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Packages that use Sample | |
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classifiers | |
io | |
statistics | |
transformations |
Uses of Sample in classifiers |
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Methods in classifiers with parameters of type Sample | |
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int |
NaiveBayes.evaluate(Sample s)
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int |
MixtureClassifier.evaluate(Sample s)
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int |
NaiveBayes.evaluate(Sample s,
double[] scores)
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int |
MixtureClassifier.evaluate(Sample s,
double[] scores)
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Uses of Sample in io |
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Methods in io that return types with arguments of type Sample | |
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java.util.List<Sample> |
ChunkedDataSet.cachedData()
Cache all chunks into a List |
Uses of Sample in statistics |
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Fields in statistics with type parameters of type Sample | |
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java.util.ArrayList<Sample> |
DataSet.samples
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java.util.HashMap<java.lang.Integer,java.util.ArrayList<Sample>> |
DataSet.samplesByClass
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Methods in statistics that return Sample | |
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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 | |
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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 | |
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void |
DataSet.addSample(Sample s)
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Method parameters in statistics with type arguments of type Sample | |
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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 | |
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Sample(Sample s)
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Uses of Sample in transformations |
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Methods in transformations that return types with arguments of type Sample | |
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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 | |
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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. |
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