Uses of Class
statistics.Sample

Packages that use Sample
classifiers   
io   
statistics   
transformations   
 

Uses of Sample in classifiers
 

Methods in classifiers with parameters of type Sample
 int NaiveBayes.evaluate(Sample s)
           
 int MixtureClassifier.evaluate(Sample s)
           
 int NaiveBayes.evaluate(Sample s, double[] scores)
           
 int MixtureClassifier.evaluate(Sample s, double[] scores)
           
 

Uses of Sample in io
 

Methods in io that return types with arguments of type Sample
 java.util.List<Sample> ChunkedDataSet.cachedData()
          Cache all chunks into a List for easier (single-core) access
 

Uses of Sample in statistics
 

Fields in statistics with type parameters of type Sample
 java.util.ArrayList<Sample> DataSet.samples
           
 java.util.HashMap<java.lang.Integer,java.util.ArrayList<Sample>> DataSet.samplesByClass
           
 

Methods in statistics that return Sample
static Sample Sample.meanSubstract(java.util.List<Sample> data)
          Subtract the mean value from all samples
 

Methods in statistics that return types with arguments of type Sample
static java.util.ArrayList<Sample> Sample.reduceToClass(java.util.ArrayList<Sample> data, int id)
          Remove all data from a list which is not of label id
static java.util.ArrayList<Sample> Sample.removeClass(java.util.ArrayList<Sample> data, int id)
          Remove samples of a certain class from the data set
static java.util.ArrayList<Sample> Sample.unlabeledArrayListFromArray(double[][] data)
          Create an ArrayList of Samples from a double array; one sample per row
 

Methods in statistics with parameters of type Sample
 void DataSet.addSample(Sample s)
           
 

Method parameters in statistics with type arguments of type Sample
static MixtureDensity Trainer.em(MixtureDensity initial, java.util.List<Sample> data)
          Given an initial mixture density, perform a single EM iteration w/ out the use of parallelization.
static MixtureDensity Trainer.em(MixtureDensity initial, java.util.List<Sample> data, int iterations)
          Perform a number of EM iterations (single-core, cached posteriors) using the initial density and the given data.
static MixtureDensity Initialization.gMeansClustering(java.util.List<Sample> data, double alpha, int maxc, boolean diagonalCovariances)
          Perform a Gaussian-means (G-means) clustering on the given data set.
static MixtureDensity Initialization.hierarchicalGaussianClustering(java.util.List<Sample> data, int maxc, boolean diagonalCovariances, Initialization.DensityRankingMethod rank)
          Perform a hierarchical Gaussian clustering: Beginning with only one density, always split the cluster with highest variance in two parts, finding the new means by following the strongest eigen vector.
static MixtureDensity Initialization.kMeansClustering(java.util.List<Sample> data, int nd, boolean diagonalCovariances)
          Perform a simple k-means clustering on the data.
static MixtureDensity Trainer.map(MixtureDensity initial, java.util.List<Sample> data, double r, int iterations, java.lang.String update)
          Perform a number of MAP iterations, based on the initial estimate
static MixtureDensity Trainer.map(MixtureDensity initial, java.util.List<Sample> data, double r, java.lang.String update)
          Perform a single MAP iteration on the given initial estimate
static Sample Sample.meanSubstract(java.util.List<Sample> data)
          Subtract the mean value from all samples
static Density Trainer.ml(java.util.List<Sample> data, boolean diagonalCovariances)
          Standard (single-core) maximum likelihood estimation for a single Gaussian density
static java.util.ArrayList<Sample> Sample.reduceToClass(java.util.ArrayList<Sample> data, int id)
          Remove all data from a list which is not of label id
static java.util.ArrayList<Sample> Sample.removeClass(java.util.ArrayList<Sample> data, int id)
          Remove samples of a certain class from the data set
 

Constructors in statistics with parameters of type Sample
Sample(Sample s)
           
 

Uses of Sample in transformations
 

Methods in transformations that return types with arguments of type Sample
 java.util.ArrayList<Sample> PCA.transform(java.util.ArrayList<Sample> in)
          Transform a list of samples using the pre-computed principal components.
 java.util.ArrayList<Sample> PCA.transform(java.util.ArrayList<Sample> in, int dim)
          Transform a list of samples using the pre-computed principal components and reduce the dimsions to the given number.
 

Method parameters in transformations with type arguments of type Sample
 void PCA.computePCA(java.util.List<Sample> data)
          Compute the parameters for the principal components analysis (PCA).
 java.util.ArrayList<Sample> PCA.transform(java.util.ArrayList<Sample> in)
          Transform a list of samples using the pre-computed principal components.
 java.util.ArrayList<Sample> PCA.transform(java.util.ArrayList<Sample> in, int dim)
          Transform a list of samples using the pre-computed principal components and reduce the dimsions to the given number.