#2435 (Update AlgLib to most recent version) – HeuristicLab

comment:1 Changed 9 years ago by gkronber

Owner set to gkronber
Status changed from new to accepted

comment:2 Changed 9 years ago by gkronber

Script to test alglib changes (including persistence)

// use 'vars' to access variables in the script's variable store (e.g. vars.x = 5)
// use 'vars[string]' to access variables via runtime strings (e.g. vars["x"] = 5)
// use 'vars.Contains(string)' to check if a variable exists
// use 'vars.Clear()' to remove all variables
// use 'foreach (KeyValuePair<string, object> v in vars) { ... }' to iterate over all variables
// use 'variables' to work with IEnumerable<T> extension methods on the script's variable store

using System;
using System.Linq;
using System.Collections.Generic;
using System.Threading;

using HeuristicLab.Core;
using HeuristicLab.Common;
using HeuristicLab.Algorithms.DataAnalysis;
using HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm;
using HeuristicLab.Problems.Instances.DataAnalysis;
using HeuristicLab.Optimization;
using HeuristicLab.Algorithms.CMAEvolutionStrategy;
using HeuristicLab.Algorithms.GradientDescent;
using HeuristicLab.Problems.TestFunctions;
using HeuristicLab.Problems.DataAnalysis;
using HeuristicLab.Problems.DataAnalysis.Symbolic;
using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
//using HeuristicLab.Collections;
using HeuristicLab.Data;
using HeuristicLab.ParallelEngine;

public class MyScript : HeuristicLab.Scripting.CSharpScriptBase {
  public override void Main() {
    TestRegressionTower();

    TestCMAES();
    TestLmbgfs();
    TestSymbReg();
  }
  
  public void TestRegressionTower() {
    // create tower problem
    var prov = new RegressionRealWorldInstanceProvider();
    var dd = prov.GetDataDescriptors().First(n=>n.Name.Contains("Tower"));
    var problemData = prov.LoadData(dd);
    var regProblem = new RegressionProblem();
    regProblem.Load(problemData);
    
    RunAlg("lr", new LinearRegression(), (IRegressionProblem)regProblem.Clone());
    RunAlg("rf", new RandomForestRegression(), (IRegressionProblem)regProblem.Clone());
       
    var smallProblem = (IRegressionProblem)regProblem.Clone();
    smallProblem.ProblemData.TrainingPartition.End = 300;
    RunAlg("gp", new GaussianProcessRegression(), smallProblem);
    
    RunAlg("nn", new NeuralNetworkRegression(), (IRegressionProblem)smallProblem.Clone());
    RunAlg("nne", new NeuralNetworkEnsembleRegression(), (IRegressionProblem)smallProblem.Clone());
    RunAlg("knn", new NearestNeighbourRegression(), (IRegressionProblem)regProblem.Clone());
  }
 
  
  public void TestCMAES() {   
    var cmaes = new CMAEvolutionStrategy();
    var prob = new SingleObjectiveTestFunctionProblem();
    prob.EvaluatorParameter.Value = new RosenbrockEvaluator();
    prob.ProblemSize = new IntValue(20);
    RunAlg("cmaes", cmaes, prob);
  }
  
  public void TestLmbgfs() {
    var lmbfgs = new LbfgsAlgorithm();
    var prob = new SingleObjectiveTestFunctionProblem();
    prob.EvaluatorParameter.Value = new RosenbrockEvaluator();
    prob.ProblemSize = new IntValue(20);
    lmbfgs.MaxIterations = 1000;
    RunAlg("lbfgs", lmbfgs, prob);
  }
  
  public void TestSymbReg() {
    // create tower problem
    var prov = new RegressionRealWorldInstanceProvider();
    var dd = prov.GetDataDescriptors().First(n=>n.Name.Contains("Tower"));
    var problemData = prov.LoadData(dd);
    var regProblem = new SymbolicRegressionSingleObjectiveProblem();
    regProblem.Load(problemData);
    regProblem.EvaluatorParameter.Value = new SymbolicRegressionConstantOptimizationEvaluator();
    regProblem.MaximumSymbolicExpressionTreeLength.Value = 100;
    regProblem.MaximumSymbolicExpressionTreeDepth.Value = 12;
    
    var gp = new OffspringSelectionGeneticAlgorithm();
    gp.Problem = regProblem;
    gp.MaximumGenerations.Value = 10;
    gp.ComparisonFactorLowerBound.Value = 1.0;
    gp.Mutator = gp.MutatorParameter.ValidValues.First(n => n.Name.Contains("TreeManipulator"));
    gp.Selector = gp.SelectorParameter.ValidValues.First(n => n.Name.Contains("GenderSpecific"));
    
    RunAlg("symbreg", gp, regProblem);
  }
  
  public void RunAlg(string name, IAlgorithm alg, IProblem prob) {
    alg.Problem = prob;
    var engineAlg = alg as EngineAlgorithm;
    if(engineAlg!=null) {
       engineAlg.Engine = new ParallelEngine();
    }
    vars[name] = alg;
    using(var wh = new AutoResetEvent(false)) {
      alg.ExceptionOccurred += (object sender, EventArgs<Exception> e) => { wh.Set(); };
      alg.Stopped += (object sender, EventArgs e) => { wh.Set(); };
      for(int i=0;i<10;i++) {
        alg.Prepare();
        alg.Start();
      
        wh.WaitOne();
      }
    }
  }
  // implement further classes and methods

}

comment:3 Changed 9 years ago by gkronber

r12790: updated alglib to version 3.9.0

Version 0, edited 9 years ago by gkronber (next)