Free cookie consent management tool by TermsFeed Policy Generator

Ignore:
Timestamp:
08/08/12 14:04:17 (12 years ago)
Author:
mkommend
Message:

#1081: Intermediate commit of trunk updates - interpreter changes must be redone.

Location:
branches/HeuristicLab.TimeSeries
Files:
2 added
24 edited
4 copied

Legend:

Unmodified
Added
Removed

  • branches/HeuristicLab.TimeSeries

    • Property svn:ignore

      •  

        old new  
        2020bin
        2121protoc.exe
         22_ReSharper.HeuristicLab.TimeSeries-3.3

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4

    • Property svn:ignore

      •  

        old new  
        55*.vs10x
        66Plugin.cs
         7*.user

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/HeuristicLab.Algorithms.DataAnalysis-3.4.csproj

    r7825 r8430  
    5252    <DebugType>pdbonly</DebugType>
    5353    <Optimize>true</Optimize>
    54     <OutputPath>$(SolutionDir)\bin\</OutputPath>
     54    <OutputPath>..\..\..\..\trunk\sources\bin\</OutputPath>
    5555    <DefineConstants>TRACE</DefineConstants>
    5656    <ErrorReport>prompt</ErrorReport>
    … …  
    102102  <ItemGroup>
    103103    <Reference Include="ALGLIB-3.5.0, Version=3.5.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
    104       <HintPath>..\..\bin\ALGLIB-3.5.0.dll</HintPath>
     104      <HintPath>..\..\..\..\trunk\sources\bin\ALGLIB-3.5.0.dll</HintPath>
    105105      <Private>False</Private>
    106106    </Reference>
     107    <Reference Include="HeuristicLab.Algorithms.Benchmarks-3.3">
     108      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Algorithms.Benchmarks-3.3.dll</HintPath>
     109    </Reference>
     110    <Reference Include="HeuristicLab.Algorithms.GradientDescent-3.3">
     111      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Algorithms.GradientDescent-3.3.dll</HintPath>
     112    </Reference>
     113    <Reference Include="HeuristicLab.Analysis-3.3">
     114      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Analysis-3.3.dll</HintPath>
     115    </Reference>
     116    <Reference Include="HeuristicLab.Collections-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
     117      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Collections-3.3.dll</HintPath>
     118    </Reference>
     119    <Reference Include="HeuristicLab.Common-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
     120      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Common-3.3.dll</HintPath>
     121    </Reference>
     122    <Reference Include="HeuristicLab.Common.Resources-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
     123      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Common.Resources-3.3.dll</HintPath>
     124    </Reference>
     125    <Reference Include="HeuristicLab.Core-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
     126      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Core-3.3.dll</HintPath>
     127    </Reference>
     128    <Reference Include="HeuristicLab.Data-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
     129      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Data-3.3.dll</HintPath>
     130    </Reference>
     131    <Reference Include="HeuristicLab.Encodings.RealVectorEncoding-3.3">
     132      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Encodings.RealVectorEncoding-3.3.dll</HintPath>
     133    </Reference>
     134    <Reference Include="HeuristicLab.Operators-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
     135      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Operators-3.3.dll</HintPath>
     136    </Reference>
     137    <Reference Include="HeuristicLab.Optimization-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
     138      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Optimization-3.3.dll</HintPath>
     139    </Reference>
     140    <Reference Include="HeuristicLab.Parameters-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
     141      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Parameters-3.3.dll</HintPath>
     142    </Reference>
     143    <Reference Include="HeuristicLab.Persistence-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
     144      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Persistence-3.3.dll</HintPath>
     145    </Reference>
     146    <Reference Include="HeuristicLab.PluginInfrastructure-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
     147      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.PluginInfrastructure-3.3.dll</HintPath>
     148    </Reference>
     149    <Reference Include="HeuristicLab.Problems.DataAnalysis.Symbolic.Classification-3.4">
     150      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Problems.DataAnalysis.Symbolic.Classification-3.4.dll</HintPath>
     151    </Reference>
     152    <Reference Include="HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4">
     153      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.dll</HintPath>
     154    </Reference>
     155    <Reference Include="HeuristicLab.Problems.Instances-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
     156      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Problems.Instances-3.3.dll</HintPath>
     157    </Reference>
     158    <Reference Include="HeuristicLab.Random-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
     159      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Random-3.3.dll</HintPath>
     160    </Reference>
    107161    <Reference Include="LibSVM-1.6.3, Version=1.6.3.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
    108       <HintPath>..\..\bin\LibSVM-1.6.3.dll</HintPath>
     162      <HintPath>..\..\..\..\trunk\sources\bin\LibSVM-1.6.3.dll</HintPath>
    109163      <Private>False</Private>
    110164    </Reference>
    … …  
    113167      <RequiredTargetFramework>3.5</RequiredTargetFramework>
    114168    </Reference>
    115     <Reference Include="System.Data" />
    116169    <Reference Include="System.Drawing" />
    117     <Reference Include="System.Xml" />
    118170  </ItemGroup>
    119171  <ItemGroup>
    … …  
    122174    </Compile>
    123175    <Compile Include="FixedDataAnalysisAlgorithm.cs" />
     176    <Compile Include="GaussianProcess\CovarianceProd.cs" />
     177    <Compile Include="GaussianProcess\CovarianceSum.cs" />
     178    <Compile Include="GaussianProcess\CovariancePeriodic.cs" />
     179    <Compile Include="GaussianProcess\GaussianProcessHyperparameterInitializer.cs" />
     180    <Compile Include="GaussianProcess\GaussianProcessRegressionSolutionCreator.cs" />
     181    <Compile Include="GaussianProcess\GaussianProcessRegressionModelCreator.cs" />
     182    <Compile Include="GaussianProcess\CovarianceLinear.cs" />
     183    <Compile Include="GaussianProcess\GaussianProcessModelCreator.cs">
     184      <SubType>Code</SubType>
     185    </Compile>
     186    <Compile Include="GaussianProcess\MeanLinear.cs" />
     187    <Compile Include="GaussianProcess\Util.cs" />
     188    <Compile Include="GaussianProcess\MeanZero.cs" />
     189    <Compile Include="GaussianProcess\MeanConst.cs" />
     190    <Compile Include="GaussianProcess\IMeanFunction.cs" />
     191    <Compile Include="GaussianProcess\CovarianceSEard.cs" />
     192    <Compile Include="GaussianProcess\CovarianceSEiso.cs" />
     193    <Compile Include="GaussianProcess\GaussianProcessModel.cs" />
     194    <Compile Include="GaussianProcess\GaussianProcessRegression.cs" />
     195    <Compile Include="GaussianProcess\GaussianProcessRegressionSolution.cs" />
     196    <Compile Include="GaussianProcess\ICovarianceFunction.cs" />
     197    <Compile Include="Interfaces\IGaussianProcessModel.cs" />
     198    <Compile Include="Interfaces\IGaussianProcessSolution.cs" />
    124199    <Compile Include="Interfaces\INearestNeighbourClassificationSolution.cs" />
    125200    <Compile Include="Interfaces\INearestNeighbourRegressionSolution.cs" />
    … …  
    144219    </Compile>
    145220    <Compile Include="Linear\AlglibUtil.cs" />
     221    <Compile Include="Linear\Scaling.cs" />
    146222    <Compile Include="Linear\LinearDiscriminantAnalysis.cs" />
    147223    <Compile Include="Linear\LinearRegression.cs">
    … …  
    185261      <SubType>Code</SubType>
    186262    </Compile>
    187   </ItemGroup>
    188   <ItemGroup>
    189     <ProjectReference Include="..\..\HeuristicLab.Collections\3.3\HeuristicLab.Collections-3.3.csproj">
    190       <Project>{958B43BC-CC5C-4FA2-8628-2B3B01D890B6}</Project>
    191       <Name>HeuristicLab.Collections-3.3</Name>
    192       <Private>False</Private>
    193     </ProjectReference>
    194     <ProjectReference Include="..\..\HeuristicLab.Common.Resources\3.3\HeuristicLab.Common.Resources-3.3.csproj">
    195       <Project>{0E27A536-1C4A-4624-A65E-DC4F4F23E3E1}</Project>
    196       <Name>HeuristicLab.Common.Resources-3.3</Name>
    197       <Private>False</Private>
    198     </ProjectReference>
    199     <ProjectReference Include="..\..\HeuristicLab.Common\3.3\HeuristicLab.Common-3.3.csproj">
    200       <Project>{A9AD58B9-3EF9-4CC1-97E5-8D909039FF5C}</Project>
    201       <Name>HeuristicLab.Common-3.3</Name>
    202       <Private>False</Private>
    203     </ProjectReference>
    204     <ProjectReference Include="..\..\HeuristicLab.Core\3.3\HeuristicLab.Core-3.3.csproj">
    205       <Project>{C36BD924-A541-4A00-AFA8-41701378DDC5}</Project>
    206       <Name>HeuristicLab.Core-3.3</Name>
    207       <Private>False</Private>
    208     </ProjectReference>
    209     <ProjectReference Include="..\..\HeuristicLab.Data\3.3\HeuristicLab.Data-3.3.csproj">
    210       <Project>{BBAB9DF5-5EF3-4BA8-ADE9-B36E82114937}</Project>
    211       <Name>HeuristicLab.Data-3.3</Name>
    212       <Private>False</Private>
    213     </ProjectReference>
     263    <Compile Include="TimeSeries\AutoregressiveModeling.cs" />
     264  </ItemGroup>
     265  <ItemGroup>
    214266    <ProjectReference Include="..\..\HeuristicLab.Encodings.SymbolicExpressionTreeEncoding\3.4\HeuristicLab.Encodings.SymbolicExpressionTreeEncoding-3.4.csproj">
    215267      <Project>{06D4A186-9319-48A0-BADE-A2058D462EEA}</Project>
    216268      <Name>HeuristicLab.Encodings.SymbolicExpressionTreeEncoding-3.4</Name>
    217       <Private>False</Private>
    218269    </ProjectReference>
    219     <ProjectReference Include="..\..\HeuristicLab.Optimization\3.3\HeuristicLab.Optimization-3.3.csproj">
    220       <Project>{14AB8D24-25BC-400C-A846-4627AA945192}</Project>
    221       <Name>HeuristicLab.Optimization-3.3</Name>
    222       <Private>False</Private>
    223     </ProjectReference>
    224     <ProjectReference Include="..\..\HeuristicLab.Parameters\3.3\HeuristicLab.Parameters-3.3.csproj">
    225       <Project>{56F9106A-079F-4C61-92F6-86A84C2D84B7}</Project>
    226       <Name>HeuristicLab.Parameters-3.3</Name>
    227       <Private>False</Private>
    228     </ProjectReference>
    229     <ProjectReference Include="..\..\HeuristicLab.Persistence\3.3\HeuristicLab.Persistence-3.3.csproj">
    230       <Project>{102BC7D3-0EF9-439C-8F6D-96FF0FDB8E1B}</Project>
    231       <Name>HeuristicLab.Persistence-3.3</Name>
    232       <Private>False</Private>
    233     </ProjectReference>
    234     <ProjectReference Include="..\..\HeuristicLab.PluginInfrastructure\3.3\HeuristicLab.PluginInfrastructure-3.3.csproj">
    235       <Project>{94186A6A-5176-4402-AE83-886557B53CCA}</Project>
    236       <Name>HeuristicLab.PluginInfrastructure-3.3</Name>
    237       <Private>False</Private>
    238     </ProjectReference>
    239     <ProjectReference Include="..\..\HeuristicLab.Problems.DataAnalysis.Symbolic.Classification\3.4\HeuristicLab.Problems.DataAnalysis.Symbolic.Classification-3.4.csproj">
    240       <Project>{05BAE4E1-A9FA-4644-AA77-42558720159E}</Project>
    241       <Name>HeuristicLab.Problems.DataAnalysis.Symbolic.Classification-3.4</Name>
    242       <Private>False</Private>
    243     </ProjectReference>
    244     <ProjectReference Include="..\..\HeuristicLab.Problems.DataAnalysis.Symbolic.Regression\3.4\HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj">
    245       <Project>{5AC82412-911B-4FA2-A013-EDC5E3F3FCC2}</Project>
    246       <Name>HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4</Name>
    247       <Private>False</Private>
     270    <ProjectReference Include="..\..\HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis\3.4\HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis-3.4.csproj">
     271      <Project>{07486E68-1517-4B9D-A58D-A38E99AE71AB}</Project>
     272      <Name>HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis-3.4</Name>
    248273    </ProjectReference>
    249274    <ProjectReference Include="..\..\HeuristicLab.Problems.DataAnalysis.Symbolic\3.4\HeuristicLab.Problems.DataAnalysis.Symbolic-3.4.csproj">
    250275      <Project>{3D28463F-EC96-4D82-AFEE-38BE91A0CA00}</Project>
    251276      <Name>HeuristicLab.Problems.DataAnalysis.Symbolic-3.4</Name>
    252       <Private>False</Private>
    253277    </ProjectReference>
    254278    <ProjectReference Include="..\..\HeuristicLab.Problems.DataAnalysis\3.4\HeuristicLab.Problems.DataAnalysis-3.4.csproj">
    255279      <Project>{DF87C13E-A889-46FF-8153-66DCAA8C5674}</Project>
    256280      <Name>HeuristicLab.Problems.DataAnalysis-3.4</Name>
    257       <Private>False</Private>
    258     </ProjectReference>
    259     <ProjectReference Include="..\..\HeuristicLab.Problems.Instances\3.3\HeuristicLab.Problems.Instances-3.3.csproj">
    260       <Project>{3540E29E-4793-49E7-8EE2-FEA7F61C3994}</Project>
    261       <Name>HeuristicLab.Problems.Instances-3.3</Name>
    262       <Private>False</Private>
    263281    </ProjectReference>
    264282  </ItemGroup>

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/Interfaces/IDataAnalysisAlgorithm.cs

    r7259 r8430  
    2828  /// </summary>
    2929  public interface IDataAnalysisAlgorithm<T> : IAlgorithm where T : class, IDataAnalysisProblem {
    30     new T Problem { get; }
     30    new T Problem { get; set; }
    3131  }
    3232}

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/AlglibUtil.cs

    r7259 r8430  
    4545      return matrix;
    4646    }
     47    public static double[,] PrepareAndScaleInputMatrix(Dataset dataset, IEnumerable<string> variables, IEnumerable<int> rows, Scaling scaling) {
     48      List<string> variablesList = variables.ToList();
     49      List<int> rowsList = rows.ToList();
     50
     51      double[,] matrix = new double[rowsList.Count, variablesList.Count];
     52
     53      int col = 0;
     54      foreach (string column in variables) {
     55        var values = scaling.GetScaledValues(dataset, column, rows);
     56        int row = 0;
     57        foreach (var value in values) {
     58          matrix[row, col] = value;
     59          row++;
     60        }
     61        col++;
     62      }
     63
     64      return matrix;
     65    }
    4766  }
    4867}

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearDiscriminantAnalysis.cs

    r7259 r8430  
    6868      string targetVariable = problemData.TargetVariable;
    6969      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
    70       IEnumerable<int> rows = problemData.TrainingIndizes;
     70      IEnumerable<int> rows = problemData.TrainingIndices;
    7171      int nClasses = problemData.ClassNames.Count();
    7272      double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearRegression.cs

    r7588 r8430  
    7272      string targetVariable = problemData.TargetVariable;
    7373      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
    74       IEnumerable<int> rows = problemData.TrainingIndizes;
     74      IEnumerable<int> rows = problemData.TrainingIndices;
    7575      double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
    7676      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitClassification.cs

    r7259 r8430  
    6969      string targetVariable = problemData.TargetVariable;
    7070      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
    71       IEnumerable<int> rows = problemData.TrainingIndizes;
     71      IEnumerable<int> rows = problemData.TrainingIndices;
    7272      double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
    7373      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
    … …  
    8181      int nClasses = classValues.Count();
    8282      // map original class values to values [0..nClasses-1]
    83       Dictionary<double, double> classIndizes = new Dictionary<double, double>();
     83      Dictionary<double, double> classIndices = new Dictionary<double, double>();
    8484      for (int i = 0; i < nClasses; i++) {
    85         classIndizes[classValues[i]] = i;
     85        classIndices[classValues[i]] = i;
    8686      }
    8787      for (int row = 0; row < nRows; row++) {
    88         inputMatrix[row, nFeatures] = classIndizes[inputMatrix[row, nFeatures]];
     88        inputMatrix[row, nFeatures] = classIndices[inputMatrix[row, nFeatures]];
    8989      }
    9090      int info;

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourClassification.cs

    r7259 r8430  
    8787      string targetVariable = problemData.TargetVariable;
    8888      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
    89       IEnumerable<int> rows = problemData.TrainingIndizes;
     89      IEnumerable<int> rows = problemData.TrainingIndices;
    9090      double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
    9191      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
    … …  
    9999      int nClasses = classValues.Count();
    100100      // map original class values to values [0..nClasses-1]
    101       Dictionary<double, double> classIndizes = new Dictionary<double, double>();
     101      Dictionary<double, double> classIndices = new Dictionary<double, double>();
    102102      for (int i = 0; i < nClasses; i++) {
    103         classIndizes[classValues[i]] = i;
     103        classIndices[classValues[i]] = i;
    104104      }
    105105      for (int row = 0; row < nRows; row++) {
    106         inputMatrix[row, nFeatures] = classIndizes[inputMatrix[row, nFeatures]];
     106        inputMatrix[row, nFeatures] = classIndices[inputMatrix[row, nFeatures]];
    107107      }
    108108      alglib.nearestneighbor.kdtreebuild(inputMatrix, nRows, inputMatrix.GetLength(1) - 1, 1, 2, kdtree);

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourRegression.cs

    r7259 r8430  
    8787      string targetVariable = problemData.TargetVariable;
    8888      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
    89       IEnumerable<int> rows = problemData.TrainingIndizes;
     89      IEnumerable<int> rows = problemData.TrainingIndices;
    9090      double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
    9191      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkClassification.cs

    r7259 r8430  
    5353      get { return (IFixedValueParameter<DoubleValue>)Parameters[DecayParameterName]; }
    5454    }
    55     public ConstrainedValueParameter<IntValue> HiddenLayersParameter {
    56       get { return (ConstrainedValueParameter<IntValue>)Parameters[HiddenLayersParameterName]; }
     55    public IConstrainedValueParameter<IntValue> HiddenLayersParameter {
     56      get { return (IConstrainedValueParameter<IntValue>)Parameters[HiddenLayersParameterName]; }
    5757    }
    5858    public IFixedValueParameter<IntValue> NodesInFirstHiddenLayerParameter {
    … …  
    185185      string targetVariable = problemData.TargetVariable;
    186186      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
    187       IEnumerable<int> rows = problemData.TrainingIndizes;
     187      IEnumerable<int> rows = problemData.TrainingIndices;
    188188      double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
    189189      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
    … …  
    195195      int nClasses = classValues.Count();
    196196      // map original class values to values [0..nClasses-1]
    197       Dictionary<double, double> classIndizes = new Dictionary<double, double>();
     197      Dictionary<double, double> classIndices = new Dictionary<double, double>();
    198198      for (int i = 0; i < nClasses; i++) {
    199         classIndizes[classValues[i]] = i;
     199        classIndices[classValues[i]] = i;
    200200      }
    201201      for (int row = 0; row < nRows; row++) {
    202         inputMatrix[row, nFeatures] = classIndizes[inputMatrix[row, nFeatures]];
     202        inputMatrix[row, nFeatures] = classIndices[inputMatrix[row, nFeatures]];
    203203      }
    204204

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleClassification.cs

    r7259 r8430  
    5757      get { return (IFixedValueParameter<DoubleValue>)Parameters[DecayParameterName]; }
    5858    }
    59     public ConstrainedValueParameter<IntValue> HiddenLayersParameter {
    60       get { return (ConstrainedValueParameter<IntValue>)Parameters[HiddenLayersParameterName]; }
     59    public IConstrainedValueParameter<IntValue> HiddenLayersParameter {
     60      get { return (IConstrainedValueParameter<IntValue>)Parameters[HiddenLayersParameterName]; }
    6161    }
    6262    public IFixedValueParameter<IntValue> NodesInFirstHiddenLayerParameter {
    … …  
    171171      string targetVariable = problemData.TargetVariable;
    172172      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
    173       IEnumerable<int> rows = problemData.TrainingIndizes;
     173      IEnumerable<int> rows = problemData.TrainingIndices;
    174174      double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
    175175      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
    … …  
    181181      int nClasses = classValues.Count();
    182182      // map original class values to values [0..nClasses-1]
    183       Dictionary<double, double> classIndizes = new Dictionary<double, double>();
     183      Dictionary<double, double> classIndices = new Dictionary<double, double>();
    184184      for (int i = 0; i < nClasses; i++) {
    185         classIndizes[classValues[i]] = i;
     185        classIndices[classValues[i]] = i;
    186186      }
    187187      for (int row = 0; row < nRows; row++) {
    188         inputMatrix[row, nFeatures] = classIndizes[inputMatrix[row, nFeatures]];
     188        inputMatrix[row, nFeatures] = classIndices[inputMatrix[row, nFeatures]];
    189189      }
    190190

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleRegression.cs

    r7259 r8430  
    5757      get { return (IFixedValueParameter<DoubleValue>)Parameters[DecayParameterName]; }
    5858    }
    59     public ConstrainedValueParameter<IntValue> HiddenLayersParameter {
    60       get { return (ConstrainedValueParameter<IntValue>)Parameters[HiddenLayersParameterName]; }
     59    public IConstrainedValueParameter<IntValue> HiddenLayersParameter {
     60      get { return (IConstrainedValueParameter<IntValue>)Parameters[HiddenLayersParameterName]; }
    6161    }
    6262    public IFixedValueParameter<IntValue> NodesInFirstHiddenLayerParameter {
    … …  
    170170      string targetVariable = problemData.TargetVariable;
    171171      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
    172       IEnumerable<int> rows = problemData.TrainingIndizes;
     172      IEnumerable<int> rows = problemData.TrainingIndices;
    173173      double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
    174174      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkRegression.cs

    r7259 r8430  
    2626using HeuristicLab.Core;
    2727using HeuristicLab.Data;
    28 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    2928using HeuristicLab.Optimization;
     29using HeuristicLab.Parameters;
    3030using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    3131using HeuristicLab.Problems.DataAnalysis;
    32 using HeuristicLab.Problems.DataAnalysis.Symbolic;
    33 using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
    34 using HeuristicLab.Parameters;
    3532
    3633namespace HeuristicLab.Algorithms.DataAnalysis {
    … …  
    5350      get { return (IFixedValueParameter<DoubleValue>)Parameters[DecayParameterName]; }
    5451    }
    55     public ConstrainedValueParameter<IntValue> HiddenLayersParameter {
    56       get { return (ConstrainedValueParameter<IntValue>)Parameters[HiddenLayersParameterName]; }
     52    public IConstrainedValueParameter<IntValue> HiddenLayersParameter {
     53      get { return (IConstrainedValueParameter<IntValue>)Parameters[HiddenLayersParameterName]; }
    5754    }
    5855    public IFixedValueParameter<IntValue> NodesInFirstHiddenLayerParameter {
    … …  
    186183      string targetVariable = problemData.TargetVariable;
    187184      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
    188       IEnumerable<int> rows = problemData.TrainingIndizes;
     185      IEnumerable<int> rows = problemData.TrainingIndices;
    189186      double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
    190187      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/Plugin.cs.frame

    r7943 r8430  
    2626  /// Plugin class for HeuristicLab.Algorithms.DataAnalysis plugin.
    2727  /// </summary>
    28   [Plugin("HeuristicLab.Algorithms.DataAnalysis", "Provides wrappers for data analysis algorithms implemented in external libraries (linear regression, linear discriminant analysis, k-means clustering, support vector classification and regression)", "3.4.2.$WCREV$")]
     28  [Plugin("HeuristicLab.Algorithms.DataAnalysis", "Provides wrappers for data analysis algorithms implemented in external libraries (linear regression, linear discriminant analysis, k-means clustering, support vector classification and regression)", "3.4.3.$WCREV$")]
    2929  [PluginFile("HeuristicLab.Algorithms.DataAnalysis-3.4.dll", PluginFileType.Assembly)]
    3030  [PluginDependency("HeuristicLab.ALGLIB", "3.5.0")]
    31   [PluginDependency("HeuristicLab.LibSVM", "1.6.3")]
     31  [PluginDependency("HeuristicLab.Algorithms.GradientDescent", "3.3")]
     32  [PluginDependency("HeuristicLab.Analysis", "3.3")]
    3233  [PluginDependency("HeuristicLab.Collections", "3.3")]
    3334  [PluginDependency("HeuristicLab.Common", "3.3")]
    … …  
    3536  [PluginDependency("HeuristicLab.Core", "3.3")]
    3637  [PluginDependency("HeuristicLab.Data", "3.3")]
     38  [PluginDependency("HeuristicLab.Encodings.RealVectorEncoding", "3.3")]
    3739  [PluginDependency("HeuristicLab.Encodings.SymbolicExpressionTreeEncoding", "3.4")]
     40  [PluginDependency("HeuristicLab.Operators", "3.3")]
    3841  [PluginDependency("HeuristicLab.Optimization", "3.3")]
    3942  [PluginDependency("HeuristicLab.Parameters", "3.3")]
    … …  
    4346  [PluginDependency("HeuristicLab.Problems.DataAnalysis.Symbolic.Classification", "3.4")]
    4447  [PluginDependency("HeuristicLab.Problems.DataAnalysis.Symbolic.Regression", "3.4")]
     48  [PluginDependency("HeuristicLab.LibSVM", "1.6.3")]
    4549  public class HeuristicLabAlgorithmsDataAnalysisPlugin : PluginBase {
    4650  }

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/Properties/AssemblyInfo.cs.frame

    r7259 r8430  
    5353// by using the '*' as shown below:
    5454[assembly: AssemblyVersion("3.4.0.0")]
    55 [assembly: AssemblyFileVersion("3.4.2.$WCREV$")]
     55[assembly: AssemblyFileVersion("3.4.3.$WCREV$")]

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestClassification.cs

    r7259 r8430  
    9797      string targetVariable = problemData.TargetVariable;
    9898      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
    99       IEnumerable<int> rows = problemData.TrainingIndizes;
     99      IEnumerable<int> rows = problemData.TrainingIndices;
    100100      double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
    101101      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
    … …  
    111111      int nClasses = classValues.Count();
    112112      // map original class values to values [0..nClasses-1]
    113       Dictionary<double, double> classIndizes = new Dictionary<double, double>();
     113      Dictionary<double, double> classIndices = new Dictionary<double, double>();
    114114      for (int i = 0; i < nClasses; i++) {
    115         classIndizes[classValues[i]] = i;
     115        classIndices[classValues[i]] = i;
    116116      }
    117117      for (int row = 0; row < nRows; row++) {
    118         inputMatrix[row, nCols - 1] = classIndizes[inputMatrix[row, nCols - 1]];
     118        inputMatrix[row, nCols - 1] = classIndices[inputMatrix[row, nCols - 1]];
    119119      }
    120120      // execute random forest algorithm

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestRegression.cs

    r7259 r8430  
    9797      string targetVariable = problemData.TargetVariable;
    9898      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
    99       IEnumerable<int> rows = problemData.TrainingIndizes;
     99      IEnumerable<int> rows = problemData.TrainingIndices;
    100100      double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
    101101      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorClassification.cs

    r7430 r8430  
    4646
    4747    #region parameter properties
    48     public IValueParameter<StringValue> SvmTypeParameter {
    49       get { return (IValueParameter<StringValue>)Parameters[SvmTypeParameterName]; }
     48    public IConstrainedValueParameter<StringValue> SvmTypeParameter {
     49      get { return (IConstrainedValueParameter<StringValue>)Parameters[SvmTypeParameterName]; }
    5050    }
    51     public IValueParameter<StringValue> KernelTypeParameter {
    52       get { return (IValueParameter<StringValue>)Parameters[KernelTypeParameterName]; }
     51    public IConstrainedValueParameter<StringValue> KernelTypeParameter {
     52      get { return (IConstrainedValueParameter<StringValue>)Parameters[KernelTypeParameterName]; }
    5353    }
    5454    public IValueParameter<DoubleValue> NuParameter {
    … …  
    6565    public StringValue SvmType {
    6666      get { return SvmTypeParameter.Value; }
     67      set { SvmTypeParameter.Value = value; }
    6768    }
    6869    public StringValue KernelType {
    6970      get { return KernelTypeParameter.Value; }
     71      set { KernelTypeParameter.Value = value; }
    7072    }
    7173    public DoubleValue Nu {
    … …  
    130132      Dataset dataset = problemData.Dataset;
    131133      string targetVariable = problemData.TargetVariable;
    132       IEnumerable<int> rows = problemData.TrainingIndizes;
     134      IEnumerable<int> rows = problemData.TrainingIndices;
    133135
    134136      //extract SVM parameters from scope and set them

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorMachineModel.cs

    r7259 r8430  
    162162      // calculate predictions for the currently requested rows
    163163      SVM.Problem problem = SupportVectorMachineUtil.CreateSvmProblem(dataset, targetVariable, allowedInputVariables, rows);
    164       SVM.Problem scaledProblem = Scaling.Scale(RangeTransform, problem);
     164      SVM.Problem scaledProblem = SVM.Scaling.Scale(RangeTransform, problem);
    165165
    166166      for (int i = 0; i < scaledProblem.Count; i++) {

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorRegression.cs

    r7306 r8430  
    4747
    4848    #region parameter properties
    49     public IValueParameter<StringValue> SvmTypeParameter {
    50       get { return (IValueParameter<StringValue>)Parameters[SvmTypeParameterName]; }
     49    public IConstrainedValueParameter<StringValue> SvmTypeParameter {
     50      get { return (IConstrainedValueParameter<StringValue>)Parameters[SvmTypeParameterName]; }
    5151    }
    52     public IValueParameter<StringValue> KernelTypeParameter {
    53       get { return (IValueParameter<StringValue>)Parameters[KernelTypeParameterName]; }
     52    public IConstrainedValueParameter<StringValue> KernelTypeParameter {
     53      get { return (IConstrainedValueParameter<StringValue>)Parameters[KernelTypeParameterName]; }
    5454    }
    5555    public IValueParameter<DoubleValue> NuParameter {
    … …  
    6969    public StringValue SvmType {
    7070      get { return SvmTypeParameter.Value; }
     71      set { SvmTypeParameter.Value = value; }
    7172    }
    7273    public StringValue KernelType {
    7374      get { return KernelTypeParameter.Value; }
     75      set { KernelTypeParameter.Value = value; }
    7476    }
    7577    public DoubleValue Nu {
    … …  
    138140      Dataset dataset = problemData.Dataset;
    139141      string targetVariable = problemData.TargetVariable;
    140       IEnumerable<int> rows = problemData.TrainingIndizes;
     142      IEnumerable<int> rows = problemData.TrainingIndices;
    141143
    142144      //extract SVM parameters from scope and set them

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/kMeans/KMeansClustering.cs

    r8080 r8430  
    8585      Dataset dataset = problemData.Dataset;
    8686      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
    87       IEnumerable<int> rows = problemData.TrainingIndizes;
     87      IEnumerable<int> rows = problemData.TrainingIndices;
    8888      int info;
    8989      double[,] centers;

  • branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/kMeans/KMeansClusteringSolution.cs

    r7259 r8430  
    5252    public KMeansClusteringSolution(KMeansClusteringModel model, IClusteringProblemData problemData)
    5353      : base(model, problemData) {
    54       double trainingIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TrainingIndizes);
    55       double testIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TestIndizes);
     54      double trainingIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TrainingIndices);
     55      double testIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TestIndices);
    5656      this.Add(new Result(TrainingIntraClusterSumOfSquaresResultName, "The sum of squared distances of points of the training partition to the cluster center (is minimized by k-Means).", new DoubleValue(trainingIntraClusterSumOfSquares)));
    5757      this.Add(new Result(TestIntraClusterSumOfSquaresResultName, "The sum of squared distances of points of the test partition to the cluster center (is minimized by k-Means).", new DoubleValue(testIntraClusterSumOfSquares)));
Note: See TracChangeset for help on using the changeset viewer.