Example #1
0
 private LbfgsAlgorithm(LbfgsAlgorithm original, Cloner cloner)
     : base(original, cloner)
 {
     initializer     = cloner.Clone(original.initializer);
     makeStep        = cloner.Clone(original.makeStep);
     updateResults   = cloner.Clone(original.updateResults);
     analyzer        = cloner.Clone(original.analyzer);
     solutionCreator = cloner.Clone(original.solutionCreator);
     evaluator       = cloner.Clone(original.evaluator);
     RegisterEvents();
 }
 private LbfgsUpdateResults(LbfgsUpdateResults original, Cloner cloner) : base(original, cloner)
 {
 }
    protected GaussianProcessBase(IDataAnalysisProblem problem)
      : base() {
      Problem = problem;
      Parameters.Add(new ValueParameter<IMeanFunction>(MeanFunctionParameterName, "The mean function to use.", new MeanConst()));
      Parameters.Add(new ValueParameter<ICovarianceFunction>(CovarianceFunctionParameterName, "The covariance function to use.", new CovarianceSquaredExponentialIso()));
      Parameters.Add(new ValueParameter<IntValue>(MinimizationIterationsParameterName, "The number of iterations for likelihood optimization with LM-BFGS.", new IntValue(20)));
      Parameters.Add(new ValueParameter<IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
      Parameters.Add(new ValueParameter<BoolValue>(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));

      Parameters.Add(new ValueParameter<BoolValue>(ApproximateGradientsParameterName, "Indicates that gradients should not be approximated (necessary for LM-BFGS).", new BoolValue(false)));
      Parameters[ApproximateGradientsParameterName].Hidden = true; // should not be changed

      Parameters.Add(new FixedValueParameter<BoolValue>(ScaleInputValuesParameterName,
        "Determines if the input variable values are scaled to the range [0..1] for training.", new BoolValue(true)));
      Parameters[ScaleInputValuesParameterName].Hidden = true;

      // necessary for BFGS
      Parameters.Add(new ValueParameter<BoolValue>("Maximization", new BoolValue(false)));
      Parameters["Maximization"].Hidden = true;

      var randomCreator = new HeuristicLab.Random.RandomCreator();
      var gpInitializer = new GaussianProcessHyperparameterInitializer();
      var bfgsInitializer = new LbfgsInitializer();
      var makeStep = new LbfgsMakeStep();
      var branch = new ConditionalBranch();
      var modelCreator = new Placeholder();
      var updateResults = new LbfgsUpdateResults();
      var analyzer = new LbfgsAnalyzer();
      var finalModelCreator = new Placeholder();
      var finalAnalyzer = new LbfgsAnalyzer();
      var solutionCreator = new Placeholder();

      OperatorGraph.InitialOperator = randomCreator;
      randomCreator.SeedParameter.ActualName = SeedParameterName;
      randomCreator.SeedParameter.Value = null;
      randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameterName;
      randomCreator.SetSeedRandomlyParameter.Value = null;
      randomCreator.Successor = gpInitializer;

      gpInitializer.CovarianceFunctionParameter.ActualName = CovarianceFunctionParameterName;
      gpInitializer.MeanFunctionParameter.ActualName = MeanFunctionParameterName;
      gpInitializer.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
      gpInitializer.HyperparameterParameter.ActualName = HyperparameterParameterName;
      gpInitializer.RandomParameter.ActualName = randomCreator.RandomParameter.Name;
      gpInitializer.Successor = bfgsInitializer;

      bfgsInitializer.IterationsParameter.ActualName = MinimizationIterationsParameterName;
      bfgsInitializer.PointParameter.ActualName = HyperparameterParameterName;
      bfgsInitializer.ApproximateGradientsParameter.ActualName = ApproximateGradientsParameterName;
      bfgsInitializer.Successor = makeStep;

      makeStep.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
      makeStep.PointParameter.ActualName = HyperparameterParameterName;
      makeStep.Successor = branch;

      branch.ConditionParameter.ActualName = makeStep.TerminationCriterionParameter.Name;
      branch.FalseBranch = modelCreator;
      branch.TrueBranch = finalModelCreator;

      modelCreator.OperatorParameter.ActualName = ModelCreatorParameterName;
      modelCreator.Successor = updateResults;

      updateResults.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
      updateResults.QualityParameter.ActualName = NegativeLogLikelihoodParameterName;
      updateResults.QualityGradientsParameter.ActualName = HyperparameterGradientsParameterName;
      updateResults.ApproximateGradientsParameter.ActualName = ApproximateGradientsParameterName;
      updateResults.Successor = analyzer;

      analyzer.QualityParameter.ActualName = NegativeLogLikelihoodParameterName;
      analyzer.PointParameter.ActualName = HyperparameterParameterName;
      analyzer.QualityGradientsParameter.ActualName = HyperparameterGradientsParameterName;
      analyzer.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
      analyzer.PointsTableParameter.ActualName = "Hyperparameter table";
      analyzer.QualityGradientsTableParameter.ActualName = "Gradients table";
      analyzer.QualitiesTableParameter.ActualName = "Negative log likelihood table";
      analyzer.Successor = makeStep;

      finalModelCreator.OperatorParameter.ActualName = ModelCreatorParameterName;
      finalModelCreator.Successor = finalAnalyzer;

      finalAnalyzer.QualityParameter.ActualName = NegativeLogLikelihoodParameterName;
      finalAnalyzer.PointParameter.ActualName = HyperparameterParameterName;
      finalAnalyzer.QualityGradientsParameter.ActualName = HyperparameterGradientsParameterName;
      finalAnalyzer.PointsTableParameter.ActualName = analyzer.PointsTableParameter.ActualName;
      finalAnalyzer.QualityGradientsTableParameter.ActualName = analyzer.QualityGradientsTableParameter.ActualName;
      finalAnalyzer.QualitiesTableParameter.ActualName = analyzer.QualitiesTableParameter.ActualName;
      finalAnalyzer.Successor = solutionCreator;

      solutionCreator.OperatorParameter.ActualName = SolutionCreatorParameterName;
    }
 private LbfgsUpdateResults(LbfgsUpdateResults original, Cloner cloner) : base(original, cloner) { }
Example #5
0
        public LbfgsAlgorithm()
            : base()
        {
            Parameters.Add(new ValueParameter <IMultiAnalyzer>(AnalyzerParameterName, "The analyzers that will be executed on the solution.", new MultiAnalyzer()));
            Parameters.Add(new ValueParameter <IntValue>(MaxIterationsParameterName, "The maximal number of iterations for.", new IntValue(20)));
            Parameters.Add(new ValueParameter <IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
            Parameters.Add(new ValueParameter <BoolValue>(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
            Parameters.Add(new ValueParameter <BoolValue>(ApproximateGradientsParameterName, "Indicates that gradients should be approximated.", new BoolValue(true)));
            Parameters.Add(new OptionalValueParameter <DoubleValue>(GradientCheckStepSizeParameterName, "Step size for the gradient check (should be used for debugging the gradient calculation only)."));
            // these parameter should not be changed usually
            Parameters[ApproximateGradientsParameterName].Hidden  = true;
            Parameters[GradientCheckStepSizeParameterName].Hidden = true;

            var randomCreator = new RandomCreator();

            solutionCreator = new Placeholder();
            initializer     = new LbfgsInitializer();
            makeStep        = new LbfgsMakeStep();
            var branch = new ConditionalBranch();

            evaluator     = new Placeholder();
            updateResults = new LbfgsUpdateResults();
            var analyzerPlaceholder      = new Placeholder();
            var finalAnalyzerPlaceholder = new Placeholder();

            OperatorGraph.InitialOperator = randomCreator;

            randomCreator.SeedParameter.ActualName            = SeedParameterName;
            randomCreator.SeedParameter.Value                 = null;
            randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameterName;
            randomCreator.SetSeedRandomlyParameter.Value      = null;
            randomCreator.Successor = solutionCreator;

            solutionCreator.Name      = "(Solution Creator)";
            solutionCreator.Successor = initializer;

            initializer.IterationsParameter.ActualName           = MaxIterationsParameterName;
            initializer.ApproximateGradientsParameter.ActualName = ApproximateGradientsParameterName;
            initializer.Successor = makeStep;

            makeStep.StateParameter.ActualName = initializer.StateParameter.Name;
            makeStep.Successor = branch;

            branch.ConditionParameter.ActualName = makeStep.TerminationCriterionParameter.Name;
            branch.FalseBranch = evaluator;
            branch.TrueBranch  = finalAnalyzerPlaceholder;

            evaluator.Name      = "(Evaluator)";
            evaluator.Successor = updateResults;

            updateResults.StateParameter.ActualName = initializer.StateParameter.Name;
            updateResults.ApproximateGradientsParameter.ActualName = ApproximateGradientsParameterName;
            updateResults.Successor = analyzerPlaceholder;

            analyzerPlaceholder.Name = "(Analyzer)";
            analyzerPlaceholder.OperatorParameter.ActualName = AnalyzerParameterName;
            analyzerPlaceholder.Successor = makeStep;

            finalAnalyzerPlaceholder.Name = "(Analyzer)";
            finalAnalyzerPlaceholder.OperatorParameter.ActualName = AnalyzerParameterName;
            finalAnalyzerPlaceholder.Successor = null;

            analyzer = new LbfgsAnalyzer();
            analyzer.StateParameter.ActualName = initializer.StateParameter.Name;
        }
Example #6
0
    public NcaAlgorithm()
      : base() {
      Parameters.Add(new ValueParameter<IntValue>(SeedParameterName, "The seed of the random number generator.", new IntValue(0)));
      Parameters.Add(new ValueParameter<BoolValue>(SetSeedRandomlyParameterName, "A boolean flag that indicates whether the seed should be randomly reset each time the algorithm is run.", new BoolValue(true)));
      Parameters.Add(new FixedValueParameter<IntValue>(KParameterName, "The K for the nearest neighbor.", new IntValue(3)));
      Parameters.Add(new FixedValueParameter<IntValue>(DimensionsParameterName, "The number of dimensions that NCA should reduce the data to.", new IntValue(2)));
      Parameters.Add(new ConstrainedValueParameter<INcaInitializer>(InitializationParameterName, "Which method should be used to initialize the matrix. Typically LDA (linear discriminant analysis) should provide a good estimate."));
      Parameters.Add(new FixedValueParameter<IntValue>(NeighborSamplesParameterName, "How many of the neighbors should be sampled in order to speed up the calculation. This should be at least the value of k and at most the number of training instances minus one will be used.", new IntValue(60)));
      Parameters.Add(new FixedValueParameter<IntValue>(IterationsParameterName, "How many iterations the conjugate gradient (CG) method should be allowed to perform. The method might still terminate earlier if a local optima has already been reached.", new IntValue(50)));
      Parameters.Add(new FixedValueParameter<DoubleValue>(RegularizationParameterName, "A non-negative paramter which can be set to increase generalization and avoid overfitting. If set to 0 the algorithm is similar to NCA as proposed by Goldberger et al.", new DoubleValue(0)));
      Parameters.Add(new ValueParameter<INcaModelCreator>(NcaModelCreatorParameterName, "Creates an NCA model out of the matrix.", new NcaModelCreator()));
      Parameters.Add(new ValueParameter<INcaSolutionCreator>(NcaSolutionCreatorParameterName, "Creates an NCA solution given a model and some data.", new NcaSolutionCreator()));
      Parameters.Add(new ValueParameter<BoolValue>(ApproximateGradientsParameterName, "True if the gradient should be approximated otherwise they are computed exactly.", new BoolValue()));

      NcaSolutionCreatorParameter.Hidden = true;
      ApproximateGradientsParameter.Hidden = true;

      INcaInitializer defaultInitializer = null;
      foreach (var initializer in ApplicationManager.Manager.GetInstances<INcaInitializer>().OrderBy(x => x.ItemName)) {
        if (initializer is LdaInitializer) defaultInitializer = initializer;
        InitializationParameter.ValidValues.Add(initializer);
      }
      if (defaultInitializer != null) InitializationParameter.Value = defaultInitializer;

      var randomCreator = new RandomCreator();
      var ncaInitializer = new Placeholder();
      var bfgsInitializer = new LbfgsInitializer();
      var makeStep = new LbfgsMakeStep();
      var branch = new ConditionalBranch();
      var gradientCalculator = new NcaGradientCalculator();
      var modelCreator = new Placeholder();
      var updateResults = new LbfgsUpdateResults();
      var analyzer = new LbfgsAnalyzer();
      var finalModelCreator = new Placeholder();
      var finalAnalyzer = new LbfgsAnalyzer();
      var solutionCreator = new Placeholder();

      OperatorGraph.InitialOperator = randomCreator;
      randomCreator.SeedParameter.ActualName = SeedParameterName;
      randomCreator.SeedParameter.Value = null;
      randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameterName;
      randomCreator.SetSeedRandomlyParameter.Value = null;
      randomCreator.Successor = ncaInitializer;

      ncaInitializer.Name = "(NcaInitializer)";
      ncaInitializer.OperatorParameter.ActualName = InitializationParameterName;
      ncaInitializer.Successor = bfgsInitializer;

      bfgsInitializer.IterationsParameter.ActualName = IterationsParameterName;
      bfgsInitializer.PointParameter.ActualName = NcaMatrixParameterName;
      bfgsInitializer.ApproximateGradientsParameter.ActualName = ApproximateGradientsParameterName;
      bfgsInitializer.Successor = makeStep;

      makeStep.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
      makeStep.PointParameter.ActualName = NcaMatrixParameterName;
      makeStep.Successor = branch;

      branch.ConditionParameter.ActualName = makeStep.TerminationCriterionParameter.Name;
      branch.FalseBranch = gradientCalculator;
      branch.TrueBranch = finalModelCreator;

      gradientCalculator.Successor = modelCreator;

      modelCreator.OperatorParameter.ActualName = NcaModelCreatorParameterName;
      modelCreator.Successor = updateResults;

      updateResults.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
      updateResults.QualityParameter.ActualName = QualityParameterName;
      updateResults.QualityGradientsParameter.ActualName = NcaMatrixGradientsParameterName;
      updateResults.ApproximateGradientsParameter.ActualName = ApproximateGradientsParameterName;
      updateResults.Successor = analyzer;

      analyzer.QualityParameter.ActualName = QualityParameterName;
      analyzer.PointParameter.ActualName = NcaMatrixParameterName;
      analyzer.QualityGradientsParameter.ActualName = NcaMatrixGradientsParameterName;
      analyzer.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
      analyzer.PointsTableParameter.ActualName = "Matrix table";
      analyzer.QualityGradientsTableParameter.ActualName = "Gradients table";
      analyzer.QualitiesTableParameter.ActualName = "Qualities";
      analyzer.Successor = makeStep;

      finalModelCreator.OperatorParameter.ActualName = NcaModelCreatorParameterName;
      finalModelCreator.Successor = finalAnalyzer;

      finalAnalyzer.QualityParameter.ActualName = QualityParameterName;
      finalAnalyzer.PointParameter.ActualName = NcaMatrixParameterName;
      finalAnalyzer.QualityGradientsParameter.ActualName = NcaMatrixGradientsParameterName;
      finalAnalyzer.PointsTableParameter.ActualName = analyzer.PointsTableParameter.ActualName;
      finalAnalyzer.QualityGradientsTableParameter.ActualName = analyzer.QualityGradientsTableParameter.ActualName;
      finalAnalyzer.QualitiesTableParameter.ActualName = analyzer.QualitiesTableParameter.ActualName;
      finalAnalyzer.Successor = solutionCreator;

      solutionCreator.OperatorParameter.ActualName = NcaSolutionCreatorParameterName;

      Problem = new ClassificationProblem();
    }
Example #7
0
    public LbfgsAlgorithm()
      : base() {
      Parameters.Add(new ValueParameter<IMultiAnalyzer>(AnalyzerParameterName, "The analyzers that will be executed on the solution.", new MultiAnalyzer()));
      Parameters.Add(new ValueParameter<IntValue>(MaxIterationsParameterName, "The maximal number of iterations for.", new IntValue(20)));
      Parameters.Add(new ValueParameter<IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
      Parameters.Add(new ValueParameter<BoolValue>(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
      Parameters.Add(new ValueParameter<BoolValue>(ApproximateGradientsParameterName, "Indicates that gradients should be approximated.", new BoolValue(true)));
      Parameters.Add(new OptionalValueParameter<DoubleValue>(GradientCheckStepSizeParameterName, "Step size for the gradient check (should be used for debugging the gradient calculation only)."));
      // these parameter should not be changed usually
      Parameters[ApproximateGradientsParameterName].Hidden = true;
      Parameters[GradientCheckStepSizeParameterName].Hidden = true;

      var randomCreator = new RandomCreator();
      solutionCreator = new Placeholder();
      initializer = new LbfgsInitializer();
      makeStep = new LbfgsMakeStep();
      var branch = new ConditionalBranch();
      evaluator = new Placeholder();
      updateResults = new LbfgsUpdateResults();
      var analyzerPlaceholder = new Placeholder();
      var finalAnalyzerPlaceholder = new Placeholder();

      OperatorGraph.InitialOperator = randomCreator;

      randomCreator.SeedParameter.ActualName = SeedParameterName;
      randomCreator.SeedParameter.Value = null;
      randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameterName;
      randomCreator.SetSeedRandomlyParameter.Value = null;
      randomCreator.Successor = solutionCreator;

      solutionCreator.Name = "(Solution Creator)";
      solutionCreator.Successor = initializer;

      initializer.IterationsParameter.ActualName = MaxIterationsParameterName;
      initializer.ApproximateGradientsParameter.ActualName = ApproximateGradientsParameterName;
      initializer.Successor = makeStep;

      makeStep.StateParameter.ActualName = initializer.StateParameter.Name;
      makeStep.Successor = branch;

      branch.ConditionParameter.ActualName = makeStep.TerminationCriterionParameter.Name;
      branch.FalseBranch = evaluator;
      branch.TrueBranch = finalAnalyzerPlaceholder;

      evaluator.Name = "(Evaluator)";
      evaluator.Successor = updateResults;

      updateResults.StateParameter.ActualName = initializer.StateParameter.Name;
      updateResults.ApproximateGradientsParameter.ActualName = ApproximateGradientsParameterName;
      updateResults.Successor = analyzerPlaceholder;

      analyzerPlaceholder.Name = "(Analyzer)";
      analyzerPlaceholder.OperatorParameter.ActualName = AnalyzerParameterName;
      analyzerPlaceholder.Successor = makeStep;

      finalAnalyzerPlaceholder.Name = "(Analyzer)";
      finalAnalyzerPlaceholder.OperatorParameter.ActualName = AnalyzerParameterName;
      finalAnalyzerPlaceholder.Successor = null;

      analyzer = new LbfgsAnalyzer();
      analyzer.StateParameter.ActualName = initializer.StateParameter.Name;
    }
Example #8
0
 private LbfgsAlgorithm(LbfgsAlgorithm original, Cloner cloner)
   : base(original, cloner) {
   initializer = cloner.Clone(original.initializer);
   makeStep = cloner.Clone(original.makeStep);
   updateResults = cloner.Clone(original.updateResults);
   analyzer = cloner.Clone(original.analyzer);
   solutionCreator = cloner.Clone(original.solutionCreator);
   evaluator = cloner.Clone(original.evaluator);
   RegisterEvents();
 }