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();
}
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 FixedValueParameter <BoolValue>("Maximization (BFGS)", new BoolValue(false)));
Parameters["Maximization (BFGS)"].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.MaximizationParameter.ActualName = "Maximization (BFGS)";
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;
}
