
+        thrust::sort_by_key( tmpv.begin(), tmpv.end(), index_y_->begin() );
+        thrust::copy(points_z_->begin(), points_z_->end(), tmpv.begin());
+        thrust::sort_by_key( tmpv.begin(), tmpv.end(), index_z_->begin() );
+
+
+        (*aabb_min_)[0]=make_float4((*points_x_)[(*index_x_)[0]],(*points_y_)[(*index_y_)[0]],(*points_z_)[(*index_z_)[0]],0);
+
+        (*aabb_max_)[0]=make_float4((*points_x_)[(*index_x_)[points_->size()-1]],(*points_y_)[(*index_y_)[points_->size()-1]],(*points_z_)[(*index_z_)[points_->size()-1]],0);
+        #ifdef PRINT_DEBUG_TIMING
+        cudaDeviceSynchronize();
+        std::cout<<" initial stuff:"<<stepTimer.elapsed()<<std::endl;
+        stepTimer.restart();
+        #endif
+        int last_node_count=0;
+        for( int i=0;; i++ ) {
+            cuda::kd_tree_builder_detail::SplitNodes sn;
+
+            sn.maxPointsPerNode=max_leaf_size_;
+            sn.node_count=thrust::raw_pointer_cast(&allocation_info_[NodeCount]);
+            sn.nodes_allocated=thrust::raw_pointer_cast(&allocation_info_[NodesAllocated]);
+            sn.out_of_space=thrust::raw_pointer_cast(&allocation_info_[OutOfSpace]);
+            sn.child1_=thrust::raw_pointer_cast(&(*child1_)[0]);
+            sn.parent_=thrust::raw_pointer_cast(&(*parent_)[0]);
+            sn.splits=thrust::raw_pointer_cast(&(*splits_)[0]);
+
+            thrust::counting_iterator<int> cit(0);
+            thrust::for_each( thrust::make_zip_iterator(thrust::make_tuple( parent_->begin(), child1_->begin(),  splits_->begin(), aabb_min_->begin(), aabb_max_->begin(), cit  )),
+                              thrust::make_zip_iterator(thrust::make_tuple( parent_->begin()+last_node_count, child1_->begin()+last_node_count,splits_->begin()+last_node_count, aabb_min_->begin()+last_node_count, aabb_max_->begin()+last_node_count,cit+last_node_count  )),
+                              sn   );
+            // copy allocation info to host
+            thrust::host_vector<int> alloc_info = allocation_info_;
+
+            if( last_node_count == alloc_info[NodeCount] ) { // no more nodes were split -> done
+                break;
+            }
+            last_node_count=alloc_info[NodeCount];
+			
+			// a node was un-splittable due to a lack of space
+            if( alloc_info[OutOfSpace]==1 ) {
+                resize_node_vectors(alloc_info[NodesAllocated]*2);
+                alloc_info[OutOfSpace]=0;
+                alloc_info[NodesAllocated]*=2;
+                allocation_info_=alloc_info;
+            }
+            #ifdef PRINT_DEBUG_TIMING
+            cudaDeviceSynchronize();
+            std::cout<<" node split:"<<stepTimer.elapsed()<<std::endl;
+            stepTimer.restart();
+            #endif
+
+            // foreach point: point was in node that was split?move it to child (leaf) node : do nothing
+            cuda::kd_tree_builder_detail::MovePointsToChildNodes sno( thrust::raw_pointer_cast(&(*child1_)[0]),
+                                                                      thrust::raw_pointer_cast(&(*splits_)[0]),
+                                                                      thrust::raw_pointer_cast(&(*points_x_)[0]),
+                                                                      thrust::raw_pointer_cast(&(*points_y_)[0]),
+                                                                      thrust::raw_pointer_cast(&(*points_z_)[0]),
+                                                                      thrust::raw_pointer_cast(&(*owners_x_)[0]),
+                                                                      thrust::raw_pointer_cast(&(*owners_y_)[0]),
+                                                                      thrust::raw_pointer_cast(&(*owners_z_)[0]),
+                                                                      thrust::raw_pointer_cast(&(*leftright_x_)[0]),
+                                                                      thrust::raw_pointer_cast(&(*leftright_y_)[0]),
+                                                                      thrust::raw_pointer_cast(&(*leftright_z_)[0])
+                                                                      );
+            thrust::counting_iterator<int> ci0(0);
+            thrust::for_each( thrust::make_zip_iterator( thrust::make_tuple( ci0, index_x_->begin(), index_y_->begin(), index_z_->begin()) ),
+                              thrust::make_zip_iterator( thrust::make_tuple( ci0+points_->size(), index_x_->end(), index_y_->end(), index_z_->end()) ),sno  );
+
+            #ifdef PRINT_DEBUG_TIMING
+            cudaDeviceSynchronize();
+            std::cout<<" set new owners:"<<stepTimer.elapsed()<<std::endl;
+            stepTimer.restart();
+            #endif
+
+            // move points around so that each leaf node's points are continuous
+            separate_left_and_right_children(*index_x_,*owners_x_,*tmp_index_,*tmp_owners_, *leftright_x_);
+            std::swap(tmp_index_, index_x_);
+            std::swap(tmp_owners_, owners_x_);
+            separate_left_and_right_children(*index_y_,*owners_y_,*tmp_index_,*tmp_owners_, *leftright_y_,false);
+            std::swap(tmp_index_, index_y_);
+            separate_left_and_right_children(*index_z_,*owners_z_,*tmp_index_,*tmp_owners_, *leftright_z_,false);
+            std::swap(tmp_index_, index_z_);
+
+            #ifdef PRINT_DEBUG_TIMING
+            cudaDeviceSynchronize();
+            std::cout<<" split:"<<stepTimer.elapsed()<<std::endl;
+            stepTimer.restart();
+            #endif
+            // calculate new AABB etc
+            update_leftright_and_aabb( *points_x_, *points_y_, *points_z_, *index_x_, *index_y_, *index_z_, *owners_x_, *splits_,*aabb_min_, *aabb_max_);
+            #ifdef PRINT_DEBUG_TIMING
+            cudaDeviceSynchronize();
+            std::cout<<" update_leftright_and_aabb:"<<stepTimer.elapsed()<<std::endl;
+            stepTimer.restart();
+            print_vector(node_count_);
+            #endif
+
+        }
+    }
+    
+	template<class Distance> 
+	friend class KDTreeCuda3dIndex;
+
+protected:
+
+
+    //! takes the partitioned nodes, and sets the left-/right info of leaf nodes, as well as the AABBs
+    void
+    update_leftright_and_aabb( const thrust::device_vector<float>& x, const thrust::device_vector<float>& y,const thrust::device_vector<float>& z,
+                               const thrust::device_vector<int>& ix, const thrust::device_vector<int>& iy,const thrust::device_vector<int>& iz,
+                               const thrust::device_vector<int>& owners,
+                               thrust::device_vector<cuda::kd_tree_builder_detail::SplitInfo>& splits, thrust::device_vector<float4>& aabbMin,thrust::device_vector<float4>& aabbMax)
+    {
+        thrust::device_vector<int>* labelsUnique=tmp_owners_;
+        thrust::device_vector<int>* countsUnique=tmp_index_;
+		// assume: points of each node are continuous in the array
+		
+		// find which nodes are here, and where each node's points begin and end
+        int unique_labels = thrust::unique_by_key_copy( owners.begin(), owners.end(), thrust::counting_iterator<int>(0), labelsUnique->begin(), countsUnique->begin()).first - labelsUnique->begin();
+
+		// update the info
+        cuda::kd_tree_builder_detail::SetLeftAndRightAndAABB s;
+        s.maxPoints=x.size();
+        s.nElements=unique_labels;
+        s.nodes=thrust::raw_pointer_cast(&(splits[0]));
+        s.counts=thrust::raw_pointer_cast(&( (*countsUnique)[0]));
+        s.labels=thrust::raw_pointer_cast(&( (*labelsUnique)[0]));
+        s.x=thrust::raw_pointer_cast(&x[0]);
+        s.y=thrust::raw_pointer_cast(&y[0]);
+        s.z=thrust::raw_pointer_cast(&z[0]);
+        s.ix=thrust::raw_pointer_cast(&ix[0]);
+        s.iy=thrust::raw_pointer_cast(&iy[0]);
+        s.iz=thrust::raw_pointer_cast(&iz[0]);
+        s.aabbMin=thrust::raw_pointer_cast(&aabbMin[0]);
+        s.aabbMax=thrust::raw_pointer_cast(&aabbMax[0]);
+
+        thrust::counting_iterator<int> it(0);
+        thrust::for_each(it, it+unique_labels, s);
+    }
+
+    //! Separates the left and right children of each node into continuous parts of the array.
+    //! More specifically, it seperates children with even and odd node indices because nodes are always
+    //! allocated in pairs -> child1==child2+1 -> child1 even and child2 odd, or vice-versa.
+    //! Since the split operation is stable, this results in continuous partitions
+    //! for all the single nodes.
+    //! (basically the split primitive according to sengupta et al)
+    //! about twice as fast as thrust::partition
+    void separate_left_and_right_children( thrust::device_vector<int>& key_in, thrust::device_vector<int>& val_in, thrust::device_vector<int>& key_out, thrust::device_vector<int>& val_out, thrust::device_vector<int>& left_right_marks, bool scatter_val_out=true )
+    {
+        thrust::device_vector<int>* f_tmp = &val_out;
+        thrust::device_vector<int>* addr_tmp = tmp_misc_;
+
+        thrust::exclusive_scan( /*thrust::make_transform_iterator(*/ left_right_marks.begin() /*,cuda::kd_tree_builder_detail::IsEven*/
+                                                                     /*())*/, /*thrust::make_transform_iterator(*/ left_right_marks.end() /*,cuda::kd_tree_builder_detail::IsEven*/
+                                                                     /*())*/,     f_tmp->begin() );
+        cuda::kd_tree_builder_detail::set_addr3 sa;
+        sa.val_=thrust::raw_pointer_cast(&left_right_marks[0]);
+        sa.f_=thrust::raw_pointer_cast(&(*f_tmp)[0]);
+        sa.npoints_=key_in.size();
+        thrust::counting_iterator<int> it(0);
+        thrust::transform(it, it+val_in.size(), addr_tmp->begin(), sa);
+
+        thrust::scatter(key_in.begin(), key_in.end(), addr_tmp->begin(), key_out.begin());
+        if( scatter_val_out ) thrust::scatter(val_in.begin(), val_in.end(), addr_tmp->begin(), val_out.begin());
+    }
+
+    //! allocates additional space in all the node-related vectors.
+    //! new_size elements will be added to all vectors.
+    void resize_node_vectors( size_t new_size )
+    {
+        size_t add = new_size - child1_->size();
+        child1_->insert(child1_->end(), add, -1);
+        parent_->insert(parent_->end(), add, -1);
+        cuda::kd_tree_builder_detail::SplitInfo s;
+        s.left=0;
+        s.right=0;
+        splits_->insert(splits_->end(), add, s);
+        float4 f;
+        aabb_min_->insert(aabb_min_->end(), add, f);
+        aabb_max_->insert(aabb_max_->end(), add, f);
+    }
+
+
+    const thrust::device_vector<float4>* points_;
+	
+	// tree data, those are stored per-node
+	
+	//! left child of each node. (right child==left child + 1, due to the alloc mechanism)
+	//! child1_[node]==-1 if node is a leaf node
+    thrust::device_vector<int>* child1_;
+	//! parent node of each node
+    thrust::device_vector<int>* parent_;
+	//! split info (dim/value or left/right pointers)
+    thrust::device_vector<cuda::kd_tree_builder_detail::SplitInfo>* splits_;
+	//! min aabb value of each node
+    thrust::device_vector<float4>* aabb_min_;
+	//! max aabb value of each node
+    thrust::device_vector<float4>* aabb_max_;
+
+    enum AllocationInfo
+    {
+        NodeCount=0,
+        NodesAllocated=1,
+        OutOfSpace=2
+    };
+    // those were put into a single vector of 3 elements so that only one mem transfer will be needed for all three of them
+    //  thrust::device_vector<int> out_of_space_;
+    //  thrust::device_vector<int> node_count_;
+    //  thrust::device_vector<int> nodes_allocated_;
+    thrust::device_vector<int> allocation_info_;
+	
+    int max_leaf_size_;
+
+	// coordinate values of the points
+    thrust::device_vector<float>* points_x_, * points_y_, * points_z_;
+	// indices
+    thrust::device_vector<int>* index_x_,  * index_y_,  * index_z_;
+	// owner node
+    thrust::device_vector<int>* owners_x_, * owners_y_, * owners_z_;
+	// contains info about whether a point was partitioned to the left or right child after a split
+    thrust::device_vector<int>* leftright_x_, * leftright_y_, * leftright_z_;
+    thrust::device_vector<int>* tmp_index_, * tmp_owners_, * tmp_misc_;
+    bool delete_node_info_;
+};
+
+
+} // namespace flann
+#endif
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/kdtree_index.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/kdtree_index.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/kdtree_index.h	(revision 15840)
@@ -0,0 +1,620 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_KDTREE_INDEX_H_
+#define FLANN_KDTREE_INDEX_H_
+
+#include <algorithm>
+#include <map>
+#include <cassert>
+#include <cstring>
+
+#include "flann/general.h"
+#include "flann/algorithms/nn_index.h"
+#include "flann/util/dynamic_bitset.h"
+#include "flann/util/matrix.h"
+#include "flann/util/result_set.h"
+#include "flann/util/heap.h"
+#include "flann/util/allocator.h"
+#include "flann/util/random.h"
+#include "flann/util/saving.h"
+
+
+namespace flann
+{
+
+struct KDTreeIndexParams : public IndexParams
+{
+    KDTreeIndexParams(int trees = 4)
+    {
+        (*this)["algorithm"] = FLANN_INDEX_KDTREE;
+        (*this)["trees"] = trees;
+    }
+};
+
+
+/**
+ * Randomized kd-tree index
+ *
+ * Contains the k-d trees and other information for indexing a set of points
+ * for nearest-neighbor matching.
+ */
+template <typename Distance>
+class KDTreeIndex : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+    typedef bool needs_kdtree_distance;
+
+    /**
+     * KDTree constructor
+     *
+     * Params:
+     *          inputData = dataset with the input features
+     *          params = parameters passed to the kdtree algorithm
+     */
+    KDTreeIndex(const Matrix<ElementType>& inputData, const IndexParams& params = KDTreeIndexParams(),
+                Distance d = Distance() ) :
+        dataset_(inputData), index_params_(params), distance_(d)
+    {
+        size_ = dataset_.rows;
+        veclen_ = dataset_.cols;
+
+        trees_ = get_param(index_params_,"trees",4);
+        tree_roots_ = new NodePtr[trees_];
+
+        // Create a permutable array of indices to the input vectors.
+        vind_.resize(size_);
+        for (size_t i = 0; i < size_; ++i) {
+            vind_[i] = int(i);
+        }
+
+        mean_ = new DistanceType[veclen_];
+        var_ = new DistanceType[veclen_];
+    }
+
+
+    KDTreeIndex(const KDTreeIndex&);
+    KDTreeIndex& operator=(const KDTreeIndex&);
+
+    /**
+     * Standard destructor
+     */
+    ~KDTreeIndex()
+    {
+        if (tree_roots_!=NULL) {
+            delete[] tree_roots_;
+        }
+        delete[] mean_;
+        delete[] var_;
+    }
+
+    /**
+     * Builds the index
+     */
+    void buildIndex()
+    {
+        /* Construct the randomized trees. */
+        for (int i = 0; i < trees_; i++) {
+            /* Randomize the order of vectors to allow for unbiased sampling. */
+            std::random_shuffle(vind_.begin(), vind_.end());
+            tree_roots_[i] = divideTree(&vind_[0], int(size_) );
+        }
+    }
+
+
+    flann_algorithm_t getType() const
+    {
+        return FLANN_INDEX_KDTREE;
+    }
+
+
+    void saveIndex(FILE* stream)
+    {
+        save_value(stream, trees_);
+        for (int i=0; i<trees_; ++i) {
+            save_tree(stream, tree_roots_[i]);
+        }
+    }
+
+
+
+    void loadIndex(FILE* stream)
+    {
+        load_value(stream, trees_);
+        if (tree_roots_!=NULL) {
+            delete[] tree_roots_;
+        }
+        tree_roots_ = new NodePtr[trees_];
+        for (int i=0; i<trees_; ++i) {
+            load_tree(stream,tree_roots_[i]);
+        }
+
+        index_params_["algorithm"] = getType();
+        index_params_["trees"] = tree_roots_;
+    }
+
+    /**
+     *  Returns size of index.
+     */
+    size_t size() const
+    {
+        return size_;
+    }
+
+    /**
+     * Returns the length of an index feature.
+     */
+    size_t veclen() const
+    {
+        return veclen_;
+    }
+
+    /**
+     * Computes the inde memory usage
+     * Returns: memory used by the index
+     */
+    int usedMemory() const
+    {
+        return int(pool_.usedMemory+pool_.wastedMemory+dataset_.rows*sizeof(int));  // pool memory and vind array memory
+    }
+
+    /**
+     * Find set of nearest neighbors to vec. Their indices are stored inside
+     * the result object.
+     *
+     * Params:
+     *     result = the result object in which the indices of the nearest-neighbors are stored
+     *     vec = the vector for which to search the nearest neighbors
+     *     maxCheck = the maximum number of restarts (in a best-bin-first manner)
+     */
+    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
+    {
+        int maxChecks = searchParams.checks;
+        float epsError = 1+searchParams.eps;
+
+        if (maxChecks==FLANN_CHECKS_UNLIMITED) {
+            getExactNeighbors(result, vec, epsError);
+        }
+        else {
+            getNeighbors(result, vec, maxChecks, epsError);
+        }
+    }
+
+    IndexParams getParameters() const
+    {
+        return index_params_;
+    }
+
+private:
+
+
+    /*--------------------- Internal Data Structures --------------------------*/
+    struct Node
+    {
+        /**
+         * Dimension used for subdivision.
+         */
+        int divfeat;
+        /**
+         * The values used for subdivision.
+         */
+        DistanceType divval;
+        /**
+         * The child nodes.
+         */
+        Node* child1, * child2;
+    };
+    typedef Node* NodePtr;
+    typedef BranchStruct<NodePtr, DistanceType> BranchSt;
+    typedef BranchSt* Branch;
+
+
+
+    void save_tree(FILE* stream, NodePtr tree)
+    {
+        save_value(stream, *tree);
+        if (tree->child1!=NULL) {
+            save_tree(stream, tree->child1);
+        }
+        if (tree->child2!=NULL) {
+            save_tree(stream, tree->child2);
+        }
+    }
+
+
+    void load_tree(FILE* stream, NodePtr& tree)
+    {
+        tree = pool_.allocate<Node>();
+        load_value(stream, *tree);
+        if (tree->child1!=NULL) {
+            load_tree(stream, tree->child1);
+        }
+        if (tree->child2!=NULL) {
+            load_tree(stream, tree->child2);
+        }
+    }
+
+
+    /**
+     * Create a tree node that subdivides the list of vecs from vind[first]
+     * to vind[last].  The routine is called recursively on each sublist.
+     * Place a pointer to this new tree node in the location pTree.
+     *
+     * Params: pTree = the new node to create
+     *                  first = index of the first vector
+     *                  last = index of the last vector
+     */
+    NodePtr divideTree(int* ind, int count)
+    {
+        NodePtr node = pool_.allocate<Node>(); // allocate memory
+
+        /* If too few exemplars remain, then make this a leaf node. */
+        if ( count == 1) {
+            node->child1 = node->child2 = NULL;    /* Mark as leaf node. */
+            node->divfeat = *ind;    /* Store index of this vec. */
+        }
+        else {
+            int idx;
+            int cutfeat;
+            DistanceType cutval;
+            meanSplit(ind, count, idx, cutfeat, cutval);
+
+            node->divfeat = cutfeat;
+            node->divval = cutval;
+            node->child1 = divideTree(ind, idx);
+            node->child2 = divideTree(ind+idx, count-idx);
+        }
+
+        return node;
+    }
+
+
+    /**
+     * Choose which feature to use in order to subdivide this set of vectors.
+     * Make a random choice among those with the highest variance, and use
+     * its variance as the threshold value.
+     */
+    void meanSplit(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval)
+    {
+        memset(mean_,0,veclen_*sizeof(DistanceType));
+        memset(var_,0,veclen_*sizeof(DistanceType));
+
+        /* Compute mean values.  Only the first SAMPLE_MEAN values need to be
+            sampled to get a good estimate.
+         */
+        int cnt = std::min((int)SAMPLE_MEAN+1, count);
+        for (int j = 0; j < cnt; ++j) {
+            ElementType* v = dataset_[ind[j]];
+            for (size_t k=0; k<veclen_; ++k) {
+                mean_[k] += v[k];
+            }
+        }
+        for (size_t k=0; k<veclen_; ++k) {
+            mean_[k] /= cnt;
+        }
+
+        /* Compute variances (no need to divide by count). */
+        for (int j = 0; j < cnt; ++j) {
+            ElementType* v = dataset_[ind[j]];
+            for (size_t k=0; k<veclen_; ++k) {
+                DistanceType dist = v[k] - mean_[k];
+                var_[k] += dist * dist;
+            }
+        }
+        /* Select one of the highest variance indices at random. */
+        cutfeat = selectDivision(var_);
+        cutval = mean_[cutfeat];
+
+        int lim1, lim2;
+        planeSplit(ind, count, cutfeat, cutval, lim1, lim2);
+
+        if (lim1>count/2) index = lim1;
+        else if (lim2<count/2) index = lim2;
+        else index = count/2;
+
+        /* If either list is empty, it means that all remaining features
+         * are identical. Split in the middle to maintain a balanced tree.
+         */
+        if ((lim1==count)||(lim2==0)) index = count/2;
+    }
+
+
+    /**
+     * Select the top RAND_DIM largest values from v and return the index of
+     * one of these selected at random.
+     */
+    int selectDivision(DistanceType* v)
+    {
+        int num = 0;
+        size_t topind[RAND_DIM];
+
+        /* Create a list of the indices of the top RAND_DIM values. */
+        for (size_t i = 0; i < veclen_; ++i) {
+            if ((num < RAND_DIM)||(v[i] > v[topind[num-1]])) {
+                /* Put this element at end of topind. */
+                if (num < RAND_DIM) {
+                    topind[num++] = i;            /* Add to list. */
+                }
+                else {
+                    topind[num-1] = i;         /* Replace last element. */
+                }
+                /* Bubble end value down to right location by repeated swapping. */
+                int j = num - 1;
+                while (j > 0  &&  v[topind[j]] > v[topind[j-1]]) {
+                    std::swap(topind[j], topind[j-1]);
+                    --j;
+                }
+            }
+        }
+        /* Select a random integer in range [0,num-1], and return that index. */
+        int rnd = rand_int(num);
+        return (int)topind[rnd];
+    }
+
+
+    /**
+     *  Subdivide the list of points by a plane perpendicular on axe corresponding
+     *  to the 'cutfeat' dimension at 'cutval' position.
+     *
+     *  On return:
+     *  dataset[ind[0..lim1-1]][cutfeat]<cutval
+     *  dataset[ind[lim1..lim2-1]][cutfeat]==cutval
+     *  dataset[ind[lim2..count]][cutfeat]>cutval
+     */
+    void planeSplit(int* ind, int count, int cutfeat, DistanceType cutval, int& lim1, int& lim2)
+    {
+        /* Move vector indices for left subtree to front of list. */
+        int left = 0;
+        int right = count-1;
+        for (;; ) {
+            while (left<=right && dataset_[ind[left]][cutfeat]<cutval) ++left;
+            while (left<=right && dataset_[ind[right]][cutfeat]>=cutval) --right;
+            if (left>right) break;
+            std::swap(ind[left], ind[right]); ++left; --right;
+        }
+        lim1 = left;
+        right = count-1;
+        for (;; ) {
+            while (left<=right && dataset_[ind[left]][cutfeat]<=cutval) ++left;
+            while (left<=right && dataset_[ind[right]][cutfeat]>cutval) --right;
+            if (left>right) break;
+            std::swap(ind[left], ind[right]); ++left; --right;
+        }
+        lim2 = left;
+    }
+
+    /**
+     * Performs an exact nearest neighbor search. The exact search performs a full
+     * traversal of the tree.
+     */
+    void getExactNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, float epsError)
+    {
+        //		checkID -= 1;  /* Set a different unique ID for each search. */
+
+        if (trees_ > 1) {
+            fprintf(stderr,"It doesn't make any sense to use more than one tree for exact search");
+        }
+        if (trees_>0) {
+            searchLevelExact(result, vec, tree_roots_[0], 0.0, epsError);
+        }
+    }
+
+    /**
+     * Performs the approximate nearest-neighbor search. The search is approximate
+     * because the tree traversal is abandoned after a given number of descends in
+     * the tree.
+     */
+    void getNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, int maxCheck, float epsError)
+    {
+        int i;
+        BranchSt branch;
+
+        int checkCount = 0;
+        Heap<BranchSt>* heap = new Heap<BranchSt>((int)size_);
+        DynamicBitset checked(size_);
+
+        /* Search once through each tree down to root. */
+        for (i = 0; i < trees_; ++i) {
+            searchLevel(result, vec, tree_roots_[i], 0, checkCount, maxCheck, epsError, heap, checked);
+        }
+
+        /* Keep searching other branches from heap until finished. */
+        while ( heap->popMin(branch) && (checkCount < maxCheck || !result.full() )) {
+            searchLevel(result, vec, branch.node, branch.mindist, checkCount, maxCheck, epsError, heap, checked);
+        }
+
+        delete heap;
+
+    }
+
+
+    /**
+     *  Search starting from a given node of the tree.  Based on any mismatches at
+     *  higher levels, all exemplars below this level must have a distance of
+     *  at least "mindistsq".
+     */
+    void searchLevel(ResultSet<DistanceType>& result_set, const ElementType* vec, NodePtr node, DistanceType mindist, int& checkCount, int maxCheck,
+                     float epsError, Heap<BranchSt>* heap, DynamicBitset& checked)
+    {
+        if (result_set.worstDist()<mindist) {
+            //			printf("Ignoring branch, too far\n");
+            return;
+        }
+
+        /* If this is a leaf node, then do check and return. */
+        if ((node->child1 == NULL)&&(node->child2 == NULL)) {
+            /*  Do not check same node more than once when searching multiple trees.
+                Once a vector is checked, we set its location in vind to the
+                current checkID.
+             */
+            int index = node->divfeat;
+            if ( checked.test(index) || ((checkCount>=maxCheck)&& result_set.full()) ) return;
+            checked.set(index);
+            checkCount++;
+
+            DistanceType dist = distance_(dataset_[index], vec, veclen_);
+            result_set.addPoint(dist,index);
+
+            return;
+        }
+
+        /* Which child branch should be taken first? */
+        ElementType val = vec[node->divfeat];
+        DistanceType diff = val - node->divval;
+        NodePtr bestChild = (diff < 0) ? node->child1 : node->child2;
+        NodePtr otherChild = (diff < 0) ? node->child2 : node->child1;
+
+        /* Create a branch record for the branch not taken.  Add distance
+            of this feature boundary (we don't attempt to correct for any
+            use of this feature in a parent node, which is unlikely to
+            happen and would have only a small effect).  Don't bother
+            adding more branches to heap after halfway point, as cost of
+            adding exceeds their value.
+         */
+
+        DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat);
+        //		if (2 * checkCount < maxCheck  ||  !result.full()) {
+        if ((new_distsq*epsError < result_set.worstDist())||  !result_set.full()) {
+            heap->insert( BranchSt(otherChild, new_distsq) );
+        }
+
+        /* Call recursively to search next level down. */
+        searchLevel(result_set, vec, bestChild, mindist, checkCount, maxCheck, epsError, heap, checked);
+    }
+
+    /**
+     * Performs an exact search in the tree starting from a node.
+     */
+    void searchLevelExact(ResultSet<DistanceType>& result_set, const ElementType* vec, const NodePtr node, DistanceType mindist, const float epsError)
+    {
+        /* If this is a leaf node, then do check and return. */
+        if ((node->child1 == NULL)&&(node->child2 == NULL)) {
+            int index = node->divfeat;
+            DistanceType dist = distance_(dataset_[index], vec, veclen_);
+            result_set.addPoint(dist,index);
+            return;
+        }
+
+        /* Which child branch should be taken first? */
+        ElementType val = vec[node->divfeat];
+        DistanceType diff = val - node->divval;
+        NodePtr bestChild = (diff < 0) ? node->child1 : node->child2;
+        NodePtr otherChild = (diff < 0) ? node->child2 : node->child1;
+
+        /* Create a branch record for the branch not taken.  Add distance
+            of this feature boundary (we don't attempt to correct for any
+            use of this feature in a parent node, which is unlikely to
+            happen and would have only a small effect).  Don't bother
+            adding more branches to heap after halfway point, as cost of
+            adding exceeds their value.
+         */
+
+        DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat);
+
+        /* Call recursively to search next level down. */
+        searchLevelExact(result_set, vec, bestChild, mindist, epsError);
+
+        if (new_distsq*epsError<=result_set.worstDist()) {
+            searchLevelExact(result_set, vec, otherChild, new_distsq, epsError);
+        }
+    }
+
+
+private:
+
+    enum
+    {
+        /**
+         * To improve efficiency, only SAMPLE_MEAN random values are used to
+         * compute the mean and variance at each level when building a tree.
+         * A value of 100 seems to perform as well as using all values.
+         */
+        SAMPLE_MEAN = 100,
+        /**
+         * Top random dimensions to consider
+         *
+         * When creating random trees, the dimension on which to subdivide is
+         * selected at random from among the top RAND_DIM dimensions with the
+         * highest variance.  A value of 5 works well.
+         */
+        RAND_DIM=5
+    };
+
+
+    /**
+     * Number of randomized trees that are used
+     */
+    int trees_;
+
+    /**
+     *  Array of indices to vectors in the dataset.
+     */
+    std::vector<int> vind_;
+
+    /**
+     * The dataset used by this index
+     */
+    const Matrix<ElementType> dataset_;
+
+    IndexParams index_params_;
+
+    size_t size_;
+    size_t veclen_;
+
+
+    DistanceType* mean_;
+    DistanceType* var_;
+
+
+    /**
+     * Array of k-d trees used to find neighbours.
+     */
+    NodePtr* tree_roots_;
+
+    /**
+     * Pooled memory allocator.
+     *
+     * Using a pooled memory allocator is more efficient
+     * than allocating memory directly when there is a large
+     * number small of memory allocations.
+     */
+    PooledAllocator pool_;
+
+    Distance distance_;
+
+
+};   // class KDTreeForest
+
+}
+
+#endif //FLANN_KDTREE_INDEX_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/kdtree_single_index.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/kdtree_single_index.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/kdtree_single_index.h	(revision 15840)
@@ -0,0 +1,607 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_KDTREE_SINGLE_INDEX_H_
+#define FLANN_KDTREE_SINGLE_INDEX_H_
+
+#include <algorithm>
+#include <map>
+#include <cassert>
+#include <cstring>
+
+#include "flann/general.h"
+#include "flann/algorithms/nn_index.h"
+#include "flann/util/matrix.h"
+#include "flann/util/result_set.h"
+#include "flann/util/heap.h"
+#include "flann/util/allocator.h"
+#include "flann/util/random.h"
+#include "flann/util/saving.h"
+
+namespace flann
+{
+
+struct KDTreeSingleIndexParams : public IndexParams
+{
+    KDTreeSingleIndexParams(int leaf_max_size = 10, bool reorder = true)
+    {
+        (*this)["algorithm"] = FLANN_INDEX_KDTREE_SINGLE;
+        (*this)["leaf_max_size"] = leaf_max_size;
+        (*this)["reorder"] = reorder;
+    }
+};
+
+
+/**
+ * Single kd-tree index
+ *
+ * Contains the k-d trees and other information for indexing a set of points
+ * for nearest-neighbor matching.
+ */
+template <typename Distance>
+class KDTreeSingleIndex : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+    typedef bool needs_kdtree_distance;
+
+    /**
+     * KDTree constructor
+     *
+     * Params:
+     *          inputData = dataset with the input features
+     *          params = parameters passed to the kdtree algorithm
+     */
+    KDTreeSingleIndex(const Matrix<ElementType>& inputData, const IndexParams& params = KDTreeSingleIndexParams(),
+                      Distance d = Distance() ) :
+        dataset_(inputData), index_params_(params), distance_(d)
+    {
+        size_ = dataset_.rows;
+        dim_ = dataset_.cols;
+        leaf_max_size_ = get_param(params,"leaf_max_size",10);
+        reorder_ = get_param(params,"reorder",true);
+
+        // Create a permutable array of indices to the input vectors.
+        vind_.resize(size_);
+        for (size_t i = 0; i < size_; i++) {
+            vind_[i] = i;
+        }
+    }
+
+    KDTreeSingleIndex(const KDTreeSingleIndex&);
+    KDTreeSingleIndex& operator=(const KDTreeSingleIndex&);
+
+    /**
+     * Standard destructor
+     */
+    ~KDTreeSingleIndex()
+    {
+        if (reorder_) delete[] data_.ptr();
+    }
+
+    /**
+     * Builds the index
+     */
+    void buildIndex()
+    {
+        computeBoundingBox(root_bbox_);
+        root_node_ = divideTree(0, size_, root_bbox_ );   // construct the tree
+
+        if (reorder_) {
+            data_ = flann::Matrix<ElementType>(new ElementType[size_*dim_], size_, dim_);
+            for (size_t i=0; i<size_; ++i) {
+                for (size_t j=0; j<dim_; ++j) {
+                    data_[i][j] = dataset_[vind_[i]][j];
+                }
+            }
+        }
+        else {
+            data_ = dataset_;
+        }
+    }
+
+    flann_algorithm_t getType() const
+    {
+        return FLANN_INDEX_KDTREE_SINGLE;
+    }
+
+
+    void saveIndex(FILE* stream)
+    {
+        save_value(stream, size_);
+        save_value(stream, dim_);
+        save_value(stream, root_bbox_);
+        save_value(stream, reorder_);
+        save_value(stream, leaf_max_size_);
+        save_value(stream, vind_);
+        if (reorder_) {
+            save_value(stream, data_);
+        }
+        save_tree(stream, root_node_);
+    }
+
+
+    void loadIndex(FILE* stream)
+    {
+        load_value(stream, size_);
+        load_value(stream, dim_);
+        load_value(stream, root_bbox_);
+        load_value(stream, reorder_);
+        load_value(stream, leaf_max_size_);
+        load_value(stream, vind_);
+        if (reorder_) {
+            load_value(stream, data_);
+        }
+        else {
+            data_ = dataset_;
+        }
+        load_tree(stream, root_node_);
+
+
+        index_params_["algorithm"] = getType();
+        index_params_["leaf_max_size"] = leaf_max_size_;
+        index_params_["reorder"] = reorder_;
+    }
+
+    /**
+     *  Returns size of index.
+     */
+    size_t size() const
+    {
+        return size_;
+    }
+
+    /**
+     * Returns the length of an index feature.
+     */
+    size_t veclen() const
+    {
+        return dim_;
+    }
+
+    /**
+     * Computes the inde memory usage
+     * Returns: memory used by the index
+     */
+    int usedMemory() const
+    {
+        return pool_.usedMemory+pool_.wastedMemory+dataset_.rows*sizeof(int);  // pool memory and vind array memory
+    }
+
+    IndexParams getParameters() const
+    {
+        return index_params_;
+    }
+
+    /**
+     * Find set of nearest neighbors to vec. Their indices are stored inside
+     * the result object.
+     *
+     * Params:
+     *     result = the result object in which the indices of the nearest-neighbors are stored
+     *     vec = the vector for which to search the nearest neighbors
+     *     maxCheck = the maximum number of restarts (in a best-bin-first manner)
+     */
+    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
+    {
+        float epsError = 1+searchParams.eps;
+
+        std::vector<DistanceType> dists(dim_,0);
+        DistanceType distsq = computeInitialDistances(vec, dists);
+        searchLevel(result, vec, root_node_, distsq, dists, epsError);
+    }
+
+private:
+
+
+    /*--------------------- Internal Data Structures --------------------------*/
+    struct Node
+    {
+    	/**
+    	 * Indices of points in leaf node
+    	 */
+    	int left, right;
+    	/**
+    	 * Dimension used for subdivision.
+    	 */
+    	int divfeat;
+    	/**
+    	 * The values used for subdivision.
+    	 */
+    	DistanceType divlow, divhigh;
+        /**
+         * The child nodes.
+         */
+        Node* child1, * child2;
+    };
+    typedef Node* NodePtr;
+
+
+    struct Interval
+    {
+        DistanceType low, high;
+    };
+
+    typedef std::vector<Interval> BoundingBox;
+
+    typedef BranchStruct<NodePtr, DistanceType> BranchSt;
+    typedef BranchSt* Branch;
+
+
+
+
+    void save_tree(FILE* stream, NodePtr tree)
+    {
+        save_value(stream, *tree);
+        if (tree->child1!=NULL) {
+            save_tree(stream, tree->child1);
+        }
+        if (tree->child2!=NULL) {
+            save_tree(stream, tree->child2);
+        }
+    }
+
+
+    void load_tree(FILE* stream, NodePtr& tree)
+    {
+        tree = pool_.allocate<Node>();
+        load_value(stream, *tree);
+        if (tree->child1!=NULL) {
+            load_tree(stream, tree->child1);
+        }
+        if (tree->child2!=NULL) {
+            load_tree(stream, tree->child2);
+        }
+    }
+
+
+    void computeBoundingBox(BoundingBox& bbox)
+    {
+        bbox.resize(dim_);
+        for (size_t i=0; i<dim_; ++i) {
+            bbox[i].low = (DistanceType)dataset_[0][i];
+            bbox[i].high = (DistanceType)dataset_[0][i];
+        }
+        for (size_t k=1; k<dataset_.rows; ++k) {
+            for (size_t i=0; i<dim_; ++i) {
+                if (dataset_[k][i]<bbox[i].low) bbox[i].low = (DistanceType)dataset_[k][i];
+                if (dataset_[k][i]>bbox[i].high) bbox[i].high = (DistanceType)dataset_[k][i];
+            }
+        }
+    }
+
+
+    /**
+     * Create a tree node that subdivides the list of vecs from vind[first]
+     * to vind[last].  The routine is called recursively on each sublist.
+     * Place a pointer to this new tree node in the location pTree.
+     *
+     * Params: pTree = the new node to create
+     *                  first = index of the first vector
+     *                  last = index of the last vector
+     */
+    NodePtr divideTree(int left, int right, BoundingBox& bbox)
+    {
+        NodePtr node = pool_.allocate<Node>(); // allocate memory
+
+        /* If too few exemplars remain, then make this a leaf node. */
+        if ( (right-left) <= leaf_max_size_) {
+            node->child1 = node->child2 = NULL;    /* Mark as leaf node. */
+            node->left = left;
+            node->right = right;
+
+            // compute bounding-box of leaf points
+            for (size_t i=0; i<dim_; ++i) {
+                bbox[i].low = (DistanceType)dataset_[vind_[left]][i];
+                bbox[i].high = (DistanceType)dataset_[vind_[left]][i];
+            }
+            for (int k=left+1; k<right; ++k) {
+                for (size_t i=0; i<dim_; ++i) {
+                    if (bbox[i].low>dataset_[vind_[k]][i]) bbox[i].low=(DistanceType)dataset_[vind_[k]][i];
+                    if (bbox[i].high<dataset_[vind_[k]][i]) bbox[i].high=(DistanceType)dataset_[vind_[k]][i];
+                }
+            }
+        }
+        else {
+            int idx;
+            int cutfeat;
+            DistanceType cutval;
+            middleSplit(&vind_[0]+left, right-left, idx, cutfeat, cutval, bbox);
+
+            node->divfeat = cutfeat;
+
+            BoundingBox left_bbox(bbox);
+            left_bbox[cutfeat].high = cutval;
+            node->child1 = divideTree(left, left+idx, left_bbox);
+
+            BoundingBox right_bbox(bbox);
+            right_bbox[cutfeat].low = cutval;
+            node->child2 = divideTree(left+idx, right, right_bbox);
+
+            node->divlow = left_bbox[cutfeat].high;
+            node->divhigh = right_bbox[cutfeat].low;
+
+            for (size_t i=0; i<dim_; ++i) {
+            	bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
+            	bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
+            }
+        }
+
+        return node;
+    }
+
+    void computeMinMax(int* ind, int count, int dim, ElementType& min_elem, ElementType& max_elem)
+    {
+        min_elem = dataset_[ind[0]][dim];
+        max_elem = dataset_[ind[0]][dim];
+        for (int i=1; i<count; ++i) {
+            ElementType val = dataset_[ind[i]][dim];
+            if (val<min_elem) min_elem = val;
+            if (val>max_elem) max_elem = val;
+        }
+    }
+
+    void middleSplit(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval, const BoundingBox& bbox)
+    {
+        // find the largest span from the approximate bounding box
+        ElementType max_span = bbox[0].high-bbox[0].low;
+        cutfeat = 0;
+        cutval = (bbox[0].high+bbox[0].low)/2;
+        for (size_t i=1; i<dim_; ++i) {
+            ElementType span = bbox[i].low-bbox[i].low;
+            if (span>max_span) {
+                max_span = span;
+                cutfeat = i;
+                cutval = (bbox[i].high+bbox[i].low)/2;
+            }
+        }
+
+        // compute exact span on the found dimension
+        ElementType min_elem, max_elem;
+        computeMinMax(ind, count, cutfeat, min_elem, max_elem);
+        cutval = (min_elem+max_elem)/2;
+        max_span = max_elem - min_elem;
+
+        // check if a dimension of a largest span exists
+        size_t k = cutfeat;
+        for (size_t i=0; i<dim_; ++i) {
+            if (i==k) continue;
+            ElementType span = bbox[i].high-bbox[i].low;
+            if (span>max_span) {
+                computeMinMax(ind, count, i, min_elem, max_elem);
+                span = max_elem - min_elem;
+                if (span>max_span) {
+                    max_span = span;
+                    cutfeat = i;
+                    cutval = (min_elem+max_elem)/2;
+                }
+            }
+        }
+        int lim1, lim2;
+        planeSplit(ind, count, cutfeat, cutval, lim1, lim2);
+
+        if (lim1>count/2) index = lim1;
+        else if (lim2<count/2) index = lim2;
+        else index = count/2;
+    }
+
+
+    void middleSplit_(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval, const BoundingBox& bbox)
+    {
+        const float eps_val=0.00001f;
+        DistanceType max_span = bbox[0].high-bbox[0].low;
+        for (size_t i=1; i<dim_; ++i) {
+            DistanceType span = bbox[i].high-bbox[i].low;
+            if (span>max_span) {
+                max_span = span;
+            }
+        }
+        DistanceType max_spread = -1;
+        cutfeat = 0;
+        for (size_t i=0; i<dim_; ++i) {
+            DistanceType span = bbox[i].high-bbox[i].low;
+            if (span>(DistanceType)((1-eps_val)*max_span)) {
+                ElementType min_elem, max_elem;
+                computeMinMax(ind, count, cutfeat, min_elem, max_elem);
+                DistanceType spread = (DistanceType)(max_elem-min_elem);
+                if (spread>max_spread) {
+                    cutfeat = i;
+                    max_spread = spread;
+                }
+            }
+        }
+        // split in the middle
+        DistanceType split_val = (bbox[cutfeat].low+bbox[cutfeat].high)/2;
+        ElementType min_elem, max_elem;
+        computeMinMax(ind, count, cutfeat, min_elem, max_elem);
+
+        if (split_val<min_elem) cutval = (DistanceType)min_elem;
+        else if (split_val>max_elem) cutval = (DistanceType)max_elem;
+        else cutval = split_val;
+
+        int lim1, lim2;
+        planeSplit(ind, count, cutfeat, cutval, lim1, lim2);
+
+        if (lim1>count/2) index = lim1;
+        else if (lim2<count/2) index = lim2;
+        else index = count/2;
+    }
+
+
+    /**
+     *  Subdivide the list of points by a plane perpendicular on axe corresponding
+     *  to the 'cutfeat' dimension at 'cutval' position.
+     *
+     *  On return:
+     *  dataset[ind[0..lim1-1]][cutfeat]<cutval
+     *  dataset[ind[lim1..lim2-1]][cutfeat]==cutval
+     *  dataset[ind[lim2..count]][cutfeat]>cutval
+     */
+    void planeSplit(int* ind, int count, int cutfeat, DistanceType cutval, int& lim1, int& lim2)
+    {
+        /* Move vector indices for left subtree to front of list. */
+        int left = 0;
+        int right = count-1;
+        for (;; ) {
+            while (left<=right && dataset_[ind[left]][cutfeat]<cutval) ++left;
+            while (left<=right && dataset_[ind[right]][cutfeat]>=cutval) --right;
+            if (left>right) break;
+            std::swap(ind[left], ind[right]); ++left; --right;
+        }
+        /* If either list is empty, it means that all remaining features
+         * are identical. Split in the middle to maintain a balanced tree.
+         */
+        lim1 = left;
+        right = count-1;
+        for (;; ) {
+            while (left<=right && dataset_[ind[left]][cutfeat]<=cutval) ++left;
+            while (left<=right && dataset_[ind[right]][cutfeat]>cutval) --right;
+            if (left>right) break;
+            std::swap(ind[left], ind[right]); ++left; --right;
+        }
+        lim2 = left;
+    }
+
+    DistanceType computeInitialDistances(const ElementType* vec, std::vector<DistanceType>& dists)
+    {
+        DistanceType distsq = 0.0;
+
+        for (size_t i = 0; i < dim_; ++i) {
+            if (vec[i] < root_bbox_[i].low) {
+                dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].low, i);
+                distsq += dists[i];
+            }
+            if (vec[i] > root_bbox_[i].high) {
+                dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].high, i);
+                distsq += dists[i];
+            }
+        }
+
+        return distsq;
+    }
+
+    /**
+     * Performs an exact search in the tree starting from a node.
+     */
+    void searchLevel(ResultSet<DistanceType>& result_set, const ElementType* vec, const NodePtr node, DistanceType mindistsq,
+                     std::vector<DistanceType>& dists, const float epsError)
+    {
+        /* If this is a leaf node, then do check and return. */
+        if ((node->child1 == NULL)&&(node->child2 == NULL)) {
+            DistanceType worst_dist = result_set.worstDist();
+            for (int i=node->left; i<node->right; ++i) {
+                int index = reorder_ ? i : vind_[i];
+                DistanceType dist = distance_(vec, data_[index], dim_, worst_dist);
+                if (dist<worst_dist) {
+                    result_set.addPoint(dist,vind_[i]);
+                }
+            }
+            return;
+        }
+
+        /* Which child branch should be taken first? */
+        int idx = node->divfeat;
+        ElementType val = vec[idx];
+        DistanceType diff1 = val - node->divlow;
+        DistanceType diff2 = val - node->divhigh;
+
+        NodePtr bestChild;
+        NodePtr otherChild;
+        DistanceType cut_dist;
+        if ((diff1+diff2)<0) {
+            bestChild = node->child1;
+            otherChild = node->child2;
+            cut_dist = distance_.accum_dist(val, node->divhigh, idx);
+        }
+        else {
+            bestChild = node->child2;
+            otherChild = node->child1;
+            cut_dist = distance_.accum_dist( val, node->divlow, idx);
+        }
+
+        /* Call recursively to search next level down. */
+        searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError);
+
+        DistanceType dst = dists[idx];
+        mindistsq = mindistsq + cut_dist - dst;
+        dists[idx] = cut_dist;
+        if (mindistsq*epsError<=result_set.worstDist()) {
+            searchLevel(result_set, vec, otherChild, mindistsq, dists, epsError);
+        }
+        dists[idx] = dst;
+    }
+
+private:
+
+    /**
+     * The dataset used by this index
+     */
+    const Matrix<ElementType> dataset_;
+
+    IndexParams index_params_;
+
+    int leaf_max_size_;
+    bool reorder_;
+
+
+    /**
+     *  Array of indices to vectors in the dataset.
+     */
+    std::vector<int> vind_;
+
+    Matrix<ElementType> data_;
+
+    size_t size_;
+    size_t dim_;
+
+    /**
+     * Array of k-d trees used to find neighbours.
+     */
+    NodePtr root_node_;
+
+    BoundingBox root_bbox_;
+
+    /**
+     * Pooled memory allocator.
+     *
+     * Using a pooled memory allocator is more efficient
+     * than allocating memory directly when there is a large
+     * number small of memory allocations.
+     */
+    PooledAllocator pool_;
+
+    Distance distance_;
+};   // class KDTree
+
+}
+
+#endif //FLANN_KDTREE_SINGLE_INDEX_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/kmeans_index.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/kmeans_index.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/kmeans_index.h	(revision 15840)
@@ -0,0 +1,1115 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_KMEANS_INDEX_H_
+#define FLANN_KMEANS_INDEX_H_
+
+#include <algorithm>
+#include <string>
+#include <map>
+#include <cassert>
+#include <limits>
+#include <cmath>
+
+#include "flann/general.h"
+#include "flann/algorithms/nn_index.h"
+#include "flann/algorithms/dist.h"
+#include "flann/util/matrix.h"
+#include "flann/util/result_set.h"
+#include "flann/util/heap.h"
+#include "flann/util/allocator.h"
+#include "flann/util/random.h"
+#include "flann/util/saving.h"
+#include "flann/util/logger.h"
+
+
+namespace flann
+{
+
+struct KMeansIndexParams : public IndexParams
+{
+    KMeansIndexParams(int branching = 32, int iterations = 11,
+                      flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 )
+    {
+        (*this)["algorithm"] = FLANN_INDEX_KMEANS;
+        // branching factor
+        (*this)["branching"] = branching;
+        // max iterations to perform in one kmeans clustering (kmeans tree)
+        (*this)["iterations"] = iterations;
+        // algorithm used for picking the initial cluster centers for kmeans tree
+        (*this)["centers_init"] = centers_init;
+        // cluster boundary index. Used when searching the kmeans tree
+        (*this)["cb_index"] = cb_index;
+    }
+};
+
+
+/**
+ * Hierarchical kmeans index
+ *
+ * Contains a tree constructed through a hierarchical kmeans clustering
+ * and other information for indexing a set of points for nearest-neighbour matching.
+ */
+template <typename Distance>
+class KMeansIndex : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+    typedef bool needs_vector_space_distance;
+
+
+    typedef void (KMeansIndex::* centersAlgFunction)(int, int*, int, int*, int&);
+
+    /**
+     * The function used for choosing the cluster centers.
+     */
+    centersAlgFunction chooseCenters;
+
+
+
+    /**
+     * Chooses the initial centers in the k-means clustering in a random manner.
+     *
+     * Params:
+     *     k = number of centers
+     *     vecs = the dataset of points
+     *     indices = indices in the dataset
+     *     indices_length = length of indices vector
+     *
+     */
+    void chooseCentersRandom(int k, int* indices, int indices_length, int* centers, int& centers_length)
+    {
+        UniqueRandom r(indices_length);
+
+        int index;
+        for (index=0; index<k; ++index) {
+            bool duplicate = true;
+            int rnd;
+            while (duplicate) {
+                duplicate = false;
+                rnd = r.next();
+                if (rnd<0) {
+                    centers_length = index;
+                    return;
+                }
+
+                centers[index] = indices[rnd];
+
+                for (int j=0; j<index; ++j) {
+                    DistanceType sq = distance_(dataset_[centers[index]], dataset_[centers[j]], dataset_.cols);
+                    if (sq<1e-16) {
+                        duplicate = true;
+                    }
+                }
+            }
+        }
+
+        centers_length = index;
+    }
+
+
+    /**
+     * Chooses the initial centers in the k-means using Gonzales' algorithm
+     * so that the centers are spaced apart from each other.
+     *
+     * Params:
+     *     k = number of centers
+     *     vecs = the dataset of points
+     *     indices = indices in the dataset
+     * Returns:
+     */
+    void chooseCentersGonzales(int k, int* indices, int indices_length, int* centers, int& centers_length)
+    {
+        int n = indices_length;
+
+        int rnd = rand_int(n);
+        assert(rnd >=0 && rnd < n);
+
+        centers[0] = indices[rnd];
+
+        int index;
+        for (index=1; index<k; ++index) {
+
+            int best_index = -1;
+            DistanceType best_val = 0;
+            for (int j=0; j<n; ++j) {
+                DistanceType dist = distance_(dataset_[centers[0]],dataset_[indices[j]],dataset_.cols);
+                for (int i=1; i<index; ++i) {
+                    DistanceType tmp_dist = distance_(dataset_[centers[i]],dataset_[indices[j]],dataset_.cols);
+                    if (tmp_dist<dist) {
+                        dist = tmp_dist;
+                    }
+                }
+                if (dist>best_val) {
+                    best_val = dist;
+                    best_index = j;
+                }
+            }
+            if (best_index!=-1) {
+                centers[index] = indices[best_index];
+            }
+            else {
+                break;
+            }
+        }
+        centers_length = index;
+    }
+
+
+    /**
+     * Chooses the initial centers in the k-means using the algorithm
+     * proposed in the KMeans++ paper:
+     * Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding
+     *
+     * Implementation of this function was converted from the one provided in Arthur's code.
+     *
+     * Params:
+     *     k = number of centers
+     *     vecs = the dataset of points
+     *     indices = indices in the dataset
+     * Returns:
+     */
+    void chooseCentersKMeanspp(int k, int* indices, int indices_length, int* centers, int& centers_length)
+    {
+        int n = indices_length;
+
+        double currentPot = 0;
+        DistanceType* closestDistSq = new DistanceType[n];
+
+        // Choose one random center and set the closestDistSq values
+        int index = rand_int(n);
+        assert(index >=0 && index < n);
+        centers[0] = indices[index];
+
+        for (int i = 0; i < n; i++) {
+            closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
+            currentPot += closestDistSq[i];
+        }
+
+
+        const int numLocalTries = 1;
+
+        // Choose each center
+        int centerCount;
+        for (centerCount = 1; centerCount < k; centerCount++) {
+
+            // Repeat several trials
+            double bestNewPot = -1;
+            int bestNewIndex = -1;
+            for (int localTrial = 0; localTrial < numLocalTries; localTrial++) {
+
+                // Choose our center - have to be slightly careful to return a valid answer even accounting
+                // for possible rounding errors
+                double randVal = rand_double(currentPot);
+                for (index = 0; index < n-1; index++) {
+                    if (randVal <= closestDistSq[index]) break;
+                    else randVal -= closestDistSq[index];
+                }
+
+                // Compute the new potential
+                double newPot = 0;
+                for (int i = 0; i < n; i++) newPot += std::min( distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols), closestDistSq[i] );
+
+                // Store the best result
+                if ((bestNewPot < 0)||(newPot < bestNewPot)) {
+                    bestNewPot = newPot;
+                    bestNewIndex = index;
+                }
+            }
+
+            // Add the appropriate center
+            centers[centerCount] = indices[bestNewIndex];
+            currentPot = bestNewPot;
+            for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols), closestDistSq[i] );
+        }
+
+        centers_length = centerCount;
+
+        delete[] closestDistSq;
+    }
+
+
+
+public:
+
+    flann_algorithm_t getType() const
+    {
+        return FLANN_INDEX_KMEANS;
+    }
+
+    /**
+     * Index constructor
+     *
+     * Params:
+     *          inputData = dataset with the input features
+     *          params = parameters passed to the hierarchical k-means algorithm
+     */
+    KMeansIndex(const Matrix<ElementType>& inputData, const IndexParams& params = KMeansIndexParams(),
+                Distance d = Distance())
+        : dataset_(inputData), index_params_(params), root_(NULL), indices_(NULL), distance_(d)
+    {
+        memoryCounter_ = 0;
+
+        size_ = dataset_.rows;
+        veclen_ = dataset_.cols;
+
+        branching_ = get_param(params,"branching",32);
+        iterations_ = get_param(params,"iterations",11);
+        if (iterations_<0) {
+            iterations_ = (std::numeric_limits<int>::max)();
+        }
+        centers_init_  = get_param(params,"centers_init",FLANN_CENTERS_RANDOM);
+
+        if (centers_init_==FLANN_CENTERS_RANDOM) {
+            chooseCenters = &KMeansIndex::chooseCentersRandom;
+        }
+        else if (centers_init_==FLANN_CENTERS_GONZALES) {
+            chooseCenters = &KMeansIndex::chooseCentersGonzales;
+        }
+        else if (centers_init_==FLANN_CENTERS_KMEANSPP) {
+            chooseCenters = &KMeansIndex::chooseCentersKMeanspp;
+        }
+        else {
+            throw FLANNException("Unknown algorithm for choosing initial centers.");
+        }
+        cb_index_ = 0.4f;
+
+    }
+
+
+    KMeansIndex(const KMeansIndex&);
+    KMeansIndex& operator=(const KMeansIndex&);
+
+
+    /**
+     * Index destructor.
+     *
+     * Release the memory used by the index.
+     */
+    virtual ~KMeansIndex()
+    {
+        if (root_ != NULL) {
+            free_centers(root_);
+        }
+        if (indices_!=NULL) {
+            delete[] indices_;
+        }
+    }
+
+    /**
+     *  Returns size of index.
+     */
+    size_t size() const
+    {
+        return size_;
+    }
+
+    /**
+     * Returns the length of an index feature.
+     */
+    size_t veclen() const
+    {
+        return veclen_;
+    }
+
+
+    void set_cb_index( float index)
+    {
+        cb_index_ = index;
+    }
+
+    /**
+     * Computes the inde memory usage
+     * Returns: memory used by the index
+     */
+    int usedMemory() const
+    {
+        return pool_.usedMemory+pool_.wastedMemory+memoryCounter_;
+    }
+
+    /**
+     * Builds the index
+     */
+    void buildIndex()
+    {
+        if (branching_<2) {
+            throw FLANNException("Branching factor must be at least 2");
+        }
+
+        indices_ = new int[size_];
+        for (size_t i=0; i<size_; ++i) {
+            indices_[i] = int(i);
+        }
+
+        root_ = pool_.allocate<KMeansNode>();
+        computeNodeStatistics(root_, indices_, (int)size_);
+        computeClustering(root_, indices_, (int)size_, branching_,0);
+    }
+
+
+    void saveIndex(FILE* stream)
+    {
+        save_value(stream, branching_);
+        save_value(stream, iterations_);
+        save_value(stream, memoryCounter_);
+        save_value(stream, cb_index_);
+        save_value(stream, *indices_, (int)size_);
+
+        save_tree(stream, root_);
+    }
+
+
+    void loadIndex(FILE* stream)
+    {
+        load_value(stream, branching_);
+        load_value(stream, iterations_);
+        load_value(stream, memoryCounter_);
+        load_value(stream, cb_index_);
+        if (indices_!=NULL) {
+            delete[] indices_;
+        }
+        indices_ = new int[size_];
+        load_value(stream, *indices_, size_);
+
+        if (root_!=NULL) {
+            free_centers(root_);
+        }
+        load_tree(stream, root_);
+
+        index_params_["algorithm"] = getType();
+        index_params_["branching"] = branching_;
+        index_params_["iterations"] = iterations_;
+        index_params_["centers_init"] = centers_init_;
+        index_params_["cb_index"] = cb_index_;
+
+    }
+
+
+    /**
+     * Find set of nearest neighbors to vec. Their indices are stored inside
+     * the result object.
+     *
+     * Params:
+     *     result = the result object in which the indices of the nearest-neighbors are stored
+     *     vec = the vector for which to search the nearest neighbors
+     *     searchParams = parameters that influence the search algorithm (checks, cb_index)
+     */
+    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
+    {
+
+        int maxChecks = searchParams.checks;
+
+        if (maxChecks==FLANN_CHECKS_UNLIMITED) {
+            findExactNN(root_, result, vec);
+        }
+        else {
+            // Priority queue storing intermediate branches in the best-bin-first search
+            Heap<BranchSt>* heap = new Heap<BranchSt>((int)size_);
+
+            int checks = 0;
+            findNN(root_, result, vec, checks, maxChecks, heap);
+
+            BranchSt branch;
+            while (heap->popMin(branch) && (checks<maxChecks || !result.full())) {
+                KMeansNodePtr node = branch.node;
+                findNN(node, result, vec, checks, maxChecks, heap);
+            }
+
+            delete heap;
+        }
+
+    }
+
+    /**
+     * Clustering function that takes a cut in the hierarchical k-means
+     * tree and return the clusters centers of that clustering.
+     * Params:
+     *     numClusters = number of clusters to have in the clustering computed
+     * Returns: number of cluster centers
+     */
+    int getClusterCenters(Matrix<DistanceType>& centers)
+    {
+        int numClusters = centers.rows;
+        if (numClusters<1) {
+            throw FLANNException("Number of clusters must be at least 1");
+        }
+
+        DistanceType variance;
+        KMeansNodePtr* clusters = new KMeansNodePtr[numClusters];
+
+        int clusterCount = getMinVarianceClusters(root_, clusters, numClusters, variance);
+
+        Logger::info("Clusters requested: %d, returning %d\n",numClusters, clusterCount);
+
+        for (int i=0; i<clusterCount; ++i) {
+            DistanceType* center = clusters[i]->pivot;
+            for (size_t j=0; j<veclen_; ++j) {
+                centers[i][j] = center[j];
+            }
+        }
+        delete[] clusters;
+
+        return clusterCount;
+    }
+
+    IndexParams getParameters() const
+    {
+        return index_params_;
+    }
+
+
+private:
+    /**
+     * Struture representing a node in the hierarchical k-means tree.
+     */
+    struct KMeansNode
+    {
+        /**
+         * The cluster center.
+         */
+        DistanceType* pivot;
+        /**
+         * The cluster radius.
+         */
+        DistanceType radius;
+        /**
+         * The cluster mean radius.
+         */
+        DistanceType mean_radius;
+        /**
+         * The cluster variance.
+         */
+        DistanceType variance;
+        /**
+         * The cluster size (number of points in the cluster)
+         */
+        int size;
+        /**
+         * Child nodes (only for non-terminal nodes)
+         */
+        KMeansNode** childs;
+        /**
+         * Node points (only for terminal nodes)
+         */
+        int* indices;
+        /**
+         * Level
+         */
+        int level;
+    };
+    typedef KMeansNode* KMeansNodePtr;
+
+    /**
+     * Alias definition for a nicer syntax.
+     */
+    typedef BranchStruct<KMeansNodePtr, DistanceType> BranchSt;
+
+
+
+
+    void save_tree(FILE* stream, KMeansNodePtr node)
+    {
+        save_value(stream, *node);
+        save_value(stream, *(node->pivot), (int)veclen_);
+        if (node->childs==NULL) {
+            int indices_offset = (int)(node->indices - indices_);
+            save_value(stream, indices_offset);
+        }
+        else {
+            for(int i=0; i<branching_; ++i) {
+                save_tree(stream, node->childs[i]);
+            }
+        }
+    }
+
+
+    void load_tree(FILE* stream, KMeansNodePtr& node)
+    {
+        node = pool_.allocate<KMeansNode>();
+        load_value(stream, *node);
+        node->pivot = new DistanceType[veclen_];
+        load_value(stream, *(node->pivot), (int)veclen_);
+        if (node->childs==NULL) {
+            int indices_offset;
+            load_value(stream, indices_offset);
+            node->indices = indices_ + indices_offset;
+        }
+        else {
+            node->childs = pool_.allocate<KMeansNodePtr>(branching_);
+            for(int i=0; i<branching_; ++i) {
+                load_tree(stream, node->childs[i]);
+            }
+        }
+    }
+
+
+    /**
+     * Helper function
+     */
+    void free_centers(KMeansNodePtr node)
+    {
+        delete[] node->pivot;
+        if (node->childs!=NULL) {
+            for (int k=0; k<branching_; ++k) {
+                free_centers(node->childs[k]);
+            }
+        }
+    }
+
+    /**
+     * Computes the statistics of a node (mean, radius, variance).
+     *
+     * Params:
+     *     node = the node to use
+     *     indices = the indices of the points belonging to the node
+     */
+    void computeNodeStatistics(KMeansNodePtr node, int* indices, int indices_length)
+    {
+
+        DistanceType radius = 0;
+        DistanceType variance = 0;
+        DistanceType* mean = new DistanceType[veclen_];
+        memoryCounter_ += int(veclen_*sizeof(DistanceType));
+
+        memset(mean,0,veclen_*sizeof(DistanceType));
+
+        for (size_t i=0; i<size_; ++i) {
+            ElementType* vec = dataset_[indices[i]];
+            for (size_t j=0; j<veclen_; ++j) {
+                mean[j] += vec[j];
+            }
+            variance += distance_(vec, ZeroIterator<ElementType>(), veclen_);
+        }
+        for (size_t j=0; j<veclen_; ++j) {
+            mean[j] /= size_;
+        }
+        variance /= size_;
+        variance -= distance_(mean, ZeroIterator<ElementType>(), veclen_);
+
+        DistanceType tmp = 0;
+        for (int i=0; i<indices_length; ++i) {
+            tmp = distance_(mean, dataset_[indices[i]], veclen_);
+            if (tmp>radius) {
+                radius = tmp;
+            }
+        }
+
+        node->variance = variance;
+        node->radius = radius;
+        node->pivot = mean;
+    }
+
+
+    /**
+     * The method responsible with actually doing the recursive hierarchical
+     * clustering
+     *
+     * Params:
+     *     node = the node to cluster
+     *     indices = indices of the points belonging to the current node
+     *     branching = the branching factor to use in the clustering
+     *
+     * TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point)
+     */
+    void computeClustering(KMeansNodePtr node, int* indices, int indices_length, int branching, int level)
+    {
+        node->size = indices_length;
+        node->level = level;
+
+        if (indices_length < branching) {
+            node->indices = indices;
+            std::sort(node->indices,node->indices+indices_length);
+            node->childs = NULL;
+            return;
+        }
+
+        int* centers_idx = new int[branching];
+        int centers_length;
+        (this->*chooseCenters)(branching, indices, indices_length, centers_idx, centers_length);
+
+        if (centers_length<branching) {
+            node->indices = indices;
+            std::sort(node->indices,node->indices+indices_length);
+            node->childs = NULL;
+            delete [] centers_idx;
+            return;
+        }
+
+
+        Matrix<double> dcenters(new double[branching*veclen_],branching,veclen_);
+        for (int i=0; i<centers_length; ++i) {
+            ElementType* vec = dataset_[centers_idx[i]];
+            for (size_t k=0; k<veclen_; ++k) {
+                dcenters[i][k] = double(vec[k]);
+            }
+        }
+        delete[] centers_idx;
+
+        DistanceType* radiuses = new DistanceType[branching];
+        int* count = new int[branching];
+        for (int i=0; i<branching; ++i) {
+            radiuses[i] = 0;
+            count[i] = 0;
+        }
+
+        //	assign points to clusters
+        int* belongs_to = new int[indices_length];
+        for (int i=0; i<indices_length; ++i) {
+
+            DistanceType sq_dist = distance_(dataset_[indices[i]], dcenters[0], veclen_);
+            belongs_to[i] = 0;
+            for (int j=1; j<branching; ++j) {
+                DistanceType new_sq_dist = distance_(dataset_[indices[i]], dcenters[j], veclen_);
+                if (sq_dist>new_sq_dist) {
+                    belongs_to[i] = j;
+                    sq_dist = new_sq_dist;
+                }
+            }
+            if (sq_dist>radiuses[belongs_to[i]]) {
+                radiuses[belongs_to[i]] = sq_dist;
+            }
+            count[belongs_to[i]]++;
+        }
+
+        bool converged = false;
+        int iteration = 0;
+        while (!converged && iteration<iterations_) {
+            converged = true;
+            iteration++;
+
+            // compute the new cluster centers
+            for (int i=0; i<branching; ++i) {
+                memset(dcenters[i],0,sizeof(double)*veclen_);
+                radiuses[i] = 0;
+            }
+            for (int i=0; i<indices_length; ++i) {
+                ElementType* vec = dataset_[indices[i]];
+                double* center = dcenters[belongs_to[i]];
+                for (size_t k=0; k<veclen_; ++k) {
+                    center[k] += vec[k];
+                }
+            }
+            for (int i=0; i<branching; ++i) {
+                int cnt = count[i];
+                for (size_t k=0; k<veclen_; ++k) {
+                    dcenters[i][k] /= cnt;
+                }
+            }
+
+            // reassign points to clusters
+            for (int i=0; i<indices_length; ++i) {
+                DistanceType sq_dist = distance_(dataset_[indices[i]], dcenters[0], veclen_);
+                int new_centroid = 0;
+                for (int j=1; j<branching; ++j) {
+                    DistanceType new_sq_dist = distance_(dataset_[indices[i]], dcenters[j], veclen_);
+                    if (sq_dist>new_sq_dist) {
+                        new_centroid = j;
+                        sq_dist = new_sq_dist;
+                    }
+                }
+                if (sq_dist>radiuses[new_centroid]) {
+                    radiuses[new_centroid] = sq_dist;
+                }
+                if (new_centroid != belongs_to[i]) {
+                    count[belongs_to[i]]--;
+                    count[new_centroid]++;
+                    belongs_to[i] = new_centroid;
+
+                    converged = false;
+                }
+            }
+
+            for (int i=0; i<branching; ++i) {
+                // if one cluster converges to an empty cluster,
+                // move an element into that cluster
+                if (count[i]==0) {
+                    int j = (i+1)%branching;
+                    while (count[j]<=1) {
+                        j = (j+1)%branching;
+                    }
+
+                    for (int k=0; k<indices_length; ++k) {
+                        if (belongs_to[k]==j) {
+                            belongs_to[k] = i;
+                            count[j]--;
+                            count[i]++;
+                            break;
+                        }
+                    }
+                    converged = false;
+                }
+            }
+
+        }
+
+        DistanceType** centers = new DistanceType*[branching];
+
+        for (int i=0; i<branching; ++i) {
+            centers[i] = new DistanceType[veclen_];
+            memoryCounter_ += veclen_*sizeof(DistanceType);
+            for (size_t k=0; k<veclen_; ++k) {
+                centers[i][k] = (DistanceType)dcenters[i][k];
+            }
+        }
+
+
+        // compute kmeans clustering for each of the resulting clusters
+        node->childs = pool_.allocate<KMeansNodePtr>(branching);
+        int start = 0;
+        int end = start;
+        for (int c=0; c<branching; ++c) {
+            int s = count[c];
+
+            DistanceType variance = 0;
+            DistanceType mean_radius =0;
+            for (int i=0; i<indices_length; ++i) {
+                if (belongs_to[i]==c) {
+                    DistanceType d = distance_(dataset_[indices[i]], ZeroIterator<ElementType>(), veclen_);
+                    variance += d;
+                    mean_radius += sqrt(d);
+                    std::swap(indices[i],indices[end]);
+                    std::swap(belongs_to[i],belongs_to[end]);
+                    end++;
+                }
+            }
+            variance /= s;
+            mean_radius /= s;
+            variance -= distance_(centers[c], ZeroIterator<ElementType>(), veclen_);
+
+            node->childs[c] = pool_.allocate<KMeansNode>();
+            node->childs[c]->radius = radiuses[c];
+            node->childs[c]->pivot = centers[c];
+            node->childs[c]->variance = variance;
+            node->childs[c]->mean_radius = mean_radius;
+            node->childs[c]->indices = NULL;
+            computeClustering(node->childs[c],indices+start, end-start, branching, level+1);
+            start=end;
+        }
+
+        delete[] dcenters.ptr();
+        delete[] centers;
+        delete[] radiuses;
+        delete[] count;
+        delete[] belongs_to;
+    }
+
+
+
+    /**
+     * Performs one descent in the hierarchical k-means tree. The branches not
+     * visited are stored in a priority queue.
+     *
+     * Params:
+     *      node = node to explore
+     *      result = container for the k-nearest neighbors found
+     *      vec = query points
+     *      checks = how many points in the dataset have been checked so far
+     *      maxChecks = maximum dataset points to checks
+     */
+
+
+    void findNN(KMeansNodePtr node, ResultSet<DistanceType>& result, const ElementType* vec, int& checks, int maxChecks,
+                Heap<BranchSt>* heap)
+    {
+        // Ignore those clusters that are too far away
+        {
+            DistanceType bsq = distance_(vec, node->pivot, veclen_);
+            DistanceType rsq = node->radius;
+            DistanceType wsq = result.worstDist();
+
+            DistanceType val = bsq-rsq-wsq;
+            DistanceType val2 = val*val-4*rsq*wsq;
+
+            //if (val>0) {
+            if ((val>0)&&(val2>0)) {
+                return;
+            }
+        }
+
+        if (node->childs==NULL) {
+            if (checks>=maxChecks) {
+                if (result.full()) return;
+            }
+            checks += node->size;
+            for (int i=0; i<node->size; ++i) {
+                int index = node->indices[i];
+                DistanceType dist = distance_(dataset_[index], vec, veclen_);
+                result.addPoint(dist, index);
+            }
+        }
+        else {
+            DistanceType* domain_distances = new DistanceType[branching_];
+            int closest_center = exploreNodeBranches(node, vec, domain_distances, heap);
+            delete[] domain_distances;
+            findNN(node->childs[closest_center],result,vec, checks, maxChecks, heap);
+        }
+    }
+
+    /**
+     * Helper function that computes the nearest childs of a node to a given query point.
+     * Params:
+     *     node = the node
+     *     q = the query point
+     *     distances = array with the distances to each child node.
+     * Returns:
+     */
+    int exploreNodeBranches(KMeansNodePtr node, const ElementType* q, DistanceType* domain_distances, Heap<BranchSt>* heap)
+    {
+
+        int best_index = 0;
+        domain_distances[best_index] = distance_(q, node->childs[best_index]->pivot, veclen_);
+        for (int i=1; i<branching_; ++i) {
+            domain_distances[i] = distance_(q, node->childs[i]->pivot, veclen_);
+            if (domain_distances[i]<domain_distances[best_index]) {
+                best_index = i;
+            }
+        }
+
+        //		float* best_center = node->childs[best_index]->pivot;
+        for (int i=0; i<branching_; ++i) {
+            if (i != best_index) {
+                domain_distances[i] -= cb_index_*node->childs[i]->variance;
+
+                //				float dist_to_border = getDistanceToBorder(node.childs[i].pivot,best_center,q);
+                //				if (domain_distances[i]<dist_to_border) {
+                //					domain_distances[i] = dist_to_border;
+                //				}
+                heap->insert(BranchSt(node->childs[i],domain_distances[i]));
+            }
+        }
+
+        return best_index;
+    }
+
+
+    /**
+     * Function the performs exact nearest neighbor search by traversing the entire tree.
+     */
+    void findExactNN(KMeansNodePtr node, ResultSet<DistanceType>& result, const ElementType* vec)
+    {
+        // Ignore those clusters that are too far away
+        {
+            DistanceType bsq = distance_(vec, node->pivot, veclen_);
+            DistanceType rsq = node->radius;
+            DistanceType wsq = result.worstDist();
+
+            DistanceType val = bsq-rsq-wsq;
+            DistanceType val2 = val*val-4*rsq*wsq;
+
+            //                  if (val>0) {
+            if ((val>0)&&(val2>0)) {
+                return;
+            }
+        }
+
+
+        if (node->childs==NULL) {
+            for (int i=0; i<node->size; ++i) {
+                int index = node->indices[i];
+                DistanceType dist = distance_(dataset_[index], vec, veclen_);
+                result.addPoint(dist, index);
+            }
+        }
+        else {
+            int* sort_indices = new int[branching_];
+
+            getCenterOrdering(node, vec, sort_indices);
+
+            for (int i=0; i<branching_; ++i) {
+                findExactNN(node->childs[sort_indices[i]],result,vec);
+            }
+
+            delete[] sort_indices;
+        }
+    }
+
+
+    /**
+     * Helper function.
+     *
+     * I computes the order in which to traverse the child nodes of a particular node.
+     */
+    void getCenterOrdering(KMeansNodePtr node, const ElementType* q, int* sort_indices)
+    {
+        DistanceType* domain_distances = new DistanceType[branching_];
+        for (int i=0; i<branching_; ++i) {
+            DistanceType dist = distance_(q, node->childs[i]->pivot, veclen_);
+
+            int j=0;
+            while (domain_distances[j]<dist && j<i) j++;
+            for (int k=i; k>j; --k) {
+                domain_distances[k] = domain_distances[k-1];
+                sort_indices[k] = sort_indices[k-1];
+            }
+            domain_distances[j] = dist;
+            sort_indices[j] = i;
+        }
+        delete[] domain_distances;
+    }
+
+    /**
+     * Method that computes the squared distance from the query point q
+     * from inside region with center c to the border between this
+     * region and the region with center p
+     */
+    DistanceType getDistanceToBorder(DistanceType* p, DistanceType* c, DistanceType* q)
+    {
+        DistanceType sum = 0;
+        DistanceType sum2 = 0;
+
+        for (int i=0; i<veclen_; ++i) {
+            DistanceType t = c[i]-p[i];
+            sum += t*(q[i]-(c[i]+p[i])/2);
+            sum2 += t*t;
+        }
+
+        return sum*sum/sum2;
+    }
+
+
+    /**
+     * Helper function the descends in the hierarchical k-means tree by spliting those clusters that minimize
+     * the overall variance of the clustering.
+     * Params:
+     *     root = root node
+     *     clusters = array with clusters centers (return value)
+     *     varianceValue = variance of the clustering (return value)
+     * Returns:
+     */
+    int getMinVarianceClusters(KMeansNodePtr root, KMeansNodePtr* clusters, int clusters_length, DistanceType& varianceValue)
+    {
+        int clusterCount = 1;
+        clusters[0] = root;
+
+        DistanceType meanVariance = root->variance*root->size;
+
+        while (clusterCount<clusters_length) {
+            DistanceType minVariance = (std::numeric_limits<DistanceType>::max)();
+            int splitIndex = -1;
+
+            for (int i=0; i<clusterCount; ++i) {
+                if (clusters[i]->childs != NULL) {
+
+                    DistanceType variance = meanVariance - clusters[i]->variance*clusters[i]->size;
+
+                    for (int j=0; j<branching_; ++j) {
+                        variance += clusters[i]->childs[j]->variance*clusters[i]->childs[j]->size;
+                    }
+                    if (variance<minVariance) {
+                        minVariance = variance;
+                        splitIndex = i;
+                    }
+                }
+            }
+
+            if (splitIndex==-1) break;
+            if ( (branching_+clusterCount-1) > clusters_length) break;
+
+            meanVariance = minVariance;
+
+            // split node
+            KMeansNodePtr toSplit = clusters[splitIndex];
+            clusters[splitIndex] = toSplit->childs[0];
+            for (int i=1; i<branching_; ++i) {
+                clusters[clusterCount++] = toSplit->childs[i];
+            }
+        }
+
+        varianceValue = meanVariance/root->size;
+        return clusterCount;
+    }
+
+private:
+    /** The branching factor used in the hierarchical k-means clustering */
+    int branching_;
+
+    /** Maximum number of iterations to use when performing k-means clustering */
+    int iterations_;
+
+    /** Algorithm for choosing the cluster centers */
+    flann_centers_init_t centers_init_;
+
+    /**
+     * Cluster border index. This is used in the tree search phase when determining
+     * the closest cluster to explore next. A zero value takes into account only
+     * the cluster centres, a value greater then zero also take into account the size
+     * of the cluster.
+     */
+    float cb_index_;
+
+    /**
+     * The dataset used by this index
+     */
+    const Matrix<ElementType> dataset_;
+
+    /** Index parameters */
+    IndexParams index_params_;
+
+    /**
+     * Number of features in the dataset.
+     */
+    size_t size_;
+
+    /**
+     * Length of each feature.
+     */
+    size_t veclen_;
+
+    /**
+     * The root node in the tree.
+     */
+    KMeansNodePtr root_;
+
+    /**
+     *  Array of indices to vectors in the dataset.
+     */
+    int* indices_;
+
+    /**
+     * The distance
+     */
+    Distance distance_;
+
+    /**
+     * Pooled memory allocator.
+     */
+    PooledAllocator pool_;
+
+    /**
+     * Memory occupied by the index.
+     */
+    int memoryCounter_;
+};
+
+}
+
+#endif //FLANN_KMEANS_INDEX_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/linear_index.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/linear_index.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/linear_index.h	(revision 15840)
@@ -0,0 +1,131 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_LINEAR_INDEX_H_
+#define FLANN_LINEAR_INDEX_H_
+
+#include "flann/general.h"
+#include "flann/algorithms/nn_index.h"
+
+namespace flann
+{
+
+struct LinearIndexParams : public IndexParams
+{
+    LinearIndexParams()
+    {
+        (* this)["algorithm"] = FLANN_INDEX_LINEAR;
+    }
+};
+
+template <typename Distance>
+class LinearIndex : public NNIndex<Distance>
+{
+public:
+
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+
+    LinearIndex(const Matrix<ElementType>& inputData, const IndexParams& params = LinearIndexParams(),
+                Distance d = Distance()) :
+        dataset_(inputData), index_params_(params), distance_(d)
+    {
+    }
+
+    LinearIndex(const LinearIndex&);
+    LinearIndex& operator=(const LinearIndex&);
+
+    flann_algorithm_t getType() const
+    {
+        return FLANN_INDEX_LINEAR;
+    }
+
+
+    size_t size() const
+    {
+        return dataset_.rows;
+    }
+
+    size_t veclen() const
+    {
+        return dataset_.cols;
+    }
+
+
+    int usedMemory() const
+    {
+        return 0;
+    }
+
+    void buildIndex()
+    {
+        /* nothing to do here for linear search */
+    }
+
+    void saveIndex(FILE*)
+    {
+        /* nothing to do here for linear search */
+    }
+
+
+    void loadIndex(FILE*)
+    {
+        /* nothing to do here for linear search */
+
+        index_params_["algorithm"] = getType();
+    }
+
+    void findNeighbors(ResultSet<DistanceType>& resultSet, const ElementType* vec, const SearchParams& /*searchParams*/)
+    {
+        for (size_t i = 0; i < dataset_.rows; ++i) {
+            DistanceType dist = distance_(dataset_[i], vec, dataset_.cols);
+            resultSet.addPoint(dist, i);
+        }
+    }
+
+    IndexParams getParameters() const
+    {
+        return index_params_;
+    }
+
+private:
+    /** The dataset */
+    const Matrix<ElementType> dataset_;
+    /** Index parameters */
+    IndexParams index_params_;
+    /** Index distance */
+    Distance distance_;
+
+};
+
+}
+
+#endif // FLANN_LINEAR_INDEX_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/lsh_index.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/lsh_index.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/lsh_index.h	(revision 15840)
@@ -0,0 +1,451 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+/***********************************************************************
+ * Author: Vincent Rabaud
+ *************************************************************************/
+
+#ifndef FLANN_LSH_INDEX_H_
+#define FLANN_LSH_INDEX_H_
+
+#include <algorithm>
+#include <cassert>
+#include <cstring>
+#include <map>
+#include <vector>
+
+#include "flann/general.h"
+#include "flann/algorithms/nn_index.h"
+#include "flann/util/matrix.h"
+#include "flann/util/result_set.h"
+#include "flann/util/heap.h"
+#include "flann/util/lsh_table.h"
+#include "flann/util/allocator.h"
+#include "flann/util/random.h"
+#include "flann/util/saving.h"
+
+namespace flann
+{
+
+struct LshIndexParams : public IndexParams
+{
+    LshIndexParams(unsigned int table_number = 12, unsigned int key_size = 20, unsigned int multi_probe_level = 2)
+    {
+        (* this)["algorithm"] = FLANN_INDEX_LSH;
+        // The number of hash tables to use
+        (*this)["table_number"] = table_number;
+        // The length of the key in the hash tables
+        (*this)["key_size"] = key_size;
+        // Number of levels to use in multi-probe (0 for standard LSH)
+        (*this)["multi_probe_level"] = multi_probe_level;
+    }
+};
+
+/**
+ * Randomized kd-tree index
+ *
+ * Contains the k-d trees and other information for indexing a set of points
+ * for nearest-neighbor matching.
+ */
+template<typename Distance>
+class LshIndex : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+    /** Constructor
+     * @param input_data dataset with the input features
+     * @param params parameters passed to the LSH algorithm
+     * @param d the distance used
+     */
+    LshIndex(const Matrix<ElementType>& input_data, const IndexParams& params = LshIndexParams(),
+             Distance d = Distance()) :
+        dataset_(input_data), index_params_(params), distance_(d)
+    {
+        table_number_ = get_param<unsigned int>(index_params_,"table_number",12);
+        key_size_ = get_param<unsigned int>(index_params_,"key_size",20);
+        multi_probe_level_ = get_param<unsigned int>(index_params_,"multi_probe_level",2);
+
+        feature_size_ = dataset_.cols;
+        fill_xor_mask(0, key_size_, multi_probe_level_, xor_masks_);
+    }
+
+
+    LshIndex(const LshIndex&);
+    LshIndex& operator=(const LshIndex&);
+
+    /**
+     * Builds the index
+     */
+    void buildIndex()
+    {
+        tables_.resize(table_number_);
+        for (unsigned int i = 0; i < table_number_; ++i) {
+            lsh::LshTable<ElementType>& table = tables_[i];
+            table = lsh::LshTable<ElementType>(feature_size_, key_size_);
+
+            // Add the features to the table
+            table.add(dataset_);
+        }
+    }
+
+    flann_algorithm_t getType() const
+    {
+        return FLANN_INDEX_LSH;
+    }
+
+
+    void saveIndex(FILE* stream)
+    {
+        save_value(stream,table_number_);
+        save_value(stream,key_size_);
+        save_value(stream,multi_probe_level_);
+        save_value(stream, dataset_);
+    }
+
+    void loadIndex(FILE* stream)
+    {
+        load_value(stream, table_number_);
+        load_value(stream, key_size_);
+        load_value(stream, multi_probe_level_);
+        load_value(stream, dataset_);
+        // Building the index is so fast we can afford not storing it
+        buildIndex();
+
+        index_params_["algorithm"] = getType();
+        index_params_["table_number"] = table_number_;
+        index_params_["key_size"] = key_size_;
+        index_params_["multi_probe_level"] = multi_probe_level_;
+    }
+
+    /**
+     *  Returns size of index.
+     */
+    size_t size() const
+    {
+        return dataset_.rows;
+    }
+
+    /**
+     * Returns the length of an index feature.
+     */
+    size_t veclen() const
+    {
+        return feature_size_;
+    }
+
+    /**
+     * Computes the index memory usage
+     * Returns: memory used by the index
+     */
+    int usedMemory() const
+    {
+        return dataset_.rows * sizeof(int);
+    }
+
+
+    IndexParams getParameters() const
+    {
+        return index_params_;
+    }
+
+    /**
+     * \brief Perform k-nearest neighbor search
+     * \param[in] queries The query points for which to find the nearest neighbors
+     * \param[out] indices The indices of the nearest neighbors found
+     * \param[out] dists Distances to the nearest neighbors found
+     * \param[in] knn Number of nearest neighbors to return
+     * \param[in] params Search parameters
+     */
+    virtual int knnSearch(const Matrix<ElementType>& queries,
+    					Matrix<int>& indices,
+    					Matrix<DistanceType>& dists,
+    					size_t knn,
+    					const SearchParams& params)
+    {
+        assert(queries.cols == veclen());
+        assert(indices.rows >= queries.rows);
+        assert(dists.rows >= queries.rows);
+        assert(indices.cols >= knn);
+        assert(dists.cols >= knn);
+
+        int count = 0;
+        if (params.use_heap==FLANN_True) {
+        	KNNUniqueResultSet<DistanceType> resultSet(knn);
+        	for (size_t i = 0; i < queries.rows; i++) {
+        		resultSet.clear();
+        		findNeighbors(resultSet, queries[i], params);
+        		resultSet.copy(indices[i], dists[i], knn, params.sorted);
+        		count += resultSet.size();
+        	}
+        }
+        else {
+        	KNNResultSet<DistanceType> resultSet(knn);
+        	for (size_t i = 0; i < queries.rows; i++) {
+        		resultSet.clear();
+        		findNeighbors(resultSet, queries[i], params);
+        		resultSet.copy(indices[i], dists[i], knn, params.sorted);
+        		count += resultSet.size();
+        	}
+        }
+
+        return count;
+    }
+
+    /**
+     * \brief Perform k-nearest neighbor search
+     * \param[in] queries The query points for which to find the nearest neighbors
+     * \param[out] indices The indices of the nearest neighbors found
+     * \param[out] dists Distances to the nearest neighbors found
+     * \param[in] knn Number of nearest neighbors to return
+     * \param[in] params Search parameters
+     */
+    virtual int knnSearch(const Matrix<ElementType>& queries,
+					std::vector< std::vector<int> >& indices,
+					std::vector<std::vector<DistanceType> >& dists,
+    				size_t knn,
+    				const SearchParams& params)
+    {
+        assert(queries.cols == veclen());
+		if (indices.size() < queries.rows ) indices.resize(queries.rows);
+		if (dists.size() < queries.rows ) dists.resize(queries.rows);
+
+		int count = 0;
+		if (params.use_heap==FLANN_True) {
+			KNNUniqueResultSet<DistanceType> resultSet(knn);
+			for (size_t i = 0; i < queries.rows; i++) {
+				resultSet.clear();
+				findNeighbors(resultSet, queries[i], params);
+				size_t n = std::min(resultSet.size(), knn);
+				indices[i].resize(n);
+				dists[i].resize(n);
+				resultSet.copy(&indices[i][0], &dists[i][0], n, params.sorted);
+				count += n;
+			}
+		}
+		else {
+			KNNResultSet<DistanceType> resultSet(knn);
+			for (size_t i = 0; i < queries.rows; i++) {
+				resultSet.clear();
+				findNeighbors(resultSet, queries[i], params);
+				size_t n = std::min(resultSet.size(), knn);
+				indices[i].resize(n);
+				dists[i].resize(n);
+				resultSet.copy(&indices[i][0], &dists[i][0], n, params.sorted);
+				count += n;
+			}
+		}
+
+		return count;
+    }
+
+    /**
+     * Find set of nearest neighbors to vec. Their indices are stored inside
+     * the result object.
+     *
+     * Params:
+     *     result = the result object in which the indices of the nearest-neighbors are stored
+     *     vec = the vector for which to search the nearest neighbors
+     *     maxCheck = the maximum number of restarts (in a best-bin-first manner)
+     */
+    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& /*searchParams*/)
+    {
+        getNeighbors(vec, result);
+    }
+
+private:
+    /** Defines the comparator on score and index
+     */
+    typedef std::pair<float, unsigned int> ScoreIndexPair;
+    struct SortScoreIndexPairOnSecond
+    {
+        bool operator()(const ScoreIndexPair& left, const ScoreIndexPair& right) const
+        {
+            return left.second < right.second;
+        }
+    };
+
+    /** Fills the different xor masks to use when getting the neighbors in multi-probe LSH
+     * @param key the key we build neighbors from
+     * @param lowest_index the lowest index of the bit set
+     * @param level the multi-probe level we are at
+     * @param xor_masks all the xor mask
+     */
+    void fill_xor_mask(lsh::BucketKey key, int lowest_index, unsigned int level,
+                       std::vector<lsh::BucketKey>& xor_masks)
+    {
+        xor_masks.push_back(key);
+        if (level == 0) return;
+        for (int index = lowest_index - 1; index >= 0; --index) {
+            // Create a new key
+            lsh::BucketKey new_key = key | (1 << index);
+            fill_xor_mask(new_key, index, level - 1, xor_masks);
+        }
+    }
+
+    /** Performs the approximate nearest-neighbor search.
+     * @param vec the feature to analyze
+     * @param do_radius flag indicating if we check the radius too
+     * @param radius the radius if it is a radius search
+     * @param do_k flag indicating if we limit the number of nn
+     * @param k_nn the number of nearest neighbors
+     * @param checked_average used for debugging
+     */
+    void getNeighbors(const ElementType* vec, bool do_radius, float radius, bool do_k, unsigned int k_nn,
+                      float& checked_average)
+    {
+        static std::vector<ScoreIndexPair> score_index_heap;
+
+        if (do_k) {
+            unsigned int worst_score = std::numeric_limits<unsigned int>::max();
+            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
+            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
+            for (; table != table_end; ++table) {
+                size_t key = table->getKey(vec);
+                std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
+                std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
+                for (; xor_mask != xor_mask_end; ++xor_mask) {
+                    size_t sub_key = key ^ (*xor_mask);
+                    const lsh::Bucket* bucket = table->getBucketFromKey(sub_key);
+                    if (bucket == 0) continue;
+
+                    // Go over each descriptor index
+                    std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
+                    std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
+                    DistanceType hamming_distance;
+
+                    // Process the rest of the candidates
+                    for (; training_index < last_training_index; ++training_index) {
+                        hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols);
+
+                        if (hamming_distance < worst_score) {
+                            // Insert the new element
+                            score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index));
+                            std::push_heap(score_index_heap.begin(), score_index_heap.end());
+
+                            if (score_index_heap.size() > (unsigned int)k_nn) {
+                                // Remove the highest distance value as we have too many elements
+                                std::pop_heap(score_index_heap.begin(), score_index_heap.end());
+                                score_index_heap.pop_back();
+                                // Keep track of the worst score
+                                worst_score = score_index_heap.front().first;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        else {
+            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
+            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
+            for (; table != table_end; ++table) {
+                size_t key = table->getKey(vec);
+                std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
+                std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
+                for (; xor_mask != xor_mask_end; ++xor_mask) {
+                    size_t sub_key = key ^ (*xor_mask);
+                    const lsh::Bucket* bucket = table->getBucketFromKey(sub_key);
+                    if (bucket == 0) continue;
+
+                    // Go over each descriptor index
+                    std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
+                    std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
+                    DistanceType hamming_distance;
+
+                    // Process the rest of the candidates
+                    for (; training_index < last_training_index; ++training_index) {
+                        // Compute the Hamming distance
+                        hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols);
+                        if (hamming_distance < radius) score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index));
+                    }
+                }
+            }
+        }
+    }
+
+    /** Performs the approximate nearest-neighbor search.
+     * This is a slower version than the above as it uses the ResultSet
+     * @param vec the feature to analyze
+     */
+    void getNeighbors(const ElementType* vec, ResultSet<DistanceType>& result)
+    {
+        typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
+        typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
+        for (; table != table_end; ++table) {
+            size_t key = table->getKey(vec);
+            std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
+            std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
+            for (; xor_mask != xor_mask_end; ++xor_mask) {
+                size_t sub_key = key ^ (*xor_mask);
+                const lsh::Bucket* bucket = table->getBucketFromKey(sub_key);
+                if (bucket == 0) continue;
+
+                // Go over each descriptor index
+                std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
+                std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
+                DistanceType hamming_distance;
+
+                // Process the rest of the candidates
+                for (; training_index < last_training_index; ++training_index) {
+                    // Compute the Hamming distance
+                    hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols);
+                    result.addPoint(hamming_distance, *training_index);
+                }
+            }
+        }
+    }
+
+    /** The different hash tables */
+    std::vector<lsh::LshTable<ElementType> > tables_;
+
+    /** The data the LSH tables where built from */
+    Matrix<ElementType> dataset_;
+
+    /** The size of the features (as ElementType[]) */
+    unsigned int feature_size_;
+
+    IndexParams index_params_;
+
+    /** table number */
+    unsigned int table_number_;
+    /** key size */
+    unsigned int key_size_;
+    /** How far should we look for neighbors in multi-probe LSH */
+    unsigned int multi_probe_level_;
+
+    /** The XOR masks to apply to a key to get the neighboring buckets */
+    std::vector<lsh::BucketKey> xor_masks_;
+
+    Distance distance_;
+};
+}
+
+#endif //FLANN_LSH_INDEX_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/nn_index.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/nn_index.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/algorithms/nn_index.h	(revision 15840)
@@ -0,0 +1,440 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_NNINDEX_H
+#define FLANN_NNINDEX_H
+
+#include <string>
+
+#ifdef TBB
+  #include <tbb/parallel_for.h>
+  #include <tbb/blocked_range.h>
+  #include <tbb/atomic.h>
+  #include <tbb/task_scheduler_init.h>
+#endif
+
+
+#include "flann/general.h"
+#include "flann/util/matrix.h"
+#include "flann/util/result_set.h"
+#include "flann/util/params.h"
+#ifdef TBB
+  #include "flann/tbb/bodies.hpp"
+#endif
+
+namespace flann
+{
+
+#define KNN_HEAP_THRESHOLD 250
+
+/**
+ * Nearest-neighbour index base class
+ */
+template <typename Distance>
+class NNIndex
+{
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+public:
+
+    virtual ~NNIndex() {}
+
+    /**
+     * \brief Builds the index
+     */
+    virtual void buildIndex() = 0;
+
+    /**
+     * \brief Perform k-nearest neighbor search
+     * \param[in] queries The query points for which to find the nearest neighbors
+     * \param[out] indices The indices of the nearest neighbors found
+     * \param[out] dists Distances to the nearest neighbors found
+     * \param[in] knn Number of nearest neighbors to return
+     * \param[in] params Search parameters
+     */
+    virtual int knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, size_t knn, const SearchParams& params)
+    {
+        assert(queries.cols == veclen());
+        assert(indices.rows >= queries.rows);
+        assert(dists.rows >= queries.rows);
+        assert(indices.cols >= knn);
+        assert(dists.cols >= knn);
+        bool use_heap;
+
+        if (params.use_heap==FLANN_Undefined) {
+        	use_heap = (knn>KNN_HEAP_THRESHOLD)?true:false;
+        }
+        else {
+        	use_heap = (params.use_heap==FLANN_True)?true:false;
+        }
+        int count = 0;
+
+#ifdef TBB
+        // Check if we need to do multicore search or stick with single core FLANN (less overhead)
+        if(params.cores == 1)
+        {
+#endif
+        	if (use_heap) {
+        		KNNResultSet2<DistanceType> resultSet(knn);
+        		for (size_t i = 0; i < queries.rows; i++) {
+        			resultSet.clear();
+        			findNeighbors(resultSet, queries[i], params);
+        			resultSet.copy(indices[i], dists[i], knn, params.sorted);
+        			count += resultSet.size();
+        		}
+        	}
+        	else {
+        		KNNSimpleResultSet<DistanceType> resultSet(knn);
+        		for (size_t i = 0; i < queries.rows; i++) {
+        			resultSet.clear();
+        			findNeighbors(resultSet, queries[i], params);
+        			resultSet.copy(indices[i], dists[i], knn, params.sorted);
+        			count += resultSet.size();
+        		}
+        	}
+#ifdef TBB
+    }
+    else
+    {
+        // Initialise the task scheduler for the use of Intel TBB parallel constructs
+        tbb::task_scheduler_init task_sched(params.cores);
+
+        // Make an atomic integer count, such that we can keep track of amount of neighbors found
+        tbb::atomic<int> atomic_count;
+        atomic_count = 0;
+        // Use auto partitioner to choose the optimal grainsize for dividing the query points
+        flann::parallel_knnSearch<Distance> parallel_knn(queries, indices, dists, knn, params, this, atomic_count);
+        tbb::parallel_for(tbb::blocked_range<size_t>(0,queries.rows),
+                          parallel_knn,
+                          tbb::auto_partitioner());
+
+        count = atomic_count;
+    }
+#endif
+
+        return count;
+    }
+
+
+
+    /**
+     * \brief Perform k-nearest neighbor search
+     * \param[in] queries The query points for which to find the nearest neighbors
+     * \param[out] indices The indices of the nearest neighbors found
+     * \param[out] dists Distances to the nearest neighbors found
+     * \param[in] knn Number of nearest neighbors to return
+     * \param[in] params Search parameters
+     */
+    virtual int knnSearch(const Matrix<ElementType>& queries,
+					std::vector< std::vector<int> >& indices,
+					std::vector<std::vector<DistanceType> >& dists,
+    				size_t knn,
+    				const SearchParams& params)
+    {
+        assert(queries.cols == veclen());
+        bool use_heap;
+        if (params.use_heap==FLANN_Undefined) {
+        	use_heap = (knn>KNN_HEAP_THRESHOLD)?true:false;
+        }
+        else {
+        	use_heap = (params.use_heap==FLANN_True)?true:false;
+        }
+
+        if (indices.size() < queries.rows ) indices.resize(queries.rows);
+		if (dists.size() < queries.rows ) dists.resize(queries.rows);
+
+		int count = 0;
+#ifdef TBB
+        // Check if we need to do multicore search or stick with single core FLANN (less overhead)
+        if(params.cores == 1)
+        {
+#endif
+        	if (use_heap) {
+        		KNNResultSet2<DistanceType> resultSet(knn);
+        		for (size_t i = 0; i < queries.rows; i++) {
+        			resultSet.clear();
+        			findNeighbors(resultSet, queries[i], params);
+        			size_t n = std::min(resultSet.size(), knn);
+        			indices[i].resize(n);
+        			dists[i].resize(n);
+        			resultSet.copy(&indices[i][0], &dists[i][0], n, params.sorted);
+        			count += n;
+        		}
+        	}
+        	else {
+        		KNNSimpleResultSet<DistanceType> resultSet(knn);
+        		for (size_t i = 0; i < queries.rows; i++) {
+        			resultSet.clear();
+        			findNeighbors(resultSet, queries[i], params);
+        			size_t n = std::min(resultSet.size(), knn);
+        			indices[i].resize(n);
+        			dists[i].resize(n);
+        			resultSet.copy(&indices[i][0], &dists[i][0], n, params.sorted);
+        			count += n;
+        		}
+        	}
+#ifdef TBB
+        }
+        else
+        {
+            // Initialise the task scheduler for the use of Intel TBB parallel constructs
+            tbb::task_scheduler_init task_sched(params.cores);
+
+            // Make an atomic integer count, such that we can keep track of amount of neighbors found
+            tbb::atomic<int> atomic_count;
+            atomic_count = 0;
+
+            // Use auto partitioner to choose the optimal grainsize for dividing the query points
+            flann::parallel_knnSearch2<Distance> parallel_knn(queries, indices, dists, knn, params, this, atomic_count);
+            tbb::parallel_for(tbb::blocked_range<size_t>(0,queries.rows),
+                              parallel_knn,
+                              tbb::auto_partitioner());
+
+            count = atomic_count;
+        }
+#endif
+		return count;
+    }
+
+
+    /**
+     * \brief Perform radius search
+     * \param[in] query The query point
+     * \param[out] indices The indinces of the neighbors found within the given radius
+     * \param[out] dists The distances to the nearest neighbors found
+     * \param[in] radius The radius used for search
+     * \param[in] params Search parameters
+     * \returns Number of neighbors found
+     */
+    virtual int radiusSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists,
+    		float radius, const SearchParams& params)
+    {
+        assert(queries.cols == veclen());
+        int count = 0;
+#ifdef TBB
+        // Check if we need to do multicore search or stick with single core FLANN (less overhead)
+        if(params.cores == 1)
+        {
+#endif
+			size_t num_neighbors = std::min(indices.cols, dists.cols);
+			int max_neighbors = params.max_neighbors;
+			if (max_neighbors<0) max_neighbors = num_neighbors;
+			else max_neighbors = std::min(max_neighbors,(int)num_neighbors);
+
+    		if (max_neighbors==0) {
+    			CountRadiusResultSet<DistanceType> resultSet(radius);
+    			for (size_t i = 0; i < queries.rows; i++) {
+    				resultSet.clear();
+    				findNeighbors(resultSet, queries[i], params);
+    				count += resultSet.size();
+    			}
+    		}
+    		else {
+
+    			// explicitly indicated to use unbounded radius result set
+    			// or we know there'll be enough room for resulting indices and dists
+    			if (params.max_neighbors<0 && (num_neighbors>=size())) {
+    				RadiusResultSet<DistanceType> resultSet(radius);
+    				for (size_t i = 0; i < queries.rows; i++) {
+    					resultSet.clear();
+    					findNeighbors(resultSet, queries[i], params);
+    					size_t n = resultSet.size();
+    					count += n;
+    					if (n>num_neighbors) n = num_neighbors;
+    					resultSet.copy(indices[i], dists[i], n, params.sorted);
+
+    					// mark the next element in the output buffers as unused
+    					if (n<indices.cols) indices[i][n] = -1;
+    					if (n<dists.cols) dists[i][n] = std::numeric_limits<DistanceType>::infinity();
+    				}
+    			}
+    			else {
+    				// number of neighbors limited to max_neighbors
+    				KNNRadiusResultSet<DistanceType> resultSet(radius, max_neighbors);
+    				for (size_t i = 0; i < queries.rows; i++) {
+    					resultSet.clear();
+    					findNeighbors(resultSet, queries[i], params);
+    					size_t n = resultSet.size();
+    					count += n;
+    					if ((int)n>max_neighbors) n = max_neighbors;
+    					resultSet.copy(indices[i], dists[i], n, params.sorted);
+
+    					// mark the next element in the output buffers as unused
+    					if (n<indices.cols) indices[i][n] = -1;
+    					if (n<dists.cols) dists[i][n] = std::numeric_limits<DistanceType>::infinity();
+    				}
+    			}
+    		}
+#ifdef TBB
+        }
+        else
+        {
+            // Initialise the task scheduler for the use of Intel TBB parallel constructs
+            tbb::task_scheduler_init task_sched(params.cores);
+
+            // Make an atomic integer count, such that we can keep track of amount of neighbors found
+            tbb::atomic<int> atomic_count;
+            atomic_count = 0;
+
+            // Use auto partitioner to choose the optimal grainsize for dividing the query points
+            flann::parallel_radiusSearch<Distance> parallel_radius(queries, indices, dists, radius, params, this, atomic_count);
+            tbb::parallel_for(tbb::blocked_range<size_t>(0,queries.rows),
+                              parallel_radius,
+                              tbb::auto_partitioner());
+
+            count = atomic_count;
+        }
+#endif
+        return count;
+    }
+
+    virtual int radiusSearch(const Matrix<ElementType>& queries, std::vector< std::vector<int> >& indices,
+    		std::vector<std::vector<DistanceType> >& dists, float radius, const SearchParams& params)
+    {
+        assert(queries.cols == veclen());
+    	int count = 0;
+#ifdef TBB
+        // Check if we need to do multicore search or stick with single core FLANN (less overhead)
+        if(params.cores == 1)
+        {
+#endif
+        	// just count neighbors
+        	if (params.max_neighbors==0) {
+        		CountRadiusResultSet<DistanceType> resultSet(radius);
+        		for (size_t i = 0; i < queries.rows; i++) {
+        			resultSet.clear();
+        			findNeighbors(resultSet, queries[i], params);
+        			count += resultSet.size();
+        		}
+        	}
+        	else {
+        		if (indices.size() < queries.rows ) indices.resize(queries.rows);
+        		if (dists.size() < queries.rows ) dists.resize(queries.rows);
+
+        		if (params.max_neighbors<0) {
+        			// search for all neighbors
+        			RadiusResultSet<DistanceType> resultSet(radius);
+        			for (size_t i = 0; i < queries.rows; i++) {
+        				resultSet.clear();
+        				findNeighbors(resultSet, queries[i], params);
+        				size_t n = resultSet.size();
+        				count += n;
+        				indices[i].resize(n);
+        				dists[i].resize(n);
+        				resultSet.copy(&indices[i][0], &dists[i][0], n, params.sorted);
+        			}
+        		}
+        		else {
+        			// number of neighbors limited to max_neighbors
+        			KNNRadiusResultSet<DistanceType> resultSet(radius, params.max_neighbors);
+        			for (size_t i = 0; i < queries.rows; i++) {
+        				resultSet.clear();
+        				findNeighbors(resultSet, queries[i], params);
+        				size_t n = resultSet.size();
+        				count += n;
+        				if ((int)n>params.max_neighbors) n = params.max_neighbors;
+        				indices[i].resize(n);
+        				dists[i].resize(n);
+        				resultSet.copy(&indices[i][0], &dists[i][0], n, params.sorted);
+        			}
+        		}
+        	}
+#ifdef TBB
+        }
+        else
+        {
+          // Initialise the task scheduler for the use of Intel TBB parallel constructs
+          tbb::task_scheduler_init task_sched(params.cores);
+
+          // Reset atomic count before passing it on to the threads, such that we can keep track of amount of neighbors found
+          tbb::atomic<int> atomic_count;
+          atomic_count = 0;
+
+          // Use auto partitioner to choose the optimal grainsize for dividing the query points
+          flann::parallel_radiusSearch2<Distance> parallel_radius(queries, indices, dists, radius, params, this, atomic_count);
+          tbb::parallel_for(tbb::blocked_range<size_t>(0,queries.rows),
+                            parallel_radius,
+                            tbb::auto_partitioner());
+
+          count = atomic_count;
+        }
+#endif
+        return count;
+    }
+
+
+    /**
+     * \brief Saves the index to a stream
+     * \param stream The stream to save the index to
+     */
+    virtual void saveIndex(FILE* stream) = 0;
+
+    /**
+     * \brief Loads the index from a stream
+     * \param stream The stream from which the index is loaded
+     */
+    virtual void loadIndex(FILE* stream) = 0;
+
+    /**
+     * \returns number of features in this index.
+     */
+    virtual size_t size() const = 0;
+
+    /**
+     * \returns The dimensionality of the features in this index.
+     */
+    virtual size_t veclen() const = 0;
+
+    /**
+     * \returns The amount of memory (in bytes) used by the index.
+     */
+    virtual int usedMemory() const = 0;
+
+    /**
+     * \returns The index type (kdtree, kmeans,...)
+     */
+    virtual flann_algorithm_t getType() const = 0;
+
+    /**
+     * \returns The index parameters
+     */
+    virtual IndexParams getParameters() const = 0;
+
+
+    /**
+     * \brief Method that searches for nearest-neighbours
+     */
+    virtual void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) = 0;
+};
+
+}
+
+#endif //FLANN_NNINDEX_H
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/config.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/config.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/config.h	(revision 15840)
@@ -0,0 +1,38 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2011  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2011  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+
+#ifndef FLANN_CONFIG_H_
+#define FLANN_CONFIG_H_
+
+#ifdef FLANN_VERSION_
+#undef FLANN_VERSION_
+#endif
+#define FLANN_VERSION_ "1.7.1"
+
+#endif /* FLANN_CONFIG_H_ */
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/defines.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/defines.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/defines.h	(revision 15840)
@@ -0,0 +1,181 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2011  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2011  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+
+#ifndef FLANN_DEFINES_H_
+#define FLANN_DEFINES_H_
+
+#include "config.h"
+
+#ifdef FLANN_EXPORT
+#undef FLANN_EXPORT
+#endif
+#ifdef WIN32
+/* win32 dll export/import directives */
+ #ifdef FLANN_EXPORTS
+  #define FLANN_EXPORT __declspec(dllexport)
+ #elif defined(FLANN_STATIC)
+  #define FLANN_EXPORT
+ #else
+  #define FLANN_EXPORT __declspec(dllimport)
+ #endif
+#else
+/* unix needs nothing */
+ #define FLANN_EXPORT
+#endif
+
+#ifdef FLANN_DEPRECATED
+#undef FLANN_DEPRECATED
+#endif
+#ifdef __GNUC__
+#define FLANN_DEPRECATED __attribute__ ((deprecated))
+#elif defined(_MSC_VER)
+#define FLANN_DEPRECATED __declspec(deprecated)
+#else
+#pragma message("WARNING: You need to implement FLANN_DEPRECATED for this compiler")
+#define FLANN_DEPRECATED
+#endif
+
+#undef FLANN_PLATFORM_64_BIT
+#undef FLANN_PLATFORM_32_BIT
+#if __amd64__ || __x86_64__ || _WIN64 || _M_X64
+#define FLANN_PLATFORM_64_BIT
+#else
+#define FLANN_PLATFORM_32_BIT
+#endif
+
+#undef FLANN_ARRAY_LEN
+#define FLANN_ARRAY_LEN(a) (sizeof(a)/sizeof(a[0]))
+
+#ifdef __cplusplus
+namespace flann {
+#endif
+
+/* Nearest neighbour index algorithms */
+enum flann_algorithm_t
+{
+    FLANN_INDEX_LINEAR = 0,
+    FLANN_INDEX_KDTREE = 1,
+    FLANN_INDEX_KMEANS = 2,
+    FLANN_INDEX_COMPOSITE = 3,
+    FLANN_INDEX_KDTREE_SINGLE = 4,
+    FLANN_INDEX_HIERARCHICAL = 5,
+    FLANN_INDEX_LSH = 6,
+    FLANN_INDEX_KDTREE_CUDA = 7,
+    FLANN_INDEX_SAVED = 254,
+    FLANN_INDEX_AUTOTUNED = 255,
+
+    // deprecated constants, should use the FLANN_INDEX_* ones instead
+    LINEAR = 0,
+    KDTREE = 1,
+    KMEANS = 2,
+    COMPOSITE = 3,
+    KDTREE_SINGLE = 4,
+    SAVED = 254,
+    AUTOTUNED = 255
+};
+
+
+
+enum flann_centers_init_t
+{
+    FLANN_CENTERS_RANDOM = 0,
+    FLANN_CENTERS_GONZALES = 1,
+    FLANN_CENTERS_KMEANSPP = 2,
+
+    // deprecated constants, should use the FLANN_CENTERS_* ones instead
+    CENTERS_RANDOM = 0,
+    CENTERS_GONZALES = 1,
+    CENTERS_KMEANSPP = 2
+};
+
+enum flann_log_level_t
+{
+    FLANN_LOG_NONE = 0,
+    FLANN_LOG_FATAL = 1,
+    FLANN_LOG_ERROR = 2,
+    FLANN_LOG_WARN = 3,
+    FLANN_LOG_INFO = 4
+};
+
+enum flann_distance_t
+{
+    FLANN_DIST_EUCLIDEAN = 1,
+    FLANN_DIST_L2 = 1,
+    FLANN_DIST_MANHATTAN = 2,
+    FLANN_DIST_L1 = 2,
+    FLANN_DIST_MINKOWSKI = 3,
+    FLANN_DIST_MAX   = 4,
+    FLANN_DIST_HIST_INTERSECT   = 5,
+    FLANN_DIST_HELLINGER = 6,
+    FLANN_DIST_CHI_SQUARE = 7,
+    FLANN_DIST_CS         = 7,
+    FLANN_DIST_KULLBACK_LEIBLER  = 8,
+    FLANN_DIST_KL                = 8,
+    FLANN_DIST_HAMMING         	= 9,
+    FLANN_DIST_HAMMING_LUT		= 10,
+    FLANN_DIST_HAMMING_POPCNT   = 11,
+
+    // deprecated constants, should use the FLANN_DIST_* ones instead
+    EUCLIDEAN = 1,
+    MANHATTAN = 2,
+    MINKOWSKI = 3,
+    MAX_DIST   = 4,
+    HIST_INTERSECT   = 5,
+    HELLINGER = 6,
+    CS         = 7,
+    KL         = 8,
+    KULLBACK_LEIBLER  = 8
+};
+
+enum flann_datatype_t
+{
+    FLANN_NONE = -1,
+    FLANN_INT8 = 0,
+    FLANN_INT16 = 1,
+    FLANN_INT32 = 2,
+    FLANN_INT64 = 3,
+    FLANN_UINT8 = 4,
+    FLANN_UINT16 = 5,
+    FLANN_UINT32 = 6,
+    FLANN_UINT64 = 7,
+    FLANN_FLOAT32 = 8,
+    FLANN_FLOAT64 = 9
+};
+
+enum flann_checks_t {
+    FLANN_CHECKS_UNLIMITED = -1,
+    FLANN_CHECKS_AUTOTUNED = -2
+};
+
+#ifdef __cplusplus
+}
+#endif
+
+
+#endif /* FLANN_DEFINES_H_ */
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/flann.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/flann.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/flann.h	(revision 15840)
@@ -0,0 +1,474 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_H_
+#define FLANN_H_
+
+#include "defines.h"
+
+#ifdef __cplusplus
+extern "C"
+{
+    using namespace flann;
+#endif
+
+
+struct FLANNParameters
+{
+    enum flann_algorithm_t algorithm; /* the algorithm to use */
+
+    /* search time parameters */
+    int checks;                /* how many leafs (features) to check in one search */
+    float cb_index;            /* cluster boundary index. Used when searching the kmeans tree */
+    float eps;     /* eps parameter for eps-knn search */
+
+    /*  kdtree index parameters */
+    int trees;                 /* number of randomized trees to use (for kdtree) */
+    int leaf_max_size;
+
+    /* kmeans index parameters */
+    int branching;             /* branching factor (for kmeans tree) */
+    int iterations;            /* max iterations to perform in one kmeans cluetering (kmeans tree) */
+    enum flann_centers_init_t centers_init;  /* algorithm used for picking the initial cluster centers for kmeans tree */
+
+    /* autotuned index parameters */
+    float target_precision;    /* precision desired (used for autotuning, -1 otherwise) */
+    float build_weight;        /* build tree time weighting factor */
+    float memory_weight;       /* index memory weigthing factor */
+    float sample_fraction;     /* what fraction of the dataset to use for autotuning */
+
+    /* LSH parameters */
+    unsigned int table_number_; /** The number of hash tables to use */
+    unsigned int key_size_;     /** The length of the key in the hash tables */
+    unsigned int multi_probe_level_; /** Number of levels to use in multi-probe LSH, 0 for standard LSH */
+
+    /* other parameters */
+    enum flann_log_level_t log_level;    /* determines the verbosity of each flann function */
+    long random_seed;            /* random seed to use */
+};
+
+
+typedef void* FLANN_INDEX; /* deprecated */
+typedef void* flann_index_t;
+
+FLANN_EXPORT extern struct FLANNParameters DEFAULT_FLANN_PARAMETERS;
+
+/**
+   Sets the log level used for all flann functions (unless
+   specified in FLANNParameters for each call
+
+   Params:
+    level = verbosity level
+ */
+FLANN_EXPORT void flann_log_verbosity(int level);
+
+
+/**
+ * Sets the distance type to use throughout FLANN.
+ * If distance type specified is MINKOWSKI, the second argument
+ * specifies which order the minkowski distance should have.
+ */
+FLANN_EXPORT void flann_set_distance_type(enum flann_distance_t distance_type, int order);
+
+
+/**
+   Builds and returns an index. It uses autotuning if the target_precision field of index_params
+   is between 0 and 1, or the parameters specified if it's -1.
+
+   Params:
+    dataset = pointer to a data set stored in row major order
+    rows = number of rows (features) in the dataset
+    cols = number of columns in the dataset (feature dimensionality)
+    speedup = speedup over linear search, estimated if using autotuning, output parameter
+    index_params = index related parameters
+    flann_params = generic flann parameters
+
+   Returns: the newly created index or a number <0 for error
+ */
+FLANN_EXPORT flann_index_t flann_build_index(float* dataset,
+                                             int rows,
+                                             int cols,
+                                             float* speedup,
+                                             struct FLANNParameters* flann_params);
+
+FLANN_EXPORT flann_index_t flann_build_index_float(float* dataset,
+                                                   int rows,
+                                                   int cols,
+                                                   float* speedup,
+                                                   struct FLANNParameters* flann_params);
+
+FLANN_EXPORT flann_index_t flann_build_index_double(double* dataset,
+                                                    int rows,
+                                                    int cols,
+                                                    float* speedup,
+                                                    struct FLANNParameters* flann_params);
+
+FLANN_EXPORT flann_index_t flann_build_index_byte(unsigned char* dataset,
+                                                  int rows,
+                                                  int cols,
+                                                  float* speedup,
+                                                  struct FLANNParameters* flann_params);
+
+FLANN_EXPORT flann_index_t flann_build_index_int(int* dataset,
+                                                 int rows,
+                                                 int cols,
+                                                 float* speedup,
+                                                 struct FLANNParameters* flann_params);
+
+/**
+ * Saves the index to a file. Only the index is saved into the file, the dataset corresponding to the index is not saved.
+ *
+ * @param index_id The index that should be saved
+ * @param filename The filename the index should be saved to
+ * @return Returns 0 on success, negative value on error.
+ */
+FLANN_EXPORT int flann_save_index(flann_index_t index_id,
+                                  char* filename);
+
+FLANN_EXPORT int flann_save_index_float(flann_index_t index_id,
+                                        char* filename);
+
+FLANN_EXPORT int flann_save_index_double(flann_index_t index_id,
+                                         char* filename);
+
+FLANN_EXPORT int flann_save_index_byte(flann_index_t index_id,
+                                       char* filename);
+
+FLANN_EXPORT int flann_save_index_int(flann_index_t index_id,
+                                      char* filename);
+
+/**
+ * Loads an index from a file.
+ *
+ * @param filename File to load the index from.
+ * @param dataset The dataset corresponding to the index.
+ * @param rows Dataset tors
+ * @param cols Dataset columns
+ * @return
+ */
+FLANN_EXPORT flann_index_t flann_load_index(char* filename,
+                                            float* dataset,
+                                            int rows,
+                                            int cols);
+
+FLANN_EXPORT flann_index_t flann_load_index_float(char* filename,
+                                                  float* dataset,
+                                                  int rows,
+                                                  int cols);
+
+FLANN_EXPORT flann_index_t flann_load_index_double(char* filename,
+                                                   double* dataset,
+                                                   int rows,
+                                                   int cols);
+
+FLANN_EXPORT flann_index_t flann_load_index_byte(char* filename,
+                                                 unsigned char* dataset,
+                                                 int rows,
+                                                 int cols);
+
+FLANN_EXPORT flann_index_t flann_load_index_int(char* filename,
+                                                int* dataset,
+                                                int rows,
+                                                int cols);
+
+
+/**
+   Builds an index and uses it to find nearest neighbors.
+
+   Params:
+    dataset = pointer to a data set stored in row major order
+    rows = number of rows (features) in the dataset
+    cols = number of columns in the dataset (feature dimensionality)
+    testset = pointer to a query set stored in row major order
+    trows = number of rows (features) in the query dataset (same dimensionality as features in the dataset)
+    indices = pointer to matrix for the indices of the nearest neighbors of the testset features in the dataset
+            (must have trows number of rows and nn number of columns)
+    nn = how many nearest neighbors to return
+    flann_params = generic flann parameters
+
+   Returns: zero or -1 for error
+ */
+FLANN_EXPORT int flann_find_nearest_neighbors(float* dataset,
+                                              int rows,
+                                              int cols,
+                                              float* testset,
+                                              int trows,
+                                              int* indices,
+                                              float* dists,
+                                              int nn,
+                                              struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_find_nearest_neighbors_float(float* dataset,
+                                                    int rows,
+                                                    int cols,
+                                                    float* testset,
+                                                    int trows,
+                                                    int* indices,
+                                                    float* dists,
+                                                    int nn,
+                                                    struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_find_nearest_neighbors_double(double* dataset,
+                                                     int rows,
+                                                     int cols,
+                                                     double* testset,
+                                                     int trows,
+                                                     int* indices,
+                                                     double* dists,
+                                                     int nn,
+                                                     struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_find_nearest_neighbors_byte(unsigned char* dataset,
+                                                   int rows,
+                                                   int cols,
+                                                   unsigned char* testset,
+                                                   int trows,
+                                                   int* indices,
+                                                   float* dists,
+                                                   int nn,
+                                                   struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_find_nearest_neighbors_int(int* dataset,
+                                                  int rows,
+                                                  int cols,
+                                                  int* testset,
+                                                  int trows,
+                                                  int* indices,
+                                                  float* dists,
+                                                  int nn,
+                                                  struct FLANNParameters* flann_params);
+
+
+/**
+   Searches for nearest neighbors using the index provided
+
+   Params:
+    index_id = the index (constructed previously using flann_build_index).
+    testset = pointer to a query set stored in row major order
+    trows = number of rows (features) in the query dataset (same dimensionality as features in the dataset)
+    indices = pointer to matrix for the indices of the nearest neighbors of the testset features in the dataset
+            (must have trows number of rows and nn number of columns)
+    dists = pointer to matrix for the distances of the nearest neighbors of the testset features in the dataset
+            (must have trows number of rows and 1 column)
+    nn = how many nearest neighbors to return
+    flann_params = generic flann parameters
+
+   Returns: zero or a number <0 for error
+ */
+FLANN_EXPORT int flann_find_nearest_neighbors_index(flann_index_t index_id,
+                                                    float* testset,
+                                                    int trows,
+                                                    int* indices,
+                                                    float* dists,
+                                                    int nn,
+                                                    struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_find_nearest_neighbors_index_float(flann_index_t index_id,
+                                                          float* testset,
+                                                          int trows,
+                                                          int* indices,
+                                                          float* dists,
+                                                          int nn,
+                                                          struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_find_nearest_neighbors_index_double(flann_index_t index_id,
+                                                           double* testset,
+                                                           int trows,
+                                                           int* indices,
+                                                           double* dists,
+                                                           int nn,
+                                                           struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_find_nearest_neighbors_index_byte(flann_index_t index_id,
+                                                         unsigned char* testset,
+                                                         int trows,
+                                                         int* indices,
+                                                         float* dists,
+                                                         int nn,
+                                                         struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_find_nearest_neighbors_index_int(flann_index_t index_id,
+                                                        int* testset,
+                                                        int trows,
+                                                        int* indices,
+                                                        float* dists,
+                                                        int nn,
+                                                        struct FLANNParameters* flann_params);
+
+
+/**
+ * Performs an radius search using an already constructed index.
+ *
+ * In case of radius search, instead of always returning a predetermined
+ * number of nearest neighbours (for example the 10 nearest neighbours), the
+ * search will return all the neighbours found within a search radius
+ * of the query point.
+ *
+ * The check parameter in the FLANNParameters below sets the level of approximation
+ * for the search by only visiting "checks" number of features in the index
+ * (the same way as for the KNN search). A lower value for checks will give
+ * a higher search speedup at the cost of potentially not returning all the
+ * neighbours in the specified radius.
+ *
+ * The cores parameter in the FLANNParameters below sets the number of cores
+ * that will be used for the radius search, in case Intel TBB is present on
+ * the system and FLANN is built with multicore support on. Auto core selection
+ * can be achieved by setting the number of cores to -1.
+ */
+FLANN_EXPORT int flann_radius_search(flann_index_t index_ptr, /* the index */
+                                     float* query, /* query point */
+                                     int* indices, /* array for storing the indices found (will be modified) */
+                                     float* dists, /* similar, but for storing distances */
+                                     int max_nn,  /* size of arrays indices and dists */
+                                     float radius, /* search radius (squared radius for euclidian metric) */
+                                     struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_radius_search_float(flann_index_t index_ptr, /* the index */
+                                           float* query, /* query point */
+                                           int* indices, /* array for storing the indices found (will be modified) */
+                                           float* dists, /* similar, but for storing distances */
+                                           int max_nn,  /* size of arrays indices and dists */
+                                           float radius, /* search radius (squared radius for euclidian metric) */
+                                           struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_radius_search_double(flann_index_t index_ptr, /* the index */
+                                            double* query, /* query point */
+                                            int* indices, /* array for storing the indices found (will be modified) */
+                                            double* dists, /* similar, but for storing distances */
+                                            int max_nn,  /* size of arrays indices and dists */
+                                            float radius, /* search radius (squared radius for euclidian metric) */
+                                            struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_radius_search_byte(flann_index_t index_ptr, /* the index */
+                                          unsigned char* query, /* query point */
+                                          int* indices, /* array for storing the indices found (will be modified) */
+                                          float* dists, /* similar, but for storing distances */
+                                          int max_nn,  /* size of arrays indices and dists */
+                                          float radius, /* search radius (squared radius for euclidian metric) */
+                                          struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_radius_search_int(flann_index_t index_ptr, /* the index */
+                                         int* query, /* query point */
+                                         int* indices, /* array for storing the indices found (will be modified) */
+                                         float* dists, /* similar, but for storing distances */
+                                         int max_nn,  /* size of arrays indices and dists */
+                                         float radius, /* search radius (squared radius for euclidian metric) */
+                                         struct FLANNParameters* flann_params);
+
+/**
+   Deletes an index and releases the memory used by it.
+
+   Params:
+    index_id = the index (constructed previously using flann_build_index).
+    flann_params = generic flann parameters
+
+   Returns: zero or a number <0 for error
+ */
+FLANN_EXPORT int flann_free_index(flann_index_t index_id,
+                                  struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_free_index_float(flann_index_t index_id,
+                                        struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_free_index_double(flann_index_t index_id,
+                                         struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_free_index_byte(flann_index_t index_id,
+                                       struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_free_index_int(flann_index_t index_id,
+                                      struct FLANNParameters* flann_params);
+
+/**
+   Clusters the features in the dataset using a hierarchical kmeans clustering approach.
+   This is significantly faster than using a flat kmeans clustering for a large number
+   of clusters.
+
+   Params:
+    dataset = pointer to a data set stored in row major order
+    rows = number of rows (features) in the dataset
+    cols = number of columns in the dataset (feature dimensionality)
+    clusters = number of cluster to compute
+    result = memory buffer where the output cluster centers are storred
+    index_params = used to specify the kmeans tree parameters (branching factor, max number of iterations to use)
+    flann_params = generic flann parameters
+
+   Returns: number of clusters computed or a number <0 for error. This number can be different than the number of clusters requested, due to the
+    way hierarchical clusters are computed. The number of clusters returned will be the highest number of the form
+    (branch_size-1)*K+1 smaller than the number of clusters requested.
+ */
+
+FLANN_EXPORT int flann_compute_cluster_centers(float* dataset,
+                                               int rows,
+                                               int cols,
+                                               int clusters,
+                                               float* result,
+                                               struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_compute_cluster_centers_float(float* dataset,
+                                                     int rows,
+                                                     int cols,
+                                                     int clusters,
+                                                     float* result,
+                                                     struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_compute_cluster_centers_double(double* dataset,
+                                                      int rows,
+                                                      int cols,
+                                                      int clusters,
+                                                      double* result,
+                                                      struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_compute_cluster_centers_byte(unsigned char* dataset,
+                                                    int rows,
+                                                    int cols,
+                                                    int clusters,
+                                                    float* result,
+                                                    struct FLANNParameters* flann_params);
+
+FLANN_EXPORT int flann_compute_cluster_centers_int(int* dataset,
+                                                   int rows,
+                                                   int cols,
+                                                   int clusters,
+                                                   float* result,
+                                                   struct FLANNParameters* flann_params);
+
+
+#ifdef __cplusplus
+}
+
+
+#include "flann.hpp"
+
+#endif
+
+
+#endif /*FLANN_H_*/
+
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/flann.hpp
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/flann.hpp	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/flann.hpp	(revision 15840)
@@ -0,0 +1,329 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_HPP_
+#define FLANN_HPP_
+
+#include <vector>
+#include <string>
+#include <cassert>
+#include <cstdio>
+
+#include "flann/general.h"
+#include "flann/util/matrix.h"
+#include "flann/util/params.h"
+#include "flann/util/saving.h"
+
+#include "flann/algorithms/all_indices.h"
+
+namespace flann
+{
+
+/**
+ * Sets the log level used for all flann functions
+ * @param level Verbosity level
+ */
+inline void log_verbosity(int level)
+{
+    if (level >= 0) {
+        Logger::setLevel(level);
+    }
+}
+
+/**
+ * (Deprecated) Index parameters for creating a saved index.
+ */
+struct SavedIndexParams : public IndexParams
+{
+    SavedIndexParams(std::string filename)
+    {
+        (*this)["algorithm"] = FLANN_INDEX_SAVED;
+        (*this)["filename"] = filename;
+    }
+};
+
+
+template<typename Distance>
+NNIndex<Distance>* load_saved_index(const Matrix<typename Distance::ElementType>& dataset, const std::string& filename, Distance distance)
+{
+    typedef typename Distance::ElementType ElementType;
+
+    FILE* fin = fopen(filename.c_str(), "rb");
+    if (fin == NULL) {
+        return NULL;
+    }
+    IndexHeader header = load_header(fin);
+    if (header.data_type != flann_datatype<ElementType>::value) {
+        throw FLANNException("Datatype of saved index is different than of the one to be created.");
+    }
+    if ((size_t(header.rows) != dataset.rows)||(size_t(header.cols) != dataset.cols)) {
+        throw FLANNException("The index saved belongs to a different dataset");
+    }
+
+    IndexParams params;
+    params["algorithm"] = header.index_type;
+    NNIndex<Distance>* nnIndex = create_index_by_type<Distance>(dataset, params, distance);
+    nnIndex->loadIndex(fin);
+    fclose(fin);
+
+    return nnIndex;
+}
+
+
+template<typename Distance>
+class Index
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+    Index(const Matrix<ElementType>& features, const IndexParams& params, Distance distance = Distance() )
+        : index_params_(params)
+    {
+        flann_algorithm_t index_type = get_param<flann_algorithm_t>(params,"algorithm");
+        loaded_ = false;
+
+        if (index_type == FLANN_INDEX_SAVED) {
+            nnIndex_ = load_saved_index<Distance>(features, get_param<std::string>(params,"filename"), distance);
+            loaded_ = true;
+        }
+        else {
+            nnIndex_ = create_index_by_type<Distance>(features, params, distance);
+        }
+    }
+
+    virtual ~Index()
+    {
+        delete nnIndex_;
+    }
+
+    /**
+     * Builds the index.
+     */
+    void buildIndex()
+    {
+        if (!loaded_) {
+            nnIndex_->buildIndex();
+        }
+    }
+
+    void save(std::string filename)
+    {
+        FILE* fout = fopen(filename.c_str(), "wb");
+        if (fout == NULL) {
+            throw FLANNException("Cannot open file");
+        }
+        save_header(fout, *nnIndex_);
+        saveIndex(fout);
+        fclose(fout);
+    }
+
+    /**
+     * \brief Saves the index to a stream
+     * \param stream The stream to save the index to
+     */
+    void saveIndex(FILE* stream)
+    {
+        nnIndex_->saveIndex(stream);
+    }
+
+    /**
+     * \brief Loads the index from a stream
+     * \param stream The stream from which the index is loaded
+     */
+    void loadIndex(FILE* stream)
+    {
+        nnIndex_->loadIndex(stream);
+    }
+
+    /**
+     * \returns number of features in this index.
+     */
+    size_t veclen() const
+    {
+        return nnIndex_->veclen();
+    }
+
+    /**
+     * \returns The dimensionality of the features in this index.
+     */
+    size_t size() const
+    {
+        return nnIndex_->size();
+    }
+
+    /**
+     * \returns The index type (kdtree, kmeans,...)
+     */
+    flann_algorithm_t getType() const
+    {
+        return nnIndex_->getType();
+    }
+
+    /**
+     * \returns The amount of memory (in bytes) used by the index.
+     */
+    int usedMemory() const
+    {
+        return nnIndex_->usedMemory();
+    }
+
+
+    /**
+     * \returns The index parameters
+     */
+    IndexParams getParameters() const
+    {
+        return nnIndex_->getParameters();
+    }
+
+    /**
+     * \brief Perform k-nearest neighbor search
+     * \param[in] queries The query points for which to find the nearest neighbors
+     * \param[out] indices The indices of the nearest neighbors found
+     * \param[out] dists Distances to the nearest neighbors found
+     * \param[in] knn Number of nearest neighbors to return
+     * \param[in] params Search parameters
+     */
+    int knnSearch(const Matrix<ElementType>& queries,
+                                 Matrix<int>& indices,
+                                 Matrix<DistanceType>& dists,
+                                 size_t knn,
+                           const SearchParams& params)
+    {
+    	return nnIndex_->knnSearch(queries, indices, dists, knn, params);
+    }
+
+
+    /**
+     * \brief Perform k-nearest neighbor search
+     * \param[in] queries The query points for which to find the nearest neighbors
+     * \param[out] indices The indices of the nearest neighbors found
+     * \param[out] dists Distances to the nearest neighbors found
+     * \param[in] knn Number of nearest neighbors to return
+     * \param[in] params Search parameters
+     */
+    int knnSearch(const Matrix<ElementType>& queries,
+                                 std::vector< std::vector<int> >& indices,
+                                 std::vector<std::vector<DistanceType> >& dists,
+                                 size_t knn,
+                           const SearchParams& params)
+    {
+    	return nnIndex_->knnSearch(queries, indices, dists, knn, params);
+    }
+
+
+    /**
+     * \brief Perform radius search
+     * \param[in] queries The query points
+     * \param[out] indices The indinces of the neighbors found within the given radius
+     * \param[out] dists The distances to the nearest neighbors found
+     * \param[in] radius The radius used for search
+     * \param[in] params Search parameters
+     * \returns Number of neighbors found
+     */
+    int radiusSearch(const Matrix<ElementType>& queries,
+                                    Matrix<int>& indices,
+                                    Matrix<DistanceType>& dists,
+                                    float radius,
+                              const SearchParams& params)
+    {
+    	return nnIndex_->radiusSearch(queries, indices, dists, radius, params);
+    }
+
+
+    /**
+     * \brief Perform radius search
+     * \param[in] queries The query points
+     * \param[out] indices The indinces of the neighbors found within the given radius
+     * \param[out] dists The distances to the nearest neighbors found
+     * \param[in] radius The radius used for search
+     * \param[in] params Search parameters
+     * \returns Number of neighbors found
+     */
+    int radiusSearch(const Matrix<ElementType>& queries,
+                                    std::vector< std::vector<int> >& indices,
+                                    std::vector<std::vector<DistanceType> >& dists,
+                                    float radius,
+                              const SearchParams& params)
+    {
+    	return nnIndex_->radiusSearch(queries, indices, dists, radius, params);
+    }
+
+    /**
+     * \brief Returns actual index
+     */
+    FLANN_DEPRECATED NNIndex<Distance>* getIndex()
+    {
+        return nnIndex_;
+    }
+
+    /**
+     * \brief Returns index parameters.
+     * \deprecated use getParameters() instead.
+     */
+    FLANN_DEPRECATED  const IndexParams* getIndexParameters()
+    {
+        return &index_params_;
+    }
+
+private:
+    /** Pointer to actual index class */
+    NNIndex<Distance>* nnIndex_;
+    /** Indices if the index was loaded from a file */
+    bool loaded_;
+    /** Parameters passed to the index */
+    IndexParams index_params_;
+};
+
+/**
+ * Performs a hierarchical clustering of the points passed as argument and then takes a cut in the
+ * the clustering tree to return a flat clustering.
+ * @param[in] points Points to be clustered
+ * @param centers The computed cluster centres. Matrix should be preallocated and centers.rows is the
+ *  number of clusters requested.
+ * @param params Clustering parameters (The same as for flann::KMeansIndex)
+ * @param d Distance to be used for clustering (eg: flann::L2)
+ * @return number of clusters computed (can be different than clusters.rows and is the highest number
+ * of the form (branching-1)*K+1 smaller than clusters.rows).
+ */
+template <typename Distance>
+int hierarchicalClustering(const Matrix<typename Distance::ElementType>& points, Matrix<typename Distance::ResultType>& centers,
+                           const KMeansIndexParams& params, Distance d = Distance())
+{
+    KMeansIndex<Distance> kmeans(points, params, d);
+    kmeans.buildIndex();
+
+    int clusterNum = kmeans.getClusterCenters(centers);
+    return clusterNum;
+}
+
+}
+#endif /* FLANN_HPP_ */
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/general.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/general.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/general.h	(revision 15840)
@@ -0,0 +1,144 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_GENERAL_H_
+#define FLANN_GENERAL_H_
+
+#include "defines.h"
+#include <stdexcept>
+#include <cassert>
+
+namespace flann
+{
+
+class FLANNException : public std::runtime_error
+{
+public:
+    FLANNException(const char* message) : std::runtime_error(message) { }
+
+    FLANNException(const std::string& message) : std::runtime_error(message) { }
+};
+
+inline size_t flann_datatype_size(flann_datatype_t type)
+{
+	switch (type) {
+	case FLANN_INT8:
+		return 1;
+	break;
+	case FLANN_INT16:
+		return 2;
+	break;
+	case FLANN_INT32:
+		return 4;
+	break;
+	case FLANN_INT64:
+		return 8;
+	break;
+	case FLANN_UINT8:
+		return 1;
+	break;
+	case FLANN_UINT16:
+		return 2;
+	break;
+	case FLANN_UINT32:
+		return 4;
+	break;
+	case FLANN_UINT64:
+		return 8;
+	break;
+	case FLANN_FLOAT32:
+		return 4;
+	break;
+	case FLANN_FLOAT64:
+		return 8;
+	break;
+	default:
+		return 1;
+	}
+}
+
+template <typename T>
+struct flann_datatype
+{
+	static const flann_datatype_t value = FLANN_NONE;
+};
+
+template<>
+struct flann_datatype<char>
+{
+	static const flann_datatype_t value = FLANN_INT8;
+};
+
+template<>
+struct flann_datatype<short>
+{
+	static const flann_datatype_t value = FLANN_INT16;
+};
+
+template<>
+struct flann_datatype<int>
+{
+	static const flann_datatype_t value = FLANN_INT32;
+};
+
+template<>
+struct flann_datatype<unsigned char>
+{
+	static const flann_datatype_t value = FLANN_UINT8;
+};
+
+template<>
+struct flann_datatype<unsigned short>
+{
+	static const flann_datatype_t value = FLANN_UINT16;
+};
+
+template<>
+struct flann_datatype<unsigned int>
+{
+	static const flann_datatype_t value = FLANN_UINT32;
+};
+
+template<>
+struct flann_datatype<float>
+{
+	static const flann_datatype_t value = FLANN_FLOAT32;
+};
+
+template<>
+struct flann_datatype<double>
+{
+	static const flann_datatype_t value = FLANN_FLOAT64;
+};
+
+}
+
+
+#endif  /* FLANN_GENERAL_H_ */
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/io/hdf5.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/io/hdf5.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/io/hdf5.h	(revision 15840)
@@ -0,0 +1,231 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+
+#ifndef FLANN_HDF5_H_
+#define FLANN_HDF5_H_
+
+#include <hdf5.h>
+
+#include "flann/util/matrix.h"
+
+
+namespace flann
+{
+
+namespace
+{
+
+template<typename T>
+hid_t get_hdf5_type()
+{
+    throw FLANNException("Unsupported type for IO operations");
+}
+
+template<>
+hid_t get_hdf5_type<char>() { return H5T_NATIVE_CHAR; }
+template<>
+hid_t get_hdf5_type<unsigned char>() { return H5T_NATIVE_UCHAR; }
+template<>
+hid_t get_hdf5_type<short int>() { return H5T_NATIVE_SHORT; }
+template<>
+hid_t get_hdf5_type<unsigned short int>() { return H5T_NATIVE_USHORT; }
+template<>
+hid_t get_hdf5_type<int>() { return H5T_NATIVE_INT; }
+template<>
+hid_t get_hdf5_type<unsigned int>() { return H5T_NATIVE_UINT; }
+template<>
+hid_t get_hdf5_type<long>() { return H5T_NATIVE_LONG; }
+template<>
+hid_t get_hdf5_type<unsigned long>() { return H5T_NATIVE_ULONG; }
+template<>
+hid_t get_hdf5_type<float>() { return H5T_NATIVE_FLOAT; }
+template<>
+hid_t get_hdf5_type<double>() { return H5T_NATIVE_DOUBLE; }
+template<>
+hid_t get_hdf5_type<long double>() { return H5T_NATIVE_LDOUBLE; }
+}
+
+
+#define CHECK_ERROR(x,y) if ((x)<0) throw FLANNException((y));
+
+template<typename T>
+void save_to_file(const flann::Matrix<T>& dataset, const std::string& filename, const std::string& name)
+{
+
+#if H5Eset_auto_vers == 2
+    H5Eset_auto( H5E_DEFAULT, NULL, NULL );
+#else
+    H5Eset_auto( NULL, NULL );
+#endif
+
+    herr_t status;
+    hid_t file_id;
+    file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, H5P_DEFAULT);
+    if (file_id < 0) {
+        file_id = H5Fcreate(filename.c_str(), H5F_ACC_EXCL, H5P_DEFAULT, H5P_DEFAULT);
+    }
+    CHECK_ERROR(file_id,"Error creating hdf5 file.");
+
+    hsize_t     dimsf[2];              // dataset dimensions
+    dimsf[0] = dataset.rows;
+    dimsf[1] = dataset.cols;
+
+    hid_t space_id = H5Screate_simple(2, dimsf, NULL);
+    hid_t memspace_id = H5Screate_simple(2, dimsf, NULL);
+
+    hid_t dataset_id;
+#if H5Dcreate_vers == 2
+    dataset_id = H5Dcreate2(file_id, name.c_str(), get_hdf5_type<T>(), space_id, H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT);
+#else
+    dataset_id = H5Dcreate(file_id, name.c_str(), get_hdf5_type<T>(), space_id, H5P_DEFAULT);
+#endif
+
+    if (dataset_id<0) {
+#if H5Dopen_vers == 2
+        dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT);
+#else
+        dataset_id = H5Dopen(file_id, name.c_str());
+#endif
+    }
+    CHECK_ERROR(dataset_id,"Error creating or opening dataset in file.");
+
+    status = H5Dwrite(dataset_id, get_hdf5_type<T>(), memspace_id, space_id, H5P_DEFAULT, dataset.ptr() );
+    CHECK_ERROR(status, "Error writing to dataset");
+
+    H5Sclose(memspace_id);
+    H5Sclose(space_id);
+    H5Dclose(dataset_id);
+    H5Fclose(file_id);
+
+}
+
+
+template<typename T>
+void load_from_file(flann::Matrix<T>& dataset, const std::string& filename, const std::string& name)
+{
+    herr_t status;
+    hid_t file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, H5P_DEFAULT);
+    CHECK_ERROR(file_id,"Error opening hdf5 file.");
+
+    hid_t dataset_id;
+#if H5Dopen_vers == 2
+    dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT);
+#else
+    dataset_id = H5Dopen(file_id, name.c_str());
+#endif
+    CHECK_ERROR(dataset_id,"Error opening dataset in file.");
+
+    hid_t space_id = H5Dget_space(dataset_id);
+
+    hsize_t dims_out[2];
+    H5Sget_simple_extent_dims(space_id, dims_out, NULL);
+
+    dataset = flann::Matrix<T>(new T[dims_out[0]*dims_out[1]], dims_out[0], dims_out[1]);
+
+    status = H5Dread(dataset_id, get_hdf5_type<T>(), H5S_ALL, H5S_ALL, H5P_DEFAULT, dataset[0]);
+    CHECK_ERROR(status, "Error reading dataset");
+
+    H5Sclose(space_id);
+    H5Dclose(dataset_id);
+    H5Fclose(file_id);
+}
+
+
+#ifdef HAVE_MPI
+
+namespace mpi
+{
+/**
+ * Loads a the hyperslice corresponding to this processor from a hdf5 file.
+ * @param flann_dataset Dataset where the data is loaded
+ * @param filename HDF5 file name
+ * @param name Name of dataset inside file
+ */
+template<typename T>
+void load_from_file(flann::Matrix<T>& dataset, const std::string& filename, const std::string& name)
+{
+    MPI_Comm comm  = MPI_COMM_WORLD;
+    MPI_Info info  = MPI_INFO_NULL;
+
+    int mpi_size, mpi_rank;
+    MPI_Comm_size(comm, &mpi_size);
+    MPI_Comm_rank(comm, &mpi_rank);
+
+    herr_t status;
+
+    hid_t plist_id = H5Pcreate(H5P_FILE_ACCESS);
+    H5Pset_fapl_mpio(plist_id, comm, info);
+    hid_t file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, plist_id);
+    CHECK_ERROR(file_id,"Error opening hdf5 file.");
+    H5Pclose(plist_id);
+    hid_t dataset_id;
+#if H5Dopen_vers == 2
+    dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT);
+#else
+    dataset_id = H5Dopen(file_id, name.c_str());
+#endif
+    CHECK_ERROR(dataset_id,"Error opening dataset in file.");
+
+    hid_t space_id = H5Dget_space(dataset_id);
+    hsize_t dims[2];
+    H5Sget_simple_extent_dims(space_id, dims, NULL);
+
+    hsize_t count[2];
+    hsize_t offset[2];
+
+    hsize_t item_cnt = dims[0]/mpi_size+(dims[0]%mpi_size==0 ? 0 : 1);
+    hsize_t cnt = (mpi_rank<mpi_size-1 ? item_cnt : dims[0]-item_cnt*(mpi_size-1));
+
+    count[0] = cnt;
+    count[1] = dims[1];
+    offset[0] = mpi_rank*item_cnt;
+    offset[1] = 0;
+
+    hid_t memspace_id = H5Screate_simple(2,count,NULL);
+
+    H5Sselect_hyperslab(space_id, H5S_SELECT_SET, offset, NULL, count, NULL);
+
+    dataset = flann::Matrix<T>(new T[count[0]*count[1]], count[0], count[1]);
+
+    plist_id = H5Pcreate(H5P_DATASET_XFER);
+//    H5Pset_dxpl_mpio(plist_id, H5FD_MPIO_COLLECTIVE);
+    status = H5Dread(dataset_id, get_hdf5_type<T>(), memspace_id, space_id, plist_id, dataset[0]);
+    CHECK_ERROR(status, "Error reading dataset");
+
+    H5Pclose(plist_id);
+    H5Sclose(space_id);
+    H5Sclose(memspace_id);
+    H5Dclose(dataset_id);
+    H5Fclose(file_id);
+}
+}
+#endif // HAVE_MPI
+} // namespace flann::mpi
+
+#endif /* FLANN_HDF5_H_ */
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/mpi/client.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/mpi/client.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/mpi/client.h	(revision 15840)
@@ -0,0 +1,88 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2011  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2011  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+
+#ifndef MPI_CLIENT_H_
+#define MPI_CLIENT_H_
+
+#include <cstdlib>
+#include <boost/asio.hpp>
+#include <flann/util/matrix.h>
+#include <flann/util/params.h>
+#include "queries.h"
+
+namespace flann {
+namespace mpi {
+
+template<typename ElementType, typename DistanceType>
+class Client
+{
+public:
+	Client(const std::string& host, const std::string& service)
+	{
+	    tcp::resolver resolver(io_service_);
+	    tcp::resolver::query query(tcp::v4(), host, service);
+	    iterator_ = resolver.resolve(query);
+	}
+
+
+	void knnSearch(const flann::Matrix<ElementType>& queries, flann::Matrix<int>& indices, flann::Matrix<DistanceType>& dists, int knn, const SearchParams& params)
+	{
+	    tcp::socket sock(io_service_);
+	    sock.connect(*iterator_);
+
+	    Request<ElementType> req;
+	    req.nn = knn;
+	    req.queries = queries;
+	    req.checks = params.checks;
+	    // send request
+	    write_object(sock,req);
+
+	    Response<DistanceType> resp;
+	    // read response
+	    read_object(sock, resp);
+
+	    for (size_t i=0;i<indices.rows;++i) {
+	    	for (size_t j=0;j<indices.cols;++j) {
+	    		indices[i][j] = resp.indices[i][j];
+	    		dists[i][j] = resp.dists[i][j];
+	    	}
+	    }
+	}
+
+
+private:
+	boost::asio::io_service io_service_;
+	tcp::resolver::iterator iterator_;
+};
+
+
+} //namespace mpi
+} // namespace flann
+
+#endif // MPI_CLIENT_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/mpi/index.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/mpi/index.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/mpi/index.h	(revision 15840)
@@ -0,0 +1,272 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2010  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2010  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+
+#ifndef FLANN_MPI_HPP_
+#define FLANN_MPI_HPP_
+
+#include <boost/mpi.hpp>
+#include <boost/serialization/array.hpp>
+#include <flann/flann.hpp>
+#include <flann/io/hdf5.h>
+
+namespace flann
+{
+namespace mpi
+{
+
+template<typename DistanceType>
+struct SearchResults
+{
+    flann::Matrix<int> indices;
+    flann::Matrix<DistanceType> dists;
+
+    template<typename Archive>
+    void serialize(Archive& ar, const unsigned int version)
+    {
+        ar& indices.rows;
+        ar& indices.cols;
+        if (Archive::is_loading::value) {
+            indices = Matrix<int>(new int[indices.rows*indices.cols], indices.rows, indices.cols);
+        }
+        ar& boost::serialization::make_array(indices.ptr(), indices.rows*indices.cols);
+        if (Archive::is_saving::value) {
+            delete[] indices.ptr();
+        }
+        ar& dists.rows;
+        ar& dists.cols;
+        if (Archive::is_loading::value) {
+            dists = Matrix<DistanceType>(new DistanceType[dists.rows*dists.cols], dists.rows, dists.cols);
+        }
+        ar& boost::serialization::make_array(dists.ptr(), dists.rows*dists.cols);
+        if (Archive::is_saving::value) {
+            delete[] dists.ptr();
+        }
+    }
+};
+
+template<typename DistanceType>
+struct ResultsMerger
+{
+    SearchResults<DistanceType> operator()(SearchResults<DistanceType> a, SearchResults<DistanceType> b)
+    {
+        SearchResults<DistanceType> results;
+        results.indices = flann::Matrix<int>(new int[a.indices.rows*a.indices.cols],a.indices.rows,a.indices.cols);
+        results.dists = flann::Matrix<DistanceType>(new DistanceType[a.dists.rows*a.dists.cols],a.dists.rows,a.dists.cols);
+
+
+        for (size_t i = 0; i < results.dists.rows; ++i) {
+            size_t idx = 0;
+            size_t a_idx = 0;
+            size_t b_idx = 0;
+            while (idx < results.dists.cols) {
+                if (a.dists[i][a_idx] <= b.dists[i][b_idx]) {
+                    results.dists[i][idx] = a.dists[i][a_idx];
+                    results.indices[i][idx] = a.indices[i][a_idx];
+                    idx++;
+                    a_idx++;
+                }
+                else {
+                    results.dists[i][idx] = b.dists[i][b_idx];
+                    results.indices[i][idx] = b.indices[i][b_idx];
+                    idx++;
+                    b_idx++;
+                }
+            }
+        }
+        delete[] a.indices.ptr();
+        delete[] a.dists.ptr();
+        delete[] b.indices.ptr();
+        delete[] b.dists.ptr();
+        return results;
+    }
+};
+
+
+
+template<typename Distance>
+class Index
+{
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+    flann::Index<Distance>* flann_index;
+    flann::Matrix<ElementType> dataset;
+    int size_;
+    int offset_;
+
+public:
+    Index(const std::string& file_name,
+          const std::string& dataset_name,
+          const IndexParams& params);
+
+    ~Index();
+
+    void buildIndex()
+    {
+        flann_index->buildIndex();
+    }
+
+    void knnSearch(const flann::Matrix<ElementType>& queries,
+                   flann::Matrix<int>& indices,
+                   flann::Matrix<DistanceType>& dists,
+                   int knn, const
+                   SearchParams& params);
+
+    int radiusSearch(const flann::Matrix<ElementType>& query,
+                     flann::Matrix<int>& indices,
+                     flann::Matrix<DistanceType>& dists,
+                     float radius,
+                     const SearchParams& params);
+
+    // void save(std::string filename);
+
+    int veclen() const
+    {
+        return flann_index->veclen();
+    }
+
+    int size() const
+    {
+        return size_;
+    }
+
+    IndexParams getIndexParameters()
+    {
+        return flann_index->getParameters();
+    }
+};
+
+
+template<typename Distance>
+Index<Distance>::Index(const std::string& file_name, const std::string& dataset_name, const IndexParams& params)
+{
+    boost::mpi::communicator world;
+    flann_algorithm_t index_type = get_param<flann_algorithm_t>(params,"algorithm");
+    if (index_type == SAVED) {
+        throw FLANNException("Saving/loading of MPI indexes is not currently supported.");
+    }
+    flann::mpi::load_from_file(dataset, file_name, dataset_name);
+    flann_index = new flann::Index<Distance>(dataset, params);
+
+    std::vector<int> sizes;
+    // get the sizes of all MPI indices
+    all_gather(world, (int)flann_index->size(), sizes);
+    size_ = 0;
+    offset_ = 0;
+    for (size_t i = 0; i < sizes.size(); ++i) {
+        if ((int)i < world.rank()) offset_ += sizes[i];
+        size_ += sizes[i];
+    }
+}
+
+template<typename Distance>
+Index<Distance>::~Index()
+{
+    delete flann_index;
+    delete[] dataset.ptr();
+}
+
+template<typename Distance>
+void Index<Distance>::knnSearch(const flann::Matrix<ElementType>& queries, flann::Matrix<int>& indices, flann::Matrix<DistanceType>& dists, int knn, const SearchParams& params)
+{
+    boost::mpi::communicator world;
+    flann::Matrix<int> local_indices(new int[queries.rows*knn], queries.rows, knn);
+    flann::Matrix<DistanceType> local_dists(new DistanceType[queries.rows*knn], queries.rows, knn);
+
+    flann_index->knnSearch(queries, local_indices, local_dists, knn, params);
+    for (size_t i = 0; i < local_indices.rows; ++i) {
+        for (size_t j = 0; j < local_indices.cols; ++j) {
+            local_indices[i][j] += offset_;
+        }
+    }
+    SearchResults<DistanceType> local_results;
+    local_results.indices = local_indices;
+    local_results.dists = local_dists;
+    SearchResults<DistanceType> results;
+
+    // perform MPI reduce
+    reduce(world, local_results, results, ResultsMerger<DistanceType>(), 0);
+
+    if (world.rank() == 0) {
+        for (size_t i = 0; i < results.indices.rows; ++i) {
+            for (size_t j = 0; j < results.indices.cols; ++j) {
+                indices[i][j] = results.indices[i][j];
+                dists[i][j] = results.dists[i][j];
+            }
+        }
+        delete[] results.indices.ptr();
+        delete[] results.dists.ptr();
+    }
+}
+
+template<typename Distance>
+int Index<Distance>::radiusSearch(const flann::Matrix<ElementType>& query, flann::Matrix<int>& indices, flann::Matrix<DistanceType>& dists, float radius, const SearchParams& params)
+{
+    boost::mpi::communicator world;
+    flann::Matrix<int> local_indices(new int[indices.rows*indices.cols], indices.rows, indices.cols);
+    flann::Matrix<DistanceType> local_dists(new DistanceType[dists.rows*dists.cols], dists.rows, dists.cols);
+
+    flann_index->radiusSearch(query, local_indices, local_dists, radius, params);
+    for (size_t i = 0; i < local_indices.rows; ++i) {
+        for (size_t j = 0; j < local_indices.cols; ++j) {
+            local_indices[i][j] += offset_;
+        }
+    }
+    SearchResults<DistanceType> local_results;
+    local_results.indices = local_indices;
+    local_results.dists = local_dists;
+    SearchResults<DistanceType> results;
+
+    // perform MPI reduce
+    reduce(world, local_results, results, ResultsMerger<DistanceType>(), 0);
+
+    if (world.rank() == 0) {
+        for (int i = 0; i < std::min(results.indices.rows, indices.rows); ++i) {
+            for (int j = 0; j < std::min(results.indices.cols, indices.cols); ++j) {
+                indices[i][j] = results.indices[i][j];
+                dists[i][j] = results.dists[i][j];
+            }
+        }
+        delete[] results.indices.ptr();
+        delete[] results.dists.ptr();
+    }
+    return 0;
+}
+
+}
+} //namespace flann::mpi
+
+namespace boost { namespace mpi {
+template<>
+template<typename DistanceType>
+struct is_commutative<flann::mpi::ResultsMerger<DistanceType>, flann::mpi::SearchResults<DistanceType> > : mpl::true_ { };
+} } // end namespace boost::mpi
+
+
+#endif /* FLANN_MPI_HPP_ */
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/mpi/matrix.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/mpi/matrix.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/mpi/matrix.h	(revision 15840)
@@ -0,0 +1,54 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2011  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2011  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+
+#ifndef MPI_MATRIX_H_
+#define MPI_MATRIX_H_
+
+#include <flann/util/matrix.h>
+#include <boost/serialization/array.hpp>
+
+
+namespace boost {
+namespace serialization {
+
+template<class Archive, class T>
+void serialize(Archive & ar, flann::Matrix<T> & matrix, const unsigned int version)
+{
+	ar & matrix.rows & matrix.cols & matrix.stride;
+    if (Archive::is_loading::value) {
+        matrix = flann::Matrix<T>(new T[matrix.rows*matrix.cols], matrix.rows, matrix.cols, matrix.stride);
+    }
+    ar & boost::serialization::make_array(matrix.ptr(), matrix.rows*matrix.cols);
+}
+
+}
+}
+
+
+#endif /* MPI_MATRIX_H_ */
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/mpi/queries.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/mpi/queries.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/mpi/queries.h	(revision 15840)
@@ -0,0 +1,103 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2011  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2011  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+
+#ifndef MPI_QUERIES_H_
+#define MPI_QUERIES_H_
+
+#include <flann/mpi/matrix.h>
+#include <boost/archive/binary_iarchive.hpp>
+#include <boost/archive/binary_oarchive.hpp>
+#include <boost/asio.hpp>
+
+namespace flann
+{
+
+template<typename T>
+struct Request
+{
+	flann::Matrix<T> queries;
+	int nn;
+	int checks;
+
+	template<typename Archive>
+	void serialize(Archive& ar, const unsigned int version)
+	{
+		ar & queries & nn & checks;
+	}
+};
+
+template<typename T>
+struct Response
+{
+	flann::Matrix<int> indices;
+	flann::Matrix<T> dists;
+
+	template<typename Archive>
+	void serialize(Archive& ar, const unsigned int version)
+	{
+		ar & indices & dists;
+	}
+};
+
+
+using boost::asio::ip::tcp;
+
+template <typename T>
+void read_object(tcp::socket& sock, T& val)
+{
+	uint32_t size;
+	boost::asio::read(sock, boost::asio::buffer(&size, sizeof(size)));
+	size = ntohl(size);
+
+	boost::asio::streambuf archive_stream;
+	boost::asio::read(sock, archive_stream, boost::asio::transfer_at_least(size));
+
+	boost::archive::binary_iarchive archive(archive_stream);
+	archive >> val;
+}
+
+template <typename T>
+void write_object(tcp::socket& sock, const T& val)
+{
+	boost::asio::streambuf archive_stream;
+	boost::archive::binary_oarchive archive(archive_stream);
+	archive << val;
+
+	uint32_t size = archive_stream.size();
+	size = htonl(size);
+	boost::asio::write(sock, boost::asio::buffer(&size, sizeof(size)));
+	boost::asio::write(sock, archive_stream);
+
+}
+
+}
+
+
+
+#endif /* MPI_QUERIES_H_ */
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/mpi/server.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/mpi/server.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/mpi/server.h	(revision 15840)
@@ -0,0 +1,153 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2011  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2011  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+
+#ifndef MPI_SERVER_H_
+#define MPI_SERVER_H_
+
+#include <flann/mpi/index.h>
+#include <stdio.h>
+#include <time.h>
+
+#include <cstdlib>
+#include <iostream>
+#include <boost/bind.hpp>
+#include <boost/shared_ptr.hpp>
+#include <boost/asio.hpp>
+#include <boost/thread/thread.hpp>
+
+#include "queries.h"
+
+namespace flann {
+
+namespace mpi {
+
+template<typename Distance>
+class Server
+{
+
+	typedef typename Distance::ElementType ElementType;
+	typedef typename Distance::ResultType DistanceType;
+	typedef boost::shared_ptr<tcp::socket> socket_ptr;
+	typedef flann::mpi::Index<Distance> FlannIndex;
+
+	void session(socket_ptr sock)
+	{
+		boost::mpi::communicator world;
+		try {
+			Request<ElementType> req;
+			if (world.rank()==0) {
+				read_object(*sock,req);
+				std::cout << "Received query\n";
+			}
+			// broadcast request to all MPI processes
+			boost::mpi::broadcast(world, req, 0);
+
+			Response<DistanceType> resp;
+			if (world.rank()==0) {
+				int rows = req.queries.rows;
+				int cols = req.nn;
+				resp.indices = flann::Matrix<int>(new int[rows*cols], rows, cols);
+				resp.dists = flann::Matrix<DistanceType>(new DistanceType[rows*cols], rows, cols);
+			}
+
+			std::cout << "Searching in process " << world.rank() << "\n";
+			index_->knnSearch(req.queries, resp.indices, resp.dists, req.nn, flann::SearchParams(req.checks));
+
+			if (world.rank()==0) {
+				std::cout << "Sending result\n";
+				write_object(*sock,resp);
+			}
+
+			delete[] req.queries.ptr();
+			if (world.rank()==0) {
+				delete[] resp.indices.ptr();
+				delete[] resp.dists.ptr();
+			}
+
+		}
+		catch (std::exception& e) {
+			std::cerr << "Exception in thread: " << e.what() << "\n";
+		}
+	}
+
+
+
+public:
+	Server(const std::string& filename, const std::string& dataset, short port, const IndexParams& params) :
+		port_(port)
+	{
+		boost::mpi::communicator world;
+		if (world.rank()==0) {
+			std::cout << "Reading dataset and building index...";
+			std::flush(std::cout);
+		}
+		index_ = new FlannIndex(filename, dataset, params);
+		index_->buildIndex();
+		world.barrier(); // wait for data to be loaded and indexes to be created
+		if (world.rank()==0) {
+			std::cout << "done.\n";
+		}
+	}
+
+
+	void run()
+	{
+		boost::mpi::communicator world;
+		boost::shared_ptr<boost::asio::io_service> io_service;
+		boost::shared_ptr<tcp::acceptor> acceptor;
+
+		if (world.rank()==0) {
+			io_service.reset(new boost::asio::io_service());
+			acceptor.reset(new tcp::acceptor(*io_service, tcp::endpoint(tcp::v4(), port_)));
+			std::cout << "Start listening for queries...\n";
+		}
+		for (;;) {
+			socket_ptr sock;
+			if (world.rank()==0) {
+				sock.reset(new tcp::socket(*io_service));
+				acceptor->accept(*sock);
+				std::cout << "Accepted connection\n";
+			}
+			world.barrier(); // everybody waits here for a connection
+			boost::thread t(boost::bind(&Server::session, this, sock));
+			t.join();
+		}
+
+	}
+
+private:
+	FlannIndex* index_;
+	short port_;
+};
+
+
+} // namespace mpi
+} // namespace flann
+
+#endif // MPI_SERVER_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/nn/ground_truth.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/nn/ground_truth.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/nn/ground_truth.h	(revision 15840)
@@ -0,0 +1,98 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_GROUND_TRUTH_H_
+#define FLANN_GROUND_TRUTH_H_
+
+#include "flann/algorithms/dist.h"
+#include "flann/util/matrix.h"
+
+
+namespace flann
+{
+
+template <typename Distance>
+void find_nearest(const Matrix<typename Distance::ElementType>& dataset, typename Distance::ElementType* query, int* matches, int nn,
+                  int skip = 0, Distance distance = Distance())
+{
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+    int n = nn + skip;
+
+    int* match = new int[n];
+    DistanceType* dists = new DistanceType[n];
+
+    dists[0] = distance(dataset[0], query, dataset.cols);
+    match[0] = 0;
+    int dcnt = 1;
+
+    for (size_t i=1; i<dataset.rows; ++i) {
+        DistanceType tmp = distance(dataset[i], query, dataset.cols);
+
+        if (dcnt<n) {
+            match[dcnt] = i;
+            dists[dcnt++] = tmp;
+        }
+        else if (tmp < dists[dcnt-1]) {
+            dists[dcnt-1] = tmp;
+            match[dcnt-1] = i;
+        }
+
+        int j = dcnt-1;
+        // bubble up
+        while (j>=1 && dists[j]<dists[j-1]) {
+            std::swap(dists[j],dists[j-1]);
+            std::swap(match[j],match[j-1]);
+            j--;
+        }
+    }
+
+    for (int i=0; i<nn; ++i) {
+        matches[i] = match[i+skip];
+    }
+
+    delete[] match;
+    delete[] dists;
+}
+
+
+template <typename Distance>
+void compute_ground_truth(const Matrix<typename Distance::ElementType>& dataset, const Matrix<typename Distance::ElementType>& testset, Matrix<int>& matches,
+                          int skip=0, Distance d = Distance())
+{
+    for (size_t i=0; i<testset.rows; ++i) {
+        find_nearest<Distance>(dataset, testset[i], matches[i], (int)matches.cols, skip, d);
+    }
+}
+
+
+}
+
+#endif //FLANN_GROUND_TRUTH_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/nn/index_testing.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/nn/index_testing.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/nn/index_testing.h	(revision 15840)
@@ -0,0 +1,322 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_INDEX_TESTING_H_
+#define FLANN_INDEX_TESTING_H_
+
+#include <cstring>
+#include <cassert>
+#include <cmath>
+
+#include "flann/util/matrix.h"
+#include "flann/algorithms/nn_index.h"
+#include "flann/util/result_set.h"
+#include "flann/util/logger.h"
+#include "flann/util/timer.h"
+
+
+namespace flann
+{
+
+inline int countCorrectMatches(int* neighbors, int* groundTruth, int n)
+{
+    int count = 0;
+    for (int i=0; i<n; ++i) {
+        for (int k=0; k<n; ++k) {
+            if (neighbors[i]==groundTruth[k]) {
+                count++;
+                break;
+            }
+        }
+    }
+    return count;
+}
+
+
+template <typename Distance>
+typename Distance::ResultType computeDistanceRaport(const Matrix<typename Distance::ElementType>& inputData, typename Distance::ElementType* target,
+                                                    int* neighbors, int* groundTruth, int veclen, int n, const Distance& distance)
+{
+    typedef typename Distance::ResultType DistanceType;
+
+    DistanceType ret = 0;
+    for (int i=0; i<n; ++i) {
+        DistanceType den = distance(inputData[groundTruth[i]], target, veclen);
+        DistanceType num = distance(inputData[neighbors[i]], target, veclen);
+
+        if ((den==0)&&(num==0)) {
+            ret += 1;
+        }
+        else {
+            ret += num/den;
+        }
+    }
+
+    return ret;
+}
+
+template <typename Distance>
+float search_with_ground_truth(NNIndex<Distance>& index, const Matrix<typename Distance::ElementType>& inputData,
+                               const Matrix<typename Distance::ElementType>& testData, const Matrix<int>& matches, int nn, int checks,
+                               float& time, typename Distance::ResultType& dist, const Distance& distance, int skipMatches)
+{
+    typedef typename Distance::ResultType DistanceType;
+
+    if (matches.cols<size_t(nn)) {
+        Logger::info("matches.cols=%d, nn=%d\n",matches.cols,nn);
+
+        throw FLANNException("Ground truth is not computed for as many neighbors as requested");
+    }
+
+    KNNResultSet<DistanceType> resultSet(nn+skipMatches);
+    SearchParams searchParams(checks);
+
+    int* indices = new int[nn+skipMatches];
+    DistanceType* dists = new DistanceType[nn+skipMatches];
+    int* neighbors = indices + skipMatches;
+
+    int correct = 0;
+    DistanceType distR = 0;
+    StartStopTimer t;
+    int repeats = 0;
+    while (t.value<0.2) {
+        repeats++;
+        t.start();
+        correct = 0;
+        distR = 0;
+        for (size_t i = 0; i < testData.rows; i++) {
+            resultSet.clear();
+            index.findNeighbors(resultSet, testData[i], searchParams);
+            resultSet.copy(indices,dists,nn+skipMatches);
+
+            correct += countCorrectMatches(neighbors,matches[i], nn);
+            distR += computeDistanceRaport<Distance>(inputData, testData[i], neighbors, matches[i], testData.cols, nn, distance);
+        }
+        t.stop();
+    }
+    time = float(t.value/repeats);
+
+    delete[] indices;
+    delete[] dists;
+
+    float precicion = (float)correct/(nn*testData.rows);
+
+    dist = distR/(testData.rows*nn);
+
+    Logger::info("%8d %10.4g %10.5g %10.5g %10.5g\n",
+                 checks, precicion, time, 1000.0 * time / testData.rows, dist);
+
+    return precicion;
+}
+
+
+template <typename Distance>
+float test_index_checks(NNIndex<Distance>& index, const Matrix<typename Distance::ElementType>& inputData,
+                        const Matrix<typename Distance::ElementType>& testData, const Matrix<int>& matches,
+                        int checks, float& precision, const Distance& distance, int nn = 1, int skipMatches = 0)
+{
+    typedef typename Distance::ResultType DistanceType;
+
+    Logger::info("  Nodes  Precision(%)   Time(s)   Time/vec(ms)  Mean dist\n");
+    Logger::info("---------------------------------------------------------\n");
+
+    float time = 0;
+    DistanceType dist = 0;
+    precision = search_with_ground_truth(index, inputData, testData, matches, nn, checks, time, dist, distance, skipMatches);
+
+    return time;
+}
+
+template <typename Distance>
+float test_index_precision(NNIndex<Distance>& index, const Matrix<typename Distance::ElementType>& inputData,
+                           const Matrix<typename Distance::ElementType>& testData, const Matrix<int>& matches,
+                           float precision, int& checks, const Distance& distance, int nn = 1, int skipMatches = 0)
+{
+    typedef typename Distance::ResultType DistanceType;
+    const float SEARCH_EPS = 0.001f;
+
+    Logger::info("  Nodes  Precision(%)   Time(s)   Time/vec(ms)  Mean dist\n");
+    Logger::info("---------------------------------------------------------\n");
+
+    int c2 = 1;
+    float p2;
+    int c1 = 1;
+//     float p1;
+    float time;
+    DistanceType dist;
+
+    p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
+
+    if (p2>precision) {
+        Logger::info("Got as close as I can\n");
+        checks = c2;
+        return time;
+    }
+
+    while (p2<precision) {
+        c1 = c2;
+//         p1 = p2;
+        c2 *=2;
+        p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
+    }
+
+    int cx;
+    float realPrecision;
+    if (fabs(p2-precision)>SEARCH_EPS) {
+        Logger::info("Start linear estimation\n");
+        // after we got to values in the vecinity of the desired precision
+        // use linear approximation get a better estimation
+
+        cx = (c1+c2)/2;
+        realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
+        while (fabs(realPrecision-precision)>SEARCH_EPS) {
+
+            if (realPrecision<precision) {
+                c1 = cx;
+            }
+            else {
+                c2 = cx;
+            }
+            cx = (c1+c2)/2;
+            if (cx==c1) {
+                Logger::info("Got as close as I can\n");
+                break;
+            }
+            realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
+        }
+
+        c2 = cx;
+        p2 = realPrecision;
+
+    }
+    else {
+        Logger::info("No need for linear estimation\n");
+        cx = c2;
+        realPrecision = p2;
+    }
+
+    checks = cx;
+    return time;
+}
+
+
+template <typename Distance>
+void test_index_precisions(NNIndex<Distance>& index, const Matrix<typename Distance::ElementType>& inputData,
+                           const Matrix<typename Distance::ElementType>& testData, const Matrix<int>& matches,
+                           float* precisions, int precisions_length, const Distance& distance, int nn = 1, int skipMatches = 0, float maxTime = 0)
+{
+    typedef typename Distance::ResultType DistanceType;
+
+    const float SEARCH_EPS = 0.001;
+
+    // make sure precisions array is sorted
+    std::sort(precisions, precisions+precisions_length);
+
+    int pindex = 0;
+    float precision = precisions[pindex];
+
+    Logger::info("  Nodes  Precision(%)   Time(s)   Time/vec(ms)  Mean dist\n");
+    Logger::info("---------------------------------------------------------\n");
+
+    int c2 = 1;
+    float p2;
+
+    int c1 = 1;
+    float p1;
+
+    float time;
+    DistanceType dist;
+
+    p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
+
+    // if precision for 1 run down the tree is already
+    // better then some of the requested precisions, then
+    // skip those
+    while (precisions[pindex]<p2 && pindex<precisions_length) {
+        pindex++;
+    }
+
+    if (pindex==precisions_length) {
+        Logger::info("Got as close as I can\n");
+        return;
+    }
+
+    for (int i=pindex; i<precisions_length; ++i) {
+
+        precision = precisions[i];
+        while (p2<precision) {
+            c1 = c2;
+            p1 = p2;
+            c2 *=2;
+            p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
+            if ((maxTime> 0)&&(time > maxTime)&&(p2<precision)) return;
+        }
+
+        int cx;
+        float realPrecision;
+        if (fabs(p2-precision)>SEARCH_EPS) {
+            Logger::info("Start linear estimation\n");
+            // after we got to values in the vecinity of the desired precision
+            // use linear approximation get a better estimation
+
+            cx = (c1+c2)/2;
+            realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
+            while (fabs(realPrecision-precision)>SEARCH_EPS) {
+
+                if (realPrecision<precision) {
+                    c1 = cx;
+                }
+                else {
+                    c2 = cx;
+                }
+                cx = (c1+c2)/2;
+                if (cx==c1) {
+                    Logger::info("Got as close as I can\n");
+                    break;
+                }
+                realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
+            }
+
+            c2 = cx;
+            p2 = realPrecision;
+
+        }
+        else {
+            Logger::info("No need for linear estimation\n");
+            cx = c2;
+            realPrecision = p2;
+        }
+
+    }
+}
+
+}
+
+#endif //FLANN_INDEX_TESTING_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/nn/simplex_downhill.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/nn/simplex_downhill.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/nn/simplex_downhill.h	(revision 15840)
@@ -0,0 +1,186 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_SIMPLEX_DOWNHILL_H_
+#define FLANN_SIMPLEX_DOWNHILL_H_
+
+namespace flann
+{
+
+/**
+    Adds val to array vals (and point to array points) and keeping the arrays sorted by vals.
+ */
+template <typename T>
+void addValue(int pos, float val, float* vals, T* point, T* points, int n)
+{
+    vals[pos] = val;
+    for (int i=0; i<n; ++i) {
+        points[pos*n+i] = point[i];
+    }
+
+    // bubble down
+    int j=pos;
+    while (j>0 && vals[j]<vals[j-1]) {
+        swap(vals[j],vals[j-1]);
+        for (int i=0; i<n; ++i) {
+            swap(points[j*n+i],points[(j-1)*n+i]);
+        }
+        --j;
+    }
+}
+
+
+/**
+    Simplex downhill optimization function.
+    Preconditions: points is a 2D mattrix of size (n+1) x n
+                    func is the cost function taking n an array of n params and returning float
+                    vals is the cost function in the n+1 simplex points, if NULL it will be computed
+
+    Postcondition: returns optimum value and points[0..n] are the optimum parameters
+ */
+template <typename T, typename F>
+float optimizeSimplexDownhill(T* points, int n, F func, float* vals = NULL )
+{
+    const int MAX_ITERATIONS = 10;
+
+    assert(n>0);
+
+    T* p_o = new T[n];
+    T* p_r = new T[n];
+    T* p_e = new T[n];
+
+    int alpha = 1;
+
+    int iterations = 0;
+
+    bool ownVals = false;
+    if (vals == NULL) {
+        ownVals = true;
+        vals = new float[n+1];
+        for (int i=0; i<n+1; ++i) {
+            float val = func(points+i*n);
+            addValue(i, val, vals, points+i*n, points, n);
+        }
+    }
+    int nn = n*n;
+
+    while (true) {
+
+        if (iterations++ > MAX_ITERATIONS) break;
+
+        // compute average of simplex points (except the highest point)
+        for (int j=0; j<n; ++j) {
+            p_o[j] = 0;
+            for (int i=0; i<n; ++i) {
+                p_o[i] += points[j*n+i];
+            }
+        }
+        for (int i=0; i<n; ++i) {
+            p_o[i] /= n;
+        }
+
+        bool converged = true;
+        for (int i=0; i<n; ++i) {
+            if (p_o[i] != points[nn+i]) {
+                converged = false;
+            }
+        }
+        if (converged) break;
+
+        // trying a reflection
+        for (int i=0; i<n; ++i) {
+            p_r[i] = p_o[i] + alpha*(p_o[i]-points[nn+i]);
+        }
+        float val_r = func(p_r);
+
+        if ((val_r>=vals[0])&&(val_r<vals[n])) {
+            // reflection between second highest and lowest
+            // add it to the simplex
+            Logger::info("Choosing reflection\n");
+            addValue(n, val_r,vals, p_r, points, n);
+            continue;
+        }
+
+        if (val_r<vals[0]) {
+            // value is smaller than smalest in simplex
+
+            // expand some more to see if it drops further
+            for (int i=0; i<n; ++i) {
+                p_e[i] = 2*p_r[i]-p_o[i];
+            }
+            float val_e = func(p_e);
+
+            if (val_e<val_r) {
+                Logger::info("Choosing reflection and expansion\n");
+                addValue(n, val_e,vals,p_e,points,n);
+            }
+            else {
+                Logger::info("Choosing reflection\n");
+                addValue(n, val_r,vals,p_r,points,n);
+            }
+            continue;
+        }
+        if (val_r>=vals[n]) {
+            for (int i=0; i<n; ++i) {
+                p_e[i] = (p_o[i]+points[nn+i])/2;
+            }
+            float val_e = func(p_e);
+
+            if (val_e<vals[n]) {
+                Logger::info("Choosing contraction\n");
+                addValue(n,val_e,vals,p_e,points,n);
+                continue;
+            }
+        }
+        {
+            Logger::info("Full contraction\n");
+            for (int j=1; j<=n; ++j) {
+                for (int i=0; i<n; ++i) {
+                    points[j*n+i] = (points[j*n+i]+points[i])/2;
+                }
+                float val = func(points+j*n);
+                addValue(j,val,vals,points+j*n,points,n);
+            }
+        }
+    }
+
+    float bestVal = vals[0];
+
+    delete[] p_r;
+    delete[] p_o;
+    delete[] p_e;
+    if (ownVals) delete[] vals;
+
+    return bestVal;
+}
+
+}
+
+#endif //FLANN_SIMPLEX_DOWNHILL_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/tbb/bodies.hpp
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/tbb/bodies.hpp	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/tbb/bodies.hpp	(revision 15840)
@@ -0,0 +1,464 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2010-2011  Nick Vanbaelen (nickon@acm.org). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_TBB_BODIES_H
+#define FLANN_TBB_BODIES_H
+
+#include <tbb/blocked_range.h>
+#include <tbb/atomic.h>
+
+#include "flann/util/matrix.h"
+#include "flann/util/params.h"
+#include "flann/util/result_set.h"
+
+namespace flann
+{
+
+template <typename Distance> class NNIndex;
+
+
+template<typename Distance>
+class parallel_knnSearch
+{
+public:
+  typedef typename Distance::ElementType ElementType;
+  typedef typename Distance::ResultType DistanceType;
+
+  parallel_knnSearch(const Matrix<ElementType>& queries,
+                           Matrix<int>& indices,
+                           Matrix<DistanceType>& distances,
+                           size_t knn,
+                     const SearchParams& params,
+                           NNIndex<Distance>* nnIndex,
+                           tbb::atomic<int>& count)
+    : queries_(queries),
+      indices_(indices),
+      distances_(distances),
+      knn_(knn),
+      params_(params),
+      nnIndex_(nnIndex),
+      count_(count)
+
+  {}
+
+  /* default destructor will do */
+
+  /* default copy constructor will do,
+     parallel for will use this to create a separate parallel_knnSearch object
+     for each worker thread (pointers will be copied, which is OK) */
+
+  /**
+   * Perform knnSearch for the query points assigned to this worker thread
+   * \param r query point range assigned for this worker thread to operate on
+   */
+  void operator()( const tbb::blocked_range<size_t>& r ) const
+  {
+    if (params_.use_heap==FLANN_True)
+    {
+      KNNResultSet2<DistanceType> resultSet(knn_);
+      for (size_t i=r.begin(); i!=r.end(); ++i)
+      {
+        resultSet.clear();
+        nnIndex_->findNeighbors(resultSet, queries_[i], params_);
+        resultSet.copy(indices_[i], distances_[i], knn_, params_.sorted);
+        count_ += resultSet.size();
+      }
+    }
+    else
+    {
+      KNNSimpleResultSet<DistanceType> resultSet(knn_);
+      for (size_t i=r.begin(); i!=r.end(); ++i)
+      {
+        resultSet.clear();
+        nnIndex_->findNeighbors(resultSet, queries_[i], params_);
+        resultSet.copy(indices_[i], distances_[i], knn_, params_.sorted);
+        count_ += resultSet.size();
+      }
+    }
+  }
+
+private:
+  //! All query points to perform search on
+  //! \note each worker thread only operates on a specified range
+  const Matrix<ElementType>& queries_;
+
+  //! Matrix for storing the indices of the nearest neighbors
+  //! \note no need for this to be a parallel container, each worker thread
+  //!       solely operates on its specified range!
+  Matrix<int>& indices_;
+
+  //! Matrix for storing the distances to the nearest neighbors
+  //! \note no need for this to be a parallel container, each worker thread
+  //!       solely operates on its specified range!
+  Matrix<DistanceType>& distances_;
+
+  //! Number of nearest neighbors to search for
+  size_t knn_;
+
+  //! The search parameters to take into account
+  const SearchParams& params_;
+
+  //! The nearest neighbor index to perform the search with
+  NNIndex<Distance>* nnIndex_;
+
+  //! Atomic count variable to keep track of the number of neighbors found
+  //! \note must be mutable because body will be casted as const in parallel_for
+  tbb::atomic<int>& count_;
+};
+
+
+template<typename Distance>
+class parallel_knnSearch2
+{
+public:
+  typedef typename Distance::ElementType ElementType;
+  typedef typename Distance::ResultType DistanceType;
+
+  parallel_knnSearch2(const Matrix<ElementType>& queries,
+                            std::vector< std::vector<int> >& indices,
+                            std::vector<std::vector<DistanceType> >& distances,
+                            size_t knn,
+                      const SearchParams& params,
+                            NNIndex<Distance>* nnIndex,
+                            tbb::atomic<int>& count)
+    : queries_(queries),
+      indices_(indices),
+      distances_(distances),
+      knn_(knn),
+      params_(params),
+      nnIndex_(nnIndex),
+      count_(count)
+
+  {}
+
+  /* default destructor will do */
+
+  /* default copy constructor will do,
+     parallel for will use this to create a separate parallel_knnSearch object
+     for each worker thread (pointers will be copied, which is OK) */
+
+  /**
+   * Perform knnSearch for the query points assigned to this worker thread
+   * (specified by the blocked_range parameter)
+   */
+  void operator()( const tbb::blocked_range<size_t>& r ) const
+  {
+    if (params_.use_heap==FLANN_True) {
+        KNNResultSet2<DistanceType> resultSet(knn_);
+        for (size_t i=r.begin(); i!=r.end(); ++i)
+        {
+            resultSet.clear();
+            nnIndex_->findNeighbors(resultSet, queries_[i], params_);
+            size_t n = std::min(resultSet.size(), knn_);
+            indices_[i].resize(n);
+            distances_[i].resize(n);
+            resultSet.copy(&indices_[i][0], &distances_[i][0], n, params_.sorted);
+            count_ += n;
+        }
+    }
+    else {
+        KNNSimpleResultSet<DistanceType> resultSet(knn_);
+        for (size_t i=r.begin(); i!=r.end(); ++i)
+        {
+            resultSet.clear();
+            nnIndex_->findNeighbors(resultSet, queries_[i], params_);
+            size_t n = std::min(resultSet.size(), knn_);
+            indices_[i].resize(n);
+            distances_[i].resize(n);
+            resultSet.copy(&indices_[i][0], &distances_[i][0], n, params_.sorted);
+            count_ += n;
+        }
+    }
+  }
+
+private:
+  //! All query points to perform search on
+  //! \note each worker thread only operates on a specified range
+  const Matrix<ElementType>& queries_;
+
+  //! Vector for storing the indices of the nearest neighbors
+  //! \note no need for this to be a parallel container, each worker thread
+  //!       solely operates on its specified range!
+  std::vector< std::vector<int> >& indices_;
+
+  //! Vector for storing the distances to the nearest neighbors
+  //! \note no need for this to be a parallel container, each worker thread
+  //!       solely operates on its specified range!
+  std::vector< std::vector<DistanceType> >& distances_;
+
+  //! Number of nearest neighbors to search for
+  size_t knn_;
+
+  //! The search parameters to take into account
+  const SearchParams& params_;
+
+  //! The nearest neighbor index to perform the search with
+  NNIndex<Distance>* nnIndex_;
+
+  //! Atomic count variable to keep track of the number of neighbors found
+  //! \note must be mutable because body will be casted as const in parallel_for
+  tbb::atomic<int>& count_;
+};
+
+
+template<typename Distance>
+class parallel_radiusSearch
+{
+public:
+  typedef typename Distance::ElementType ElementType;
+  typedef typename Distance::ResultType DistanceType;
+
+  /* default destructor will do */
+
+  /* default copy constructor will do,
+     parallel for will use this to create a separate parallel_knnSearch object
+     for each worker thread (pointers will be copied, which is OK) */
+
+  /**
+   * Perform radiusSearch for the query points assigned to this worker thread
+   * (specified by the blocked_range parameter)
+   */
+  parallel_radiusSearch(const Matrix<ElementType>& queries,
+                              Matrix<int>& indices,
+                              Matrix<DistanceType>& distances,
+                              float radius,
+                        const SearchParams& params,
+                              NNIndex<Distance>* nnIndex,
+                              tbb::atomic<int>& count)
+    : queries_(queries),
+      indices_(indices),
+      distances_(distances),
+      radius_(radius),
+      params_(params),
+      nnIndex_(nnIndex),
+      count_(count)
+
+  {}
+
+  void operator()( const tbb::blocked_range<size_t>& r ) const
+  {
+		size_t num_neighbors = std::min(indices_.cols, distances_.cols);
+		int max_neighbors = params_.max_neighbors;
+		if (max_neighbors<0) max_neighbors = num_neighbors;
+		else max_neighbors = std::min(max_neighbors,(int)num_neighbors);
+
+      if (max_neighbors==0) {
+          CountRadiusResultSet<DistanceType> resultSet(radius_);
+          for (size_t i=r.begin(); i!=r.end(); ++i)
+          {
+              resultSet.clear();
+              nnIndex_->findNeighbors(resultSet, queries_[i], params_);
+              count_ += resultSet.size();
+          }
+      }
+      else {
+
+          // explicitly indicated to use unbounded radius result set
+          // or we know there'll be enough room for resulting indices and dists
+          if (params_.max_neighbors<0 && (num_neighbors>=nnIndex_->size())) {
+              RadiusResultSet<DistanceType> resultSet(radius_);
+              for (size_t i=r.begin(); i!=r.end(); ++i)
+              {
+                  resultSet.clear();
+                  nnIndex_->findNeighbors(resultSet, queries_[i], params_);
+                  size_t n = resultSet.size();
+                  count_ += n;
+                  if (n>num_neighbors) n = num_neighbors;
+                  resultSet.copy(indices_[i], distances_[i], n, params_.sorted);
+
+                  // mark the next element in the output buffers as unused
+                  if (n<indices_.cols) indices_[i][n] = -1;
+                  if (n<distances_.cols) distances_[i][n] = std::numeric_limits<DistanceType>::infinity();
+              }
+          }
+          else {
+              // number of neighbors limited to max_neighbors
+              KNNRadiusResultSet<DistanceType> resultSet(radius_, max_neighbors);
+              for (size_t i=r.begin(); i!=r.end(); ++i)
+              {
+                  resultSet.clear();
+                  nnIndex_->findNeighbors(resultSet, queries_[i], params_);
+                  size_t n = resultSet.size();
+                  count_ += n ;
+                  if ((int)n>max_neighbors) n = max_neighbors;
+                  resultSet.copy(indices_[i], distances_[i], n, params_.sorted);
+
+                  // mark the next element in the output buffers as unused
+                  if (n<indices_.cols) indices_[i][n] = -1;
+                  if (n<distances_.cols) distances_[i][n] = std::numeric_limits<DistanceType>::infinity();
+              }
+          }
+      }
+  }
+
+private:
+  //! All query points to perform search on
+  //! \note each worker thread only operates on a specified range
+  const Matrix<ElementType>& queries_;
+
+  //! Matrix for storing the indices of the nearest neighbors
+  //! \note no need for this to be a parallel container, each worker thread
+  //!       solely operates on its specified range!
+  Matrix<int>& indices_;
+
+  //! Matrix for storing the distances to the nearest neighbors
+  //! \note no need for this to be a parallel container, each worker thread
+  //!       solely operates on its specified range!
+  Matrix<DistanceType>& distances_;
+
+  //! Radius size bound on the search for nearest neighbors
+  float radius_;
+
+  //! The search parameters to take into account
+  const SearchParams& params_;
+
+  //! The nearest neighbor index to perform the search with
+  NNIndex<Distance>* nnIndex_;
+
+  //! Atomic count variable to keep track of the number of neighbors found
+  //! \note must be mutable because body will be casted as const in parallel_for
+  tbb::atomic<int>& count_;
+};
+
+
+template<typename Distance>
+class parallel_radiusSearch2
+{
+public:
+  typedef typename Distance::ElementType ElementType;
+  typedef typename Distance::ResultType DistanceType;
+
+  /* default destructor will do */
+
+  /* default copy constructor will do,
+     parallel for will use this to create a separate parallel_knnSearch object
+     for each worker thread (pointers will be copied, which is OK) */
+
+  /**
+   * Perform radiusSearch for the query points assigned to this worker thread
+   * (specified by the blocked_range parameter)
+   */
+  parallel_radiusSearch2(const Matrix<ElementType>& queries,
+                               std::vector< std::vector<int> >& indices,
+                               std::vector<std::vector<DistanceType> >& distances,
+                               float radius,
+                         const SearchParams& params,
+                               NNIndex<Distance>* nnIndex,
+                               tbb::atomic<int>& count)
+    : queries_(queries),
+      indices_(indices),
+      distances_(distances),
+      radius_(radius),
+      params_(params),
+      nnIndex_(nnIndex),
+      count_(count)
+
+  {}
+
+  void operator()( const tbb::blocked_range<size_t>& r ) const
+  {
+      int max_neighbors = params_.max_neighbors;
+      // just count neighbors
+      if (max_neighbors==0) {
+          CountRadiusResultSet<DistanceType> resultSet(radius_);
+          for (size_t i=r.begin(); i!=r.end(); ++i)
+          {
+            resultSet.clear();
+            nnIndex_->findNeighbors(resultSet, queries_[i], params_);
+            count_ += resultSet.size();
+          }
+      }
+      else {
+          if (indices_.size() < queries_.rows ) indices_.resize(queries_.rows);
+          if (distances_.size() < queries_.rows ) distances_.resize(queries_.rows);
+
+          if (max_neighbors<0) {
+              // search for all neighbors
+              RadiusResultSet<DistanceType> resultSet(radius_);
+              for (size_t i=r.begin(); i!=r.end(); ++i)
+              {
+                  resultSet.clear();
+                  nnIndex_->findNeighbors(resultSet, queries_[i], params_);
+                  size_t n = resultSet.size();
+                  count_ += n;
+                  indices_[i].resize(n);
+                  distances_[i].resize(n);
+                  resultSet.copy(&indices_[i][0], &distances_[i][0], n, params_.sorted);
+              }
+          }
+          else {
+              // number of neighbors limited to max_neighbors
+              KNNRadiusResultSet<DistanceType> resultSet(radius_, params_.max_neighbors);
+              for (size_t i=r.begin(); i!=r.end(); ++i)
+              {
+                  resultSet.clear();
+                  nnIndex_->findNeighbors(resultSet, queries_[i], params_);
+                  size_t n = resultSet.size();
+                  count_ += n;
+                  if ((int)n>max_neighbors) n = max_neighbors;
+                  indices_[i].resize(n);
+                  distances_[i].resize(n);
+                  resultSet.copy(&indices_[i][0], &distances_[i][0], n, params_.sorted);
+              }
+          }
+      }
+  }
+
+private:
+    //! All query points to perform search on
+    //! \note each worker thread only operates on a specified range
+    const Matrix<ElementType>& queries_;
+
+    //! Vector for storing the indices of the nearest neighbors
+    //! \note no need for this to be a parallel container, each worker thread
+    //!       solely operates on its specified range!
+    std::vector< std::vector<int> >& indices_;
+
+    //! Vector for storing the distances to the nearest neighbors
+    //! \note no need for this to be a parallel container, each worker thread
+    //!       solely operates on its specified range!
+    std::vector< std::vector<DistanceType> >& distances_;
+
+    //! Radius size bound on the search for nearest neighbors
+    float radius_;
+
+    //! The search parameters to take into account
+    const SearchParams& params_;
+
+    //! The nearest neighbor index to perform the search with
+    NNIndex<Distance>* nnIndex_;
+
+    //! Atomic count variable to keep track of the number of neighbors found
+    //! \note must be mutable because body will be casted as const in parallel_for
+    tbb::atomic<int>& count_;
+};
+
+}
+
+#endif //FLANN_TBB_BODIES_H
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/allocator.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/allocator.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/allocator.h	(revision 15840)
@@ -0,0 +1,188 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_ALLOCATOR_H_
+#define FLANN_ALLOCATOR_H_
+
+#include <stdlib.h>
+#include <stdio.h>
+
+
+namespace flann
+{
+
+/**
+ * Allocates (using C's malloc) a generic type T.
+ *
+ * Params:
+ *     count = number of instances to allocate.
+ * Returns: pointer (of type T*) to memory buffer
+ */
+template <typename T>
+T* allocate(size_t count = 1)
+{
+    T* mem = (T*) ::malloc(sizeof(T)*count);
+    return mem;
+}
+
+
+/**
+ * Pooled storage allocator
+ *
+ * The following routines allow for the efficient allocation of storage in
+ * small chunks from a specified pool.  Rather than allowing each structure
+ * to be freed individually, an entire pool of storage is freed at once.
+ * This method has two advantages over just using malloc() and free().  First,
+ * it is far more efficient for allocating small objects, as there is
+ * no overhead for remembering all the information needed to free each
+ * object or consolidating fragmented memory.  Second, the decision about
+ * how long to keep an object is made at the time of allocation, and there
+ * is no need to track down all the objects to free them.
+ *
+ */
+
+const size_t     WORDSIZE=16;
+const  size_t     BLOCKSIZE=8192;
+
+class PooledAllocator
+{
+    /* We maintain memory alignment to word boundaries by requiring that all
+        allocations be in multiples of the machine wordsize.  */
+    /* Size of machine word in bytes.  Must be power of 2. */
+    /* Minimum number of bytes requested at a time from	the system.  Must be multiple of WORDSIZE. */
+
+
+    int     remaining;  /* Number of bytes left in current block of storage. */
+    void*   base;     /* Pointer to base of current block of storage. */
+    void*   loc;      /* Current location in block to next allocate memory. */
+    int     blocksize;
+
+
+public:
+    int     usedMemory;
+    int     wastedMemory;
+
+    /**
+        Default constructor. Initializes a new pool.
+     */
+    PooledAllocator(int blocksize = BLOCKSIZE)
+    {
+        this->blocksize = blocksize;
+        remaining = 0;
+        base = NULL;
+
+        usedMemory = 0;
+        wastedMemory = 0;
+    }
+
+    /**
+     * Destructor. Frees all the memory allocated in this pool.
+     */
+    ~PooledAllocator()
+    {
+        void* prev;
+
+        while (base != NULL) {
+            prev = *((void**) base); /* Get pointer to prev block. */
+            ::free(base);
+            base = prev;
+        }
+    }
+
+    /**
+     * Returns a pointer to a piece of new memory of the given size in bytes
+     * allocated from the pool.
+     */
+    void* allocateMemory(int size)
+    {
+        int blocksize;
+
+        /* Round size up to a multiple of wordsize.  The following expression
+            only works for WORDSIZE that is a power of 2, by masking last bits of
+            incremented size to zero.
+         */
+        size = (size + (WORDSIZE - 1)) & ~(WORDSIZE - 1);
+
+        /* Check whether a new block must be allocated.  Note that the first word
+            of a block is reserved for a pointer to the previous block.
+         */
+        if (size > remaining) {
+
+            wastedMemory += remaining;
+
+            /* Allocate new storage. */
+            blocksize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ?
+                        size + sizeof(void*) + (WORDSIZE-1) : BLOCKSIZE;
+
+            // use the standard C malloc to allocate memory
+            void* m = ::malloc(blocksize);
+            if (!m) {
+                fprintf(stderr,"Failed to allocate memory.\n");
+                return NULL;
+            }
+
+            /* Fill first word of new block with pointer to previous block. */
+            ((void**) m)[0] = base;
+            base = m;
+
+            int shift = 0;
+            //int shift = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) & (WORDSIZE-1))) & (WORDSIZE-1);
+
+            remaining = blocksize - sizeof(void*) - shift;
+            loc = ((char*)m + sizeof(void*) + shift);
+        }
+        void* rloc = loc;
+        loc = (char*)loc + size;
+        remaining -= size;
+
+        usedMemory += size;
+
+        return rloc;
+    }
+
+    /**
+     * Allocates (using this pool) a generic type T.
+     *
+     * Params:
+     *     count = number of instances to allocate.
+     * Returns: pointer (of type T*) to memory buffer
+     */
+    template <typename T>
+    T* allocate(size_t count = 1)
+    {
+        T* mem = (T*) this->allocateMemory((int)(sizeof(T)*count));
+        return mem;
+    }
+
+};
+
+}
+
+#endif //FLANN_ALLOCATOR_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/any.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/any.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/any.h	(revision 15840)
@@ -0,0 +1,292 @@
+#ifndef FLANN_ANY_H_
+#define FLANN_ANY_H_
+/*
+ * (C) Copyright Christopher Diggins 2005-2011
+ * (C) Copyright Pablo Aguilar 2005
+ * (C) Copyright Kevlin Henney 2001
+ *
+ * Distributed under the Boost Software License, Version 1.0. (See
+ * accompanying file LICENSE_1_0.txt or copy at
+ * http://www.boost.org/LICENSE_1_0.txt
+ *
+ * Adapted for FLANN by Marius Muja
+ */
+
+#include <stdexcept>
+#include <ostream>
+#include <typeinfo>
+
+namespace flann
+{
+
+namespace anyimpl
+{
+
+struct bad_any_cast : public std::runtime_error
+{
+	bad_any_cast() : std::runtime_error("Cannot convert 'any' value") { }
+};
+
+struct empty_any
+{
+};
+
+inline std::ostream& operator <<(std::ostream& out, const empty_any&)
+{
+    out << "[empty_any]";
+    return out;
+}
+
+struct base_any_policy
+{
+    virtual void static_delete(void** x) = 0;
+    virtual void copy_from_value(void const* src, void** dest) = 0;
+    virtual void clone(void* const* src, void** dest) = 0;
+    virtual void move(void* const* src, void** dest) = 0;
+    virtual void* get_value(void** src) = 0;
+    virtual size_t get_size() = 0;
+    virtual const std::type_info& type() = 0;
+    virtual void print(std::ostream& out, void* const* src) = 0;
+};
+
+template<typename T>
+struct typed_base_any_policy : base_any_policy
+{
+    virtual size_t get_size() { return sizeof(T); }
+    virtual const std::type_info& type() { return typeid(T); }
+
+};
+
+template<typename T>
+struct small_any_policy : typed_base_any_policy<T>
+{
+    virtual void static_delete(void**) { }
+    virtual void copy_from_value(void const* src, void** dest)
+    {
+        new (dest) T(* reinterpret_cast<T const*>(src));
+    }
+    virtual void clone(void* const* src, void** dest) { *dest = *src; }
+    virtual void move(void* const* src, void** dest) { *dest = *src; }
+    virtual void* get_value(void** src) { return reinterpret_cast<void*>(src); }
+    virtual void print(std::ostream& out, void* const* src) { out << *reinterpret_cast<T const*>(src); }
+};
+
+template<typename T>
+struct big_any_policy : typed_base_any_policy<T>
+{
+    virtual void static_delete(void** x)
+    {
+        if (* x) delete (* reinterpret_cast<T**>(x)); *x = NULL;
+    }
+    virtual void copy_from_value(void const* src, void** dest)
+    {
+        *dest = new T(*reinterpret_cast<T const*>(src));
+    }
+    virtual void clone(void* const* src, void** dest)
+    {
+        *dest = new T(**reinterpret_cast<T* const*>(src));
+    }
+    virtual void move(void* const* src, void** dest)
+    {
+        (*reinterpret_cast<T**>(dest))->~T();
+        **reinterpret_cast<T**>(dest) = **reinterpret_cast<T* const*>(src);
+    }
+    virtual void* get_value(void** src) { return *src; }
+    virtual void print(std::ostream& out, void* const* src) { out << *reinterpret_cast<T const*>(*src); }
+};
+
+template<typename T>
+struct choose_policy
+{
+    typedef big_any_policy<T> type;
+};
+
+template<typename T>
+struct choose_policy<T*>
+{
+    typedef small_any_policy<T*> type;
+};
+
+struct any;
+
+/// Choosing the policy for an any type is illegal, but should never happen.
+/// This is designed to throw a compiler error.
+template<>
+struct choose_policy<any>
+{
+    typedef void type;
+};
+
+/// Specializations for small types.
+#define SMALL_POLICY(TYPE) \
+    template<> \
+    struct choose_policy<TYPE> { typedef small_any_policy<TYPE> type; \
+    }
+
+SMALL_POLICY(signed char);
+SMALL_POLICY(unsigned char);
+SMALL_POLICY(signed short);
+SMALL_POLICY(unsigned short);
+SMALL_POLICY(signed int);
+SMALL_POLICY(unsigned int);
+SMALL_POLICY(signed long);
+SMALL_POLICY(unsigned long);
+SMALL_POLICY(float);
+SMALL_POLICY(bool);
+
+#undef SMALL_POLICY
+
+/// This function will return a different policy for each type.
+template<typename T>
+base_any_policy* get_policy()
+{
+    static typename choose_policy<T>::type policy;
+    return &policy;
+}
+} // namespace anyimpl
+
+class any
+{
+	typedef any any_t; // workaround for the NVCC compiler under windows
+private:
+    // fields
+    anyimpl::base_any_policy* policy;
+    void* object;
+
+public:
+    /// Initializing constructor.
+    template <typename T>
+    any(const T& x)
+        : policy(anyimpl::get_policy<anyimpl::empty_any>()), object(NULL)
+    {
+        assign(x);
+    }
+
+    /// Empty constructor.
+    any()
+        : policy(anyimpl::get_policy<anyimpl::empty_any>()), object(NULL)
+    { }
+
+    /// Special initializing constructor for string literals.
+    any(const char* x)
+        : policy(anyimpl::get_policy<anyimpl::empty_any>()), object(NULL)
+    {
+        assign(x);
+    }
+
+    /// Copy constructor.
+    any(const any& x)
+        : policy(anyimpl::get_policy<anyimpl::empty_any>()), object(NULL)
+    {
+        assign(x);
+    }
+
+    /// Destructor.
+    ~any()
+    {
+        policy->static_delete(&object);
+    }
+
+    /// Assignment function from another any.
+    any& assign(const any& x)
+    {
+        reset();
+        policy = x.policy;
+        policy->clone(&x.object, &object);
+        return *this;
+    }
+
+    /// Assignment function.
+    template <typename T>
+    any_t& assign(const T& x)
+    {
+        reset();
+        policy = anyimpl::get_policy<T>();
+        policy->copy_from_value(&x, &object);
+        return *this;
+    }
+
+    /// Assignment operator.
+    template<typename T>
+    any_t& operator=(const T& x)
+    {
+        return assign(x);
+    }
+
+    /// Assignment operator, specialed for literal strings.
+    /// They have types like const char [6] which don't work as expected.
+    any& operator=(const char* x)
+    {
+        return assign(x);
+    }
+
+    /// Utility functions
+    any& swap(any& x)
+    {
+        std::swap(policy, x.policy);
+        std::swap(object, x.object);
+        return *this;
+    }
+
+    /// Cast operator. You can only cast to the original type.
+    template<typename T>
+    T& cast()
+    {
+        if (policy->type() != typeid(T)) throw anyimpl::bad_any_cast();
+        T* r = reinterpret_cast<T*>(policy->get_value(&object));
+        return *r;
+    }
+
+    /// Cast operator. You can only cast to the original type.
+    template<typename T>
+    const T& cast() const
+    {
+        if (policy->type() != typeid(T)) throw anyimpl::bad_any_cast();
+        void* obj = const_cast<void*>(object);
+        T* r = reinterpret_cast<T*>(policy->get_value(&obj));
+        return *r;
+    }
+
+    /// Returns true if the any contains no value.
+    bool empty() const
+    {
+        return policy->type() == typeid(anyimpl::empty_any);
+    }
+
+    /// Frees any allocated memory, and sets the value to NULL.
+    void reset()
+    {
+        policy->static_delete(&object);
+        policy = anyimpl::get_policy<anyimpl::empty_any>();
+    }
+
+    /// Returns true if the two types are the same.
+    bool compatible(const any& x) const
+    {
+        return policy->type() == x.policy->type();
+    }
+
+    /// Returns if the type is compatible with the policy
+    template<typename T>
+    bool has_type()
+    {
+        return policy->type() == typeid(T);
+    }
+
+    const std::type_info& type() const
+    {
+        return policy->type();
+    }
+
+    friend std::ostream& operator <<(std::ostream& out, const any& any_val);
+};
+
+inline std::ostream& operator <<(std::ostream& out, const any& any_val)
+{
+    any_val.policy->print(out,&any_val.object);
+    return out;
+}
+
+}
+
+#endif // FLANN_ANY_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/cuda/heap.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/cuda/heap.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/cuda/heap.h	(revision 15840)
@@ -0,0 +1,139 @@
+#ifndef FLANN_UTIL_CUDA_HEAP_H
+#define FLANN_UTIL_CUDA_HEAP_H
+
+/*
+    Copyright (c) 2011, Andreas MÃŒtzel <andreas.muetzel@gmx.net>
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+ * Redistributions of source code must retain the above copyright
+        notice, this list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution.
+ * Neither the name of the <organization> nor the
+        names of its contributors may be used to endorse or promote products
+        derived from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY Andreas MÃŒtzel <andreas.muetzel@gmx.net> ''AS IS'' AND ANY
+    EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL Andreas MÃŒtzel <andreas.muetzel@gmx.net> BE LIABLE FOR ANY
+    DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+    LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+    ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+    SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+
+namespace flann
+{
+namespace cuda
+{
+template <class T>
+__device__ __host__ void swap( T& x, T& y )
+{
+    T t=x;
+    x=y;
+    y=t;
+}
+
+namespace heap
+{
+
+//! moves an element down the heap until all children are smaller than the elemnent
+//! if c is a less-than comparator, it do this until all children are larger
+template <class GreaterThan, class RandomAccessIterator>
+__host__ __device__ void
+sift_down( RandomAccessIterator array, size_t begin, size_t length, GreaterThan c = GreaterThan() )
+{
+
+    while( 2*begin+1 < length ) {
+        size_t left = 2*begin+1;
+        size_t right = 2*begin+2;
+        size_t largest=begin;
+        if((left < length)&& c(array[left], array[largest]) ) largest=left;
+
+        if((right < length)&& c(array[right], array[largest]) ) largest=right;
+
+        if( largest != begin ) {
+            cuda::swap( array[begin], array[largest] );
+            begin=largest;
+        }
+        else return;
+    }
+}
+
+//! creates a max-heap in the array beginning at begin of length "length"
+//! if c is a less-than comparator, it will create a min-heap
+template <class GreaterThan, class RandomAccessIterator>
+__host__ __device__ void
+make_heap( RandomAccessIterator begin, size_t length, GreaterThan c = GreaterThan() )
+{
+    int i=length/2-1;
+    while( i>=0 ) {
+        sift_down( begin, i, length, c );
+        i--;
+    }
+}
+
+
+//! verifies if the array is a max-heap
+//! if c is a less-than comparator, it will verify if it is a min-heap
+template <class GreaterThan, class RandomAccessIterator>
+__host__ __device__ bool
+is_heap( RandomAccessIterator begin, size_t length, GreaterThan c = GreaterThan() )
+{
+    for( unsigned i=0; i<length; i++ ) {
+        if((2*i+1 < length)&& c(begin[2*i+1],begin[i]) ) return false;
+        if((2*i+2 < length)&& c(begin[2*i+2],begin[i]) ) return false;
+    }
+    return true;
+}
+
+
+//! moves an element down the heap until all children are smaller than the elemnent
+//! if c is a less-than comparator, it do this until all children are larger
+template <class GreaterThan, class RandomAccessIterator, class RandomAccessIterator2>
+__host__ __device__ void
+sift_down( RandomAccessIterator key, RandomAccessIterator2 value, size_t begin, size_t length, GreaterThan c = GreaterThan() )
+{
+
+    while( 2*begin+1 < length ) {
+        size_t left = 2*begin+1;
+        size_t right = 2*begin+2;
+        size_t largest=begin;
+        if((left < length)&& c(key[left], key[largest]) ) largest=left;
+
+        if((right < length)&& c(key[right], key[largest]) ) largest=right;
+
+        if( largest != begin ) {
+            cuda::swap( key[begin], key[largest] );
+            cuda::swap( value[begin], value[largest] );
+            begin=largest;
+        }
+        else return;
+    }
+}
+
+//! creates a max-heap in the array beginning at begin of length "length"
+//! if c is a less-than comparator, it will create a min-heap
+template <class GreaterThan, class RandomAccessIterator, class RandomAccessIterator2>
+__host__ __device__ void
+make_heap( RandomAccessIterator key,  RandomAccessIterator2 value, size_t length, GreaterThan c = GreaterThan() )
+{
+    int i=length/2-1;
+    while( i>=0 ) {
+        sift_down( key, value, i, length, c );
+        i--;
+    }
+}
+
+}
+
+}
+}
+
+#endif
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/cuda/result_set.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/cuda/result_set.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/cuda/result_set.h	(revision 15840)
@@ -0,0 +1,536 @@
+/**********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2011  Andreas Muetzel (amuetzel@uni-koblenz.de). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+#ifndef FLANN_UTIL_CUDA_RESULTSET_H
+#define FLANN_UTIL_CUDA_RESULTSET_H
+
+#include <flann/util/cuda/heap.h>
+#include <limits>
+
+__device__ __forceinline__
+float infinity()
+{
+       return __int_as_float(0x7f800000);
+}
+
+#ifndef INFINITY
+#define INFINITY infinity()
+#endif
+
+namespace flann
+{
+namespace cuda
+{
+//! result set for the 1nn search. Doesn't do any global memory accesses on its own,
+template< typename DistanceType >
+struct SingleResultSet
+{
+    int bestIndex;
+    DistanceType bestDist;
+    const DistanceType epsError;
+
+    __device__
+    SingleResultSet( DistanceType eps ) : bestIndex(-1),bestDist(INFINITY), epsError(eps){ }
+
+    __device__
+    inline float
+    worstDist()
+    {
+        return bestDist;
+    }
+
+    __device__
+    inline void
+    insert(int index, DistanceType dist)
+    {
+        if( dist <= bestDist ) {
+            bestIndex=index;
+            bestDist=dist;
+        }
+    }
+
+    DistanceType* resultDist;
+    int* resultIndex;
+
+    __device__
+    inline void
+    setResultLocation( DistanceType* dists, int* index, int thread, int stride )
+    {
+        resultDist=dists+thread*stride;
+        resultIndex=index+thread*stride;
+        if( stride != 1 ) {
+            for( int i=1; i<stride; i++ ) {
+                resultDist[i]=INFINITY;
+                resultIndex[i]=-1;
+            }
+        }
+    }
+
+    __device__
+    inline void
+    finish()
+    {
+        resultDist[0]=bestDist;
+        resultIndex[0]=bestIndex;
+    }
+};
+
+template< typename DistanceType >
+struct GreaterThan
+{
+    __device__
+    bool operator()(DistanceType a, DistanceType b)
+    {
+        return a>b;
+    }
+};
+
+
+// using this and the template uses 2 or 3 registers more than the direct implementation in the kNearestKernel, but
+// there is no speed difference.
+// Setting useHeap as a template parameter leads to a whole lot of things being
+// optimized away by nvcc.
+// Register counts are the same as when removing not-needed variables in explicit specializations
+// and the "if( useHeap )" branches are eliminated at compile time.
+// The downside of this: a bit more complex kernel launch code.
+template< typename DistanceType, bool useHeap >
+struct KnnResultSet
+{
+    int foundNeighbors;
+    DistanceType largestHeapDist;
+    int maxDistIndex;
+    const int k;
+    const bool sorted;
+    const DistanceType epsError;
+
+
+    __device__
+    KnnResultSet(int knn, bool sortResults, DistanceType eps) : foundNeighbors(0),largestHeapDist(INFINITY),k(knn), sorted(sortResults), epsError(eps){ }
+
+    //          __host__ __device__
+    //          KnnResultSet(const KnnResultSet& o):foundNeighbors(o.foundNeighbors),largestHeapDist(o.largestHeapDist),k(o.k){ }
+
+    __device__
+    inline DistanceType
+    worstDist()
+    {
+        return largestHeapDist;
+    }
+
+    __device__
+    inline void
+    insert(int index, DistanceType dist)
+    {
+        if( foundNeighbors<k ) {
+            resultDist[foundNeighbors]=dist;
+            resultIndex[foundNeighbors]=index;
+            if( foundNeighbors==k-1) {
+                if( useHeap ) {
+                    flann::cuda::heap::make_heap(resultDist,resultIndex,k,GreaterThan<DistanceType>());
+                    largestHeapDist=resultDist[0];
+                }
+                else {
+                    findLargestDistIndex();
+                }
+
+            }
+            foundNeighbors++;
+        }
+        else if( dist < largestHeapDist ) {
+            if( useHeap ) {
+                resultDist[0]=dist;
+                resultIndex[0]=index;
+                flann::cuda::heap::sift_down(resultDist,resultIndex,0,k,GreaterThan<DistanceType>());
+                largestHeapDist=resultDist[0];
+            }
+            else {
+                resultDist[maxDistIndex]=dist;
+                resultIndex[maxDistIndex]=index;
+                findLargestDistIndex();
+            }
+
+        }
+    }
+
+    __device__
+    void
+    findLargestDistIndex( )
+    {
+        largestHeapDist=resultDist[0];
+        maxDistIndex=0;
+        for( int i=1; i<k; i++ )
+            if( resultDist[i] > largestHeapDist ) {
+                maxDistIndex=i;
+                largestHeapDist=resultDist[i];
+            }
+    }
+
+    float* resultDist;
+    int* resultIndex;
+
+    __device__
+    inline void
+    setResultLocation( DistanceType* dists, int* index, int thread, int stride )
+    {
+        resultDist=dists+stride*thread;
+        resultIndex=index+stride*thread;
+        for( int i=0; i<stride; i++ ) {
+            resultDist[i]=INFINITY;
+            resultIndex[i]=-1;
+            //                  resultIndex[tid+i*blockDim.x]=-1;
+            //                  resultDist[tid+i*blockDim.x]=INFINITY;
+        }
+    }
+
+    __host__ __device__
+    inline void
+    finish()
+    {
+        if( sorted ) {
+            if( !useHeap ) flann::cuda::heap::make_heap(resultDist,resultIndex,k,GreaterThan<DistanceType>());
+            for( int i=k-1; i>0; i-- ) {
+                flann::cuda::swap( resultDist[0], resultDist[i] );
+                flann::cuda::swap( resultIndex[0], resultIndex[i] );
+                flann::cuda::heap::sift_down( resultDist,resultIndex, 0, i, GreaterThan<DistanceType>() );
+            }
+        }
+    }
+};
+
+template <typename DistanceType>
+struct CountingRadiusResultSet
+{
+    int count_;
+    DistanceType radius_sq_;
+    int max_neighbors_;
+
+    __device__
+    CountingRadiusResultSet(DistanceType radius, int max_neighbors) : count_(0),radius_sq_(radius), max_neighbors_(max_neighbors){ }
+
+    __device__
+    inline DistanceType
+    worstDist()
+    {
+        return radius_sq_;
+    }
+
+    __device__
+    inline void
+    insert(int index, float dist)
+    {
+        if( dist < radius_sq_ ) {
+            count_++;
+        }
+    }
+
+    int* resultIndex;
+
+    __device__
+    inline void
+    setResultLocation( DistanceType* /*dists*/, int* count, int thread, int stride )
+    {
+        resultIndex=count+thread*stride;
+    }
+
+    __device__
+    inline void
+    finish()
+    {
+        if(( max_neighbors_<=0) ||( count_<=max_neighbors_) ) resultIndex[0]=count_;
+        else resultIndex[0]=max_neighbors_;
+    }
+};
+
+template<typename DistanceType, bool useHeap>
+struct RadiusKnnResultSet
+{
+    int foundNeighbors;
+    DistanceType largestHeapDist;
+    int maxDistElem;
+    const int k;
+    const bool sorted;
+    const DistanceType radius_sq_;
+    int* segment_starts_;
+    //          int count_;
+
+
+    __device__
+    RadiusKnnResultSet(DistanceType radius, int knn, int* segment_starts, bool sortResults) : foundNeighbors(0),largestHeapDist(radius),k(knn), sorted(sortResults), radius_sq_(radius),segment_starts_(segment_starts) { }
+
+    //          __host__ __device__
+    //          KnnResultSet(const KnnResultSet& o):foundNeighbors(o.foundNeighbors),largestHeapDist(o.largestHeapDist),k(o.k){ }
+
+    __device__
+    inline DistanceType
+    worstDist()
+    {
+        return largestHeapDist;
+    }
+
+    __device__
+    inline void
+    insert(int index, DistanceType dist)
+    {
+        if( dist < radius_sq_ ) {
+            if( foundNeighbors<k ) {
+                resultDist[foundNeighbors]=dist;
+                resultIndex[foundNeighbors]=index;
+                if(( foundNeighbors==k-1) && useHeap) {
+                    if( useHeap ) {
+                        flann::cuda::heap::make_heap(resultDist,resultIndex,k,GreaterThan<DistanceType>());
+                        largestHeapDist=resultDist[0];
+                    }
+                    else {
+                        findLargestDistIndex();
+                    }
+                }
+                foundNeighbors++;
+
+            }
+            else if( dist < largestHeapDist ) {
+                if( useHeap ) {
+                    resultDist[0]=dist;
+                    resultIndex[0]=index;
+                    flann::cuda::heap::sift_down(resultDist,resultIndex,0,k,GreaterThan<DistanceType>());
+                    largestHeapDist=resultDist[0];
+                }
+                else {
+                    resultDist[maxDistElem]=dist;
+                    resultIndex[maxDistElem]=index;
+                    findLargestDistIndex();
+                }
+            }
+        }
+    }
+
+    __device__
+    void
+    findLargestDistIndex( )
+    {
+        largestHeapDist=resultDist[0];
+        maxDistElem=0;
+        for( int i=1; i<k; i++ )
+            if( resultDist[i] > largestHeapDist ) {
+                maxDistElem=i;
+                largestHeapDist=resultDist[i];
+            }
+    }
+
+
+    DistanceType* resultDist;
+    int* resultIndex;
+
+    __device__
+    inline void
+    setResultLocation( DistanceType* dists, int* index, int thread, int /*stride*/ )
+    {
+        resultDist=dists+segment_starts_[thread];
+        resultIndex=index+segment_starts_[thread];
+    }
+
+    __device__
+    inline void
+    finish()
+    {
+        if( sorted ) {
+            if( !useHeap ) flann::cuda::heap::make_heap(resultDist,resultIndex,k,GreaterThan<DistanceType>());
+            for( int i=foundNeighbors-1; i>0; i-- ) {
+                flann::cuda::swap( resultDist[0], resultDist[i] );
+                flann::cuda::swap( resultIndex[0], resultIndex[i] );
+                flann::cuda::heap::sift_down( resultDist,resultIndex, 0, i, GreaterThan<DistanceType>() );
+            }
+        }
+    }
+};
+
+// Difference to RadiusKnnResultSet: Works like KnnResultSet, doesn't pack the results densely (as the RadiusResultSet does)
+template <typename DistanceType, bool useHeap>
+struct KnnRadiusResultSet
+{
+    int foundNeighbors;
+    DistanceType largestHeapDist;
+    int maxDistIndex;
+    const int k;
+    const bool sorted;
+    const DistanceType epsError;
+    const DistanceType radius_sq;
+
+
+    __device__
+    KnnRadiusResultSet(int knn, bool sortResults, DistanceType eps, DistanceType radius) : foundNeighbors(0),largestHeapDist(radius),k(knn), sorted(sortResults), epsError(eps),radius_sq(radius){ }
+
+    //          __host__ __device__
+    //          KnnResultSet(const KnnResultSet& o):foundNeighbors(o.foundNeighbors),largestHeapDist(o.largestHeapDist),k(o.k){ }
+
+    __device__
+    inline DistanceType
+    worstDist()
+    {
+        return largestHeapDist;
+    }
+
+    __device__
+    inline void
+    insert(int index, DistanceType dist)
+    {
+        if( dist < largestHeapDist ) {
+            if( foundNeighbors<k ) {
+                resultDist[foundNeighbors]=dist;
+                resultIndex[foundNeighbors]=index;
+                if( foundNeighbors==k-1 ) {
+                    if( useHeap ) {
+                        flann::cuda::heap::make_heap(resultDist,resultIndex,k,GreaterThan<DistanceType>());
+                        largestHeapDist=resultDist[0];
+                    }
+                    else {
+                        findLargestDistIndex();
+                    }
+                }
+                foundNeighbors++;
+            }
+            else { //if( dist < largestHeapDist )
+                if( useHeap ) {
+                    resultDist[0]=dist;
+                    resultIndex[0]=index;
+                    flann::cuda::heap::sift_down(resultDist,resultIndex,0,k,GreaterThan<DistanceType>());
+                    largestHeapDist=resultDist[0];
+                }
+                else {
+                    resultDist[maxDistIndex]=dist;
+                    resultIndex[maxDistIndex]=index;
+                    findLargestDistIndex();
+                }
+            }
+        }
+    }
+    __device__
+    void
+    findLargestDistIndex( )
+    {
+        largestHeapDist=resultDist[0];
+        maxDistIndex=0;
+        for( int i=1; i<k; i++ )
+            if( resultDist[i] > largestHeapDist ) {
+                maxDistIndex=i;
+                largestHeapDist=resultDist[i];
+            }
+    }
+
+    DistanceType* resultDist;
+    int* resultIndex;
+
+    __device__
+    inline void
+    setResultLocation( DistanceType* dists, int* index, int thread, int stride )
+    {
+        resultDist=dists+stride*thread;
+        resultIndex=index+stride*thread;
+        for( int i=0; i<stride; i++ ) {
+            resultDist[i]=INFINITY;
+            resultIndex[i]=-1;
+            //                  resultIndex[tid+i*blockDim.x]=-1;
+            //                  resultDist[tid+i*blockDim.x]=INFINITY;
+        }
+    }
+
+    __device__
+    inline void
+    finish()
+    {
+        if( sorted ) {
+            if( !useHeap ) flann::cuda::heap::make_heap(resultDist,resultIndex,k,GreaterThan<DistanceType>());
+            for( int i=k-1; i>0; i-- ) {
+                flann::cuda::swap( resultDist[0], resultDist[i] );
+                flann::cuda::swap( resultIndex[0], resultIndex[i] );
+                flann::cuda::heap::sift_down( resultDist,resultIndex, 0, i, GreaterThan<DistanceType>() );
+            }
+        }
+    }
+};
+
+//! fills the radius output buffer.
+//! IMPORTANT ASSERTION: ASSUMES THAT THERE IS ENOUGH SPACE FOR EVERY NEIGHBOR! IF THIS ISN'T
+//! TRUE, USE KnnRadiusResultSet! (Otherwise, the neighbors of one element might overflow into the next element, or past the buffer.)
+template< typename DistanceType >
+struct RadiusResultSet
+{
+    DistanceType radius_sq_;
+    int* segment_starts_;
+    int count_;
+    bool sorted_;
+
+    __device__
+    RadiusResultSet(DistanceType radius, int* segment_starts, bool sorted) : radius_sq_(radius), segment_starts_(segment_starts), count_(0), sorted_(sorted){ }
+
+    __device__
+    inline DistanceType
+    worstDist()
+    {
+        return radius_sq_;
+    }
+
+    __device__
+    inline void
+    insert(int index, DistanceType dist)
+    {
+        if( dist < radius_sq_ ) {
+            resultIndex[count_]=index;
+            resultDist[count_]=dist;
+            count_++;
+        }
+    }
+
+    int* resultIndex;
+    DistanceType* resultDist;
+
+    __device__
+    inline void
+    setResultLocation( DistanceType* dists, int* index, int thread, int /*stride*/ )
+    {
+        resultIndex=index+segment_starts_[thread];
+        resultDist=dists+segment_starts_[thread];
+    }
+
+    __device__
+    inline void
+    finish()
+    {
+        if( sorted_ ) {
+            flann::cuda::heap::make_heap( resultDist,resultIndex, count_, GreaterThan<DistanceType>() );
+            for( int i=count_-1; i>0; i-- ) {
+                flann::cuda::swap( resultDist[0], resultDist[i] );
+                flann::cuda::swap( resultIndex[0], resultIndex[i] );
+                flann::cuda::heap::sift_down( resultDist,resultIndex, 0, i, GreaterThan<DistanceType>() );
+            }
+        }
+    }
+};
+}
+}
+
+#endif
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/cutil_math.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/cutil_math.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/cutil_math.h	(revision 15840)
@@ -0,0 +1,1328 @@
+/*
+ * Copyright 1993-2010 NVIDIA Corporation.  All rights reserved.
+ *
+ * Please refer to the NVIDIA end user license agreement (EULA) associated
+ * with this source code for terms and conditions that govern your use of
+ * this software. Any use, reproduction, disclosure, or distribution of
+ * this software and related documentation outside the terms of the EULA
+ * is strictly prohibited.
+ *
+ */
+
+/*
+    This file implements common mathematical operations on vector types
+    (float3, float4 etc.) since these are not provided as standard by CUDA.
+
+    The syntax is modelled on the Cg standard library.
+
+    This is part of the CUTIL library and is not supported by NVIDIA.
+
+    Thanks to Linh Hah for additions and fixes.
+*/
+
+#ifndef CUTIL_MATH_H
+#define CUTIL_MATH_H
+
+#include "cuda_runtime.h"
+
+typedef unsigned int uint;
+typedef unsigned short ushort;
+
+#ifndef __CUDACC__
+#include <math.h>
+
+////////////////////////////////////////////////////////////////////////////////
+// host implementations of CUDA functions
+////////////////////////////////////////////////////////////////////////////////
+
+inline float fminf(float a, float b)
+{
+  return a < b ? a : b;
+}
+
+inline float fmaxf(float a, float b)
+{
+  return a > b ? a : b;
+}
+
+inline int max(int a, int b)
+{
+  return a > b ? a : b;
+}
+
+inline int min(int a, int b)
+{
+  return a < b ? a : b;
+}
+
+inline float rsqrtf(float x)
+{
+    return 1.0f / sqrtf(x);
+}
+#endif
+
+////////////////////////////////////////////////////////////////////////////////
+// constructors
+////////////////////////////////////////////////////////////////////////////////
+
+inline __host__ __device__ float2 make_float2(float s)
+{
+    return make_float2(s, s);
+}
+inline __host__ __device__ float2 make_float2(float3 a)
+{
+    return make_float2(a.x, a.y);
+}
+inline __host__ __device__ float2 make_float2(int2 a)
+{
+    return make_float2(float(a.x), float(a.y));
+}
+inline __host__ __device__ float2 make_float2(uint2 a)
+{
+    return make_float2(float(a.x), float(a.y));
+}
+
+inline __host__ __device__ int2 make_int2(int s)
+{
+    return make_int2(s, s);
+}
+inline __host__ __device__ int2 make_int2(int3 a)
+{
+    return make_int2(a.x, a.y);
+}
+inline __host__ __device__ int2 make_int2(uint2 a)
+{
+    return make_int2(int(a.x), int(a.y));
+}
+inline __host__ __device__ int2 make_int2(float2 a)
+{
+    return make_int2(int(a.x), int(a.y));
+}
+
+inline __host__ __device__ uint2 make_uint2(uint s)
+{
+    return make_uint2(s, s);
+}
+inline __host__ __device__ uint2 make_uint2(uint3 a)
+{
+    return make_uint2(a.x, a.y);
+}
+inline __host__ __device__ uint2 make_uint2(int2 a)
+{
+    return make_uint2(uint(a.x), uint(a.y));
+}
+
+inline __host__ __device__ float3 make_float3(float s)
+{
+    return make_float3(s, s, s);
+}
+inline __host__ __device__ float3 make_float3(float2 a)
+{
+    return make_float3(a.x, a.y, 0.0f);
+}
+inline __host__ __device__ float3 make_float3(float2 a, float s)
+{
+    return make_float3(a.x, a.y, s);
+}
+inline __host__ __device__ float3 make_float3(float4 a)
+{
+    return make_float3(a.x, a.y, a.z);
+}
+inline __host__ __device__ float3 make_float3(int3 a)
+{
+    return make_float3(float(a.x), float(a.y), float(a.z));
+}
+inline __host__ __device__ float3 make_float3(uint3 a)
+{
+    return make_float3(float(a.x), float(a.y), float(a.z));
+}
+
+inline __host__ __device__ int3 make_int3(int s)
+{
+    return make_int3(s, s, s);
+}
+inline __host__ __device__ int3 make_int3(int2 a)
+{
+    return make_int3(a.x, a.y, 0);
+}
+inline __host__ __device__ int3 make_int3(int2 a, int s)
+{
+    return make_int3(a.x, a.y, s);
+}
+inline __host__ __device__ int3 make_int3(uint3 a)
+{
+    return make_int3(int(a.x), int(a.y), int(a.z));
+}
+inline __host__ __device__ int3 make_int3(float3 a)
+{
+    return make_int3(int(a.x), int(a.y), int(a.z));
+}
+
+inline __host__ __device__ uint3 make_uint3(uint s)
+{
+    return make_uint3(s, s, s);
+}
+inline __host__ __device__ uint3 make_uint3(uint2 a)
+{
+    return make_uint3(a.x, a.y, 0);
+}
+inline __host__ __device__ uint3 make_uint3(uint2 a, uint s)
+{
+    return make_uint3(a.x, a.y, s);
+}
+inline __host__ __device__ uint3 make_uint3(uint4 a)
+{
+    return make_uint3(a.x, a.y, a.z);
+}
+inline __host__ __device__ uint3 make_uint3(int3 a)
+{
+    return make_uint3(uint(a.x), uint(a.y), uint(a.z));
+}
+
+inline __host__ __device__ float4 make_float4(float s)
+{
+    return make_float4(s, s, s, s);
+}
+inline __host__ __device__ float4 make_float4(float3 a)
+{
+    return make_float4(a.x, a.y, a.z, 0.0f);
+}
+inline __host__ __device__ float4 make_float4(float3 a, float w)
+{
+    return make_float4(a.x, a.y, a.z, w);
+}
+inline __host__ __device__ float4 make_float4(int4 a)
+{
+    return make_float4(float(a.x), float(a.y), float(a.z), float(a.w));
+}
+inline __host__ __device__ float4 make_float4(uint4 a)
+{
+    return make_float4(float(a.x), float(a.y), float(a.z), float(a.w));
+}
+
+inline __host__ __device__ int4 make_int4(int s)
+{
+    return make_int4(s, s, s, s);
+}
+inline __host__ __device__ int4 make_int4(int3 a)
+{
+    return make_int4(a.x, a.y, a.z, 0);
+}
+inline __host__ __device__ int4 make_int4(int3 a, int w)
+{
+    return make_int4(a.x, a.y, a.z, w);
+}
+inline __host__ __device__ int4 make_int4(uint4 a)
+{
+    return make_int4(int(a.x), int(a.y), int(a.z), int(a.w));
+}
+inline __host__ __device__ int4 make_int4(float4 a)
+{
+    return make_int4(int(a.x), int(a.y), int(a.z), int(a.w));
+}
+
+
+inline __host__ __device__ uint4 make_uint4(uint s)
+{
+    return make_uint4(s, s, s, s);
+}
+inline __host__ __device__ uint4 make_uint4(uint3 a)
+{
+    return make_uint4(a.x, a.y, a.z, 0);
+}
+inline __host__ __device__ uint4 make_uint4(uint3 a, uint w)
+{
+    return make_uint4(a.x, a.y, a.z, w);
+}
+inline __host__ __device__ uint4 make_uint4(int4 a)
+{
+    return make_uint4(uint(a.x), uint(a.y), uint(a.z), uint(a.w));
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// negate
+////////////////////////////////////////////////////////////////////////////////
+
+inline __host__ __device__ float2 operator-(float2 &a)
+{
+    return make_float2(-a.x, -a.y);
+}
+inline __host__ __device__ int2 operator-(int2 &a)
+{
+    return make_int2(-a.x, -a.y);
+}
+inline __host__ __device__ float3 operator-(float3 &a)
+{
+    return make_float3(-a.x, -a.y, -a.z);
+}
+inline __host__ __device__ int3 operator-(int3 &a)
+{
+    return make_int3(-a.x, -a.y, -a.z);
+}
+inline __host__ __device__ float4 operator-(float4 &a)
+{
+    return make_float4(-a.x, -a.y, -a.z, -a.w);
+}
+inline __host__ __device__ int4 operator-(int4 &a)
+{
+    return make_int4(-a.x, -a.y, -a.z, -a.w);
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// addition
+////////////////////////////////////////////////////////////////////////////////
+
+inline __host__ __device__ float2 operator+(float2 a, float2 b)
+{
+    return make_float2(a.x + b.x, a.y + b.y);
+}
+inline __host__ __device__ void operator+=(float2 &a, float2 b)
+{
+    a.x += b.x; a.y += b.y;
+}
+inline __host__ __device__ float2 operator+(float2 a, float b)
+{
+    return make_float2(a.x + b, a.y + b);
+}
+inline __host__ __device__ float2 operator+(float b, float2 a)
+{
+    return make_float2(a.x + b, a.y + b);
+}
+inline __host__ __device__ void operator+=(float2 &a, float b)
+{
+    a.x += b; a.y += b;
+}
+
+inline __host__ __device__ int2 operator+(int2 a, int2 b)
+{
+    return make_int2(a.x + b.x, a.y + b.y);
+}
+inline __host__ __device__ void operator+=(int2 &a, int2 b)
+{
+    a.x += b.x; a.y += b.y;
+}
+inline __host__ __device__ int2 operator+(int2 a, int b)
+{
+    return make_int2(a.x + b, a.y + b);
+}
+inline __host__ __device__ int2 operator+(int b, int2 a)
+{
+    return make_int2(a.x + b, a.y + b);
+}
+inline __host__ __device__ void operator+=(int2 &a, int b)
+{
+    a.x += b; a.y += b;
+}
+
+inline __host__ __device__ uint2 operator+(uint2 a, uint2 b)
+{
+    return make_uint2(a.x + b.x, a.y + b.y);
+}
+inline __host__ __device__ void operator+=(uint2 &a, uint2 b)
+{
+    a.x += b.x; a.y += b.y;
+}
+inline __host__ __device__ uint2 operator+(uint2 a, uint b)
+{
+    return make_uint2(a.x + b, a.y + b);
+}
+inline __host__ __device__ uint2 operator+(uint b, uint2 a)
+{
+    return make_uint2(a.x + b, a.y + b);
+}
+inline __host__ __device__ void operator+=(uint2 &a, uint b)
+{
+    a.x += b; a.y += b;
+}
+
+
+inline __host__ __device__ float3 operator+(float3 a, float3 b)
+{
+    return make_float3(a.x + b.x, a.y + b.y, a.z + b.z);
+}
+inline __host__ __device__ void operator+=(float3 &a, float3 b)
+{
+    a.x += b.x; a.y += b.y; a.z += b.z;
+}
+inline __host__ __device__ float3 operator+(float3 a, float b)
+{
+    return make_float3(a.x + b, a.y + b, a.z + b);
+}
+inline __host__ __device__ void operator+=(float3 &a, float b)
+{
+    a.x += b; a.y += b; a.z += b;
+}
+
+inline __host__ __device__ int3 operator+(int3 a, int3 b)
+{
+    return make_int3(a.x + b.x, a.y + b.y, a.z + b.z);
+}
+inline __host__ __device__ void operator+=(int3 &a, int3 b)
+{
+    a.x += b.x; a.y += b.y; a.z += b.z;
+}
+inline __host__ __device__ int3 operator+(int3 a, int b)
+{
+    return make_int3(a.x + b, a.y + b, a.z + b);
+}
+inline __host__ __device__ void operator+=(int3 &a, int b)
+{
+    a.x += b; a.y += b; a.z += b;
+}
+
+inline __host__ __device__ uint3 operator+(uint3 a, uint3 b)
+{
+    return make_uint3(a.x + b.x, a.y + b.y, a.z + b.z);
+}
+inline __host__ __device__ void operator+=(uint3 &a, uint3 b)
+{
+    a.x += b.x; a.y += b.y; a.z += b.z;
+}
+inline __host__ __device__ uint3 operator+(uint3 a, uint b)
+{
+    return make_uint3(a.x + b, a.y + b, a.z + b);
+}
+inline __host__ __device__ void operator+=(uint3 &a, uint b)
+{
+    a.x += b; a.y += b; a.z += b;
+}
+
+inline __host__ __device__ int3 operator+(int b, int3 a)
+{
+    return make_int3(a.x + b, a.y + b, a.z + b);
+}
+inline __host__ __device__ uint3 operator+(uint b, uint3 a)
+{
+    return make_uint3(a.x + b, a.y + b, a.z + b);
+}
+inline __host__ __device__ float3 operator+(float b, float3 a)
+{
+    return make_float3(a.x + b, a.y + b, a.z + b);
+}
+
+inline __host__ __device__ float4 operator+(float4 a, float4 b)
+{
+    return make_float4(a.x + b.x, a.y + b.y, a.z + b.z,  a.w + b.w);
+}
+inline __host__ __device__ void operator+=(float4 &a, float4 b)
+{
+    a.x += b.x; a.y += b.y; a.z += b.z; a.w += b.w;
+}
+inline __host__ __device__ float4 operator+(float4 a, float b)
+{
+    return make_float4(a.x + b, a.y + b, a.z + b, a.w + b);
+}
+inline __host__ __device__ float4 operator+(float b, float4 a)
+{
+    return make_float4(a.x + b, a.y + b, a.z + b, a.w + b);
+}
+inline __host__ __device__ void operator+=(float4 &a, float b)
+{
+    a.x += b; a.y += b; a.z += b; a.w += b;
+}
+
+inline __host__ __device__ int4 operator+(int4 a, int4 b)
+{
+    return make_int4(a.x + b.x, a.y + b.y, a.z + b.z,  a.w + b.w);
+}
+inline __host__ __device__ void operator+=(int4 &a, int4 b)
+{
+    a.x += b.x; a.y += b.y; a.z += b.z; a.w += b.w;
+}
+inline __host__ __device__ int4 operator+(int4 a, int b)
+{
+    return make_int4(a.x + b, a.y + b, a.z + b,  a.w + b);
+}
+inline __host__ __device__ int4 operator+(int b, int4 a)
+{
+    return make_int4(a.x + b, a.y + b, a.z + b,  a.w + b);
+}
+inline __host__ __device__ void operator+=(int4 &a, int b)
+{
+    a.x += b; a.y += b; a.z += b; a.w += b;
+}
+
+inline __host__ __device__ uint4 operator+(uint4 a, uint4 b)
+{
+    return make_uint4(a.x + b.x, a.y + b.y, a.z + b.z,  a.w + b.w);
+}
+inline __host__ __device__ void operator+=(uint4 &a, uint4 b)
+{
+    a.x += b.x; a.y += b.y; a.z += b.z; a.w += b.w;
+}
+inline __host__ __device__ uint4 operator+(uint4 a, uint b)
+{
+    return make_uint4(a.x + b, a.y + b, a.z + b,  a.w + b);
+}
+inline __host__ __device__ uint4 operator+(uint b, uint4 a)
+{
+    return make_uint4(a.x + b, a.y + b, a.z + b,  a.w + b);
+}
+inline __host__ __device__ void operator+=(uint4 &a, uint b)
+{
+    a.x += b; a.y += b; a.z += b; a.w += b;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// subtract
+////////////////////////////////////////////////////////////////////////////////
+
+inline __host__ __device__ float2 operator-(float2 a, float2 b)
+{
+    return make_float2(a.x - b.x, a.y - b.y);
+}
+inline __host__ __device__ void operator-=(float2 &a, float2 b)
+{
+    a.x -= b.x; a.y -= b.y;
+}
+inline __host__ __device__ float2 operator-(float2 a, float b)
+{
+    return make_float2(a.x - b, a.y - b);
+}
+inline __host__ __device__ float2 operator-(float b, float2 a)
+{
+    return make_float2(b - a.x, b - a.y);
+}
+inline __host__ __device__ void operator-=(float2 &a, float b)
+{
+    a.x -= b; a.y -= b;
+}
+
+inline __host__ __device__ int2 operator-(int2 a, int2 b)
+{
+    return make_int2(a.x - b.x, a.y - b.y);
+}
+inline __host__ __device__ void operator-=(int2 &a, int2 b)
+{
+    a.x -= b.x; a.y -= b.y;
+}
+inline __host__ __device__ int2 operator-(int2 a, int b)
+{
+    return make_int2(a.x - b, a.y - b);
+}
+inline __host__ __device__ int2 operator-(int b, int2 a)
+{
+    return make_int2(b - a.x, b - a.y);
+}
+inline __host__ __device__ void operator-=(int2 &a, int b)
+{
+    a.x -= b; a.y -= b;
+}
+
+inline __host__ __device__ uint2 operator-(uint2 a, uint2 b)
+{
+    return make_uint2(a.x - b.x, a.y - b.y);
+}
+inline __host__ __device__ void operator-=(uint2 &a, uint2 b)
+{
+    a.x -= b.x; a.y -= b.y;
+}
+inline __host__ __device__ uint2 operator-(uint2 a, uint b)
+{
+    return make_uint2(a.x - b, a.y - b);
+}
+inline __host__ __device__ uint2 operator-(uint b, uint2 a)
+{
+    return make_uint2(b - a.x, b - a.y);
+}
+inline __host__ __device__ void operator-=(uint2 &a, uint b)
+{
+    a.x -= b; a.y -= b;
+}
+
+inline __host__ __device__ float3 operator-(float3 a, float3 b)
+{
+    return make_float3(a.x - b.x, a.y - b.y, a.z - b.z);
+}
+inline __host__ __device__ void operator-=(float3 &a, float3 b)
+{
+    a.x -= b.x; a.y -= b.y; a.z -= b.z;
+}
+inline __host__ __device__ float3 operator-(float3 a, float b)
+{
+    return make_float3(a.x - b, a.y - b, a.z - b);
+}
+inline __host__ __device__ float3 operator-(float b, float3 a)
+{
+    return make_float3(b - a.x, b - a.y, b - a.z);
+}
+inline __host__ __device__ void operator-=(float3 &a, float b)
+{
+    a.x -= b; a.y -= b; a.z -= b;
+}
+
+inline __host__ __device__ int3 operator-(int3 a, int3 b)
+{
+    return make_int3(a.x - b.x, a.y - b.y, a.z - b.z);
+}
+inline __host__ __device__ void operator-=(int3 &a, int3 b)
+{
+    a.x -= b.x; a.y -= b.y; a.z -= b.z;
+}
+inline __host__ __device__ int3 operator-(int3 a, int b)
+{
+    return make_int3(a.x - b, a.y - b, a.z - b);
+}
+inline __host__ __device__ int3 operator-(int b, int3 a)
+{
+    return make_int3(b - a.x, b - a.y, b - a.z);
+}
+inline __host__ __device__ void operator-=(int3 &a, int b)
+{
+    a.x -= b; a.y -= b; a.z -= b;
+}
+
+inline __host__ __device__ uint3 operator-(uint3 a, uint3 b)
+{
+    return make_uint3(a.x - b.x, a.y - b.y, a.z - b.z);
+}
+inline __host__ __device__ void operator-=(uint3 &a, uint3 b)
+{
+    a.x -= b.x; a.y -= b.y; a.z -= b.z;
+}
+inline __host__ __device__ uint3 operator-(uint3 a, uint b)
+{
+    return make_uint3(a.x - b, a.y - b, a.z - b);
+}
+inline __host__ __device__ uint3 operator-(uint b, uint3 a)
+{
+    return make_uint3(b - a.x, b - a.y, b - a.z);
+}
+inline __host__ __device__ void operator-=(uint3 &a, uint b)
+{
+    a.x -= b; a.y -= b; a.z -= b;
+}
+
+inline __host__ __device__ float4 operator-(float4 a, float4 b)
+{
+    return make_float4(a.x - b.x, a.y - b.y, a.z - b.z,  a.w - b.w);
+}
+inline __host__ __device__ void operator-=(float4 &a, float4 b)
+{
+    a.x -= b.x; a.y -= b.y; a.z -= b.z; a.w -= b.w;
+}
+inline __host__ __device__ float4 operator-(float4 a, float b)
+{
+    return make_float4(a.x - b, a.y - b, a.z - b,  a.w - b);
+}
+inline __host__ __device__ void operator-=(float4 &a, float b)
+{
+    a.x -= b; a.y -= b; a.z -= b; a.w -= b;
+}
+
+inline __host__ __device__ int4 operator-(int4 a, int4 b)
+{
+    return make_int4(a.x - b.x, a.y - b.y, a.z - b.z,  a.w - b.w);
+}
+inline __host__ __device__ void operator-=(int4 &a, int4 b)
+{
+    a.x -= b.x; a.y -= b.y; a.z -= b.z; a.w -= b.w;
+}
+inline __host__ __device__ int4 operator-(int4 a, int b)
+{
+    return make_int4(a.x - b, a.y - b, a.z - b,  a.w - b);
+}
+inline __host__ __device__ int4 operator-(int b, int4 a)
+{
+    return make_int4(b - a.x, b - a.y, b - a.z, b - a.w);
+}
+inline __host__ __device__ void operator-=(int4 &a, int b)
+{
+    a.x -= b; a.y -= b; a.z -= b; a.w -= b;
+}
+
+inline __host__ __device__ uint4 operator-(uint4 a, uint4 b)
+{
+    return make_uint4(a.x - b.x, a.y - b.y, a.z - b.z,  a.w - b.w);
+}
+inline __host__ __device__ void operator-=(uint4 &a, uint4 b)
+{
+    a.x -= b.x; a.y -= b.y; a.z -= b.z; a.w -= b.w;
+}
+inline __host__ __device__ uint4 operator-(uint4 a, uint b)
+{
+    return make_uint4(a.x - b, a.y - b, a.z - b,  a.w - b);
+}
+inline __host__ __device__ uint4 operator-(uint b, uint4 a)
+{
+    return make_uint4(b - a.x, b - a.y, b - a.z, b - a.w);
+}
+inline __host__ __device__ void operator-=(uint4 &a, uint b)
+{
+    a.x -= b; a.y -= b; a.z -= b; a.w -= b;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// multiply
+////////////////////////////////////////////////////////////////////////////////
+
+inline __host__ __device__ float2 operator*(float2 a, float2 b)
+{
+    return make_float2(a.x * b.x, a.y * b.y);
+}
+inline __host__ __device__ void operator*=(float2 &a, float2 b)
+{
+    a.x *= b.x; a.y *= b.y;
+}
+inline __host__ __device__ float2 operator*(float2 a, float b)
+{
+    return make_float2(a.x * b, a.y * b);
+}
+inline __host__ __device__ float2 operator*(float b, float2 a)
+{
+    return make_float2(b * a.x, b * a.y);
+}
+inline __host__ __device__ void operator*=(float2 &a, float b)
+{
+    a.x *= b; a.y *= b;
+}
+
+inline __host__ __device__ int2 operator*(int2 a, int2 b)
+{
+    return make_int2(a.x * b.x, a.y * b.y);
+}
+inline __host__ __device__ void operator*=(int2 &a, int2 b)
+{
+    a.x *= b.x; a.y *= b.y;
+}
+inline __host__ __device__ int2 operator*(int2 a, int b)
+{
+    return make_int2(a.x * b, a.y * b);
+}
+inline __host__ __device__ int2 operator*(int b, int2 a)
+{
+    return make_int2(b * a.x, b * a.y);
+}
+inline __host__ __device__ void operator*=(int2 &a, int b)
+{
+    a.x *= b; a.y *= b;
+}
+
+inline __host__ __device__ uint2 operator*(uint2 a, uint2 b)
+{
+    return make_uint2(a.x * b.x, a.y * b.y);
+}
+inline __host__ __device__ void operator*=(uint2 &a, uint2 b)
+{
+    a.x *= b.x; a.y *= b.y;
+}
+inline __host__ __device__ uint2 operator*(uint2 a, uint b)
+{
+    return make_uint2(a.x * b, a.y * b);
+}
+inline __host__ __device__ uint2 operator*(uint b, uint2 a)
+{
+    return make_uint2(b * a.x, b * a.y);
+}
+inline __host__ __device__ void operator*=(uint2 &a, uint b)
+{
+    a.x *= b; a.y *= b;
+}
+
+inline __host__ __device__ float3 operator*(float3 a, float3 b)
+{
+    return make_float3(a.x * b.x, a.y * b.y, a.z * b.z);
+}
+inline __host__ __device__ void operator*=(float3 &a, float3 b)
+{
+    a.x *= b.x; a.y *= b.y; a.z *= b.z;
+}
+inline __host__ __device__ float3 operator*(float3 a, float b)
+{
+    return make_float3(a.x * b, a.y * b, a.z * b);
+}
+inline __host__ __device__ float3 operator*(float b, float3 a)
+{
+    return make_float3(b * a.x, b * a.y, b * a.z);
+}
+inline __host__ __device__ void operator*=(float3 &a, float b)
+{
+    a.x *= b; a.y *= b; a.z *= b;
+}
+
+inline __host__ __device__ int3 operator*(int3 a, int3 b)
+{
+    return make_int3(a.x * b.x, a.y * b.y, a.z * b.z);
+}
+inline __host__ __device__ void operator*=(int3 &a, int3 b)
+{
+    a.x *= b.x; a.y *= b.y; a.z *= b.z;
+}
+inline __host__ __device__ int3 operator*(int3 a, int b)
+{
+    return make_int3(a.x * b, a.y * b, a.z * b);
+}
+inline __host__ __device__ int3 operator*(int b, int3 a)
+{
+    return make_int3(b * a.x, b * a.y, b * a.z);
+}
+inline __host__ __device__ void operator*=(int3 &a, int b)
+{
+    a.x *= b; a.y *= b; a.z *= b;
+}
+
+inline __host__ __device__ uint3 operator*(uint3 a, uint3 b)
+{
+    return make_uint3(a.x * b.x, a.y * b.y, a.z * b.z);
+}
+inline __host__ __device__ void operator*=(uint3 &a, uint3 b)
+{
+    a.x *= b.x; a.y *= b.y; a.z *= b.z;
+}
+inline __host__ __device__ uint3 operator*(uint3 a, uint b)
+{
+    return make_uint3(a.x * b, a.y * b, a.z * b);
+}
+inline __host__ __device__ uint3 operator*(uint b, uint3 a)
+{
+    return make_uint3(b * a.x, b * a.y, b * a.z);
+}
+inline __host__ __device__ void operator*=(uint3 &a, uint b)
+{
+    a.x *= b; a.y *= b; a.z *= b;
+}
+
+inline __host__ __device__ float4 operator*(float4 a, float4 b)
+{
+    return make_float4(a.x * b.x, a.y * b.y, a.z * b.z,  a.w * b.w);
+}
+inline __host__ __device__ void operator*=(float4 &a, float4 b)
+{
+    a.x *= b.x; a.y *= b.y; a.z *= b.z; a.w *= b.w;
+}
+inline __host__ __device__ float4 operator*(float4 a, float b)
+{
+    return make_float4(a.x * b, a.y * b, a.z * b,  a.w * b);
+}
+inline __host__ __device__ float4 operator*(float b, float4 a)
+{
+    return make_float4(b * a.x, b * a.y, b * a.z, b * a.w);
+}
+inline __host__ __device__ void operator*=(float4 &a, float b)
+{
+    a.x *= b; a.y *= b; a.z *= b; a.w *= b;
+}
+
+inline __host__ __device__ int4 operator*(int4 a, int4 b)
+{
+    return make_int4(a.x * b.x, a.y * b.y, a.z * b.z,  a.w * b.w);
+}
+inline __host__ __device__ void operator*=(int4 &a, int4 b)
+{
+    a.x *= b.x; a.y *= b.y; a.z *= b.z; a.w *= b.w;
+}
+inline __host__ __device__ int4 operator*(int4 a, int b)
+{
+    return make_int4(a.x * b, a.y * b, a.z * b,  a.w * b);
+}
+inline __host__ __device__ int4 operator*(int b, int4 a)
+{
+    return make_int4(b * a.x, b * a.y, b * a.z, b * a.w);
+}
+inline __host__ __device__ void operator*=(int4 &a, int b)
+{
+    a.x *= b; a.y *= b; a.z *= b; a.w *= b;
+}
+
+inline __host__ __device__ uint4 operator*(uint4 a, uint4 b)
+{
+    return make_uint4(a.x * b.x, a.y * b.y, a.z * b.z,  a.w * b.w);
+}
+inline __host__ __device__ void operator*=(uint4 &a, uint4 b)
+{
+    a.x *= b.x; a.y *= b.y; a.z *= b.z; a.w *= b.w;
+}
+inline __host__ __device__ uint4 operator*(uint4 a, uint b)
+{
+    return make_uint4(a.x * b, a.y * b, a.z * b,  a.w * b);
+}
+inline __host__ __device__ uint4 operator*(uint b, uint4 a)
+{
+    return make_uint4(b * a.x, b * a.y, b * a.z, b * a.w);
+}
+inline __host__ __device__ void operator*=(uint4 &a, uint b)
+{
+    a.x *= b; a.y *= b; a.z *= b; a.w *= b;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// divide
+////////////////////////////////////////////////////////////////////////////////
+
+inline __host__ __device__ float2 operator/(float2 a, float2 b)
+{
+    return make_float2(a.x / b.x, a.y / b.y);
+}
+inline __host__ __device__ void operator/=(float2 &a, float2 b)
+{
+    a.x /= b.x; a.y /= b.y;
+}
+inline __host__ __device__ float2 operator/(float2 a, float b)
+{
+    return make_float2(a.x / b, a.y / b);
+}
+inline __host__ __device__ void operator/=(float2 &a, float b)
+{
+    a.x /= b; a.y /= b;
+}
+inline __host__ __device__ float2 operator/(float b, float2 a)
+{
+    return make_float2(b / a.x, b / a.y);
+}
+
+inline __host__ __device__ float3 operator/(float3 a, float3 b)
+{
+    return make_float3(a.x / b.x, a.y / b.y, a.z / b.z);
+}
+inline __host__ __device__ void operator/=(float3 &a, float3 b)
+{
+    a.x /= b.x; a.y /= b.y; a.z /= b.z;
+}
+inline __host__ __device__ float3 operator/(float3 a, float b)
+{
+    return make_float3(a.x / b, a.y / b, a.z / b);
+}
+inline __host__ __device__ void operator/=(float3 &a, float b)
+{
+    a.x /= b; a.y /= b; a.z /= b;
+}
+inline __host__ __device__ float3 operator/(float b, float3 a)
+{
+    return make_float3(b / a.x, b / a.y, b / a.z);
+}
+
+inline __host__ __device__ float4 operator/(float4 a, float4 b)
+{
+    return make_float4(a.x / b.x, a.y / b.y, a.z / b.z,  a.w / b.w);
+}
+inline __host__ __device__ void operator/=(float4 &a, float4 b)
+{
+    a.x /= b.x; a.y /= b.y; a.z /= b.z; a.w /= b.w;
+}
+inline __host__ __device__ float4 operator/(float4 a, float b)
+{
+    return make_float4(a.x / b, a.y / b, a.z / b,  a.w / b);
+}
+inline __host__ __device__ void operator/=(float4 &a, float b)
+{
+    a.x /= b; a.y /= b; a.z /= b; a.w /= b;
+}
+inline __host__ __device__ float4 operator/(float b, float4 a){
+    return make_float4(b / a.x, b / a.y, b / a.z, b / a.w);
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// min
+////////////////////////////////////////////////////////////////////////////////
+
+inline  __host__ __device__ float2 fminf(float2 a, float2 b)
+{
+	return make_float2(fminf(a.x,b.x), fminf(a.y,b.y));
+}
+inline __host__ __device__ float3 fminf(float3 a, float3 b)
+{
+	return make_float3(fminf(a.x,b.x), fminf(a.y,b.y), fminf(a.z,b.z));
+}
+inline  __host__ __device__ float4 fminf(float4 a, float4 b)
+{
+	return make_float4(fminf(a.x,b.x), fminf(a.y,b.y), fminf(a.z,b.z), fminf(a.w,b.w));
+}
+
+inline __host__ __device__ int2 min(int2 a, int2 b)
+{
+    return make_int2(min(a.x,b.x), min(a.y,b.y));
+}
+inline __host__ __device__ int3 min(int3 a, int3 b)
+{
+    return make_int3(min(a.x,b.x), min(a.y,b.y), min(a.z,b.z));
+}
+inline __host__ __device__ int4 min(int4 a, int4 b)
+{
+    return make_int4(min(a.x,b.x), min(a.y,b.y), min(a.z,b.z), min(a.w,b.w));
+}
+
+inline __host__ __device__ uint2 min(uint2 a, uint2 b)
+{
+    return make_uint2(min(a.x,b.x), min(a.y,b.y));
+}
+inline __host__ __device__ uint3 min(uint3 a, uint3 b)
+{
+    return make_uint3(min(a.x,b.x), min(a.y,b.y), min(a.z,b.z));
+}
+inline __host__ __device__ uint4 min(uint4 a, uint4 b)
+{
+    return make_uint4(min(a.x,b.x), min(a.y,b.y), min(a.z,b.z), min(a.w,b.w));
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// max
+////////////////////////////////////////////////////////////////////////////////
+
+inline __host__ __device__ float2 fmaxf(float2 a, float2 b)
+{
+	return make_float2(fmaxf(a.x,b.x), fmaxf(a.y,b.y));
+}
+inline __host__ __device__ float3 fmaxf(float3 a, float3 b)
+{
+	return make_float3(fmaxf(a.x,b.x), fmaxf(a.y,b.y), fmaxf(a.z,b.z));
+}
+inline __host__ __device__ float4 fmaxf(float4 a, float4 b)
+{
+	return make_float4(fmaxf(a.x,b.x), fmaxf(a.y,b.y), fmaxf(a.z,b.z), fmaxf(a.w,b.w));
+}
+
+inline __host__ __device__ int2 max(int2 a, int2 b)
+{
+    return make_int2(max(a.x,b.x), max(a.y,b.y));
+}
+inline __host__ __device__ int3 max(int3 a, int3 b)
+{
+    return make_int3(max(a.x,b.x), max(a.y,b.y), max(a.z,b.z));
+}
+inline __host__ __device__ int4 max(int4 a, int4 b)
+{
+    return make_int4(max(a.x,b.x), max(a.y,b.y), max(a.z,b.z), max(a.w,b.w));
+}
+
+inline __host__ __device__ uint2 max(uint2 a, uint2 b)
+{
+    return make_uint2(max(a.x,b.x), max(a.y,b.y));
+}
+inline __host__ __device__ uint3 max(uint3 a, uint3 b)
+{
+    return make_uint3(max(a.x,b.x), max(a.y,b.y), max(a.z,b.z));
+}
+inline __host__ __device__ uint4 max(uint4 a, uint4 b)
+{
+    return make_uint4(max(a.x,b.x), max(a.y,b.y), max(a.z,b.z), max(a.w,b.w));
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// lerp
+// - linear interpolation between a and b, based on value t in [0, 1] range
+////////////////////////////////////////////////////////////////////////////////
+
+inline __device__ __host__ float lerp(float a, float b, float t)
+{
+    return a + t*(b-a);
+}
+inline __device__ __host__ float2 lerp(float2 a, float2 b, float t)
+{
+    return a + t*(b-a);
+}
+inline __device__ __host__ float3 lerp(float3 a, float3 b, float t)
+{
+    return a + t*(b-a);
+}
+inline __device__ __host__ float4 lerp(float4 a, float4 b, float t)
+{
+    return a + t*(b-a);
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// clamp
+// - clamp the value v to be in the range [a, b]
+////////////////////////////////////////////////////////////////////////////////
+
+inline __device__ __host__ float clamp(float f, float a, float b)
+{
+    return fmaxf(a, fminf(f, b));
+}
+inline __device__ __host__ int clamp(int f, int a, int b)
+{
+    return max(a, min(f, b));
+}
+inline __device__ __host__ uint clamp(uint f, uint a, uint b)
+{
+    return max(a, min(f, b));
+}
+
+inline __device__ __host__ float2 clamp(float2 v, float a, float b)
+{
+    return make_float2(clamp(v.x, a, b), clamp(v.y, a, b));
+}
+inline __device__ __host__ float2 clamp(float2 v, float2 a, float2 b)
+{
+    return make_float2(clamp(v.x, a.x, b.x), clamp(v.y, a.y, b.y));
+}
+inline __device__ __host__ float3 clamp(float3 v, float a, float b)
+{
+    return make_float3(clamp(v.x, a, b), clamp(v.y, a, b), clamp(v.z, a, b));
+}
+inline __device__ __host__ float3 clamp(float3 v, float3 a, float3 b)
+{
+    return make_float3(clamp(v.x, a.x, b.x), clamp(v.y, a.y, b.y), clamp(v.z, a.z, b.z));
+}
+inline __device__ __host__ float4 clamp(float4 v, float a, float b)
+{
+    return make_float4(clamp(v.x, a, b), clamp(v.y, a, b), clamp(v.z, a, b), clamp(v.w, a, b));
+}
+inline __device__ __host__ float4 clamp(float4 v, float4 a, float4 b)
+{
+    return make_float4(clamp(v.x, a.x, b.x), clamp(v.y, a.y, b.y), clamp(v.z, a.z, b.z), clamp(v.w, a.w, b.w));
+}
+
+inline __device__ __host__ int2 clamp(int2 v, int a, int b)
+{
+    return make_int2(clamp(v.x, a, b), clamp(v.y, a, b));
+}
+inline __device__ __host__ int2 clamp(int2 v, int2 a, int2 b)
+{
+    return make_int2(clamp(v.x, a.x, b.x), clamp(v.y, a.y, b.y));
+}
+inline __device__ __host__ int3 clamp(int3 v, int a, int b)
+{
+    return make_int3(clamp(v.x, a, b), clamp(v.y, a, b), clamp(v.z, a, b));
+}
+inline __device__ __host__ int3 clamp(int3 v, int3 a, int3 b)
+{
+    return make_int3(clamp(v.x, a.x, b.x), clamp(v.y, a.y, b.y), clamp(v.z, a.z, b.z));
+}
+inline __device__ __host__ int4 clamp(int4 v, int a, int b)
+{
+    return make_int4(clamp(v.x, a, b), clamp(v.y, a, b), clamp(v.z, a, b), clamp(v.w, a, b));
+}
+inline __device__ __host__ int4 clamp(int4 v, int4 a, int4 b)
+{
+    return make_int4(clamp(v.x, a.x, b.x), clamp(v.y, a.y, b.y), clamp(v.z, a.z, b.z), clamp(v.w, a.w, b.w));
+}
+
+inline __device__ __host__ uint2 clamp(uint2 v, uint a, uint b)
+{
+    return make_uint2(clamp(v.x, a, b), clamp(v.y, a, b));
+}
+inline __device__ __host__ uint2 clamp(uint2 v, uint2 a, uint2 b)
+{
+    return make_uint2(clamp(v.x, a.x, b.x), clamp(v.y, a.y, b.y));
+}
+inline __device__ __host__ uint3 clamp(uint3 v, uint a, uint b)
+{
+    return make_uint3(clamp(v.x, a, b), clamp(v.y, a, b), clamp(v.z, a, b));
+}
+inline __device__ __host__ uint3 clamp(uint3 v, uint3 a, uint3 b)
+{
+    return make_uint3(clamp(v.x, a.x, b.x), clamp(v.y, a.y, b.y), clamp(v.z, a.z, b.z));
+}
+inline __device__ __host__ uint4 clamp(uint4 v, uint a, uint b)
+{
+    return make_uint4(clamp(v.x, a, b), clamp(v.y, a, b), clamp(v.z, a, b), clamp(v.w, a, b));
+}
+inline __device__ __host__ uint4 clamp(uint4 v, uint4 a, uint4 b)
+{
+    return make_uint4(clamp(v.x, a.x, b.x), clamp(v.y, a.y, b.y), clamp(v.z, a.z, b.z), clamp(v.w, a.w, b.w));
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// dot product
+////////////////////////////////////////////////////////////////////////////////
+
+inline __host__ __device__ float dot(float2 a, float2 b)
+{ 
+    return a.x * b.x + a.y * b.y;
+}
+inline __host__ __device__ float dot(float3 a, float3 b)
+{ 
+    return a.x * b.x + a.y * b.y + a.z * b.z;
+}
+inline __host__ __device__ float dot(float4 a, float4 b)
+{ 
+    return a.x * b.x + a.y * b.y + a.z * b.z + a.w * b.w;
+}
+
+inline __host__ __device__ int dot(int2 a, int2 b)
+{ 
+    return a.x * b.x + a.y * b.y;
+}
+inline __host__ __device__ int dot(int3 a, int3 b)
+{ 
+    return a.x * b.x + a.y * b.y + a.z * b.z;
+}
+inline __host__ __device__ int dot(int4 a, int4 b)
+{ 
+    return a.x * b.x + a.y * b.y + a.z * b.z + a.w * b.w;
+}
+
+inline __host__ __device__ uint dot(uint2 a, uint2 b)
+{ 
+    return a.x * b.x + a.y * b.y;
+}
+inline __host__ __device__ uint dot(uint3 a, uint3 b)
+{ 
+    return a.x * b.x + a.y * b.y + a.z * b.z;
+}
+inline __host__ __device__ uint dot(uint4 a, uint4 b)
+{ 
+    return a.x * b.x + a.y * b.y + a.z * b.z + a.w * b.w;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// length
+////////////////////////////////////////////////////////////////////////////////
+
+inline __host__ __device__ float length(float2 v)
+{
+    return sqrtf(dot(v, v));
+}
+inline __host__ __device__ float length(float3 v)
+{
+    return sqrtf(dot(v, v));
+}
+inline __host__ __device__ float length(float4 v)
+{
+    return sqrtf(dot(v, v));
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// normalize
+////////////////////////////////////////////////////////////////////////////////
+
+inline __host__ __device__ float2 normalize(float2 v)
+{
+    float invLen = rsqrtf(dot(v, v));
+    return v * invLen;
+}
+inline __host__ __device__ float3 normalize(float3 v)
+{
+    float invLen = rsqrtf(dot(v, v));
+    return v * invLen;
+}
+inline __host__ __device__ float4 normalize(float4 v)
+{
+    float invLen = rsqrtf(dot(v, v));
+    return v * invLen;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// floor
+////////////////////////////////////////////////////////////////////////////////
+
+inline __host__ __device__ float2 floorf(float2 v)
+{
+    return make_float2(floorf(v.x), floorf(v.y));
+}
+inline __host__ __device__ float3 floorf(float3 v)
+{
+    return make_float3(floorf(v.x), floorf(v.y), floorf(v.z));
+}
+inline __host__ __device__ float4 floorf(float4 v)
+{
+    return make_float4(floorf(v.x), floorf(v.y), floorf(v.z), floorf(v.w));
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// frac - returns the fractional portion of a scalar or each vector component
+////////////////////////////////////////////////////////////////////////////////
+
+inline __host__ __device__ float fracf(float v)
+{
+    return v - floorf(v);
+}
+inline __host__ __device__ float2 fracf(float2 v)
+{
+    return make_float2(fracf(v.x), fracf(v.y));
+}
+inline __host__ __device__ float3 fracf(float3 v)
+{
+    return make_float3(fracf(v.x), fracf(v.y), fracf(v.z));
+}
+inline __host__ __device__ float4 fracf(float4 v)
+{
+    return make_float4(fracf(v.x), fracf(v.y), fracf(v.z), fracf(v.w));
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// fmod
+////////////////////////////////////////////////////////////////////////////////
+
+inline __host__ __device__ float2 fmodf(float2 a, float2 b)
+{
+    return make_float2(fmodf(a.x, b.x), fmodf(a.y, b.y));
+}
+inline __host__ __device__ float3 fmodf(float3 a, float3 b)
+{
+    return make_float3(fmodf(a.x, b.x), fmodf(a.y, b.y), fmodf(a.z, b.z));
+}
+inline __host__ __device__ float4 fmodf(float4 a, float4 b)
+{
+    return make_float4(fmodf(a.x, b.x), fmodf(a.y, b.y), fmodf(a.z, b.z), fmodf(a.w, b.w));
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// absolute value
+////////////////////////////////////////////////////////////////////////////////
+
+inline __host__ __device__ float2 fabs(float2 v)
+{
+	return make_float2(fabs(v.x), fabs(v.y));
+}
+inline __host__ __device__ float3 fabs(float3 v)
+{
+	return make_float3(fabs(v.x), fabs(v.y), fabs(v.z));
+}
+inline __host__ __device__ float4 fabs(float4 v)
+{
+	return make_float4(fabs(v.x), fabs(v.y), fabs(v.z), fabs(v.w));
+}
+
+inline __host__ __device__ int2 abs(int2 v)
+{
+	return make_int2(abs(v.x), abs(v.y));
+}
+inline __host__ __device__ int3 abs(int3 v)
+{
+	return make_int3(abs(v.x), abs(v.y), abs(v.z));
+}
+inline __host__ __device__ int4 abs(int4 v)
+{
+	return make_int4(abs(v.x), abs(v.y), abs(v.z), abs(v.w));
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// reflect
+// - returns reflection of incident ray I around surface normal N
+// - N should be normalized, reflected vector's length is equal to length of I
+////////////////////////////////////////////////////////////////////////////////
+
+inline __host__ __device__ float3 reflect(float3 i, float3 n)
+{
+	return i - 2.0f * n * dot(n,i);
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// cross product
+////////////////////////////////////////////////////////////////////////////////
+
+inline __host__ __device__ float3 cross(float3 a, float3 b)
+{ 
+    return make_float3(a.y*b.z - a.z*b.y, a.z*b.x - a.x*b.z, a.x*b.y - a.y*b.x); 
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// smoothstep
+// - returns 0 if x < a
+// - returns 1 if x > b
+// - otherwise returns smooth interpolation between 0 and 1 based on x
+////////////////////////////////////////////////////////////////////////////////
+
+inline __device__ __host__ float smoothstep(float a, float b, float x)
+{
+	float y = clamp((x - a) / (b - a), 0.0f, 1.0f);
+	return (y*y*(3.0f - (2.0f*y)));
+}
+inline __device__ __host__ float2 smoothstep(float2 a, float2 b, float2 x)
+{
+	float2 y = clamp((x - a) / (b - a), 0.0f, 1.0f);
+	return (y*y*(make_float2(3.0f) - (make_float2(2.0f)*y)));
+}
+inline __device__ __host__ float3 smoothstep(float3 a, float3 b, float3 x)
+{
+	float3 y = clamp((x - a) / (b - a), 0.0f, 1.0f);
+	return (y*y*(make_float3(3.0f) - (make_float3(2.0f)*y)));
+}
+inline __device__ __host__ float4 smoothstep(float4 a, float4 b, float4 x)
+{
+	float4 y = clamp((x - a) / (b - a), 0.0f, 1.0f);
+	return (y*y*(make_float4(3.0f) - (make_float4(2.0f)*y)));
+}
+
+#endif
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/dynamic_bitset.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/dynamic_bitset.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/dynamic_bitset.h	(revision 15840)
@@ -0,0 +1,156 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+/***********************************************************************
+ * Author: Vincent Rabaud
+ *************************************************************************/
+
+#ifndef FLANN_DYNAMIC_BITSET_H_
+#define FLANN_DYNAMIC_BITSET_H_
+
+//#define FLANN_USE_BOOST 1
+#if FLANN_USE_BOOST
+#include <boost/dynamic_bitset.hpp>
+typedef boost::dynamic_bitset<> DynamicBitset;
+#else
+
+#include <limits.h>
+
+#include "flann/algorithms/dist.h"
+
+namespace flann {
+
+/** Class re-implementing the boost version of it
+ * This helps not depending on boost, it also does not do the bound checks
+ * and has a way to reset a block for speed
+ */
+class DynamicBitset
+{
+public:
+    /** @param default constructor
+     */
+    DynamicBitset()
+    {
+    }
+
+    /** @param only constructor we use in our code
+     * @param the size of the bitset (in bits)
+     */
+    DynamicBitset(size_t size)
+    {
+        resize(size);
+        reset();
+    }
+
+    /** Sets all the bits to 0
+     */
+    void clear()
+    {
+        std::fill(bitset_.begin(), bitset_.end(), 0);
+    }
+
+    /** @brief checks if the bitset is empty
+     * @return true if the bitset is empty
+     */
+    bool empty() const
+    {
+        return bitset_.empty();
+    }
+
+    /** @param set all the bits to 0
+     */
+    void reset()
+    {
+        std::fill(bitset_.begin(), bitset_.end(), 0);
+    }
+
+    /** @brief set one bit to 0
+     * @param
+     */
+    void reset(size_t index)
+    {
+        bitset_[index / cell_bit_size_] &= ~(size_t(1) << (index % cell_bit_size_));
+    }
+
+    /** @brief sets a specific bit to 0, and more bits too
+     * This function is useful when resetting a given set of bits so that the
+     * whole bitset ends up being 0: if that's the case, we don't care about setting
+     * other bits to 0
+     * @param
+     */
+    void reset_block(size_t index)
+    {
+        bitset_[index / cell_bit_size_] = 0;
+    }
+
+    /** @param resize the bitset so that it contains at least size bits
+     * @param size
+     */
+    void resize(size_t size)
+    {
+        size_ = size;
+        bitset_.resize(size / cell_bit_size_ + 1);
+    }
+
+    /** @param set a bit to true
+     * @param index the index of the bit to set to 1
+     */
+    void set(size_t index)
+    {
+        bitset_[index / cell_bit_size_] |= size_t(1) << (index % cell_bit_size_);
+    }
+
+    /** @param gives the number of contained bits
+     */
+    size_t size() const
+    {
+        return size_;
+    }
+
+    /** @param check if a bit is set
+     * @param index the index of the bit to check
+     * @return true if the bit is set
+     */
+    bool test(size_t index) const
+    {
+        return (bitset_[index / cell_bit_size_] & (size_t(1) << (index % cell_bit_size_))) != 0;
+    }
+
+private:
+    std::vector<size_t> bitset_;
+    size_t size_;
+    static const unsigned int cell_bit_size_ = CHAR_BIT * sizeof(size_t);
+};
+
+} // namespace flann
+
+#endif
+
+#endif // FLANN_DYNAMIC_BITSET_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/heap.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/heap.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/heap.h	(revision 15840)
@@ -0,0 +1,165 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_HEAP_H_
+#define FLANN_HEAP_H_
+
+#include <algorithm>
+#include <vector>
+
+namespace flann
+{
+
+/**
+ * Priority Queue Implementation
+ *
+ * The priority queue is implemented with a heap.  A heap is a complete
+ * (full) binary tree in which each parent is less than both of its
+ * children, but the order of the children is unspecified.
+ */
+template <typename T>
+class Heap
+{
+
+    /**
+     * Storage array for the heap.
+     * Type T must be comparable.
+     */
+    std::vector<T> heap;
+    int length;
+
+    /**
+     * Number of element in the heap
+     */
+    int count;
+
+
+
+public:
+    /**
+     * Constructor.
+     *
+     * Params:
+     *     size = heap size
+     */
+
+    Heap(int size)
+    {
+        length = size;
+        heap.reserve(length);
+        count = 0;
+    }
+
+    /**
+     *
+     * Returns: heap size
+     */
+    int size()
+    {
+        return count;
+    }
+
+    /**
+     * Tests if the heap is empty
+     *
+     * Returns: true is heap empty, false otherwise
+     */
+    bool empty()
+    {
+        return size()==0;
+    }
+
+    /**
+     * Clears the heap.
+     */
+    void clear()
+    {
+        heap.clear();
+        count = 0;
+    }
+
+    struct CompareT
+    {
+        bool operator()(const T& t_1, const T& t_2) const
+        {
+            return t_2 < t_1;
+        }
+    };
+
+    /**
+     * Insert a new element in the heap.
+     *
+     * We select the next empty leaf node, and then keep moving any larger
+     * parents down until the right location is found to store this element.
+     *
+     * Params:
+     *     value = the new element to be inserted in the heap
+     */
+    void insert(T value)
+    {
+        /* If heap is full, then return without adding this element. */
+        if (count == length) {
+            return;
+        }
+
+        heap.push_back(value);
+        static CompareT compareT;
+        std::push_heap(heap.begin(), heap.end(), compareT);
+        ++count;
+    }
+
+
+
+    /**
+     * Returns the node of minimum value from the heap (top of the heap).
+     *
+     * Params:
+     *     value = out parameter used to return the min element
+     * Returns: false if heap empty
+     */
+    bool popMin(T& value)
+    {
+        if (count == 0) {
+            return false;
+        }
+
+        value = heap[0];
+        static CompareT compareT;
+        std::pop_heap(heap.begin(), heap.end(), compareT);
+        heap.pop_back();
+        --count;
+
+        return true;  /* Return old last node. */
+    }
+};
+
+}
+
+#endif //FLANN_HEAP_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/logger.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/logger.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/logger.h	(revision 15840)
@@ -0,0 +1,130 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_LOGGER_H
+#define FLANN_LOGGER_H
+
+#include <stdio.h>
+#include <stdarg.h>
+
+#include "flann/defines.h"
+
+
+namespace flann
+{
+
+class Logger
+{
+    Logger() : stream(stdout), logLevel(FLANN_LOG_WARN) {}
+
+    ~Logger()
+    {
+        if ((stream!=NULL)&&(stream!=stdout)) {
+            fclose(stream);
+        }
+    }
+
+    static Logger& instance()
+    {
+        static Logger logger;
+        return logger;
+    }
+
+    void _setDestination(const char* name)
+    {
+        if (name==NULL) {
+            stream = stdout;
+        }
+        else {
+            stream = fopen(name,"w");
+            if (stream == NULL) {
+                stream = stdout;
+            }
+        }
+    }
+
+    int _log(int level, const char* fmt, va_list arglist)
+    {
+        if (level > logLevel ) return -1;
+        int ret = vfprintf(stream, fmt, arglist);
+        return ret;
+    }
+
+public:
+    /**
+     * Sets the logging level. All messages with lower priority will be ignored.
+     * @param level Logging level
+     */
+    static void setLevel(int level) { instance().logLevel = level; }
+
+    /**
+     * Sets the logging destination
+     * @param name Filename or NULL for console
+     */
+    static void setDestination(const char* name) { instance()._setDestination(name); }
+
+    /**
+     * Print log message
+     * @param level Log level
+     * @param fmt Message format
+     * @return
+     */
+    static int log(int level, const char* fmt, ...)
+    {
+        va_list arglist;
+        va_start(arglist, fmt);
+        int ret = instance()._log(level,fmt,arglist);
+        va_end(arglist);
+        return ret;
+    }
+
+#define LOG_METHOD(NAME,LEVEL) \
+    static int NAME(const char* fmt, ...) \
+    { \
+        va_list ap; \
+        va_start(ap, fmt); \
+        int ret = instance()._log(LEVEL, fmt, ap); \
+        va_end(ap); \
+        return ret; \
+    }
+
+    LOG_METHOD(fatal, FLANN_LOG_FATAL)
+    LOG_METHOD(error, FLANN_LOG_ERROR)
+    LOG_METHOD(warn, FLANN_LOG_WARN)
+    LOG_METHOD(info, FLANN_LOG_INFO)
+
+private:
+    FILE* stream;
+    int logLevel;
+};
+
+}
+
+#endif //FLANN_LOGGER_H
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/lsh_table.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/lsh_table.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/lsh_table.h	(revision 15840)
@@ -0,0 +1,477 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+/***********************************************************************
+ * Author: Vincent Rabaud
+ *************************************************************************/
+
+#ifndef FLANN_LSH_TABLE_H_
+#define FLANN_LSH_TABLE_H_
+
+#include <algorithm>
+#include <iostream>
+#include <iomanip>
+#include <limits.h>
+// TODO as soon as we use C++0x, use the code in USE_UNORDERED_MAP
+#if USE_UNORDERED_MAP
+#include <unordered_map>
+#else
+#include <map>
+#endif
+#include <math.h>
+#include <stddef.h>
+
+#include "flann/util/dynamic_bitset.h"
+#include "flann/util/matrix.h"
+
+namespace flann
+{
+
+namespace lsh
+{
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** What is stored in an LSH bucket
+ */
+typedef uint32_t FeatureIndex;
+/** The id from which we can get a bucket back in an LSH table
+ */
+typedef unsigned int BucketKey;
+
+/** A bucket in an LSH table
+ */
+typedef std::vector<FeatureIndex> Bucket;
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** POD for stats about an LSH table
+ */
+struct LshStats
+{
+    std::vector<unsigned int> bucket_sizes_;
+    size_t n_buckets_;
+    size_t bucket_size_mean_;
+    size_t bucket_size_median_;
+    size_t bucket_size_min_;
+    size_t bucket_size_max_;
+    size_t bucket_size_std_dev;
+    /** Each contained vector contains three value: beginning/end for interval, number of elements in the bin
+     */
+    std::vector<std::vector<unsigned int> > size_histogram_;
+};
+
+/** Overload the << operator for LshStats
+ * @param out the streams
+ * @param stats the stats to display
+ * @return the streams
+ */
+inline std::ostream& operator <<(std::ostream& out, const LshStats& stats)
+{
+    size_t w = 20;
+    out << "Lsh Table Stats:\n" << std::setw(w) << std::setiosflags(std::ios::right) << "N buckets : "
+    << stats.n_buckets_ << "\n" << std::setw(w) << std::setiosflags(std::ios::right) << "mean size : "
+    << std::setiosflags(std::ios::left) << stats.bucket_size_mean_ << "\n" << std::setw(w)
+    << std::setiosflags(std::ios::right) << "median size : " << stats.bucket_size_median_ << "\n" << std::setw(w)
+    << std::setiosflags(std::ios::right) << "min size : " << std::setiosflags(std::ios::left)
+    << stats.bucket_size_min_ << "\n" << std::setw(w) << std::setiosflags(std::ios::right) << "max size : "
+    << std::setiosflags(std::ios::left) << stats.bucket_size_max_;
+
+    // Display the histogram
+    out << std::endl << std::setw(w) << std::setiosflags(std::ios::right) << "histogram : "
+    << std::setiosflags(std::ios::left);
+    for (std::vector<std::vector<unsigned int> >::const_iterator iterator = stats.size_histogram_.begin(), end =
+             stats.size_histogram_.end(); iterator != end; ++iterator) out << (*iterator)[0] << "-" << (*iterator)[1] << ": " << (*iterator)[2] << ",  ";
+
+    return out;
+}
+
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** Lsh hash table. As its key is a sub-feature, and as usually
+ * the size of it is pretty small, we keep it as a continuous memory array.
+ * The value is an index in the corpus of features (we keep it as an unsigned
+ * int for pure memory reasons, it could be a size_t)
+ */
+template<typename ElementType>
+class LshTable
+{
+public:
+    /** A container of all the feature indices. Optimized for space
+     */
+#if USE_UNORDERED_MAP
+    typedef std::unordered_map<BucketKey, Bucket> BucketsSpace;
+#else
+    typedef std::map<BucketKey, Bucket> BucketsSpace;
+#endif
+
+    /** A container of all the feature indices. Optimized for speed
+     */
+    typedef std::vector<Bucket> BucketsSpeed;
+
+    /** Default constructor
+     */
+    LshTable()
+    {
+    }
+
+    /** Default constructor
+     * Create the mask and allocate the memory
+     * @param feature_size is the size of the feature (considered as a ElementType[])
+     * @param key_size is the number of bits that are turned on in the feature
+     */
+    LshTable(unsigned int /*feature_size*/, unsigned int /*key_size*/)
+    {
+        std::cerr << "LSH is not implemented for that type" << std::endl;
+        throw;
+    }
+
+    /** Add a feature to the table
+     * @param value the value to store for that feature
+     * @param feature the feature itself
+     */
+    void add(unsigned int value, const ElementType* feature)
+    {
+        // Add the value to the corresponding bucket
+        BucketKey key = getKey(feature);
+
+        switch (speed_level_) {
+        case kArray:
+            // That means we get the buckets from an array
+            buckets_speed_[key].push_back(value);
+            break;
+        case kBitsetHash:
+            // That means we can check the bitset for the presence of a key
+            key_bitset_.set(key);
+            buckets_space_[key].push_back(value);
+            break;
+        case kHash:
+        {
+            // That means we have to check for the hash table for the presence of a key
+            buckets_space_[key].push_back(value);
+            break;
+        }
+        }
+    }
+
+    /** Add a set of features to the table
+     * @param dataset the values to store
+     */
+    void add(Matrix<ElementType> dataset)
+    {
+#if USE_UNORDERED_MAP
+        if (!use_speed_) buckets_space_.rehash((buckets_space_.size() + dataset.rows) * 1.2);
+#endif
+        // Add the features to the table
+        for (unsigned int i = 0; i < dataset.rows; ++i) add(i, dataset[i]);
+        // Now that the table is full, optimize it for speed/space
+        optimize();
+    }
+
+    /** Get a bucket given the key
+     * @param key
+     * @return
+     */
+    inline const Bucket* getBucketFromKey(BucketKey key) const
+    {
+        // Generate other buckets
+        switch (speed_level_) {
+        case kArray:
+            // That means we get the buckets from an array
+            return &buckets_speed_[key];
+            break;
+        case kBitsetHash:
+            // That means we can check the bitset for the presence of a key
+            if (key_bitset_.test(key)) return &buckets_space_.find(key)->second;
+            else return 0;
+            break;
+        case kHash:
+        {
+            // That means we have to check for the hash table for the presence of a key
+            BucketsSpace::const_iterator bucket_it, bucket_end = buckets_space_.end();
+            bucket_it = buckets_space_.find(key);
+            // Stop here if that bucket does not exist
+            if (bucket_it == bucket_end) return 0;
+            else return &bucket_it->second;
+            break;
+        }
+        }
+        return 0;
+    }
+
+    /** Compute the sub-signature of a feature
+     */
+    size_t getKey(const ElementType* /*feature*/) const
+    {
+        std::cerr << "LSH is not implemented for that type" << std::endl;
+        throw;
+        return 1;
+    }
+
+    /** Get statistics about the table
+     * @return
+     */
+    LshStats getStats() const;
+
+private:
+    /** defines the speed fo the implementation
+     * kArray uses a vector for storing data
+     * kBitsetHash uses a hash map but checks for the validity of a key with a bitset
+     * kHash uses a hash map only
+     */
+    enum SpeedLevel
+    {
+        kArray, kBitsetHash, kHash
+    };
+
+    /** Initialize some variables
+     */
+    void initialize(size_t key_size)
+    {
+        speed_level_ = kHash;
+        key_size_ = key_size;
+    }
+
+    /** Optimize the table for speed/space
+     */
+    void optimize()
+    {
+        // If we are already using the fast storage, no need to do anything
+        if (speed_level_ == kArray) return;
+
+        // Use an array if it will be more than half full
+        if (buckets_space_.size() > (unsigned int)((1 << key_size_) / 2)) {
+            speed_level_ = kArray;
+            // Fill the array version of it
+            buckets_speed_.resize(1 << key_size_);
+            for (BucketsSpace::const_iterator key_bucket = buckets_space_.begin(); key_bucket != buckets_space_.end(); ++key_bucket) buckets_speed_[key_bucket->first] = key_bucket->second;
+
+            // Empty the hash table
+            buckets_space_.clear();
+            return;
+        }
+
+        // If the bitset is going to use less than 10% of the RAM of the hash map (at least 1 size_t for the key and two
+        // for the vector) or less than 512MB (key_size_ <= 30)
+        if (((std::max(buckets_space_.size(), buckets_speed_.size()) * CHAR_BIT * 3 * sizeof(BucketKey)) / 10
+             >= size_t(1 << key_size_)) || (key_size_ <= 32)) {
+            speed_level_ = kBitsetHash;
+            key_bitset_.resize(1 << key_size_);
+            key_bitset_.reset();
+            // Try with the BucketsSpace
+            for (BucketsSpace::const_iterator key_bucket = buckets_space_.begin(); key_bucket != buckets_space_.end(); ++key_bucket) key_bitset_.set(key_bucket->first);
+        }
+        else {
+            speed_level_ = kHash;
+            key_bitset_.clear();
+        }
+    }
+
+    /** The vector of all the buckets if they are held for speed
+     */
+    BucketsSpeed buckets_speed_;
+
+    /** The hash table of all the buckets in case we cannot use the speed version
+     */
+    BucketsSpace buckets_space_;
+
+    /** What is used to store the data */
+    SpeedLevel speed_level_;
+
+    /** If the subkey is small enough, it will keep track of which subkeys are set through that bitset
+     * That is just a speedup so that we don't look in the hash table (which can be mush slower that checking a bitset)
+     */
+    DynamicBitset key_bitset_;
+
+    /** The size of the sub-signature in bits
+     */
+    unsigned int key_size_;
+
+    // Members only used for the unsigned char specialization
+    /** The mask to apply to a feature to get the hash key
+     * Only used in the unsigned char case
+     */
+    std::vector<size_t> mask_;
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// Specialization for unsigned char
+
+template<>
+inline LshTable<unsigned char>::LshTable(unsigned int feature_size, unsigned int subsignature_size)
+{
+    initialize(subsignature_size);
+    // Allocate the mask
+    mask_ = std::vector<size_t>((size_t)ceil((float)(feature_size * sizeof(char)) / (float)sizeof(size_t)), 0);
+
+    // A bit brutal but fast to code
+    std::vector<size_t> indices(feature_size * CHAR_BIT);
+    for (size_t i = 0; i < feature_size * CHAR_BIT; ++i) indices[i] = i;
+    std::random_shuffle(indices.begin(), indices.end());
+
+    // Generate a random set of order of subsignature_size_ bits
+    for (unsigned int i = 0; i < key_size_; ++i) {
+        size_t index = indices[i];
+
+        // Set that bit in the mask
+        size_t divisor = CHAR_BIT * sizeof(size_t);
+        size_t idx = index / divisor; //pick the right size_t index
+        mask_[idx] |= size_t(1) << (index % divisor); //use modulo to find the bit offset
+    }
+
+    // Set to 1 if you want to display the mask for debug
+#if 0
+    {
+        size_t bcount = 0;
+        BOOST_FOREACH(size_t mask_block, mask_){
+            out << std::setw(sizeof(size_t) * CHAR_BIT / 4) << std::setfill('0') << std::hex << mask_block
+                << std::endl;
+            bcount += __builtin_popcountll(mask_block);
+        }
+        out << "bit count : " << std::dec << bcount << std::endl;
+        out << "mask size : " << mask_.size() << std::endl;
+        return out;
+    }
+#endif
+}
+
+/** Return the Subsignature of a feature
+ * @param feature the feature to analyze
+ */
+template<>
+inline size_t LshTable<unsigned char>::getKey(const unsigned char* feature) const
+{
+    // no need to check if T is dividable by sizeof(size_t) like in the Hamming
+    // distance computation as we have a mask
+    const size_t* feature_block_ptr = reinterpret_cast<const size_t*> (feature);
+
+    // Figure out the subsignature of the feature
+    // Given the feature ABCDEF, and the mask 001011, the output will be
+    // 000CEF
+    size_t subsignature = 0;
+    size_t bit_index = 1;
+
+    for (std::vector<size_t>::const_iterator pmask_block = mask_.begin(); pmask_block != mask_.end(); ++pmask_block) {
+        // get the mask and signature blocks
+        size_t feature_block = *feature_block_ptr;
+        size_t mask_block = *pmask_block;
+        while (mask_block) {
+            // Get the lowest set bit in the mask block
+            size_t lowest_bit = mask_block & (-(ptrdiff_t)mask_block);
+            // Add it to the current subsignature if necessary
+            subsignature += (feature_block & lowest_bit) ? bit_index : 0;
+            // Reset the bit in the mask block
+            mask_block ^= lowest_bit;
+            // increment the bit index for the subsignature
+            bit_index <<= 1;
+        }
+        // Check the next feature block
+        ++feature_block_ptr;
+    }
+    return subsignature;
+}
+
+template<>
+inline LshStats LshTable<unsigned char>::getStats() const
+{
+    LshStats stats;
+    stats.bucket_size_mean_ = 0;
+    if ((buckets_speed_.empty()) && (buckets_space_.empty())) {
+        stats.n_buckets_ = 0;
+        stats.bucket_size_median_ = 0;
+        stats.bucket_size_min_ = 0;
+        stats.bucket_size_max_ = 0;
+        return stats;
+    }
+
+    if (!buckets_speed_.empty()) {
+        for (BucketsSpeed::const_iterator pbucket = buckets_speed_.begin(); pbucket != buckets_speed_.end(); ++pbucket) {
+            stats.bucket_sizes_.push_back(pbucket->size());
+            stats.bucket_size_mean_ += pbucket->size();
+        }
+        stats.bucket_size_mean_ /= buckets_speed_.size();
+        stats.n_buckets_ = buckets_speed_.size();
+    }
+    else {
+        for (BucketsSpace::const_iterator x = buckets_space_.begin(); x != buckets_space_.end(); ++x) {
+            stats.bucket_sizes_.push_back(x->second.size());
+            stats.bucket_size_mean_ += x->second.size();
+        }
+        stats.bucket_size_mean_ /= buckets_space_.size();
+        stats.n_buckets_ = buckets_space_.size();
+    }
+
+    std::sort(stats.bucket_sizes_.begin(), stats.bucket_sizes_.end());
+
+    //  BOOST_FOREACH(int size, stats.bucket_sizes_)
+    //          std::cout << size << " ";
+    //  std::cout << std::endl;
+    stats.bucket_size_median_ = stats.bucket_sizes_[stats.bucket_sizes_.size() / 2];
+    stats.bucket_size_min_ = stats.bucket_sizes_.front();
+    stats.bucket_size_max_ = stats.bucket_sizes_.back();
+
+    // TODO compute mean and std
+    /*float mean, stddev;
+       stats.bucket_size_mean_ = mean;
+       stats.bucket_size_std_dev = stddev;*/
+
+    // Include a histogram of the buckets
+    unsigned int bin_start = 0;
+    unsigned int bin_end = 20;
+    bool is_new_bin = true;
+    for (std::vector<unsigned int>::iterator iterator = stats.bucket_sizes_.begin(), end = stats.bucket_sizes_.end(); iterator
+         != end; )
+        if (*iterator < bin_end) {
+            if (is_new_bin) {
+                stats.size_histogram_.push_back(std::vector<unsigned int>(3, 0));
+                stats.size_histogram_.back()[0] = bin_start;
+                stats.size_histogram_.back()[1] = bin_end - 1;
+                is_new_bin = false;
+            }
+            ++stats.size_histogram_.back()[2];
+            ++iterator;
+        }
+        else {
+            bin_start += 20;
+            bin_end += 20;
+            is_new_bin = true;
+        }
+
+    return stats;
+}
+
+// End the two namespaces
+}
+}
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+#endif /* FLANN_LSH_TABLE_H_ */
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/matrix.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/matrix.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/matrix.h	(revision 15840)
@@ -0,0 +1,130 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_DATASET_H_
+#define FLANN_DATASET_H_
+
+#include "flann/general.h"
+#include <stdio.h>
+
+namespace flann
+{
+
+typedef unsigned char uchar;
+
+class Matrix_
+{
+public:
+
+	Matrix_() : rows(0), cols(0), stride(0), data(NULL)
+	{
+	};
+
+    Matrix_(void* data_, size_t rows_, size_t cols_, flann_datatype_t type, size_t stride_ = 0) :
+        rows(rows_), cols(cols_),  stride(stride_)
+    {
+    	data = static_cast<uchar*>(data_);
+    	if (stride==0) stride = flann_datatype_size(type)*cols;
+    }
+
+    /**
+     * Operator that returns a (pointer to a) row of the data.
+     */
+    inline void* operator[](size_t index) const
+    {
+        return data+index*stride;
+    }
+
+    void* ptr() const
+    {
+        return data;
+    }
+
+    size_t rows;
+    size_t cols;
+    size_t stride;
+    flann_datatype_t type;
+protected:
+    uchar* data;
+
+};
+
+
+/**
+ * Class that implements a simple rectangular matrix stored in a memory buffer and
+ * provides convenient matrix-like access using the [] operators.
+ *
+ * This class has the same memory structure as the un-templated class flann::Matrix_ and
+ * it's directly convertible from it.
+ */
+template <typename T>
+class Matrix : public Matrix_
+{
+public:
+    typedef T type;
+
+    Matrix() : Matrix_()
+    {
+    }
+
+    Matrix(T* data_, size_t rows_, size_t cols_, size_t stride_ = 0) :
+    	Matrix_(data_, rows_, cols_, flann_datatype<T>::value, stride_)
+    {
+    }
+
+    /**
+     * Convenience function for deallocating the storage data.
+     */
+    FLANN_DEPRECATED void free()
+    {
+        fprintf(stderr, "The flann::Matrix<T>::free() method is deprecated "
+                "and it does not do any memory deallocation any more.  You are"
+                "responsible for deallocating the matrix memory (by doing"
+                "'delete[] matrix.ptr()' for example)");
+    }
+
+    /**
+     * Operator that returns a (pointer to a) row of the data.
+     */
+    inline T* operator[](size_t index) const
+    {
+    	return reinterpret_cast<T*>(static_cast<uchar*>(Matrix_::data)+index*stride);
+//    	return (T*)(Matrix_::operator [](index));
+    }
+
+    T* ptr() const
+    {
+    	return reinterpret_cast<T*>(Matrix_::data);
+    }
+};
+
+}
+
+#endif //FLANN_DATASET_H_
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/object_factory.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/object_factory.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/object_factory.h	(revision 15840)
@@ -0,0 +1,91 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_OBJECT_FACTORY_H_
+#define FLANN_OBJECT_FACTORY_H_
+
+#include <map>
+
+namespace flann
+{
+
+class CreatorNotFound
+{
+};
+
+template<typename BaseClass,
+         typename UniqueIdType,
+         typename ObjectCreator = BaseClass* (*)()>
+class ObjectFactory
+{
+    typedef ObjectFactory<BaseClass,UniqueIdType,ObjectCreator> ThisClass;
+    typedef std::map<UniqueIdType, ObjectCreator> ObjectRegistry;
+
+    // singleton class, private constructor
+    ObjectFactory() {}
+
+public:
+
+    bool subscribe(UniqueIdType id, ObjectCreator creator)
+    {
+        if (object_registry.find(id) != object_registry.end()) return false;
+
+        object_registry[id] = creator;
+        return true;
+    }
+
+    bool unregister(UniqueIdType id)
+    {
+        return object_registry.erase(id) == 1;
+    }
+
+    ObjectCreator create(UniqueIdType id)
+    {
+        typename ObjectRegistry::const_iterator iter = object_registry.find(id);
+
+        if (iter == object_registry.end()) {
+            throw CreatorNotFound();
+        }
+
+        return iter->second;
+    }
+
+    static ThisClass& instance()
+    {
+        static ThisClass the_factory;
+        return the_factory;
+    }
+private:
+    ObjectRegistry object_registry;
+};
+
+}
+
+#endif /* FLANN_OBJECT_FACTORY_H_ */
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/params.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/params.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/params.h	(revision 15840)
@@ -0,0 +1,130 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2011  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2011  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+
+#ifndef FLANN_PARAMS_H_
+#define FLANN_PARAMS_H_
+
+#include "any.h"
+#include "flann/general.h"
+#include <iostream>
+#include <map>
+
+
+namespace flann
+{
+
+typedef std::map<std::string, any> IndexParams;
+
+
+typedef enum {
+	FLANN_False = 0,
+	FLANN_True = 1,
+	FLANN_Undefined
+} tri_type;
+
+struct SearchParams
+{
+    SearchParams(int checks_ = 32, float eps_ = 0.0, bool sorted_ = true ) :
+    	checks(checks_), eps(eps_), sorted(sorted_)
+    {
+    	max_neighbors = -1;
+    	use_heap = FLANN_Undefined;
+    	cores = 1;
+    	matrices_in_gpu_ram = false;
+    }
+
+    // how many leafs to visit when searching for neighbours (-1 for unlimited)
+    int checks;
+    // search for eps-approximate neighbours (default: 0)
+    float eps;
+    // only for radius search, require neighbours sorted by distance (default: true)
+    bool sorted;
+    // maximum number of neighbors radius search should return (-1 for unlimited)
+    int max_neighbors;
+    // use a heap to manage the result set (default: FLANN_Undefined)
+    tri_type use_heap;
+    // how many cores to assign to the search
+    // this parameter will be ignored if Intel TBB isn't available on the system or no "TBB" macro is defined
+    int cores;
+    // for GPU search indicates if matrices are already in GPU ram
+    bool matrices_in_gpu_ram;
+};
+
+
+inline bool has_param(const IndexParams& params, std::string name)
+{
+	return params.find(name)!=params.end();
+}
+
+template<typename T>
+T get_param(const IndexParams& params, std::string name, const T& default_value)
+{
+    IndexParams::const_iterator it = params.find(name);
+    if (it != params.end()) {
+        return it->second.cast<T>();
+    }
+    else {
+        return default_value;
+    }
+}
+
+template<typename T>
+T get_param(const IndexParams& params, std::string name)
+{
+    IndexParams::const_iterator it = params.find(name);
+    if (it != params.end()) {
+        return it->second.cast<T>();
+    }
+    else {
+        throw FLANNException(std::string("Missing parameter '")+name+std::string("' in the parameters given"));
+    }
+}
+
+inline void print_params(const IndexParams& params)
+{
+    IndexParams::const_iterator it;
+
+    for(it=params.begin(); it!=params.end(); ++it) {
+        std::cout << it->first << " : " << it->second << std::endl;
+    }
+}
+
+inline void print_params(const SearchParams& params)
+{
+	std::cout << "checks : " << params.checks << std::endl;
+	std::cout << "eps : " << params.eps << std::endl;
+	std::cout << "sorted : " << params.sorted << std::endl;
+	std::cout << "max_neighbors : " << params.max_neighbors << std::endl;
+}
+
+
+}
+
+
+#endif /* FLANN_PARAMS_H_ */
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/random.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/random.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/random.h	(revision 15840)
@@ -0,0 +1,135 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_RANDOM_H
+#define FLANN_RANDOM_H
+
+#include <algorithm>
+#include <cstdlib>
+#include <vector>
+
+#include "flann/general.h"
+
+namespace flann
+{
+
+/**
+ * Seeds the random number generator
+ *  @param seed Random seed
+ */
+inline void seed_random(unsigned int seed)
+{
+    srand(seed);
+}
+
+/*
+ * Generates a random double value.
+ */
+/**
+ * Generates a random double value.
+ * @param high Upper limit
+ * @param low Lower limit
+ * @return Random double value
+ */
+inline double rand_double(double high = 1.0, double low = 0)
+{
+    return low + ((high-low) * (std::rand() / (RAND_MAX + 1.0)));
+}
+
+/**
+ * Generates a random integer value.
+ * @param high Upper limit
+ * @param low Lower limit
+ * @return Random integer value
+ */
+inline int rand_int(int high = RAND_MAX, int low = 0)
+{
+    return low + (int) ( double(high-low) * (std::rand() / (RAND_MAX + 1.0)));
+}
+
+/**
+ * Random number generator that returns a distinct number from
+ * the [0,n) interval each time.
+ */
+class UniqueRandom
+{
+    std::vector<int> vals_;
+    int size_;
+    int counter_;
+
+public:
+    /**
+     * Constructor.
+     * @param n Size of the interval from which to generate
+     * @return
+     */
+    UniqueRandom(int n)
+    {
+        init(n);
+    }
+
+    /**
+     * Initializes the number generator.
+     * @param n the size of the interval from which to generate random numbers.
+     */
+    void init(int n)
+    {
+        // create and initialize an array of size n
+        vals_.resize(n);
+        size_ = n;
+        for (int i = 0; i < size_; ++i) vals_[i] = i;
+
+        // shuffle the elements in the array
+        std::random_shuffle(vals_.begin(), vals_.end());
+
+        counter_ = 0;
+    }
+
+    /**
+     * Return a distinct random integer in greater or equal to 0 and less
+     * than 'n' on each call. It should be called maximum 'n' times.
+     * Returns: a random integer
+     */
+    int next()
+    {
+        if (counter_ == size_) {
+            return -1;
+        }
+        else {
+            return vals_[counter_++];
+        }
+    }
+};
+
+}
+
+#endif //FLANN_RANDOM_H
+
+
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/result_set.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/result_set.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/result_set.h	(revision 15840)
@@ -0,0 +1,942 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_RESULTSET_H
+#define FLANN_RESULTSET_H
+
+#include <algorithm>
+#include <cstring>
+#include <iostream>
+#include <limits>
+#include <set>
+#include <vector>
+
+namespace flann
+{
+
+/* This record represents a branch point when finding neighbors in
+    the tree.  It contains a record of the minimum distance to the query
+    point, as well as the node at which the search resumes.
+ */
+
+template <typename T, typename DistanceType>
+struct BranchStruct
+{
+    T node;           /* Tree node at which search resumes */
+    DistanceType mindist;     /* Minimum distance to query for all nodes below. */
+
+    BranchStruct() {}
+    BranchStruct(const T& aNode, DistanceType dist) : node(aNode), mindist(dist) {}
+
+    bool operator<(const BranchStruct<T, DistanceType>& rhs) const
+    {
+        return mindist<rhs.mindist;
+    }
+};
+
+
+template <typename DistanceType>
+struct DistanceIndex
+{
+    DistanceIndex(DistanceType dist, size_t index) :
+        dist_(dist), index_(index)
+    {
+    }
+    bool operator<(const DistanceIndex& dist_index) const
+    {
+        return (dist_ < dist_index.dist_) || ((dist_ == dist_index.dist_) && index_ < dist_index.index_);
+    }
+    DistanceType dist_;
+    size_t index_;
+};
+
+
+template <typename DistanceType>
+class ResultSet
+{
+public:
+    virtual ~ResultSet() {}
+
+    virtual bool full() const = 0;
+
+    virtual void addPoint(DistanceType dist, size_t index) = 0;
+
+    virtual DistanceType worstDist() const = 0;
+
+};
+
+
+
+/**
+ * KNNSimpleResultSet does not ensure that the element it holds are unique.
+ * Is used in those cases where the nearest neighbour algorithm used does not
+ * attempt to insert the same element multiple times.
+ */
+template <typename DistanceType>
+class KNNSimpleResultSet : public ResultSet<DistanceType>
+{
+public:
+	typedef DistanceIndex<DistanceType> DistIndex;
+
+	KNNSimpleResultSet(size_t capacity_) :
+        capacity_(capacity_)
+    {
+		// reserving capacity to prevent memory re-allocations
+		dist_index_.resize(capacity_, DistIndex(std::numeric_limits<DistanceType>::max(),-1));
+    	clear();
+    }
+
+    ~KNNSimpleResultSet()
+    {
+    }
+
+    /**
+     * Clears the result set
+     */
+    void clear()
+    {
+        worst_distance_ = std::numeric_limits<DistanceType>::max();
+        dist_index_[capacity_-1].dist_ = worst_distance_;
+        count_ = 0;
+    }
+
+    /**
+     *
+     * @return Number of elements in the result set
+     */
+    size_t size() const
+    {
+        return count_;
+    }
+
+    /**
+     * Radius search result set always reports full
+     * @return
+     */
+    bool full() const
+    {
+        return count_==capacity_;
+    }
+
+    /**
+     * Add a point to result set
+     * @param dist distance to point
+     * @param index index of point
+     */
+    void addPoint(DistanceType dist, size_t index)
+    {
+    	if (dist>=worst_distance_) return;
+
+        if (count_ < capacity_) ++count_;
+        size_t i;
+        for (i=count_-1; i>0; --i) {
+#ifdef FLANN_FIRST_MATCH
+            if ( (dist_index_[i-1].dist_>dist) || ((dist==dist_index_[i-1].dist_)&&(dist_index_[i-1].index_>index)) )
+#else
+            if (dist_index_[i-1].dist_>dist)
+#endif
+            {
+            	dist_index_[i] = dist_index_[i-1];
+            }
+            else break;
+        }
+        dist_index_[i].dist_ = dist;
+        dist_index_[i].index_ = index;
+        worst_distance_ = dist_index_[capacity_-1].dist_;
+    }
+
+    /**
+     * Copy indices and distances to output buffers
+     * @param indices
+     * @param dists
+     * @param num_elements Number of elements to copy
+     * @param sorted Indicates if results should be sorted
+     */
+    void copy(int* indices, DistanceType* dists, size_t num_elements, bool sorted = true)
+    {
+    	size_t n = std::min(count_, num_elements);
+    	for (size_t i=0; i<n; ++i) {
+    		*indices++ = dist_index_[i].index_;
+    		*dists++ = dist_index_[i].dist_;
+    	}
+    }
+
+    DistanceType worstDist() const
+    {
+    	return worst_distance_;
+    }
+
+private:
+    size_t capacity_;
+    size_t count_;
+    DistanceType worst_distance_;
+    std::vector<DistIndex> dist_index_;
+};
+
+/**
+ * K-Nearest neighbour result set. Ensures that the elements inserted are unique
+ */
+template <typename DistanceType>
+class KNNResultSet : public ResultSet<DistanceType>
+{
+public:
+	typedef DistanceIndex<DistanceType> DistIndex;
+
+    KNNResultSet(int capacity) : capacity_(capacity)
+    {
+		// reserving capacity to prevent memory re-allocations
+		dist_index_.resize(capacity_, DistIndex(std::numeric_limits<DistanceType>::max(),-1));
+    	clear();
+    }
+
+    ~KNNResultSet()
+    {
+
+    }
+
+    /**
+     * Clears the result set
+     */
+    void clear()
+    {
+        worst_distance_ = std::numeric_limits<DistanceType>::max();
+        dist_index_[capacity_-1].dist_ = worst_distance_;
+        count_ = 0;
+    }
+
+    size_t size() const
+    {
+        return count_;
+    }
+
+    bool full() const
+    {
+        return count_ == capacity_;
+    }
+
+
+    void addPoint(DistanceType dist, size_t index)
+    {
+        if (dist >= worst_distance_) return;
+        size_t i;
+        for (i = count_; i > 0; --i) {
+#ifdef FLANN_FIRST_MATCH
+            if ( (dist_index_[i-1].dist_<=dist) && ((dist!=dist_index_[i-1].dist_)||(dist_index_[i-1].index_<=index)) )
+#else
+            if (dist_index_[i-1].dist_<=dist)
+#endif
+            {
+                // Check for duplicate indices
+                size_t j = i - 1;
+                while (dist_index_[j].dist_ == dist) {
+                    if (dist_index_[j].index_ == index) {
+                        return;
+                    }
+                    --j;
+                }
+                break;
+            }
+        }
+        if (count_ < capacity_) ++count_;
+        for (size_t j = count_-1; j > i; --j) {
+            dist_index_[j] = dist_index_[j-1];
+        }
+        dist_index_[i].dist_ = dist;
+        dist_index_[i].index_ = index;
+        worst_distance_ = dist_index_[capacity_-1].dist_;
+    }
+
+    /**
+     * Copy indices and distances to output buffers
+     * @param indices
+     * @param dists
+     * @param num_elements Number of elements to copy
+     * @param sorted Indicates if results should be sorted
+     */
+    void copy(int* indices, DistanceType* dists, size_t num_elements, bool sorted = true)
+    {
+    	size_t n = std::min(count_, num_elements);
+    	for (size_t i=0; i<n; ++i) {
+    		*indices++ = dist_index_[i].index_;
+    		*dists++ = dist_index_[i].dist_;
+    	}
+    }
+
+    DistanceType worstDist() const
+    {
+        return worst_distance_;
+    }
+
+private:
+    size_t capacity_;
+    size_t count_;
+    DistanceType worst_distance_;
+    std::vector<DistIndex> dist_index_;
+
+};
+
+
+
+template <typename DistanceType>
+class KNNResultSet2 : public ResultSet<DistanceType>
+{
+public:
+	typedef DistanceIndex<DistanceType> DistIndex;
+
+	KNNResultSet2(size_t capacity_) :
+        capacity_(capacity_)
+    {
+		// reserving capacity to prevent memory re-allocations
+		dist_index_.reserve(capacity_);
+    	clear();
+    }
+
+    ~KNNResultSet2()
+    {
+    }
+
+    /**
+     * Clears the result set
+     */
+    void clear()
+    {
+        dist_index_.clear();
+        worst_dist_ = std::numeric_limits<DistanceType>::max();
+        is_full_ = false;
+    }
+
+    /**
+     *
+     * @return Number of elements in the result set
+     */
+    size_t size() const
+    {
+        return dist_index_.size();
+    }
+
+    /**
+     * Radius search result set always reports full
+     * @return
+     */
+    bool full() const
+    {
+        return is_full_;
+    }
+
+    /**
+     * Add another point to result set
+     * @param dist distance to point
+     * @param index index of point
+     * Pre-conditions: capacity_>0
+     */
+    void addPoint(DistanceType dist, size_t index)
+    {
+    	if (dist>=worst_dist_) return;
+
+    	if (dist_index_.size()==capacity_) {
+    		// if result set if filled to capacity, remove farthest element
+    		std::pop_heap(dist_index_.begin(), dist_index_.end());
+        	dist_index_.pop_back();
+    	}
+
+    	// add new element
+    	dist_index_.push_back(DistIndex(dist,index));
+    	if (is_full_) { // when is_full_==true, we have a heap
+    		std::push_heap(dist_index_.begin(), dist_index_.end());
+    	}
+
+    	if (dist_index_.size()==capacity_) {
+    		if (!is_full_) {
+    			std::make_heap(dist_index_.begin(), dist_index_.end());
+            	is_full_ = true;
+    		}
+    		// we replaced the farthest element, update worst distance
+        	worst_dist_ = dist_index_[0].dist_;
+        }
+    }
+
+    /**
+     * Copy indices and distances to output buffers
+     * @param indices
+     * @param dists
+     * @param num_elements Number of elements to copy
+     * @param sorted Indicates if results should be sorted
+     */
+    void copy(int* indices, DistanceType* dists, size_t num_elements, bool sorted = true)
+    {
+    	if (sorted) {
+    		// std::sort_heap(dist_index_.begin(), dist_index_.end());
+    		// sort seems faster here, even though dist_index_ is a heap
+    		std::sort(dist_index_.begin(), dist_index_.end());
+    	}
+    	else {
+    		if (num_elements<size()) {
+    			std::nth_element(dist_index_.begin(), dist_index_.begin()+num_elements, dist_index_.end());
+    		}
+    	}
+
+    	size_t n = std::min(dist_index_.size(), num_elements);
+    	for (size_t i=0; i<n; ++i) {
+    		*indices++ = dist_index_[i].index_;
+    		*dists++ = dist_index_[i].dist_;
+    	}
+    }
+
+    DistanceType worstDist() const
+    {
+    	return worst_dist_;
+    }
+
+private:
+    size_t capacity_;
+    DistanceType worst_dist_;
+    std::vector<DistIndex> dist_index_;
+    bool is_full_;
+};
+
+
+/**
+ * Unbounded radius result set. It will hold as many elements as
+ * are added to it.
+ */
+template <typename DistanceType>
+class RadiusResultSet : public ResultSet<DistanceType>
+{
+public:
+	typedef DistanceIndex<DistanceType> DistIndex;
+
+	RadiusResultSet(DistanceType radius_) :
+        radius_(radius_)
+    {
+		// reserving some memory to limit number of re-allocations
+		dist_index_.reserve(1024);
+    	clear();
+    }
+
+    ~RadiusResultSet()
+    {
+    }
+
+    /**
+     * Clears the result set
+     */
+    void clear()
+    {
+        dist_index_.clear();
+    }
+
+    /**
+     *
+     * @return Number of elements in the result set
+     */
+    size_t size() const
+    {
+        return dist_index_.size();
+    }
+
+    /**
+     * Radius search result set always reports full
+     * @return
+     */
+    bool full() const
+    {
+        return true;
+    }
+
+    /**
+     * Add another point to result set
+     * @param dist distance to point
+     * @param index index of point
+     * Pre-conditions: capacity_>0
+     */
+    void addPoint(DistanceType dist, size_t index)
+    {
+    	if (dist<radius_) {
+    		// add new element
+    		dist_index_.push_back(DistIndex(dist,index));
+    	}
+    }
+
+    /**
+     * Copy indices and distances to output buffers
+     * @param indices
+     * @param dists
+     * @param num_elements Number of elements to copy
+     * @param sorted Indicates if results should be sorted
+     */
+    void copy(int* indices, DistanceType* dists, size_t num_elements, bool sorted = true)
+    {
+    	if (sorted) {
+    		// std::sort_heap(dist_index_.begin(), dist_index_.end());
+    		// sort seems faster here, even though dist_index_ is a heap
+    		std::sort(dist_index_.begin(), dist_index_.end());
+    	}
+    	else {
+    		if (num_elements<size()) {
+    			std::nth_element(dist_index_.begin(), dist_index_.begin()+num_elements, dist_index_.end());
+    		}
+    	}
+
+    	size_t n = std::min(dist_index_.size(), num_elements);
+    	for (size_t i=0; i<n; ++i) {
+    		*indices++ = dist_index_[i].index_;
+    		*dists++ = dist_index_[i].dist_;
+    	}
+    }
+
+    DistanceType worstDist() const
+    {
+    	return radius_;
+    }
+
+private:
+    DistanceType radius_;
+    std::vector<DistIndex> dist_index_;
+};
+
+
+
+/**
+ * Bounded radius result set. It limits the number of elements
+ * it can hold to a preset capacity.
+ */
+template <typename DistanceType>
+class KNNRadiusResultSet : public ResultSet<DistanceType>
+{
+public:
+	typedef DistanceIndex<DistanceType> DistIndex;
+
+	KNNRadiusResultSet(DistanceType radius_, size_t capacity_) :
+        radius_(radius_), capacity_(capacity_)
+    {
+		// reserving capacity to prevent memory re-allocations
+		dist_index_.reserve(capacity_);
+    	clear();
+    }
+
+    ~KNNRadiusResultSet()
+    {
+    }
+
+    /**
+     * Clears the result set
+     */
+    void clear()
+    {
+        dist_index_.clear();
+        worst_dist_ = radius_;
+        is_heap_ = false;
+    }
+
+    /**
+     *
+     * @return Number of elements in the result set
+     */
+    size_t size() const
+    {
+        return dist_index_.size();
+    }
+
+    /**
+     * Radius search result set always reports full
+     * @return
+     */
+    bool full() const
+    {
+        return true;
+    }
+
+    /**
+     * Add another point to result set
+     * @param dist distance to point
+     * @param index index of point
+     * Pre-conditions: capacity_>0
+     */
+    void addPoint(DistanceType dist, size_t index)
+    {
+    	if (dist>=worst_dist_) return;
+
+    	if (dist_index_.size()==capacity_) {
+    		// if result set is filled to capacity, remove farthest element
+    		std::pop_heap(dist_index_.begin(), dist_index_.end());
+        	dist_index_.pop_back();
+    	}
+
+    	// add new element
+    	dist_index_.push_back(DistIndex(dist,index));
+    	if (is_heap_) {
+    		std::push_heap(dist_index_.begin(), dist_index_.end());
+    	}
+
+    	if (dist_index_.size()==capacity_) {
+    		// when got to full capacity, make it a heap
+    		if (!is_heap_) {
+    			std::make_heap(dist_index_.begin(), dist_index_.end());
+    			is_heap_ = true;
+    		}
+    		// we replaced the farthest element, update worst distance
+        	worst_dist_ = dist_index_[0].dist_;
+        }
+    }
+
+    /**
+     * Copy indices and distances to output buffers
+     * @param indices
+     * @param dists
+     * @param num_elements Number of elements to copy
+     * @param sorted Indicates if results should be sorted
+     */
+    void copy(int* indices, DistanceType* dists, size_t num_elements, bool sorted = true)
+    {
+    	if (sorted) {
+    		// std::sort_heap(dist_index_.begin(), dist_index_.end());
+    		// sort seems faster here, even though dist_index_ is a heap
+    		std::sort(dist_index_.begin(), dist_index_.end());
+    	}
+    	else {
+    		if (num_elements<size()) {
+    			std::nth_element(dist_index_.begin(), dist_index_.begin()+num_elements, dist_index_.end());
+    		}
+    	}
+
+    	size_t n = std::min(dist_index_.size(), num_elements);
+    	for (size_t i=0; i<n; ++i) {
+    		*indices++ = dist_index_[i].index_;
+    		*dists++ = dist_index_[i].dist_;
+    	}
+    }
+
+    DistanceType worstDist() const
+    {
+    	return worst_dist_;
+    }
+
+private:
+    bool is_heap_;
+    DistanceType radius_;
+    size_t capacity_;
+    DistanceType worst_dist_;
+    std::vector<DistIndex> dist_index_;
+};
+
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+/**
+ * This is a result set that only counts the neighbors within a radius.
+ */
+
+template <typename DistanceType>
+class CountRadiusResultSet : public ResultSet<DistanceType>
+{
+    DistanceType radius;
+    size_t count;
+
+public:
+    CountRadiusResultSet(DistanceType radius_ ) :
+        radius(radius_)
+    {
+        clear();
+    }
+
+    ~CountRadiusResultSet()
+    {
+    }
+
+    void clear()
+    {
+        count = 0;
+    }
+
+    size_t size() const
+    {
+        return count;
+    }
+
+    bool full() const
+    {
+        return true;
+    }
+
+    void addPoint(DistanceType dist, size_t index)
+    {
+        if (dist<radius) {
+            count++;
+        }
+    }
+
+    DistanceType worstDist() const
+    {
+        return radius;
+    }
+
+};
+
+
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** Class that holds the k NN neighbors
+ */
+template<typename DistanceType>
+class UniqueResultSet : public ResultSet<DistanceType>
+{
+public:
+    struct DistIndex
+    {
+        DistIndex(DistanceType dist, unsigned int index) :
+            dist_(dist), index_(index)
+        {
+        }
+        bool operator<(const DistIndex dist_index) const
+        {
+            return (dist_ < dist_index.dist_) || ((dist_ == dist_index.dist_) && index_ < dist_index.index_);
+        }
+        DistanceType dist_;
+        unsigned int index_;
+    };
+
+    /** Default cosntructor */
+    UniqueResultSet() :
+        worst_distance_(std::numeric_limits<DistanceType>::max())
+    {
+    }
+
+    /** Check the status of the set
+     * @return true if we have k NN
+     */
+    inline bool full() const
+    {
+        return is_full_;
+    }
+
+    /** Remove all elements in the set
+     */
+    virtual void clear() = 0;
+
+    /** Copy the set to two C arrays
+     * @param indices pointer to a C array of indices
+     * @param dist pointer to a C array of distances
+     * @param n_neighbors the number of neighbors to copy
+     */
+    virtual void copy(int* indices, DistanceType* dist, int n_neighbors, bool sorted = true)
+    {
+    	if (n_neighbors<0) n_neighbors = dist_indices_.size();
+    	int i = 0;
+    	typedef typename std::set<DistIndex>::const_iterator Iterator;
+    	for (Iterator dist_index = dist_indices_.begin(), dist_index_end =
+    			dist_indices_.end(); (dist_index != dist_index_end) && (i < n_neighbors); ++dist_index, ++indices, ++dist, ++i) {
+    		*indices = dist_index->index_;
+    		*dist = dist_index->dist_;
+    	}
+    }
+
+    /** The number of neighbors in the set
+     * @return
+     */
+    size_t size() const
+    {
+        return dist_indices_.size();
+    }
+
+    /** The distance of the furthest neighbor
+     * If we don't have enough neighbors, it returns the max possible value
+     * @return
+     */
+    inline DistanceType worstDist() const
+    {
+        return worst_distance_;
+    }
+protected:
+    /** Flag to say if the set is full */
+    bool is_full_;
+
+    /** The worst distance found so far */
+    DistanceType worst_distance_;
+
+    /** The best candidates so far */
+    std::set<DistIndex> dist_indices_;
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** Class that holds the k NN neighbors
+ * Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays
+ */
+template<typename DistanceType>
+class KNNUniqueResultSet : public UniqueResultSet<DistanceType>
+{
+public:
+    /** Constructor
+     * @param capacity the number of neighbors to store at max
+     */
+    KNNUniqueResultSet(unsigned int capacity) : capacity_(capacity)
+    {
+        this->is_full_ = false;
+        this->clear();
+    }
+
+    /** Add a possible candidate to the best neighbors
+     * @param dist distance for that neighbor
+     * @param index index of that neighbor
+     */
+    inline void addPoint(DistanceType dist, size_t index)
+    {
+        // Don't do anything if we are worse than the worst
+        if (dist >= worst_distance_) return;
+        dist_indices_.insert(DistIndex(dist, index));
+
+        if (is_full_) {
+            if (dist_indices_.size() > capacity_) {
+                dist_indices_.erase(*dist_indices_.rbegin());
+                worst_distance_ = dist_indices_.rbegin()->dist_;
+            }
+        }
+        else if (dist_indices_.size() == capacity_) {
+            is_full_ = true;
+            worst_distance_ = dist_indices_.rbegin()->dist_;
+        }
+    }
+
+    /** Remove all elements in the set
+     */
+    void clear()
+    {
+        dist_indices_.clear();
+        worst_distance_ = std::numeric_limits<DistanceType>::max();
+        is_full_ = false;
+    }
+
+protected:
+    typedef typename UniqueResultSet<DistanceType>::DistIndex DistIndex;
+    using UniqueResultSet<DistanceType>::is_full_;
+    using UniqueResultSet<DistanceType>::worst_distance_;
+    using UniqueResultSet<DistanceType>::dist_indices_;
+
+    /** The number of neighbors to keep */
+    unsigned int capacity_;
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** Class that holds the radius nearest neighbors
+ * It is more accurate than RadiusResult as it is not limited in the number of neighbors
+ */
+template<typename DistanceType>
+class RadiusUniqueResultSet : public UniqueResultSet<DistanceType>
+{
+public:
+    /** Constructor
+     * @param capacity the number of neighbors to store at max
+     */
+    RadiusUniqueResultSet(DistanceType radius) :
+        radius_(radius)
+    {
+        is_full_ = true;
+    }
+
+    /** Add a possible candidate to the best neighbors
+     * @param dist distance for that neighbor
+     * @param index index of that neighbor
+     */
+    void addPoint(DistanceType dist, size_t index)
+    {
+        if (dist < radius_) dist_indices_.insert(DistIndex(dist, index));
+    }
+
+    /** Remove all elements in the set
+     */
+    inline void clear()
+    {
+        dist_indices_.clear();
+    }
+
+
+    /** Check the status of the set
+     * @return alwys false
+     */
+    inline bool full() const
+    {
+        return true;
+    }
+
+    /** The distance of the furthest neighbor
+     * If we don't have enough neighbors, it returns the max possible value
+     * @return
+     */
+    inline DistanceType worstDist() const
+    {
+        return radius_;
+    }
+private:
+    typedef typename UniqueResultSet<DistanceType>::DistIndex DistIndex;
+    using UniqueResultSet<DistanceType>::dist_indices_;
+    using UniqueResultSet<DistanceType>::is_full_;
+
+    /** The furthest distance a neighbor can be */
+    DistanceType radius_;
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** Class that holds the k NN neighbors within a radius distance
+ */
+template<typename DistanceType>
+class KNNRadiusUniqueResultSet : public KNNUniqueResultSet<DistanceType>
+{
+public:
+    /** Constructor
+     * @param capacity the number of neighbors to store at max
+     */
+    KNNRadiusUniqueResultSet(DistanceType radius, size_t capacity) : KNNUniqueResultSet<DistanceType>(capacity)
+    {
+        this->radius_ = radius;
+        this->clear();
+    }
+
+    /** Remove all elements in the set
+     */
+    void clear()
+    {
+        dist_indices_.clear();
+        worst_distance_ = radius_;
+        is_full_ = true;
+    }
+private:
+    using KNNUniqueResultSet<DistanceType>::dist_indices_;
+    using KNNUniqueResultSet<DistanceType>::is_full_;
+    using KNNUniqueResultSet<DistanceType>::worst_distance_;
+
+    /** The maximum distance of a neighbor */
+    DistanceType radius_;
+};
+}
+
+#endif //FLANN_RESULTSET_H
+
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/sampling.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/sampling.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/sampling.h	(revision 15840)
@@ -0,0 +1,81 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+
+#ifndef FLANN_SAMPLING_H_
+#define FLANN_SAMPLING_H_
+
+#include "flann/util/matrix.h"
+#include "flann/util/random.h"
+
+namespace flann
+{
+
+template<typename T>
+Matrix<T> random_sample(Matrix<T>& srcMatrix, long size, bool remove = false)
+{
+    Matrix<T> newSet(new T[size * srcMatrix.cols], size,srcMatrix.cols);
+
+    T* src,* dest;
+    for (long i=0; i<size; ++i) {
+        long r = rand_int(srcMatrix.rows-i);
+        dest = newSet[i];
+        src = srcMatrix[r];
+        std::copy(src, src+srcMatrix.cols, dest);
+        if (remove) {
+            src = srcMatrix[srcMatrix.rows-i-1];
+            dest = srcMatrix[r];
+            std::copy(src, src+srcMatrix.cols, dest);
+        }
+    }
+    if (remove) {
+        srcMatrix.rows -= size;
+    }
+    return newSet;
+}
+
+template<typename T>
+Matrix<T> random_sample(const Matrix<T>& srcMatrix, size_t size)
+{
+    UniqueRandom rand(srcMatrix.rows);
+    Matrix<T> newSet(new T[size * srcMatrix.cols], size,srcMatrix.cols);
+
+    T* src,* dest;
+    for (size_t i=0; i<size; ++i) {
+        long r = rand.next();
+        dest = newSet[i];
+        src = srcMatrix[r];
+        std::copy(src, src+srcMatrix.cols, dest);
+    }
+    return newSet;
+}
+
+} // namespace
+
+
+#endif /* FLANN_SAMPLING_H_ */
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/saving.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/saving.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/saving.h	(revision 15840)
@@ -0,0 +1,168 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE NNIndexGOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_SAVING_H_
+#define FLANN_SAVING_H_
+
+#include <cstring>
+#include <vector>
+#include <stdio.h>
+
+#include "flann/general.h"
+#include "flann/algorithms/nn_index.h"
+
+
+#ifdef FLANN_SIGNATURE_
+#undef FLANN_SIGNATURE_
+#endif
+#define FLANN_SIGNATURE_ "FLANN_INDEX"
+
+namespace flann
+{
+
+/**
+ * Structure representing the index header.
+ */
+struct IndexHeader
+{
+    char signature[16];
+    char version[16];
+    flann_datatype_t data_type;
+    flann_algorithm_t index_type;
+    size_t rows;
+    size_t cols;
+};
+
+/**
+ * Saves index header to stream
+ *
+ * @param stream - Stream to save to
+ * @param index - The index to save
+ */
+template<typename Distance>
+void save_header(FILE* stream, const NNIndex<Distance>& index)
+{
+    IndexHeader header;
+    memset(header.signature, 0, sizeof(header.signature));
+    strcpy(header.signature, FLANN_SIGNATURE_);
+    memset(header.version, 0, sizeof(header.version));
+    strcpy(header.version, FLANN_VERSION_);
+    header.data_type = flann_datatype<typename Distance::ElementType>::value;
+    header.index_type = index.getType();
+    header.rows = index.size();
+    header.cols = index.veclen();
+
+    fwrite(&header, sizeof(header),1,stream);
+}
+
+
+/**
+ *
+ * @param stream - Stream to load from
+ * @return Index header
+ */
+inline IndexHeader load_header(FILE* stream)
+{
+    IndexHeader header;
+    int read_size = fread(&header,sizeof(header),1,stream);
+
+    if (read_size!=1) {
+        throw FLANNException("Invalid index file, cannot read");
+    }
+
+    if (strcmp(header.signature,FLANN_SIGNATURE_)!=0) {
+        throw FLANNException("Invalid index file, wrong signature");
+    }
+
+    return header;
+}
+
+
+template<typename T>
+void save_value(FILE* stream, const T& value, size_t count = 1)
+{
+    fwrite(&value, sizeof(value),count, stream);
+}
+
+template<typename T>
+void save_value(FILE* stream, const flann::Matrix<T>& value)
+{
+    fwrite(&value, sizeof(value),1, stream);
+    fwrite(value.ptr(), sizeof(T),value.rows*value.cols, stream);
+}
+
+template<typename T>
+void save_value(FILE* stream, const std::vector<T>& value)
+{
+    size_t size = value.size();
+    fwrite(&size, sizeof(size_t), 1, stream);
+    fwrite(&value[0], sizeof(T), size, stream);
+}
+
+template<typename T>
+void load_value(FILE* stream, T& value, size_t count = 1)
+{
+    size_t read_cnt = fread(&value, sizeof(value), count, stream);
+    if (read_cnt != count) {
+        throw FLANNException("Cannot read from file");
+    }
+}
+
+template<typename T>
+void load_value(FILE* stream, flann::Matrix<T>& value)
+{
+    size_t read_cnt = fread(&value, sizeof(value), 1, stream);
+    if (read_cnt != 1) {
+        throw FLANNException("Cannot read from file");
+    }
+    value = Matrix<T>(new T[value.rows*value.cols], value.rows, value.cols);
+    read_cnt = fread(value.ptr(), sizeof(T), value.rows*value.cols, stream);
+    if (read_cnt != value.rows*value.cols) {
+        throw FLANNException("Cannot read from file");
+    }
+}
+
+
+template<typename T>
+void load_value(FILE* stream, std::vector<T>& value)
+{
+    size_t size;
+    size_t read_cnt = fread(&size, sizeof(size_t), 1, stream);
+    if (read_cnt!=1) {
+        throw FLANNException("Cannot read from file");
+    }
+    value.resize(size);
+    read_cnt = fread(&value[0], sizeof(T), size, stream);
+    if (int(read_cnt)!=int(size)) {
+        throw FLANNException("Cannot read from file");
+    }
+}
+
+}
+
+#endif /* FLANN_SAVING_H_ */
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/timer.h
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/timer.h	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/include/flann/util/timer.h	(revision 15840)
@@ -0,0 +1,93 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef FLANN_TIMER_H
+#define FLANN_TIMER_H
+
+#include <time.h>
+
+
+namespace flann
+{
+
+/**
+ * A start-stop timer class.
+ *
+ * Can be used to time portions of code.
+ */
+class StartStopTimer
+{
+    clock_t startTime;
+
+public:
+    /**
+     * Value of the timer.
+     */
+    double value;
+
+
+    /**
+     * Constructor.
+     */
+    StartStopTimer()
+    {
+        reset();
+    }
+
+    /**
+     * Starts the timer.
+     */
+    void start()
+    {
+        startTime = clock();
+    }
+
+    /**
+     * Stops the timer and updates timer value.
+     */
+    void stop()
+    {
+        clock_t stopTime = clock();
+        value += ( (double)stopTime - startTime) / CLOCKS_PER_SEC;
+    }
+
+    /**
+     * Resets the timer value to 0.
+     */
+    void reset()
+    {
+        value = 0;
+    }
+
+};
+
+}
+
+#endif // FLANN_TIMER_H
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/lib/pkgconfig/flann.pc
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/lib/pkgconfig/flann.pc	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/flann/lib/pkgconfig/flann.pc	(revision 15840)
@@ -0,0 +1,13 @@
+# This file was generated by CMake for flann
+prefix=C:/Projets/PCL/SuperBuild-test/CMakeExternal/Install/Flann
+exec_prefix=${prefix}
+libdir=${prefix}/lib
+includedir=${prefix}/include
+
+Name: flann
+Description: Fast Library for Approximate Nearest Neighbors
+Version: 1.7.1
+Requires: 
+Libs: -L${libdir} -lflann_cpp -lflann_cpp-gd
+Cflags: -I${includedir}
+
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/happieclust-1.6.0-win/evaluations.csv
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/happieclust-1.6.0-win/evaluations.csv	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/happieclust-1.6.0-win/evaluations.csv	(revision 15840)
@@ -0,0 +1,5097 @@
+0.743010198754702	0.715359937708825	1.13531255239001	0.808080594542403	0.789064889769946	0.782273805461257	0.778815724128822	0.776740816028054	0.775373292035981	0.774417254977278	0.773722951828938	0.773206760198254	0.772818616987859	0.772527041429234	0.772311602389258	0.772158870554826	0.772060143634175	0.77201014527815	0.772006305503046	0.772048429744939	0.772138674335002	0.772281822477458	0.77248592872075	0.772763501943867	0.773133575577937	0.773625372345576	0.774285062530018	0.775189032448579	0.776472253980092	0.778396366205603	0.781541894477844	0.787503712534228	0.802816198324031	0.923522445531124	0.703017945454793	0.740631628003064	0.750407935250843	0.754867933913013	0.757397736203571	0.759010088320132	0.760113385871048	0.760903644228839	0.761486374325708	0.761923113090991	0.762251857455581	0.762496997053219	0.762674520184827	0.762794893355281	0.762864703084368	0.762887585355828	0.762864703084368	0.762794893355281	0.762674520184827	0.762496997053219	0.762251857455581	0.761923113090991	0.761486374325708	0.760903644228839	0.760113385871048	0.759010088320132	0.757397736203571	0.754867933913013	0.750407935250843	0.740631628003064	0.703017945454793	0.923522445531119	0.802816198324031	0.787503712534228	0.781541894477844	0.778396366205603	0.776472253980092	0.775189032448579	0.774285062530018	0.773625372345576	0.773133575577937	0.772763501943867	0.77248592872075	0.772281822477458	0.772138674335002	0.772048429744939	0.772006305503046	0.77201014527815	0.772060143634175	0.772158870554826	0.772311602389258	0.772527041429234	0.772818616987859	0.773206760198254	0.773722951828938	0.774417254977278	0.775373292035981	0.776740816028054	0.778815724128822	0.782273805461257	0.789064889769946	0.808080594542403	1.13531255238999	0.715359937708826	0.743010198754702	0.75137872280963
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Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/happieclust-1.6.0-win/happieclust.exe.stackdump
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/happieclust-1.6.0-win/happieclust.exe.stackdump	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/happieclust-1.6.0-win/happieclust.exe.stackdump	(revision 15840)
@@ -0,0 +1,20 @@
+Exception: STATUS_ACCESS_VIOLATION at eip=00406079
+eax=00000002 ebx=002EBF18 ecx=036A0F50 edx=7FEA6330 esi=00000000 edi=7FEAFFD0
+ebp=0085C1D8 esp=0085C1B0 program=C:\reps\HeuristicLab\branches\2886_SymRegGrammarEnumeration\ExpressionClustering\happieclust-1.6.0-win\happieclust.exe, pid 13324, thread main
+cs=0023 ds=002B es=002B fs=0053 gs=002B ss=002B
+Stack trace:
+Frame     Function  Args
+0085C1D8  00406079  (00000259, 00000117, 00000002, 7FFC0000)
+0085C208  004061D8  (00000117, 00000259, 2594C37D, D4B249AD)
+0085C248  004093BB  (00000000, 00000000, 00000000, 0000027D)
+0085C2A8  00409657  (00000000, 00000000, 00000000, 0000027D)
+0085C308  0040957E  (00000000, 00000000, 00000000, 000004FA)
+0085C368  0040957E  (00000000, 00000000, 00000000, 000009F4)
+0085C3C8  0040957E  (00000000, 00000000, 00000000, 000013E8)
+0085C428  00409973  (00000000, 00000000, 00000000, 00000000)
+0085C7F8  004033AC  (03673A70, 00000006, 03673AE0, 00405959)
+0085CC48  00405CCE  (0000000B, 03672BA8, 03670090, 0085CE64)
+0085CD58  610060D8  (00000000, 0085CD90, 61005450, 0085CD90)
+61005450  61004416  (0000009C, A02404C7, E8611021, FFFFFF48)
+  80763 [main] happieclust 13324 _cygtls::handle_exceptions: Exception: STATUS_ACCESS_VIOLATION
+  81903 [main] happieclust 13324 _cygtls::handle_exceptions: Error while dumping state (probably corrupted stack)
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/happieclust-1.6.0-win/happieclust_matrix.bat
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/happieclust-1.6.0-win/happieclust_matrix.bat	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/happieclust-1.6.0-win/happieclust_matrix.bat	(revision 15840)
@@ -0,0 +1,7 @@
+REM tab2nc.exe '{string empty,colname[c]} r*{string rowname[]; double matrix[][c]}' %1 %2.nc
+tab2nc.exe tab2nc_script.txt %1 %2.nc
+REM happieclust.exe -i %2.nc -o %2.row_clust.nc -D matrix -O -C rows -X row_ %3 %4 %5 %6 %7 %8 %9
+REM happieclust.exe -i %2.row_clust.nc -o %2.row_col_clust.nc -D matrix -O -C cols -X col_ %3 %4 %5 %6 %7 %8 %9
+happieclust.exe -i %2.nc -o %2.row_clust.nc -C rows -d euclidean -M 10
+nc2cdt.exe %2.row_col_clust.nc %2 -M matrix -r row_ -c col_ -R rowname -C colname
+
Index: branches/2886_SymRegGrammarEnumeration/ExpressionClustering/happieclust-1.6.0-win/tab2nc_script.txt
===================================================================
--- branches/2886_SymRegGrammarEnumeration/ExpressionClustering/happieclust-1.6.0-win/tab2nc_script.txt	(revision 15840)
+++ branches/2886_SymRegGrammarEnumeration/ExpressionClustering/happieclust-1.6.0-win/tab2nc_script.txt	(revision 15840)
@@ -0,0 +1,1 @@
+r*{double matrix[][c]}
Index: branches/2886_SymRegGrammarEnumeration/SymRegGrammarEnumeration.sln
===================================================================
--- branches/2886_SymRegGrammarEnumeration/SymRegGrammarEnumeration.sln	(revision 15838)
+++ branches/2886_SymRegGrammarEnumeration/SymRegGrammarEnumeration.sln	(revision 15840)
@@ -7,4 +7,6 @@
 EndProject
 Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Test", "Test\Test.csproj", "{C828DECC-CAF5-4E23-B594-8D4F51255072}"
+EndProject
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "ExpressionClustering", "ExpressionClustering\ExpressionClustering.csproj", "{956C4ADE-F86D-4DCC-BE2B-BA8C5BE4851B}"
 EndProject
 Global
@@ -22,4 +24,8 @@
 		{C828DECC-CAF5-4E23-B594-8D4F51255072}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{C828DECC-CAF5-4E23-B594-8D4F51255072}.Release|Any CPU.Build.0 = Release|Any CPU
+		{956C4ADE-F86D-4DCC-BE2B-BA8C5BE4851B}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{956C4ADE-F86D-4DCC-BE2B-BA8C5BE4851B}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{956C4ADE-F86D-4DCC-BE2B-BA8C5BE4851B}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{956C4ADE-F86D-4DCC-BE2B-BA8C5BE4851B}.Release|Any CPU.Build.0 = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
