- absDet() - Method in class jsat.linear.QRDecomposition
-
- absDet() - Method in class jsat.linear.SingularValueDecomposition
-
Computes the absolute value of the determinant for the full matrix.
- absFunc - Static variable in class jsat.math.MathTricks
-
Convenience object for taking the
abs of the
first index
- AbsoluteLoss - Class in jsat.lossfunctions
-
The AbsoluteLoss loss function for regression L(x, y) = |x-y|.
- AbsoluteLoss() - Constructor for class jsat.lossfunctions.AbsoluteLoss
-
- AbstractClusterDissimilarity - Class in jsat.clustering.dissimilarity
-
This base class does not currently provide any inheritable functionality, but
stores static methods.
- AbstractClusterDissimilarity() - Constructor for class jsat.clustering.dissimilarity.AbstractClusterDissimilarity
-
- accel - Variable in class jsat.clustering.kmeans.KernelKMeans
-
THe acceleration cache for the kernel
- accelCache - Variable in class jsat.classifiers.linear.kernelized.DUOL
-
- accelCache - Variable in class jsat.classifiers.svm.SupportVectorLearner
-
Kernel evaluation acceleration cache
- accessingRow(int) - Method in class jsat.classifiers.svm.SupportVectorLearner
-
This method allows the caller to hint that they are about to access many
kernel values for a specific row.
- accumulateSum(Vec, Vec, Vec, Function) - Static method in class jsat.linear.VecOps
-
Computes the result of ∑∀ i ∈ |w| wi
f(xi-yi)
- Accuracy - Class in jsat.classifiers.evaluation
-
Evaluates a classifier based on its accuracy in predicting the correct class.
- Accuracy() - Constructor for class jsat.classifiers.evaluation.Accuracy
-
- Accuracy(Accuracy) - Constructor for class jsat.classifiers.evaluation.Accuracy
-
- aConst(boolean, int, int, int) - Method in class jsat.clustering.dissimilarity.AverageLinkDissimilarity
-
- aConst(boolean, int, int, int) - Method in class jsat.clustering.dissimilarity.CentroidDissimilarity
-
- aConst(boolean, int, int, int) - Method in class jsat.clustering.dissimilarity.CompleteLinkDissimilarity
-
- aConst(boolean, int, int, int) - Method in class jsat.clustering.dissimilarity.LanceWilliamsDissimilarity
-
This method computes the value of the α variable.
- aConst(boolean, int, int, int) - Method in class jsat.clustering.dissimilarity.MedianDissimilarity
-
- aConst(boolean, int, int, int) - Method in class jsat.clustering.dissimilarity.SingleLinkDissimilarity
-
- aConst(boolean, int, int, int) - Method in class jsat.clustering.dissimilarity.WardsDissimilarity
-
- acosh(double) - Static method in class jsat.math.TrigMath
-
- acotch(double) - Static method in class jsat.math.TrigMath
-
- acsch(double) - Static method in class jsat.math.TrigMath
-
- activate(Vec, Vec) - Method in interface jsat.classifiers.neuralnetwork.activations.ActivationLayer
-
Computes the activation function of this layer on the given input.
- activate(Matrix, Matrix, boolean) - Method in interface jsat.classifiers.neuralnetwork.activations.ActivationLayer
-
Computes the activation function of this layer on the given input.
- activate(Vec, Vec) - Method in class jsat.classifiers.neuralnetwork.activations.LinearLayer
-
- activate(Matrix, Matrix, boolean) - Method in class jsat.classifiers.neuralnetwork.activations.LinearLayer
-
- activate(Vec, Vec) - Method in class jsat.classifiers.neuralnetwork.activations.ReLU
-
- activate(Matrix, Matrix, boolean) - Method in class jsat.classifiers.neuralnetwork.activations.ReLU
-
- activate(Vec, Vec) - Method in class jsat.classifiers.neuralnetwork.activations.SigmoidLayer
-
- activate(Matrix, Matrix, boolean) - Method in class jsat.classifiers.neuralnetwork.activations.SigmoidLayer
-
- activate(Vec, Vec) - Method in class jsat.classifiers.neuralnetwork.activations.SoftmaxLayer
-
- activate(Matrix, Matrix, boolean) - Method in class jsat.classifiers.neuralnetwork.activations.SoftmaxLayer
-
- activate(Vec, Vec) - Method in class jsat.classifiers.neuralnetwork.activations.SoftSignLayer
-
- activate(Matrix, Matrix, boolean) - Method in class jsat.classifiers.neuralnetwork.activations.SoftSignLayer
-
- activate(Vec, Vec) - Method in class jsat.classifiers.neuralnetwork.activations.TanhLayer
-
- activate(Matrix, Matrix, boolean) - Method in class jsat.classifiers.neuralnetwork.activations.TanhLayer
-
- ActivationFunction() - Constructor for class jsat.classifiers.neuralnetwork.BackPropagationNet.ActivationFunction
-
- ActivationLayer - Interface in jsat.classifiers.neuralnetwork.activations
-
This interface defines a type of activation layer for use in a Neural Network
- AdaBoostM1 - Class in jsat.classifiers.boosting
-
Implementation of Experiments with a New Boosting Algorithm, by Yoav Freund&Robert E.
- AdaBoostM1(Classifier, int) - Constructor for class jsat.classifiers.boosting.AdaBoostM1
-
- AdaBoostM1PL - Class in jsat.classifiers.boosting
-
An extension to the original AdaBoostM1 algorithm for parallel training.
- AdaBoostM1PL(Classifier, int) - Constructor for class jsat.classifiers.boosting.AdaBoostM1PL
-
- AdaDelta - Class in jsat.math.optimization.stochastic
-
AdaDelta is inspired by
AdaGrad and was developed for use primarily
in neural networks.
- AdaDelta() - Constructor for class jsat.math.optimization.stochastic.AdaDelta
-
Creates a new AdaDelta updater using a decay rate of 0.95
- AdaDelta(double) - Constructor for class jsat.math.optimization.stochastic.AdaDelta
-
Creates a new AdaDelta updater
- AdaDelta(AdaDelta) - Constructor for class jsat.math.optimization.stochastic.AdaDelta
-
Copy constructor
- AdaGrad - Class in jsat.math.optimization.stochastic
-
AdaGrad provides an adaptive learning rate for each individual feature
See: Duchi, J., Hazan, E.,&Singer, Y.
- AdaGrad() - Constructor for class jsat.math.optimization.stochastic.AdaGrad
-
Creates a new AdaGrad updater
- AdaGrad(AdaGrad) - Constructor for class jsat.math.optimization.stochastic.AdaGrad
-
Copy constructor
- Adam - Class in jsat.math.optimization.stochastic
-
Adam is inspired by
RMSProp and
AdaGrad, where the former can
be seen as a special case of Adam.
- Adam() - Constructor for class jsat.math.optimization.stochastic.Adam
-
- Adam(double, double, double, double, double) - Constructor for class jsat.math.optimization.stochastic.Adam
-
- Adam(Adam) - Constructor for class jsat.math.optimization.stochastic.Adam
-
Copy constructor
- add(Matrix) - Method in class jsat.linear.Matrix
-
Creates a new Matrix that stores the result of A+B
- add(Matrix, ExecutorService) - Method in class jsat.linear.Matrix
-
Creates a new Matrix that stores the result of A+B
- add(double) - Method in class jsat.linear.Matrix
-
Creates a new Matrix that stores the result of A+c
- add(double, ExecutorService) - Method in class jsat.linear.Matrix
-
Creates a new Matrix that stores the result of A+c
- add(double) - Method in class jsat.linear.Vec
-
Returns a new vector that is the result of this + c
- add(Vec) - Method in class jsat.linear.Vec
-
Returns a new vector that is the result of this + b
- add(double) - Method in class jsat.linear.VecPaired
-
- add(Vec) - Method in class jsat.linear.VecPaired
-
- add(V) - Method in class jsat.linear.vectorcollection.RTree
-
- add(V) - Method in class jsat.linear.vectorcollection.VectorArray
-
- add(Complex) - Method in class jsat.math.Complex
-
Creates a new complex number containing the resulting addition of this and another
- add(double) - Method in class jsat.math.ExponentialMovingStatistics
-
Adds the given data point to the statistics
- add(double) - Method in class jsat.math.OnLineStatistics
-
Adds a data sample with unit weight to the counts.
- add(double, double) - Method in class jsat.math.OnLineStatistics
-
Adds a data sample the the counts with the provided weight of influence.
- add(OnLineStatistics, OnLineStatistics) - Static method in class jsat.math.OnLineStatistics
-
Computes a new set of counts that is the sum of the counts from the given distributions.
- add(OnLineStatistics) - Method in class jsat.math.OnLineStatistics
-
Adds to the current statistics all the samples that were collected in
B.
- add(DataPoint) - Method in class jsat.SimpleDataSet
-
Adds a new datapoint to this set.
- add(E) - Method in class jsat.utils.BoundedSortedList
-
- add(int, E) - Method in class jsat.utils.BoundedSortedList
-
- add(V) - Method in class jsat.utils.BoundedSortedSet
-
- add(double) - Method in class jsat.utils.DoubleList
-
- add(Double) - Method in class jsat.utils.DoubleList
-
- add(int, double) - Method in class jsat.utils.DoubleList
-
- add(int, Double) - Method in class jsat.utils.DoubleList
-
- add(int, int) - Method in class jsat.utils.IntList
-
- add(int, Integer) - Method in class jsat.utils.IntList
-
- add(int) - Method in class jsat.utils.IntList
-
- add(Integer) - Method in class jsat.utils.IntList
-
- add(Integer) - Method in class jsat.utils.IntSet
-
- add(int) - Method in class jsat.utils.IntSet
-
- add(Integer) - Method in class jsat.utils.IntSetFixedSize
-
- add(int) - Method in class jsat.utils.IntSetFixedSize
-
Adds a new integer into the set
- add(Integer) - Method in class jsat.utils.IntSortedSet
-
- add(int, long) - Method in class jsat.utils.LongList
-
- add(int, Long) - Method in class jsat.utils.LongList
-
- add(long) - Method in class jsat.utils.LongList
-
- add(Long) - Method in class jsat.utils.LongList
-
- add(int, E) - Method in class jsat.utils.SimpleList
-
- add(T) - Method in class jsat.utils.SortedArrayList
-
- add(int, T) - Method in class jsat.utils.SortedArrayList
-
- addAll(Collection<? extends V>) - Method in class jsat.linear.vectorcollection.VectorArray
-
- addAll(Collection<? extends V>) - Method in class jsat.utils.BoundedSortedSet
-
- addAll(Collection<? extends Integer>) - Method in class jsat.utils.IntList
-
- addAll(Collection<? extends T>) - Method in class jsat.utils.SortedArrayList
-
- addAll(int, Collection<? extends T>) - Method in class jsat.utils.SortedArrayList
-
- addAndGet(double) - Method in class jsat.utils.concurrent.AtomicDouble
-
Atomically adds the given value to the current value.
- addAndGet(int, double) - Method in class jsat.utils.concurrent.AtomicDoubleArray
-
Atomically adds the given value to the element at index i.
- addChild(FibHeap.FibNode<T>) - Method in class jsat.utils.FibHeap.FibNode
-
Adds the given node directly to the children list of this node.
- addCol(Matrix, int, int, int, double) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of column j in the given matrix to be A[:,j] = A[:,j]+ c
- addCol(Matrix, int, double) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of column j in the given matrix to be A[:,j] = A[:,j]+ c
- addDataPoint(Vec, int[], int) - Method in class jsat.classifiers.ClassificationDataSet
-
Creates a new data point and adds it to this data set.
- addDataPoint(Vec, int) - Method in class jsat.classifiers.ClassificationDataSet
-
Creates a new data point with no categorical variables and adds it to
this data set.
- addDataPoint(Vec, int, double) - Method in class jsat.classifiers.ClassificationDataSet
-
Creates a new data point with no categorical variables and adds it to
this data set.
- addDataPoint(Vec, int[], int, double) - Method in class jsat.classifiers.ClassificationDataSet
-
Creates a new data point and add its to this data set.
- addDataPoint(DataPoint, int) - Method in class jsat.classifiers.ClassificationDataSet
-
Creates a new data point and add it
- addDataPoint(Vec, double) - Method in class jsat.regression.RegressionDataSet
-
Creates a new data point with no categorical variables to be added to the
data set.
- addDataPoint(Vec, int[], double) - Method in class jsat.regression.RegressionDataSet
-
Creates a new data point to be added to the data set.
- addDataPoint(DataPoint, double) - Method in class jsat.regression.RegressionDataSet
-
- addDataPointPair(DataPointPair<Double>) - Method in class jsat.regression.RegressionDataSet
-
- addDiag(Matrix, int, int, double) - Static method in class jsat.linear.RowColumnOps
-
Updates the values along the main diagonal of the matrix by adding a constant to them
- addEdge(N, N) - Method in class jsat.classifiers.bayesian.graphicalmodel.DirectedGraph
-
Adds a directed edge into the network from a to b.
- addFeature(int, int, Set<Integer>, Set<Integer>) - Static method in class jsat.datatransform.featureselection.SFS
-
- addMultCol(Matrix, int, int, int, double, double[]) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of column j in the given matrix to be A[:,j] = A[:,j]+c[:]*t.
The Matrix A and array c do not need to have the same dimensions, so long as they both have indices in the given range.
- addMultCol(Matrix, int, int, int, double, Vec) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of column j in the given matrix to be A[:,j] = A[:,j]+c[:]*t.
The Matrix A and vector c do not need to have the same dimensions, so long as they both have indices in the given range.
- addMultRow(Matrix, int, int, int, double, double[]) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of row i in the given matrix to be A[i,:] = A[i,:]+c[:]*t.
The Matrix A and array c do not need to have the same dimensions, so long as they both have indices in the given range.
- addMultRow(Matrix, int, int, int, double, Vec) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of row i in the given matrix to be A[i,:] = A[i,:]+c[:]*t.
The Matrix A and array c do not need to have the same dimensions, so long as they both have indices in the given range.
- addNewKernelPoint() - Method in class jsat.distributions.kernels.KernelPoints
-
Adds a new Kernel Point to the internal list this object represents.
- addNode(N) - Method in class jsat.classifiers.bayesian.graphicalmodel.DirectedGraph
-
Adds a new node to the graph
- addNodes(Collection<? extends N>) - Method in class jsat.classifiers.bayesian.graphicalmodel.DirectedGraph
-
Adds all the objects in c as nodes in the graph
- addOriginalDocument(String) - Method in class jsat.text.ClassificationHashedTextDataLoader
-
- addOriginalDocument(String, int) - Method in class jsat.text.ClassificationHashedTextDataLoader
-
- addOriginalDocument(String) - Method in class jsat.text.ClassificationTextDataLoader
-
- addOriginalDocument(String, int) - Method in class jsat.text.ClassificationTextDataLoader
-
- addOriginalDocument(String) - Method in class jsat.text.HashedTextDataLoader
-
- addOriginalDocument(String) - Method in class jsat.text.TextDataLoader
-
- addParameter(DoubleParameter, double...) - Method in class jsat.parameters.GridSearch
-
Adds a new double parameter to be altered for the model being tuned.
- addParameter(String, double...) - Method in class jsat.parameters.GridSearch
-
Adds a new double parameter to be altered for the model being tuned.
- addParameter(IntParameter, int...) - Method in class jsat.parameters.GridSearch
-
Adds a new int parameter to be altered for the model being tuned.
- addParameter(String, int...) - Method in class jsat.parameters.GridSearch
-
Adds a new integer parameter to be altered for the model being tuned.
- addParameter(DoubleParameter, Distribution) - Method in class jsat.parameters.RandomSearch
-
Adds a new double parameter to be altered for the model being tuned.
- addParameter(IntParameter, Distribution) - Method in class jsat.parameters.RandomSearch
-
Adds a new double parameter to be altered for the model being tuned.
- addParameter(String, Distribution) - Method in class jsat.parameters.RandomSearch
-
Adds a new parameter to be altered for the model being tuned.
- addPoint(DataPoint, int) - Method in class jsat.classifiers.trees.ImpurityScore
-
Adds one more point to the impurity score
- addPoint(double, int) - Method in class jsat.classifiers.trees.ImpurityScore
-
Adds one more point to the impurity score
- addRange(Collection<Integer>, int, int, int) - Static method in class jsat.utils.ListUtils
-
Adds values into the given collection using integer in the specified range and step size.
- addResult(CategoricalResults, int, double) - Method in class jsat.classifiers.evaluation.Accuracy
-
- addResult(CategoricalResults, int, double) - Method in class jsat.classifiers.evaluation.AUC
-
- addResult(CategoricalResults, int, double) - Method in interface jsat.classifiers.evaluation.ClassificationScore
-
Adds the given result to the score
- addResult(CategoricalResults, int, double) - Method in class jsat.classifiers.evaluation.Kappa
-
- addResult(CategoricalResults, int, double) - Method in class jsat.classifiers.evaluation.LogLoss
-
- addResult(CategoricalResults, int, double) - Method in class jsat.classifiers.evaluation.SimpleBinaryClassMetric
-
- addResult(double, double, double) - Method in class jsat.regression.evaluation.MeanAbsoluteError
-
- addResult(double, double, double) - Method in class jsat.regression.evaluation.MeanSquaredError
-
- addResult(double, double, double) - Method in interface jsat.regression.evaluation.RegressionScore
-
Adds the given result to the score
- addResult(double, double, double) - Method in class jsat.regression.evaluation.TotalHistoryRegressionScore
-
- addResults(ClassificationScore) - Method in class jsat.classifiers.evaluation.Accuracy
-
- addResults(ClassificationScore) - Method in class jsat.classifiers.evaluation.AUC
-
- addResults(ClassificationScore) - Method in interface jsat.classifiers.evaluation.ClassificationScore
-
The score contained in this object is augmented with the results
already accumulated in the other object.
- addResults(ClassificationScore) - Method in class jsat.classifiers.evaluation.Kappa
-
- addResults(ClassificationScore) - Method in class jsat.classifiers.evaluation.LogLoss
-
- addResults(ClassificationScore) - Method in class jsat.classifiers.evaluation.SimpleBinaryClassMetric
-
- addResults(RegressionScore) - Method in class jsat.regression.evaluation.MeanAbsoluteError
-
- addResults(RegressionScore) - Method in class jsat.regression.evaluation.MeanSquaredError
-
- addResults(RegressionScore) - Method in interface jsat.regression.evaluation.RegressionScore
-
The score contained in this object is augmented with the results
already accumulated in the other object.
- addResults(RegressionScore) - Method in class jsat.regression.evaluation.TotalHistoryRegressionScore
-
- addRow(Matrix, int, int, int, double) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of row i in the given matrix to be A[i,:] = A[i,:]+ c
- addRow(Matrix, int, double) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of row i in the given matrix to be A[i,:] = A[i,:]+ c
- addSample(double, V) - Method in class jsat.driftdetectors.ADWIN
-
- addSample(double, V) - Method in class jsat.driftdetectors.BaseDriftDetector
-
Adds a new point to the drift detector.
- addSample(boolean, V) - Method in class jsat.driftdetectors.DDM
-
Adds a new boolean trial to the detector, with the goal of detecting when
the number of successful trials (true) drifts to a new value.
- addSample(double, V) - Method in class jsat.driftdetectors.DDM
-
- addScorer(ClassificationScore) - Method in class jsat.classifiers.ClassificationModelEvaluation
-
- addScorer(RegressionScore) - Method in class jsat.regression.RegressionModelEvaluation
-
- addToCache(Vec, List<Double>) - Method in class jsat.distributions.kernels.BaseKernelTrick
-
- addToCache(Vec, List<Double>) - Method in class jsat.distributions.kernels.BaseL2Kernel
-
- addToCache(Vec, List<Double>) - Method in class jsat.distributions.kernels.DistanceMetricBasedKernel
-
- addToCache(Vec, List<Double>) - Method in interface jsat.distributions.kernels.KernelTrick
-
Appends the new cache values for the given vector to the list of cache
values.
- addToCache(Vec, List<Double>) - Method in class jsat.distributions.kernels.NormalizedKernel
-
- addToHistory(V) - Method in class jsat.driftdetectors.BaseDriftDetector
-
Adds the given item to the history, creating a new history holder if
needed.
- addTransform(DataTransform) - Method in class jsat.datatransform.DataTransformProcess
-
Adds a transform to the list of transforms.
- AdjustedRandIndex - Class in jsat.clustering.evaluation
-
Adjusted Rand Index (ARI) is a measure to evaluate a cluster based
on the true class labels for the data set.
- AdjustedRandIndex() - Constructor for class jsat.clustering.evaluation.AdjustedRandIndex
-
- ADWIN<V> - Class in jsat.driftdetectors
-
Adaptive Windowing (ADWIN) is an algorithm for detecting
changes in an input stream.
- ADWIN(double) - Constructor for class jsat.driftdetectors.ADWIN
-
Creates a new ADWIN object for detecting changes in the mean value of a
stream of inputs.
- ADWIN(double, int) - Constructor for class jsat.driftdetectors.ADWIN
-
Creates a new ADWIN object for detecting changes in the mean value of a
stream of inputs.
- ADWIN(ADWIN<V>) - Constructor for class jsat.driftdetectors.ADWIN
-
Copy constructor
- allEpsNeighbors(VectorCollection<V0>, List<V1>, double, ExecutorService) - Static method in class jsat.linear.vectorcollection.VectorCollectionUtils
-
Searches the given collection for all the neighbors within a distance of radius for every data point in the given search list.
- allNearestNeighbors(VectorCollection<V0>, List<V1>, int) - Static method in class jsat.linear.vectorcollection.VectorCollectionUtils
-
Searches the given collection for the k nearest neighbors for every data point in the given search list.
- allNearestNeighbors(VectorCollection<V0>, V1[], int) - Static method in class jsat.linear.vectorcollection.VectorCollectionUtils
-
Searches the given collection for the k nearest neighbors for every data point in the given search list.
- allNearestNeighbors(VectorCollection<V0>, List<V1>, int, ExecutorService) - Static method in class jsat.linear.vectorcollection.VectorCollectionUtils
-
Searches the given collection for the k nearest neighbors for every data point in the given search list.
- allNearestNeighbors(VectorCollection<V0>, V1[], int, ExecutorService) - Static method in class jsat.linear.vectorcollection.VectorCollectionUtils
-
Searches the given collection for the k nearest neighbors for every data point in the given search list.
- allWords - Variable in class jsat.text.TextDataLoader
-
list of all word tokens encountered in order of first observation
- ALMA2 - Class in jsat.classifiers.linear
-
Provides a linear implementation of the ALMAp algorithm for p = 2, which is
considerably more efficient to compute.
- ALMA2() - Constructor for class jsat.classifiers.linear.ALMA2
-
Creates a new ALMA learner using an alpha of 0.8
- ALMA2(double) - Constructor for class jsat.classifiers.linear.ALMA2
-
Creates a new ALMA learner using the given alpha
- ALMA2(ALMA2) - Constructor for class jsat.classifiers.linear.ALMA2
-
Copy constructor
- ALMA2K - Class in jsat.classifiers.linear.kernelized
-
Provides a kernelized version of the
ALMA2 algorithm.
- ALMA2K(KernelTrick, double) - Constructor for class jsat.classifiers.linear.kernelized.ALMA2K
-
Creates a new kernelized ALMA2 object
- ALMA2K(ALMA2K) - Constructor for class jsat.classifiers.linear.kernelized.ALMA2K
-
Copy constructor
- alpha - Variable in class jsat.distributions.kernels.KernelPoint
-
- alpha - Static variable in class jsat.text.GreekLetters
-
- alphas - Variable in class jsat.classifiers.linear.kernelized.DUOL
-
Signed weights for each support vector.
- alphas - Variable in class jsat.classifiers.svm.SupportVectorLearner
-
The array of coefficients associated with each support vector.
- AMM - Class in jsat.classifiers.svm.extended
-
This is the batch variant of the Adaptive Multi-Hyperplane Machine (AMM)
algorithm.
- AMM() - Constructor for class jsat.classifiers.svm.extended.AMM
-
Creates a new batch AMM learner
- AMM(double) - Constructor for class jsat.classifiers.svm.extended.AMM
-
Creates a new batch AMM learner
- AMM(double, int) - Constructor for class jsat.classifiers.svm.extended.AMM
-
Creates a new batch AMM learner
- AMM(AMM) - Constructor for class jsat.classifiers.svm.extended.AMM
-
Copy constructor
- AODE - Class in jsat.classifiers.bayesian
-
Averaged One-Dependence Estimators (AODE) is an extension of Naive Bayes that
attempts to be more accurate by reducing the independence assumption.
- AODE() - Constructor for class jsat.classifiers.bayesian.AODE
-
Creates a new AODE classifier.
- AODE(AODE) - Constructor for class jsat.classifiers.bayesian.AODE
-
Creates a copy of an AODE classifier
- apply(double[]) - Method in class jsat.utils.IndexTable
-
Applies this index table to the specified target, putting target
into the same ordering as this IndexTable.
- apply(List) - Method in class jsat.utils.IndexTable
-
Applies this index table to the specified target, putting target
into the same ordering as this IndexTable.
- apply(List, List) - Method in class jsat.utils.IndexTable
-
Applies this index table to the specified target, putting target
into the same ordering as this IndexTable.
- applyFunction(Function) - Method in class jsat.linear.SparseVector
-
- applyFunction(Function) - Method in class jsat.linear.Vec
-
Applies the given function to each and every value in the vector.
- applyIndexFunction(IndexFunction) - Method in class jsat.linear.SparseVector
-
- applyIndexFunction(IndexFunction) - Method in class jsat.linear.Vec
-
Applies the given function to each and every value in the vector.
- applyMeanUpdates(double[], double[]) - Method in class jsat.clustering.kmeans.KernelKMeans
-
- applyRegularization(Matrix, Vec) - Method in class jsat.classifiers.neuralnetwork.regularizers.Max2NormRegularizer
-
- applyRegularization(Matrix, Vec, ExecutorService) - Method in class jsat.classifiers.neuralnetwork.regularizers.Max2NormRegularizer
-
- applyRegularization(Matrix, Vec) - Method in interface jsat.classifiers.neuralnetwork.regularizers.WeightRegularizer
-
Applies regularization to one matrix, where the rows of the matrix
correspond tot he weights associated to one neuron's input.
- applyRegularization(Matrix, Vec, ExecutorService) - Method in interface jsat.classifiers.neuralnetwork.regularizers.WeightRegularizer
-
Applies regularization to one matrix, where the rows of the matrix
correspond tot he weights associated to one neuron's input.
- applyRegularizationToRow(Vec, double) - Method in class jsat.classifiers.neuralnetwork.regularizers.Max2NormRegularizer
-
- applyRegularizationToRow(Vec, double) - Method in interface jsat.classifiers.neuralnetwork.regularizers.WeightRegularizer
-
Applies the regularization to one row of the weight matrix, where the row
corresponds to the weights into one neuron.
- applyTo(List<String>) - Method in class jsat.text.stemming.Stemmer
-
Replaces each value in the list with the stemmed version of the word
- applyTo(String[]) - Method in class jsat.text.stemming.Stemmer
-
Replaces each value in the array with the stemmed version of the word
- applyTo(Vec) - Method in class jsat.text.wordweighting.BinaryWordPresent
-
- applyTo(Vec) - Method in class jsat.text.wordweighting.OkapiBM25
-
- applyTo(Vec) - Method in class jsat.text.wordweighting.TfIdf
-
- applyTo(Vec) - Method in class jsat.text.wordweighting.WordCount
-
- applyTo(Vec) - Method in class jsat.text.wordweighting.WordWeighting
-
The implementation may want to pre compute come values based on the
vector it is about to be applied to.
- applyTransform(DataTransform) - Method in class jsat.DataSet
-
Applies the given transformation to all points in this data set,
replacing each data point with the new value.
- applyTransform(DataTransform, ExecutorService) - Method in class jsat.DataSet
-
Applies the given transformation to all points in this data set in
parallel, replacing each data point with the new value.
- applyTransform(DataTransform, boolean) - Method in class jsat.DataSet
-
Applies the given transformation to all points in this data set.
- applyTransform(DataTransform, boolean, ExecutorService) - Method in class jsat.DataSet
-
Applies the given transformation to all points in this data set in
parallel.
- ArcX4 - Class in jsat.classifiers.boosting
-
Arc-x4 is a ensemble-classifier that performs re-weighting of the data points
based on the total number of errors that have occurred for the data point.
- ArcX4(Classifier, int) - Constructor for class jsat.classifiers.boosting.ArcX4
-
Creates a new Arc-X4 classifier
- ARFFLoader - Class in jsat
-
Class for loading ARFF files.
- ARFFLoader() - Constructor for class jsat.ARFFLoader
-
- AROW - Class in jsat.classifiers.linear
-
An implementation of Adaptive Regularization of Weight Vectors (AROW), which
uses second order information to learn a large margin binary classifier.
- AROW() - Constructor for class jsat.classifiers.linear.AROW
-
Creates a new AROW learner
- AROW(double, boolean) - Constructor for class jsat.classifiers.linear.AROW
-
Creates a new AROW learner
- AROW(AROW) - Constructor for class jsat.classifiers.linear.AROW
-
Copy constructor
- array - Variable in class jsat.linear.DenseVector
-
- arrayCopy() - Method in class jsat.linear.DenseVector
-
- arrayCopy() - Method in class jsat.linear.ScaledVector
-
- arrayCopy() - Method in class jsat.linear.SparseVector
-
- arrayCopy() - Method in class jsat.linear.Vec
-
Creates a new array that contains all the values of this vector in the
appropriate indices
- arrayCopy() - Method in class jsat.linear.VecPaired
-
- ArrayUtils - Class in jsat.utils
-
Extra utilities for working on array types
- asClassificationDataSet(int) - Method in class jsat.SimpleDataSet
-
Converts this dataset into one meant for classification problems.
- asech(double) - Static method in class jsat.math.TrigMath
-
- asinh(double) - Static method in class jsat.math.TrigMath
-
- asRegressionDataSet(int) - Method in class jsat.SimpleDataSet
-
Converts this dataset into one meant for regression problems.
- assignClusterDesignations(int[], int, int[]) - Static method in class jsat.clustering.hierarchical.PriorityHAC
-
Goes through the merge array in order from last merge to first, and sets the cluster assignment for each data point based on the merge list.
- atanh(double) - Static method in class jsat.math.TrigMath
-
- AtomicDouble - Class in jsat.utils.concurrent
-
- AtomicDouble(double) - Constructor for class jsat.utils.concurrent.AtomicDouble
-
- AtomicDoubleArray - Class in jsat.utils.concurrent
-
Provides a double array that can have individual values updated
atomically.
- AtomicDoubleArray(int) - Constructor for class jsat.utils.concurrent.AtomicDoubleArray
-
Creates a new AtomicDoubleArray of the given length, with all values
initialized to zero
- AtomicDoubleArray(double[]) - Constructor for class jsat.utils.concurrent.AtomicDoubleArray
-
Creates a new AtomixDouble Array that is of the same length
as the input array.
- AUC - Class in jsat.classifiers.evaluation
-
Computes the Area Under the ROC Curve as an evaluation of classification
scores.
- AUC() - Constructor for class jsat.classifiers.evaluation.AUC
-
Creates a new AUC object
- AUC(AUC) - Constructor for class jsat.classifiers.evaluation.AUC
-
Copy constructor
- autoAddParameters(DataSet) - Method in class jsat.parameters.GridSearch
-
This method will automatically populate the search space with parameters
based on which Parameter objects return non-null distributions.
- autoAddParameters(DataSet, int) - Method in class jsat.parameters.GridSearch
-
This method will automatically populate the search space with parameters
based on which Parameter objects return non-null distributions.
Note, using this method with Cross Validation has the potential for
over-estimating the accuracy of results if the data set is actually used
to for parameter guessing.
- autoAddParameters(DataSet) - Method in class jsat.parameters.RandomSearch
-
This method will automatically populate the search space with parameters
based on which Parameter objects return non-null distributions.
Note, using this method with Cross Validation has the potential for
over-estimating the accuracy of results if the data set is actually used
to for parameter guessing.
It is possible for this method to return 0, indicating that no default
parameters could be found.
- AutoDeskewTransform - Class in jsat.datatransform
-
This transform applies a shifted Box-Cox transform for several fixed values
of λ, and selects the one that provides the greatest reduction in the
skewness of the distribution.
- AutoDeskewTransform() - Constructor for class jsat.datatransform.AutoDeskewTransform
-
Creates a new AutoDeskew transform
- AutoDeskewTransform(double...) - Constructor for class jsat.datatransform.AutoDeskewTransform
-
Creates a new AutoDeskew transform
- AutoDeskewTransform(List<Double>) - Constructor for class jsat.datatransform.AutoDeskewTransform
-
Creates a new AutoDeskew transform
- AutoDeskewTransform(boolean, List<Double>) - Constructor for class jsat.datatransform.AutoDeskewTransform
-
Creates a new AutoDeskew transform
- AutoDeskewTransform(DataSet) - Constructor for class jsat.datatransform.AutoDeskewTransform
-
Creates a new deskewing object from the given data set
- AutoDeskewTransform(DataSet, List<Double>) - Constructor for class jsat.datatransform.AutoDeskewTransform
-
Creates a new deskewing object from the given data set
- AutoDeskewTransform(DataSet, boolean, List<Double>) - Constructor for class jsat.datatransform.AutoDeskewTransform
-
Creates a new deskewing object from the given data set
- AutoDeskewTransform(AutoDeskewTransform) - Constructor for class jsat.datatransform.AutoDeskewTransform
-
Copy constructor
- autoFeatureSample() - Method in class jsat.classifiers.trees.RandomForest
-
Tells the class to automatically select the number of features to use.
- autoKernel(Vec) - Static method in class jsat.distributions.empirical.KernelDensityEstimator
-
Automatically selects a good Kernel function for the data set that balances Execution time and accuracy
- AveragedRegressor - Class in jsat.regression
-
Creates a regressor that averages the results of several voting regression methods.
- AveragedRegressor(Regressor...) - Constructor for class jsat.regression.AveragedRegressor
-
Constructs a new averaged regressor using the given array of voters
- AveragedRegressor(List<Regressor>) - Constructor for class jsat.regression.AveragedRegressor
-
Constructs a new averaged regressor using the given list of voters.
- AverageLinkDissimilarity - Class in jsat.clustering.dissimilarity
-
Also known as Group-Average Agglomerative Clustering (GAAC) and UPGMA, this
measure computer the dissimilarity by summing the distances between all
possible data point pairs in the union of the clusters.
- AverageLinkDissimilarity() - Constructor for class jsat.clustering.dissimilarity.AverageLinkDissimilarity
-
- AverageLinkDissimilarity(DistanceMetric) - Constructor for class jsat.clustering.dissimilarity.AverageLinkDissimilarity
-
Creates a new AverageLinkDissimilarity
- await(int) - Method in class jsat.utils.concurrent.TreeBarrier
-
Waits for all threads to reach this barrier.
- await() - Method in class jsat.utils.ModifiableCountDownLatch
-
Waits until the count gets reduced to zero, and then all threads waiting will get to run.
- awaitTermination(long, TimeUnit) - Method in class jsat.utils.FakeExecutor
-
- d - Variable in class jsat.distributions.kernels.DistanceMetricBasedKernel
-
the distance metric to use for the Kernel
- dag - Variable in class jsat.classifiers.bayesian.graphicalmodel.DiscreteBayesNetwork
-
The directed Graph that represents this BN
- DANN - Class in jsat.classifiers.knn
-
DANN is an implementation of Discriminant Adaptive Nearest Neighbor.
- DANN() - Constructor for class jsat.classifiers.knn.DANN
-
Creates a new DANN classifier
- DANN(int, int) - Constructor for class jsat.classifiers.knn.DANN
-
Creates a new DANN classifier
- DANN(int, int, double) - Constructor for class jsat.classifiers.knn.DANN
-
Creates a new DANN classifier
- DANN(int, int, double, VectorCollectionFactory<VecPaired<Vec, Integer>>) - Constructor for class jsat.classifiers.knn.DANN
-
Creates a new DANN classifier
- DANN(int, int, double, int, VectorCollectionFactory<VecPaired<Vec, Integer>>) - Constructor for class jsat.classifiers.knn.DANN
-
Creates a new DANN classifier
- DataModelPipeline - Class in jsat.datatransform
-
A Data Model Pipeline combines several data transforms and a base Classifier
or Regressor into a unified object for performing classification and
Regression with.
- DataModelPipeline(Classifier, DataTransformProcess) - Constructor for class jsat.datatransform.DataModelPipeline
-
Creates a new Data Model Pipeline from the given transform process and
base classifier
- DataModelPipeline(Classifier, DataTransform...) - Constructor for class jsat.datatransform.DataModelPipeline
-
Creates a new Data Model Pipeline from the given transform factories and
base classifier
- DataModelPipeline(Regressor, DataTransformProcess) - Constructor for class jsat.datatransform.DataModelPipeline
-
Creates a new Data Model Pipeline from the given transform process and
base regressor
- DataModelPipeline(Regressor, DataTransform...) - Constructor for class jsat.datatransform.DataModelPipeline
-
Creates a new Data Model Pipeline from the given transform factories and
base classifier
- DataModelPipeline(DataModelPipeline) - Constructor for class jsat.datatransform.DataModelPipeline
-
Copy constructor
- DataPoint - Class in jsat.classifiers
-
This is the general class object for representing a singular data point in a data set.
- DataPoint(Vec, int[], CategoricalData[]) - Constructor for class jsat.classifiers.DataPoint
-
Creates a new data point with the default weight of 1.0
- DataPoint(Vec, int[], CategoricalData[], double) - Constructor for class jsat.classifiers.DataPoint
-
Creates a new data point
- DataPoint(Vec, double) - Constructor for class jsat.classifiers.DataPoint
-
Creates a new data point that has no categorical variables
- DataPoint(Vec) - Constructor for class jsat.classifiers.DataPoint
-
Creates a new data point that has no categorical variables and a weight of 1.0
- DataPointPair<P> - Class in jsat.classifiers
-
This class exists so that any data point can be arbitrarily paired with some value
- DataPointPair(DataPoint, P) - Constructor for class jsat.classifiers.DataPointPair
-
- datapoints - Variable in class jsat.classifiers.ClassificationDataSet
-
- dataPoints - Variable in class jsat.regression.RegressionDataSet
-
The list of all data points, paired with their true regression output
- dataPoints - Variable in class jsat.SimpleDataSet
-
- dataPointToCord(DataPointPair<Integer>, int, int[]) - Method in class jsat.classifiers.bayesian.ConditionalProbabilityTable
-
Converts a data point pair into a coordinate.
- DataSet<Type extends DataSet> - Class in jsat
-
This is the base class for representing a data set.
- DataSet() - Constructor for class jsat.DataSet
-
- DataTransform - Interface in jsat.datatransform
-
A pre-processing step may be desirable before training.
- DataTransformBase - Class in jsat.datatransform
-
This abstract class implements the Parameterized interface to ease the
development of simple Data Transforms.
- DataTransformBase() - Constructor for class jsat.datatransform.DataTransformBase
-
- DataTransformProcess - Class in jsat.datatransform
-
Performing a transform on the whole data set before training a classifier can
add bias to the results.
- DataTransformProcess() - Constructor for class jsat.datatransform.DataTransformProcess
-
Creates a new transform process that is empty.
- DataTransformProcess(DataTransform...) - Constructor for class jsat.datatransform.DataTransformProcess
-
Creates a new transform process from the listed factories, which will be
applied in order by index.
- DaviesBouldinIndex - Class in jsat.clustering.evaluation
-
A measure for evaluating the quality of a clustering by measuring the
distances of points to their centroids.
- DaviesBouldinIndex() - Constructor for class jsat.clustering.evaluation.DaviesBouldinIndex
-
- DaviesBouldinIndex(DaviesBouldinIndex) - Constructor for class jsat.clustering.evaluation.DaviesBouldinIndex
-
Copy constructor
- DaviesBouldinIndex(DistanceMetric) - Constructor for class jsat.clustering.evaluation.DaviesBouldinIndex
-
Creates a new DaviesBouldinIndex
- DBSCAN - Class in jsat.clustering
-
A density-based algorithm for discovering clusters in large spatial databases
with noise (1996) by Martin Ester , Hans-peter Kriegel , Jörg S , Xiaowei Xu
- DBSCAN(DistanceMetric, VectorCollectionFactory<VecPaired<Vec, Integer>>) - Constructor for class jsat.clustering.DBSCAN
-
- DBSCAN() - Constructor for class jsat.clustering.DBSCAN
-
- DBSCAN(DistanceMetric) - Constructor for class jsat.clustering.DBSCAN
-
- DBSCAN(DBSCAN) - Constructor for class jsat.clustering.DBSCAN
-
Copy constructor
- dCalc(ContinuousDistribution) - Method in class jsat.testing.goodnessoffit.KSTest
-
Calculates the D statistic for comparison against a continous distribution
- dCaldO(Vec) - Method in class jsat.testing.goodnessoffit.KSTest
-
Calculates the D statistic for comparison against another data set
- DCD - Class in jsat.classifiers.svm
-
Implements Dual Coordinate Descent (DCD) training algorithms for a Linear
L1 or L2 Support Vector Machine for binary
classification and regression.
- DCD() - Constructor for class jsat.classifiers.svm.DCD
-
Creates a new DCDL2 SVM object
- DCD(int, boolean) - Constructor for class jsat.classifiers.svm.DCD
-
Creates a new DCD SVM object.
- DCD(int, double, boolean) - Constructor for class jsat.classifiers.svm.DCD
-
Creates a new DCD SVM object
- DCDs - Class in jsat.classifiers.svm
-
Implements Dual Coordinate Descent with shrinking (DCDs) training algorithms
for a Linear L1 or L2 Support Vector Machine for binary
classification and regression.
- DCDs() - Constructor for class jsat.classifiers.svm.DCDs
-
Creates a new DCDL2 SVM object
- DCDs(int, boolean) - Constructor for class jsat.classifiers.svm.DCDs
-
Creates a new DCD SVM object
- DCDs(int, double, double, boolean) - Constructor for class jsat.classifiers.svm.DCDs
-
Creates a new DCD SVM object
- DCSVM - Class in jsat.classifiers.svm
-
This is an implementation of the Divide-and-Conquer Support Vector Machine
(DC-SVM).
- DCSVM(KernelTrick) - Constructor for class jsat.classifiers.svm.DCSVM
-
Creates a new DC-SVM for the given kernel
- DCSVM() - Constructor for class jsat.classifiers.svm.DCSVM
-
Creates a new DC-SVM for the RBF kernel
- DCSVM(DCSVM) - Constructor for class jsat.classifiers.svm.DCSVM
-
Copy Constructor
- DDAG - Class in jsat.classifiers
-
Decision Directed Acyclic Graph (DDAG) classifier.
- DDAG(Classifier, boolean) - Constructor for class jsat.classifiers.DDAG
-
Creates a new DDAG classifier to extend a binary classifier to handle multi-class problems.
- DDAG(Classifier) - Constructor for class jsat.classifiers.DDAG
-
Creates a new DDAG classifier to extend a binary classifier to handle multi-class problems.
- DDM<V> - Class in jsat.driftdetectors
-
DDM (Drift Detection Method) is a drift detector for binary events, and is
meant to detect decreases in the success rate over time.
- DDM() - Constructor for class jsat.driftdetectors.DDM
-
Creates a new DDM drift detector using the default warning and drift
thresholds of 2 and 3 respectively.
- DDM(double, double) - Constructor for class jsat.driftdetectors.DDM
-
Creates a new DDM drift detector
- DDM(DDM<V>) - Constructor for class jsat.driftdetectors.DDM
-
Copy constructor
- DecayRate - Interface in jsat.math.decayrates
-
Many algorithms use a learning rate to adjust the step size by which the
search space is covered.
- DecayRateParameter - Class in jsat.parameters
-
A parameter for changing between the default
decay rates.
- DecayRateParameter() - Constructor for class jsat.parameters.DecayRateParameter
-
- decisionFunction(int) - Method in class jsat.classifiers.svm.PlattSMO
-
Returns the local decision function for classification training purposes
without the bias term
- decisionFunctionR(int) - Method in class jsat.classifiers.svm.PlattSMO
-
Returns the local decision function for regression training purposes
without the bias term
- DecisionStump - Class in jsat.classifiers.trees
-
This class is a 1-rule.
- DecisionStump() - Constructor for class jsat.classifiers.trees.DecisionStump
-
Creates a new decision stump
- DecisionTree - Class in jsat.classifiers.trees
-
- DecisionTree() - Constructor for class jsat.classifiers.trees.DecisionTree
-
- DecisionTree(int) - Constructor for class jsat.classifiers.trees.DecisionTree
-
Creates a Decision Tree that does not do any pruning, and is built out only to the specified depth
- DecisionTree(int, int, TreePruner.PruningMethod, double) - Constructor for class jsat.classifiers.trees.DecisionTree
-
Creates a new decision tree classifier
- DecisionTree(DecisionTree) - Constructor for class jsat.classifiers.trees.DecisionTree
-
Copy constructor
- DecisionTree.Node - Class in jsat.classifiers.trees
-
- decreaseKey(FibHeap.FibNode<T>, double) - Method in class jsat.utils.FibHeap
-
- deepCopy() - Method in class jsat.linear.DenseVector
-
- DEFAULT_ALPHA - Static variable in class jsat.clustering.LSDBC
-
4.0 is the default scale value used when performing clustering.
- DEFAULT_ALPHA - Static variable in class jsat.math.optimization.stochastic.Adam
-
- DEFAULT_BATCH_SIZE - Static variable in class jsat.classifiers.svm.Pegasos
-
The default batch size is 1
- DEFAULT_BETA_1 - Static variable in class jsat.math.optimization.stochastic.Adam
-
- DEFAULT_BETA_2 - Static variable in class jsat.math.optimization.stochastic.Adam
-
- DEFAULT_CLASS_BUDGET - Static variable in class jsat.classifiers.svm.extended.OnlineAMM
-
- DEFAULT_COMMENT - Static variable in class jsat.io.CSV
-
- DEFAULT_DELIMITER - Static variable in class jsat.io.CSV
-
- DEFAULT_EPOCHS - Static variable in class jsat.classifiers.linear.StochasticSTLinearL1
-
- DEFAULT_EPOCHS - Static variable in class jsat.classifiers.svm.Pegasos
-
The default number of epochs is 5
- DEFAULT_EPS - Static variable in class jsat.classifiers.knn.DANN
-
The default regularization used when building a metric is
1.0
- DEFAULT_EPS - Static variable in class jsat.classifiers.linear.NewGLMNET
-
The default tolerance for training is 0.01.
- DEFAULT_EPS - Static variable in class jsat.classifiers.neuralnetwork.LVQ
-
The default eps distance factor between the two wining vectors
0.3
- DEFAULT_EPS - Static variable in class jsat.math.optimization.stochastic.Adam
-
- DEFAULT_ERROR - Static variable in class jsat.regression.OrdinaryKriging
-
- DEFAULT_EXTRA_SAMPLES - Static variable in class jsat.classifiers.boosting.Bagging
-
The number of extra samples to take when bagging in each round used by default in the constructor: 0
- DEFAULT_EXTRACTION_METHOD - Static variable in class jsat.clustering.OPTICS
-
The default method used to extract clusters in OPTICS
- DEFAULT_FOLDS - Static variable in class jsat.classifiers.boosting.Stacking
-
- DEFAULT_ITERATIONS - Static variable in class jsat.classifiers.knn.DANN
-
The default number of iterations for creating the metric is
1
- DEFAULT_ITERATIONS - Static variable in class jsat.classifiers.neuralnetwork.LVQ
-
The default number of iterations is 200
- DEFAULT_K - Static variable in class jsat.classifiers.knn.DANN
-
The default number of neighbors to use when classifying is
1
- DEFAULT_K - Static variable in class jsat.distributions.multivariate.MetricKDE
-
When estimating the bandwidth, the distances of the k'th nearest
neighbors are used to perform the estimate.
- DEFAULT_KF - Static variable in class jsat.classifiers.neuralnetwork.SOM
-
- DEFAULT_KF - Static variable in class jsat.distributions.multivariate.MetricKDE
-
When estimating the bandwidth, the distances of the k'th nearest
neighbors are used to perform the estimate.
- DEFAULT_KN - Static variable in class jsat.classifiers.knn.DANN
-
The default number of neighbors to use when building a metric is
40.
- DEFAULT_LAMBDA - Static variable in class jsat.math.optimization.stochastic.Adam
-
- DEFAULT_LEARNING_DECAY - Static variable in class jsat.classifiers.neuralnetwork.SOM
-
- DEFAULT_LEARNING_RATE - Static variable in class jsat.classifiers.neuralnetwork.LVQ
-
The default learning rate 0.1
- DEFAULT_LEARNING_RATE - Static variable in class jsat.classifiers.neuralnetwork.SOM
-
- DEFAULT_LEARNING_RATE - Static variable in class jsat.regression.StochasticGradientBoosting
-
- DEFAULT_LOSS - Static variable in class jsat.classifiers.linear.StochasticSTLinearL1
-
- DEFAULT_LVQ_METHOD - Static variable in class jsat.classifiers.neuralnetwork.LVQ
-
The default method of LVQ to use LVQ3
- DEFAULT_MAX_ITERS - Static variable in class jsat.classifiers.neuralnetwork.SOM
-
- DEFAULT_MAX_OUTER_ITER - Static variable in class jsat.classifiers.linear.NewGLMNET
-
The default number of outer iterations of the training algorithm is
100 .
- DEFAULT_MIN_POINTS - Static variable in class jsat.clustering.OPTICS
-
The default number of points to consider is 10.
- DEFAULT_MSCALE - Static variable in class jsat.classifiers.neuralnetwork.LVQ
-
- DEFAULT_NEIGHBOR_DECAY - Static variable in class jsat.classifiers.neuralnetwork.SOM
-
- DEFAULT_NEIGHBORS - Static variable in class jsat.clustering.LSDBC
-
15 is the default number of neighbors used when performing clustering
- DEFAULT_NUGGET - Static variable in class jsat.regression.OrdinaryKriging
-
The default nugget value is 0.1
- DEFAULT_PRUNE_CONSTANT - Static variable in class jsat.classifiers.svm.extended.OnlineAMM
-
- DEFAULT_PRUNE_FREQUENCY - Static variable in class jsat.classifiers.svm.extended.OnlineAMM
-
- DEFAULT_REG - Static variable in class jsat.classifiers.linear.StochasticSTLinearL1
-
- DEFAULT_REG - Static variable in class jsat.classifiers.svm.Pegasos
-
The default regularization value is 1.0E-4
- DEFAULT_REGULARIZER - Static variable in class jsat.classifiers.svm.extended.OnlineAMM
-
- DEFAULT_REPS_PER_CLASS - Static variable in class jsat.classifiers.neuralnetwork.LVQ
-
The default number of representatives per class is
3
- DEFAULT_ROUNDS - Static variable in class jsat.classifiers.boosting.Bagging
-
The number of rounds of bagging that will be used by default in the constructor: 20
- DEFAULT_SEED - Static variable in class jsat.utils.random.RandomUtil
-
If not specified, this will be the seed used for random objects used
internally within JSAT.
You may change this to any desired value at the start of any experiments
to consistently get new experimental results.
- DEFAULT_SEED_SELECTION - Static variable in class jsat.classifiers.neuralnetwork.LVQ
-
The default seed selection method is SeedSelection.KPP
- DEFAULT_SEED_SELECTION - Static variable in class jsat.clustering.kmeans.KMeans
-
This is the default seed selection method used in ElkanKMeans.
- DEFAULT_SIMULTANIOUS_TRAINING - Static variable in class jsat.classifiers.boosting.Bagging
-
- DEFAULT_STND_DEV - Static variable in class jsat.distributions.multivariate.MetricKDE
-
When estimating the bandwidth, the distances of the k'th nearest
neighbors are used to perform the estimate.
- DEFAULT_STOPPING_DIST - Static variable in class jsat.classifiers.neuralnetwork.LVQ
-
The default stopping distance for convergence is
0.001
- DEFAULT_TRAINING_PROPORTION - Static variable in class jsat.regression.StochasticGradientBoosting
-
- DEFAULT_USE_PRIORS - Static variable in class jsat.classifiers.bayesian.graphicalmodel.DiscreteBayesNetwork
-
Whether or not the classifier should take into account the prior probabilities.
- DEFAULT_XI - Static variable in class jsat.clustering.OPTICS
-
The default value for xi is 0.005
- defaultHandling - Static variable in class jsat.classifiers.bayesian.NaiveBayes
-
- DefaultMaxIterations - Static variable in class jsat.clustering.MeanShift
-
- DefaultScaleBandwidthFactor - Static variable in class jsat.clustering.MeanShift
-
- DefaultVectorCollectionFactory<V extends Vec> - Class in jsat.linear.vectorcollection
-
DefaultVectorCollectionFactory is a generic factory that attempts to return a
good vector collection for the given input.
- DefaultVectorCollectionFactory() - Constructor for class jsat.linear.vectorcollection.DefaultVectorCollectionFactory
-
- DELETED - Static variable in class jsat.utils.ClosedHashingUtil
-
This value indicates that the status of an open addressing space is
DELETED, meaning i should not stop a search for a value - but the code
is free to overwrite the values at this location and change the status
to
ClosedHashingUtil.OCCUPIED.
- delta - Static variable in class jsat.text.GreekLetters
-
- DenseMatrix - Class in jsat.linear
-
- DenseMatrix(Vec, Vec) - Constructor for class jsat.linear.DenseMatrix
-
Creates a new matrix based off the given vectors.
- DenseMatrix(int, int) - Constructor for class jsat.linear.DenseMatrix
-
Creates a new matrix of zeros
- DenseMatrix(double[][]) - Constructor for class jsat.linear.DenseMatrix
-
Creates a new matrix that is a clone of the given matrix.
- DenseMatrix(Matrix) - Constructor for class jsat.linear.DenseMatrix
-
Creates a new dense matrix that has a copy of all the same values as the
given one
- DenseSparceTransform - Class in jsat.datatransform
-
Dense sparce transform alters the vectors that store the numerical values.
- DenseSparceTransform(double) - Constructor for class jsat.datatransform.DenseSparceTransform
-
Creates a new Dense Sparce Transform.
- DenseSparseMetric - Interface in jsat.linear.distancemetrics
-
Many algorithms require computing the distances from a small set of points to
many other points.
- DenseVector - Class in jsat.linear
-
A vector implementation that is dense, meaning all values are allocated -
even if their values will be implicitly zero.
- DenseVector(int) - Constructor for class jsat.linear.DenseVector
-
Creates a new Dense Vector of zeros
- DenseVector(List<Double>) - Constructor for class jsat.linear.DenseVector
-
Creates a new vector of the length of the given list, and values copied
over in order.
- DenseVector(double[]) - Constructor for class jsat.linear.DenseVector
-
Creates a new Dense Vector that uses the given array as its values.
- DenseVector(double[], int, int) - Constructor for class jsat.linear.DenseVector
-
Creates a new Dense Vector that uses the given array as its values.
- DenseVector(Vec) - Constructor for class jsat.linear.DenseVector
-
Creates a new Dense Vector that contains a copy of the values in the
given vector
- dependent - Variable in class jsat.classifiers.bayesian.ODE
-
The attribute we will be dependent on
- depends(int, int) - Method in class jsat.classifiers.bayesian.graphicalmodel.DiscreteBayesNetwork
-
Adds a dependency relation ship between two variables that will be in the network.
- depTargets - Variable in class jsat.classifiers.bayesian.ODE
-
The number of possible values for the dependent variable
- deriv(double, double) - Method in enum jsat.classifiers.linear.StochasticSTLinearL1.Loss
-
Returns the value of the derivative of the loss function
- deriv(double, double) - Static method in class jsat.lossfunctions.AbsoluteLoss
-
Returns the derivative of the absolute loss
- deriv(double, double, double) - Static method in class jsat.lossfunctions.EpsilonInsensitiveLoss
-
Computes the first derivative of the ε-insensitive loss
- deriv(double, double) - Static method in class jsat.lossfunctions.HingeLoss
-
Computes the first derivative of the HingeLoss loss
- deriv(Vec, Vec, int) - Method in class jsat.lossfunctions.HingeLoss
-
- deriv(double, double, double) - Static method in class jsat.lossfunctions.HuberLoss
-
Computes the first derivative of the HuberLoss loss
- deriv(double, double) - Static method in class jsat.lossfunctions.LogisticLoss
-
Computes the first derivative of the logistic loss
- deriv(Vec, Vec, int) - Method in interface jsat.lossfunctions.LossMC
-
Computes the derivatives with respect to each output
processed and derivs may be the same object, and will
simply have all its values altered if so.
- deriv(Vec, Vec, int) - Method in class jsat.lossfunctions.SoftmaxLoss
-
- deriv(double, double) - Static method in class jsat.lossfunctions.SquaredLoss
-
Computes the first derivative of the squared loss
- deriv1(double) - Method in enum jsat.datatransform.FastICA.DefaultNegEntropyFunc
-
- deriv1(double) - Method in interface jsat.datatransform.FastICA.NegEntropyFunc
-
- deriv2(double, double) - Method in enum jsat.datatransform.FastICA.DefaultNegEntropyFunc
-
- deriv2(double, double) - Method in interface jsat.datatransform.FastICA.NegEntropyFunc
-
- deriv2(double, double, double) - Static method in class jsat.lossfunctions.HuberLoss
-
Computes the second derivative of the HuberLoss loss, which only exists for
values < c
- deriv2(double, double) - Static method in class jsat.lossfunctions.LogisticLoss
-
Computes the second derivative of the logistic loss
- deriv2(double, double) - Static method in class jsat.lossfunctions.SquaredLoss
-
Computes the second derivative of the squared loss, which is always
1
- DescriptiveStatistics - Class in jsat.math
-
- DescriptiveStatistics() - Constructor for class jsat.math.DescriptiveStatistics
-
- det() - Method in class jsat.linear.LUPDecomposition
-
- diag(Vec) - Static method in class jsat.linear.Matrix
-
Returns a new dense square matrix such that the main diagonal contains
the values given in a
- diagMult(Matrix, Vec) - Static method in class jsat.linear.Matrix
-
Alters the matrix A so that it contains the result of A
times a sparse matrix represented by only its diagonal values or
A = A*diag(b).
- diagMult(Vec, Matrix) - Static method in class jsat.linear.Matrix
-
Alters the matrix A so that it contains the result of
sparse matrix represented by only its diagonal values times A or
A = diag(b)*A.
- digamma(double) - Static method in class jsat.math.FastMath
-
Computes the digamma function of the input
- digamma(double) - Static method in class jsat.math.SpecialMath
-
- dimensions - Variable in class jsat.datatransform.WhitenedPCA
-
The number of dimensions to project down to
- DirectedGraph<N> - Class in jsat.classifiers.bayesian.graphicalmodel
-
Provides a class representing an undirected graph.
- DirectedGraph() - Constructor for class jsat.classifiers.bayesian.graphicalmodel.DirectedGraph
-
- Dirichlet - Class in jsat.distributions.multivariate
-
An implementation of the Dirichlet distribution.
- Dirichlet(Vec) - Constructor for class jsat.distributions.multivariate.Dirichlet
-
Creates a new Dirichlet distribution.
- disablePath(int) - Method in class jsat.classifiers.trees.DecisionTree.Node
-
- disablePath(int) - Method in class jsat.classifiers.trees.TreeNodeVisitor
-
Disables the selected path to the specified child from the current node.
- DiscreteBayesNetwork - Class in jsat.classifiers.bayesian.graphicalmodel
-
A class for representing a Baysian Network (BN) for discrete variables.
- DiscreteBayesNetwork() - Constructor for class jsat.classifiers.bayesian.graphicalmodel.DiscreteBayesNetwork
-
- DiscreteDistribution - Class in jsat.distributions.discrete
-
- DiscreteDistribution() - Constructor for class jsat.distributions.discrete.DiscreteDistribution
-
- dissimilarity(List<DataPoint>, List<DataPoint>) - Method in class jsat.clustering.dissimilarity.AverageLinkDissimilarity
-
- dissimilarity(Set<Integer>, Set<Integer>, double[][]) - Method in class jsat.clustering.dissimilarity.AverageLinkDissimilarity
-
- dissimilarity(int, int, int, int, double[][]) - Method in class jsat.clustering.dissimilarity.AverageLinkDissimilarity
-
- dissimilarity(int, int, int, int, int, int, double[][]) - Method in class jsat.clustering.dissimilarity.AverageLinkDissimilarity
-
- dissimilarity(List<DataPoint>, List<DataPoint>) - Method in class jsat.clustering.dissimilarity.CentroidDissimilarity
-
- dissimilarity(Set<Integer>, Set<Integer>, double[][]) - Method in class jsat.clustering.dissimilarity.CentroidDissimilarity
-
- dissimilarity(int, int, int, int, double[][]) - Method in class jsat.clustering.dissimilarity.CentroidDissimilarity
-
- dissimilarity(int, int, int, int, int, int, double[][]) - Method in class jsat.clustering.dissimilarity.CentroidDissimilarity
-
- dissimilarity(List<DataPoint>, List<DataPoint>) - Method in interface jsat.clustering.dissimilarity.ClusterDissimilarity
-
Provides the notion of dissimilarity between two sets of points, that may
not have the same number of points.
- dissimilarity(Set<Integer>, Set<Integer>, double[][]) - Method in interface jsat.clustering.dissimilarity.ClusterDissimilarity
-
Provides the notion of dissimilarity between two sets of points, that may
not have the same number of points.
- dissimilarity(List<DataPoint>, List<DataPoint>) - Method in class jsat.clustering.dissimilarity.CompleteLinkDissimilarity
-
- dissimilarity(Set<Integer>, Set<Integer>, double[][]) - Method in class jsat.clustering.dissimilarity.CompleteLinkDissimilarity
-
- dissimilarity(int, int, int, int, double[][]) - Method in class jsat.clustering.dissimilarity.CompleteLinkDissimilarity
-
- dissimilarity(int, int, int, int, int, int, double[][]) - Method in class jsat.clustering.dissimilarity.CompleteLinkDissimilarity
-
- dissimilarity(int, int, int, double, double, double) - Method in class jsat.clustering.dissimilarity.CompleteLinkDissimilarity
-
- dissimilarity(List<DataPoint>, List<DataPoint>) - Method in class jsat.clustering.dissimilarity.LanceWilliamsDissimilarity
-
- dissimilarity(Set<Integer>, Set<Integer>, double[][]) - Method in class jsat.clustering.dissimilarity.LanceWilliamsDissimilarity
-
- dissimilarity(int, int, int, int, double[][]) - Method in class jsat.clustering.dissimilarity.LanceWilliamsDissimilarity
-
- dissimilarity(int, int, int, int, int, int, double[][]) - Method in class jsat.clustering.dissimilarity.LanceWilliamsDissimilarity
-
- dissimilarity(int, int, int, double, double, double) - Method in class jsat.clustering.dissimilarity.LanceWilliamsDissimilarity
-
Provides the notion of dissimilarity between two sets of points, that may
not have the same number of points.
- dissimilarity(List<DataPoint>, List<DataPoint>) - Method in class jsat.clustering.dissimilarity.SingleLinkDissimilarity
-
- dissimilarity(Set<Integer>, Set<Integer>, double[][]) - Method in class jsat.clustering.dissimilarity.SingleLinkDissimilarity
-
- dissimilarity(int, int, int, int, double[][]) - Method in class jsat.clustering.dissimilarity.SingleLinkDissimilarity
-
- dissimilarity(int, int, int, int, int, int, double[][]) - Method in class jsat.clustering.dissimilarity.SingleLinkDissimilarity
-
- dissimilarity(int, int, int, double, double, double) - Method in class jsat.clustering.dissimilarity.SingleLinkDissimilarity
-
- dissimilarity(int, int, int, int, double[][]) - Method in interface jsat.clustering.dissimilarity.UpdatableClusterDissimilarity
-
Provides the notion of dissimilarity between two sets of points, that may
not have the same number of points.
- dissimilarity(int, int, int, int, int, int, double[][]) - Method in interface jsat.clustering.dissimilarity.UpdatableClusterDissimilarity
-
Provides the notion of dissimilarity between two sets of points, that may
not have the same number of points.
- dist(Vec) - Method in class jsat.distributions.kernels.KernelPoint
-
Computes the Euclidean distance between this kernel point and the given
input in the kernel space
- dist(Vec, List<Double>) - Method in class jsat.distributions.kernels.KernelPoint
-
Computes the Euclidean distance between this kernel point and the given
input in the kernel space
- dist(KernelPoint) - Method in class jsat.distributions.kernels.KernelPoint
-
Computes the Euclidean distance between this kernel point and the given
kernel point in the kernel space
- dist(int, Vec, List<Double>) - Method in class jsat.distributions.kernels.KernelPoints
-
Computes the Euclidean distance in the kernel space between the
k'th KernelPoint and the given vector
- dist(int, KernelPoint) - Method in class jsat.distributions.kernels.KernelPoints
-
Computes the Euclidean distance in the kernel space between the
k'th KernelPoint and the given KernelPoint
- dist(int, KernelPoints, int) - Method in class jsat.distributions.kernels.KernelPoints
-
Computes the Euclidean distance in the kernel space between the
k'th KernelPoint and the j'th KernelPoint in the given
set
- dist(Vec, Vec) - Method in class jsat.linear.distancemetrics.ChebyshevDistance
-
- dist(int, int, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.ChebyshevDistance
-
- dist(int, Vec, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.ChebyshevDistance
-
- dist(int, Vec, List<Double>, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.ChebyshevDistance
-
- dist(Vec, Vec) - Method in class jsat.linear.distancemetrics.CosineDistance
-
- dist(int, int, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.CosineDistance
-
- dist(int, Vec, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.CosineDistance
-
- dist(int, Vec, List<Double>, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.CosineDistance
-
- dist(Vec, Vec) - Method in class jsat.linear.distancemetrics.CosineDistanceNormalized
-
- dist(int, int, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.CosineDistanceNormalized
-
- dist(int, Vec, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.CosineDistanceNormalized
-
- dist(int, Vec, List<Double>, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.CosineDistanceNormalized
-
- dist(double, Vec, Vec) - Method in interface jsat.linear.distancemetrics.DenseSparseMetric
-
Efficiently computes the distance from one main vector that is used many
times, to some sparse target vector.
- dist(Vec, Vec) - Method in class jsat.linear.distancemetrics.DistanceCounter
-
- dist(int, int, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.DistanceCounter
-
- dist(int, Vec, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.DistanceCounter
-
- dist(int, Vec, List<Double>, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.DistanceCounter
-
- dist(Vec, Vec) - Method in interface jsat.linear.distancemetrics.DistanceMetric
-
Computes the distance between 2 vectors.
- dist(int, int, List<? extends Vec>, List<Double>) - Method in interface jsat.linear.distancemetrics.DistanceMetric
-
Computes the distance between 2 vectors in the original list of vectors.
- dist(int, Vec, List<? extends Vec>, List<Double>) - Method in interface jsat.linear.distancemetrics.DistanceMetric
-
Computes the distance between one vector in the original list of vectors
with that of another vector not from the original list.
- dist(int, Vec, List<Double>, List<? extends Vec>, List<Double>) - Method in interface jsat.linear.distancemetrics.DistanceMetric
-
- dist(Vec, Vec) - Method in class jsat.linear.distancemetrics.EuclideanDistance
-
- dist(double, Vec, Vec) - Method in class jsat.linear.distancemetrics.EuclideanDistance
-
- dist(int, int, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.EuclideanDistance
-
- dist(int, Vec, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.EuclideanDistance
-
- dist(int, Vec, List<Double>, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.EuclideanDistance
-
- dist(Vec, Vec) - Method in class jsat.linear.distancemetrics.KernelDistance
-
Returns the square of the distance function expanded as kernel methods.
- dist(int, int, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.KernelDistance
-
- dist(int, Vec, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.KernelDistance
-
- dist(int, Vec, List<Double>, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.KernelDistance
-
- dist(Vec, Vec) - Method in class jsat.linear.distancemetrics.MahalanobisDistance
-
- dist(int, int, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.MahalanobisDistance
-
- dist(int, Vec, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.MahalanobisDistance
-
- dist(int, Vec, List<Double>, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.MahalanobisDistance
-
- dist(Vec, Vec) - Method in class jsat.linear.distancemetrics.ManhattanDistance
-
- dist(double, Vec, Vec) - Method in class jsat.linear.distancemetrics.ManhattanDistance
-
- dist(int, int, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.ManhattanDistance
-
- dist(int, Vec, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.ManhattanDistance
-
- dist(int, Vec, List<Double>, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.ManhattanDistance
-
- dist(Vec, Vec) - Method in class jsat.linear.distancemetrics.MinkowskiDistance
-
- dist(double, Vec, Vec) - Method in class jsat.linear.distancemetrics.MinkowskiDistance
-
- dist(int, int, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.MinkowskiDistance
-
- dist(int, Vec, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.MinkowskiDistance
-
- dist(int, Vec, List<Double>, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.MinkowskiDistance
-
- dist(Vec, Vec) - Method in class jsat.linear.distancemetrics.NormalizedEuclideanDistance
-
- dist(int, int, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.NormalizedEuclideanDistance
-
- dist(int, Vec, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.NormalizedEuclideanDistance
-
- dist(int, Vec, List<Double>, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.NormalizedEuclideanDistance
-
- dist(Vec, Vec) - Method in class jsat.linear.distancemetrics.PearsonDistance
-
- dist(int, int, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.PearsonDistance
-
- dist(int, Vec, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.PearsonDistance
-
- dist(int, Vec, List<Double>, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.PearsonDistance
-
- dist(Vec, Vec) - Method in class jsat.linear.distancemetrics.SquaredEuclideanDistance
-
- dist(int, int, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.SquaredEuclideanDistance
-
- dist(int, Vec, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.SquaredEuclideanDistance
-
- dist(int, Vec, List<Double>, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.SquaredEuclideanDistance
-
- dist(Vec, Vec) - Method in class jsat.linear.distancemetrics.WeightedEuclideanDistance
-
- dist(int, int, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.WeightedEuclideanDistance
-
- dist(int, Vec, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.WeightedEuclideanDistance
-
- dist(int, Vec, List<Double>, List<? extends Vec>, List<Double>) - Method in class jsat.linear.distancemetrics.WeightedEuclideanDistance
-
- distance(DataPoint, DataPoint) - Method in interface jsat.clustering.dissimilarity.ClusterDissimilarity
-
Provides the notion of distance, or dissimilarity, between two data points
- distance(DataPoint, DataPoint) - Method in class jsat.clustering.dissimilarity.DistanceMetricDissimilarity
-
- distance(int, int, int[]) - Method in class jsat.clustering.kmeans.KernelKMeans
-
Computes the distance between one data point and a specified mean
- distance(Vec, int) - Method in class jsat.clustering.kmeans.KernelKMeans
-
Returns the distance between the given data point and the the specified cluster
- distance(Vec, List<Double>, int) - Method in class jsat.clustering.kmeans.KernelKMeans
-
Returns the distance between the given data point and the the specified cluster
- DistanceCounter - Class in jsat.linear.distancemetrics
-
This class exists primarily as a sanity/benchmarking utility.
- DistanceCounter(DistanceMetric) - Constructor for class jsat.linear.distancemetrics.DistanceCounter
-
Creates a new distance counter to wrap the given base metric
- DistanceCounter(DistanceCounter) - Constructor for class jsat.linear.distancemetrics.DistanceCounter
-
Copies the given distance counter, while sharing the same underlying
counter between the original and this new object.
- DistanceMetric - Interface in jsat.linear.distancemetrics
-
A distance metric defines the distance between two points in a metric space.
- DistanceMetricBasedKernel - Class in jsat.distributions.kernels
-
This abstract class provides the means of implementing a Kernel based off
some
DistanceMetric.
- DistanceMetricBasedKernel(DistanceMetric) - Constructor for class jsat.distributions.kernels.DistanceMetricBasedKernel
-
Creates a new distance based kerenel
- DistanceMetricDissimilarity - Class in jsat.clustering.dissimilarity
-
A base class for Dissimilarity measures that are build ontop the use of some
distance metric.
- DistanceMetricDissimilarity(DistanceMetric) - Constructor for class jsat.clustering.dissimilarity.DistanceMetricDissimilarity
-
- distanceToCosine(double) - Static method in class jsat.linear.distancemetrics.CosineDistance
-
- Distribution - Class in jsat.distributions
-
Base distribution class for distributions that have only one input.
- Distribution() - Constructor for class jsat.distributions.Distribution
-
- DistributionSearch - Class in jsat.distributions
-
Provides methods for selecting the distribution that best fits a given data set.
- DistributionSearch() - Constructor for class jsat.distributions.DistributionSearch
-
- distributMissing(List<List<DataPointPair<T>>>, List<DataPointPair<T>>) - Static method in class jsat.classifiers.trees.DecisionStump
-
Distributes a list of datapoints that had missing values to each split, re-weighted by the indicated fractions
- distributMissing(List<List<DataPointPair<T>>>, double[], List<DataPointPair<T>>) - Static method in class jsat.classifiers.trees.DecisionStump
-
Distributes a list of datapoints that had missing values to each split, re-weighted by the indicated fractions
- divCol(Matrix, int, int, int, double) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of column j in the given matrix to be A[:,j] = A[:,j]/c
- divCol(Matrix, int, double) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of column j in the given matrix to be A[:,j] = A[:,j]/c
- divCol(Matrix, int, int, int, Vec) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of column j in the given matrix to be A[:,j] = A[:,j]/c[j].
The Matrix A and vector c do not need to have the same dimensions,
so long as they both have indices in the given range.
- divCol(Matrix, int, int, int, double[]) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of column j in the given matrix to be A[:,j] = A[:,j]/c[j].
The Matrix A and array c do not need to have the same dimensions, so long as they both have indices in the given range.
- divide(double) - Method in class jsat.linear.Vec
-
Returns a new vector that is the result of this / c
- divide(double) - Method in class jsat.linear.VecPaired
-
- divide(Complex) - Method in class jsat.math.Complex
-
Creates a new complex number containing the resulting division of this by
another
- divideConst(double) - Method in class jsat.classifiers.CategoricalResults
-
Divides all the probabilities by a constant value in order to scale them
- DivisiveGlobalClusterer - Class in jsat.clustering.hierarchical
-
DivisiveGlobalClusterer is a hierarchical clustering method that works by
splitting the data set into sub trees from the top down.
- DivisiveGlobalClusterer(KClusterer, ClusterEvaluation) - Constructor for class jsat.clustering.hierarchical.DivisiveGlobalClusterer
-
- DivisiveGlobalClusterer(DivisiveGlobalClusterer) - Constructor for class jsat.clustering.hierarchical.DivisiveGlobalClusterer
-
Copy constructor
- DivisiveLocalClusterer - Class in jsat.clustering.hierarchical
-
DivisiveLocalClusterer is a hierarchical clustering method that works by
splitting the data set into sub trees from the top down.
- DivisiveLocalClusterer(KClusterer, ClusterEvaluation) - Constructor for class jsat.clustering.hierarchical.DivisiveLocalClusterer
-
- DivisiveLocalClusterer(DivisiveLocalClusterer) - Constructor for class jsat.clustering.hierarchical.DivisiveLocalClusterer
-
Copy constructor
- divRow(Matrix, int, int, int, double) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of row i in the given matrix to be A[i,:] = A[i,:] / c
- divRow(Matrix, int, double) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of row i in the given matrix to be A[i,:] = A[i,:] / c
- dm - Variable in class jsat.classifiers.neuralnetwork.LVQ
-
The distance metric to use
- dm - Variable in class jsat.clustering.dissimilarity.DistanceMetricDissimilarity
-
The distance metric that will back this dissimilarity measure.
- dm - Variable in class jsat.clustering.kmeans.KMeans
-
- dm - Variable in class jsat.clustering.PAM
-
- doseStoreResults() - Method in class jsat.classifiers.ClassificationModelEvaluation
-
- dot(Vec) - Method in class jsat.distributions.kernels.KernelPoint
-
Computes the dot product between the kernel point this object represents
and the given input vector in the kernel space.
- dot(Vec, List<Double>) - Method in class jsat.distributions.kernels.KernelPoint
-
Computes the dot product between the kernel point this object represents
and the given input vector in the kernel space
- dot(KernelPoint) - Method in class jsat.distributions.kernels.KernelPoint
-
Returns the dot product between this point and another in the kernel
space
- dot(int, Vec, List<Double>) - Method in class jsat.distributions.kernels.KernelPoints
-
Computes the dot product between the k'th KernelPoint and the
given vector in the kernel space.
- dot(Vec, List<Double>) - Method in class jsat.distributions.kernels.KernelPoints
-
Computes the dot product between each KernelPoint in this set and the
given vector in the kernel space.
- dot(int, KernelPoint) - Method in class jsat.distributions.kernels.KernelPoints
-
Computes the dot product between the k'th KernelPoint and the
given KernelPoint
- dot(int, KernelPoints, int) - Method in class jsat.distributions.kernels.KernelPoints
-
Computes the dot product between the k'th KernelPoint and the
j'th KernelPoint in the given set of points.
- dot(Vec) - Method in class jsat.linear.DenseVector
-
- dot(Vec) - Method in class jsat.linear.RandomVector
-
- dot(Vec) - Method in class jsat.linear.ScaledVector
-
- dot(Vec) - Method in class jsat.linear.ShiftedVec
-
- dot(Vec) - Method in class jsat.linear.SparseVector
-
- dot(Vec) - Method in class jsat.linear.Vec
-
Computes the dot product between two vectors, which is equivalent to
Σ thisi*vi
This method should be overloaded for a serious implementation.
- dot(Vec) - Method in class jsat.linear.VecPaired
-
- DoubleList - Class in jsat.utils
-
Provides a modifiable implementation of a List using a double array.
- DoubleList() - Constructor for class jsat.utils.DoubleList
-
Creates a new empty DoubleList
- DoubleList(int) - Constructor for class jsat.utils.DoubleList
-
Creates a new empty DoubleList
- DoubleList(Collection<Double>) - Constructor for class jsat.utils.DoubleList
-
Creates a new DoubleList containing the values of the given collection
- DoubleParameter - Class in jsat.parameters
-
A double parameter that may be altered.
- DoubleParameter() - Constructor for class jsat.parameters.DoubleParameter
-
- DReDNetSimple - Class in jsat.classifiers.neuralnetwork
-
This class provides a neural network based on Geoffrey Hinton's
Deep Rectified Dropout Nets.
- DReDNetSimple() - Constructor for class jsat.classifiers.neuralnetwork.DReDNetSimple
-
Creates a new DRedNet that uses two hidden layers with 1024 neurons each.
- DReDNetSimple(int...) - Constructor for class jsat.classifiers.neuralnetwork.DReDNetSimple
-
Create a new DReDNet that uses the specified number of hidden layers.
- driftHandled(boolean) - Method in class jsat.driftdetectors.ADWIN
-
This implementation of ADWIN allows for choosing to drop either the old
values, as is normal for a drift detector, or to drop the newer
values.
- driftHandled() - Method in class jsat.driftdetectors.ADWIN
-
- driftHandled() - Method in class jsat.driftdetectors.BaseDriftDetector
-
This method should be called once the drift is handled.
- driftHandled() - Method in class jsat.driftdetectors.DDM
-
- drifting - Variable in class jsat.driftdetectors.BaseDriftDetector
-
Set to true to indicate that concept drift has occurred
- driftStart - Variable in class jsat.driftdetectors.BaseDriftDetector
-
Set this value to the time point where the drift is believed to have
started from.
- DunnIndex - Class in jsat.clustering.evaluation
-
Computes the Dunn Index (DI) using a customizable manner.
- DunnIndex(IntraClusterEvaluation, ClusterDissimilarity) - Constructor for class jsat.clustering.evaluation.DunnIndex
-
Creates a new DunnIndex
- DunnIndex(DunnIndex) - Constructor for class jsat.clustering.evaluation.DunnIndex
-
Copy constructor
- DUOL - Class in jsat.classifiers.linear.kernelized
-
Provides an implementation of Double Update Online Learning (DUOL) algorithm.
- DUOL(KernelTrick) - Constructor for class jsat.classifiers.linear.kernelized.DUOL
-
Creates a new DUOL learner
- DUOL(DUOL) - Constructor for class jsat.classifiers.linear.kernelized.DUOL
-
Copy constructor
- gain(ImpurityScore, ImpurityScore...) - Static method in class jsat.classifiers.trees.ImpurityScore
-
Computes the gain in score from a splitting of the data set
- gain(ImpurityScore, double, ImpurityScore...) - Static method in class jsat.classifiers.trees.ImpurityScore
-
Computes the gain in score from a splitting of the data set
- gamma - Variable in class jsat.classifiers.linear.kernelized.CSKLRBatch
-
- Gamma - Class in jsat.distributions
-
- Gamma(double, double) - Constructor for class jsat.distributions.Gamma
-
- gamma(double) - Static method in class jsat.math.SpecialMath
-
The gamma function is a generalization of the factorial function.
- gamma - Static variable in class jsat.text.GreekLetters
-
- gammaIncLow(double, double) - Static method in class jsat.math.SpecialMath
-
Computes the lower incomplete gamma function, γ(a,z).
- gammaIncUp(double, double) - Static method in class jsat.math.SpecialMath
-
Computes the incomplete gamma function, Γ(a,z).
- gammaP(double, double) - Static method in class jsat.math.SpecialMath
-
Returns the regularized gamma function P(a,z) = γ(a,z)/Γ(a).
- gammaPSeries(double, double) - Static method in class jsat.math.SpecialMath
-
- gammaQ(double, double) - Static method in class jsat.math.SpecialMath
-
Computes the regularized gamma function Q(a,z) = Γ(a,z)/Γ(a).
- gammToSigma(double) - Static method in class jsat.distributions.kernels.RBFKernel
-
Another common (equivalent) form of the RBF kernel is k(x, y) =
exp(-γ||x-y||2).
- GapStatistic - Class in jsat.clustering
-
This class implements a method for estimating the number of clusters in a
data set called the Gap Statistic.
- GapStatistic() - Constructor for class jsat.clustering.GapStatistic
-
Creates a new Gap clusterer using k-means as the base clustering algorithm
- GapStatistic(KClusterer) - Constructor for class jsat.clustering.GapStatistic
-
Creates a new Gap clusterer using the base clustering algorithm given.
- GapStatistic(KClusterer, boolean) - Constructor for class jsat.clustering.GapStatistic
-
Creates a new Gap clsuterer using the base clustering algorithm given.
- GapStatistic(KClusterer, boolean, int, DistanceMetric) - Constructor for class jsat.clustering.GapStatistic
-
Creates a new Gap clsuterer using the base clustering algorithm given.
- GapStatistic(GapStatistic) - Constructor for class jsat.clustering.GapStatistic
-
Copy constructor
- GaussianNormalInit - Class in jsat.classifiers.neuralnetwork.initializers
-
This object initializes the values of weights by sampling from the zero mean
Gaussian
- GaussianNormalInit(double) - Constructor for class jsat.classifiers.neuralnetwork.initializers.GaussianNormalInit
-
Creates a new GuassianNormalInit object for initializing weights
- GaussKF - Class in jsat.distributions.empirical.kernelfunc
-
- GeneralRBFKernel - Class in jsat.distributions.kernels
-
- GeneralRBFKernel(DistanceMetric, double) - Constructor for class jsat.distributions.kernels.GeneralRBFKernel
-
Creates a new Generic RBF Kernel
- generateData(int) - Method in class jsat.utils.GridDataGenerator
-
Generates a new data set.
- GenericMatrix - Class in jsat.linear
-
This Class provides default implementations of most all functions in row major form.
- GenericMatrix() - Constructor for class jsat.linear.GenericMatrix
-
- get(int) - Method in class jsat.linear.ConcatenatedVec
-
- get(int) - Method in class jsat.linear.ConstantVector
-
- get(int, int) - Method in class jsat.linear.DenseMatrix
-
- get(int) - Method in class jsat.linear.DenseVector
-
- get(int, int) - Method in class jsat.linear.Matrix
-
Returns the value stored at at the matrix position Ai,j
- get(int, int) - Method in class jsat.linear.MatrixOfVecs
-
- get(int) - Method in class jsat.linear.Poly2Vec
-
- get(int, int) - Method in class jsat.linear.RandomMatrix
-
- get(int) - Method in class jsat.linear.RandomVector
-
- get(int) - Method in class jsat.linear.ScaledVector
-
- get(int) - Method in class jsat.linear.ShiftedVec
-
- get(int, int) - Method in class jsat.linear.SparseMatrix
-
- get(int) - Method in class jsat.linear.SparseVector
-
- get(int, int) - Method in class jsat.linear.SubMatrix
-
- get(int) - Method in class jsat.linear.SubVector
-
- get(int, int) - Method in class jsat.linear.TransposeView
-
- get(int) - Method in class jsat.linear.Vec
-
Gets the value stored at a specific index in the vector
- get(int) - Method in class jsat.linear.VecPaired
-
- get(int) - Method in class jsat.linear.VecWithNorm
-
- get() - Method in class jsat.utils.concurrent.AtomicDouble
-
- get(int) - Method in class jsat.utils.concurrent.AtomicDoubleArray
-
Gets the current value at position i.
- get(K) - Method in class jsat.utils.concurrent.ConcurrentCacheLRU
-
- get(int) - Method in class jsat.utils.DoubleList
-
- get(int) - Method in class jsat.utils.IntDoubleMap
-
Returns the value to which the specified key is mapped, or
Double.NaN if this map contains no mapping for the key.
- get(Object) - Method in class jsat.utils.IntDoubleMap
-
- get(int) - Method in class jsat.utils.IntDoubleMapArray
-
Returns the value to which the specified key is mapped, or
Double.NaN if this map contains no mapping for the key.
- get(Object) - Method in class jsat.utils.IntDoubleMapArray
-
- get(int) - Method in class jsat.utils.IntList
-
- get(int) - Method in class jsat.utils.LongList
-
- get(int) - Method in class jsat.utils.SimpleList
-
- getA(int, double...) - Method in class jsat.math.ContinuedFraction
-
The a term of a continued fraction is the value that occurs as one of the
numerators, an its depth starts at 1.
- getAccelerationCache(List<? extends Vec>) - Method in class jsat.distributions.kernels.BaseKernelTrick
-
- getAccelerationCache(List<? extends Vec>) - Method in class jsat.distributions.kernels.BaseL2Kernel
-
- getAccelerationCache(List<? extends Vec>) - Method in class jsat.distributions.kernels.DistanceMetricBasedKernel
-
- getAccelerationCache(List<? extends Vec>) - Method in interface jsat.distributions.kernels.KernelTrick
-
Creates a new list cache values from a given list of training set
vectors.
- getAccelerationCache(List<? extends Vec>) - Method in class jsat.distributions.kernels.NormalizedKernel
-
- getAccelerationCache(List<? extends Vec>) - Method in class jsat.linear.distancemetrics.ChebyshevDistance
-
- getAccelerationCache(List<? extends Vec>, ExecutorService) - Method in class jsat.linear.distancemetrics.ChebyshevDistance
-
- getAccelerationCache(List<? extends Vec>) - Method in class jsat.linear.distancemetrics.CosineDistance
-
- getAccelerationCache(List<? extends Vec>, ExecutorService) - Method in class jsat.linear.distancemetrics.CosineDistance
-
- getAccelerationCache(List<? extends Vec>) - Method in class jsat.linear.distancemetrics.CosineDistanceNormalized
-
- getAccelerationCache(List<? extends Vec>, ExecutorService) - Method in class jsat.linear.distancemetrics.CosineDistanceNormalized
-
- getAccelerationCache(List<? extends Vec>) - Method in class jsat.linear.distancemetrics.DistanceCounter
-
- getAccelerationCache(List<? extends Vec>, ExecutorService) - Method in class jsat.linear.distancemetrics.DistanceCounter
-
- getAccelerationCache(List<? extends Vec>) - Method in interface jsat.linear.distancemetrics.DistanceMetric
-
Returns a cache of double values associated with the given list of
vectors in the given order.
- getAccelerationCache(List<? extends Vec>, ExecutorService) - Method in interface jsat.linear.distancemetrics.DistanceMetric
-
Returns a cache of double values associated with the given list of
vectors in the given order.
- getAccelerationCache(List<? extends Vec>) - Method in class jsat.linear.distancemetrics.EuclideanDistance
-
- getAccelerationCache(List<? extends Vec>, ExecutorService) - Method in class jsat.linear.distancemetrics.EuclideanDistance
-
- getAccelerationCache(List<? extends Vec>) - Method in class jsat.linear.distancemetrics.KernelDistance
-
- getAccelerationCache(List<? extends Vec>, ExecutorService) - Method in class jsat.linear.distancemetrics.KernelDistance
-
- getAccelerationCache(List<? extends Vec>) - Method in class jsat.linear.distancemetrics.MahalanobisDistance
-
- getAccelerationCache(List<? extends Vec>, ExecutorService) - Method in class jsat.linear.distancemetrics.MahalanobisDistance
-
- getAccelerationCache(List<? extends Vec>) - Method in class jsat.linear.distancemetrics.ManhattanDistance
-
- getAccelerationCache(List<? extends Vec>, ExecutorService) - Method in class jsat.linear.distancemetrics.ManhattanDistance
-
- getAccelerationCache(List<? extends Vec>) - Method in class jsat.linear.distancemetrics.MinkowskiDistance
-
- getAccelerationCache(List<? extends Vec>, ExecutorService) - Method in class jsat.linear.distancemetrics.MinkowskiDistance
-
- getAccelerationCache(List<? extends Vec>) - Method in class jsat.linear.distancemetrics.NormalizedEuclideanDistance
-
- getAccelerationCache(List<? extends Vec>, ExecutorService) - Method in class jsat.linear.distancemetrics.NormalizedEuclideanDistance
-
- getAccelerationCache(List<? extends Vec>) - Method in class jsat.linear.distancemetrics.PearsonDistance
-
- getAccelerationCache(List<? extends Vec>, ExecutorService) - Method in class jsat.linear.distancemetrics.PearsonDistance
-
- getAccelerationCache(List<? extends Vec>) - Method in class jsat.linear.distancemetrics.SquaredEuclideanDistance
-
- getAccelerationCache(List<? extends Vec>, ExecutorService) - Method in class jsat.linear.distancemetrics.SquaredEuclideanDistance
-
- getAccelerationCache(List<? extends Vec>) - Method in class jsat.linear.distancemetrics.WeightedEuclideanDistance
-
- getAccelerationCache(List<? extends Vec>, ExecutorService) - Method in class jsat.linear.distancemetrics.WeightedEuclideanDistance
-
- getActivationFunction() - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
Returns the activation function used for training the network
- getAlpha() - Method in class jsat.classifiers.linear.ALMA2
-
Returns the approximation coefficient used
- getAlpha() - Method in class jsat.classifiers.linear.kernelized.ALMA2K
-
Returns the approximation coefficient used
- getAlpha() - Method in class jsat.classifiers.linear.NewGLMNET
-
- getAlpha() - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Returns the extra parameter value
- getAlpha() - Method in class jsat.classifiers.neuralnetwork.RBFNet
-
Returns the alpha bandwidth learning parameter
- getAlpha() - Method in class jsat.clustering.LSDBC
-
Returns the scale value that will control how many points are added to a
cluster.
- getAlpha() - Method in class jsat.datatransform.visualization.TSNE
-
- getAlpha() - Method in class jsat.distributions.kernels.PolynomialKernel
-
Returns the scaling parameter
- getAlpha() - Method in class jsat.distributions.kernels.SigmoidKernel
-
Returns the scaling parameter
- getAlpha() - Method in class jsat.distributions.multivariate.SymmetricDirichlet
-
Returns the alpha value used by this distribution
- getAlpha() - Method in class jsat.math.decayrates.InverseDecay
-
Returns the scaling parameter
- getAlpha() - Method in class jsat.math.decayrates.PowerDecay
-
Returns the scaling parameter
- getAlpha() - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
- getAlphas() - Method in class jsat.distributions.multivariate.Dirichlet
-
Returns the backing vector that contains the alphas specifying the current distribution.
- getAltVar() - Method in interface jsat.testing.onesample.OneSampleTest
-
- getAltVar() - Method in class jsat.testing.onesample.TTest
-
- getAltVar() - Method in class jsat.testing.onesample.ZTest
-
- getAndAdd(double) - Method in class jsat.utils.concurrent.AtomicDouble
-
- getAndAdd(int, double) - Method in class jsat.utils.concurrent.AtomicDoubleArray
-
Atomically adds the given value to the element at index i.
- getAndDecrement(int) - Method in class jsat.utils.concurrent.AtomicDoubleArray
-
Atomically decrements by one the element at index i.
- getAndIncrement(int) - Method in class jsat.utils.concurrent.AtomicDoubleArray
-
Atomically increments by one the element at index i.
- getAndSet(int, double) - Method in class jsat.utils.concurrent.AtomicDoubleArray
-
Atomically sets the element at position i to the given value
and returns the old value.
- getArg() - Method in class jsat.math.Complex
-
Computes the Argument, also called phase, of this complex number.
- getASCIIName() - Method in class jsat.parameters.DecayRateParameter
-
- getASCIIName() - Method in class jsat.parameters.KernelFunctionParameter
-
- getASCIIName() - Method in class jsat.parameters.MetricParameter
-
- getASCIIName() - Method in class jsat.parameters.Parameter
-
Returns the name of this parameter using only valid ACII characters.
- getAsDPPList() - Method in class jsat.classifiers.ClassificationDataSet
-
- getAsDPPList() - Method in class jsat.regression.RegressionDataSet
-
Returns a new list containing copies of the data points in this data set,
paired with their regression target values.
- getAsFloatDPPList() - Method in class jsat.classifiers.ClassificationDataSet
-
- getB() - Method in class jsat.classifiers.linear.ALMA2
-
Returns the B value of the ALMA algorithm
- getB() - Method in class jsat.classifiers.linear.kernelized.ALMA2K
-
Returns the B value of the ALMA algorithm
- getB() - Method in class jsat.distributions.Laplace
-
- getB(int, double...) - Method in class jsat.math.ContinuedFraction
-
The b term of a continued fraction is the value that is added to the
continuing fraction, its depth starts at 0.
- getBackingArray() - Method in class jsat.utils.DoubleList
-
Returns the reference to the array that backs this list.
- getBackingList() - Method in class jsat.SimpleDataSet
-
- getBandwith() - Method in class jsat.distributions.empirical.KernelDensityEstimator
-
- getBandwith() - Method in class jsat.distributions.multivariate.MetricKDE
-
Returns the current bandwidth used
- getBase() - Method in class jsat.linear.ScaledVector
-
Returns the base vector that is being scaled
- getBase() - Method in class jsat.linear.ShiftedVec
-
- getBase() - Method in class jsat.linear.VecWithNorm
-
Return the base vector that is having its norm tracked
- getBaseClassifier() - Method in class jsat.parameters.ModelSearch
-
Returns the base classifier that was originally passed in when
constructing this GridSearch.
- getBaseMatrix() - Method in class jsat.linear.SubMatrix
-
Returns the matrix that is the base for this sub matrix.
- getBaseRegressor() - Method in class jsat.parameters.ModelSearch
-
Returns the base regressor that was originally passed in when
constructing this GridSearch.
- getBaseRegressorClone() - Method in class jsat.regression.RANSAC
-
Once RANSAC is complete, it maintains its trained version of the
finalized regressor.
- getBasisSamplingMethod() - Method in class jsat.datatransform.kernel.KernelPCA
-
Returns the method of selecting the basis vectors
- getBasisSamplingMethod() - Method in class jsat.datatransform.kernel.Nystrom
-
Returns the method of selecting the basis vectors
- getBasisSize() - Method in class jsat.datatransform.kernel.KernelPCA
-
Returns the number of basis vectors to use
- getBasisSize() - Method in class jsat.datatransform.kernel.Nystrom
-
Returns the number of basis vectors to use
- getBasisSize() - Method in class jsat.distributions.kernels.KernelPoint
-
Returns the number of vectors serving as the basis set
- getBasisSize() - Method in class jsat.distributions.kernels.KernelPoints
-
Returns the number of basis vectors in use.
- getBatchSize() - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
Returns the training batch size
- getBatchSize() - Method in class jsat.classifiers.neuralnetwork.DReDNetSimple
-
- getBatchSize() - Method in class jsat.classifiers.svm.Pegasos
-
Returns the number of points used in each iteration
- getBatchSize() - Method in class jsat.clustering.kmeans.MiniBatchKMeans
-
Returns the batch size used at each iteration
- getBatchSize() - Method in class jsat.regression.StochasticRidgeRegression
-
Returns the batch size for updates
- getBestDistribution(Vec) - Static method in class jsat.distributions.DistributionSearch
-
Searches the distributions that are known for a possible fit, and returns
what appears to be the best fit.
- getBestDistribution(Vec, double) - Static method in class jsat.distributions.DistributionSearch
-
Searches the distributions that are known for a possible fit, and returns
what appears to be the best fit.
- getBestDistribution(Vec, ContinuousDistribution...) - Static method in class jsat.distributions.DistributionSearch
-
Searches the distributions that are given for a possible fit, and returns
what appears to be the best fit.
- getBestDistribution(Vec, double, ContinuousDistribution...) - Static method in class jsat.distributions.DistributionSearch
-
Searches the distributions that are given for a possible fit, and returns
what appears to be the best fit.
- getBeta() - Method in class jsat.math.optimization.ModifiedOWLQN
-
- getBias() - Method in class jsat.classifiers.linear.ALMA2
-
- getBias(int) - Method in class jsat.classifiers.linear.ALMA2
-
- getBias() - Method in class jsat.classifiers.linear.AROW
-
- getBias(int) - Method in class jsat.classifiers.linear.AROW
-
- getBias() - Method in class jsat.classifiers.linear.BBR
-
- getBias(int) - Method in class jsat.classifiers.linear.BBR
-
- getBias(int) - Method in class jsat.classifiers.linear.LinearBatch
-
- getBias(int) - Method in class jsat.classifiers.linear.LinearSGD
-
- getBias() - Method in class jsat.classifiers.linear.LogisticRegressionDCD
-
- getBias(int) - Method in class jsat.classifiers.linear.LogisticRegressionDCD
-
- getBias() - Method in class jsat.classifiers.linear.NewGLMNET
-
- getBias(int) - Method in class jsat.classifiers.linear.NewGLMNET
-
- getBias() - Method in class jsat.classifiers.linear.NHERD
-
- getBias(int) - Method in class jsat.classifiers.linear.NHERD
-
- getBias() - Method in class jsat.classifiers.linear.PassiveAggressive
-
- getBias(int) - Method in class jsat.classifiers.linear.PassiveAggressive
-
- getBias() - Method in class jsat.classifiers.linear.ROMMA
-
- getBias(int) - Method in class jsat.classifiers.linear.ROMMA
-
- getBias() - Method in class jsat.classifiers.linear.SCD
-
- getBias(int) - Method in class jsat.classifiers.linear.SCD
-
- getBias() - Method in class jsat.classifiers.linear.SCW
-
- getBias(int) - Method in class jsat.classifiers.linear.SCW
-
- getBias(int) - Method in class jsat.classifiers.linear.SPA
-
- getBias() - Method in class jsat.classifiers.linear.STGD
-
- getBias(int) - Method in class jsat.classifiers.linear.STGD
-
- getBias(int) - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
- getBias() - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
- getBias(int) - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
- getBias() - Method in class jsat.classifiers.neuralnetwork.Perceptron
-
- getBias(int) - Method in class jsat.classifiers.neuralnetwork.Perceptron
-
- getBias() - Method in class jsat.classifiers.svm.DCD
-
- getBias(int) - Method in class jsat.classifiers.svm.DCD
-
- getBias() - Method in class jsat.classifiers.svm.DCDs
-
- getBias(int) - Method in class jsat.classifiers.svm.DCDs
-
- getBias() - Method in class jsat.classifiers.svm.Pegasos
-
- getBias(int) - Method in class jsat.classifiers.svm.Pegasos
-
- getBias() - Method in class jsat.regression.LogisticRegression
-
- getBias(int) - Method in class jsat.regression.LogisticRegression
-
- getBias() - Method in class jsat.regression.MultipleLinearRegression
-
- getBias(int) - Method in class jsat.regression.MultipleLinearRegression
-
- getBias() - Method in class jsat.regression.StochasticRidgeRegression
-
- getBias(int) - Method in class jsat.regression.StochasticRidgeRegression
-
- getBias(int) - Method in interface jsat.SimpleWeightVectorModel
-
Returns the bias term used with the weight vector for the given class
index.
- getBias() - Method in interface jsat.SingleWeightVectorModel
-
Returns the bias term used for the model, or 0 of the model does not
support or was not trained with a bias term.
- getBiasInit() - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
- getBudget() - Method in class jsat.classifiers.linear.kernelized.BOGD
-
Returns the maximum number of allowed support vectors
- getBudget() - Method in class jsat.classifiers.linear.kernelized.Forgetron
-
Returns the current budget
- getBudgetSize() - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
Returns the budget size, or maximum number of allowed support vectors.
- getBudgetStrategy() - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
Returns the method of budget maintenance
- getBudgetStrategy() - Method in class jsat.distributions.kernels.KernelPoint
-
Returns the budget method used
- getBudgetStrategy() - Method in class jsat.distributions.kernels.KernelPoints
-
Returns the budget method used
- getBurnIn() - Method in class jsat.classifiers.linear.kernelized.OSKL
-
Returns the number of burn in rounds
- getBurnIn() - Method in class jsat.classifiers.svm.SBP
-
- getC() - Method in class jsat.classifiers.linear.ALMA2
-
- getC() - Method in class jsat.classifiers.linear.kernelized.ALMA2K
-
- getC() - Method in class jsat.classifiers.linear.kernelized.DUOL
-
Returns the aggressiveness parameter
- getC() - Method in class jsat.classifiers.linear.LogisticRegressionDCD
-
Returns the regularization tradeoff parameter
- getC() - Method in class jsat.classifiers.linear.NewGLMNET
-
- getC() - Method in class jsat.classifiers.linear.NHERD
-
Returns the aggressiveness parameter
- getC() - Method in class jsat.classifiers.linear.PassiveAggressive
-
Returns the aggressiveness parameter
- getC() - Method in class jsat.classifiers.linear.SCW
-
Returns the aggressiveness parameter
- getC() - Method in class jsat.classifiers.linear.SPA
-
Returns the aggressiveness parameter
- getC() - Method in class jsat.classifiers.svm.DCD
-
Returns the penalty parameter for misclassifications.
- getC() - Method in class jsat.classifiers.svm.DCDs
-
Returns the penalty parameter for misclassifications.
- getC() - Method in class jsat.classifiers.svm.DCSVM
-
Returns the soft margin complexity parameter of the SVM
- getC() - Method in class jsat.classifiers.svm.extended.OnlineAMM
-
Returns the pruning constant
- getC() - Method in class jsat.classifiers.svm.LSSVM
-
Returns the regularization parameter value used
- getC() - Method in class jsat.classifiers.svm.PlattSMO
-
Returns the soft margin complexity parameter of the SVM
- getC() - Method in class jsat.classifiers.svm.SVMnoBias
-
Returns the soft margin complexity parameter of the SVM
- getC() - Method in class jsat.datatransform.FastICA
-
- getC() - Method in class jsat.distributions.kernels.LinearKernel
-
Returns the positive additive term
- getC() - Method in class jsat.distributions.kernels.PolynomialKernel
-
Returns the additive constant
- getC() - Method in class jsat.distributions.kernels.RationalQuadraticKernel
-
Returns the positive additive coefficient
- getC() - Method in class jsat.distributions.kernels.SigmoidKernel
-
Returns the additive constant
- getC() - Method in class jsat.linear.vectorcollection.lsh.E2LSH
-
Returns the multiplier used on the radius that controls the degree
of approximation.
- getC1() - Method in class jsat.math.optimization.BacktrackingArmijoLineSearch
-
Returns the sufficient decrease condition constant
- getC1() - Method in class jsat.math.optimization.WolfeNWLineSearch
-
Returns the sufficient decrease condition constant
- getC2() - Method in class jsat.math.optimization.WolfeNWLineSearch
-
Returns the curvature condition constant
- getC45Tree() - Static method in class jsat.classifiers.trees.DecisionTree
-
Returns a Decision Tree with settings initialized so that its behavior is
approximately that of the C4.5 decision tree algorithm when used on
classification data.
- getCacheMode() - Method in class jsat.classifiers.svm.SupportVectorLearner
-
Returns the current caching mode in use
- getCacheValue() - Method in class jsat.classifiers.svm.SupportVectorLearner
-
Returns the current cache value
- getCalibrationFolds() - Method in class jsat.classifiers.calibration.BinaryCalibration
-
Returns the number of cross validation folds to use
- getCalibrationHoldOut() - Method in class jsat.classifiers.calibration.BinaryCalibration
-
Returns the portion of the data set that will be held out for calibration
- getCalibrationMode() - Method in class jsat.classifiers.calibration.BinaryCalibration
-
Returns the calibration mode used during training
- getCallCount() - Method in class jsat.linear.distancemetrics.DistanceCounter
-
- getCategoricalData() - Method in class jsat.classifiers.DataPoint
-
Returns the array of Categorical Data information
- getCategoricalValue(int) - Method in class jsat.classifiers.DataPoint
-
- getCategoricalValues() - Method in class jsat.classifiers.DataPoint
-
Returns the array of values for each category.
- getCategories() - Method in class jsat.DataSet
-
Returns the array containing the categorical data information for this data
set.
- getCategoryName() - Method in class jsat.classifiers.CategoricalData
-
- getCategoryName(int) - Method in class jsat.DataSet
-
Returns the name used for the i'th categorical attribute.
- getCentroids(DataSet, int, DistanceMetric, ExecutorService) - Method in enum jsat.classifiers.neuralnetwork.RBFNet.Phase1Learner
-
Obtains the centroids for the given data set
- getChild(int) - Method in class jsat.classifiers.trees.DecisionTree.Node
-
- getChild(int) - Method in class jsat.classifiers.trees.TreeNodeVisitor
-
Returns the node for the specific child, or null if the child index was
not valid
- getChildren(N) - Method in class jsat.classifiers.bayesian.graphicalmodel.DirectedGraph
-
Returns the set of all children of the requested node, or null if the node does not exist in the graph.
- getClassBudget() - Method in class jsat.classifiers.svm.extended.OnlineAMM
-
Returns the maximum number of hyperplanes allowed per class
- getClassification(double) - Method in class jsat.lossfunctions.HingeLoss
-
- getClassification(Vec) - Method in class jsat.lossfunctions.HingeLoss
-
- getClassification(double) - Method in class jsat.lossfunctions.LogisticLoss
-
- getClassification(double) - Method in interface jsat.lossfunctions.LossC
-
Given the score value of a data point, this returns the classification
results.
- getClassification(Vec) - Method in interface jsat.lossfunctions.LossMC
-
Given the
processed
predictions, returns the classification results for said predictions.
- getClassification(Vec) - Method in class jsat.lossfunctions.SoftmaxLoss
-
- getClassificationTargetScore() - Method in class jsat.parameters.ModelSearch
-
Returns the classification score that is trying to be optimized via grid
search
- getClassifier() - Method in class jsat.classifiers.ClassificationModelEvaluation
-
Returns the classifier that was original given for evaluation.
- getClassSize() - Method in class jsat.classifiers.ClassificationDataSet
-
Returns the number of target classes in this classification data set.
- getClusterDesignations(int[], int) - Method in class jsat.clustering.hierarchical.NNChainHAC
-
Returns the assignment array for that would have been computed for the
previous data set with the desired number of clusters.
- getClusterDesignations(int, DataSet) - Method in class jsat.clustering.hierarchical.NNChainHAC
-
Returns the assignment array for that would have been computed for the
previous data set with the desired number of clusters.
- getClusterDesignations(int[], int) - Method in class jsat.clustering.hierarchical.PriorityHAC
-
Returns the assignment array for that would have been computed for the
previous data set with the desired number of clusters.
- getClusterDesignations(int) - Method in class jsat.clustering.hierarchical.PriorityHAC
-
Returns the assignment array for that would have been computed for the
previous data set with the desired number of clusters.
- getClusterSampleSize() - Method in class jsat.classifiers.svm.DCSVM
-
- getCoefficents() - Method in class jsat.regression.LogisticRegression
-
Returns the backing vector that containing the learned coefficients for the logistic regression.
- getCoefficient() - Method in class jsat.classifiers.boosting.ArcX4
-
Returns the coefficient use when re-weighting
- getCoefficientVector(int) - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Returns the raw coefficient vector used without the bias term.
- getColumn(int) - Method in class jsat.linear.Matrix
-
Creates a vector that has a copy of the values in column j of this
matrix.
- getColumnMeanVariance() - Method in class jsat.DataSet
-
Computes the weighted mean and variance for each column of feature
values.
- getColumnView(int) - Method in class jsat.linear.Matrix
-
Obtains a vector that is backed by this, at very little memory
cost.
- getColumnView(int) - Method in class jsat.linear.SubMatrix
-
- getColumnView(int) - Method in class jsat.linear.TransposeView
-
- getCondition() - Method in class jsat.linear.SingularValueDecomposition
-
Returns the condition number of the matrix.
- getConfusionMatrix() - Method in class jsat.classifiers.ClassificationModelEvaluation
-
- getConjugate() - Method in class jsat.math.Complex
-
Returns a new complex number representing the complex conjugate of this
one
- getConsensusSet() - Method in class jsat.regression.RANSAC
-
Returns an boolean array where the indices correspond to data points in
the original training set.
- getConstant() - Method in class jsat.classifiers.neuralnetwork.initializers.ConstantInit
-
- getCorrectWeights() - Method in class jsat.classifiers.ClassificationModelEvaluation
-
Returns the total value of the weights for data points that were classified correctly.
- getCovMode() - Method in class jsat.classifiers.linear.NHERD
-
Returns the mode for forming the covariance
- getCurrentVariableValues() - Method in class jsat.distributions.Beta
-
- getCurrentVariableValues() - Method in class jsat.distributions.Cauchy
-
- getCurrentVariableValues() - Method in class jsat.distributions.ChiSquared
-
- getCurrentVariableValues() - Method in class jsat.distributions.ContinuousDistribution
-
Returns an array, where each value contains the value of a parameter in the distribution.
- getCurrentVariableValues() - Method in class jsat.distributions.empirical.KernelDensityEstimator
-
- getCurrentVariableValues() - Method in class jsat.distributions.Exponential
-
- getCurrentVariableValues() - Method in class jsat.distributions.FisherSendor
-
- getCurrentVariableValues() - Method in class jsat.distributions.Gamma
-
- getCurrentVariableValues() - Method in class jsat.distributions.Kolmogorov
-
- getCurrentVariableValues() - Method in class jsat.distributions.Laplace
-
- getCurrentVariableValues() - Method in class jsat.distributions.Levy
-
- getCurrentVariableValues() - Method in class jsat.distributions.Logistic
-
- getCurrentVariableValues() - Method in class jsat.distributions.LogNormal
-
- getCurrentVariableValues() - Method in class jsat.distributions.LogUniform
-
- getCurrentVariableValues() - Method in class jsat.distributions.MaxwellBoltzmann
-
- getCurrentVariableValues() - Method in class jsat.distributions.Normal
-
- getCurrentVariableValues() - Method in class jsat.distributions.Pareto
-
- getCurrentVariableValues() - Method in class jsat.distributions.Rayleigh
-
- getCurrentVariableValues() - Method in class jsat.distributions.StudentT
-
- getCurrentVariableValues() - Method in class jsat.distributions.TruncatedDistribution
-
- getCurrentVariableValues() - Method in class jsat.distributions.Uniform
-
- getCurrentVariableValues() - Method in class jsat.distributions.Weibull
-
- getD() - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet.ActivationFunction
-
Returns the function object for the derivative of this activation
function.
- getD() - Method in class jsat.linear.EigenValueDecomposition
-
Return the block diagonal eigenvalue matrix
- getD() - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
Returns the approximate number of documents that will be observed, or
-1 if this object is not ready to learn
- getD(int) - Method in class jsat.utils.DoubleList
-
- getDataMatrix() - Method in class jsat.DataSet
-
Creates a matrix from the data set, where each row represent a data
point, and each column is one of the numeric example from the data set.
- getDataMatrixView() - Method in class jsat.DataSet
-
Creates a matrix backed by the data set, where each row is a data point
from the dataset, and each column is one of the numeric examples from the
data set.
- getDataPoint(int) - Method in class jsat.classifiers.ClassificationDataSet
-
Returns the i'th data point from the data set
- getDataPoint() - Method in class jsat.classifiers.DataPointPair
-
- getDataPoint(int) - Method in class jsat.DataSet
-
Returns the i'th data point in this set.
- getDataPoint(int) - Method in class jsat.regression.RegressionDataSet
-
- getDataPoint(int) - Method in class jsat.SimpleDataSet
-
- getDataPointCategory(int) - Method in class jsat.classifiers.ClassificationDataSet
-
Returns the integer value corresponding to the true category of the i'th data point.
- getDataPointIterator() - Method in class jsat.DataSet
-
Returns an iterator that will iterate over all data points in the set.
- getDataPointPair(int) - Method in class jsat.classifiers.ClassificationDataSet
-
Returns the i'th data point from the data set, paired with the integer indicating its true class
- getDataPointPair(int) - Method in class jsat.regression.RegressionDataSet
-
Returns the i'th data point in the data set paired with its target regressor value.
- getDataPoints() - Method in class jsat.DataSet
-
Creates a list containing the same DataPoints in this set.
- getDatapointsFromCluster(int, int[], DataSet, int[]) - Static method in class jsat.clustering.ClustererBase
-
Gets a list of the datapoints in a data set that belong to the indicated cluster
- getDataSet() - Method in class jsat.text.ClassificationHashedTextDataLoader
-
- getDataSet() - Method in class jsat.text.ClassificationTextDataLoader
-
- getDataSet() - Method in class jsat.text.HashedTextDataLoader
-
Returns a new data set containing the original data points that were
loaded with this loader.
- getDataSet() - Method in class jsat.text.TextDataLoader
-
Returns a new data set containing the original data points that were
loaded with this loader.
- getDataVectors() - Method in class jsat.DataSet
-
Creates a list of the vectors values for each data point in the correct order.
- getDataWeights() - Method in class jsat.DataSet
-
This method returns the weight of each data point in a single Vector.
- getDefaultK() - Method in class jsat.distributions.multivariate.MetricKDE
-
Returns the default value of the k'th nearest neighbor to use when not specified.
- getDefaultStndDev() - Method in class jsat.distributions.multivariate.MetricKDE
-
Returns the multiple of the standard deviations that is added to the bandwidth estimate
- getDegree() - Method in class jsat.datatransform.PolynomialTransform
-
Returns the polynomial degree to use
- getDegree() - Method in class jsat.distributions.kernels.PolynomialKernel
-
Returns the degree of the polynomial
- getDelta() - Method in class jsat.driftdetectors.ADWIN
-
Returns the upper bound on false positives
- getDeriv(double, double) - Method in class jsat.lossfunctions.AbsoluteLoss
-
- getDeriv(double, double) - Method in class jsat.lossfunctions.EpsilonInsensitiveLoss
-
- getDeriv(double, double) - Method in class jsat.lossfunctions.HingeLoss
-
- getDeriv(double, double) - Method in class jsat.lossfunctions.HuberLoss
-
- getDeriv(double, double) - Method in class jsat.lossfunctions.LogisticLoss
-
- getDeriv(double, double) - Method in interface jsat.lossfunctions.LossC
-
Computes the first derivative of the getLoss function.
- getDeriv(double, double) - Method in interface jsat.lossfunctions.LossFunc
-
Computes the first derivative of the loss function.
- getDeriv(double, double) - Method in interface jsat.lossfunctions.LossR
-
Computes the first derivative of the getLoss function.
- getDeriv(double, double) - Method in class jsat.lossfunctions.SquaredLoss
-
- getDeriv2(double, double) - Method in class jsat.lossfunctions.AbsoluteLoss
-
- getDeriv2(double, double) - Method in class jsat.lossfunctions.EpsilonInsensitiveLoss
-
- getDeriv2(double, double) - Method in class jsat.lossfunctions.HingeLoss
-
- getDeriv2(double, double) - Method in class jsat.lossfunctions.HuberLoss
-
- getDeriv2(double, double) - Method in class jsat.lossfunctions.LogisticLoss
-
- getDeriv2(double, double) - Method in interface jsat.lossfunctions.LossC
-
Computes the second derivative of the getLoss function.
- getDeriv2(double, double) - Method in interface jsat.lossfunctions.LossFunc
-
Computes the second derivative of the getLoss function.
- getDeriv2(double, double) - Method in interface jsat.lossfunctions.LossR
-
Computes the second derivative of the getLoss function.
- getDeriv2(double, double) - Method in class jsat.lossfunctions.SquaredLoss
-
- getDeriv2Max() - Method in class jsat.lossfunctions.AbsoluteLoss
-
- getDeriv2Max() - Method in class jsat.lossfunctions.EpsilonInsensitiveLoss
-
- getDeriv2Max() - Method in class jsat.lossfunctions.HingeLoss
-
- getDeriv2Max() - Method in class jsat.lossfunctions.HuberLoss
-
- getDeriv2Max() - Method in class jsat.lossfunctions.LogisticLoss
-
- getDeriv2Max() - Method in interface jsat.lossfunctions.LossFunc
-
Returns an upper bound on the maximum value of the second derivative.
- getDeriv2Max() - Method in class jsat.lossfunctions.SquaredLoss
-
- getDerivative() - Method in class jsat.math.optimization.RosenbrockFunction
-
Returns the gradient of the Rosenbrock function
- getDescriptiveName() - Method in class jsat.distributions.ContinuousDistribution
-
The descriptive name of a distribution returns the name of the distribution, followed by the parameters of the distribution and their values.
- getDescriptiveName() - Method in class jsat.distributions.Exponential
-
- getDescriptiveName() - Method in class jsat.distributions.Normal
-
- getDescriptiveName() - Method in class jsat.distributions.StudentT
-
- getDet() - Method in class jsat.linear.CholeskyDecomposition
-
Computes the determinant of A
- getDimension() - Method in class jsat.datatransform.kernel.Nystrom
-
Returns the number of dimensions to project down too
- getDimension() - Method in class jsat.distributions.multivariate.SymmetricDirichlet
-
Returns the dimension size of the current distribution
- getDimensions() - Method in class jsat.datatransform.kernel.KernelPCA
-
Returns the number of dimensions to project down too
- getDimensions() - Method in class jsat.datatransform.kernel.RFF_RBF
-
Returns the number of dimensions that will be used in the projected space
- getDimensions() - Method in class jsat.datatransform.WhitenedPCA
-
Returns the number of dimensions to project down to
- getDimensionSize() - Method in class jsat.classifiers.bayesian.ConditionalProbabilityTable
-
Returns the number of dimensions in the CPT
- getDistance(double[][], int, int) - Static method in class jsat.clustering.dissimilarity.AbstractClusterDissimilarity
-
A convenience method.
- getDistanceMetric() - Method in class jsat.classifiers.knn.LWL
-
Returns the distance metric in use
- getDistanceMetric() - Method in class jsat.classifiers.knn.NearestNeighbour
-
- getDistanceMetric() - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Returns the distance metric to use
- getDistanceMetric() - Method in class jsat.classifiers.neuralnetwork.RBFNet
-
Returns the distance metric in use
- getDistanceMetric() - Method in class jsat.clustering.evaluation.intra.SumOfSqrdPairwiseDistances
-
- getDistanceMetric() - Method in class jsat.clustering.FLAME
-
Returns the distance metric to use for the nearest neighbor search
- getDistanceMetric() - Method in class jsat.clustering.GapStatistic
-
- getDistanceMetric() - Method in class jsat.clustering.kmeans.KMeans
-
Returns the distance metric in use
- getDistanceMetric() - Method in class jsat.clustering.kmeans.MiniBatchKMeans
-
Returns the distance metric used for determining the nearest cluster center
- getDistanceMetric() - Method in class jsat.clustering.OPTICS
-
Returns the distance metric used to compute distances in the algorithm.
- getDistanceMetric() - Method in class jsat.datatransform.featureselection.ReliefF
-
Returns the distance metric to use
- getDistanceMetric() - Method in class jsat.distributions.multivariate.MetricKDE
-
Returns the distance metric that is used for density estimation
- getDistanceMetric() - Method in class jsat.linear.vectorcollection.VectorArray
-
- getDistanceMetrics() - Method in class jsat.clustering.HDBSCAN
-
- getDistribution() - Method in class jsat.classifiers.boosting.Wagging
-
Returns the distribution used for weight sampling
- getDistribution() - Method in class jsat.classifiers.boosting.WaggingNormal
-
- getDistributionName() - Method in class jsat.distributions.Beta
-
- getDistributionName() - Method in class jsat.distributions.Cauchy
-
- getDistributionName() - Method in class jsat.distributions.ChiSquared
-
- getDistributionName() - Method in class jsat.distributions.ContinuousDistribution
-
Return the name of the distribution.
- getDistributionName() - Method in class jsat.distributions.empirical.KernelDensityEstimator
-
- getDistributionName() - Method in class jsat.distributions.Exponential
-
- getDistributionName() - Method in class jsat.distributions.FisherSendor
-
- getDistributionName() - Method in class jsat.distributions.Gamma
-
- getDistributionName() - Method in class jsat.distributions.Kolmogorov
-
- getDistributionName() - Method in class jsat.distributions.Laplace
-
- getDistributionName() - Method in class jsat.distributions.Levy
-
- getDistributionName() - Method in class jsat.distributions.Logistic
-
- getDistributionName() - Method in class jsat.distributions.LogNormal
-
- getDistributionName() - Method in class jsat.distributions.LogUniform
-
- getDistributionName() - Method in class jsat.distributions.MaxwellBoltzmann
-
- getDistributionName() - Method in class jsat.distributions.Normal
-
- getDistributionName() - Method in class jsat.distributions.Pareto
-
- getDistributionName() - Method in class jsat.distributions.Rayleigh
-
- getDistributionName() - Method in class jsat.distributions.StudentT
-
- getDistributionName() - Method in class jsat.distributions.TruncatedDistribution
-
- getDistributionName() - Method in class jsat.distributions.Uniform
-
- getDistributionName() - Method in class jsat.distributions.Weibull
-
- getDPPList() - Method in class jsat.regression.RegressionDataSet
-
Returns a new list containing the data points in this data set, paired with
their regression target values.
- getDriftAge() - Method in class jsat.driftdetectors.BaseDriftDetector
-
Returns the number of items in recent history that differed from the
historical values, or -1 if there has not been any detected
drift.
- getDriftedHistory() - Method in class jsat.driftdetectors.BaseDriftDetector
-
- getDriftThreshold() - Method in class jsat.driftdetectors.DDM
-
Returns the threshold multiple for controlling the false positive /
negative rate on detecting changes.
- getDropoutHidden() - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
- getDropoutInput() - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
- getElogW() - Method in class jsat.clustering.GapStatistic
-
Returns the array of expected E[log(Wk)] scores
computed from sampling new data sets.
- getElogWkStndDev() - Method in class jsat.clustering.GapStatistic
-
- getEmbeddingMetric() - Method in class jsat.datatransform.visualization.MDS
-
- getEndBlock(int, int, int) - Static method in class jsat.utils.concurrent.ParallelUtils
-
Gets the ending index (exclusive) for splitting up a list of items into
P evenly sized blocks.
- getEndBlock(int, int) - Static method in class jsat.utils.concurrent.ParallelUtils
-
Gets the ending index (exclusive) for splitting up a list of items into
SystemInfo.LogicalCores evenly sized blocks.
- getEndLevel() - Method in class jsat.classifiers.svm.DCSVM
-
- getEntropyThreshold() - Method in class jsat.classifiers.svm.extended.CPM
-
- getEpochs() - Method in class jsat.classifiers.BaseUpdateableClassifier
-
Returns the number of epochs used for training
- getEpochs() - Method in class jsat.classifiers.linear.kernelized.CSKLRBatch
-
Returns the number of passes through the data set
- getEpochs() - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
Returns the number of epochs to use
- getEpochs() - Method in class jsat.classifiers.linear.PassiveAggressive
-
Returns the number of epochs used for training
- getEpochs() - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Returns the maximum number of epochs
- getEpochs() - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
Returns the number of iterations of updating that will be done
- getEpochs() - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
Returns the number of epochs of training epochs for learning
- getEpochs() - Method in class jsat.classifiers.neuralnetwork.DReDNetSimple
-
- getEpochs() - Method in class jsat.classifiers.svm.extended.CPM
-
Returns the number of epochs used for training
- getEpochs() - Method in class jsat.classifiers.svm.Pegasos
-
Returns the number of iterations of updating that will be done
- getEpochs() - Method in class jsat.regression.BaseUpdateableRegressor
-
Returns the number of epochs used for training
- getEpochs() - Method in class jsat.regression.StochasticRidgeRegression
-
Returns the number of training iterations
- getEpochs() - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
Returns the number of training iterations over the data set that will be
used
- getEps() - Method in class jsat.classifiers.linear.PassiveAggressive
-
Returns the maximum acceptable difference in prediction and truth
- getEps() - Method in class jsat.classifiers.svm.DCD
-
Returns the epsilon insensitivity parameter used in regression problems.
- getEps() - Method in class jsat.classifiers.svm.DCDs
-
Returns the epsilon insensitivity parameter used in regression problems.
- getEps() - Method in class jsat.clustering.FLAME
-
Returns the minimum difference in scores to consider FLAME converged
- getEps() - Method in class jsat.math.optimization.ModifiedOWLQN
-
- getEpsilon() - Method in class jsat.classifiers.knn.DANN
-
Returns the regularization parameter that is applied to the diagonal of
the matrix when creating each new metric.
- getEpsilon() - Method in class jsat.classifiers.svm.PlattSMO
-
Returns the epsilon insensitive loss value
- getEpsilonDistance() - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Sets the epsilon scale distance between learning vectors that may be
allowed to two at a time.
- getErrorRate() - Method in class jsat.classifiers.ClassificationModelEvaluation
-
Computes the weighted error rate of the classifier.
- getErrorRateStats() - Method in class jsat.classifiers.ClassificationModelEvaluation
-
Returns the object that keeps track of the error on
individual evaluations.
- getErrorStndDev() - Method in class jsat.regression.RegressionModelEvaluation
-
Returns the standard deviation of the error from all runs
- getErrorTolerance() - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
Returns the error tolerance that would be used
- getErrorTolerance() - Method in class jsat.distributions.kernels.KernelPoint
-
- getErrorTolerance() - Method in class jsat.distributions.kernels.KernelPoints
-
- getErrorTolerance() - Method in class jsat.regression.KernelRLS
-
Returns the projection approximation tolerance
- getEta() - Method in class jsat.classifiers.linear.kernelized.BOGD
-
Returns the learning rate in use
- getEta() - Method in class jsat.classifiers.linear.kernelized.CSKLR
-
Returns the learning rate to use
- getEta() - Method in class jsat.classifiers.linear.kernelized.CSKLRBatch
-
Returns the learning rate to use
- getEta() - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
Returns the base learning rate
- getEta() - Method in class jsat.classifiers.linear.kernelized.OSKL
-
Returns the learning rate in use
- getEta() - Method in class jsat.classifiers.linear.kernelized.Projectron
-
Returns the sparsity parameter value
- getEta() - Method in class jsat.classifiers.linear.LinearSGD
-
Returns the current learning rate in use
- getEta() - Method in class jsat.classifiers.linear.SCW
-
Returns the target correction confidence
- getEta() - Method in class jsat.classifiers.linear.SMIDAS
-
Returns the current learning rate used during training
- getEta() - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
- getEta() - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
- getEtaDecay() - Method in class jsat.classifiers.linear.LinearSGD
-
Returns the decay rate in use
- getEtaDecay() - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
- getExponent() - Method in class jsat.classifiers.boosting.ArcX4
-
Returns the exponent used when re-weighting
- getExtractionMethod() - Method in class jsat.clustering.OPTICS
-
Returns the method used to extract clusters from the reachability plot.
- getExtraSamples() - Method in class jsat.classifiers.boosting.Bagging
-
- getExtraSamples() - Method in class jsat.classifiers.trees.RandomForest
-
- getFaultException() - Method in exception jsat.exceptions.FailedToFitException
-
Returns the exception that caused the issue.
- getFeatureCount() - Method in class jsat.datatransform.featureselection.BDS
-
Returns the number of features to use
- getFeatureCount() - Method in class jsat.datatransform.featureselection.MutualInfoFS
-
Returns the number of features to select
- getFeatureCount() - Method in class jsat.datatransform.featureselection.ReliefF
-
Returns the number of features to sue
- getFeatureImportance() - Method in class jsat.classifiers.trees.RandomForest
-
Random Forest can obtain an unbiased estimate of feature importance using
a
TreeFeatureImportanceInference method on the out-of-bag samples
during training.
- getFeaturesToAdd() - Method in class jsat.datatransform.featureselection.LRS
-
Returns the number of features to add
- getFeaturesToRemove() - Method in class jsat.datatransform.featureselection.LRS
-
Returns the number of features to remove
- getFirstColumn() - Method in class jsat.linear.SubMatrix
-
Returns the column offset used from the base matrix
- getFirstItem() - Method in class jsat.utils.Pair
-
- getFirstItem() - Method in class jsat.utils.PairedReturn
-
Returns the first object stored.
- getFirstRow() - Method in class jsat.linear.SubMatrix
-
Returns the row offset used from the base matrix
- getfKs() - Method in class jsat.clustering.kmeans.KMeansPDN
-
Returns the array of f(K) values generated for the last data set.
- getFolds() - Method in class jsat.classifiers.boosting.Stacking
-
- getFolds() - Method in class jsat.datatransform.featureselection.BDS
-
- getFolds() - Method in class jsat.datatransform.featureselection.LRS
-
- getFolds() - Method in class jsat.datatransform.featureselection.SBS
-
- getFolds() - Method in class jsat.datatransform.featureselection.SFS
-
- getForrestSize() - Method in class jsat.classifiers.trees.ERTrees
-
- getFunctionCDF(Distribution) - Static method in class jsat.distributions.Distribution
-
- getFunctionPDF(ContinuousDistribution) - Static method in class jsat.distributions.ContinuousDistribution
-
- getG() - Method in class jsat.classifiers.linear.kernelized.OSKL
-
Returns the sparsification parameter
- getGain(ImpurityScore, List<List<DataPointPair<Integer>>>) - Method in class jsat.classifiers.trees.DecisionStump
-
From the score for the original set that is being split, this computes
the gain as the improvement in classification from the original split.
- getGainMethod() - Method in class jsat.classifiers.trees.DecisionStump
-
- getGainMethod() - Method in class jsat.classifiers.trees.DecisionTree
-
- getGamma() - Method in class jsat.classifiers.linear.kernelized.CSKLR
-
Returns the gamma sparsity parameter value
- getGamma() - Method in class jsat.classifiers.linear.kernelized.CSKLRBatch
-
Returns the gamma sparsity parameter value
- getGamma() - Method in class jsat.datatransform.visualization.LargeViz
-
- getGap() - Method in class jsat.clustering.GapStatistic
-
Returns the array of gap statistic values.
- getGradientUpdater() - Method in class jsat.classifiers.linear.LinearSGD
-
- getGradientUpdater() - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
- getGravity() - Method in class jsat.classifiers.linear.STGD
-
Returns the regularization parameter
- getGuess(DataSet) - Method in class jsat.parameters.DoubleParameter
-
This method allows one to obtain a distribution that represents a
reasonable "guess" at the range of values that would work for this
parameter.
- getGuess(DataSet) - Method in class jsat.parameters.IntParameter
-
This method allows one to obtain a distribution that represents a
reasonable "guess" at the range of values that would work for this
parameter.
- getH(double, double, double) - Static method in class jsat.distributions.kernels.KernelPoint
-
Gets the minimum of H in [0, 1] the for RBF merging
amkmn(1-h)^2 + ankmnh^2
.
- getHandling() - Method in class jsat.datatransform.featureselection.MutualInfoFS
-
Returns the method of numericHandling numeric features
- getHiddenSizes() - Method in class jsat.classifiers.neuralnetwork.DReDNetSimple
-
- getI(int) - Method in class jsat.utils.IntList
-
- getImag() - Method in class jsat.math.Complex
-
Returns the imaginary part of this complex number
- getImagEigenvalues() - Method in class jsat.linear.EigenValueDecomposition
-
Return the imaginary parts of the eigenvalues
- getImportanceStats(TreeLearner, DataSet<Type>) - Method in class jsat.classifiers.trees.ImportanceByUses
-
- getImportanceStats(TreeLearner, DataSet<Type>) - Method in class jsat.classifiers.trees.MDA
-
- getImportanceStats(TreeLearner, DataSet<Type>) - Method in class jsat.classifiers.trees.MDI
-
- getImportanceStats(TreeLearner, DataSet<Type>) - Method in interface jsat.classifiers.trees.TreeFeatureImportanceInference
-
- getImpurityMeasure() - Method in class jsat.classifiers.trees.ExtraTree
-
Returns the impurity measure in use
- getImpurityMeasure() - Method in class jsat.classifiers.trees.ImpurityScore
-
Returns the impurity measure being used
- getIndex() - Method in class jsat.linear.IndexValue
-
Returns the index of the stored value
- getInitialLearningRate() - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Returns the current initial learning rate
- getInitialLearningRate() - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
Returns the learning rate used
- getInitialLearningRate() - Method in class jsat.classifiers.neuralnetwork.SOM
-
Returns the rate at which input is incorporated at each iteration of the SOM
- getInitialTrainSize() - Method in class jsat.regression.RANSAC
-
Returns the number of data points to be sampled from the training set to
create initial models.
- getInstance() - Static method in class jsat.distributions.empirical.kernelfunc.BiweightKF
-
Returns the singleton instance of this class
- getInstance() - Static method in class jsat.distributions.empirical.kernelfunc.EpanechnikovKF
-
Returns the singleton instance of this class
- getInstance() - Static method in class jsat.distributions.empirical.kernelfunc.GaussKF
-
Returns the singleton instance of this class
- getInstance() - Static method in class jsat.distributions.empirical.kernelfunc.TriweightKF
-
Returns the singleton instance of this class
- getInstance() - Static method in class jsat.distributions.empirical.kernelfunc.UniformKF
-
Returns the singleton instance of this class
- getIntegrationMax() - Method in class jsat.distributions.ContinuousDistribution
-
- getIntegrationMin() - Method in class jsat.distributions.ContinuousDistribution
-
- getInverseSingularValues() - Method in class jsat.linear.SingularValueDecomposition
-
Returns an array containing the inverse singular values.
- getInverseSingularValues(double) - Method in class jsat.linear.SingularValueDecomposition
-
Returns an array containing the inverse singular values.
- getItem() - Method in class jsat.utils.UnionFind
-
- getIterationLimit() - Method in class jsat.clustering.EMGaussianMixture
-
Returns the maximum number of iterations of the ElkanKMeans algorithm that will be performed.
- getIterationLimit() - Method in class jsat.clustering.kmeans.GMeans
-
- getIterationLimit() - Method in class jsat.clustering.kmeans.KMeans
-
Returns the maximum number of iterations of the ElkanKMeans algorithm that will be performed.
- getIterationLimit() - Method in class jsat.clustering.kmeans.XMeans
-
- getIterations() - Method in class jsat.classifiers.boosting.ArcX4
-
Returns the number of iterations to learn
- getIterations() - Method in class jsat.classifiers.boosting.Wagging
-
Returns the number of iterations to create weak learners
- getIterations() - Method in class jsat.classifiers.linear.SCD
-
Returns the number of iterations used
- getIterations() - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Returns the number of iterations of the algorithm to apply
- getIterations() - Method in class jsat.classifiers.svm.PegasosK
-
Returns the number of iterations used during training
- getIterations() - Method in class jsat.classifiers.svm.SBP
-
Returns the number of iterations the algorithm will perform
- getIterations() - Method in class jsat.clustering.kmeans.MiniBatchKMeans
-
Returns the number of mini-batch iterations used
- getIterations() - Method in class jsat.datatransform.featureselection.ReliefF
-
Returns the number of iterations to use
- getIterations() - Method in class jsat.datatransform.visualization.TSNE
-
- getIterations() - Method in class jsat.regression.RANSAC
-
Returns the number models that will be tested on the data set.
- getIterativeRefine() - Method in class jsat.clustering.kmeans.GMeans
-
- getIterativeRefine() - Method in class jsat.clustering.kmeans.XMeans
-
- getK() - Method in class jsat.classifiers.knn.DANN
-
Returns the number of nearest neighbors to use when predicting
- getK() - Method in class jsat.classifiers.linear.STGD
-
Returns the frequency of regularization application
- getK() - Method in class jsat.classifiers.svm.extended.CPM
-
- getK() - Method in class jsat.clustering.FLAME
-
Returns the number of neighbors used
- getK() - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
Returns the number of topics to learn, or -1 if this
object is not ready to learn
- getKappa() - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
- getKeptModels() - Method in class jsat.classifiers.ClassificationModelEvaluation
-
Returns the models that were kept after the last evaluation.
- getKeptModels() - Method in class jsat.regression.RegressionModelEvaluation
-
Returns the models that were kept after the last evaluation.
- getKeptNominal() - Method in class jsat.datatransform.RemoveAttributeTransform
-
Returns an unmodifiable list of the original indices of the nominal
attributes that will be kept when this transform is applied.
- getKeptNumeric() - Method in class jsat.datatransform.RemoveAttributeTransform
-
Returns an unmodifiable list of the original indices of the numeric
attributes that will be kept when this transform is applied.
- getKernel() - Method in class jsat.classifiers.linear.kernelized.BOGD
-
Returns the kernel to use
- getKernel() - Method in class jsat.classifiers.linear.kernelized.CSKLR
-
Returns the kernel trick in use
- getKernel() - Method in class jsat.classifiers.linear.kernelized.DUOL
-
Returns the kernel trick in use
- getKernel() - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
Returns the kernel in use
- getKernel() - Method in class jsat.classifiers.linear.kernelized.OSKL
-
Returns the kernel to use
- getKernel() - Method in class jsat.classifiers.linear.kernelized.Projectron
-
Returns the kernel trick in use
- getKernel() - Method in class jsat.classifiers.svm.SupportVectorLearner
-
- getKernel() - Method in class jsat.datatransform.kernel.KernelPCA
-
- getKernel() - Method in class jsat.datatransform.kernel.Nystrom
-
- getKernel() - Method in class jsat.distributions.kernels.KernelPoints
-
- getKernel() - Method in class jsat.regression.KernelRidgeRegression
-
Returns the kernel in use
- getKernelFunction() - Method in class jsat.classifiers.knn.LWL
-
Returns the kernel function that will be used to set the weights.
- getKernelFunction() - Method in class jsat.distributions.multivariate.MetricKDE
-
- getKernelFunction() - Method in class jsat.distributions.multivariate.MultivariateKDE
-
- getKernelFunction() - Method in class jsat.distributions.multivariate.ProductKDE
-
- getKernelTrick() - Method in class jsat.classifiers.linear.kernelized.ALMA2K
-
Returns the kernel in use
- getKernelTrick() - Method in class jsat.classifiers.linear.kernelized.Forgetron
-
Returns the current kernel trick
- getKn() - Method in class jsat.classifiers.knn.DANN
-
Returns the number of nearest neighbors to use when adapting the distance
metric
- getKthNeighborStats(VectorCollection<V0>, List<V1>, int) - Static method in class jsat.linear.vectorcollection.VectorCollectionUtils
-
Computes statistics about the distance of the k'th nearest neighbor for each data point in the search list.
- getKthNeighborStats(VectorCollection<V0>, V1[], int) - Static method in class jsat.linear.vectorcollection.VectorCollectionUtils
-
Computes statistics about the distance of the k'th nearest neighbor for each data point in the search list.
- getKthNeighborStats(VectorCollection<V0>, List<V1>, int, ExecutorService) - Static method in class jsat.linear.vectorcollection.VectorCollectionUtils
-
Computes statistics about the distance of the k'th nearest neighbor for each data point in the search list.
- getKthNeighborStats(VectorCollection<V0>, V1[], int, ExecutorService) - Static method in class jsat.linear.vectorcollection.VectorCollectionUtils
-
Computes statistics about the distance of the k'th nearest neighbor for each data point in the search list.
- getKurtosis() - Method in class jsat.math.OnLineStatistics
-
- getL() - Method in class jsat.linear.vectorcollection.lsh.E2LSH
-
Returns how many separate hash tables have been created for this distance
metric.
- getL(int) - Method in class jsat.utils.LongList
-
- getLambda() - Method in class jsat.classifiers.boosting.EmphasisBoost
-
Returns the value of the λ trade off parameter
- getLambda() - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
Returns the L2 regularization parameter
- getLambda() - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
Returns the amount of regularization to used in training
- getLambda() - Method in class jsat.classifiers.svm.extended.CPM
-
- getLambda() - Method in class jsat.classifiers.svm.extended.OnlineAMM
-
Returns the regularization parameter
- getLambda() - Method in class jsat.distributions.discrete.Poisson
-
- getLambda() - Method in class jsat.regression.KernelRidgeRegression
-
Returns the regularization constant in use
- getLambda() - Method in class jsat.regression.RidgeRegression
-
Returns the regularization constant in use
- getLambda() - Method in class jsat.regression.StochasticRidgeRegression
-
Returns the regularization constant in use
- getLambda0() - Method in class jsat.classifiers.linear.LinearBatch
-
Returns the L2 regularization term in use
- getLambda0() - Method in class jsat.classifiers.linear.LinearSGD
-
Returns the L2 regularization term in use
- getLambda1() - Method in class jsat.classifiers.linear.LinearSGD
-
Returns the L1 regularization term in use
- getLambdaMultipler() - Method in class jsat.math.optimization.ModifiedOWLQN
-
- getLastNonZeroIndex() - Method in class jsat.linear.SparseVector
-
Returns the index of the last non-zero value, or -1 if all values are zero.
- getLayerSizes() - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
- getLearningDecay() - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Returns the method used to decay the learning rate over each iteration
- getLearningDecay() - Method in class jsat.classifiers.neuralnetwork.SOM
-
The rate the SOM learns decays over each iteration, and this defines the
way in which the rate decays.
- getLearningDecay() - Method in class jsat.regression.StochasticRidgeRegression
-
Returns the learning decay rate used
- getLearningRate() - Method in class jsat.classifiers.linear.STGD
-
Returns the learning rate to use
- getLearningRate() - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Returns the learning rate at which to apply updates during the algorithm.
- getLearningRate() - Method in class jsat.regression.StochasticGradientBoosting
-
Returns the learning rate of the algorithm used to control overfitting.
- getLearningRate() - Method in class jsat.regression.StochasticRidgeRegression
-
Returns the learning rate in use.
- getLearningRateDecay() - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Returns the decay rate in use
- getLearningRateDecay() - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
Returns the decay rate used to adjust the learning rate after each epoch
- getLineSearch() - Method in class jsat.math.optimization.BFGS
-
Returns the line search method used at each iteration
- getLineSearch() - Method in class jsat.math.optimization.LBFGS
-
Returns the line search method used at each iteration
- getListOfLists(int) - Static method in class jsat.clustering.kmeans.KMeans
-
- getLocalClassifier() - Method in class jsat.classifiers.neuralnetwork.LVQLLC
-
Returns the classifier used for each prototype
- getLocation() - Method in class jsat.distributions.Cauchy
-
- getLocation() - Method in class jsat.distributions.Levy
-
Returns the location parameter used by this distribution.
- getLogPrb(int[], int) - Method in class jsat.classifiers.bayesian.ODE
-
- getLogW() - Method in class jsat.clustering.GapStatistic
-
Returns the array of empirical log(Wk) scores computed
from the data set last clustered.
- getLoss() - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
Returns the loss function in use
- getLoss() - Method in class jsat.classifiers.linear.LinearBatch
-
Returns the loss function in use
- getLoss() - Method in class jsat.classifiers.linear.LinearSGD
-
Returns the loss function in use
- getLoss() - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
returns the loss function in use
- getLoss(double, double) - Method in class jsat.lossfunctions.AbsoluteLoss
-
- getLoss(double, double) - Method in class jsat.lossfunctions.EpsilonInsensitiveLoss
-
- getLoss(double, double) - Method in class jsat.lossfunctions.HingeLoss
-
- getLoss(Vec, int) - Method in class jsat.lossfunctions.HingeLoss
-
- getLoss(double, double) - Method in class jsat.lossfunctions.HuberLoss
-
- getLoss(double, double) - Method in class jsat.lossfunctions.LogisticLoss
-
- getLoss(double, double) - Method in interface jsat.lossfunctions.LossC
-
Computes the getLoss for a classification problem.
- getLoss(double, double) - Method in interface jsat.lossfunctions.LossFunc
-
Computes the loss for some problem.
- getLoss(Vec, int) - Method in interface jsat.lossfunctions.LossMC
-
Computes the scalar loss for on the given example
- getLoss(double, double) - Method in interface jsat.lossfunctions.LossR
-
Computes the getLoss for a regression problem.
- getLoss(Vec, int) - Method in class jsat.lossfunctions.SoftmaxLoss
-
- getLoss(double, double) - Method in class jsat.lossfunctions.SquaredLoss
-
- getLT() - Method in class jsat.linear.CholeskyDecomposition
-
The Cholesky Decomposition computes the factorization A = L LT.
- getLVQMethod() - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Returns the version of the LVQ algorithm to use.
- getM() - Method in class jsat.classifiers.bayesian.AODE
-
Returns the minimum needed score
- getM() - Method in class jsat.driftdetectors.ADWIN
-
Returns the accuracy / speed parameter for ADWIN
- getM() - Method in class jsat.math.optimization.LBFGS
-
Returns the number of history items that will be used
- getM() - Method in class jsat.math.optimization.ModifiedOWLQN
-
Returns the number of history items that will be used
- getMagnitude() - Method in class jsat.math.Complex
-
Computes the magnitude of this complex number, which is
sqrt(
Re2+
Im2)
- getMatch() - Method in class jsat.utils.ProbailityMatch
-
- getMatrixOfSameType(int, int) - Method in class jsat.linear.DenseMatrix
-
- getMatrixOfSameType(int, int) - Method in class jsat.linear.GenericMatrix
-
Creates a new matrix of the same type
- getMatrixOfSameType(int, int) - Method in class jsat.linear.MatrixOfVecs
-
- getMatrixOfSameType(int, int) - Method in class jsat.linear.RandomMatrix
-
- getMatrixOfSameType(int, int) - Method in class jsat.linear.SubMatrix
-
- getMatrixOfSameType(int, int) - Method in class jsat.linear.TransposeView
-
- getMax() - Method in class jsat.distributions.discrete.UniformDiscrete
-
- getMax() - Method in class jsat.math.OnLineStatistics
-
- getMaxBudget() - Method in class jsat.distributions.kernels.KernelPoint
-
Returns the current maximum budget for support vectors
- getMaxBudget() - Method in class jsat.distributions.kernels.KernelPoints
-
Returns the current maximum budget for support vectors
- getMaxCoeff() - Method in class jsat.classifiers.linear.kernelized.BOGD
-
Returns the maximum allowed value for any support vector
- getMaxDecrease() - Method in class jsat.datatransform.featureselection.SBS
-
Returns the maximum allowable decrease in accuracy from one set of
features to the next
- getMaxDepth() - Method in class jsat.classifiers.trees.DecisionTree
-
The maximum depth that this classifier may build trees to.
- getMaxError() - Method in class jsat.regression.RegressionModelEvaluation
-
Returns the maximum squared error observed from all runs.
- getMaxFeatures() - Method in class jsat.datatransform.featureselection.SBS
-
Returns the maximum number of features to find
- getMaxFeatures() - Method in class jsat.datatransform.featureselection.SFS
-
Returns the maximum number of features to find
- getMaxForestSize() - Method in class jsat.classifiers.trees.RandomForest
-
Returns the number of rounds of boosting that will be done, which is also the number of base learners that will be trained
- getMaxHistory() - Method in class jsat.driftdetectors.BaseDriftDetector
-
Returns the maximum number of items that will be kept in the history.
- getMaximumIterations() - Method in class jsat.clustering.kmeans.KernelKMeans
-
Returns the maximum number of iterations of the KMeans algorithm that will be performed.
- getMaximumIterations() - Method in class jsat.math.optimization.BFGS
-
- getMaximumIterations() - Method in class jsat.math.optimization.LBFGS
-
- getMaximumIterations() - Method in class jsat.math.optimization.ModifiedOWLQN
-
- getMaximumIterations() - Method in interface jsat.math.optimization.Optimizer2
-
Returns the maximum number of iterations to perform
- getMaxIncrease() - Method in class jsat.datatransform.featureselection.SFS
-
- getMaxIterations() - Method in class jsat.classifiers.boosting.AdaBoostM1
-
Returns the maximum number of iterations used
- getMaxIterations() - Method in class jsat.classifiers.boosting.EmphasisBoost
-
Returns the maximum number of iterations used
- getMaxIterations() - Method in class jsat.classifiers.boosting.LogitBoost
-
The maximum number of iterations of boosting that may occur.
- getMaxIterations() - Method in class jsat.classifiers.boosting.ModestAdaBoost
-
Returns the maximum number of iterations used
- getMaxIterations() - Method in class jsat.classifiers.knn.DANN
-
Returns the number of times the distance metric will be updated.
- getMaxIterations() - Method in class jsat.classifiers.linear.BBR
-
Returns the maximum number of iterations allowed
- getMaxIterations() - Method in class jsat.classifiers.linear.LogisticRegressionDCD
-
Returns the maximum number of iterations the algorithm is allowed to run
- getMaxIterations() - Method in class jsat.classifiers.neuralnetwork.SOM
-
Returns the maximum number of iterations that will be used to converge
- getMaxIterations() - Method in class jsat.classifiers.svm.DCD
-
Returns the maximum number of allowed training epochs
- getMaxIterations() - Method in class jsat.classifiers.svm.DCDs
-
Returns the maximum number of allowed training epochs
- getMaxIterations() - Method in class jsat.classifiers.svm.PlattSMO
-
Returns the maximum number of iterations
- getMaxIterations() - Method in class jsat.clustering.FLAME
-
Returns the maximum number of iterations to perform
- getMaxIterations() - Method in class jsat.clustering.MeanShift
-
Returns the maximum number of iterations the algorithm will go through,
terminating early if convergence has not occurred.
- getMaxIterations() - Method in class jsat.clustering.PAM
-
- getMaxIterations() - Method in class jsat.regression.StochasticGradientBoosting
-
Returns the maximum number of iterations used in SGB
- getMaxIters() - Method in class jsat.classifiers.linear.NewGLMNET
-
- getMaxLeafSize() - Method in class jsat.linear.vectorcollection.VPTree
-
Returns the maximum leaf node size.
- getMaxNorm() - Method in class jsat.classifiers.neuralnetwork.regularizers.Max2NormRegularizer
-
- getMaxParents() - Method in class jsat.classifiers.bayesian.graphicalmodel.K2NetworkLearner
-
Returns the maximum number of parents allowed when learning a network structure, or zero if any number of parents are valid.
- getMaxPCs() - Method in class jsat.datatransform.PCA
-
- getMaxPointError() - Method in class jsat.regression.RANSAC
-
Each data point not in the initial training set will be tested against.
- getMaxScaled() - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
Returns the maximum feature value after scaling
- getMaxSize() - Method in class jsat.utils.BoundedSortedSet
-
Returns the maximum size allowed for the bounded set
- getMaxTime() - Method in class jsat.math.decayrates.ExponetialDecay
-
Returns the maximum time to use in the rate decay
- getMaxTime() - Method in class jsat.math.decayrates.LinearDecay
-
Returns the maximum time to use in the rate decay
- getMaxTokenLength() - Method in class jsat.text.tokenizer.NaiveTokenizer
-
Returns the maximum allowed token length
- getMean() - Method in class jsat.classifiers.boosting.WaggingNormal
-
Returns the mean value used for the normal distribution
- getMean() - Method in class jsat.driftdetectors.ADWIN
-
Returns the mean value for all inputs contained in the current window
- getMean() - Method in class jsat.math.ExponentialMovingStatistics
-
- getMean() - Method in class jsat.math.OnLineStatistics
-
- getMeanError() - Method in class jsat.regression.RegressionModelEvaluation
-
Returns the mean squared error from all runs.
- getMeans() - Method in class jsat.clustering.kmeans.KMeans
-
Returns the raw list of means that were used for each class.
- getMeans() - Method in class jsat.clustering.kmeans.MiniBatchKMeans
-
Returns the raw list of means that were used for each class.
- getMeasurementError() - Method in class jsat.regression.OrdinaryKriging
-
Returns the measurement error used for Kriging, which is equivalent to
altering the diagonal values of the covariance.
- getMedoids() - Method in class jsat.clustering.PAM
-
Returns the raw array of indices that indicate which data point acted as
the center for each cluster.
- getMethod(DataSet<Type>, JSATData.FloatStorageMethod) - Static method in enum jsat.io.JSATData.FloatStorageMethod
-
- getMetric() - Method in class jsat.parameters.MetricParameter
-
Returns the distance metric that was used for this learner
- getMin() - Method in class jsat.distributions.discrete.UniformDiscrete
-
- getMin() - Method in class jsat.math.OnLineStatistics
-
- getMinClusterSize() - Method in class jsat.clustering.HDBSCAN
-
- getMinClusterSize() - Method in class jsat.clustering.kmeans.GMeans
-
- getMinClusterSize() - Method in class jsat.clustering.kmeans.XMeans
-
- getMinError() - Method in class jsat.regression.RegressionModelEvaluation
-
Returns the minimum squared error from all runs.
- getMinFeatures() - Method in class jsat.datatransform.featureselection.SBS
-
Returns the minimum number of features to find
- getMinFeatures() - Method in class jsat.datatransform.featureselection.SFS
-
Returns the minimum number of features to find
- getMiniBatchSize() - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Returns the number of data points used to perform each gradient update
- getMinimumOccurrenceDTF(int) - Method in class jsat.text.TextDataLoader
-
Creates a new transform factory to remove all features for tokens that
did not occur a certain number of times
- getMinKey() - Method in class jsat.utils.FibHeap
-
- getMinPoints() - Method in class jsat.clustering.HDBSCAN
-
- getMinPts() - Method in class jsat.clustering.OPTICS
-
Sets the minimum number of points needed to compute the core distance.
- getMinRate() - Method in class jsat.math.decayrates.ExponetialDecay
-
Returns the minimum value to return from he rate methods
- getMinRate() - Method in class jsat.math.decayrates.LinearDecay
-
Returns the minimum value to return from he rate methods
- getMinResultSize() - Method in class jsat.regression.RANSAC
-
RANSAC requires an initial model to be accurate enough to include a
minimum number of inliers before being considered as a potentially good
model.
- getMinResultSplitSize() - Method in class jsat.classifiers.trees.DecisionStump
-
Returns the minimum result split size that may be considered for use as
the attribute to split on.
- getMinResultSplitSize() - Method in class jsat.classifiers.trees.DecisionTree
-
Returns the minimum result split size that may be considered for use as
the attribute to split on.
- getMinSamples() - Method in class jsat.classifiers.trees.DecisionTree
-
The minimum number of samples needed at each step in order to continue branching
- getMinScaled() - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
Returns the minimum feature value after scaling
- getMinTokenLength() - Method in class jsat.text.tokenizer.NaiveTokenizer
-
Returns the minimum allowed token length
- getMinValue() - Method in class jsat.utils.FibHeap
-
- getMissingDropped() - Method in class jsat.DataSet
-
This method returns a dataset that is a subset of this dataset, where
only the rows that have no missing values are kept.
- getMode() - Method in class jsat.classifiers.linear.kernelized.CSKLR
-
Returns the update mode in use
- getMode() - Method in class jsat.classifiers.linear.kernelized.CSKLRBatch
-
Returns the update mode in use
- getMode() - Method in class jsat.classifiers.linear.PassiveAggressive
-
Returns which version of the PA update is used
- getMode() - Method in class jsat.classifiers.linear.SCW
-
Returns which algorithm is used
- getMode() - Method in class jsat.classifiers.linear.SPA
-
Returns which version of the PA update is used
- getMode() - Method in class jsat.datatransform.JLTransform
-
- getModelError() - Method in class jsat.regression.RANSAC
-
Returns the model error, which is the average absolute difference between
the model and all points in the set of inliers.
- getModels() - Method in class jsat.classifiers.boosting.AdaBoostM1
-
- getModels() - Method in class jsat.classifiers.boosting.EmphasisBoost
-
- getModels() - Method in class jsat.classifiers.boosting.LogitBoost
-
- getModels() - Method in class jsat.classifiers.boosting.ModestAdaBoost
-
- getModels() - Method in class jsat.classifiers.boosting.SAMME
-
- getModelSize() - Method in class jsat.regression.KernelRLS
-
Returns the number of basis vectors that make up the model
- getModelWeights() - Method in class jsat.classifiers.boosting.AdaBoostM1
-
- getModelWeights() - Method in class jsat.classifiers.boosting.EmphasisBoost
-
- getModelWeights() - Method in class jsat.classifiers.boosting.ModestAdaBoost
-
- getModelWeights() - Method in class jsat.classifiers.boosting.SAMME
-
- getMomentum() - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
Returns the momentum in use
- getMomentum() - Method in class jsat.math.optimization.stochastic.SGDMomentum
-
- getMScale() - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Returns the scale used for the LVQ 3 learning algorithm update set.
- getMu() - Method in class jsat.distributions.Laplace
-
- getMu() - Method in class jsat.distributions.Logistic
-
- getName() - Method in class jsat.classifiers.evaluation.Accuracy
-
- getName() - Method in class jsat.classifiers.evaluation.AUC
-
- getName() - Method in interface jsat.classifiers.evaluation.ClassificationScore
-
Returns the name to present for this score
- getName() - Method in class jsat.classifiers.evaluation.F1Score
-
- getName() - Method in class jsat.classifiers.evaluation.FbetaScore
-
- getName() - Method in class jsat.classifiers.evaluation.Kappa
-
- getName() - Method in class jsat.classifiers.evaluation.LogLoss
-
- getName() - Method in class jsat.classifiers.evaluation.MatthewsCorrelationCoefficient
-
- getName() - Method in class jsat.classifiers.evaluation.Precision
-
- getName() - Method in class jsat.classifiers.evaluation.Recall
-
- getName() - Method in class jsat.parameters.Parameter
-
Returns the display name of this parameter.
- getName() - Method in class jsat.regression.evaluation.CoefficientOfDetermination
-
- getName() - Method in class jsat.regression.evaluation.MeanAbsoluteError
-
- getName() - Method in class jsat.regression.evaluation.MeanSquaredError
-
- getName() - Method in interface jsat.regression.evaluation.RegressionScore
-
Returns the name to present for this score
- getName() - Method in class jsat.regression.evaluation.RelativeAbsoluteError
-
- getName() - Method in class jsat.regression.evaluation.RelativeSquaredError
-
- getNearby(Vec) - Method in class jsat.distributions.multivariate.MetricKDE
-
- getNearby(Vec) - Method in class jsat.distributions.multivariate.MultivariateKDE
-
Returns the list of vectors that have a non zero contribution to the density of the query point x.
- getNearby(Vec) - Method in class jsat.distributions.multivariate.ProductKDE
-
- getNearbyRaw(Vec) - Method in class jsat.distributions.multivariate.MetricKDE
-
- getNearbyRaw(Vec) - Method in class jsat.distributions.multivariate.MultivariateKDE
-
Returns the list of vectors that have a non zero contribution to the density of the query point x.
- getNearbyRaw(Vec) - Method in class jsat.distributions.multivariate.ProductKDE
-
- getNegativeSamples() - Method in class jsat.datatransform.visualization.LargeViz
-
- getNegEntropyFunction() - Method in class jsat.datatransform.FastICA
-
- getNeighborDecay() - Method in class jsat.classifiers.neuralnetwork.SOM
-
The range of effect each data point has decays with each iteration, and
this defines the way in which the rate decays.
- getNeighbors() - Method in class jsat.classifiers.knn.LWL
-
Returns the number of neighbors that will be used to create each local model
- getNeighbors() - Method in class jsat.classifiers.knn.NearestNeighbour
-
Returns the number of neighbors currently consulted to make decisions
- getNeighbors(int) - Method in class jsat.classifiers.knn.NearestNeighbour
-
- getNeighbors() - Method in class jsat.clustering.LSDBC
-
Returns the number of neighbors that will be considered when clustering
data points
- getNeighbors() - Method in class jsat.datatransform.featureselection.ReliefF
-
Returns the number of neighbors that will be used at each step of the
algorithm.
- getNeighbors() - Method in class jsat.datatransform.visualization.Isomap
-
- getNewMean() - Method in class jsat.driftdetectors.ADWIN
-
Returns the mean value determined for the newer values that we have
drifted into.
- getNewStndDev() - Method in class jsat.driftdetectors.ADWIN
-
Returns the standard deviation for the newer values that we have
drifted into.
- getNewVariance() - Method in class jsat.driftdetectors.ADWIN
-
Returns the variance for the newer values that we have
drifted into.
- getNextPow2TwinPrime(int) - Static method in class jsat.utils.ClosedHashingUtil
-
Gets the next twin prime that is near a power of 2 and greater than or
equal to the given value
- getNodes() - Method in class jsat.classifiers.bayesian.graphicalmodel.DirectedGraph
-
Returns the set of all nodes currently in the graph
- getNonZeroIterator(int) - Method in class jsat.linear.ConcatenatedVec
-
- getNonZeroIterator(int) - Method in class jsat.linear.Poly2Vec
-
- getNonZeroIterator(int) - Method in class jsat.linear.ScaledVector
-
- getNonZeroIterator(int) - Method in class jsat.linear.ShiftedVec
-
- getNonZeroIterator(int) - Method in class jsat.linear.SparseVector
-
- getNonZeroIterator(int) - Method in class jsat.linear.SubVector
-
- getNonZeroIterator() - Method in class jsat.linear.Vec
-
Returns an iterator that will go over the non zero values in the given
vector.
- getNonZeroIterator(int) - Method in class jsat.linear.Vec
-
Returns an iterator that will go over the non zero values starting from
the specified index in the given vector.
- getNonZeroIterator() - Method in class jsat.linear.VecPaired
-
- getNonZeroIterator() - Method in class jsat.linear.VecWithNorm
-
- getNonZeroIterator(int) - Method in class jsat.linear.VecWithNorm
-
- getNorm2() - Method in class jsat.linear.SingularValueDecomposition
-
Returns the 2 norm of the matrix, which is the maximal singular value.
- getNu() - Method in class jsat.classifiers.svm.SBP
-
Returns the nu SVM parameter
- getNugget() - Method in class jsat.regression.OrdinaryKriging
-
Returns the nugget value passed to the variogram during training.
- getNullVar() - Method in interface jsat.testing.onesample.OneSampleTest
-
- getNullVar() - Method in class jsat.testing.onesample.TTest
-
- getNullVar() - Method in class jsat.testing.onesample.ZTest
-
- getNumberOfBins() - Method in class jsat.datatransform.NumericalToHistogram
-
- getNumberOfPaths() - Method in class jsat.classifiers.trees.DecisionStump
-
Returns the number of paths that this decision stump leads to.
- getNumberOfTransforms() - Method in class jsat.datatransform.DataTransformProcess
-
- getNumCategoricalVars() - Method in class jsat.DataSet
-
Returns the number of categorical variables for each data point in the set
- getNumCentroids() - Method in class jsat.classifiers.neuralnetwork.RBFNet
-
Returns the number of centroids to use when training
- getNumericalHandling() - Method in class jsat.classifiers.bayesian.NaiveBayes
-
Returns the method used to handle numerical attributes
- getNumericalValues() - Method in class jsat.classifiers.DataPoint
-
Returns the vector containing the numerical values.
- getNumericColumn(int) - Method in class jsat.DataSet
-
The data set can be seen as a NxM matrix, were each row is a
data point, and each column the values for a particular
variable.
- getNumericColumns() - Method in class jsat.DataSet
-
Creates an array of column vectors for every numeric variable in this
data set.
- getNumericColumns(Set<Integer>) - Method in class jsat.DataSet
-
Creates an array of column vectors for every numeric variable in this
data set.
- getNumericName(int) - Method in class jsat.DataSet
-
Returns the name used for the i'th numeric attribute.
- getNumFeatures() - Method in class jsat.DataSet
-
- getNumNumericalVars() - Method in class jsat.DataSet
-
Returns the number of numerical variables for each data point in the set
- getNumOfCategories() - Method in class jsat.classifiers.CategoricalData
-
- getObject() - Method in class jsat.parameters.ObjectParameter
-
Returns the current object value
- getOldMean() - Method in class jsat.driftdetectors.ADWIN
-
Returns the mean value determined for the older values that we have
drifted away from.
- getOldStndDev() - Method in class jsat.driftdetectors.ADWIN
-
Returns the standard deviation for the older values that we have
drifted away from.
- getOldVariance() - Method in class jsat.driftdetectors.ADWIN
-
Returns the variance for the older values that we have
drifted away from.
- getOmega() - Method in class jsat.distributions.kernels.PukKernel
-
- getOnlineColumnStats(boolean) - Method in class jsat.DataSet
-
Returns summary statistics computed in an online fashion for each numeric
variable.
- getOnlineDenseStats() - Method in class jsat.DataSet
-
Returns an
OnLineStatistics object that is built by observing
what proportion of each data point contains non zero numerical values.
- getOptimizer() - Method in class jsat.classifiers.linear.LinearBatch
-
Returns the optimization method in use, or null.
- getOptionName(int) - Method in class jsat.classifiers.CategoricalData
-
- getOutOfBagError() - Method in class jsat.classifiers.trees.RandomForest
-
- getP() - Method in class jsat.classifiers.neuralnetwork.RBFNet
-
Returns the nearest neighbors parameter.
- getP() - Method in class jsat.distributions.discrete.Binomial
-
- getP() - Method in class jsat.linear.distancemetrics.MinkowskiDistance
-
- getPair() - Method in class jsat.classifiers.DataPointPair
-
- getPair() - Method in class jsat.linear.VecPaired
-
- getParameter(String) - Method in class jsat.classifiers.bayesian.BestClassDistribution
-
- getParameter(String) - Method in class jsat.classifiers.bayesian.MultinomialNaiveBayes
-
- getParameter(String) - Method in class jsat.classifiers.bayesian.NaiveBayes
-
- getParameter(String) - Method in class jsat.classifiers.boosting.AdaBoostM1
-
- getParameter(String) - Method in class jsat.classifiers.boosting.ArcX4
-
- getParameter(String) - Method in class jsat.classifiers.boosting.Bagging
-
- getParameter(String) - Method in class jsat.classifiers.boosting.EmphasisBoost
-
- getParameter(String) - Method in class jsat.classifiers.boosting.LogitBoost
-
- getParameter(String) - Method in class jsat.classifiers.boosting.ModestAdaBoost
-
- getParameter(String) - Method in class jsat.classifiers.boosting.SAMME
-
- getParameter(String) - Method in class jsat.classifiers.boosting.Wagging
-
- getParameter(String) - Method in class jsat.classifiers.calibration.BinaryCalibration
-
- getParameter(String) - Method in class jsat.classifiers.knn.DANN
-
- getParameter(String) - Method in class jsat.classifiers.knn.LWL
-
- getParameter(String) - Method in class jsat.classifiers.knn.NearestNeighbour
-
- getParameter(String) - Method in class jsat.classifiers.linear.AROW
-
- getParameter(String) - Method in class jsat.classifiers.linear.BBR
-
- getParameter(String) - Method in class jsat.classifiers.linear.kernelized.ALMA2K
-
- getParameter(String) - Method in class jsat.classifiers.linear.kernelized.BOGD
-
- getParameter(String) - Method in class jsat.classifiers.linear.kernelized.CSKLR
-
- getParameter(String) - Method in class jsat.classifiers.linear.kernelized.CSKLRBatch
-
- getParameter(String) - Method in class jsat.classifiers.linear.kernelized.DUOL
-
- getParameter(String) - Method in class jsat.classifiers.linear.kernelized.Forgetron
-
- getParameter(String) - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
- getParameter(String) - Method in class jsat.classifiers.linear.kernelized.OSKL
-
- getParameter(String) - Method in class jsat.classifiers.linear.kernelized.Projectron
-
- getParameter(String) - Method in class jsat.classifiers.linear.LinearBatch
-
- getParameter(String) - Method in class jsat.classifiers.linear.LinearSGD
-
- getParameter(String) - Method in class jsat.classifiers.linear.LogisticRegressionDCD
-
- getParameter(String) - Method in class jsat.classifiers.linear.NewGLMNET
-
- getParameter(String) - Method in class jsat.classifiers.linear.NHERD
-
- getParameter(String) - Method in class jsat.classifiers.linear.PassiveAggressive
-
- getParameter(String) - Method in class jsat.classifiers.linear.SCD
-
- getParameter(String) - Method in class jsat.classifiers.linear.SCW
-
- getParameter(String) - Method in class jsat.classifiers.linear.SPA
-
- getParameter(String) - Method in class jsat.classifiers.linear.STGD
-
- getParameter(String) - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
- getParameter(String) - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
- getParameter(String) - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
- getParameter(String) - Method in class jsat.classifiers.neuralnetwork.DReDNetSimple
-
- getParameter(String) - Method in class jsat.classifiers.neuralnetwork.LVQ
-
- getParameter(String) - Method in class jsat.classifiers.neuralnetwork.RBFNet
-
- getParameter(String) - Method in class jsat.classifiers.neuralnetwork.SOM
-
- getParameter(String) - Method in class jsat.classifiers.OneVSAll
-
- getParameter(String) - Method in class jsat.classifiers.OneVSOne
-
- getParameter(String) - Method in class jsat.classifiers.RegressorToClassifier
-
- getParameter(String) - Method in class jsat.classifiers.svm.DCD
-
- getParameter(String) - Method in class jsat.classifiers.svm.DCDs
-
- getParameter(String) - Method in class jsat.classifiers.svm.DCSVM
-
- getParameter(String) - Method in class jsat.classifiers.svm.extended.CPM
-
- getParameter(String) - Method in class jsat.classifiers.svm.extended.OnlineAMM
-
- getParameter(String) - Method in class jsat.classifiers.svm.LSSVM
-
- getParameter(String) - Method in class jsat.classifiers.svm.Pegasos
-
- getParameter(String) - Method in class jsat.classifiers.svm.PegasosK
-
- getParameter(String) - Method in class jsat.classifiers.svm.PlattSMO
-
- getParameter(String) - Method in class jsat.classifiers.svm.SBP
-
- getParameter(String) - Method in class jsat.classifiers.trees.DecisionStump
-
- getParameter(String) - Method in class jsat.classifiers.trees.DecisionTree
-
- getParameter(String) - Method in class jsat.classifiers.trees.ExtraTree
-
- getParameter(String) - Method in class jsat.classifiers.trees.RandomForest
-
- getParameter(String) - Method in class jsat.clustering.FLAME
-
- getParameter(String) - Method in class jsat.clustering.GapStatistic
-
- getParameter(String) - Method in class jsat.clustering.HDBSCAN
-
- getParameter(String) - Method in class jsat.clustering.kmeans.KernelKMeans
-
- getParameter(String) - Method in class jsat.clustering.kmeans.KMeans
-
- getParameter(String) - Method in class jsat.clustering.LSDBC
-
- getParameter(String) - Method in class jsat.clustering.OPTICS
-
- getParameter(String) - Method in class jsat.datatransform.DataModelPipeline
-
- getParameter(String) - Method in class jsat.datatransform.DataTransformBase
-
- getParameter(String) - Method in class jsat.datatransform.DataTransformProcess
-
- getParameter(String) - Method in class jsat.distributions.kernels.BaseL2Kernel
-
- getParameter(String) - Method in class jsat.distributions.kernels.DistanceMetricBasedKernel
-
- getParameter(String) - Method in class jsat.distributions.kernels.LinearKernel
-
- getParameter(String) - Method in class jsat.distributions.kernels.NormalizedKernel
-
- getParameter(String) - Method in class jsat.distributions.kernels.PolynomialKernel
-
- getParameter(String) - Method in class jsat.distributions.kernels.SigmoidKernel
-
- getParameter(String) - Method in class jsat.distributions.multivariate.MetricKDE
-
- getParameter(String) - Method in class jsat.math.decayrates.ExponetialDecay
-
- getParameter(String) - Method in class jsat.math.decayrates.InverseDecay
-
- getParameter(String) - Method in class jsat.math.decayrates.LinearDecay
-
- getParameter(String) - Method in class jsat.math.decayrates.PowerDecay
-
- getParameter(String) - Method in interface jsat.parameters.Parameterized
-
Returns the parameter with the given name.
- getParameter(String) - Method in class jsat.regression.KernelRidgeRegression
-
- getParameter(String) - Method in class jsat.regression.KernelRLS
-
- getParameter(String) - Method in class jsat.regression.NadarayaWatson
-
- getParameter(String) - Method in class jsat.regression.OrdinaryKriging
-
- getParameter(String) - Method in class jsat.regression.RANSAC
-
- getParameter(String) - Method in class jsat.regression.RidgeRegression
-
- getParameter(String) - Method in class jsat.regression.StochasticGradientBoosting
-
- getParameter(String) - Method in class jsat.regression.StochasticRidgeRegression
-
- getParameter(String) - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
- getParameterByName(String) - Method in class jsat.parameters.ModelSearch
-
Finds the parameter object with the given name, or throws an exception if
a parameter with the given name does not exist.
- getParameters() - Method in class jsat.classifiers.bayesian.BestClassDistribution
-
- getParameters() - Method in class jsat.classifiers.bayesian.MultinomialNaiveBayes
-
- getParameters() - Method in class jsat.classifiers.bayesian.NaiveBayes
-
- getParameters() - Method in class jsat.classifiers.boosting.AdaBoostM1
-
- getParameters() - Method in class jsat.classifiers.boosting.ArcX4
-
- getParameters() - Method in class jsat.classifiers.boosting.Bagging
-
- getParameters() - Method in class jsat.classifiers.boosting.EmphasisBoost
-
- getParameters() - Method in class jsat.classifiers.boosting.LogitBoost
-
- getParameters() - Method in class jsat.classifiers.boosting.ModestAdaBoost
-
- getParameters() - Method in class jsat.classifiers.boosting.SAMME
-
- getParameters() - Method in class jsat.classifiers.boosting.Wagging
-
- getParameters() - Method in class jsat.classifiers.calibration.BinaryCalibration
-
- getParameters() - Method in class jsat.classifiers.knn.DANN
-
- getParameters() - Method in class jsat.classifiers.knn.LWL
-
- getParameters() - Method in class jsat.classifiers.knn.NearestNeighbour
-
- getParameters() - Method in class jsat.classifiers.linear.AROW
-
- getParameters() - Method in class jsat.classifiers.linear.BBR
-
- getParameters() - Method in class jsat.classifiers.linear.kernelized.ALMA2K
-
- getParameters() - Method in class jsat.classifiers.linear.kernelized.BOGD
-
- getParameters() - Method in class jsat.classifiers.linear.kernelized.CSKLR
-
- getParameters() - Method in class jsat.classifiers.linear.kernelized.CSKLRBatch
-
- getParameters() - Method in class jsat.classifiers.linear.kernelized.DUOL
-
- getParameters() - Method in class jsat.classifiers.linear.kernelized.Forgetron
-
- getParameters() - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
- getParameters() - Method in class jsat.classifiers.linear.kernelized.OSKL
-
- getParameters() - Method in class jsat.classifiers.linear.kernelized.Projectron
-
- getParameters() - Method in class jsat.classifiers.linear.LinearBatch
-
- getParameters() - Method in class jsat.classifiers.linear.LinearSGD
-
- getParameters() - Method in class jsat.classifiers.linear.LogisticRegressionDCD
-
- getParameters() - Method in class jsat.classifiers.linear.NewGLMNET
-
- getParameters() - Method in class jsat.classifiers.linear.NHERD
-
- getParameters() - Method in class jsat.classifiers.linear.PassiveAggressive
-
- getParameters() - Method in class jsat.classifiers.linear.SCD
-
- getParameters() - Method in class jsat.classifiers.linear.SCW
-
- getParameters() - Method in class jsat.classifiers.linear.SPA
-
- getParameters() - Method in class jsat.classifiers.linear.STGD
-
- getParameters() - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
- getParameters() - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
- getParameters() - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
- getParameters() - Method in class jsat.classifiers.neuralnetwork.DReDNetSimple
-
- getParameters() - Method in class jsat.classifiers.neuralnetwork.LVQ
-
- getParameters() - Method in class jsat.classifiers.neuralnetwork.RBFNet
-
- getParameters() - Method in class jsat.classifiers.neuralnetwork.SOM
-
- getParameters() - Method in class jsat.classifiers.OneVSAll
-
- getParameters() - Method in class jsat.classifiers.OneVSOne
-
- getParameters() - Method in class jsat.classifiers.RegressorToClassifier
-
- getParameters() - Method in class jsat.classifiers.svm.DCD
-
- getParameters() - Method in class jsat.classifiers.svm.DCDs
-
- getParameters() - Method in class jsat.classifiers.svm.DCSVM
-
- getParameters() - Method in class jsat.classifiers.svm.extended.CPM
-
- getParameters() - Method in class jsat.classifiers.svm.extended.OnlineAMM
-
- getParameters() - Method in class jsat.classifiers.svm.LSSVM
-
- getParameters() - Method in class jsat.classifiers.svm.Pegasos
-
- getParameters() - Method in class jsat.classifiers.svm.PegasosK
-
- getParameters() - Method in class jsat.classifiers.svm.PlattSMO
-
- getParameters() - Method in class jsat.classifiers.svm.SBP
-
- getParameters() - Method in class jsat.classifiers.trees.DecisionStump
-
- getParameters() - Method in class jsat.classifiers.trees.DecisionTree
-
- getParameters() - Method in class jsat.classifiers.trees.ExtraTree
-
- getParameters() - Method in class jsat.classifiers.trees.RandomForest
-
- getParameters() - Method in class jsat.clustering.FLAME
-
- getParameters() - Method in class jsat.clustering.GapStatistic
-
- getParameters() - Method in class jsat.clustering.HDBSCAN
-
- getParameters() - Method in class jsat.clustering.kmeans.KernelKMeans
-
- getParameters() - Method in class jsat.clustering.kmeans.KMeans
-
- getParameters() - Method in class jsat.clustering.LSDBC
-
- getParameters() - Method in class jsat.clustering.OPTICS
-
- getParameters() - Method in class jsat.datatransform.DataModelPipeline
-
- getParameters() - Method in class jsat.datatransform.DataTransformBase
-
- getParameters() - Method in class jsat.datatransform.DataTransformProcess
-
- getParameters() - Method in class jsat.distributions.kernels.BaseL2Kernel
-
- getParameters() - Method in class jsat.distributions.kernels.DistanceMetricBasedKernel
-
- getParameters() - Method in class jsat.distributions.kernels.LinearKernel
-
- getParameters() - Method in class jsat.distributions.kernels.NormalizedKernel
-
- getParameters() - Method in class jsat.distributions.kernels.PolynomialKernel
-
- getParameters() - Method in class jsat.distributions.kernels.SigmoidKernel
-
- getParameters() - Method in class jsat.distributions.multivariate.MetricKDE
-
- getParameters() - Method in class jsat.math.decayrates.ExponetialDecay
-
- getParameters() - Method in class jsat.math.decayrates.InverseDecay
-
- getParameters() - Method in class jsat.math.decayrates.LinearDecay
-
- getParameters() - Method in class jsat.math.decayrates.PowerDecay
-
- getParameters() - Method in interface jsat.parameters.Parameterized
-
Returns the list of parameters that can be altered for this learner.
- getParameters() - Method in class jsat.regression.KernelRidgeRegression
-
- getParameters() - Method in class jsat.regression.KernelRLS
-
- getParameters() - Method in class jsat.regression.NadarayaWatson
-
- getParameters() - Method in class jsat.regression.OrdinaryKriging
-
- getParameters() - Method in class jsat.regression.RANSAC
-
- getParameters() - Method in class jsat.regression.RidgeRegression
-
- getParameters() - Method in class jsat.regression.StochasticGradientBoosting
-
- getParameters() - Method in class jsat.regression.StochasticRidgeRegression
-
- getParameters() - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
- getParamsFromMethods(Object) - Static method in class jsat.parameters.Parameter
-
Given an object, this method will use reflection to automatically find
getter and setter method pairs, and create Parameter object for each
getter setter pair.
Getters are found by searching for no argument methods that start with
"get" or "is".
- getParents(N) - Method in class jsat.classifiers.bayesian.graphicalmodel.DirectedGraph
-
Returns the set of all parents of the requested node, or null if the node does not exist in the graph
- getPath(DataPoint) - Method in class jsat.classifiers.trees.DecisionTree.Node
-
- getPath(DataPoint) - Method in class jsat.classifiers.trees.TreeNodeVisitor
-
Returns the path down the tree the given data point would have taken, or
-1 if this node was a leaf node OR if a missing value prevent traversal
down the path
- getPathWeight(int) - Method in class jsat.classifiers.trees.DecisionTree.Node
-
- getPathWeight(int) - Method in class jsat.classifiers.trees.TreeNodeVisitor
-
Returns the relative weight of each path, which should be an indication
of how much of the training data went down each path.
- getPerplexity() - Method in class jsat.datatransform.visualization.LargeViz
-
- getPerplexity() - Method in class jsat.datatransform.visualization.TSNE
-
- getPhase1Learner() - Method in class jsat.classifiers.neuralnetwork.RBFNet
-
Returns the method to use for learning the centroids of the network.
- getPhase2Learner() - Method in class jsat.classifiers.neuralnetwork.RBFNet
-
Returns the learning method to use for determining the bandwidths of each
center in the network.
- getPivotSelectionMethod() - Method in class jsat.linear.vectorcollection.KDTree.KDTreeFactory
-
- getPointWeights() - Method in class jsat.classifiers.ClassificationModelEvaluation
-
- getPredicting() - Method in class jsat.classifiers.ClassificationDataSet
-
- getPredictions() - Method in class jsat.classifiers.ClassificationModelEvaluation
-
- getPrior() - Method in class jsat.classifiers.linear.BBR
-
Returns the regularizing prior in use
- getPrior() - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Returns the prior used for regularization
- getPriority() - Method in class jsat.utils.FibHeap.FibNode
-
- getPriors() - Method in class jsat.classifiers.ClassificationDataSet
-
Computes the prior probabilities of each class, and returns an array containing the values.
- getProb(int) - Method in class jsat.classifiers.CategoricalResults
-
Returns the stored probability for the given category
- getProbability() - Method in class jsat.utils.ProbailityMatch
-
- getProjectedDimension() - Method in class jsat.datatransform.JLTransform
-
- getPruneFrequency() - Method in class jsat.classifiers.svm.extended.OnlineAMM
-
Returns the number of iterations between each pruning
- getPruningMethod() - Method in class jsat.classifiers.trees.DecisionTree
-
Returns the method of pruning used after tree construction
- getPseudoDet() - Method in class jsat.linear.SingularValueDecomposition
-
Computes the pseudo determinant of the matrix, which corresponds to absolute value of
the determinant of the full rank square sub matrix that contains all non zero singular values.
- getPseudoDet(double) - Method in class jsat.linear.SingularValueDecomposition
-
Computes the pseudo determinant of the matrix, which corresponds to absolute value of
the determinant of the full rank square sub matrix that contains all non singular values > tol.
- getPseudoInverse() - Method in class jsat.linear.SingularValueDecomposition
-
Returns the Moore–Penrose pseudo inverse of the matrix.
- getPseudoInverse(double) - Method in class jsat.linear.SingularValueDecomposition
-
Returns the Moore–Penrose pseudo inverse of the matrix.
- getQueryInfo(Vec) - Method in class jsat.distributions.kernels.BaseKernelTrick
-
- getQueryInfo(Vec) - Method in class jsat.distributions.kernels.BaseL2Kernel
-
- getQueryInfo(Vec) - Method in class jsat.distributions.kernels.DistanceMetricBasedKernel
-
- getQueryInfo(Vec) - Method in interface jsat.distributions.kernels.KernelTrick
-
- getQueryInfo(Vec) - Method in class jsat.distributions.kernels.NormalizedKernel
-
- getQueryInfo(Vec) - Method in class jsat.linear.distancemetrics.ChebyshevDistance
-
- getQueryInfo(Vec) - Method in class jsat.linear.distancemetrics.CosineDistance
-
- getQueryInfo(Vec) - Method in class jsat.linear.distancemetrics.CosineDistanceNormalized
-
- getQueryInfo(Vec) - Method in class jsat.linear.distancemetrics.DistanceCounter
-
- getQueryInfo(Vec) - Method in interface jsat.linear.distancemetrics.DistanceMetric
-
- getQueryInfo(Vec) - Method in class jsat.linear.distancemetrics.EuclideanDistance
-
- getQueryInfo(Vec) - Method in class jsat.linear.distancemetrics.KernelDistance
-
- getQueryInfo(Vec) - Method in class jsat.linear.distancemetrics.MahalanobisDistance
-
- getQueryInfo(Vec) - Method in class jsat.linear.distancemetrics.ManhattanDistance
-
- getQueryInfo(Vec) - Method in class jsat.linear.distancemetrics.MinkowskiDistance
-
- getQueryInfo(Vec) - Method in class jsat.linear.distancemetrics.NormalizedEuclideanDistance
-
- getQueryInfo(Vec) - Method in class jsat.linear.distancemetrics.PearsonDistance
-
- getQueryInfo(Vec) - Method in class jsat.linear.distancemetrics.SquaredEuclideanDistance
-
- getQueryInfo(Vec) - Method in class jsat.linear.distancemetrics.WeightedEuclideanDistance
-
- getR() - Method in class jsat.classifiers.linear.AROW
-
Returns the regularization parameter
- getR() - Method in class jsat.classifiers.linear.kernelized.CSKLR
-
Returns the maximal norm of the algorithm
- getR() - Method in class jsat.classifiers.linear.kernelized.CSKLRBatch
-
Returns the maximal norm of the algorithm
- getR() - Method in class jsat.classifiers.linear.kernelized.OSKL
-
Returns the maximum allowed norm for the model learned
- getRadius() - Method in class jsat.linear.vectorcollection.lsh.E2LSH
-
Returns the desired approximate radius for which to return results
- getRandom() - Static method in class jsat.utils.random.RandomUtil
-
Returns a new Random object that can be used.
- getRandom(int) - Static method in class jsat.utils.random.RandomUtil
-
Returns a new Random object that can be used, initiated with the given
seed.
- getRandomFeatureCount() - Method in class jsat.classifiers.trees.RandomDecisionTree
-
Returns the number of random features used at each node of the tree
- getRank() - Method in class jsat.linear.SingularValueDecomposition
-
Returns the numerical rank of the matrix.
- getRank(double) - Method in class jsat.linear.SingularValueDecomposition
-
Returns the numerical rank of the matrix.
- getRawBasisVecs() - Method in class jsat.distributions.kernels.KernelPoint
-
Returns the list of the raw vectors being used by the kernel points.
- getRawBasisVecs() - Method in class jsat.distributions.kernels.KernelPoints
-
Returns a list of the raw vectors being used by the kernel points.
- getRawKeyTable() - Method in class jsat.utils.IntDoubleMap
-
- getRawStatusTable() - Method in class jsat.utils.IntDoubleMap
-
- getRawValueTable() - Method in class jsat.utils.IntDoubleMap
-
- getRawWeight() - Method in class jsat.classifiers.linear.ALMA2
-
- getRawWeight(int) - Method in class jsat.classifiers.linear.ALMA2
-
- getRawWeight() - Method in class jsat.classifiers.linear.AROW
-
- getRawWeight(int) - Method in class jsat.classifiers.linear.AROW
-
- getRawWeight() - Method in class jsat.classifiers.linear.BBR
-
- getRawWeight(int) - Method in class jsat.classifiers.linear.BBR
-
- getRawWeight(int) - Method in class jsat.classifiers.linear.LinearBatch
-
- getRawWeight(int) - Method in class jsat.classifiers.linear.LinearSGD
-
- getRawWeight() - Method in class jsat.classifiers.linear.LogisticRegressionDCD
-
- getRawWeight(int) - Method in class jsat.classifiers.linear.LogisticRegressionDCD
-
- getRawWeight() - Method in class jsat.classifiers.linear.NewGLMNET
-
- getRawWeight(int) - Method in class jsat.classifiers.linear.NewGLMNET
-
- getRawWeight() - Method in class jsat.classifiers.linear.NHERD
-
- getRawWeight(int) - Method in class jsat.classifiers.linear.NHERD
-
- getRawWeight() - Method in class jsat.classifiers.linear.PassiveAggressive
-
- getRawWeight(int) - Method in class jsat.classifiers.linear.PassiveAggressive
-
- getRawWeight() - Method in class jsat.classifiers.linear.ROMMA
-
- getRawWeight(int) - Method in class jsat.classifiers.linear.ROMMA
-
- getRawWeight() - Method in class jsat.classifiers.linear.SCD
-
- getRawWeight(int) - Method in class jsat.classifiers.linear.SCD
-
- getRawWeight() - Method in class jsat.classifiers.linear.SCW
-
- getRawWeight(int) - Method in class jsat.classifiers.linear.SCW
-
- getRawWeight(int) - Method in class jsat.classifiers.linear.SPA
-
- getRawWeight() - Method in class jsat.classifiers.linear.STGD
-
- getRawWeight(int) - Method in class jsat.classifiers.linear.STGD
-
- getRawWeight(int) - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
- getRawWeight() - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
- getRawWeight(int) - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
- getRawWeight() - Method in class jsat.classifiers.neuralnetwork.Perceptron
-
- getRawWeight(int) - Method in class jsat.classifiers.neuralnetwork.Perceptron
-
- getRawWeight() - Method in class jsat.classifiers.svm.DCD
-
- getRawWeight(int) - Method in class jsat.classifiers.svm.DCD
-
- getRawWeight() - Method in class jsat.classifiers.svm.DCDs
-
- getRawWeight(int) - Method in class jsat.classifiers.svm.DCDs
-
- getRawWeight() - Method in class jsat.classifiers.svm.Pegasos
-
- getRawWeight(int) - Method in class jsat.classifiers.svm.Pegasos
-
- getRawWeight() - Method in class jsat.regression.LogisticRegression
-
- getRawWeight(int) - Method in class jsat.regression.LogisticRegression
-
- getRawWeight() - Method in class jsat.regression.MultipleLinearRegression
-
- getRawWeight(int) - Method in class jsat.regression.MultipleLinearRegression
-
- getRawWeight() - Method in class jsat.regression.StochasticRidgeRegression
-
- getRawWeight(int) - Method in class jsat.regression.StochasticRidgeRegression
-
- getRawWeight(int) - Method in interface jsat.SimpleWeightVectorModel
-
Returns the raw weight vector associated with the given class index.
- getRawWeight() - Method in interface jsat.SingleWeightVectorModel
-
Returns the only weight vector used for the model
- getReachabilityArray() - Method in class jsat.clustering.OPTICS
-
Returns a copy of the reachability array in correct reachability order.
- getReal() - Method in class jsat.math.Complex
-
Returns the real part of this complex number
- getRealEigenvalues() - Method in class jsat.linear.EigenValueDecomposition
-
Return the real parts of the eigenvalues
- getRegression(double) - Method in class jsat.lossfunctions.AbsoluteLoss
-
- getRegression(double) - Method in class jsat.lossfunctions.EpsilonInsensitiveLoss
-
- getRegression(double) - Method in class jsat.lossfunctions.HuberLoss
-
- getRegression(double) - Method in interface jsat.lossfunctions.LossR
-
Given the score value of a data point, this returns the correct numeric
result.
- getRegression(double) - Method in class jsat.lossfunctions.SquaredLoss
-
- getRegressionTargetScore() - Method in class jsat.parameters.ModelSearch
-
Returns the regression score that is trying to be optimized via grid
search
- getRegressor() - Method in class jsat.regression.RegressionModelEvaluation
-
Returns the regressor that was to be evaluated
- getRegularization() - Method in class jsat.classifiers.linear.BBR
-
Returns the regularization penalty used if the auto value is not used
- getRegularization() - Method in class jsat.classifiers.linear.kernelized.BOGD
-
Returns the regularization parameter used
- getRegularization() - Method in class jsat.classifiers.linear.SCD
-
Returns the regularization parameter value used for learning.
- getRegularization() - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Returns the regularization coefficient in use
- getRegularization() - Method in class jsat.classifiers.svm.Pegasos
-
Returns the amount of regularization to used in training
- getRegularization() - Method in class jsat.classifiers.svm.PegasosK
-
Returns the amount of regularization used
- getRegularization() - Method in class jsat.datatransform.WhitenedPCA
-
- getRegularizer() - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
- getRepresentativesPerClass() - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Returns the number of representatives to create for each class.
- getResults() - Method in class jsat.classifiers.trees.ImpurityScore
-
Obtains the current categorical results by prior probability
- getReverse(Comparator<T>) - Static method in class jsat.utils.IndexTable
-
Obtains the reverse order comparator
- getReverseNominalMap() - Method in class jsat.datatransform.RemoveAttributeTransform
-
Returns a mapping from the nominal indices in the transformed space back
to their original indices
- getReverseNumericMap() - Method in class jsat.datatransform.RemoveAttributeTransform
-
Returns a mapping from the numeric indices in the transformed space back
to their original indices
- getRho() - Method in class jsat.classifiers.linear.kernelized.DUOL
-
Returns the "conflict" parameter value for the threshold of performing double updates
- getRho() - Method in class jsat.math.optimization.stochastic.AdaDelta
-
- getRho() - Method in class jsat.math.optimization.stochastic.RMSProp
-
- getRidge() - Method in class jsat.datatransform.kernel.Nystrom
-
Returns the regularization value added to each eigen value
- getRounds() - Method in class jsat.classifiers.boosting.Bagging
-
Returns the number of rounds of boosting that will be done, which is also the number of base learners that will be trained
- getRow(int) - Method in class jsat.linear.Matrix
-
Creates a vector that has a copy of the values in row i of this
matrix.
- getRowView(int) - Method in class jsat.linear.DenseMatrix
-
- getRowView(int) - Method in class jsat.linear.Matrix
-
Obtains a vector that is backed by this, at very little memory
cost.
- getRowView(int) - Method in class jsat.linear.MatrixOfVecs
-
- getRowView(int) - Method in class jsat.linear.SparseMatrix
-
- getRowView(int) - Method in class jsat.linear.SubMatrix
-
- getRowView(int) - Method in class jsat.linear.TransposeView
-
- getS() - Method in class jsat.distributions.Logistic
-
- getS() - Method in class jsat.linear.SingularValueDecomposition
-
Returns the diagonal matrix S such that the SVD product results in the original matrix.
- getSampleCount() - Method in class jsat.clustering.CLARA
-
- getSampledDataSet(ClassificationDataSet, int[]) - Static method in class jsat.classifiers.boosting.Bagging
-
Creates a new data set from the given sample counts.
- getSampledDataSet(RegressionDataSet, int[]) - Static method in class jsat.classifiers.boosting.Bagging
-
Creates a new data set from the given sample counts.
- getSamples(int) - Method in class jsat.classifiers.ClassificationDataSet
-
Returns the list of all examples that belong to the given category.
- getSamples() - Method in class jsat.clustering.GapStatistic
-
- getSampleSize() - Method in class jsat.classifiers.ClassificationDataSet
-
- getSampleSize() - Method in class jsat.clustering.CLARA
-
- getSampleSize() - Method in class jsat.DataSet
-
Returns the number of data points in this data set
- getSampleSize() - Method in class jsat.regression.RegressionDataSet
-
- getSampleSize() - Method in class jsat.SimpleDataSet
-
- getSampleVariableVector(int, int) - Method in class jsat.classifiers.ClassificationDataSet
-
- getScale() - Method in class jsat.distributions.Cauchy
-
- getScale() - Method in class jsat.distributions.Levy
-
Returns the scale parameter used by this distribution
- getScale() - Method in class jsat.distributions.Rayleigh
-
- getScale() - Method in class jsat.linear.ScaledVector
-
Returns the current scale in use
- getScaleBandwidthFactor() - Method in class jsat.clustering.MeanShift
-
Returns the value by which the bandwidth of the
MultivariateKDE will
be scaled by.
- getScore(DataPoint) - Method in class jsat.classifiers.boosting.EmphasisBoost
-
- getScore(DataPoint) - Method in class jsat.classifiers.boosting.ModestAdaBoost
-
- getScore(DataPoint) - Method in interface jsat.classifiers.calibration.BinaryScoreClassifier
-
Returns the numeric score for predicting a class of a given data point,
where the sign of the value indicates which class the data point is
predicted to belong to.
- getScore() - Method in class jsat.classifiers.evaluation.Accuracy
-
- getScore() - Method in class jsat.classifiers.evaluation.AUC
-
- getScore() - Method in interface jsat.classifiers.evaluation.ClassificationScore
-
- getScore() - Method in class jsat.classifiers.evaluation.F1Score
-
- getScore() - Method in class jsat.classifiers.evaluation.FbetaScore
-
- getScore() - Method in class jsat.classifiers.evaluation.Kappa
-
- getScore() - Method in class jsat.classifiers.evaluation.LogLoss
-
- getScore() - Method in class jsat.classifiers.evaluation.MatthewsCorrelationCoefficient
-
- getScore() - Method in class jsat.classifiers.evaluation.Precision
-
- getScore() - Method in class jsat.classifiers.evaluation.Recall
-
- getScore() - Method in class jsat.classifiers.evaluation.SimpleBinaryClassMetric
-
- getScore(DataPoint) - Method in class jsat.classifiers.linear.ALMA2
-
- getScore(DataPoint) - Method in class jsat.classifiers.linear.AROW
-
- getScore(DataPoint) - Method in class jsat.classifiers.linear.kernelized.ALMA2K
-
- getScore(DataPoint) - Method in class jsat.classifiers.linear.kernelized.BOGD
-
- getScore(double, double) - Static method in class jsat.classifiers.linear.kernelized.CSKLR
-
Returns the binary logistic regression score
- getScore(DataPoint) - Method in class jsat.classifiers.linear.kernelized.DUOL
-
- getScore(DataPoint) - Method in class jsat.classifiers.linear.kernelized.Forgetron
-
- getScore(DataPoint) - Method in class jsat.classifiers.linear.kernelized.OSKL
-
- getScore(DataPoint) - Method in class jsat.classifiers.linear.kernelized.Projectron
-
- getScore(DataPoint) - Method in class jsat.classifiers.linear.NHERD
-
- getScore(DataPoint) - Method in class jsat.classifiers.linear.PassiveAggressive
-
- getScore(DataPoint) - Method in class jsat.classifiers.linear.ROMMA
-
- getScore(DataPoint) - Method in class jsat.classifiers.linear.SCW
-
- getScore(DataPoint) - Method in class jsat.classifiers.linear.STGD
-
- getScore(DataPoint) - Method in class jsat.classifiers.neuralnetwork.Perceptron
-
- getScore(DataPoint) - Method in class jsat.classifiers.RegressorToClassifier
-
- getScore(DataPoint) - Method in class jsat.classifiers.svm.DCD
-
- getScore(DataPoint) - Method in class jsat.classifiers.svm.DCDs
-
- getScore(DataPoint) - Method in class jsat.classifiers.svm.DCSVM
-
- getScore(DataPoint) - Method in class jsat.classifiers.svm.extended.CPM
-
- getScore(DataPoint) - Method in class jsat.classifiers.svm.LSSVM
-
- getScore(DataPoint) - Method in class jsat.classifiers.svm.Pegasos
-
- getScore(DataPoint) - Method in class jsat.classifiers.svm.PegasosK
-
- getScore(DataPoint) - Method in class jsat.classifiers.svm.PlattSMO
-
- getScore(DataPoint) - Method in class jsat.classifiers.svm.SBP
-
- getScore(DataPoint) - Method in class jsat.classifiers.svm.SVMnoBias
-
- getScore() - Method in class jsat.classifiers.trees.ImpurityScore
-
Computes the current impurity score for the points that have been added.
- getScore(DataSet, Object, int, Random) - Static method in class jsat.datatransform.featureselection.SFS
-
The score function for a data set and a learner by cross validation of a
classifier
- getScore() - Method in class jsat.regression.evaluation.CoefficientOfDetermination
-
- getScore() - Method in class jsat.regression.evaluation.MeanAbsoluteError
-
- getScore() - Method in class jsat.regression.evaluation.MeanSquaredError
-
- getScore() - Method in interface jsat.regression.evaluation.RegressionScore
-
- getScore() - Method in class jsat.regression.evaluation.RelativeAbsoluteError
-
- getScore() - Method in class jsat.regression.evaluation.RelativeSquaredError
-
- getScoreStats(ClassificationScore) - Method in class jsat.classifiers.ClassificationModelEvaluation
-
Gets the statistics associated with the given score.
- getScoreStats(RegressionScore) - Method in class jsat.regression.RegressionModelEvaluation
-
Gets the statistics associated with the given score.
- getSecondItem() - Method in class jsat.utils.Pair
-
- getSecondItem() - Method in class jsat.utils.PairedReturn
-
Returns the second object stored
- getSeedSelection() - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Returns the method of seed selection used
- getSeedSelection() - Method in class jsat.clustering.EMGaussianMixture
-
- getSeedSelection() - Method in class jsat.clustering.kmeans.GMeans
-
- getSeedSelection() - Method in class jsat.clustering.kmeans.KMeans
-
- getSeedSelection() - Method in class jsat.clustering.kmeans.MiniBatchKMeans
-
Returns the method of seed selection to use
- getSeedSelection() - Method in class jsat.clustering.kmeans.XMeans
-
- getSeedSelection() - Method in class jsat.clustering.PAM
-
- getSelectedCategorical() - Method in class jsat.datatransform.featureselection.BDS
-
Returns a copy of the set of categorical features selected by the search
algorithm
- getSelectedCategorical() - Method in class jsat.datatransform.featureselection.LRS
-
Returns a copy of the set of categorical features selected by the search
algorithm
- getSelectedCategorical() - Method in class jsat.datatransform.featureselection.SBS
-
Returns a copy of the set of categorical features selected by the search
algorithm
- getSelectedCategorical() - Method in class jsat.datatransform.featureselection.SFS
-
Returns a copy of the set of categorical features selected by the search
algorithm
- getSelectedNumerical() - Method in class jsat.datatransform.featureselection.BDS
-
Returns a copy of the set of numerical features selected by the search
algorithm.
- getSelectedNumerical() - Method in class jsat.datatransform.featureselection.LRS
-
Returns a copy of the set of numerical features selected by the search
algorithm.
- getSelectedNumerical() - Method in class jsat.datatransform.featureselection.SBS
-
Returns a copy of the set of numerical features selected by the search
algorithm.
- getSelectedNumerical() - Method in class jsat.datatransform.featureselection.SFS
-
Returns a copy of the set of numerical features selected by the search
algorithm.
- getSelectionCount() - Method in class jsat.classifiers.trees.ExtraTree
-
Returns the number of random features chosen at each level in the tree
- getShift() - Method in class jsat.linear.ShiftedVec
-
- getSigma() - Method in class jsat.datatransform.kernel.RFF_RBF
-
Returns the σ value used for the RBF kernel approximation.
- getSigma() - Method in class jsat.distributions.kernels.GeneralRBFKernel
-
- getSigma() - Method in class jsat.distributions.kernels.PukKernel
-
- getSigma() - Method in class jsat.distributions.kernels.RBFKernel
-
- getSignatureBitLength() - Method in class jsat.linear.vectorcollection.lsh.RandomProjectionLSH
-
Returns the signature or encoding length in bits.
- getSingularValues() - Method in class jsat.linear.SingularValueDecomposition
-
Returns a copy of the sorted array of the singular values, include the near zero ones.
- getSkewness() - Method in class jsat.math.OnLineStatistics
-
- getSmoothing() - Method in class jsat.classifiers.bayesian.MultinomialNaiveBayes
-
- getSmoothing() - Method in class jsat.math.ExponentialMovingStatistics
-
- getSolverMode() - Method in class jsat.regression.RidgeRegression
-
Returns the solver in use
- getSomHeight() - Method in class jsat.classifiers.neuralnetwork.SOM
-
Returns the height of the SOM lattice to create
- getSomWidth() - Method in class jsat.classifiers.neuralnetwork.SOM
-
Returns the width of the SOM lattice to create
- getSparsityStats() - Method in class jsat.DataSet
-
Returns statistics on the sparsity of the vectors in this data set.
- getSplitIndex(List<Integer>, int) - Method in class jsat.linear.vectorcollection.KDTree
-
- getSplittingAttribute() - Method in class jsat.classifiers.trees.DecisionStump
-
Returns the attribute that this stump has decided to use to compute
results.
- getSqrdNorm(int, int, List<? extends Vec>, List<Double>) - Method in class jsat.distributions.kernels.BaseL2Kernel
-
Returns the squared L2 norm between two points from the cache values.
- getSqrdNorm(int, List<? extends Vec>, List<Double>) - Method in class jsat.distributions.kernels.BaseL2Kernel
-
Returns the squared L2 norm of the given point from the cache
- getSqrdNorm(int, Vec, List<Double>, List<? extends Vec>, List<Double>) - Method in class jsat.distributions.kernels.BaseL2Kernel
-
Returns the squared L2 norm between a point in the cache and one with a provided qi value
- getSqrdNorm() - Method in class jsat.distributions.kernels.KernelPoint
-
Returns the squared values of the 2 norm of the point this object
represents
- getSqrdNorm(int) - Method in class jsat.distributions.kernels.KernelPoints
-
Returns the squared 2 norm value of the k'th KernelPoint
- getStandardDeviation() - Method in class jsat.math.ExponentialMovingStatistics
-
- getStandardDeviation() - Method in class jsat.math.OnLineStatistics
-
- getStandardDeviations() - Method in class jsat.classifiers.boosting.WaggingNormal
-
Returns the standard deviation used for the normal distribution
- getStartBlock(int, int, int) - Static method in class jsat.utils.concurrent.ParallelUtils
-
Gets the starting index (inclusive) for splitting up a list of items into
P evenly sized blocks.
- getStartBlock(int, int) - Static method in class jsat.utils.concurrent.ParallelUtils
-
Gets the starting index (inclusive) for splitting up a list of items into
SystemInfo.LogicalCores evenly sized blocks.
- getStartLevel() - Method in class jsat.classifiers.svm.DCSVM
-
- getStndDev() - Method in class jsat.classifiers.neuralnetwork.initializers.GaussianNormalInit
-
- getStndDev() - Method in class jsat.driftdetectors.ADWIN
-
Returns the standard deviation for all inputs contained in the current
window.
- getStndDevs() - Method in class jsat.clustering.FLAME
-
Returns the number of standard deviations away from the mean
density an outlier must be
- getStoppingDist() - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Returns the stopping distance used to terminate the algorithm early
- getStopSize() - Method in class jsat.classifiers.trees.ExtraTree
-
Returns the stopping size for tree growth
- getSubEpochs() - Method in class jsat.classifiers.svm.extended.AMM
-
Returns the number of passes through the data set done on each iteration
- getSubset(List<Integer>) - Method in class jsat.classifiers.ClassificationDataSet
-
- getSubset(List<Integer>) - Method in class jsat.DataSet
-
Creates a new dataset that is a subset of this dataset.
- getSubset(List<Integer>) - Method in class jsat.regression.RegressionDataSet
-
- getSubset(List<Integer>) - Method in class jsat.SimpleDataSet
-
- getSuccessRate() - Method in class jsat.driftdetectors.DDM
-
Returns the current estimate of the success rate (number of true
inputs) for the model.
- getSumOfWeights() - Method in class jsat.classifiers.ClassificationModelEvaluation
-
Returns the total value of the weights for all data points that were tested against
- getSumOfWeights() - Method in class jsat.classifiers.trees.ImpurityScore
-
Returns the sum of the weights for all points currently in the impurity
score
- getSumOfWeights() - Method in class jsat.math.OnLineStatistics
-
Returns the sum of the weights for all data points added to the statistics.
- getSupportVectorCount() - Method in class jsat.classifiers.linear.kernelized.OSKL
-
Returns the number of data points accepted as support vectors
- getTargetDimension() - Method in class jsat.datatransform.visualization.Isomap
-
- getTargetDimension() - Method in class jsat.datatransform.visualization.LargeViz
-
- getTargetDimension() - Method in class jsat.datatransform.visualization.MDS
-
- getTargetDimension() - Method in class jsat.datatransform.visualization.TSNE
-
- getTargetDimension() - Method in interface jsat.datatransform.visualization.VisualizationTransform
-
- getTargetValue(int) - Method in class jsat.regression.RegressionDataSet
-
Returns the target regression value for the i'th data point in the data set.
- getTargetValues() - Method in class jsat.regression.RegressionDataSet
-
Returns a vector containing the target regression values for each
data point.
- getTau() - Method in class jsat.math.decayrates.InverseDecay
-
Returns the early rate dampening parameter
- getTau() - Method in class jsat.math.decayrates.PowerDecay
-
Returns the early rate dampening parameter
- getTermFrequency(int) - Method in class jsat.text.TextDataLoader
-
Return the number of times a token has been seen in the document
- getTestProportion() - Method in class jsat.classifiers.trees.DecisionTree
-
Returns the proportion of the training set that is put aside to perform pruning with
- getTestVars() - Method in interface jsat.testing.onesample.OneSampleTest
-
- getTestVars() - Method in class jsat.testing.onesample.TTest
-
- getTestVars() - Method in class jsat.testing.onesample.ZTest
-
- getTextVectorCreator() - Method in class jsat.text.HashedTextDataLoader
-
Returns the
TextVectorCreator used by this data loader to convert
documents into vectors.
- getTextVectorCreator() - Method in class jsat.text.TextDataLoader
-
Returns the
TextVectorCreator used by this data loader to convert
documents into vectors.
- getThreshold() - Method in class jsat.classifiers.linear.STGD
-
Returns the coefficient threshold parameter
- getThreshold() - Method in class jsat.datatransform.PCA
-
- getTolerance() - Method in class jsat.classifiers.linear.BBR
-
Returns the tolerance parameter that controls convergence
- getTolerance() - Method in class jsat.classifiers.linear.LinearBatch
-
Returns the value of the convergence tolerance parameter
- getTolerance() - Method in class jsat.classifiers.linear.NewGLMNET
-
- getTolerance() - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Returns the minimum tolerance for early stopping.
- getTolerance() - Method in class jsat.classifiers.svm.DCDs
-
Returns the tolerance value used to terminate early
- getTolerance() - Method in class jsat.classifiers.svm.PlattSMO
-
Returns the solution tolerance
- getTolerance() - Method in class jsat.classifiers.svm.SVMnoBias
-
Returns the solution tolerance
- getTolerance() - Method in class jsat.datatransform.visualization.MDS
-
- getTopics(Vec) - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
Computes the topic distribution for the given document.
Note that the returned vector will be dense, but many of the values may
be very nearly zero.
- getTopicVec(int) - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
Returns the topic vector for a given topic.
- getTotalClassificationTime() - Method in class jsat.classifiers.ClassificationModelEvaluation
-
Returns the total number of milliseconds spent performing classification on the testing set.
- getTotalClassificationTime() - Method in class jsat.regression.RegressionModelEvaluation
-
Returns the total number of milliseconds spent performing regression on the testing set.
- getTotalTrainingTime() - Method in class jsat.classifiers.ClassificationModelEvaluation
-
Returns the total number of milliseconds spent training the classifier.
- getTotalTrainingTime() - Method in class jsat.regression.RegressionModelEvaluation
-
Returns the total number of milliseconds spent training the regressor.
- getTrainedClassifier() - Method in class jsat.parameters.ModelSearch
-
Returns the resultant classifier trained on the whole data set after
performing parameter tuning.
- getTrainedRegressor() - Method in class jsat.parameters.ModelSearch
-
Returns the resultant regressor trained on the whole data set after
performing parameter tuning.
- getTrainingProportion() - Method in class jsat.regression.StochasticGradientBoosting
-
Returns the fraction of the data points used during each iteration of the
training algorithm.
- getTreeNodeVisitor() - Method in class jsat.classifiers.trees.DecisionTree
-
- getTreeNodeVisitor() - Method in class jsat.classifiers.trees.ERTrees
-
- getTreeNodeVisitor() - Method in class jsat.classifiers.trees.ExtraTree
-
- getTreeNodeVisitor() - Method in interface jsat.classifiers.trees.TreeLearner
-
Obtains a node visitor for the tree learner that can be used to traverse
and predict from the learned tree
- getTrials() - Method in class jsat.distributions.discrete.Binomial
-
- getTrials() - Method in class jsat.parameters.RandomSearch
-
- getTrustH0() - Method in class jsat.clustering.kmeans.GMeans
-
- getTruths() - Method in class jsat.classifiers.ClassificationModelEvaluation
-
- getTwiceShallowClone() - Method in class jsat.classifiers.ClassificationDataSet
-
- getTwiceShallowClone() - Method in class jsat.DataSet
-
Returns a new version of this data set that is of the same type, and
contains a different listing pointing to shallow data point copies.
- getTwiceShallowClone() - Method in class jsat.regression.RegressionDataSet
-
- getTwiceShallowClone() - Method in class jsat.SimpleDataSet
-
- getU() - Method in class jsat.linear.SingularValueDecomposition
-
Returns the backing matrix U of the SVD.
- getUseDefaultSelectionCount() - Method in class jsat.classifiers.trees.ERTrees
-
Returns if the default heuristic for the selection count is used
- getUseDefaultStopSize() - Method in class jsat.classifiers.trees.ERTrees
-
Returns if the default heuristic for the stop size is used
- getV() - Method in class jsat.linear.EigenValueDecomposition
-
Return a copy of the eigenvector matrix
- getV() - Method in class jsat.linear.SingularValueDecomposition
-
Returns the backing matrix V of the SVD.
- getVal(Random) - Method in class jsat.linear.RandomMatrix
-
Computes the value of an index given the already initialized
Random object.
- getVal(Random) - Method in class jsat.linear.RandomVector
-
Computes the value of an index given the already initialized
Random object.
- getValue() - Method in class jsat.linear.IndexValue
-
Returns the value of the stored index
- getValue() - Method in class jsat.parameters.BooleanParameter
-
Returns the current value for the parameter.
- getValue() - Method in class jsat.parameters.DoubleParameter
-
Returns the current value for the parameter.
- getValue() - Method in class jsat.parameters.IntParameter
-
Returns the current value for the parameter.
- getValue() - Method in class jsat.utils.FibHeap.FibNode
-
- getValueString() - Method in class jsat.parameters.BooleanParameter
-
- getValueString() - Method in class jsat.parameters.DoubleParameter
-
- getValueString() - Method in class jsat.parameters.IntParameter
-
- getValueString() - Method in class jsat.parameters.MetricParameter
-
- getValueString() - Method in class jsat.parameters.ObjectParameter
-
- getValueString() - Method in class jsat.parameters.Parameter
-
Returns a string indicating the value currently held by the Parameter
- getVarance() - Method in class jsat.math.OnLineStatistics
-
Computes the population variance
- getVariables() - Method in class jsat.distributions.Beta
-
- getVariables() - Method in class jsat.distributions.Cauchy
-
- getVariables() - Method in class jsat.distributions.ChiSquared
-
- getVariables() - Method in class jsat.distributions.ContinuousDistribution
-
Returns an array, where each value contains the name of a parameter in the distribution.
- getVariables() - Method in class jsat.distributions.empirical.KernelDensityEstimator
-
- getVariables() - Method in class jsat.distributions.Exponential
-
- getVariables() - Method in class jsat.distributions.FisherSendor
-
- getVariables() - Method in class jsat.distributions.Gamma
-
- getVariables() - Method in class jsat.distributions.Kolmogorov
-
- getVariables() - Method in class jsat.distributions.Laplace
-
- getVariables() - Method in class jsat.distributions.Levy
-
- getVariables() - Method in class jsat.distributions.Logistic
-
- getVariables() - Method in class jsat.distributions.LogNormal
-
- getVariables() - Method in class jsat.distributions.LogUniform
-
- getVariables() - Method in class jsat.distributions.MaxwellBoltzmann
-
- getVariables() - Method in class jsat.distributions.Normal
-
- getVariables() - Method in class jsat.distributions.Pareto
-
- getVariables() - Method in class jsat.distributions.Rayleigh
-
- getVariables() - Method in class jsat.distributions.StudentT
-
- getVariables() - Method in class jsat.distributions.TruncatedDistribution
-
- getVariables() - Method in class jsat.distributions.Uniform
-
- getVariables() - Method in class jsat.distributions.Weibull
-
- getVariance() - Method in class jsat.driftdetectors.ADWIN
-
Returns the variance for all inputs contained in the current window
- getVariance() - Method in class jsat.math.ExponentialMovingStatistics
-
- getVector() - Method in class jsat.classifiers.DataPointPair
-
- getVector() - Method in class jsat.linear.VecPaired
-
- getVectorCollection(List<V>, DistanceMetric) - Method in class jsat.linear.vectorcollection.CoverTree.CoverTreeFactory
-
- getVectorCollection(List<V>, DistanceMetric, ExecutorService) - Method in class jsat.linear.vectorcollection.CoverTree.CoverTreeFactory
-
- getVectorCollection(List<V>, DistanceMetric) - Method in class jsat.linear.vectorcollection.DefaultVectorCollectionFactory
-
- getVectorCollection(List<V>, DistanceMetric, ExecutorService) - Method in class jsat.linear.vectorcollection.DefaultVectorCollectionFactory
-
- getVectorCollection(List<V>, DistanceMetric) - Method in class jsat.linear.vectorcollection.EuclideanCollection.EuclideanCollectionFactory
-
- getVectorCollection(List<V>, DistanceMetric, ExecutorService) - Method in class jsat.linear.vectorcollection.EuclideanCollection.EuclideanCollectionFactory
-
- getVectorCollection(List<V>, DistanceMetric) - Method in class jsat.linear.vectorcollection.KDTree.KDTreeFactory
-
- getVectorCollection(List<V>, DistanceMetric, ExecutorService) - Method in class jsat.linear.vectorcollection.KDTree.KDTreeFactory
-
- getVectorCollection(List<V>, DistanceMetric) - Method in class jsat.linear.vectorcollection.lsh.RandomProjectionLSH.RandomProjectionLSHFactory
-
- getVectorCollection(List<V>, DistanceMetric, ExecutorService) - Method in class jsat.linear.vectorcollection.lsh.RandomProjectionLSH.RandomProjectionLSHFactory
-
- getVectorCollection(List<V>, DistanceMetric) - Method in class jsat.linear.vectorcollection.RandomBallCover.RandomBallCoverFactory
-
- getVectorCollection(List<V>, DistanceMetric, ExecutorService) - Method in class jsat.linear.vectorcollection.RandomBallCover.RandomBallCoverFactory
-
- getVectorCollection(List<V>, DistanceMetric) - Method in class jsat.linear.vectorcollection.RandomBallCoverOneShot.RandomBallCoverOneShotFactory
-
- getVectorCollection(List<V>, DistanceMetric, ExecutorService) - Method in class jsat.linear.vectorcollection.RandomBallCoverOneShot.RandomBallCoverOneShotFactory
-
- getVectorCollection(List<V>, DistanceMetric) - Method in class jsat.linear.vectorcollection.RTree.RTreeFactory
-
- getVectorCollection(List<V>, DistanceMetric, ExecutorService) - Method in class jsat.linear.vectorcollection.RTree.RTreeFactory
-
- getVectorCollection(List<V>, DistanceMetric) - Method in class jsat.linear.vectorcollection.VectorArray.VectorArrayFactory
-
- getVectorCollection(List<V>, DistanceMetric, ExecutorService) - Method in class jsat.linear.vectorcollection.VectorArray.VectorArrayFactory
-
- getVectorCollection(List<V>, DistanceMetric) - Method in interface jsat.linear.vectorcollection.VectorCollectionFactory
-
Creates a new Vector Collection from the given source using the provided metric.
- getVectorCollection(List<V>, DistanceMetric, ExecutorService) - Method in interface jsat.linear.vectorcollection.VectorCollectionFactory
-
Creates a new Vector Collection from the given source using the provided metric.
- getVectorCollection(List<V>, DistanceMetric) - Method in class jsat.linear.vectorcollection.VPTree.VPTreeFactory
-
- getVectorCollection(List<V>, DistanceMetric, ExecutorService) - Method in class jsat.linear.vectorcollection.VPTree.VPTreeFactory
-
- getVectorCollection(List<V>, DistanceMetric) - Method in class jsat.linear.vectorcollection.VPTreeMV.VPTreeMVFactory
-
- getVectorCollection(List<V>, DistanceMetric, ExecutorService) - Method in class jsat.linear.vectorcollection.VPTreeMV.VPTreeMVFactory
-
- getVectorConstant(Vec) - Method in interface jsat.linear.distancemetrics.DenseSparseMetric
-
Computes a summary constant value for the vector that is based on the
distance metric in use.
- getVectorConstant(Vec) - Method in class jsat.linear.distancemetrics.EuclideanDistance
-
- getVectorConstant(Vec) - Method in class jsat.linear.distancemetrics.ManhattanDistance
-
- getVectorConstant(Vec) - Method in class jsat.linear.distancemetrics.MinkowskiDistance
-
- getVecView() - Method in class jsat.classifiers.CategoricalResults
-
- getVecView() - Method in class jsat.utils.DoubleList
-
Obtains a view of this double list as a dense vector with equal length.
- getVocabSize() - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
Returns the size of the vocabulary for LDA, or -1 if this
object is not ready to learn
- getVRaw() - Method in class jsat.linear.EigenValueDecomposition
-
Returns the raw eigenvector matrix.
- getVT() - Method in class jsat.linear.EigenValueDecomposition
-
Returns a copy of the transposed eigenvector matrix.
- getW() - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
Returns a copy of the weight vector used to compute results via a dot
product.
- getWarningThreshold() - Method in class jsat.driftdetectors.DDM
-
Returns the threshold multiple for controlling the false positive /
negative rate on detecting changes.
- getWeakClassifier() - Method in class jsat.classifiers.boosting.Wagging
-
Returns the weak learner used for classification.
- getWeakLearner() - Method in class jsat.classifiers.boosting.AdaBoostM1
-
Returns the weak learner currently being used by this method.
- getWeakLearner() - Method in class jsat.classifiers.boosting.ArcX4
-
Returns the weak learner used
- getWeakLearner() - Method in class jsat.classifiers.boosting.EmphasisBoost
-
Returns the weak learner currently being used by this method.
- getWeakLearner() - Method in class jsat.classifiers.boosting.ModestAdaBoost
-
Returns the weak learner currently being used by this method.
- getWeakRegressor() - Method in class jsat.classifiers.boosting.Wagging
-
Returns the weak learner used for regression
- getWeight() - Method in class jsat.classifiers.DataPoint
-
Returns the weight that this data point carries.
- getWeight(int, int, double, Random) - Method in enum jsat.classifiers.neuralnetwork.BackPropagationNet.WeightInitialization
-
- getWeight() - Method in class jsat.linear.distancemetrics.WeightedEuclideanDistance
-
Returns the weight vector used by this object.
- getWeightDecay() - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
Returns the weight decay used for each update
- getWeightInit() - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
- getWeightInitialization() - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
Returns the method of weight initialization used
- getWeights() - Method in class jsat.datatransform.featureselection.ReliefF
-
Returns accesses to the learned weight data.
- getWeightSampledDataSet(ClassificationDataSet, int[]) - Static method in class jsat.classifiers.boosting.Bagging
-
Creates a new data set from the given sample counts.
- getWeightSampledDataSet(RegressionDataSet, int[]) - Static method in class jsat.classifiers.boosting.Bagging
-
Creates a new data set from the given sample counts.
- getWeightVec() - Method in class jsat.classifiers.linear.ALMA2
-
Returns the weight vector used to compute results via a dot product.
- getWeightVec() - Method in class jsat.classifiers.linear.AROW
-
Returns the weight vector used to compute results via a dot product.
- getWeightVec() - Method in class jsat.classifiers.linear.BBR
-
Returns the weight vector used to compute results via a dot product.
- getWeightVec() - Method in class jsat.classifiers.linear.LogisticRegressionDCD
-
Returns the weight vector used to compute results via a dot product.
- getWeightVec() - Method in class jsat.classifiers.linear.NHERD
-
Returns the weight vector used to compute results via a dot product.
- getWeightVec() - Method in class jsat.classifiers.linear.ROMMA
-
Returns the weight vector used to compute results via a dot product.
- getWeightVec() - Method in class jsat.classifiers.linear.SCW
-
Returns the weight vector used to compute results via a dot product.
- getWidnowLength() - Method in class jsat.driftdetectors.ADWIN
-
This returns the current "length" of the window, which is the number of
items that have been added to the ADWIN object since the last drift, and
is reduced when drift occurres.
- getWordForIndex(int) - Method in class jsat.text.TextDataLoader
-
Returns the original token for the given index in the data set
- getWRaw() - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
Returns the weight vector used to compute results via a dot product.
- getX() - Method in class jsat.utils.Tuple3
-
- getXi() - Method in class jsat.clustering.OPTICS
-
- getY() - Method in class jsat.utils.Tuple3
-
- getZ() - Method in class jsat.utils.Tuple3
-
- getzMax() - Method in class jsat.classifiers.boosting.LogitBoost
-
Returns the maximum miss-classification penalty used by the algorithm.
- GMeans - Class in jsat.clustering.kmeans
-
This class provides a method of performing
KMeans clustering when the
value of
K is not known.
- GMeans() - Constructor for class jsat.clustering.kmeans.GMeans
-
- GMeans(KMeans) - Constructor for class jsat.clustering.kmeans.GMeans
-
- GMeans(GMeans) - Constructor for class jsat.clustering.kmeans.GMeans
-
- GoldenSearch - Class in jsat.math.optimization
-
Minimizes a single variate function in the same way that
- GoldenSearch() - Constructor for class jsat.math.optimization.GoldenSearch
-
- GoldenSearch - Class in jsat.math.optimization.oned
-
- GoldenSearch() - Constructor for class jsat.math.optimization.oned.GoldenSearch
-
- grad(double, double, double, double) - Method in enum jsat.classifiers.linear.kernelized.CSKLR.UpdateMode
-
Get the gradient value that should be applied based on the input
variable from the current model
- GradFunction(DataSet, LossFunc) - Constructor for class jsat.classifiers.linear.LinearBatch.GradFunction
-
- GRADIENT - Static variable in class jsat.math.optimization.RosenbrockFunction
-
The gradient of the Rosenbrock function
- gradientError(double, double) - Method in enum jsat.classifiers.linear.StochasticMultinomialLogisticRegression.Prior
-
- gradientError(double, double, double) - Method in enum jsat.classifiers.linear.StochasticMultinomialLogisticRegression.Prior
-
- GradientUpdater - Interface in jsat.math.optimization.stochastic
-
This interface defines the method of updating some weight vector using a
gradient and a learning rate.
- GreekLetters - Class in jsat.text
-
- GreekLetters() - Constructor for class jsat.text.GreekLetters
-
- GridDataGenerator - Class in jsat.utils
-
This is a utility to generate data in a grid fashion.
- GridDataGenerator(ContinuousDistribution, Random, int...) - Constructor for class jsat.utils.GridDataGenerator
-
Creates a new Grid data generator, that can be queried for new data sets.
- GridDataGenerator(ContinuousDistribution, int...) - Constructor for class jsat.utils.GridDataGenerator
-
Creates a new Grid data generator, that can be queried for new data sets.
- GridDataGenerator() - Constructor for class jsat.utils.GridDataGenerator
-
Creates a new grid data generator for a 2 x 5 with uniform noise in the range [-1/4, 1/4]
- GridSearch - Class in jsat.parameters
-
GridSearch is a simple method for tuning the parameters of a classification
or regression algorithm.
- GridSearch(Regressor, int) - Constructor for class jsat.parameters.GridSearch
-
Creates a new GridSearch to tune the specified parameters of a regression
model.
- GridSearch(Classifier, int) - Constructor for class jsat.parameters.GridSearch
-
Creates a new GridSearch to tune the specified parameters of a
classification model.
- GridSearch(GridSearch) - Constructor for class jsat.parameters.GridSearch
-
Copy constructor
- guessAlpha(DataSet) - Static method in class jsat.classifiers.linear.kernelized.ALMA2K
-
Guesses the distribution to use for the α parameter
- guessAlpha(DataSet) - Static method in class jsat.classifiers.linear.NewGLMNET
-
Guess the distribution to use for the trade off term term
(double) α in Elastic Net regularization.
- guessAlpha(DataSet) - Static method in class jsat.classifiers.neuralnetwork.RBFNet
-
- guessAlpha(DataSet) - Static method in class jsat.distributions.kernels.SigmoidKernel
-
Guesses a distribution for the α parameter
- guessC(DataSet) - Static method in class jsat.classifiers.linear.kernelized.DUOL
-
Guesses the distribution to use for the C parameter
- guessC(DataSet) - Static method in class jsat.classifiers.linear.LogisticRegressionDCD
-
Guess the distribution to use for the regularization term
C in Logistic Regression.
- guessC(DataSet) - Static method in class jsat.classifiers.linear.NewGLMNET
-
Guess the distribution to use for the regularization term
C in Logistic Regression.
- guessC(DataSet) - Static method in class jsat.classifiers.linear.NHERD
-
Guess the distribution to use for the regularization term
C .
- guessC(DataSet) - Static method in class jsat.classifiers.linear.PassiveAggressive
-
Guess the distribution to use for the regularization term
C in PassiveAggressive.
- guessC(DataSet) - Static method in class jsat.classifiers.linear.SCW
-
Guess the distribution to use for the regularization term
C .
- guessC(DataSet) - Static method in class jsat.classifiers.linear.SPA
-
Guess the distribution to use for the regularization term
C in Support PassiveAggressive.
- guessC(DataSet) - Static method in class jsat.classifiers.svm.DCDs
-
Guess the distribution to use for the regularization term
C in a SVM.
- guessC(DataSet) - Static method in class jsat.classifiers.svm.LSSVM
-
Guess the distribution to use for the regularization term
C in a LS-SVM.
- guessC(DataSet) - Static method in class jsat.classifiers.svm.PlattSMO
-
Guess the distribution to use for the regularization term
C in a SVM.
- guessC(DataSet) - Static method in class jsat.distributions.kernels.RationalQuadraticKernel
-
Guess the distribution to use for the C parameter.
- guessC(DataSet) - Static method in class jsat.distributions.kernels.SigmoidKernel
-
Guesses a distribution for the α parameter
- guessDegree(DataSet) - Static method in class jsat.distributions.kernels.PolynomialKernel
-
Guesses the distribution to use for the degree parameter
- guessDimensions(DataSet) - Static method in class jsat.datatransform.kernel.KernelPCA
-
- guessDimensions(DataSet) - Static method in class jsat.datatransform.WhitenedPCA
-
- guessEntropyThreshold(DataSet) - Static method in class jsat.classifiers.svm.extended.CPM
-
- guessesNeeded() - Method in class jsat.math.rootfinding.Bisection
-
- guessesNeeded() - Method in class jsat.math.rootfinding.RiddersMethod
-
- guessesNeeded() - Method in interface jsat.math.rootfinding.RootFinder
-
Different root finding methods require different numbers of initial guesses.
- guessesNeeded() - Method in class jsat.math.rootfinding.Secant
-
- guessesNeeded() - Method in class jsat.math.rootfinding.Zeroin
-
- guessEta(DataSet) - Static method in class jsat.classifiers.linear.kernelized.BOGD
-
Guesses the distribution to use for the η parameter
- guessEta(DataSet) - Static method in class jsat.classifiers.linear.SCW
-
Guess the distribution to use for the regularization term
η .
- guessK(DataSet) - Static method in class jsat.classifiers.knn.DANN
-
Guesses the distribution to use for the number of neighbors to consider
- guessKn(DataSet) - Static method in class jsat.classifiers.knn.DANN
-
Guesses the distribution to use for the number of neighbors to consider
- guessLambda(DataSet) - Static method in class jsat.classifiers.boosting.EmphasisBoost
-
Guesses the distribution to use for the λ parameter
- guessLambda(DataSet) - Static method in class jsat.classifiers.linear.kernelized.KernelSGD
-
Guess the distribution to use for the regularization term
λ .
- guessLambda(DataSet) - Static method in class jsat.classifiers.svm.extended.CPM
-
Provides a distribution of reasonable values for the
λ parameter
- guessLambda(DataSet) - Static method in class jsat.classifiers.svm.extended.OnlineAMM
-
Guess the distribution to use for the regularization term
λ in AMM.
- guessLambda(DataSet) - Static method in class jsat.regression.KernelRidgeRegression
-
Guesses the distribution to use for the λ parameter
- guessLambda0(DataSet) - Static method in class jsat.classifiers.linear.LinearBatch
-
Guess the distribution to use for the regularization term
λ0 .
- guessLambda0(DataSet) - Static method in class jsat.classifiers.linear.LinearSGD
-
Guess the distribution to use for the regularization term
λ0 .
- guessLambda1(DataSet) - Static method in class jsat.classifiers.linear.LinearSGD
-
Guess the distribution to use for the regularization term
λ1 .
- guessMaxCoeff(DataSet) - Static method in class jsat.classifiers.linear.kernelized.BOGD
-
Guesses the distribution to use for the MaxCoeff parameter
- guessNeighbors(DataSet) - Static method in class jsat.classifiers.knn.LWL
-
Guesses the distribution to use for the number of neighbors to consider
- guessNeighbors(DataSet) - Static method in class jsat.classifiers.knn.NearestNeighbour
-
Guesses the distribution to use for the number of neighbors to consider
- guessNumberOfBins(DataSet) - Static method in class jsat.datatransform.NumericalToHistogram
-
Attempts to guess the number of bins to use
- guessNumCentroids(DataSet) - Static method in class jsat.classifiers.neuralnetwork.RBFNet
-
- guessOmega(DataSet) - Static method in class jsat.distributions.kernels.PukKernel
-
Guesses the distribution to use for the ω parameter
- guessP(DataSet) - Static method in class jsat.classifiers.neuralnetwork.RBFNet
-
- guessProjectedDimension(DataSet) - Static method in class jsat.datatransform.JLTransform
-
- guessR(DataSet) - Static method in class jsat.classifiers.linear.AROW
-
Guess the distribution to use for the regularization term
r .
- guessR(DataSet) - Static method in class jsat.classifiers.linear.kernelized.CSKLR
-
Guesses the distribution to use for the R parameter
- guessR(DataSet) - Static method in class jsat.classifiers.linear.kernelized.CSKLRBatch
-
Guesses the distribution to use for the R parameter
- guessR(DataSet) - Static method in class jsat.classifiers.linear.kernelized.OSKL
-
Guesses the distribution to use for the R parameter
- guessRegularization(DataSet) - Static method in class jsat.classifiers.linear.kernelized.BOGD
-
Guesses the distribution to use for the Regularization parameter
- guessRegularization(DataSet) - Static method in class jsat.classifiers.svm.Pegasos
-
- guessSigma(DataSet) - Method in class jsat.datatransform.kernel.RFF_RBF
-
Guess the distribution to use for the kernel width term
σ in the RBF kernel being approximated.
- guessSigma(DataSet) - Method in class jsat.distributions.kernels.GeneralRBFKernel
-
Guess the distribution to use for the kernel width term
σ in the General RBF kernel.
- guessSigma(DataSet, DistanceMetric) - Static method in class jsat.distributions.kernels.GeneralRBFKernel
-
Guess the distribution to use for the kernel width term
σ in the General RBF kernel.
- guessSigma(DataSet) - Static method in class jsat.distributions.kernels.PukKernel
-
Guesses the distribution to use for the λ parameter
- guessSigma(DataSet) - Static method in class jsat.distributions.kernels.RBFKernel
-
Guess the distribution to use for the kernel width term
σ in the RBF kernel.
- MAGIC_NUMBER - Static variable in class jsat.io.JSATData
-
- MahalanobisDistance - Class in jsat.linear.distancemetrics
-
The Mahalanobis Distance is a metric that takes into account the variance of the data.
- MahalanobisDistance() - Constructor for class jsat.linear.distancemetrics.MahalanobisDistance
-
- MajorityVote - Class in jsat.classifiers
-
The Majority Vote classifier is a simple ensemble classifier.
- MajorityVote(Classifier...) - Constructor for class jsat.classifiers.MajorityVote
-
Creates a new Majority Vote classifier using the given voters.
- MajorityVote(List<Classifier>) - Constructor for class jsat.classifiers.MajorityVote
-
Creates a new Majority Vote classifier using the given voters.
- makeNodeC(List<DataPointPair<Integer>>, Set<Integer>, int, ExecutorService, ModifiableCountDownLatch) - Method in class jsat.classifiers.trees.DecisionTree
-
Makes a new node for classification
- makeNodeC(List<DataPointPair<Integer>>, Set<Integer>, int, ExecutorService, ModifiableCountDownLatch) - Method in class jsat.classifiers.trees.RandomDecisionTree
-
- makeNodeR(List<DataPointPair<Double>>, Set<Integer>, int, ExecutorService, ModifiableCountDownLatch) - Method in class jsat.classifiers.trees.DecisionTree
-
Makes a new node for regression
- makeNodeR(List<DataPointPair<Double>>, Set<Integer>, int, ExecutorService, ModifiableCountDownLatch) - Method in class jsat.classifiers.trees.RandomDecisionTree
-
- ManhattanDistance - Class in jsat.linear.distancemetrics
-
Manhattan Distance is the L1 norm.
- ManhattanDistance() - Constructor for class jsat.linear.distancemetrics.ManhattanDistance
-
- MathTricks - Class in jsat.math
-
This class provides utilities for performing specific arithmetic patterns in
numerically stable / efficient ways.
- Matrix - Class in jsat.linear
-
Generic class with some pre-implemented methods for a Matrix object.
- Matrix() - Constructor for class jsat.linear.Matrix
-
- MatrixOfVecs - Class in jsat.linear
-
This class provides a base mechanism to create a Matrix 'view' from a list of
Vec objects.
- MatrixOfVecs(Vec...) - Constructor for class jsat.linear.MatrixOfVecs
-
Creates a new Matrix of Vecs from the given array of Vec objects.
- MatrixOfVecs(List<Vec>) - Constructor for class jsat.linear.MatrixOfVecs
-
Creates a new Matrix of Vecs from the given list of Vec objects.
- MatrixOfVecs(int, int, boolean) - Constructor for class jsat.linear.MatrixOfVecs
-
Creates a new Matrix of Vecs of the desired size.
- MatrixStatistics - Class in jsat.linear
-
This class provides methods useful for statistical operations that involve matrices and vectors.
- MatthewsCorrelationCoefficient - Class in jsat.classifiers.evaluation
-
Evaluates a classifier based on Mathews Correlation Coefficient
- MatthewsCorrelationCoefficient() - Constructor for class jsat.classifiers.evaluation.MatthewsCorrelationCoefficient
-
- MatthewsCorrelationCoefficient(MatthewsCorrelationCoefficient) - Constructor for class jsat.classifiers.evaluation.MatthewsCorrelationCoefficient
-
- max() - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet.ActivationFunction
-
The maximum possible response value
- max() - Method in class jsat.distributions.Beta
-
- max() - Method in class jsat.distributions.Cauchy
-
- max() - Method in class jsat.distributions.ChiSquared
-
- max() - Method in class jsat.distributions.discrete.Binomial
-
- max() - Method in class jsat.distributions.discrete.Poisson
-
- max() - Method in class jsat.distributions.discrete.UniformDiscrete
-
- max() - Method in class jsat.distributions.Distribution
-
The maximum value for which the #pdf(double) is meant to return
a value.
- max() - Method in class jsat.distributions.empirical.KernelDensityEstimator
-
- max() - Method in class jsat.distributions.Exponential
-
- max() - Method in class jsat.distributions.FisherSendor
-
- max() - Method in class jsat.distributions.Gamma
-
- max() - Method in class jsat.distributions.Kolmogorov
-
- max() - Method in class jsat.distributions.Laplace
-
- max() - Method in class jsat.distributions.Levy
-
- max() - Method in class jsat.distributions.Logistic
-
- max() - Method in class jsat.distributions.LogNormal
-
- max() - Method in class jsat.distributions.LogUniform
-
- max() - Method in class jsat.distributions.MaxwellBoltzmann
-
- max() - Method in class jsat.distributions.Normal
-
- max() - Method in class jsat.distributions.Pareto
-
- max() - Method in class jsat.distributions.Rayleigh
-
- max() - Method in class jsat.distributions.StudentT
-
- max() - Method in class jsat.distributions.TruncatedDistribution
-
- max() - Method in class jsat.distributions.Uniform
-
- max() - Method in class jsat.distributions.Weibull
-
- max() - Method in class jsat.linear.DenseVector
-
- max() - Method in class jsat.linear.RandomVector
-
- max() - Method in class jsat.linear.ScaledVector
-
- max() - Method in class jsat.linear.ShiftedVec
-
- max() - Method in class jsat.linear.SparseVector
-
- max() - Method in class jsat.linear.Vec
-
Returns the maximum value stored in this vector
- max() - Method in class jsat.linear.VecPaired
-
- max(double...) - Static method in class jsat.math.MathTricks
-
- Max2NormRegularizer - Class in jsat.classifiers.neuralnetwork.regularizers
-
This regularizer restricts the norm of each neuron's weights to be bounded by
a fixed constant, and rescaled when the norm is exceeded.
- Max2NormRegularizer(double) - Constructor for class jsat.classifiers.neuralnetwork.regularizers.Max2NormRegularizer
-
- maxBudget - Variable in class jsat.distributions.kernels.KernelPoint
-
- MaxDistance - Class in jsat.clustering.evaluation.intra
-
Evaluates a cluster's validity by returning the
maximum distance between any two points in the cluster.
- MaxDistance() - Constructor for class jsat.clustering.evaluation.intra.MaxDistance
-
- MaxDistance(DistanceMetric) - Constructor for class jsat.clustering.evaluation.intra.MaxDistance
-
Creates a new MaxDistance
- MaxDistance(MaxDistance) - Constructor for class jsat.clustering.evaluation.intra.MaxDistance
-
Copy constructor
- maxHistory - Variable in class jsat.driftdetectors.BaseDriftDetector
-
Controls the maximum amount of history to keep
- maximumIterations - Variable in class jsat.clustering.kmeans.KernelKMeans
-
- MaxIterLimit - Variable in class jsat.clustering.EMGaussianMixture
-
Control the maximum number of iterations to perform.
- MaxIterLimit - Variable in class jsat.clustering.kmeans.KMeans
-
Control the maximum number of iterations to perform.
- maxLambdaLogisticL1(ClassificationDataSet) - Static method in class jsat.classifiers.linear.LinearTools
-
If the linear model performs logistic regression regularized by λ
||w||1, this method computes the smallest value of lambda that
produces a weight vector of all zeros.
Note, that the value returned depends on the data set size.
- maxScaled - Variable in class jsat.classifiers.linear.StochasticSTLinearL1
-
The scaled maximum
- maxSize() - Method in class jsat.utils.BoundedSortedList
-
Returns the maximum size allowed for the bounded list
- MaxwellBoltzmann - Class in jsat.distributions
-
- MaxwellBoltzmann() - Constructor for class jsat.distributions.MaxwellBoltzmann
-
- MaxwellBoltzmann(double) - Constructor for class jsat.distributions.MaxwellBoltzmann
-
- MDA - Class in jsat.classifiers.trees
-
Mean Decrease in Accuracy (MDA) measures feature importance by applying the
classifier for each feature, and corruption one feature at a time as each
dataum its pushed through the tree.
- MDA() - Constructor for class jsat.classifiers.trees.MDA
-
- MDI - Class in jsat.classifiers.trees
-
Determines the importance of features by measuring the decrease in impurity
caused by each feature used, weighted by the amount of data seen by the node
using the feature.
- MDI(ImpurityScore.ImpurityMeasure) - Constructor for class jsat.classifiers.trees.MDI
-
- MDI() - Constructor for class jsat.classifiers.trees.MDI
-
- MDS - Class in jsat.datatransform.visualization
-
Multidimensional scaling is an algorithm for finding low dimensional
embeddings of arbitrary distance matrices.
- MDS() - Constructor for class jsat.datatransform.visualization.MDS
-
- mean() - Method in class jsat.distributions.Beta
-
- mean() - Method in class jsat.distributions.Cauchy
-
The Cauchy distribution is unique in that it does not have a mean value (undefined).
- mean() - Method in class jsat.distributions.ChiSquared
-
- mean() - Method in class jsat.distributions.ContinuousDistribution
-
- mean() - Method in class jsat.distributions.discrete.Binomial
-
- mean() - Method in class jsat.distributions.discrete.Poisson
-
- mean() - Method in class jsat.distributions.discrete.UniformDiscrete
-
- mean() - Method in class jsat.distributions.Distribution
-
Computes the mean value of the distribution
- mean() - Method in class jsat.distributions.empirical.KernelDensityEstimator
-
- mean() - Method in class jsat.distributions.Exponential
-
- mean() - Method in class jsat.distributions.FisherSendor
-
- mean() - Method in class jsat.distributions.Gamma
-
- mean() - Method in class jsat.distributions.Kolmogorov
-
- mean() - Method in class jsat.distributions.Laplace
-
- mean() - Method in class jsat.distributions.Levy
-
- mean() - Method in class jsat.distributions.Logistic
-
- mean() - Method in class jsat.distributions.LogNormal
-
- mean() - Method in class jsat.distributions.LogUniform
-
- mean() - Method in class jsat.distributions.MaxwellBoltzmann
-
- mean() - Method in class jsat.distributions.Normal
-
- mean() - Method in class jsat.distributions.Pareto
-
- mean() - Method in class jsat.distributions.Rayleigh
-
- mean() - Method in class jsat.distributions.StudentT
-
- mean() - Method in class jsat.distributions.Uniform
-
- mean() - Method in class jsat.distributions.Weibull
-
- mean() - Method in class jsat.linear.ConstantVector
-
- mean() - Method in class jsat.linear.ScaledVector
-
- mean() - Method in class jsat.linear.ShiftedVec
-
- mean() - Method in class jsat.linear.Vec
-
Computes the mean value of all values stored in this vector
- mean() - Method in class jsat.linear.VecPaired
-
- MeanAbsoluteError - Class in jsat.regression.evaluation
-
Uses the Mean of Absolute Errors between the predictions and the true values.
- MeanAbsoluteError() - Constructor for class jsat.regression.evaluation.MeanAbsoluteError
-
- MeanAbsoluteError(MeanAbsoluteError) - Constructor for class jsat.regression.evaluation.MeanAbsoluteError
-
Copy constructor
- MeanCentroidDistance - Class in jsat.clustering.evaluation.intra
-
Evaluates a cluster's validity by computing the mean distance of each point
in the cluster from the cluster's centroid.
- MeanCentroidDistance() - Constructor for class jsat.clustering.evaluation.intra.MeanCentroidDistance
-
- MeanCentroidDistance(DistanceMetric) - Constructor for class jsat.clustering.evaluation.intra.MeanCentroidDistance
-
Creates a new MeanCentroidDistance.
- MeanCentroidDistance(MeanCentroidDistance) - Constructor for class jsat.clustering.evaluation.intra.MeanCentroidDistance
-
Copy constructor
- MeanDistance - Class in jsat.clustering.evaluation.intra
-
Evaluates a cluster's validity by computing the mean distance between all
combinations of points.
- MeanDistance() - Constructor for class jsat.clustering.evaluation.intra.MeanDistance
-
- MeanDistance(DistanceMetric) - Constructor for class jsat.clustering.evaluation.intra.MeanDistance
-
Creates a new MeanDistance
- MeanDistance(MeanDistance) - Constructor for class jsat.clustering.evaluation.intra.MeanDistance
-
Copy constructor
- means - Variable in class jsat.clustering.kmeans.KMeans
-
The list of means
- MeanShift - Class in jsat.clustering
-
The MeanShift algorithms performs clustering on a data set by letting the
data speak for itself and performing a mode search amongst the data set,
returning a cluster for each discovered mode.
- MeanShift() - Constructor for class jsat.clustering.MeanShift
-
- MeanShift(DistanceMetric) - Constructor for class jsat.clustering.MeanShift
-
- MeanShift(MultivariateKDE) - Constructor for class jsat.clustering.MeanShift
-
Creates a new MeanShift clustering object.
- MeanShift(MeanShift) - Constructor for class jsat.clustering.MeanShift
-
Copy constructor
- meanSqrdNorms - Variable in class jsat.clustering.kmeans.KernelKMeans
-
The value of the un-normalized squared norm for each mean
- MeanSquaredError - Class in jsat.regression.evaluation
-
Uses the Mean of the Squared Errors between the predictions and the true
values.
- MeanSquaredError() - Constructor for class jsat.regression.evaluation.MeanSquaredError
-
- MeanSquaredError(boolean) - Constructor for class jsat.regression.evaluation.MeanSquaredError
-
- MeanSquaredError(MeanSquaredError) - Constructor for class jsat.regression.evaluation.MeanSquaredError
-
Copy constructor
- meanToMeanDistance(int, int) - Method in class jsat.clustering.kmeans.KernelKMeans
-
Computes the distance between two of the means in the clustering
- meanToMeanDistance(int, int, int[]) - Method in class jsat.clustering.kmeans.KernelKMeans
-
- meanToMeanDistance(int, int, int[], ExecutorService) - Method in class jsat.clustering.kmeans.KernelKMeans
-
- meanToMeanDistance(int, int, int[], int[], double) - Method in class jsat.clustering.kmeans.KernelKMeans
-
- meanToMeanDistance(int, int, int[], int[], double, ExecutorService) - Method in class jsat.clustering.kmeans.KernelKMeans
-
- meanVector(List<V>) - Static method in class jsat.linear.MatrixStatistics
-
Computes the mean of the given data set.
- meanVector(DataSet) - Static method in class jsat.linear.MatrixStatistics
-
Computes the weighted mean of the given data set.
- meanVector(Vec, List<V>) - Static method in class jsat.linear.MatrixStatistics
-
Computes the mean of the given data set.
- meanVector(Vec, DataSet) - Static method in class jsat.linear.MatrixStatistics
-
Computes the weighted mean of the data set
- median() - Method in class jsat.distributions.Beta
-
- median() - Method in class jsat.distributions.Cauchy
-
- median() - Method in class jsat.distributions.ChiSquared
-
- median() - Method in class jsat.distributions.discrete.Binomial
-
- median() - Method in class jsat.distributions.discrete.UniformDiscrete
-
- median() - Method in class jsat.distributions.Distribution
-
Computes the median value of the distribution
- median() - Method in class jsat.distributions.Exponential
-
- median() - Method in class jsat.distributions.FisherSendor
-
- median() - Method in class jsat.distributions.Gamma
-
- median() - Method in class jsat.distributions.Kolmogorov
-
- median() - Method in class jsat.distributions.Laplace
-
- median() - Method in class jsat.distributions.Logistic
-
- median() - Method in class jsat.distributions.LogNormal
-
- median() - Method in class jsat.distributions.LogUniform
-
- median() - Method in class jsat.distributions.MaxwellBoltzmann
-
- median() - Method in class jsat.distributions.Normal
-
- median() - Method in class jsat.distributions.Rayleigh
-
- median() - Method in class jsat.distributions.StudentT
-
- median() - Method in class jsat.distributions.Uniform
-
- median() - Method in class jsat.distributions.Weibull
-
- median() - Method in class jsat.linear.ConstantVector
-
- median() - Method in class jsat.linear.DenseVector
-
- median() - Method in class jsat.linear.ScaledVector
-
- median() - Method in class jsat.linear.ShiftedVec
-
- median() - Method in class jsat.linear.SparseVector
-
- median() - Method in class jsat.linear.Vec
-
Returns the median value in this vector
- median() - Method in class jsat.linear.VecPaired
-
- MedianDissimilarity - Class in jsat.clustering.dissimilarity
-
Median link dissimilarity, also called WPGMC.
- MedianDissimilarity(DistanceMetric) - Constructor for class jsat.clustering.dissimilarity.MedianDissimilarity
-
- MedianDissimilarity(MedianDissimilarity) - Constructor for class jsat.clustering.dissimilarity.MedianDissimilarity
-
- medoids - Variable in class jsat.clustering.PAM
-
- mergedView(List<T>, List<T>) - Static method in class jsat.utils.ListUtils
-
Returns a new unmodifiable view that is the merging of two lists
- metricBound() - Method in class jsat.linear.distancemetrics.ChebyshevDistance
-
- metricBound() - Method in class jsat.linear.distancemetrics.CosineDistance
-
- metricBound() - Method in class jsat.linear.distancemetrics.CosineDistanceNormalized
-
- metricBound() - Method in class jsat.linear.distancemetrics.DistanceCounter
-
- metricBound() - Method in interface jsat.linear.distancemetrics.DistanceMetric
-
All metrics must return values greater than or equal to 0.
- metricBound() - Method in class jsat.linear.distancemetrics.EuclideanDistance
-
- metricBound() - Method in class jsat.linear.distancemetrics.KernelDistance
-
- metricBound() - Method in class jsat.linear.distancemetrics.MahalanobisDistance
-
- metricBound() - Method in class jsat.linear.distancemetrics.ManhattanDistance
-
- metricBound() - Method in class jsat.linear.distancemetrics.MinkowskiDistance
-
- metricBound() - Method in class jsat.linear.distancemetrics.NormalizedEuclideanDistance
-
- metricBound() - Method in class jsat.linear.distancemetrics.PearsonDistance
-
- metricBound() - Method in class jsat.linear.distancemetrics.SquaredEuclideanDistance
-
- metricBound() - Method in class jsat.linear.distancemetrics.WeightedEuclideanDistance
-
- MetricKDE - Class in jsat.distributions.multivariate
-
- MetricKDE() - Constructor for class jsat.distributions.multivariate.MetricKDE
-
Creates a new KDE object that still needs a data set to model the distribution of
- MetricKDE(DistanceMetric) - Constructor for class jsat.distributions.multivariate.MetricKDE
-
Creates a new KDE object that still needs a data set to model the distribution of
- MetricKDE(DistanceMetric, VectorCollectionFactory<VecPaired<Vec, Integer>>) - Constructor for class jsat.distributions.multivariate.MetricKDE
-
Creates a new KDE object that still needs a data set to model the distribution of
- MetricKDE(KernelFunction, DistanceMetric) - Constructor for class jsat.distributions.multivariate.MetricKDE
-
- MetricKDE(KernelFunction, DistanceMetric, VectorCollectionFactory<VecPaired<Vec, Integer>>) - Constructor for class jsat.distributions.multivariate.MetricKDE
-
Creates a new KDE object that still needs a data set to model the distribution of
- MetricKDE(KernelFunction, DistanceMetric, VectorCollectionFactory<VecPaired<Vec, Integer>>, int, double) - Constructor for class jsat.distributions.multivariate.MetricKDE
-
Creates a new KDE object that still needs a data set to model the distribution of
- MetricParameter - Class in jsat.parameters
-
A MetricParameter is a parameter controller for the
DistanceMetric
used by the current algorithm.
- MetricParameter() - Constructor for class jsat.parameters.MetricParameter
-
- min() - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet.ActivationFunction
-
The minimum possible response value
- min() - Method in class jsat.distributions.Beta
-
- min() - Method in class jsat.distributions.Cauchy
-
- min() - Method in class jsat.distributions.ChiSquared
-
- min() - Method in class jsat.distributions.discrete.Binomial
-
- min() - Method in class jsat.distributions.discrete.Poisson
-
- min() - Method in class jsat.distributions.discrete.UniformDiscrete
-
- min() - Method in class jsat.distributions.Distribution
-
The minimum value for which the #pdf(double) is meant to return
a value.
- min() - Method in class jsat.distributions.empirical.KernelDensityEstimator
-
- min() - Method in class jsat.distributions.Exponential
-
- min() - Method in class jsat.distributions.FisherSendor
-
- min() - Method in class jsat.distributions.Gamma
-
- min() - Method in class jsat.distributions.Kolmogorov
-
- min() - Method in class jsat.distributions.Laplace
-
- min() - Method in class jsat.distributions.Levy
-
- min() - Method in class jsat.distributions.Logistic
-
- min() - Method in class jsat.distributions.LogNormal
-
- min() - Method in class jsat.distributions.LogUniform
-
- min() - Method in class jsat.distributions.MaxwellBoltzmann
-
- min() - Method in class jsat.distributions.Normal
-
- min() - Method in class jsat.distributions.Pareto
-
- min() - Method in class jsat.distributions.Rayleigh
-
- min() - Method in class jsat.distributions.StudentT
-
- min() - Method in class jsat.distributions.TruncatedDistribution
-
- min() - Method in class jsat.distributions.Uniform
-
- min() - Method in class jsat.distributions.Weibull
-
- min() - Method in class jsat.linear.DenseVector
-
- min() - Method in class jsat.linear.RandomVector
-
- min() - Method in class jsat.linear.ScaledVector
-
- min() - Method in class jsat.linear.ShiftedVec
-
- min() - Method in class jsat.linear.SparseVector
-
- min() - Method in class jsat.linear.Vec
-
Returns the minimum value stored in this vector
- min() - Method in class jsat.linear.VecPaired
-
- min(double...) - Static method in class jsat.math.MathTricks
-
- MiniBatchKMeans - Class in jsat.clustering.kmeans
-
Implements the mini-batch algorithms for k-means.
- MiniBatchKMeans(int, int) - Constructor for class jsat.clustering.kmeans.MiniBatchKMeans
-
- MiniBatchKMeans(DistanceMetric, int, int) - Constructor for class jsat.clustering.kmeans.MiniBatchKMeans
-
Creates a new Mini-Batch k-Means object that uses
k-means++ for seed selection.
- MiniBatchKMeans(DistanceMetric, int, int, SeedSelectionMethods.SeedSelection) - Constructor for class jsat.clustering.kmeans.MiniBatchKMeans
-
Creates a new Mini-Batch k-Means object
- MiniBatchKMeans(MiniBatchKMeans) - Constructor for class jsat.clustering.kmeans.MiniBatchKMeans
-
Copy constructor
- minimize(double, int, double, double, int, Function, double...) - Static method in class jsat.math.optimization.GoldenSearch
-
Finds the local minimum of the function f.
- MinkowskiDistance - Class in jsat.linear.distancemetrics
-
Minkowski Distance is the Lp norm.
- MinkowskiDistance(double) - Constructor for class jsat.linear.distancemetrics.MinkowskiDistance
-
- minScaled - Variable in class jsat.classifiers.linear.StochasticSTLinearL1
-
The scaled minimum
- mode - Variable in class jsat.classifiers.calibration.BinaryCalibration
-
The calibration mode to use
- mode() - Method in class jsat.distributions.Beta
-
- mode() - Method in class jsat.distributions.Cauchy
-
- mode() - Method in class jsat.distributions.ChiSquared
-
- mode() - Method in class jsat.distributions.ContinuousDistribution
-
- mode() - Method in class jsat.distributions.discrete.Binomial
-
- mode() - Method in class jsat.distributions.discrete.Poisson
-
- mode() - Method in class jsat.distributions.discrete.UniformDiscrete
-
- mode() - Method in class jsat.distributions.Distribution
-
Computes the mode of the distribution.
- mode() - Method in class jsat.distributions.empirical.KernelDensityEstimator
-
- mode() - Method in class jsat.distributions.Exponential
-
- mode() - Method in class jsat.distributions.FisherSendor
-
- mode() - Method in class jsat.distributions.Gamma
-
- mode() - Method in class jsat.distributions.Kolmogorov
-
- mode() - Method in class jsat.distributions.Laplace
-
- mode() - Method in class jsat.distributions.Levy
-
- mode() - Method in class jsat.distributions.Logistic
-
- mode() - Method in class jsat.distributions.LogNormal
-
- mode() - Method in class jsat.distributions.LogUniform
-
- mode() - Method in class jsat.distributions.MaxwellBoltzmann
-
- mode() - Method in class jsat.distributions.Normal
-
- mode() - Method in class jsat.distributions.Pareto
-
- mode() - Method in class jsat.distributions.Rayleigh
-
- mode() - Method in class jsat.distributions.StudentT
-
- mode() - Method in class jsat.distributions.TruncatedDistribution
-
- mode() - Method in class jsat.distributions.Uniform
-
- mode() - Method in class jsat.distributions.Weibull
-
- model(DataSet, int) - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
Fits the LDA model against the given data set
- model(DataSet, int, ExecutorService) - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
Fits the LDA model against the given data set
- ModelMismatchException - Exception in jsat.exceptions
-
This exception is thrown when the input into a model does not match the expectation of the model.
- ModelMismatchException(String, Throwable) - Constructor for exception jsat.exceptions.ModelMismatchException
-
- ModelMismatchException(Throwable) - Constructor for exception jsat.exceptions.ModelMismatchException
-
- ModelMismatchException(String) - Constructor for exception jsat.exceptions.ModelMismatchException
-
- ModelMismatchException() - Constructor for exception jsat.exceptions.ModelMismatchException
-
- ModelSearch - Class in jsat.parameters
-
This abstract class provides boilerplate for algorithms that search a model's
parameter space to find the parameters that provide the best overall
performance.
- ModelSearch(Regressor, int) - Constructor for class jsat.parameters.ModelSearch
-
- ModelSearch(Classifier, int) - Constructor for class jsat.parameters.ModelSearch
-
- ModelSearch(ModelSearch) - Constructor for class jsat.parameters.ModelSearch
-
Copy constructor
- ModestAdaBoost - Class in jsat.classifiers.boosting
-
Modest Ada Boost is a generalization of Discrete Ada Boost that attempts to
reduce the generalization error and avoid over-fitting.
- ModestAdaBoost(Classifier, int) - Constructor for class jsat.classifiers.boosting.ModestAdaBoost
-
Creates a new ModestBoost learner
- ModestAdaBoost(ModestAdaBoost) - Constructor for class jsat.classifiers.boosting.ModestAdaBoost
-
Copy constructor
- ModifiableCountDownLatch - Class in jsat.utils
-
Provides a
CountDownLatch that can have the number of counts increased as well as decreased.
- ModifiableCountDownLatch(int) - Constructor for class jsat.utils.ModifiableCountDownLatch
-
- ModifiedOWLQN - Class in jsat.math.optimization
-
This implements the Modified Orthant-Wise Limited memory
Quasi-Newton(mOWL-QN) optimizer.
- ModifiedOWLQN() - Constructor for class jsat.math.optimization.ModifiedOWLQN
-
Creates a new mOWL-QN optimizer with no regularization penalty
- ModifiedOWLQN(double) - Constructor for class jsat.math.optimization.ModifiedOWLQN
-
Creates a new mOWL-QN optimizer
- ModifiedOWLQN(ModifiedOWLQN) - Constructor for class jsat.math.optimization.ModifiedOWLQN
-
copy constructor
- mostLikely() - Method in class jsat.classifiers.CategoricalResults
-
Returns the category that is the most likely according to the current probability values
- mu - Static variable in class jsat.text.GreekLetters
-
- multCol(Matrix, int, int, int, double) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of column j in the given matrix to be A[:,j] = A[:,j]* c
- multCol(Matrix, int, double) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of column j in the given matrix to be A[:,j] = A[:,j]* c
- MultinomialLogisticRegression - Class in jsat.classifiers
-
Multinomial Logistic Regression is an extension of
LogisticRegression for classification when
there are more then two target classes.
- MultinomialLogisticRegression() - Constructor for class jsat.classifiers.MultinomialLogisticRegression
-
- MultinomialNaiveBayes - Class in jsat.classifiers.bayesian
-
An implementation of the Multinomial Naive Bayes model (MNB).
- MultinomialNaiveBayes() - Constructor for class jsat.classifiers.bayesian.MultinomialNaiveBayes
-
Creates a new Multinomial model with laplace smoothing
- MultinomialNaiveBayes(double) - Constructor for class jsat.classifiers.bayesian.MultinomialNaiveBayes
-
Creates a new Multinomial model with the given amount of smoothing
- MultinomialNaiveBayes(MultinomialNaiveBayes) - Constructor for class jsat.classifiers.bayesian.MultinomialNaiveBayes
-
Copy constructor
- MultipleLinearRegression - Class in jsat.regression
-
- MultipleLinearRegression() - Constructor for class jsat.regression.MultipleLinearRegression
-
- MultipleLinearRegression(boolean) - Constructor for class jsat.regression.MultipleLinearRegression
-
- multiply(Vec, double, Vec) - Method in class jsat.linear.DenseMatrix
-
- multiply(Matrix, Matrix) - Method in class jsat.linear.DenseMatrix
-
- multiply(Matrix, Matrix, ExecutorService) - Method in class jsat.linear.DenseMatrix
-
- multiply(double, Matrix, Vec) - Method in class jsat.linear.DenseVector
-
- multiply(Vec, double, Vec) - Method in class jsat.linear.GenericMatrix
-
- multiply(Matrix, Matrix) - Method in class jsat.linear.GenericMatrix
-
- multiply(Matrix, Matrix, ExecutorService) - Method in class jsat.linear.GenericMatrix
-
- multiply(Vec, double, Vec) - Method in class jsat.linear.Matrix
-
If this matrix is Am x n, and b has a length of n, and c has a length of m,
then this will mutate c to store c = c + A*b*z
- multiply(Vec) - Method in class jsat.linear.Matrix
-
Creates a new vector that is equal to A*b
- multiply(Matrix) - Method in class jsat.linear.Matrix
-
Creates a new matrix that stores A*B
- multiply(Matrix, ExecutorService) - Method in class jsat.linear.Matrix
-
Creates a new matrix that stores A*B
- multiply(Matrix, Matrix) - Method in class jsat.linear.Matrix
-
Alters the matrix C to be equal to C = C+A*B
- multiply(Matrix, Matrix, ExecutorService) - Method in class jsat.linear.Matrix
-
Alters the matrix C to be equal to C = C+A*B
- multiply(double) - Method in class jsat.linear.Matrix
-
Creates a new Matrix that stores A*c
- multiply(double, ExecutorService) - Method in class jsat.linear.Matrix
-
Creates a new Matrix that stores A*c
- multiply(Vec, double, Vec) - Method in class jsat.linear.MatrixOfVecs
-
- multiply(double, Matrix, Vec) - Method in class jsat.linear.RandomVector
-
- multiply(double, Matrix, Vec) - Method in class jsat.linear.ScaledVector
-
- multiply(Vec, double, Vec) - Method in class jsat.linear.SparseMatrix
-
- multiply(Matrix, Matrix) - Method in class jsat.linear.SparseMatrix
-
- multiply(Matrix, Matrix, ExecutorService) - Method in class jsat.linear.SparseMatrix
-
- multiply(double, Matrix, Vec) - Method in class jsat.linear.SparseVector
-
- multiply(double) - Method in class jsat.linear.Vec
-
Returns a new vector that is the result of this * c
- multiply(Matrix) - Method in class jsat.linear.Vec
-
Returns a new vector that is the result of the vector matrix product
thisTA
- multiply(Matrix, Vec) - Method in class jsat.linear.Vec
-
If this is vector a, this this computes b = b + aT*A
- multiply(double, Matrix, Vec) - Method in class jsat.linear.Vec
-
If this is vector a, this this computes b = b + c aT*A
- multiply(double) - Method in class jsat.linear.VecPaired
-
- multiply(double, Matrix, Vec) - Method in class jsat.linear.VecPaired
-
- multiply(Complex) - Method in class jsat.math.Complex
-
Creates a new complex number containing the resulting multiplication between this and another
- multiplyTranspose(Matrix, Matrix) - Method in class jsat.linear.GenericMatrix
-
- multiplyTranspose(Matrix, Matrix, ExecutorService) - Method in class jsat.linear.GenericMatrix
-
- multiplyTranspose(Matrix, Matrix) - Method in class jsat.linear.Matrix
-
Alters the matrix C to be equal to C = C+A*BT
- multiplyTranspose(Matrix) - Method in class jsat.linear.Matrix
-
Returns the new matrix C that is C = A*BT
- multiplyTranspose(Matrix, Matrix, ExecutorService) - Method in class jsat.linear.Matrix
-
Alters the matrix C to be equal to C = C+A*BT
- multiplyTranspose(Matrix, ExecutorService) - Method in class jsat.linear.Matrix
-
Returns the new matrix C that is C = A*BT
- multiplyTranspose(Matrix, Matrix) - Method in class jsat.linear.SparseMatrix
-
- multiplyTranspose(Matrix, Matrix, ExecutorService) - Method in class jsat.linear.SparseMatrix
-
- MultivariateDistribution - Interface in jsat.distributions.multivariate
-
This interface represents the contract that any continuous multivariate distribution must implement
- MultivariateDistributionSkeleton - Class in jsat.distributions.multivariate
-
Common class for implementing a multivariate distribution.
- MultivariateDistributionSkeleton() - Constructor for class jsat.distributions.multivariate.MultivariateDistributionSkeleton
-
- MultivariateKDE - Class in jsat.distributions.multivariate
-
- MultivariateKDE() - Constructor for class jsat.distributions.multivariate.MultivariateKDE
-
- MultivariateNormals - Class in jsat.classifiers.bayesian
-
This classifier can be seen as an extension of
NaiveBayes.
- MultivariateNormals(boolean) - Constructor for class jsat.classifiers.bayesian.MultivariateNormals
-
- MultivariateNormals() - Constructor for class jsat.classifiers.bayesian.MultivariateNormals
-
- MultivariateNormals(MultivariateNormals) - Constructor for class jsat.classifiers.bayesian.MultivariateNormals
-
Copy constructor
- multRow(Matrix, int, int, int, double) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of row i in the given matrix to be A[i,:] = A[i,:] * c
- multRow(Matrix, int, double) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of row i in the given matrix to be A[i,:] = A[i,:] * c
- multRow(Matrix, int, int, int, Vec) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of row i in the given matrix to be A[i,:] = A[i,:] .* c[i]
The Matrix A and vector c do not need to have the same dimensions,
so long as they both have indices in the given range.
- multRow(Matrix, int, Vec) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of row i in the given matrix to be A[i,:] = A[i,:] .* c[i]
The Matrix A and vector c do not need to have the same dimensions,
so long as they both have indices in the given range.
- multRow(Matrix, int, int, int, double[]) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of row i in the given matrix to be A[i,:] = A[i,:] .* c[i]
The Matrix A and array c do not need to have the same dimensions,
so long as they both have indices in the given range.
- multRow(Matrix, int, double[]) - Static method in class jsat.linear.RowColumnOps
-
Updates the values of row i in the given matrix to be A[i,:] = A[i,:] .* c[i]
- mutableAdd(Vec) - Method in class jsat.distributions.kernels.KernelPoint
-
Alters this point to contain the given input vector as well
- mutableAdd(double, Vec) - Method in class jsat.distributions.kernels.KernelPoint
-
Alters this point to contain the given input vector as well
- mutableAdd(double, Vec, List<Double>) - Method in class jsat.distributions.kernels.KernelPoint
-
Alters this point to contain the given input vector as well
- mutableAdd(int, double, Vec, List<Double>) - Method in class jsat.distributions.kernels.KernelPoints
-
Alters ones of the KernelPoint objects by adding / subtracting a vector
from it
- mutableAdd(Vec, Vec, List<Double>) - Method in class jsat.distributions.kernels.KernelPoints
-
Alters some of the KernelPoints by adding / subtracting a vector from it
- mutableAdd(double, Vec) - Method in class jsat.linear.ConcatenatedVec
-
- mutableAdd(double, Matrix) - Method in class jsat.linear.DenseMatrix
-
- mutableAdd(double) - Method in class jsat.linear.DenseVector
-
- mutableAdd(double, Vec) - Method in class jsat.linear.DenseVector
-
- mutableAdd(double, Matrix) - Method in class jsat.linear.GenericMatrix
-
- mutableAdd(double, Matrix, ExecutorService) - Method in class jsat.linear.GenericMatrix
-
- mutableAdd(double) - Method in class jsat.linear.GenericMatrix
-
- mutableAdd(double, ExecutorService) - Method in class jsat.linear.GenericMatrix
-
- mutableAdd(Matrix) - Method in class jsat.linear.Matrix
-
Alters the current matrix to store the value A+B
- mutableAdd(double, Matrix) - Method in class jsat.linear.Matrix
-
Alters the current matrix to store the value A+c*B
- mutableAdd(Matrix, ExecutorService) - Method in class jsat.linear.Matrix
-
Alters the current matrix to store the value A+B
- mutableAdd(double, Matrix, ExecutorService) - Method in class jsat.linear.Matrix
-
Alters the current matrix to store the value A+c*B
- mutableAdd(double) - Method in class jsat.linear.Matrix
-
Alters the current matrix to store the value A+c
- mutableAdd(double, ExecutorService) - Method in class jsat.linear.Matrix
-
Alters the current matrix to store the value A+c
- mutableAdd(double) - Method in class jsat.linear.MatrixOfVecs
-
- mutableAdd(double) - Method in class jsat.linear.RandomVector
-
- mutableAdd(double, Vec) - Method in class jsat.linear.RandomVector
-
- mutableAdd(double) - Method in class jsat.linear.ScaledVector
-
- mutableAdd(double, Vec) - Method in class jsat.linear.ScaledVector
-
- mutableAdd(Vec) - Method in class jsat.linear.ShiftedVec
-
- mutableAdd(double) - Method in class jsat.linear.ShiftedVec
-
- mutableAdd(double, Vec) - Method in class jsat.linear.ShiftedVec
-
- mutableAdd(double, Matrix) - Method in class jsat.linear.SparseMatrix
-
- mutableAdd(double, Matrix, ExecutorService) - Method in class jsat.linear.SparseMatrix
-
- mutableAdd(double) - Method in class jsat.linear.SparseMatrix
-
- mutableAdd(double, ExecutorService) - Method in class jsat.linear.SparseMatrix
-
- mutableAdd(double) - Method in class jsat.linear.SparseVector
-
- mutableAdd(double, Vec) - Method in class jsat.linear.SparseVector
-
- mutableAdd(double) - Method in class jsat.linear.Vec
-
Alters this vector such that
this = this + c
This method should be overloaded for a serious implementation.
- mutableAdd(double, Vec) - Method in class jsat.linear.Vec
-
Alters this vector such that
this = this + c * b
This method should be overloaded for a serious implementation.
- mutableAdd(Vec) - Method in class jsat.linear.Vec
-
Alters this vector such that
this = this + b
- mutableAdd(double) - Method in class jsat.linear.VecPaired
-
- mutableAdd(Vec) - Method in class jsat.linear.VecPaired
-
- mutableAdd(double, Vec) - Method in class jsat.linear.VecPaired
-
- mutableAdd(double) - Method in class jsat.linear.VecWithNorm
-
- mutableAdd(double, Vec) - Method in class jsat.linear.VecWithNorm
-
- mutableAdd(double, double) - Method in class jsat.math.Complex
-
Alters this complex number as if an addition of another complex number was performed.
- mutableAdd(Complex) - Method in class jsat.math.Complex
-
Alters this complex number to contain the result of the addition of another
- mutableDivide(double) - Method in class jsat.linear.DenseVector
-
- mutableDivide(double) - Method in class jsat.linear.RandomVector
-
- mutableDivide(double) - Method in class jsat.linear.ScaledVector
-
- mutableDivide(double) - Method in class jsat.linear.ShiftedVec
-
- mutableDivide(double) - Method in class jsat.linear.SparseVector
-
- mutableDivide(double) - Method in class jsat.linear.Vec
-
Mutates this /= c
This method should be overloaded for a serious implementation.
- mutableDivide(double) - Method in class jsat.linear.VecPaired
-
- mutableDivide(double) - Method in class jsat.linear.VecWithNorm
-
- mutableDivide(double, double) - Method in class jsat.math.Complex
-
Alters this complex number as if a division by another complex number was performed.
- mutableDivide(Complex) - Method in class jsat.math.Complex
-
Alters this complex number to contain the result of the division by another
- mutableInverse(DataPoint) - Method in interface jsat.datatransform.InPlaceInvertibleTransform
-
Mutates the given data point.
- mutableInverse(DataPoint) - Method in class jsat.datatransform.LinearTransform
-
- mutableInverse(DataPoint) - Method in class jsat.datatransform.ZeroMeanTransform
-
- mutableMultiply(double) - Method in class jsat.distributions.kernels.KernelPoint
-
Alters this point to be multiplied by the given value
- mutableMultiply(int, double) - Method in class jsat.distributions.kernels.KernelPoints
-
Alters the k'th KernelPoint by multiplying it with a constant
value
- mutableMultiply(double) - Method in class jsat.distributions.kernels.KernelPoints
-
Alters all the KernelPoint objects contained in this set by the same
constant value
- mutableMultiply(double) - Method in class jsat.linear.DenseMatrix
-
- mutableMultiply(double) - Method in class jsat.linear.DenseVector
-
- mutableMultiply(double) - Method in class jsat.linear.GenericMatrix
-
- mutableMultiply(double, ExecutorService) - Method in class jsat.linear.GenericMatrix
-
- mutableMultiply(double) - Method in class jsat.linear.Matrix
-
Alters the current matrix to be equal to A*c
- mutableMultiply(double, ExecutorService) - Method in class jsat.linear.Matrix
-
Alters the current matrix to be equal to A*c
- mutableMultiply(double) - Method in class jsat.linear.MatrixOfVecs
-
- mutableMultiply(double) - Method in class jsat.linear.RandomVector
-
- mutableMultiply(double) - Method in class jsat.linear.ScaledVector
-
- mutableMultiply(double) - Method in class jsat.linear.ShiftedVec
-
- mutableMultiply(double) - Method in class jsat.linear.SparseMatrix
-
- mutableMultiply(double, ExecutorService) - Method in class jsat.linear.SparseMatrix
-
- mutableMultiply(double) - Method in class jsat.linear.SparseVector
-
- mutableMultiply(double) - Method in class jsat.linear.Vec
-
Mutates this *= c
This method should be overloaded for a serious implementation.
- mutableMultiply(double) - Method in class jsat.linear.VecPaired
-
- mutableMultiply(double) - Method in class jsat.linear.VecWithNorm
-
- mutableMultiply(double, double) - Method in class jsat.math.Complex
-
Alters this complex number as if a multiplication of another complex number was performed.
- mutableMultiply(Complex) - Method in class jsat.math.Complex
-
Alters this complex number to contain the result of the multiplication of another
- mutablePairwiseDivide(Vec) - Method in class jsat.linear.DenseVector
-
- mutablePairwiseDivide(Vec) - Method in class jsat.linear.RandomVector
-
- mutablePairwiseDivide(Vec) - Method in class jsat.linear.ScaledVector
-
- mutablePairwiseDivide(Vec) - Method in class jsat.linear.ShiftedVec
-
- mutablePairwiseDivide(Vec) - Method in class jsat.linear.SparseVector
-
- mutablePairwiseDivide(Vec) - Method in class jsat.linear.Vec
-
Mutates this by dividing each value by the value in b
that has the same index
This method should be overloaded for a serious implementation.
- mutablePairwiseDivide(Vec) - Method in class jsat.linear.VecPaired
-
- mutablePairwiseDivide(Vec) - Method in class jsat.linear.VecWithNorm
-
- mutablePairwiseMultiply(Vec) - Method in class jsat.linear.DenseVector
-
- mutablePairwiseMultiply(Vec) - Method in class jsat.linear.RandomVector
-
- mutablePairwiseMultiply(Vec) - Method in class jsat.linear.ScaledVector
-
- mutablePairwiseMultiply(Vec) - Method in class jsat.linear.ShiftedVec
-
- mutablePairwiseMultiply(Vec) - Method in class jsat.linear.SparseVector
-
- mutablePairwiseMultiply(Vec) - Method in class jsat.linear.Vec
-
Mutates this by multiplying each value by the value in b
that has the same index.
- mutablePairwiseMultiply(Vec) - Method in class jsat.linear.VecPaired
-
- mutablePairwiseMultiply(Vec) - Method in class jsat.linear.VecWithNorm
-
- mutableSubtract(double) - Method in class jsat.linear.DenseVector
-
- mutableSubtract(Matrix) - Method in class jsat.linear.Matrix
-
Alters the current matrix to store A-B
- mutableSubtract(double, Matrix) - Method in class jsat.linear.Matrix
-
Alters the current matrix to store A-c*B
- mutableSubtract(Matrix, ExecutorService) - Method in class jsat.linear.Matrix
-
Alters the current matrix to store A-B
- mutableSubtract(double, Matrix, ExecutorService) - Method in class jsat.linear.Matrix
-
Alters the current matrix to store A-c*B
- mutableSubtract(double) - Method in class jsat.linear.Matrix
-
Alters the current matrix to store A-c
- mutableSubtract(double, ExecutorService) - Method in class jsat.linear.Matrix
-
Alters the current matrix to store A-c
- mutableSubtract(double) - Method in class jsat.linear.Vec
-
Alters this vector such that
this = this - c
- mutableSubtract(double, Vec) - Method in class jsat.linear.Vec
-
Alters this vector such that
this = this - c * b
- mutableSubtract(Vec) - Method in class jsat.linear.Vec
-
Alters this vector such that
this = this - b
- mutableSubtract(Vec) - Method in class jsat.linear.VecPaired
-
- mutableSubtract(double, double) - Method in class jsat.math.Complex
-
Alters this complex number as if a subtraction of another complex number was performed.
- mutableSubtract(Complex) - Method in class jsat.math.Complex
-
Alters this complex number to contain the result of the subtraction of another
- mutableTransform(DataPoint) - Method in class jsat.datatransform.AutoDeskewTransform
-
- mutableTransform(DataPoint) - Method in class jsat.datatransform.Imputer
-
- mutableTransform(DataPoint) - Method in interface jsat.datatransform.InPlaceTransform
-
Mutates the given data point.
- mutableTransform(DataPoint) - Method in class jsat.datatransform.InsertMissingValuesTransform
-
- mutableTransform(DataPoint) - Method in class jsat.datatransform.LinearTransform
-
- mutableTransform(DataPoint) - Method in class jsat.datatransform.PNormNormalization
-
- mutableTransform(DataPoint) - Method in class jsat.datatransform.StandardizeTransform
-
- mutableTransform(DataPoint) - Method in class jsat.datatransform.UnitVarianceTransform
-
- mutableTransform(DataPoint) - Method in class jsat.datatransform.WhitenedZCA
-
- mutableTransform(DataPoint) - Method in class jsat.datatransform.ZeroMeanTransform
-
- mutableTranspose() - Method in class jsat.linear.DenseMatrix
-
- mutableTranspose() - Method in class jsat.linear.GenericMatrix
-
- mutableTranspose() - Method in class jsat.linear.Matrix
-
Transposes the current matrix in place, altering its value.
- mutableTranspose() - Method in class jsat.linear.SparseMatrix
-
- mutateConjugate() - Method in class jsat.math.Complex
-
Alters this complex number so that it represents its complex conjugate
instead.
- mutatesNominal() - Method in class jsat.datatransform.AutoDeskewTransform
-
- mutatesNominal() - Method in class jsat.datatransform.Imputer
-
- mutatesNominal() - Method in interface jsat.datatransform.InPlaceTransform
-
By default returns false.
- mutatesNominal() - Method in class jsat.datatransform.InsertMissingValuesTransform
-
- mutatesNominal() - Method in class jsat.datatransform.LinearTransform
-
- mutatesNominal() - Method in class jsat.datatransform.PNormNormalization
-
- mutatesNominal() - Method in class jsat.datatransform.StandardizeTransform
-
- mutatesNominal() - Method in class jsat.datatransform.UnitVarianceTransform
-
- mutatesNominal() - Method in class jsat.datatransform.WhitenedZCA
-
- mutatesNominal() - Method in class jsat.datatransform.ZeroMeanTransform
-
- MutualInfoFS - Class in jsat.datatransform.featureselection
-
Performs greedy feature selection based on Mutual Information of the features
with respect to the class values.
- MutualInfoFS() - Constructor for class jsat.datatransform.featureselection.MutualInfoFS
-
Creates a new Mutual Information feature selection object that attempts
to select up to 100 features.
- MutualInfoFS(int) - Constructor for class jsat.datatransform.featureselection.MutualInfoFS
-
Creates a new Mutual Information feature selection object.
- MutualInfoFS(ClassificationDataSet, int) - Constructor for class jsat.datatransform.featureselection.MutualInfoFS
-
Creates a new Mutual Information feature selection object.
- MutualInfoFS(MutualInfoFS) - Constructor for class jsat.datatransform.featureselection.MutualInfoFS
-
Copy constructor
- MutualInfoFS(int, MutualInfoFS.NumericalHandeling) - Constructor for class jsat.datatransform.featureselection.MutualInfoFS
-
Creates a new Mutual Information feature selection object.
- MutualInfoFS(ClassificationDataSet, int, MutualInfoFS.NumericalHandeling) - Constructor for class jsat.datatransform.featureselection.MutualInfoFS
-
Creates a new Mutual Information feature selection object.
- MutualInfoFS.NumericalHandeling - Enum in jsat.datatransform.featureselection
-
The definition for mutual information for continuous attributes requires
an integration of an unknown function, as such requires some form of
approximation.
- S - Variable in class jsat.classifiers.linear.kernelized.DUOL
-
Set of support vectors
- sameDimensions(Matrix, Matrix) - Static method in class jsat.linear.Matrix
-
Convenience method that will return true only if the two input
matrices have the exact same dimensions.
- SAMME - Class in jsat.classifiers.boosting
-
This is an implementation of the Multi-Class AdaBoost method SAMME (Stagewise Additive Modeling using
a Multi-Class Exponential loss function), presented in
Multi-class AdaBoost by Ji Zhu,
Saharon Rosset, Hui Zou,&Trevor Hasstie
This algorithm reduces to
AdaBoostM1 for binary classification problems.
- SAMME(Classifier, int) - Constructor for class jsat.classifiers.boosting.SAMME
-
- sample(int, Random) - Method in class jsat.clustering.EMGaussianMixture
-
- sample(int, Random) - Method in class jsat.distributions.discrete.Poisson
-
- sample(int, Random) - Method in class jsat.distributions.Distribution
-
This method returns a double array containing the values of random samples from this distribution.
- sample(int, Random) - Method in class jsat.distributions.multivariate.Dirichlet
-
- sample(int, Random) - Method in class jsat.distributions.multivariate.MetricKDE
-
Sampling not yet supported
- sample(int, Random) - Method in interface jsat.distributions.multivariate.MultivariateDistribution
-
Performs sampling on the current distribution.
- sample(int, Random) - Method in class jsat.distributions.multivariate.NormalM
-
- sample(int, Random) - Method in class jsat.distributions.multivariate.ProductKDE
-
- sample(int, Random) - Method in class jsat.distributions.multivariate.SymmetricDirichlet
-
- sampleBasisVectors(KernelTrick, DataSet, List<Vec>, Nystrom.SamplingMethod, int, boolean, Random) - Static method in class jsat.datatransform.kernel.Nystrom
-
Performs sampling of a data set for a subset of the vectors that make a
good set of basis vectors for forming an approximation of a full kernel
space.
- sampleCorCoeff(Vec, Vec) - Static method in class jsat.math.DescriptiveStatistics
-
Computes the sample correlation coefficient for two data sets X and Y.
- sampleVec(int, Random) - Method in class jsat.distributions.Distribution
-
This method returns a double array containing the values of random samples from this distribution.
- sampleWithReplacement(int[], int, Random) - Static method in class jsat.classifiers.boosting.Bagging
-
Performs the sampling based on the number of data points, storing the
counts in an array to be constructed from XXXX
- saveCentroidDistance - Variable in class jsat.clustering.kmeans.KMeans
-
Indicates whether or not the distance between a datapoint and its nearest
centroid should be saved after clustering.
- SBP - Class in jsat.classifiers.svm
-
Implementation of the Stochastic Batch Perceptron (SBP) algorithm.
- SBP(KernelTrick, SupportVectorLearner.CacheMode, int, double) - Constructor for class jsat.classifiers.svm.SBP
-
Creates a new SBP SVM learner
- SBP(SBP) - Constructor for class jsat.classifiers.svm.SBP
-
Copy constructor
- SBS - Class in jsat.datatransform.featureselection
-
Sequential Backward Selection (SBS) is a greedy method of selecting a subset
of features to use for prediction.
- SBS(int, int, Classifier, double) - Constructor for class jsat.datatransform.featureselection.SBS
-
Performs SBS feature selection for a classification problem
- SBS(int, int, ClassificationDataSet, Classifier, int, double) - Constructor for class jsat.datatransform.featureselection.SBS
-
Performs SBS feature selection for a classification problem
- SBS(int, int, Regressor, double) - Constructor for class jsat.datatransform.featureselection.SBS
-
Performs SBS feature selection for a regression problem
- SBS(int, int, RegressionDataSet, Regressor, int, double) - Constructor for class jsat.datatransform.featureselection.SBS
-
Performs SBS feature selection for a regression problem
- SBSRemoveFeature(Set<Integer>, DataSet, Set<Integer>, Set<Integer>, Set<Integer>, Set<Integer>, Object, int, Random, int, double[], double) - Static method in class jsat.datatransform.featureselection.SBS
-
Attempts to remove one feature from the list while maintaining its
accuracy
- scaleBandwidth(double) - Method in class jsat.distributions.multivariate.MetricKDE
-
- scaleBandwidth(double) - Method in class jsat.distributions.multivariate.MultivariateKDE
-
A caller may want to increase or decrease the bandwidth after training
has been completed to get smoother model, or decrease it to observe
behavior.
- scaleBandwidth(double) - Method in class jsat.distributions.multivariate.ProductKDE
-
- ScaledVector - Class in jsat.linear
-
A wrapper for a vector that represents the vector multiplied by a scalar
constant.
- ScaledVector(double, Vec) - Constructor for class jsat.linear.ScaledVector
-
Creates a new scaled vector
- ScaledVector(Vec) - Constructor for class jsat.linear.ScaledVector
-
Creates a new scaled vector with a default scale of 1.
- SCD - Class in jsat.classifiers.linear
-
Implementation of Stochastic Coordinate Descent for L1 regularized
classification and regression.
- SCD(LossFunc, double, int) - Constructor for class jsat.classifiers.linear.SCD
-
Creates anew SCD learner
- SCD(SCD) - Constructor for class jsat.classifiers.linear.SCD
-
Copy constructor
- SCW - Class in jsat.classifiers.linear
-
Provides an Implementation of Confidence-Weighted (CW) learning and Soft
Confidence-Weighted (SCW), both of which are binary linear classifiers
inspired by
PassiveAggressive.
- SCW() - Constructor for class jsat.classifiers.linear.SCW
-
Creates a new SCW learner
- SCW(double, SCW.Mode, boolean) - Constructor for class jsat.classifiers.linear.SCW
-
Creates a new SCW learner
- SCW(SCW) - Constructor for class jsat.classifiers.linear.SCW
-
Copy constructor
- SCW.Mode - Enum in jsat.classifiers.linear
-
Which version of the algorithms shuld be used
- search(Vec, double) - Method in class jsat.linear.vectorcollection.CoverTree
-
- search(Vec, int) - Method in class jsat.linear.vectorcollection.CoverTree
-
- search(Vec, double) - Method in class jsat.linear.vectorcollection.EuclideanCollection
-
- search(Vec, int) - Method in class jsat.linear.vectorcollection.EuclideanCollection
-
- search(Vec, int) - Method in class jsat.linear.vectorcollection.KDTree
-
- search(Vec, double) - Method in class jsat.linear.vectorcollection.KDTree
-
- search(Vec, double) - Method in class jsat.linear.vectorcollection.lsh.RandomProjectionLSH
-
- search(Vec, int) - Method in class jsat.linear.vectorcollection.lsh.RandomProjectionLSH
-
- search(Vec, double) - Method in class jsat.linear.vectorcollection.RandomBallCover
-
- search(Vec, int) - Method in class jsat.linear.vectorcollection.RandomBallCover
-
- search(Vec, double) - Method in class jsat.linear.vectorcollection.RandomBallCoverOneShot
-
- search(Vec, int) - Method in class jsat.linear.vectorcollection.RandomBallCoverOneShot
-
- search(Vec, double) - Method in class jsat.linear.vectorcollection.RTree
-
- search(Vec, int) - Method in class jsat.linear.vectorcollection.RTree
-
- search(Vec, double) - Method in class jsat.linear.vectorcollection.VectorArray
-
- search(Vec, int) - Method in class jsat.linear.vectorcollection.VectorArray
-
- search(Vec, double) - Method in interface jsat.linear.vectorcollection.VectorCollection
-
Searches the space for all vectors that are within a given range of the query vector.
- search(Vec, int) - Method in interface jsat.linear.vectorcollection.VectorCollection
-
Searches the space for the k neighbors that are closest to the given query vector
- search(Vec, double) - Method in class jsat.linear.vectorcollection.VPTree
-
- search(Vec, int) - Method in class jsat.linear.vectorcollection.VPTree
-
- searchParams - Variable in class jsat.parameters.ModelSearch
-
The list of parameters we will search for, currently only Int and Double
params should be used
- searchR(Vec) - Method in class jsat.linear.vectorcollection.lsh.E2LSH
-
Performs a search for points within the set
radius
of the query point.
- searchR(Vec, boolean) - Method in class jsat.linear.vectorcollection.lsh.E2LSH
-
Performs a search for points within the set
radius
of the query point.
- Secant - Class in jsat.math.rootfinding
-
- Secant() - Constructor for class jsat.math.rootfinding.Secant
-
- sech(double) - Static method in class jsat.math.TrigMath
-
- seedSelection - Variable in class jsat.clustering.kmeans.KMeans
-
- seedSelection - Variable in class jsat.clustering.PAM
-
- SeedSelectionMethods - Class in jsat.clustering
-
This class provides methods for sampling a data set for a set of initial points to act as the seeds for a clustering algorithm.
- SeedSelectionMethods.SeedSelection - Enum in jsat.clustering
-
- selectIntialPoints(DataSet, int, DistanceMetric, Random, SeedSelectionMethods.SeedSelection) - Static method in class jsat.clustering.SeedSelectionMethods
-
Selects seeds from a data set to use for a clustering algorithm.
- selectIntialPoints(DataSet, int, DistanceMetric, List<Double>, Random, SeedSelectionMethods.SeedSelection) - Static method in class jsat.clustering.SeedSelectionMethods
-
- selectIntialPoints(DataSet, int, DistanceMetric, Random, SeedSelectionMethods.SeedSelection, ExecutorService) - Static method in class jsat.clustering.SeedSelectionMethods
-
Selects seeds from a data set to use for a clustering algorithm.
- selectIntialPoints(DataSet, int, DistanceMetric, List<Double>, Random, SeedSelectionMethods.SeedSelection, ExecutorService) - Static method in class jsat.clustering.SeedSelectionMethods
-
Selects seeds from a data set to use for a clustering algorithm.
- selectIntialPoints(DataSet, int[], DistanceMetric, Random, SeedSelectionMethods.SeedSelection) - Static method in class jsat.clustering.SeedSelectionMethods
-
Selects seeds from a data set to use for a clustering algorithm.
- selectIntialPoints(DataSet, int[], DistanceMetric, List<Double>, Random, SeedSelectionMethods.SeedSelection) - Static method in class jsat.clustering.SeedSelectionMethods
-
Selects seeds from a data set to use for a clustering algorithm.
- selectIntialPoints(DataSet, int[], DistanceMetric, Random, SeedSelectionMethods.SeedSelection, ExecutorService) - Static method in class jsat.clustering.SeedSelectionMethods
-
Selects seeds from a data set to use for a clustering algorithm.
- selectIntialPoints(DataSet, int[], DistanceMetric, List<Double>, Random, SeedSelectionMethods.SeedSelection, ExecutorService) - Static method in class jsat.clustering.SeedSelectionMethods
-
Selects seeds from a data set to use for a clustering algorithm.
- selfK - Variable in class jsat.clustering.kmeans.KernelKMeans
-
- set(int, double) - Method in class jsat.linear.ConcatenatedVec
-
- set(int, double) - Method in class jsat.linear.ConstantVector
-
- set(int, int, double) - Method in class jsat.linear.DenseMatrix
-
- set(int, double) - Method in class jsat.linear.DenseVector
-
- set(int, int, double) - Method in class jsat.linear.Matrix
-
Sets the value stored at at the matrix position Ai,j
- set(int, int, double) - Method in class jsat.linear.MatrixOfVecs
-
- set(int, double) - Method in class jsat.linear.Poly2Vec
-
- set(int, int, double) - Method in class jsat.linear.RandomMatrix
-
- set(int, double) - Method in class jsat.linear.RandomVector
-
- set(int, double) - Method in class jsat.linear.ScaledVector
-
- set(int, double) - Method in class jsat.linear.ShiftedVec
-
- set(int, int, double) - Method in class jsat.linear.SparseMatrix
-
- set(int, double) - Method in class jsat.linear.SparseVector
-
- set(int, int, double) - Method in class jsat.linear.SubMatrix
-
- set(int, double) - Method in class jsat.linear.SubVector
-
- set(int, int, double) - Method in class jsat.linear.TransposeView
-
- set(int, double) - Method in class jsat.linear.Vec
-
Sets the value stored at a specified index in the vector
- set(int, double) - Method in class jsat.linear.VecPaired
-
- set(int, double) - Method in class jsat.linear.VecWithNorm
-
- set(double) - Method in class jsat.utils.concurrent.AtomicDouble
-
- set(int, double) - Method in class jsat.utils.concurrent.AtomicDoubleArray
-
Sets the element at position i to the given value.
- set(int, double) - Method in class jsat.utils.DoubleList
-
- set(int, Double) - Method in class jsat.utils.DoubleList
-
- set(int, int) - Method in class jsat.utils.IntList
-
- set(int, Integer) - Method in class jsat.utils.IntList
-
- set(int, long) - Method in class jsat.utils.LongList
-
- set(int, Long) - Method in class jsat.utils.LongList
-
- set(int, E) - Method in class jsat.utils.SimpleList
-
- setActivationFunction(BackPropagationNet.ActivationFunction) - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
Sets the activation function used for the network
- setAggressive(boolean) - Method in class jsat.classifiers.linear.ROMMA
-
Determines whether the normal or aggressive ROMMA algorithm will be used.
- setAlpha(double) - Method in class jsat.classifiers.linear.ALMA2
-
Alpha controls the approximation of the large margin formed by ALMA,
with larger values causing more updates.
- setAlpha(double) - Method in class jsat.classifiers.linear.kernelized.ALMA2K
-
Alpha controls the approximation of the large margin formed by ALMA,
with larger values causing more updates.
- setAlpha(double) - Method in class jsat.classifiers.linear.NewGLMNET
-
Using α = 1 corresponds to pure L1 regularization, and
α = 0 corresponds to pure L2 regularization.
- setAlpha(double) - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Sets the extra parameter alpha.
- setAlpha(double) - Method in class jsat.classifiers.neuralnetwork.RBFNet
-
Sets the alpha parameter.
- setAlpha(double) - Method in class jsat.clustering.LSDBC
-
Sets the scale value that will control how many points are added to a
cluster.
- setAlpha(double) - Method in class jsat.datatransform.visualization.TSNE
-
α is the "early exaggeration" constant.
- setAlpha(double) - Method in class jsat.distributions.kernels.PolynomialKernel
-
Sets the scaling factor for the dot product, this is equivalent to
multiplying each value in the data set by a constant factor
- setAlpha(double) - Method in class jsat.distributions.kernels.SigmoidKernel
-
Sets the scaling factor for the dot product, this is equivalent to
multiplying each value in the data set by a constant factor
- setAlpha(double) - Method in class jsat.distributions.multivariate.SymmetricDirichlet
-
Sets the alpha value used for the distribution
- setAlpha(double) - Method in class jsat.distributions.Pareto
-
- setAlpha(double) - Method in class jsat.distributions.Weibull
-
- setAlpha(double) - Method in class jsat.math.decayrates.InverseDecay
-
Controls the scaling of the divisor, increasing α dampens the
whole range of values.
- setAlpha(double) - Method in class jsat.math.decayrates.PowerDecay
-
Controls the scaling via exponentiation, increasing α increases the
rate at which the rate decays.
- setAlpha(double) - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
Sets the prior for the on weight vector theta.
- setAlphas(double[]) - Method in class jsat.classifiers.svm.SupportVectorLearner
-
Sets the final set of alphas, and indicates that the final accelerating
structures (if available) should be constructed for performing kernel
evaluations against unseen vectors.
- setAlphas(Vec) - Method in class jsat.distributions.multivariate.Dirichlet
-
Sets the alphas of the distribution.
- setAltHypothesis(StatisticTest.H1) - Method in class jsat.testing.onesample.TTest
-
- setAltHypothesis(StatisticTest.H1) - Method in class jsat.testing.onesample.ZTest
-
- setAltHypothesis(StatisticTest.H1) - Method in interface jsat.testing.StatisticTest
-
- setAltVar(double) - Method in interface jsat.testing.onesample.OneSampleTest
-
- setAltVar(double) - Method in class jsat.testing.onesample.TTest
-
- setAltVar(double) - Method in class jsat.testing.onesample.ZTest
-
- setAutoSetRegularization(boolean) - Method in class jsat.classifiers.linear.BBR
-
Sets whether or not the regularization term will be set automatically by
the algorithm, which is done as specified in the original paper.
- setAveraged(boolean) - Method in class jsat.classifiers.linear.kernelized.ALMA2K
-
ALMA2K supports taking the averaged output of all previous hypothesis
weighted by the number of successful uses of the hypothesis during
training.
- setB(double) - Method in class jsat.classifiers.linear.ALMA2
-
- setB(double) - Method in class jsat.classifiers.linear.kernelized.ALMA2K
-
- setB(double) - Method in class jsat.distributions.Laplace
-
- setBandwith(double) - Method in class jsat.distributions.empirical.KernelDensityEstimator
-
Sets the bandwidth used for smoothing.
- setBandwith(double) - Method in class jsat.distributions.multivariate.MetricKDE
-
Sets the bandwidth used to estimate the density of the underlying distribution.
- setBase(Vec) - Method in class jsat.linear.Poly2Vec
-
Creates a new vector that implicitly represents the degree 2 polynomial
of the base vector.
- setBaseData(Vec) - Method in class jsat.testing.goodnessoffit.KSTest
-
Change the original sample to v
- setBasisSamplingMethod(Nystrom.SamplingMethod) - Method in class jsat.datatransform.kernel.KernelPCA
-
Sets the method of selecting the basis vectors
- setBasisSamplingMethod(Nystrom.SamplingMethod) - Method in class jsat.datatransform.kernel.Nystrom
-
Sets the method of selecting the basis vectors
- setBasisSize(int) - Method in class jsat.datatransform.kernel.KernelPCA
-
Sets the basis size for the Kernel PCA to be learned from.
- setBasisSize(int) - Method in class jsat.datatransform.kernel.Nystrom
-
Sets the basis size for the Kernel PCA to be learned from.
- setBatchSize(int) - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
Sets the batch size use to estimate the gradient of the error for
training
- setBatchSize(int) - Method in class jsat.classifiers.neuralnetwork.DReDNetSimple
-
Sets the batch size for updates
- setBatchSize(int) - Method in class jsat.classifiers.svm.Pegasos
-
Sets the batch size used during training.
- setBatchSize(int) - Method in class jsat.clustering.kmeans.MiniBatchKMeans
-
Sets the batch size to use at each iteration.
- setBatchSize(int) - Method in class jsat.regression.StochasticRidgeRegression
-
Sets the batch size to learn from.
- setBeta(double) - Method in class jsat.distributions.Weibull
-
- setBeta(double) - Method in class jsat.math.optimization.ModifiedOWLQN
-
Sets the shrinkage term used for the line search.
- setBiasInit(BiastInitializer) - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
Sets the method to use when initializing neuron bias values
- setBinaryCategoricalSplitting(boolean) - Method in class jsat.classifiers.trees.ExtraTree
-
The normal implementation of ExtraTree always produces binary splits,
including for categorical features.
- setBudget(int) - Method in class jsat.classifiers.linear.kernelized.BOGD
-
Sets the budget for support vectors
- setBudget(int) - Method in class jsat.classifiers.linear.kernelized.Forgetron
-
Sets the new budget, which is the maximum number of data points the
Forgetron can use to form its decision boundary.
- setBudgetSize(int) - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
Sets the maximum budget size, or number of support vectors, to allow
during training.
- setBudgetStrategy(KernelPoint.BudgetStrategy) - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
Sets the budget maintenance strategy.
- setBudgetStrategy(KernelPoint.BudgetStrategy) - Method in class jsat.distributions.kernels.KernelPoint
-
Sets the method used for maintaining the budget of support vectors.
- setBudgetStrategy(KernelPoint.BudgetStrategy) - Method in class jsat.distributions.kernels.KernelPoints
-
Sets the method used for maintaining the budget of support vectors.
- setBurnIn(int) - Method in class jsat.classifiers.linear.kernelized.OSKL
-
Sets the number of update calls to consider as part of the "burn in"
phase.
- setBurnIn(double) - Method in class jsat.classifiers.svm.SBP
-
Sets the burn in fraction.
- setC(double) - Method in class jsat.classifiers.linear.ALMA2
-
Sets the C value of the ALMA algorithm.
- setC(double) - Method in class jsat.classifiers.linear.kernelized.ALMA2K
-
Sets the C value of the ALMA algorithm.
- setC(double) - Method in class jsat.classifiers.linear.kernelized.DUOL
-
Sets the aggressiveness parameter.
- setC(double) - Method in class jsat.classifiers.linear.LogisticRegressionDCD
-
Sets the regularization trade-off term.
- setC(double) - Method in class jsat.classifiers.linear.NewGLMNET
-
Sets the regularization term, where smaller values indicate a larger
regularization penalty.
- setC(double) - Method in class jsat.classifiers.linear.NHERD
-
Set the aggressiveness parameter.
- setC(double) - Method in class jsat.classifiers.linear.PassiveAggressive
-
Set the aggressiveness parameter.
- setC(double) - Method in class jsat.classifiers.linear.SCW
-
Set the aggressiveness parameter.
- setC(double) - Method in class jsat.classifiers.linear.SPA
-
Set the aggressiveness parameter.
- setC(double) - Method in class jsat.classifiers.svm.DCD
-
Sets the penalty parameter for misclassifications.
- setC(double) - Method in class jsat.classifiers.svm.DCDs
-
Sets the penalty parameter for misclassifications.
- setC(double) - Method in class jsat.classifiers.svm.DCSVM
-
Sets the complexity parameter of SVM.
- setC(double) - Method in class jsat.classifiers.svm.extended.OnlineAMM
-
Sets the pruning constant which controls how powerful pruning is when
pruning occurs.
- setC(double) - Method in class jsat.classifiers.svm.LSSVM
-
Sets the regularization constant when training.
- setC(double) - Method in class jsat.classifiers.svm.PlattSMO
-
Sets the complexity parameter of SVM.
- setC(double) - Method in class jsat.classifiers.svm.SVMnoBias
-
Sets the complexity parameter of SVM.
- setC(int) - Method in class jsat.datatransform.FastICA
-
Sets the number of base components to learn
- setC(double) - Method in class jsat.distributions.kernels.LinearKernel
-
The positive bias term added to the result of the dot product
- setC(double) - Method in class jsat.distributions.kernels.PolynomialKernel
-
Sets the additive term, when set to one this is equivalent to adding a
bias term of 1 to each vector.
- setC(double) - Method in class jsat.distributions.kernels.RationalQuadraticKernel
-
Sets the positive additive coefficient
- setC(double) - Method in class jsat.distributions.kernels.SigmoidKernel
-
Sets the additive term, when set to one this is equivalent to adding a
bias term of 1 to each vector.
- setC1(double) - Method in class jsat.math.optimization.BacktrackingArmijoLineSearch
-
Sets the constant used for the sufficient decrease condition
f(x+α p) ≤ f(x) + c1 α pT∇f(x)
- setC1(double) - Method in class jsat.math.optimization.WolfeNWLineSearch
-
Sets the constant used for the
sufficient decrease condition
f(x+α p) ≤ f(x) + c
1 α p
T∇f(x)
This value must always be less than
WolfeNWLineSearch.setC2(double)
- setC2(double) - Method in class jsat.math.optimization.WolfeNWLineSearch
-
Sets the constant used for the curvature condition
pT ∇f(x+α p) ≥ c2 pT∇f(x)
- setCacheMode(SupportVectorLearner.CacheMode) - Method in class jsat.classifiers.svm.SupportVectorLearner
-
Calling this sets the method of caching that will be used.
- setCacheSize(long, long) - Method in class jsat.classifiers.svm.SupportVectorLearner
-
Sets the
cache value to one that will use the
specified amount of memory.
- setCacheValue(int) - Method in class jsat.classifiers.svm.SupportVectorLearner
-
Sets the cache value, which may be interpreted differently by different
caching schemes.
- setCalibrationFolds(int) - Method in class jsat.classifiers.calibration.BinaryCalibration
-
- setCalibrationHoldOut(double) - Method in class jsat.classifiers.calibration.BinaryCalibration
-
- setCalibrationMode(BinaryCalibration.CalibrationMode) - Method in class jsat.classifiers.calibration.BinaryCalibration
-
Sets which calibration mode will be used during training
- setCategoryName(String) - Method in class jsat.classifiers.CategoricalData
-
- setCIsomap(boolean) - Method in class jsat.datatransform.visualization.Isomap
-
Controls whether the C-Isomap extension is used.
- setClassBudget(int) - Method in class jsat.classifiers.svm.extended.OnlineAMM
-
When given bad parameters there is the possibility for unbounded growth
in the number of hyperplanes used.
- setClassificationTargetScore(ClassificationScore) - Method in class jsat.parameters.ModelSearch
-
Sets the score to attempt to optimize when performing grid search on a
classification problem.
- setClipping(boolean) - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Sets whether or not the clip changes in coefficient values caused by
regularization so that they can not make the coefficients go from
positive to negative or negative to positive.
- setClusterSampleSize(int) - Method in class jsat.classifiers.svm.DCSVM
-
At each level of the DC-SVM training, a clustering algorithm is used to
divide the dataset into sub-groups for independent training.
- setCoefficient(double) - Method in class jsat.classifiers.boosting.ArcX4
-
Weights are updated as 1+coef*errorsexpo.
- setConcurrentTraining(boolean) - Method in class jsat.classifiers.OneVSAll
-
- setConcurrentTraining(boolean) - Method in class jsat.classifiers.OneVSOne
-
Controls whether or not training of the several classifiers occurs concurrently or sequentually.
- setConstant(double) - Method in class jsat.classifiers.neuralnetwork.initializers.ConstantInit
-
- setConstant(double) - Method in class jsat.linear.ConstantVector
-
Sets the constant value that will be used as the value stored in every
index of this vector.
- setContraction(double) - Method in class jsat.math.optimization.NelderMead
-
Sets the contraction constant, which must be in the range (0, 1)
- setCovariance(Matrix) - Method in class jsat.distributions.multivariate.NormalM
-
Sets the covariance matrix for this matrix.
- setCovMode(NHERD.CovMode) - Method in class jsat.classifiers.linear.NHERD
-
Sets the way in which the covariance matrix is formed.
- setD(int) - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
Sets the approximate number of documents that will be observed
- setDataPoint(int, DataPoint) - Method in class jsat.classifiers.ClassificationDataSet
-
- setDataPoint(DataPoint) - Method in class jsat.classifiers.DataPointPair
-
- setDataPoint(int, DataPoint) - Method in class jsat.DataSet
-
Replaces an already existing data point with the one given.
- setDataPoint(int, DataPoint) - Method in class jsat.regression.RegressionDataSet
-
- setDataPoint(int, DataPoint) - Method in class jsat.SimpleDataSet
-
- setDataTransformProcess(DataTransformProcess) - Method in class jsat.classifiers.ClassificationModelEvaluation
-
Sets the data transform process to use when performing cross validation.
- setDataTransformProcess(DataTransformProcess) - Method in class jsat.regression.RegressionModelEvaluation
-
Sets the data transform process to use when performing cross validation.
- setDefaultK(int) - Method in class jsat.distributions.multivariate.MetricKDE
-
When estimating the bandwidth, the mean of the k'th nearest neighbors to each data point
is used.
- setDefaultStndDev(double) - Method in class jsat.distributions.multivariate.MetricKDE
-
When estimating the bandwidth, the mean of the neighbor distances is used, and a multiple of
the standard deviations is added.
- setDegree(int) - Method in class jsat.datatransform.PolynomialTransform
-
Sets the degree of the polynomial to transform the input vector into
- setDegree(double) - Method in class jsat.distributions.kernels.PolynomialKernel
-
Sets the degree of the polynomial
- setDelta(double) - Method in class jsat.driftdetectors.ADWIN
-
Sets the upper bound on the false positive rate for detecting concept
drifts
- setDf(double) - Method in class jsat.distributions.StudentT
-
Sets the degrees of freedom used by the test.
- setDiagonalOnly(boolean) - Method in class jsat.classifiers.linear.AROW
-
Using the full covariance matrix requires O(d2) work on
mistakes, where d is the dimension of the data.
- setDiagonalOnly(boolean) - Method in class jsat.classifiers.linear.SCW
-
Using the full covariance matrix requires O(d2) work on
updates, where d is the dimension of the data.
- setDimension(int) - Method in class jsat.datatransform.kernel.Nystrom
-
Sets the dimension of the new feature space, which is the number of
principal components to select from the kernelized feature space.
- setDimension(int) - Method in class jsat.distributions.multivariate.SymmetricDirichlet
-
Sets the dimension size of the distribution
- setDimensions(int) - Method in class jsat.datatransform.kernel.KernelPCA
-
Sets the dimension of the new feature space, which is the number of
principal components to select from the kernelized feature space.
- setDimensions(int) - Method in class jsat.datatransform.kernel.RFF_RBF
-
Sets the number of dimensions in the new approximate space to use.
- setDimensions(int) - Method in class jsat.datatransform.WhitenedPCA
-
Sets the number of dimensions to project down to
- setDistance(double[][], int, int, double) - Static method in class jsat.clustering.dissimilarity.AbstractClusterDissimilarity
-
A convenience method.
- setDistanceMetric(DistanceMetric) - Method in class jsat.classifiers.knn.LWL
-
Sets the distance metric that will be used for the nearest neighbor search
- setDistanceMetric(DistanceMetric) - Method in class jsat.classifiers.knn.NearestNeighbour
-
- setDistanceMetric(DistanceMetric) - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Sets the distance used for learning
- setDistanceMetric(DistanceMetric) - Method in class jsat.classifiers.neuralnetwork.RBFNet
-
Sets the distance metric used to determine neuron activations.
- setDistanceMetric(DistanceMetric) - Method in class jsat.clustering.evaluation.intra.SumOfSqrdPairwiseDistances
-
Sets the distance metric to be used whenever this object is called to
evaluate a cluster
- setDistanceMetric(DistanceMetric) - Method in class jsat.clustering.FLAME
-
Sets the distance metric to use for the nearest neighbor search
- setDistanceMetric(DistanceMetric) - Method in class jsat.clustering.GapStatistic
-
Sets the distance metric to use when evaluating a clustering algorithm
- setDistanceMetric(DistanceMetric) - Method in class jsat.clustering.kmeans.MiniBatchKMeans
-
Sets the distance metric used for determining the nearest cluster center
- setDistanceMetric(DistanceMetric) - Method in class jsat.clustering.LSDBC
-
Sets the distance metric used when performing clustering.
- setDistanceMetric(DistanceMetric) - Method in class jsat.clustering.OPTICS
-
Sets the distance metric used to compute distances in the algorithm.
- setDistanceMetric(DistanceMetric) - Method in class jsat.datatransform.featureselection.ReliefF
-
Sets the distance metric to infer the feature importance with
- setDistanceMetric(DistanceMetric) - Method in class jsat.distributions.multivariate.MetricKDE
-
Sets the distance metric that is used for density estimation
- setDistanceMetric(DistanceMetric) - Method in class jsat.linear.vectorcollection.VectorArray
-
- setDistanceMetricEmbedding(DistanceMetric) - Method in class jsat.datatransform.visualization.LargeViz
-
Sets the distance metric to use for the embedded space.
- setDistanceMetrics(DistanceMetric) - Method in class jsat.clustering.HDBSCAN
-
Sets the distance metric to use for determining closeness between data points
- setDistanceMetricSource(DistanceMetric) - Method in class jsat.datatransform.visualization.LargeViz
-
Sets the distance metric to use for the original space.
- setDistribution(ContinuousDistribution) - Method in class jsat.classifiers.boosting.Wagging
-
Sets the distribution to select the random weights from
- setDistribution(ContinuousDistribution) - Method in class jsat.classifiers.boosting.WaggingNormal
-
- setDriftThreshold(double) - Method in class jsat.driftdetectors.DDM
-
Sets the multiplier on the standard deviation that must be exceeded to
recognize the change as a drift.
- setDropoutHidden(double) - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
Sets the probability of dropping a value from the hidden layer
- setDropoutInput(double) - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
Sets the probability of dropping a value from the input layer
- setEmbeddingMetric(DistanceMetric) - Method in class jsat.datatransform.visualization.MDS
-
Sets the distance metric to use when creating the initial dissimilarity
matrix of a new dataset.
- setEndLevel(int) - Method in class jsat.classifiers.svm.DCSVM
-
The DC-SVM algorithm works by creating a hierarchy of levels, and
iteratively refining the solution from one level to the next.
- setEntropyThreshold(double) - Method in class jsat.classifiers.svm.extended.CPM
-
Sets the entropy threshold used for training.
- setEpochs(int) - Method in class jsat.classifiers.BaseUpdateableClassifier
-
Sets the number of whole iterations through the training set that will be
performed for training
- setEpochs(int) - Method in class jsat.classifiers.linear.kernelized.CSKLRBatch
-
Sets the number of training epochs (passes) through the data set
- setEpochs(int) - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
Sets the number of iterations of the training set done during batch
training
- setEpochs(int) - Method in class jsat.classifiers.linear.PassiveAggressive
-
Sets the number of whole iterations through the training set that will be
performed for training
- setEpochs(int) - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Sets the maximum number of epochs that occur in each iteration.
- setEpochs(int) - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
Sets the number of iterations of training that will be performed.
- setEpochs(int) - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
Sets the number of epochs of training used.
- setEpochs(int) - Method in class jsat.classifiers.neuralnetwork.DReDNetSimple
-
Sets the number of epochs to perform
- setEpochs(int) - Method in class jsat.classifiers.svm.extended.CPM
-
Sets the number of whole iterations through the training set that will be
performed for training
- setEpochs(int) - Method in class jsat.classifiers.svm.Pegasos
-
Sets the number of iterations through the training set that will be
performed.
- setEpochs(int) - Method in class jsat.regression.BaseUpdateableRegressor
-
Sets the number of whole iterations through the training set that will be
performed for training
- setEpochs(int) - Method in class jsat.regression.StochasticRidgeRegression
-
Sets the number of iterations through the whole training set that will be
performed.
- setEpochs(int) - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
Sets the number of training epochs when learning in a "batch" setting
- setEps(double) - Method in class jsat.classifiers.linear.PassiveAggressive
-
Sets the range for numerical prediction.
- setEps(double) - Method in class jsat.classifiers.svm.DCD
-
Sets the eps used in the epsilon insensitive loss function used
when performing regression.
- setEps(double) - Method in class jsat.classifiers.svm.DCDs
-
Sets the eps used in the epsilon insensitive loss function used
when performing regression.
- setEps(double) - Method in class jsat.clustering.FLAME
-
Sets the convergence goal for the minimum difference in score between
rounds.
- setEps(double) - Method in class jsat.math.optimization.ModifiedOWLQN
-
Sets the epsilon term that helps control when the gradient descent step
is taken instead of the normal Quasi-Newton step.
- setEpsilon(double) - Method in class jsat.classifiers.knn.DANN
-
Sets the regularization to apply the the diagonal of the scatter matrix
when creating each new metric.
- setEpsilon(double) - Method in class jsat.classifiers.svm.PlattSMO
-
Sets the epsilon for the epsilon insensitive loss when performing
regression.
- setEpsilonDistance(double) - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Sets the epsilon multiplier that controls the maximum distance two
learning vectors can be from each other in order to be updated at the
same time.
- setErrorTolerance(double) - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
- setErrorTolerance(double) - Method in class jsat.distributions.kernels.KernelPoint
-
- setErrorTolerance(double) - Method in class jsat.distributions.kernels.KernelPoints
-
- setErrorTolerance(double) - Method in class jsat.regression.KernelRLS
-
Sets the tolerance for errors in approximating a data point by projecting
it onto the set of basis vectors.
- setEta(double) - Method in class jsat.classifiers.linear.kernelized.BOGD
-
Sets the learning rate to use for training.
- setEta(double) - Method in class jsat.classifiers.linear.kernelized.CSKLR
-
Sets the learning rate to use for the algorithm.
- setEta(double) - Method in class jsat.classifiers.linear.kernelized.CSKLRBatch
-
Sets the learning rate to use for the algorithm.
- setEta(double) - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
Sets the base learning rate to start from.
- setEta(double) - Method in class jsat.classifiers.linear.kernelized.OSKL
-
Sets the learning rate to use for training.
- setEta(double) - Method in class jsat.classifiers.linear.kernelized.Projectron
-
Sets the η parameter which controls the sparsity of the Projection
solution.
- setEta(double) - Method in class jsat.classifiers.linear.LinearSGD
-
Sets the initial learning rate η to use.
- setEta(double) - Method in class jsat.classifiers.linear.SCW
-
SCW uses a probabilistic version of the margin and attempts to make a
correction so that the confidence with correct label would be of a
certain threshold, which is set by eta.
- setEta(double) - Method in class jsat.classifiers.linear.SMIDAS
-
Sets the learning rate used during training
- setEta(double) - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
Sets the base global learning rate.
- setEta(double) - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
Prior on topics.
- setEtaDecay(DecayRate) - Method in class jsat.classifiers.linear.LinearSGD
-
Sets the rate at which
η is decayed at each
update.
- setEtaDecay(DecayRate) - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
Sets the decay rate on the global learning rate over time
- setExpansion(double) - Method in class jsat.math.optimization.NelderMead
-
Sets the expansion constant, which must be greater than 1 and the reflection constant
- setExponent(double) - Method in class jsat.classifiers.boosting.ArcX4
-
Weights are updated as 1+coef*errorsexpo.
- setExtractionMethod(OPTICS.ExtractionMethod) - Method in class jsat.clustering.OPTICS
-
Sets the method used to extract clusters from the reachability plot.
- setExtraSamples(int) - Method in class jsat.classifiers.boosting.Bagging
-
Bagging samples from the training set with replacement, and draws a sampleWithReplacement at least as large
as the training set.
- setExtraSamples(int) - Method in class jsat.classifiers.trees.RandomForest
-
RandomForest performs Bagging.
- setFeatureCount(int) - Method in class jsat.datatransform.featureselection.BDS
-
Sets the number of features to select for use from the set of all input
features
- setFeatureCount(int) - Method in class jsat.datatransform.featureselection.MutualInfoFS
-
Sets the number of features to select
- setFeatureCount(int) - Method in class jsat.datatransform.featureselection.ReliefF
-
Sets the number of features to select for use from the set of all input
features
- setFeatureSamples(int) - Method in class jsat.classifiers.trees.RandomForest
-
Instead of using a heuristic, the exact number of features to sample is provided.
- setFeaturesToAdd(int) - Method in class jsat.datatransform.featureselection.LRS
-
Sets the number of features to add (the L parameter).
- setFeaturesToRemove(int) - Method in class jsat.datatransform.featureselection.LRS
-
Sets the number of features to remove (the R parameter).
- setFinalizeAfterTraining(boolean) - Method in class jsat.classifiers.bayesian.MultinomialNaiveBayes
-
- setFirstItem(X) - Method in class jsat.utils.Pair
-
- setFolds(int) - Method in class jsat.classifiers.boosting.Stacking
-
Sets the number of folds of cross validation to use when creating the new
set of weights that will be feed into the aggregating model.
- setFolds(int) - Method in class jsat.datatransform.featureselection.BDS
-
Sets the number of folds to use for cross validation when estimating the error rate
- setFolds(int) - Method in class jsat.datatransform.featureselection.LRS
-
Sets the number of folds to use for cross validation when estimating the error rate
- setFolds(int) - Method in class jsat.datatransform.featureselection.SBS
-
Sets the number of folds to use for cross validation when estimating the error rate
- setFolds(int) - Method in class jsat.datatransform.featureselection.SFS
-
Sets the number of folds to use for cross validation when estimating the error rate
- setForrestSize(int) - Method in class jsat.classifiers.trees.ERTrees
-
- setG(double) - Method in class jsat.classifiers.linear.kernelized.OSKL
-
Sets the sparsification parameter G.
- setGainMethod(ImpurityScore.ImpurityMeasure) - Method in class jsat.classifiers.trees.DecisionStump
-
- setGainMethod(ImpurityScore.ImpurityMeasure) - Method in class jsat.classifiers.trees.DecisionTree
-
- setGamma(double) - Method in class jsat.classifiers.linear.kernelized.CSKLR
-
Sets the gamma value to use.
- setGamma(double) - Method in class jsat.classifiers.linear.kernelized.CSKLRBatch
-
Sets the gamma value to use.
- setGamma(double) - Method in class jsat.datatransform.visualization.LargeViz
-
Gamma controls the negative weight assigned to negative edges in the
optimization problem.
- setGradientUpdater(GradientUpdater) - Method in class jsat.classifiers.linear.LinearSGD
-
Sets the method that will be used to update the weight vectors given
their gradient information.
- setGradientUpdater(GradientUpdater) - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
Sets the gradient update that will be used when updating the weight
matrices and bias terms.
- setGravity(double) - Method in class jsat.classifiers.linear.STGD
-
Sets the gravity regularization parameter that "weighs down" the
coefficient values.
- setHandling(MutualInfoFS.NumericalHandeling) - Method in class jsat.datatransform.featureselection.MutualInfoFS
-
Sets the method of numericHandling numeric features
- setHiddenSizes(int[]) - Method in class jsat.classifiers.neuralnetwork.DReDNetSimple
-
Sets the hidden layer sizes for this network.
- setImag(double) - Method in class jsat.math.Complex
-
Sets the imaginary value part of this complex number
- setImpurityMeasure(ImpurityScore.ImpurityMeasure) - Method in class jsat.classifiers.trees.ExtraTree
-
Sets the impurity measure used during classification tree construction to
select the best of the features.
- setIndex(int) - Method in class jsat.linear.IndexValue
-
Sets the index associated with the value.
- setInftNormCriterion(boolean) - Method in class jsat.math.optimization.BFGS
-
By default the infinity norm is used to judge convergence.
- setInftNormCriterion(boolean) - Method in class jsat.math.optimization.LBFGS
-
By default the infinity norm is used to judge convergence.
- setInitialLearningRate(double) - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Sets the initial learning rate to use for the first epoch.
- setInitialLearningRate(double) - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
Sets the initial learning rate used for the first epoch
- setInitialLearningRate(double) - Method in class jsat.classifiers.neuralnetwork.SOM
-
Sets the rate at which input is incorporated at each iteration of the SOM
algorithm
- setInitialTrainSize(int) - Method in class jsat.regression.RANSAC
-
Sets the number of data points to be sampled from the training set to
create initial models.
- setInMemory(boolean) - Method in class jsat.datatransform.JLTransform
-
Sets whether or not the transform matrix is stored explicitly in memory
or not.
- setInMemory(boolean) - Method in class jsat.datatransform.kernel.RFF_RBF
-
Sets whether or not the transform matrix is stored explicitly in memory
or not.
- setIterationLimit(int) - Method in class jsat.clustering.EMGaussianMixture
-
Sets the maximum number of iterations allowed
- setIterationLimit(int) - Method in class jsat.clustering.kmeans.GMeans
-
- setIterationLimit(int) - Method in class jsat.clustering.kmeans.KMeans
-
Sets the maximum number of iterations allowed
- setIterationLimit(int) - Method in class jsat.clustering.kmeans.XMeans
-
- setIterations(int) - Method in class jsat.classifiers.boosting.ArcX4
-
Sets the number of iterations to perform
- setIterations(int) - Method in class jsat.classifiers.boosting.Wagging
-
Sets the number of iterations to create weak learners
- setIterations(int) - Method in class jsat.classifiers.linear.SCD
-
Sets the number of iterations that will be used.
- setIterations(int) - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Sets the number of learning iterations that will occur.
- setIterations(int) - Method in class jsat.classifiers.svm.PegasosK
-
Sets the number of iterations of the algorithm to perform.
- setIterations(int) - Method in class jsat.classifiers.svm.SBP
-
Sets the number of iterations to go through.
- setIterations(int) - Method in class jsat.clustering.kmeans.MiniBatchKMeans
-
Sets the number of mini-batch iterations to perform
- setIterations(int) - Method in class jsat.datatransform.featureselection.ReliefF
-
Sets the number of iterations of the ReliefF algorithm that will be run
- setIterations(int) - Method in class jsat.datatransform.visualization.TSNE
-
Sets the desired number of gradient descent iterations to perform.
- setIterations(int) - Method in class jsat.regression.RANSAC
-
Sets the number models that will be tested on the data set.
- setIterativeRefine(boolean) - Method in class jsat.clustering.kmeans.GMeans
-
Sets whether or not the set of all cluster centers should be refined at
every iteration.
- setIterativeRefine(boolean) - Method in class jsat.clustering.kmeans.XMeans
-
Sets whether or not the set of all cluster centers should be refined at
every iteration.
- setK(int) - Method in class jsat.classifiers.knn.DANN
-
Sets the number of nearest neighbors to use when predicting
- setK(int) - Method in class jsat.classifiers.linear.STGD
-
Sets the frequency of applying the
gravity
parameter to the weight vector.
- setK(int) - Method in class jsat.classifiers.svm.extended.CPM
-
Sets the number of hyper planes to use when training.
- setK(int) - Method in class jsat.clustering.FLAME
-
Sets the number of neighbors that will be considered in determining
Cluster Supporting Points and assignment contributions.
- setK(int) - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
Sets the number of topics that LDA will try to learn
- setKappa(double) - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
The "forgetfulness" factor in the learning rate.
- setKeepModels(boolean) - Method in class jsat.classifiers.ClassificationModelEvaluation
-
Set this to true in order to keep the trained models after
evaluation.
- setKeepModels(boolean) - Method in class jsat.regression.RegressionModelEvaluation
-
Set this to true in order to keep the trained models after
evaluation.
- setKernel(KernelTrick) - Method in class jsat.classifiers.linear.kernelized.BOGD
-
Sets the kernel to use
- setKernel(KernelTrick) - Method in class jsat.classifiers.linear.kernelized.CSKLR
-
Set which kernel trick to use
- setKernel(KernelTrick) - Method in class jsat.classifiers.linear.kernelized.DUOL
-
Sets the kernel trick to use
- setKernel(KernelTrick) - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
Sets the kernel to use
- setKernel(KernelTrick) - Method in class jsat.classifiers.linear.kernelized.OSKL
-
Sets the kernel to use
- setKernel(KernelTrick) - Method in class jsat.classifiers.linear.kernelized.Projectron
-
Sets the kernel trick to be used
- setKernel(KernelTrick) - Method in class jsat.classifiers.svm.SupportVectorLearner
-
Sets the kernel trick to use
- setKernel(KernelTrick) - Method in class jsat.classifiers.svm.SVMnoBias
-
- setKernel(KernelTrick) - Method in class jsat.datatransform.kernel.KernelPCA
-
- setKernel(KernelTrick) - Method in class jsat.datatransform.kernel.Nystrom
-
- setKernel(KernelTrick) - Method in class jsat.regression.KernelRidgeRegression
-
Sets the kernel trick to use
- setKernelFunction(KernelFunction) - Method in class jsat.classifiers.knn.LWL
-
Sets the kernel function that will be used to set the weights of each
data point in the local set
- setKernelFunction(KernelFunction) - Method in class jsat.distributions.multivariate.MetricKDE
-
- setKernelTrick(KernelTrick) - Method in class jsat.classifiers.linear.kernelized.ALMA2K
-
Sets the kernel to use
- setKernelTrick(KernelTrick) - Method in class jsat.classifiers.linear.kernelized.Forgetron
-
Sets the kernel trick to use
- setKn(int) - Method in class jsat.classifiers.knn.DANN
-
Sets the number of nearest neighbors to use when adapting the distance
metric.
- setLabelInfo() - Method in class jsat.text.ClassificationHashedTextDataLoader
-
- setLabelInfo() - Method in class jsat.text.ClassificationTextDataLoader
-
- setLambda(double) - Method in class jsat.classifiers.boosting.EmphasisBoost
-
λ controls the trade off between weighting the errors based on
their distance to the margin and the quadratic error of the output.
- setLambda(double) - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
Sets the L2 regularization parameter used during learning.
- setLambda(double) - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
Sets the regularization constant used for learning.
- setLambda(double) - Method in class jsat.classifiers.svm.extended.CPM
-
Sets the regularization parameter λ to use.
- setLambda(double) - Method in class jsat.classifiers.svm.extended.OnlineAMM
-
Sets the regularization parameter for this algorithm.
- setLambda(double) - Method in class jsat.distributions.discrete.Poisson
-
Sets the average rate of the event occurring in a unit of time
- setLambda(double) - Method in class jsat.math.optimization.ModifiedOWLQN
-
Sets the regularization term for the optimizer
- setLambda(double) - Method in class jsat.regression.KernelRidgeRegression
-
Sets the regularization parameter used.
- setLambda(double) - Method in class jsat.regression.RidgeRegression
-
Sets the regularization parameter used.
- setLambda(double) - Method in class jsat.regression.StochasticRidgeRegression
-
Sets the regularization parameter used.
- setLambda0(double) - Method in class jsat.classifiers.linear.LinearBatch
-
λ0 controls the L2 regularization penalty.
- setLambda0(double) - Method in class jsat.classifiers.linear.LinearSGD
-
λ0 controls the L2 regularization penalty.
- setLambda1(double) - Method in class jsat.classifiers.linear.LinearSGD
-
λ1 controls the L1 regularization penalty.
- setLambdaMultipler(Vec) - Method in class jsat.math.optimization.ModifiedOWLQN
-
This method sets a vector that will contain a separate multiplier for
lambda for each dimension of the problem.
- setLayersActivation(List<ActivationLayer>) - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
Sets the list of layer activations for all layers other than the input
layer.
- setLayerSizes(int...) - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
Sets the array indicating the total number of layers in the network and
the sizes of each layer.
- setLearningDecay(DecayRate) - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Sets the decay rate to apply to the learning rate.
- setLearningDecay(DecayRate) - Method in class jsat.classifiers.neuralnetwork.SOM
-
The rate the SOM learns decays over each iteration, and this defines the
way in which the rate decays.
- setLearningDecay(DecayRate) - Method in class jsat.regression.StochasticRidgeRegression
-
Sets the learning rate decay function to use.
- setLearningRate(double) - Method in class jsat.classifiers.linear.STGD
-
Sets the learning rate to use
- setLearningRate(double) - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Sets the learning rate of the algorithm.
- setLearningRate(double) - Method in class jsat.regression.StochasticGradientBoosting
-
Sets the learning rate of the algorithm.
- setLearningRate(double) - Method in class jsat.regression.StochasticRidgeRegression
-
Sets the learning rate used, and should be in the range (0, 1).
- setLearningRateDecay(DecayRate) - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Sets the decay rate used to reduce the learning rate after each epoch.
- setLearningRateDecay(DecayRate) - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
Sets the decay rate used to adjust the learning rate after each epoch
- setLength(int) - Method in class jsat.linear.ConstantVector
-
Sets the length of this vector.
- setLength(int) - Method in class jsat.linear.SparseVector
-
Because sparce vectors do not have most value set, they can
have their length increased, and sometimes decreased, without
any effort.
- setLineSearch(LineSearch) - Method in class jsat.math.optimization.BFGS
-
Sets the line search method used at each iteration
- setLineSearch(LineSearch) - Method in class jsat.math.optimization.LBFGS
-
Sets the line search method used at each iteration
- setLocalClassifier(Classifier) - Method in class jsat.classifiers.neuralnetwork.LVQLLC
-
Each prototype will create a classifier that is local to itself, and
trained on the points that belong to the prototype and those near the
border of the prototype.
- setLocation(double) - Method in class jsat.distributions.Cauchy
-
- setLocation(double) - Method in class jsat.distributions.Levy
-
Sets location of the Levy distribution.
- setLooseBounds(boolean) - Method in class jsat.linear.vectorcollection.CoverTree
-
- setLoss(LossFunc) - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
Sets the loss function to use.
- setLoss(LossFunc) - Method in class jsat.classifiers.linear.LinearBatch
-
Sets the loss function used for the model.
- setLoss(LossFunc) - Method in class jsat.classifiers.linear.LinearSGD
-
Sets the loss function used for the model.
- setLoss(StochasticSTLinearL1.Loss) - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
Sets the loss function to use.
- setLVQMethod(LVQ.LVQVersion) - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Sets the version of LVQ used.
- setM(double) - Method in class jsat.classifiers.bayesian.AODE
-
Sets the minimum prior observation value needed for an attribute
combination to have enough support to be included in the final estimate.
- setM(int) - Method in class jsat.driftdetectors.ADWIN
-
This parameter controls the trade off of space and accuracy for the
sliding window.
- setM(int) - Method in class jsat.math.optimization.LBFGS
-
Sets the number of history items to keep that are used to approximate the
Hessian of the problem
- setM(int) - Method in class jsat.math.optimization.ModifiedOWLQN
-
Sets the number of history items to keep that are used to approximate the
Hessian of the problem
- setMatch(T) - Method in class jsat.utils.ProbailityMatch
-
- setMax(int) - Method in class jsat.distributions.discrete.UniformDiscrete
-
- setMaxBudget(int) - Method in class jsat.distributions.kernels.KernelPoint
-
Sets the maximum budget for support vectors to allow.
- setMaxBudget(int) - Method in class jsat.distributions.kernels.KernelPoints
-
Sets the maximum budget for support vectors to allow.
- setMaxCoeff(double) - Method in class jsat.classifiers.linear.kernelized.BOGD
-
Sets the maximum allowed value for any support vector allowed.
- setMaxDecrease(double) - Method in class jsat.datatransform.featureselection.SBS
-
Sets the maximum allowable decrease in accuracy (increase in error) from
the previous set of features to the new current set.
- setMaxDepth(int) - Method in class jsat.classifiers.trees.DecisionTree
-
Sets the maximum depth that this classifier may build trees to.
- setMaxFeatures(int) - Method in class jsat.datatransform.featureselection.SBS
-
Sets the maximum number of features that must be selected
- setMaxFeatures(int) - Method in class jsat.datatransform.featureselection.SFS
-
Sets the maximum number of features that must be selected
- setMaxForestSize(int) - Method in class jsat.classifiers.trees.RandomForest
-
Sets the maximum number of trees to create for the forest.
- setMaxHistory(int) - Method in class jsat.driftdetectors.BaseDriftDetector
-
Sets the maximum number of items to store in history.
- setMaximumIterations(int) - Method in class jsat.clustering.kmeans.KernelKMeans
-
Sets the maximum number of iterations allowed
- setMaximumIterations(int) - Method in class jsat.math.optimization.BFGS
-
- setMaximumIterations(int) - Method in class jsat.math.optimization.LBFGS
-
- setMaximumIterations(int) - Method in class jsat.math.optimization.ModifiedOWLQN
-
- setMaximumIterations(int) - Method in interface jsat.math.optimization.Optimizer2
-
Sets the maximum number of iterations allowed for the optimization method
- setMaxIncrease(double) - Method in class jsat.datatransform.featureselection.SFS
-
Sets the maximum allowable the maximum tolerable increase in error when a
feature is added
- setMaxIterations(int) - Method in class jsat.classifiers.boosting.AdaBoostM1
-
Sets the maximal number of boosting iterations that may be performed
- setMaxIterations(int) - Method in class jsat.classifiers.boosting.EmphasisBoost
-
Sets the maximal number of boosting iterations that may be performed
- setMaxIterations(int) - Method in class jsat.classifiers.boosting.LogitBoost
-
Sets the maximum number of iterations of boosting that can occur, giving
the maximum number of base learners that may be trained
- setMaxIterations(int) - Method in class jsat.classifiers.boosting.ModestAdaBoost
-
Sets the maximal number of boosting iterations that may be performed
- setMaxIterations(int) - Method in class jsat.classifiers.knn.DANN
-
Sets the number of times a new distance metric will be created for each
query.
- setMaxIterations(int) - Method in class jsat.classifiers.linear.BBR
-
Sets the maximum number of iterations allowed before halting the
algorithm early.
- setMaxIterations(int) - Method in class jsat.classifiers.linear.LogisticRegressionDCD
-
Sets the maximum number of iterations the algorithm is allowed to run
for.
- setMaxIterations(int) - Method in class jsat.classifiers.neuralnetwork.SOM
-
Sets the maximum number of iterations that will be used to converge
- setMaxIterations(int) - Method in class jsat.classifiers.svm.DCD
-
Sets the maximum number of iterations allowed through the whole training
set.
- setMaxIterations(int) - Method in class jsat.classifiers.svm.DCDs
-
Sets the maximum number of iterations allowed through the whole training
set.
- setMaxIterations(int) - Method in class jsat.classifiers.svm.PlattSMO
-
Sets the maximum number of iterations to perform of the training loop.
- setMaxIterations(int) - Method in class jsat.clustering.FLAME
-
Sets the maximum number of iterations to perform.
- setMaxIterations(int) - Method in class jsat.clustering.MeanShift
-
Sets the maximum number of iterations the algorithm will go through,
terminating early if convergence has not occurred.
- setMaxIterations(int) - Method in class jsat.clustering.PAM
-
- setMaxIterations(int) - Method in class jsat.regression.StochasticGradientBoosting
-
Sets the maximum number of iterations used in SGB.
- setMaxIters(int) - Method in class jsat.classifiers.linear.NewGLMNET
-
Sets the maximum number of training iterations for the algorithm,
specifically the outer loop as mentioned in the original paper.
- setMaxLeafSize(int) - Method in class jsat.linear.vectorcollection.VPTree
-
Sets the maximum leaf node size.
- setMaxNorm(double) - Method in class jsat.classifiers.neuralnetwork.regularizers.Max2NormRegularizer
-
Sets the maximum allowed 2 norm for a single neuron's weights
- setMaxParents(int) - Method in class jsat.classifiers.bayesian.graphicalmodel.K2NetworkLearner
-
Sets the maximum number of parents to allow a node when learning the network structure.
- setMaxPCs(int) - Method in class jsat.datatransform.PCA
-
sets the maximum number of principal components to learn
- setMaxPointError(double) - Method in class jsat.regression.RANSAC
-
Each data point not in the initial training set will be tested against.
- setMaxScaled(double) - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
Sets the maximum value of any feature after scaling is applied.
- setMaxTime(double) - Method in class jsat.math.decayrates.ExponetialDecay
-
Sets the maximum amount of time to allow in the rate decay.
- setMaxTime(double) - Method in class jsat.math.decayrates.LinearDecay
-
Sets the maximum amount of time to allow in the rate decay.
- setMaxTokenLength(int) - Method in class jsat.text.tokenizer.NaiveTokenizer
-
Sets the maximum allowed length for any token.
- setMean(double) - Method in class jsat.classifiers.boosting.WaggingNormal
-
Sets the mean value used for the normal distribution
- setMean(double) - Method in class jsat.distributions.Normal
-
- setMeanCovariance(Vec, Matrix) - Method in class jsat.distributions.multivariate.NormalM
-
Sets the mean and covariance for this distribution.
- setMeasurementError(double) - Method in class jsat.regression.OrdinaryKriging
-
Sets the measurement error used for Kriging, which is equivalent to
altering the diagonal values of the covariance.
- setMetric(DistanceMetric) - Method in class jsat.parameters.MetricParameter
-
Sets the distance metric that should be sued
- setMin(int) - Method in class jsat.distributions.discrete.UniformDiscrete
-
- setMinClusterSize(int) - Method in class jsat.clustering.HDBSCAN
-
- setMinClusterSize(int) - Method in class jsat.clustering.kmeans.GMeans
-
Sets the minimum size for splitting a cluster.
- setMinClusterSize(int) - Method in class jsat.clustering.kmeans.XMeans
-
Sets the minimum size for splitting a cluster.
- setMinFeatures(int) - Method in class jsat.datatransform.featureselection.SBS
-
Sets the minimum number of features that must be selected
- setMinFeatures(int) - Method in class jsat.datatransform.featureselection.SFS
-
Sets the minimum number of features that must be selected
- setMiniBatchSize(int) - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Sets the amount of data points used to form each gradient update.
- setMiniBatchSize(int) - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
Sets the number of data points used at a time to perform one update of
the model parameters
- setMinMax(int, int) - Method in class jsat.distributions.discrete.UniformDiscrete
-
Sets the minimum and maximum values at the same time, this is useful if
setting them one at a time may have caused a conflict with the previous
values
- setMinMax(double, double) - Method in class jsat.distributions.LogUniform
-
Sets the minimum and maximum values for this distribution
- setMinPoints(int) - Method in class jsat.clustering.HDBSCAN
-
- setMinPts(int) - Method in class jsat.clustering.OPTICS
-
Sets the minimum number of points needed to compute the core distance.
- setMinRate(double) - Method in class jsat.math.decayrates.ExponetialDecay
-
Sets the minimum learning rate to return
- setMinRate(double) - Method in class jsat.math.decayrates.LinearDecay
-
Sets the minimum learning rate to return
- setMinResultSize(int) - Method in class jsat.regression.RANSAC
-
RANSAC requires an initial model to be accurate enough to include a
minimum number of inliers before being considered as a potentially good
model.
- setMinResultSplitSize(int) - Method in class jsat.classifiers.trees.DecisionStump
-
When a split is made, it may be that outliers cause the split to
segregate a minority of points from the majority.
- setMinResultSplitSize(int) - Method in class jsat.classifiers.trees.DecisionTree
-
When a split is made, it may be that outliers cause the split to
segregate a minority of points from the majority.
- setMinSamples(int) - Method in class jsat.classifiers.trees.DecisionTree
-
Sets the minimum number of samples needed at each step in order to continue branching
- setMinScaled(double) - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
Sets the minimum value of any feature after scaling is applied.
- setMinTokenLength(int) - Method in class jsat.text.tokenizer.NaiveTokenizer
-
Sets the minimum allowed token length.
- setMode(CSKLR.UpdateMode) - Method in class jsat.classifiers.linear.kernelized.CSKLR
-
Sets what update mode should be used.
- setMode(CSKLR.UpdateMode) - Method in class jsat.classifiers.linear.kernelized.CSKLRBatch
-
Sets what update mode should be used.
- setMode(PassiveAggressive.Mode) - Method in class jsat.classifiers.linear.PassiveAggressive
-
Sets which version of the PA update is used.
- setMode(SCW.Mode) - Method in class jsat.classifiers.linear.SCW
-
Controls which version of the algorithm is used
- setMode(PassiveAggressive.Mode) - Method in class jsat.classifiers.linear.SPA
-
Sets which version of the PA update is used.
- setMode(JLTransform.TransformMode) - Method in class jsat.datatransform.JLTransform
-
The JL transform uses a random matrix to project the data, and the mode
controls which method is used to construct this matrix.
- setModificationOne(boolean) - Method in class jsat.classifiers.svm.PlattSMO
-
Sets where or not modification one or two should be used when training.
- setMomentum(double) - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
Sets the non negative momentum used in training.
- setMomentum(double) - Method in class jsat.math.optimization.stochastic.SGDMomentum
-
Sets the momentum for accumulating gradients.
- setMScale(double) - Method in class jsat.classifiers.neuralnetwork.LVQ
-
When using
LVQ.LVQVersion.LVQ3, a 3rd case exists where up to two
learning vectors can be updated at the same time if they have the same
class.
- setMu(double) - Method in class jsat.distributions.Laplace
-
- setMu(double) - Method in class jsat.distributions.Logistic
-
- setNegativeSamples(int) - Method in class jsat.datatransform.visualization.LargeViz
-
Sets the number of negative neighbor samples to obtain for each data
point.
- setNegEntropyFunction(FastICA.NegEntropyFunc) - Method in class jsat.datatransform.FastICA
-
Sets the Negative Entropy function used to infer the base components.
- setNeighborDecay(DecayRate) - Method in class jsat.classifiers.neuralnetwork.SOM
-
The range of effect each data point has decays with each iteration, and
this defines the way in which the rate decays.
- setNeighbors(int) - Method in class jsat.classifiers.knn.LWL
-
Sets the number of neighbors that will be used to create the local model
- setNeighbors(int) - Method in class jsat.classifiers.knn.NearestNeighbour
-
Sets the number of neighbors to consult when making decisions
- setNeighbors(int) - Method in class jsat.clustering.LSDBC
-
Sets the number of neighbors that will be considered when clustering
data points
- setNeighbors(int) - Method in class jsat.datatransform.featureselection.ReliefF
-
Sets the number of neighbors to use to infer feature importance from
- setNeighbors(int) - Method in class jsat.datatransform.visualization.Isomap
-
Set the number of neighbors to consider for the initial graph in Isomap
- setNoDigits(boolean) - Method in class jsat.text.tokenizer.NaiveTokenizer
-
Sets whether digits will be accepted in tokens or treated as "other" (not
white space and not character).
- setNormalize(boolean) - Method in class jsat.classifiers.neuralnetwork.RBFNet
-
Sets whether or not to normalize the outputs of the neurons in the
network so that the activations sum to one.
- setNu(double) - Method in class jsat.classifiers.svm.SBP
-
The nu parameter for this SVM is not the same as the standard nu-SVM
formulation, though it plays a similar role.
- setNugget(double) - Method in class jsat.regression.OrdinaryKriging
-
Sets the nugget value passed to the variogram during training.
- setNumberOfBins(int) - Method in class jsat.datatransform.NumericalToHistogram
-
Sets the maximum number of histogram bins to use when creating the categorical version of numeric features.
- setNumCentroids(int) - Method in class jsat.classifiers.neuralnetwork.RBFNet
-
Sets the number of centroids to learn for this model.
- setNumericalHandling(NaiveBayes.NumericalHandeling) - Method in class jsat.classifiers.bayesian.NaiveBayes
-
Sets the method used by this instance for handling numerical attributes.
- setNumericName(String, int) - Method in class jsat.DataSet
-
Sets the unique name associated with the i'th numeric attribute.
- setObject(T) - Method in class jsat.parameters.ObjectParameter
-
Sets the parameter to the given object
- setOmega(double) - Method in class jsat.distributions.kernels.PukKernel
-
Sets the omega parameter value, which controls the shape of the kernel
- setOnlineVersion(boolean) - Method in class jsat.classifiers.svm.DCD
-
By default, Algorithm 1 is used.
- setOptimizer(Optimizer2) - Method in class jsat.classifiers.linear.LinearBatch
-
Sets the method of batch optimization that will be used.
- setOptionName(String, int) - Method in class jsat.classifiers.CategoricalData
-
Sets the name of one of the value options.
- setOtherToWhiteSpace(boolean) - Method in class jsat.text.tokenizer.NaiveTokenizer
-
Sets whether or not all non letter and digit characters are treated as
white space, or ignored completely.
- setP(int) - Method in class jsat.classifiers.neuralnetwork.RBFNet
-
Sets the nearest neighbor parameter.
- setP(double) - Method in class jsat.distributions.discrete.Binomial
-
Sets the probability of a trial being a success
- setP(double) - Method in class jsat.linear.distancemetrics.MinkowskiDistance
-
- setPair(P) - Method in class jsat.classifiers.DataPointPair
-
- setPair(P) - Method in class jsat.linear.VecPaired
-
- setPath(int, TreeNodeVisitor) - Method in class jsat.classifiers.trees.DecisionTree.Node
-
- setPath(int, TreeNodeVisitor) - Method in class jsat.classifiers.trees.TreeNodeVisitor
-
Optional operation!
This method, if supported, will set the path so that the child is set to the given value.
- setPCSampling(boolean) - Method in class jsat.clustering.GapStatistic
-
By default the null distribution is sampled from the bounding hyper-cube
of the dataset.
- setPerplexity(double) - Method in class jsat.datatransform.visualization.LargeViz
-
Sets the target perplexity of the gaussian used over each data point.
- setPerplexity(double) - Method in class jsat.datatransform.visualization.TSNE
-
Sets the target perplexity of the gaussian used over each data point.
- setPhase1Learner(RBFNet.Phase1Learner) - Method in class jsat.classifiers.neuralnetwork.RBFNet
-
Sets the method used for learning the centroids (or hidden units) of the
network.
- setPhase2Learner(RBFNet.Phase2Learner) - Method in class jsat.classifiers.neuralnetwork.RBFNet
-
Sets the method used for learning the bandwidths for each centroid in the
network.
- setPivotSelectionMethod(KDTree.PivotSelection) - Method in class jsat.linear.vectorcollection.KDTree.KDTreeFactory
-
- setPredicting(CategoricalData) - Method in class jsat.classifiers.trees.DecisionStump
-
Sets the DecisionStump's predicting information.
- setPreWhitened(boolean) - Method in class jsat.datatransform.FastICA
-
Controls where or not the implementation assumes the input data is
already whitened.
- setPrior(BBR.Prior) - Method in class jsat.classifiers.linear.BBR
-
Sets the regularizing prior used
- setPrior(StochasticMultinomialLogisticRegression.Prior) - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Sets the prior used to perform regularization
- setProb(int, double) - Method in class jsat.classifiers.CategoricalResults
-
Sets the probability that a sample belongs to a given category.
- setProbability(double) - Method in class jsat.utils.ProbailityMatch
-
- setProjectedDimension(int) - Method in class jsat.datatransform.JLTransform
-
Sets the target dimension size to use for the output
- setProjectionStep(boolean) - Method in class jsat.classifiers.svm.Pegasos
-
Sets whether or not to use the projection step after each update per
iteration
- setPruneFrequency(int) - Method in class jsat.classifiers.svm.extended.OnlineAMM
-
Sets the frequency at which the weight vectors are pruned.
- setPruningMethod(TreePruner.PruningMethod) - Method in class jsat.classifiers.trees.DecisionTree
-
Sets the method of pruning that will be used after tree construction
- setR(double) - Method in class jsat.classifiers.linear.AROW
-
Sets the r parameter of AROW, which controls the regularization.
- setR(double) - Method in class jsat.classifiers.linear.kernelized.CSKLR
-
Sets the maximal margin norm value for the algorithm.
- setR(double) - Method in class jsat.classifiers.linear.kernelized.CSKLRBatch
-
Sets the maximal margin norm value for the algorithm.
- setR(double) - Method in class jsat.classifiers.linear.kernelized.OSKL
-
Sets the maximum allowed norm of the model.
- setRandomFeatureCount(int) - Method in class jsat.classifiers.trees.RandomDecisionTree
-
Sets the number of random features to and use at each node of
the decision tree
- setRange(double, double) - Method in class jsat.datatransform.LinearTransform
-
Sets the min and max value to scale the data to.
- setReal(double) - Method in class jsat.math.Complex
-
Sets the real value part of this complex number
- setReflection(double) - Method in class jsat.math.optimization.NelderMead
-
Sets the reflection constant, which must be greater than 0
- setRegressionTargetScore(RegressionScore) - Method in class jsat.parameters.ModelSearch
-
Sets the score to attempt to optimize when performing grid search on a
regression problem.
- setRegularization(double) - Method in class jsat.classifiers.linear.BBR
-
Sets the regularization penalty to use if the algorithm has not been set
to choose one automatically.
- setRegularization(double) - Method in class jsat.classifiers.linear.kernelized.BOGD
-
Sets the regularization parameter used for training.
- setRegularization(double) - Method in class jsat.classifiers.linear.SCD
-
Sets the regularization constant used for learning.
- setRegularization(double) - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Sets the coefficient applied to the regularization penalty at each
update.
- setRegularization(double) - Method in class jsat.classifiers.svm.Pegasos
-
Sets the regularization constant used for learning.
- setRegularization(double) - Method in class jsat.classifiers.svm.PegasosK
-
Sets the amount of regularization to apply.
- setRegularization(double) - Method in class jsat.datatransform.WhitenedPCA
-
- setRegularizer(WeightRegularizer) - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
Sets the method of regularizing the connections weights
- setRemoveContinuousAttributes(boolean) - Method in class jsat.classifiers.trees.DecisionStump
-
Unlike categorical values, when a continuous attribute is selected to split on, not
all values of the attribute become the same.
- setRepresentativesPerClass(int) - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Sets the number of representatives to create for each class.
- setReScale(boolean) - Method in class jsat.classifiers.linear.StochasticSTLinearL1
-
Sets whether or not scaling should be applied on th feature values of the
training vectors.
- setReTrain(boolean) - Method in class jsat.linear.distancemetrics.MahalanobisDistance
-
It may be desirable to have the metric trained only once, and use the same parameters
for all other training sessions of the learning algorithm using the metric.
- setReuseSameCVFolds(boolean) - Method in class jsat.parameters.ModelSearch
-
Sets whether or not one set of CV folds is created and re used for every
parameter combination (the default), or if a difference set of CV folds
will be used for every parameter combination.
- setRho(double) - Method in class jsat.classifiers.linear.kernelized.DUOL
-
Sets the "conflict" parameter, which controls how often double updates
are performed.
- setRho(double) - Method in class jsat.math.optimization.stochastic.AdaDelta
-
Sets the decay rate used by AdaDelta.
- setRho(double) - Method in class jsat.math.optimization.stochastic.RMSProp
-
Sets the decay rate used by rmsprop.
- setRidge(double) - Method in class jsat.datatransform.kernel.Nystrom
-
Sets the regularization parameter to add to the eigen values of the gram
matrix.
- setRMSE(boolean) - Method in class jsat.regression.evaluation.MeanSquaredError
-
- setRounds(int) - Method in class jsat.classifiers.boosting.Bagging
-
Sets the number of rounds that bagging is done, meaning how many base learners are trained
- setS(double) - Method in class jsat.distributions.Logistic
-
- setSampleCount(int) - Method in class jsat.clustering.CLARA
-
Sets the number of times
PAM will be applied to different samples from the data set.
- setSamples(int) - Method in class jsat.clustering.GapStatistic
-
The Gap statistic is measured by sampling from a reference distribution
and comparing with the given data set.
- setSampleSize(int) - Method in class jsat.clustering.CLARA
-
Sets the number of samples CLARA should take from the data set to perform
PAM on.
- setScale(double) - Method in class jsat.distributions.Cauchy
-
- setScale(double) - Method in class jsat.distributions.Levy
-
Sets the scale of the Levy distribution
- setScale(double) - Method in class jsat.distributions.Rayleigh
-
- setScale(double) - Method in class jsat.linear.ScaledVector
-
Explicitly sets the current scale to the given value
NOTE: If the scale is set to zero, the underlying base vector will be
zeroed out, and the scale set to 1.
- setScaleBandwidthFactor(double) - Method in class jsat.clustering.MeanShift
-
Sets the value by which the bandwidth of the
MultivariateKDE will
be scaled by.
- setSecondItem(Y) - Method in class jsat.utils.Pair
-
- setSeed(long) - Method in class jsat.utils.random.CMWC4096
-
- setSeed(long) - Method in class jsat.utils.random.XOR128
-
- setSeed(long) - Method in class jsat.utils.random.XOR96
-
- setSeed(long) - Method in class jsat.utils.random.XORWOW
-
- setSeedSelection(SeedSelectionMethods.SeedSelection) - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Sets the seed selection method used to select the initial learning vectors
- setSeedSelection(SeedSelectionMethods.SeedSelection) - Method in class jsat.clustering.EMGaussianMixture
-
Sets the method of seed selection to use for this algorithm.
- setSeedSelection(SeedSelectionMethods.SeedSelection) - Method in class jsat.clustering.kmeans.GMeans
-
- setSeedSelection(SeedSelectionMethods.SeedSelection) - Method in class jsat.clustering.kmeans.KMeans
-
Sets the method of seed selection to use for this algorithm.
- setSeedSelection(SeedSelectionMethods.SeedSelection) - Method in class jsat.clustering.kmeans.MiniBatchKMeans
-
Sets the method of selecting the initial data points to
seed the clustering algorithm.
- setSeedSelection(SeedSelectionMethods.SeedSelection) - Method in class jsat.clustering.kmeans.XMeans
-
- setSeedSelection(SeedSelectionMethods.SeedSelection) - Method in class jsat.clustering.PAM
-
Sets the method of seed selection used by this algorithm
- setSelectionCount(int) - Method in class jsat.classifiers.trees.ExtraTree
-
The ExtraTree will select the best of a random subset of features at each
level, this sets the number of random features to select.
- setSelfTurned(boolean) - Method in class jsat.classifiers.linear.kernelized.Forgetron
-
Sets whether or not the self-tuned variant of the Forgetron is used, the
default is true
- setShape(double) - Method in class jsat.distributions.MaxwellBoltzmann
-
- setShift(double) - Method in class jsat.linear.ShiftedVec
-
Directly alters the shift value used for this vector.
- setShrink(double) - Method in class jsat.math.optimization.NelderMead
-
Sets the shrinkage constant, which must be in the range (0, 1)
- setSigma(double) - Method in class jsat.datatransform.kernel.RFF_RBF
-
Sets the σ parameter of the RBF kernel that is being approximated.
- setSigma(double) - Method in class jsat.distributions.kernels.GeneralRBFKernel
-
Sets the kernel width parameter, which must be a positive value.
- setSigma(double) - Method in class jsat.distributions.kernels.PukKernel
-
Sets the sigma parameter value, which controls the width of the kernel
- setSigma(double) - Method in class jsat.distributions.kernels.RBFKernel
-
Sets the sigma parameter, which must be a positive value
- setSimultaniousTraining(boolean) - Method in class jsat.classifiers.boosting.Bagging
-
Bagging produces multiple base learners.
- setSmoothing(double) - Method in class jsat.classifiers.bayesian.MultinomialNaiveBayes
-
Sets the amount of smoothing applied to the model.
- setSmoothing(double) - Method in class jsat.math.ExponentialMovingStatistics
-
Sets the smoothing parameter value to use.
- setSolverMode(RidgeRegression.SolverMode) - Method in class jsat.regression.RidgeRegression
-
Sets which solver is to be used
- setSomHeight(int) - Method in class jsat.classifiers.neuralnetwork.SOM
-
Sets the height of the SOM lattice to create
- setSomWidth(int) - Method in class jsat.classifiers.neuralnetwork.SOM
-
Sets the width of the SOM lattice to create
- setSparceInput(boolean) - Method in class jsat.classifiers.bayesian.NaiveBayes
-
Tells the Naive Bayes classifier to
assume the importance of sparseness
in the numerical values.
- setSparse(boolean) - Method in class jsat.classifiers.bayesian.NaiveBayesUpdateable
-
Sets whether or not that classifier should behave as if the input vectors
are sparse.
- setStandardDeviations(double) - Method in class jsat.classifiers.boosting.WaggingNormal
-
Sets the standard deviations used for the normal distribution
- setStandardized(boolean) - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Sets whether or not to perform implicit standardization of the feature
values when performing regularization by the prior.
- setStartLevel(int) - Method in class jsat.classifiers.svm.DCSVM
-
The DC-SVM algorithm works by creating a hierarchy of levels, and
iteratively refining the solution from one level to the next.
- setStndDev(double) - Method in class jsat.classifiers.neuralnetwork.initializers.GaussianNormalInit
-
Sets the standard deviation of the distribution that will be sampled from
- setStndDev(double) - Method in class jsat.distributions.Normal
-
- setStndDevs(double) - Method in class jsat.clustering.FLAME
-
Sets the number of standard deviations away from the mean density a
candidate outlier must be to be confirmed as an outlier.
- setStopAfterFail(boolean) - Method in class jsat.clustering.kmeans.XMeans
-
Each new cluster will be tested for improvement according to the BIC
metric.
- setStoppingDist(double) - Method in class jsat.classifiers.neuralnetwork.LVQ
-
The algorithm terminates early if the learning vectors are only moving
small distances.
- setStopSize(int) - Method in class jsat.classifiers.trees.ExtraTree
-
Sets the stopping size for tree growth.
- setStoreMeans(boolean) - Method in class jsat.clustering.kmeans.KMeans
-
If set to
true the computed means will be stored after clustering
is completed, and can then be retrieved using
KMeans.getMeans().
- setStoreMeans(boolean) - Method in class jsat.clustering.kmeans.MiniBatchKMeans
-
If set to
true the computed means will be stored after clustering
is completed, and can then be retrieved using
MiniBatchKMeans.getMeans().
- setStoreMedoids(boolean) - Method in class jsat.clustering.PAM
-
If set to
true the computed medoids will be stored after clustering
is completed, and can then be retrieved using
PAM.getMedoids().
- setSubEpochs(int) - Method in class jsat.classifiers.svm.extended.AMM
-
Each iteration of the batch AMM algorithm requires at least one epoch
over the training set.
- setTargetDimension(int) - Method in class jsat.datatransform.visualization.Isomap
-
- setTargetDimension(int) - Method in class jsat.datatransform.visualization.LargeViz
-
- setTargetDimension(int) - Method in class jsat.datatransform.visualization.MDS
-
- setTargetDimension(int) - Method in class jsat.datatransform.visualization.TSNE
-
- setTargetDimension(int) - Method in interface jsat.datatransform.visualization.VisualizationTransform
-
Sets the target dimension to embed new dataset to.
- setTargetValue(int, double) - Method in class jsat.regression.RegressionDataSet
-
Sets the target regression value associated with a given data point
- setTau(double) - Method in class jsat.math.decayrates.InverseDecay
-
Controls the rate early in time, but has a decreasing impact on the rate
returned as time goes forward.
- setTau(double) - Method in class jsat.math.decayrates.PowerDecay
-
Controls the rate early in time, but has a decreasing impact on the rate
returned as time goes forward.
- setTau0(double) - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
A learning rate constant to control the influence of early iterations on
the solution.
- setTestProportion(double) - Method in class jsat.classifiers.trees.DecisionTree
-
Sets the proportion of the training set that is put aside to perform pruning with.
- setTestUsingData(Vec) - Method in interface jsat.testing.onesample.OneSampleTest
-
Sets the statistics that will be tested against an alternate hypothesis.
- setTestUsingData(Vec) - Method in class jsat.testing.onesample.TTest
-
- setTestUsingData(Vec) - Method in class jsat.testing.onesample.ZTest
-
- setTestVars(double[]) - Method in interface jsat.testing.onesample.OneSampleTest
-
- setTestVars(double[]) - Method in class jsat.testing.onesample.TTest
-
- setTestVars(double[]) - Method in class jsat.testing.onesample.ZTest
-
- setThreshold(double) - Method in class jsat.classifiers.linear.STGD
-
Sets the threshold for a coefficient value to avoid regularization.
- setThreshold(double) - Method in class jsat.datatransform.PCA
-
- setTolerance(double) - Method in class jsat.classifiers.linear.BBR
-
Sets the convergence tolerance target.
- setTolerance(double) - Method in class jsat.classifiers.linear.LinearBatch
-
Sets the convergence tolerance to user for training.
- setTolerance(double) - Method in class jsat.classifiers.linear.NewGLMNET
-
Sets the tolerance parameter for convergence.
- setTolerance(double) - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Sets the tolerance that determines when the training stops early because
the change has become too insignificant.
- setTolerance(double) - Method in class jsat.classifiers.svm.DCDs
-
Sets the tolerance for the stopping condition when training, a small value near
zero allows training to stop early when little to no additional convergence
is possible.
- setTolerance(double) - Method in class jsat.classifiers.svm.PlattSMO
-
Sets the tolerance for the solution.
- setTolerance(double) - Method in class jsat.classifiers.svm.SVMnoBias
-
Sets the tolerance for the solution.
- setTolerance(double) - Method in class jsat.datatransform.visualization.MDS
-
Sets the tolerance parameter for determining convergence.
- setTrainFinalModel(boolean) - Method in class jsat.parameters.ModelSearch
-
If true (the default) the model that was found to be best is
trained on the whole data set at the end.
- setTrainingProportion(double) - Method in class jsat.regression.StochasticGradientBoosting
-
The GB version uses the whole data set at each iteration.
- setTrainModelsInParallel(boolean) - Method in class jsat.parameters.ModelSearch
-
When set to true (the default) parallelism is obtained by
training as many models in parallel as possible.
- setTrials(int) - Method in class jsat.distributions.discrete.Binomial
-
The number of trials for the distribution
- setTrials(int) - Method in class jsat.parameters.RandomSearch
-
Sets the number of trials or samples that will be taken.
- setTrustH0(boolean) - Method in class jsat.clustering.kmeans.GMeans
-
Each new cluster will be tested for normality, with the null hypothesis
H0 being that the cluster is normal.
- setUniformSampling(boolean) - Method in class jsat.classifiers.linear.kernelized.BOGD
-
Sets whether or not support vectors should be removed by uniform sampling
or not.
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.bayesian.AODE
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.bayesian.MultinomialNaiveBayes
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.bayesian.NaiveBayesUpdateable
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.bayesian.ODE
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.boosting.UpdatableStacking
-
- setUp(CategoricalData[], int) - Method in class jsat.classifiers.boosting.UpdatableStacking
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.linear.ALMA2
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.linear.AROW
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.linear.kernelized.ALMA2K
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.linear.kernelized.BOGD
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.linear.kernelized.CSKLR
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.linear.kernelized.DUOL
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.linear.kernelized.Forgetron
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
- setUp(CategoricalData[], int) - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.linear.kernelized.OSKL
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.linear.kernelized.Projectron
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.linear.LinearSGD
-
- setUp(CategoricalData[], int) - Method in class jsat.classifiers.linear.LinearSGD
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.linear.NHERD
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.linear.PassiveAggressive
-
- setUp(CategoricalData[], int) - Method in class jsat.classifiers.linear.PassiveAggressive
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.linear.ROMMA
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.linear.SCW
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.linear.SPA
-
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.linear.STGD
-
- setUp(CategoricalData[], int) - Method in class jsat.classifiers.linear.STGD
-
- setup() - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
Prepares the network by creating all needed structure, initializing
weights, and preparing it for updates
- setUp(CategoricalData[], int, CategoricalData) - Method in class jsat.classifiers.svm.extended.OnlineAMM
-
- setUp(CategoricalData[], int, CategoricalData) - Method in interface jsat.classifiers.UpdateableClassifier
-
- setup(int, int[], Vec) - Method in class jsat.clustering.kmeans.KernelKMeans
-
Sets up the internal structure for KenrelKMeans.
- setUp(DataSet, Set<Integer>, Set<Integer>) - Method in class jsat.datatransform.RemoveAttributeTransform
-
Sets up the Remove Attribute Transform properly
- setup(int) - Method in class jsat.math.optimization.stochastic.AdaDelta
-
- setup(int) - Method in class jsat.math.optimization.stochastic.AdaGrad
-
- setup(int) - Method in class jsat.math.optimization.stochastic.Adam
-
- setup(int) - Method in interface jsat.math.optimization.stochastic.GradientUpdater
-
Sets up this updater to update a weight vector of dimension d
by a gradient of the same dimension
- setup(int) - Method in class jsat.math.optimization.stochastic.NAdaGrad
-
- setup(int) - Method in class jsat.math.optimization.stochastic.RMSProp
-
- setup(int) - Method in class jsat.math.optimization.stochastic.Rprop
-
- setup(int) - Method in class jsat.math.optimization.stochastic.SGDMomentum
-
- setup(int) - Method in class jsat.math.optimization.stochastic.SimpleSGD
-
- setUp(CategoricalData[], int) - Method in class jsat.regression.KernelRLS
-
- setUp(CategoricalData[], int) - Method in interface jsat.regression.UpdateableRegressor
-
- setUpTransform(SingularValueDecomposition) - Method in class jsat.datatransform.WhitenedPCA
-
- setUpTransform(SingularValueDecomposition) - Method in class jsat.datatransform.WhitenedZCA
-
- setUseAverageModel(boolean) - Method in class jsat.classifiers.linear.kernelized.OSKL
-
Sets whether or not the average of all intermediate models is used or if
the most recent model is used when performing classification
- setUseBias(boolean) - Method in class jsat.classifiers.linear.ALMA2
-
Sets whether or not an implicit bias term will be added to the data set
- setUseBias(boolean) - Method in class jsat.classifiers.linear.BBR
-
Sets whether or not an implicit bias term should be added to the model.
- setUseBias(boolean) - Method in class jsat.classifiers.linear.LinearSGD
-
Sets whether or not an implicit bias term will be added to the data set
- setUseBias(boolean) - Method in class jsat.classifiers.linear.LogisticRegressionDCD
-
Sets whether or not an implicit bias term should be added to the model.
- setUseBias(boolean) - Method in class jsat.classifiers.linear.NewGLMNET
-
Controls whether or not an un-regularized bias term is added to the
model.
- setUseBias(boolean) - Method in class jsat.classifiers.linear.ROMMA
-
Sets whether or not an implicit bias term will be added to the data set
- setUseBias(boolean) - Method in class jsat.classifiers.linear.SPA
-
Sets whether or not the implementation will use an implicit bias term
appended to the inputs or not.
- setUseBias(boolean) - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Sets whether or not to learn the bias term for a model.
- setUseBias(boolean) - Method in class jsat.classifiers.svm.DCD
-
Sets whether or not an implicit bias term should be added to the inputs.
- setUseBias(boolean) - Method in class jsat.classifiers.svm.DCDs
-
Sets whether or not an implicit bias term should be added to the inputs.
- setUseBiasTerm(boolean) - Method in class jsat.classifiers.linear.LinearBatch
-
- setUseDefaultSelectionCount(boolean) - Method in class jsat.classifiers.trees.ERTrees
-
Sets whether or not to use the default heuristic for the number of random
features to select as candidates for each node.
- setUseDefaultStopSize(boolean) - Method in class jsat.classifiers.trees.ERTrees
-
Sets whether or not to us the default heuristic for the number of points
to force a new node to be a leaf.
- setUseDenseSparse(boolean) - Method in class jsat.clustering.kmeans.ElkanKMeans
-
- setUseL1(boolean) - Method in class jsat.classifiers.svm.DCD
-
Determines whether or not to use the L1 or L2 SVM
- setUseL1(boolean) - Method in class jsat.classifiers.svm.DCDs
-
Determines whether or not to use the L1 or L2 SVM
- setUseLowerCase(boolean) - Method in class jsat.text.tokenizer.NaiveTokenizer
-
Sets whether or not characters are made to be lower case or not
- setUseMarginUpdates(boolean) - Method in class jsat.classifiers.linear.kernelized.Projectron
-
Sets whether or not projection updates will be performed for margin
errors.
- setUseOutOfBagError(boolean) - Method in class jsat.classifiers.trees.RandomForest
-
Sets whether or not to compute the out of bag error during training
- setUseOutOfBagImportance(boolean) - Method in class jsat.classifiers.trees.RandomForest
-
Sets whether or not to compute the out of bag importance of each feature
during training.
- setUsePriors(boolean) - Method in class jsat.classifiers.bayesian.BestClassDistribution
-
Controls whether or not the priors will be used for classification.
- setUseWarmStarts(boolean) - Method in class jsat.parameters.GridSearch
-
Sets whether or not warm starts are used, but only if the model in use
supports warm starts.
- setUsingData(List<V>) - Method in class jsat.clustering.EMGaussianMixture
-
- setUsingData(DataSet) - Method in class jsat.clustering.EMGaussianMixture
-
- setUsingData(DataSet, ExecutorService) - Method in class jsat.clustering.EMGaussianMixture
-
- setUsingData(List<V>, ExecutorService) - Method in class jsat.clustering.EMGaussianMixture
-
- setUsingData(Vec) - Method in class jsat.distributions.Beta
-
- setUsingData(Vec) - Method in class jsat.distributions.Cauchy
-
- setUsingData(Vec) - Method in class jsat.distributions.ChiSquared
-
- setUsingData(Vec) - Method in class jsat.distributions.ContinuousDistribution
-
Attempts to set the variables used by this distribution based on population sample data,
assuming the sample data is from this type of distribution.
- setUsingData(Vec) - Method in class jsat.distributions.empirical.KernelDensityEstimator
-
- setUsingData(Vec) - Method in class jsat.distributions.Exponential
-
- setUsingData(Vec) - Method in class jsat.distributions.FisherSendor
-
- setUsingData(Vec) - Method in class jsat.distributions.Gamma
-
- setUsingData(Vec) - Method in class jsat.distributions.Kolmogorov
-
- setUsingData(Vec) - Method in class jsat.distributions.Laplace
-
- setUsingData(Vec) - Method in class jsat.distributions.Levy
-
- setUsingData(Vec) - Method in class jsat.distributions.Logistic
-
- setUsingData(Vec) - Method in class jsat.distributions.LogNormal
-
- setUsingData(Vec) - Method in class jsat.distributions.LogUniform
-
- setUsingData(Vec) - Method in class jsat.distributions.MaxwellBoltzmann
-
- setUsingData(List<V>) - Method in class jsat.distributions.multivariate.Dirichlet
-
- setUsingData(List<V>, double) - Method in class jsat.distributions.multivariate.MetricKDE
-
Sets the KDE to model the density of the given data set with the specified bandwidth
- setUsingData(List<V>, double, ExecutorService) - Method in class jsat.distributions.multivariate.MetricKDE
-
Sets the KDE to model the density of the given data set with the specified bandwidth
- setUsingData(List<V>, int) - Method in class jsat.distributions.multivariate.MetricKDE
-
Sets the KDE to model the density of the given data set by estimating the bandwidth by using
the k nearest neighbors of each data point.
- setUsingData(List<V>, int, ExecutorService) - Method in class jsat.distributions.multivariate.MetricKDE
-
Sets the KDE to model the density of the given data set by estimating the bandwidth by using
the k nearest neighbors of each data point.
- setUsingData(List<V>, int, double) - Method in class jsat.distributions.multivariate.MetricKDE
-
Sets the KDE to model the density of the given data set by estimating the bandwidth
by using the k nearest neighbors of each data data point.
- setUsingData(List<V>, int, double, ExecutorService) - Method in class jsat.distributions.multivariate.MetricKDE
-
Sets the KDE to model the density of the given data set by estimating the bandwidth
by using the k nearest neighbors of each data data point.
- setUsingData(List<V>) - Method in class jsat.distributions.multivariate.MetricKDE
-
- setUsingData(List<V>, ExecutorService) - Method in class jsat.distributions.multivariate.MetricKDE
-
- setUsingData(List<V>) - Method in interface jsat.distributions.multivariate.MultivariateDistribution
-
Sets the parameters of the distribution to attempt to fit the given list of vectors.
- setUsingData(List<V>, ExecutorService) - Method in interface jsat.distributions.multivariate.MultivariateDistribution
-
Sets the parameters of the distribution to attempt to fit the given list of vectors.
- setUsingData(DataSet) - Method in interface jsat.distributions.multivariate.MultivariateDistribution
-
Sets the parameters of the distribution to attempt to fit the given list of data points.
- setUsingData(DataSet, ExecutorService) - Method in interface jsat.distributions.multivariate.MultivariateDistribution
-
Sets the parameters of the distribution to attempt to fit the given list of data points.
- setUsingData(DataSet) - Method in class jsat.distributions.multivariate.MultivariateDistributionSkeleton
-
- setUsingData(DataSet, ExecutorService) - Method in class jsat.distributions.multivariate.MultivariateDistributionSkeleton
-
- setUsingData(List<V>, ExecutorService) - Method in class jsat.distributions.multivariate.MultivariateDistributionSkeleton
-
- setUsingData(List<V>) - Method in class jsat.distributions.multivariate.NormalM
-
- setUsingData(List<V>) - Method in class jsat.distributions.multivariate.ProductKDE
-
- setUsingData(List<V>) - Method in class jsat.distributions.multivariate.SymmetricDirichlet
-
- setUsingData(Vec) - Method in class jsat.distributions.Normal
-
- setUsingData(Vec) - Method in class jsat.distributions.Pareto
-
- setUsingData(Vec) - Method in class jsat.distributions.Rayleigh
-
- setUsingData(Vec) - Method in class jsat.distributions.StudentT
-
- setUsingData(Vec) - Method in class jsat.distributions.TruncatedDistribution
-
- setUsingData(Vec) - Method in class jsat.distributions.Uniform
-
- setUsingData(Vec) - Method in class jsat.distributions.Weibull
-
- setUsingDataList(List<DataPoint>) - Method in class jsat.clustering.EMGaussianMixture
-
- setUsingDataList(List<DataPoint>, ExecutorService) - Method in class jsat.clustering.EMGaussianMixture
-
- setUsingDataList(List<DataPoint>) - Method in class jsat.distributions.multivariate.Dirichlet
-
- setUsingDataList(List<DataPoint>) - Method in class jsat.distributions.multivariate.MetricKDE
-
- setUsingDataList(List<DataPoint>, ExecutorService) - Method in class jsat.distributions.multivariate.MetricKDE
-
- setUsingDataList(List<DataPoint>) - Method in interface jsat.distributions.multivariate.MultivariateDistribution
-
Sets the parameters of the distribution to attempt to fit the given list of data points.
- setUsingDataList(List<DataPoint>, ExecutorService) - Method in interface jsat.distributions.multivariate.MultivariateDistribution
-
Sets the parameters of the distribution to attempt to fit the given list of data points.
- setUsingDataList(List<DataPoint>, ExecutorService) - Method in class jsat.distributions.multivariate.MultivariateDistributionSkeleton
-
- setUsingDataList(List<DataPoint>) - Method in class jsat.distributions.multivariate.NormalM
-
- setUsingDataList(List<DataPoint>) - Method in class jsat.distributions.multivariate.ProductKDE
-
- setUsingDataList(List<DataPoint>) - Method in class jsat.distributions.multivariate.SymmetricDirichlet
-
- setValue(double) - Method in class jsat.linear.IndexValue
-
Sets the value associated with the index
- setValue(boolean) - Method in class jsat.parameters.BooleanParameter
-
Sets the value for this parameter.
- setValue(double) - Method in class jsat.parameters.DoubleParameter
-
Sets the value for this parameter.
- setValue(int) - Method in class jsat.parameters.IntParameter
-
Sets the value for this parameter.
- setVariable(String, double) - Method in class jsat.distributions.Beta
-
- setVariable(String, double) - Method in class jsat.distributions.Cauchy
-
- setVariable(String, double) - Method in class jsat.distributions.ChiSquared
-
- setVariable(String, double) - Method in class jsat.distributions.ContinuousDistribution
-
Sets one of the variables of this distribution by the name.
- setVariable(String, double) - Method in class jsat.distributions.empirical.KernelDensityEstimator
-
- setVariable(String, double) - Method in class jsat.distributions.Exponential
-
- setVariable(String, double) - Method in class jsat.distributions.FisherSendor
-
- setVariable(String, double) - Method in class jsat.distributions.Gamma
-
- setVariable(String, double) - Method in class jsat.distributions.Kolmogorov
-
- setVariable(String, double) - Method in class jsat.distributions.Laplace
-
- setVariable(String, double) - Method in class jsat.distributions.Levy
-
- setVariable(String, double) - Method in class jsat.distributions.Logistic
-
- setVariable(String, double) - Method in class jsat.distributions.LogNormal
-
- setVariable(String, double) - Method in class jsat.distributions.LogUniform
-
- setVariable(String, double) - Method in class jsat.distributions.MaxwellBoltzmann
-
- setVariable(String, double) - Method in class jsat.distributions.Normal
-
- setVariable(String, double) - Method in class jsat.distributions.Pareto
-
- setVariable(String, double) - Method in class jsat.distributions.Rayleigh
-
- setVariable(String, double) - Method in class jsat.distributions.StudentT
-
- setVariable(String, double) - Method in class jsat.distributions.TruncatedDistribution
-
- setVariable(String, double) - Method in class jsat.distributions.Uniform
-
- setVariable(String, double) - Method in class jsat.distributions.Weibull
-
- setVCF(VectorCollectionFactory<VecPaired<Vec, Integer>>) - Method in class jsat.clustering.OPTICS
-
- setVecCollectionFactory(VectorCollectionFactory<VecPaired<Vec, Integer>>) - Method in class jsat.classifiers.neuralnetwork.LVQ
-
Sets the vector collection factory to use when storing the final learning vectors
- setVector(V) - Method in class jsat.linear.VecPaired
-
- setVectorCollectionFactory(VectorCollectionFactory<VecPaired<Vec, Integer>>) - Method in class jsat.clustering.FLAME
-
Sets the vector collection factory used to accelerate the nearest
neighbor search.
- setVectorCollectionFactory(VectorCollectionFactory<VecPaired<Vec, Integer>>) - Method in class jsat.clustering.LSDBC
-
Sets the vector collection factory used for acceleration of neighbor searches.
- setVocabSize(int) - Method in class jsat.text.topicmodel.OnlineLDAsvi
-
Sets the vocabulary size for LDA, which is the number of dimensions in
the input feature vectors.
- setWarmModels(Classifier...) - Method in class jsat.classifiers.ClassificationModelEvaluation
-
Sets the models that will be used for warm starting training.
- setWarmModels(Regressor...) - Method in class jsat.regression.RegressionModelEvaluation
-
Sets the models that will be used for warm starting training.
- setWarningThreshold(double) - Method in class jsat.driftdetectors.DDM
-
Sets the multiplier on the standard deviation that must be exceeded to
initiate a warning state.
- setWeakLearner(Classifier) - Method in class jsat.classifiers.boosting.AdaBoostM1
-
Sets the weak learner used during training.
- setWeakLearner(Classifier) - Method in class jsat.classifiers.boosting.ArcX4
-
Sets the weak learner used at each iteration of learning
- setWeakLearner(Classifier) - Method in class jsat.classifiers.boosting.EmphasisBoost
-
Sets the weak learner used during training.
- setWeakLearner(Classifier) - Method in class jsat.classifiers.boosting.ModestAdaBoost
-
Sets the weak learner used during training.
- setWeakLearner(Classifier) - Method in class jsat.classifiers.boosting.Wagging
-
Sets the weak learner used for classification.
- setWeakLearner(Regressor) - Method in class jsat.classifiers.boosting.Wagging
-
Sets the weak learner used for regressions .
- setWeight(double) - Method in class jsat.classifiers.DataPoint
-
Set the weight that this data point should carry.
- setWeight(Vec) - Method in class jsat.linear.distancemetrics.WeightedEuclideanDistance
-
Sets the weight vector to use for the distance function
- setWeight(List<? extends Vec>, List<Integer>) - Method in class jsat.text.wordweighting.BinaryWordPresent
-
- setWeight(List<? extends Vec>, List<Integer>) - Method in class jsat.text.wordweighting.OkapiBM25
-
- setWeight(List<? extends Vec>, List<Integer>) - Method in class jsat.text.wordweighting.TfIdf
-
- setWeight(List<? extends Vec>, List<Integer>) - Method in class jsat.text.wordweighting.WordCount
-
- setWeight(List<? extends Vec>, List<Integer>) - Method in class jsat.text.wordweighting.WordWeighting
-
Prepares the word weighting to be performed on a data set.
- setWeightDecay(double) - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
Sets the weight decay used for each update.
- setWeightInit(WeightInitializer) - Method in class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
Sets the method used to initialize matrix connection weights
- setWeightInitialization(BackPropagationNet.WeightInitialization) - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
Sets how the weights are initialized before training starts
- setX(X) - Method in class jsat.utils.Tuple3
-
- setXi(double) - Method in class jsat.clustering.OPTICS
-
- setXm(double) - Method in class jsat.distributions.Pareto
-
- setY(Y) - Method in class jsat.utils.Tuple3
-
- setZ(Z) - Method in class jsat.utils.Tuple3
-
- setzMax(double) - Method in class jsat.classifiers.boosting.LogitBoost
-
Sets the penalty bound for miss-classification of results.
- SFS - Class in jsat.datatransform.featureselection
-
Sequential Forward Selection (SFS) is a greedy method of selecting a subset
of features to use for prediction.
- SFS(int, int, Classifier, double) - Constructor for class jsat.datatransform.featureselection.SFS
-
Performs SFS feature selection for a classification problem
- SFS(int, int, ClassificationDataSet, Classifier, int, double) - Constructor for class jsat.datatransform.featureselection.SFS
-
Performs SFS feature selection for a classification problem
- SFS(int, int, Regressor, double) - Constructor for class jsat.datatransform.featureselection.SFS
-
Creates SFS feature selection for a regression problem
- SFS(int, int, RegressionDataSet, Regressor, int, double) - Constructor for class jsat.datatransform.featureselection.SFS
-
Performs SFS feature selection for a regression problem
- SFSSelectFeature(Set<Integer>, DataSet, Set<Integer>, Set<Integer>, Set<Integer>, Set<Integer>, Object, int, Random, double[], int) - Static method in class jsat.datatransform.featureselection.SFS
-
Attempts to add one feature to the list of features while increasing or
maintaining the current accuracy
- SGDMomentum - Class in jsat.math.optimization.stochastic
-
Performs unaltered Stochastic Gradient Decent updates using either standard
or Nestrov momentum.
- SGDMomentum(double, boolean) - Constructor for class jsat.math.optimization.stochastic.SGDMomentum
-
Creates a new SGD with Momentum learner
- SGDMomentum(double) - Constructor for class jsat.math.optimization.stochastic.SGDMomentum
-
Creates a new SGD with Nestrov Momentum learner
- SGDMomentum(SGDMomentum) - Constructor for class jsat.math.optimization.stochastic.SGDMomentum
-
Copy constructor
- SGDNetworkTrainer - Class in jsat.classifiers.neuralnetwork
-
This class provides a highly configurable and generalized method of training
a neural network using Stochastic Gradient Decent.
Note, the API of this class may change in the future.
- SGDNetworkTrainer() - Constructor for class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
Creates a new SGD network training that uses dropout
- SGDNetworkTrainer(SGDNetworkTrainer) - Constructor for class jsat.classifiers.neuralnetwork.SGDNetworkTrainer
-
Copy constructor
- shallowClone() - Method in class jsat.classifiers.ClassificationDataSet
-
- shallowClone() - Method in class jsat.DataSet
-
Returns a new version of this data set that is of the same type, and
contains a different list pointing to the same data points.
- shallowClone() - Method in class jsat.regression.RegressionDataSet
-
- shallowClone() - Method in class jsat.SimpleDataSet
-
- ShiftedVec - Class in jsat.linear
-
A wrapper for a vector that represents the vector added with a scalar value.
- ShiftedVec(Vec, double) - Constructor for class jsat.linear.ShiftedVec
-
Creates a new vector represented as base+shift
- shuffle(int[], Random) - Static method in class jsat.utils.ArrayUtils
-
Swaps values in the given array
- shuffle(int[], int, int, Random) - Static method in class jsat.utils.ArrayUtils
-
Shuffles the values in the given array
- shutdown() - Method in class jsat.utils.FakeExecutor
-
- shutdownNow() - Method in class jsat.utils.FakeExecutor
-
- sigma - Static variable in class jsat.text.GreekLetters
-
- sigmaToGamma(double) - Static method in class jsat.distributions.kernels.RBFKernel
-
Another common (equivalent) form of the RBF kernel is k(x, y) =
exp(-γ||x-y||2).
- SigmoidKernel - Class in jsat.distributions.kernels
-
Provides an implementation of the Sigmoid (Hyperbolic Tangent) Kernel, which
is of the form:
k(x, y) = tanh(alpha * < x, y > +c)
Technically, this kernel is not positive definite.
- SigmoidKernel(double, double) - Constructor for class jsat.distributions.kernels.SigmoidKernel
-
Creates a new Sigmoid Kernel
- SigmoidKernel(double) - Constructor for class jsat.distributions.kernels.SigmoidKernel
-
Creates a new Sigmoid Kernel with a bias term of 1
- SigmoidLayer - Class in jsat.classifiers.neuralnetwork.activations
-
This layer provides the standard Sigmoid activation f(x) =
1/(1+exp(-x))
- SigmoidLayer() - Constructor for class jsat.classifiers.neuralnetwork.activations.SigmoidLayer
-
- SimpleBinaryClassMetric - Class in jsat.classifiers.evaluation
-
This is a base class for scores that can be computed from simple counts of
the true positives, true negatives, false positives, and false negatives.
- SimpleBinaryClassMetric() - Constructor for class jsat.classifiers.evaluation.SimpleBinaryClassMetric
-
- SimpleBinaryClassMetric(SimpleBinaryClassMetric) - Constructor for class jsat.classifiers.evaluation.SimpleBinaryClassMetric
-
- SimpleDataSet - Class in jsat
-
SimpleData Set is a basic implementation of a data set.
- SimpleDataSet(List<DataPoint>) - Constructor for class jsat.SimpleDataSet
-
- SimpleDataSet(CategoricalData[], int) - Constructor for class jsat.SimpleDataSet
-
- SimpleHAC - Class in jsat.clustering.hierarchical
-
Provides a naive implementation of hierarchical agglomerative clustering
(HAC).
- SimpleHAC(ClusterDissimilarity) - Constructor for class jsat.clustering.hierarchical.SimpleHAC
-
- SimpleHAC(SimpleHAC) - Constructor for class jsat.clustering.hierarchical.SimpleHAC
-
Copy constructor
- simpleInsert(V) - Method in class jsat.linear.vectorcollection.CoverTree
-
- simpleInsert(CoverTree<V>.TreeNode, int) - Method in class jsat.linear.vectorcollection.CoverTree
-
- simpleInsert_(CoverTree<V>.TreeNode, int) - Method in class jsat.linear.vectorcollection.CoverTree
-
prerequisites: d(p,x) ≤ covdist(p)
- SimpleLinearRegression - Class in jsat.math
-
- SimpleLinearRegression() - Constructor for class jsat.math.SimpleLinearRegression
-
- SimpleList<E> - Class in jsat.utils
-
An alternative implementation of an
ArrayList.
- SimpleList(int) - Constructor for class jsat.utils.SimpleList
-
Creates a new SimpleList
- SimpleList() - Constructor for class jsat.utils.SimpleList
-
Creates a new SimpleList
- SimpleList(Collection<? extends E>) - Constructor for class jsat.utils.SimpleList
-
Creates a new SimpleList
- SimpleSGD - Class in jsat.math.optimization.stochastic
-
Performs unaltered Stochastic Gradient Decent updates computing
x = x- η grad
Because the SimpleSGD requires no internal state, it is not necessary to call
SimpleSGD.setup(int).
- SimpleSGD() - Constructor for class jsat.math.optimization.stochastic.SimpleSGD
-
Creates a new SGD updater
- SimpleWeightVectorModel - Interface in jsat
-
This interface is for multi-class classification problems where there may be
K or K-1 weight vectors for K classes.
- SingleLinkDissimilarity - Class in jsat.clustering.dissimilarity
-
Measures the dissimilarity of two clusters by returning the minimum
dissimilarity between the two closest data points from the clusters, ie:
the minimum distance needed to link the two clusters.
- SingleLinkDissimilarity() - Constructor for class jsat.clustering.dissimilarity.SingleLinkDissimilarity
-
- SingleLinkDissimilarity(DistanceMetric) - Constructor for class jsat.clustering.dissimilarity.SingleLinkDissimilarity
-
Creates a new SingleLinkDissimilarity
- SingleLinkDissimilarity(SingleLinkDissimilarity) - Constructor for class jsat.clustering.dissimilarity.SingleLinkDissimilarity
-
Copy constructor
- SingleWeightVectorModel - Interface in jsat
-
This interface is for binary classification and regression problems where the
solution can be represented as a single weight vector.
- SingularValueDecomposition - Class in jsat.linear
-
The Singular Value Decomposition (SVD) of a matrix Am,n = Um,n Σn,n VTn,n ,
where S is the diagonal matrix of the singular values sorted in descending order and are all non negative.
- SingularValueDecomposition(Matrix) - Constructor for class jsat.linear.SingularValueDecomposition
-
Creates a new SVD of the matrix A such that A = U Σ VT.
- SingularValueDecomposition(Matrix, int) - Constructor for class jsat.linear.SingularValueDecomposition
-
Creates a new SVD of the matrix A such that A = U Σ VT.
- SingularValueDecomposition(Matrix, Matrix, double[]) - Constructor for class jsat.linear.SingularValueDecomposition
-
Sets the values for a SVD explicitly.
- size() - Method in class jsat.classifiers.CategoricalResults
-
Returns the number of classes that are in the result.
- size() - Method in class jsat.distributions.kernels.KernelPoints
-
Returns the number of KernelPoints stored in this set
- size() - Method in class jsat.linear.vectorcollection.CoverTree
-
- size() - Method in class jsat.linear.vectorcollection.EuclideanCollection
-
- size() - Method in class jsat.linear.vectorcollection.KDTree
-
- size() - Method in class jsat.linear.vectorcollection.lsh.RandomProjectionLSH
-
- size() - Method in class jsat.linear.vectorcollection.RandomBallCover
-
- size() - Method in class jsat.linear.vectorcollection.RandomBallCoverOneShot
-
- size() - Method in class jsat.linear.vectorcollection.RTree
-
- size() - Method in interface jsat.linear.vectorcollection.VectorCollection
-
Returns the number of vectors stored in the collection
- size() - Method in class jsat.linear.vectorcollection.VPTree
-
- size() - Method in class jsat.utils.DoubleList
-
- size() - Method in class jsat.utils.FibHeap
-
- size() - Method in class jsat.utils.IntDoubleMap
-
- size() - Method in class jsat.utils.IntList
-
- size() - Method in class jsat.utils.IntPriorityQueue
-
- size() - Method in class jsat.utils.IntSet
-
- size() - Method in class jsat.utils.IntSetFixedSize
-
- size() - Method in class jsat.utils.IntSortedSet
-
- size() - Method in class jsat.utils.LongDoubleMap
-
- size() - Method in class jsat.utils.LongList
-
- size() - Method in class jsat.utils.SimpleList
-
- skewness() - Method in class jsat.distributions.Beta
-
- skewness() - Method in class jsat.distributions.Cauchy
-
- skewness() - Method in class jsat.distributions.ChiSquared
-
- skewness() - Method in class jsat.distributions.ContinuousDistribution
-
- skewness() - Method in class jsat.distributions.discrete.Binomial
-
- skewness() - Method in class jsat.distributions.discrete.Poisson
-
- skewness() - Method in class jsat.distributions.discrete.UniformDiscrete
-
- skewness() - Method in class jsat.distributions.Distribution
-
Computes the skewness of the distribution.
- skewness() - Method in class jsat.distributions.empirical.KernelDensityEstimator
-
- skewness() - Method in class jsat.distributions.Exponential
-
- skewness() - Method in class jsat.distributions.FisherSendor
-
- skewness() - Method in class jsat.distributions.Gamma
-
- skewness() - Method in class jsat.distributions.Kolmogorov
-
- skewness() - Method in class jsat.distributions.Laplace
-
- skewness() - Method in class jsat.distributions.Levy
-
- skewness() - Method in class jsat.distributions.Logistic
-
- skewness() - Method in class jsat.distributions.LogNormal
-
- skewness() - Method in class jsat.distributions.LogUniform
-
- skewness() - Method in class jsat.distributions.MaxwellBoltzmann
-
- skewness() - Method in class jsat.distributions.Normal
-
- skewness() - Method in class jsat.distributions.Pareto
-
- skewness() - Method in class jsat.distributions.Rayleigh
-
- skewness() - Method in class jsat.distributions.StudentT
-
- skewness() - Method in class jsat.distributions.Uniform
-
- skewness() - Method in class jsat.distributions.Weibull
-
- skewness() - Method in class jsat.linear.DenseVector
-
- skewness() - Method in class jsat.linear.ScaledVector
-
- skewness() - Method in class jsat.linear.SparseVector
-
- skewness() - Method in class jsat.linear.Vec
-
Computes the skewness of this vector, which is the 3rd moment.
- skewness() - Method in class jsat.linear.VecPaired
-
- SMIDAS - Class in jsat.classifiers.linear
-
Implements the iterative and single threaded stochastic solver for
L1 regularized linear regression problems SMIDAS (Stochastic
Mirror Descent Algorithm mAde Sparse).
- SMIDAS(double) - Constructor for class jsat.classifiers.linear.SMIDAS
-
Creates a new SMIDAS learner
- SMIDAS(double, int, double, StochasticSTLinearL1.Loss) - Constructor for class jsat.classifiers.linear.SMIDAS
-
Creates a new SMIDAS learner
- SMIDAS(double, int, double, StochasticSTLinearL1.Loss, boolean) - Constructor for class jsat.classifiers.linear.SMIDAS
-
Creates a new SMIDAS learner
- softmax(double[], boolean) - Static method in class jsat.math.MathTricks
-
Applies the softmax function to the given array of values, normalizing
them so that each value is equal to
exp(xj) / Σ∀ i exp(xi)
- softmax(Vec, boolean) - Static method in class jsat.math.MathTricks
-
Applies the softmax function to the given array of values, normalizing
them so that each value is equal to
exp(xj) / Σ∀ i exp(xi)
Note: If the input is sparse, this will end up destroying sparsity
- SoftmaxLayer - Class in jsat.classifiers.neuralnetwork.activations
-
This activation layer is meant to be used as the top-most layer for
classification problems, and uses the softmax function (also known as cross
entropy) to convert the inputs into probabilities.
- SoftmaxLayer() - Constructor for class jsat.classifiers.neuralnetwork.activations.SoftmaxLayer
-
- SoftmaxLoss - Class in jsat.lossfunctions
-
The Softmax loss function is a multi-class generalization of the
Logistic loss.
- SoftmaxLoss() - Constructor for class jsat.lossfunctions.SoftmaxLoss
-
- softsignActiv - Static variable in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
The softsign activation function.
- SoftSignLayer - Class in jsat.classifiers.neuralnetwork.activations
-
This provides the Soft Sign activation function f(x) = x/(1+abs(x)), which is
similar to the
tanh activation and has a min/max of -1 and
1.
- SoftSignLayer() - Constructor for class jsat.classifiers.neuralnetwork.activations.SoftSignLayer
-
- solve(Vec) - Method in class jsat.linear.CholeskyDecomposition
-
Solves the linear system of equations A x = b
- solve(Matrix) - Method in class jsat.linear.CholeskyDecomposition
-
Solves the linear system of equations A x = B
- solve(Matrix, ExecutorService) - Method in class jsat.linear.CholeskyDecomposition
-
Solves the linear system of equations A x = B
- solve(Vec) - Method in class jsat.linear.LUPDecomposition
-
- solve(Matrix) - Method in class jsat.linear.LUPDecomposition
-
- solve(Matrix, ExecutorService) - Method in class jsat.linear.LUPDecomposition
-
- solve(Vec) - Method in class jsat.linear.QRDecomposition
-
- solve(Matrix) - Method in class jsat.linear.QRDecomposition
-
- solve(Matrix, ExecutorService) - Method in class jsat.linear.QRDecomposition
-
- solve(Vec) - Method in class jsat.linear.SingularValueDecomposition
-
Solves the linear system of equations for A x = b by using the equation
x = A-1 b = V S-1 UT b
When A is not full rank, this results in a more numerically stable approximation that minimizes the least squares error.
- solve(Matrix) - Method in class jsat.linear.SingularValueDecomposition
-
Solves the linear system of equations for A x = B by using the equation
x = A-1 B = V S-1 UT B
When A is not full rank, this results in a more numerically stable approximation that minimizes the least squares error.
- solve(Matrix, ExecutorService) - Method in class jsat.linear.SingularValueDecomposition
-
Solves the linear system of equations for A x = B by using the equation
x = A-1 B = V S-1 UT B
When A is not full rank, this results in a more numerically stable approximation that minimizes the least squares error.
- solve(double, Matrix, Vec, Vec) - Static method in class jsat.linear.solvers.ConjugateGradient
-
Uses the Conjugate Gradient method to solve a linear system of
equations involving a symmetric positive definite matrix.
A symmetric positive definite matrix is a matrix A such that:
AT = A
xT * A * x > 0 for all x != 0
NOTE: No checks will be performed to confirm these properties of the given matrix.
- solve(Matrix, Vec) - Static method in class jsat.linear.solvers.ConjugateGradient
-
- solve(double, Matrix, Vec, Vec, Matrix) - Static method in class jsat.linear.solvers.ConjugateGradient
-
Uses the Conjugate Gradient method to solve a linear system of
equations involving a symmetric positive definite matrix.
A symmetric positive definite matrix is a matrix A such that:
AT = A
xT * A * x > 0 for all x != 0
NOTE: No checks will be performed to confirm these properties of the given matrix.
- solveCGNR(double, Matrix, Vec, Vec) - Static method in class jsat.linear.solvers.ConjugateGradient
-
Uses the Conjugate Gradient method to compute the least squares solution to a system
of linear equations.
Computes the least squares solution to A x = b.
- solveCGNR(Matrix, Vec) - Static method in class jsat.linear.solvers.ConjugateGradient
-
- SOM - Class in jsat.classifiers.neuralnetwork
-
An implementation of a Self Organizing Map, also called a Kohonen Map.
- SOM(int, int) - Constructor for class jsat.classifiers.neuralnetwork.SOM
-
- SOM(DistanceMetric, int, int) - Constructor for class jsat.classifiers.neuralnetwork.SOM
-
Creates a new SOM using the given parameters
- SOM(DistanceMetric, int, int, VectorCollectionFactory<VecPaired<Vec, Integer>>) - Constructor for class jsat.classifiers.neuralnetwork.SOM
-
Creates a new SOM using the given parameters
- sort(double[]) - Method in class jsat.utils.IndexTable
-
Adjusts this index table to contain the sorted index order for the given
array
- sort(List<T>) - Method in class jsat.utils.IndexTable
-
Adjust this index table to contain the sorted index order for the given
list
- sort(List<T>, Comparator<T>) - Method in class jsat.utils.IndexTable
-
Sets up the index table based on the given list of the same size and
comparator.
- sort() - Method in class jsat.utils.IntList
-
- sort(double[], int, int) - Static method in class jsat.utils.QuickSort
-
Performs sorting based on the double values natural comparator.
- sort(double[], int, int, Collection<List<?>>) - Static method in class jsat.utils.QuickSort
-
Performs sorting based on the double values natural comparator.
- sort(float[], int, int, Collection<List<?>>) - Static method in class jsat.utils.QuickSort
-
Performs sorting based on the double values natural comparator.
- sortByEigenValue(Comparator<Double>) - Method in class jsat.linear.EigenValueDecomposition
-
Sorts the eigen values and the corresponding eigenvector columns by the
associated eigen value.
- SortedArrayList<T extends Comparable<T>> - Class in jsat.utils
-
- SortedArrayList(Collection<? extends T>) - Constructor for class jsat.utils.SortedArrayList
-
- SortedArrayList(int) - Constructor for class jsat.utils.SortedArrayList
-
- SortedArrayList() - Constructor for class jsat.utils.SortedArrayList
-
- sortedCopy() - Method in class jsat.linear.DenseVector
-
- sortedCopy() - Method in class jsat.linear.RandomVector
-
- sortedCopy() - Method in class jsat.linear.ScaledVector
-
- sortedCopy() - Method in class jsat.linear.SparseVector
-
- sortedCopy() - Method in class jsat.linear.Vec
-
Returns a copy of this array with the values moved around so that they are in sorted order
- sortedCopy() - Method in class jsat.linear.VecPaired
-
- sortR(double[]) - Method in class jsat.utils.IndexTable
-
Adjusts this index table to contain the reverse sorted index order for
the given array
- sortR(List<T>) - Method in class jsat.utils.IndexTable
-
Adjusts this index table to contain the reverse sorted index order for
the given list
- SoSCentroidDistance - Class in jsat.clustering.evaluation.intra
-
Evaluates a cluster's validity by computing the sum of squared distances from
each point to the mean of the cluster.
- SoSCentroidDistance() - Constructor for class jsat.clustering.evaluation.intra.SoSCentroidDistance
-
- SoSCentroidDistance(DistanceMetric) - Constructor for class jsat.clustering.evaluation.intra.SoSCentroidDistance
-
Creates a new MeanCentroidDistance.
- SoSCentroidDistance(SoSCentroidDistance) - Constructor for class jsat.clustering.evaluation.intra.SoSCentroidDistance
-
Copy constructor
- SPA - Class in jsat.classifiers.linear
-
Support class Passive Aggressive (SPA) is a multi class generalization of
PassiveAggressive.
- SPA() - Constructor for class jsat.classifiers.linear.SPA
-
Creates a new Passive Aggressive learner that does 10 epochs and uses
PA2.
- SPA(int, PassiveAggressive.Mode) - Constructor for class jsat.classifiers.linear.SPA
-
Creates a new Passive Aggressive learner
- SparseMatrix - Class in jsat.linear
-
Creates a new Sparse Matrix where each row is backed by a sparse vector.
- SparseMatrix(int, int, int) - Constructor for class jsat.linear.SparseMatrix
-
Creates a new sparse matrix
- SparseMatrix(SparseVector[]) - Constructor for class jsat.linear.SparseMatrix
-
Creates a new Sparse Matrix backed by the given array of SpareVectors.
- SparseMatrix(int, int) - Constructor for class jsat.linear.SparseMatrix
-
Creates a new sparse matrix
- SparseMatrix(SparseMatrix) - Constructor for class jsat.linear.SparseMatrix
-
Copy constructor
- SparseVector - Class in jsat.linear
-
Provides a vector implementation that is sparse.
- SparseVector(int) - Constructor for class jsat.linear.SparseVector
-
Creates a new sparse vector of the given length that is all zero values.
- SparseVector(List<Double>) - Constructor for class jsat.linear.SparseVector
-
Creates a new sparse vector of the same length as vals and sets
each value to the values in the list.
- SparseVector(int, int) - Constructor for class jsat.linear.SparseVector
-
Creates a new sparse vector of the specified length, and pre-allocates
enough internal state to hold capacity non zero values.
- SparseVector(int[], double[], int, int) - Constructor for class jsat.linear.SparseVector
-
Creates a new sparse vector backed by the given arrays.
- SparseVector(Vec) - Constructor for class jsat.linear.SparseVector
-
Creates a new sparse vector by copying the values from another
- sparsify() - Method in class jsat.classifiers.svm.SupportVectorLearner
-
Sparsifies the SVM by removing the vectors with α = 0 from the
dataset.
- SpecialMath - Class in jsat.math
-
This class provides static methods for computing accurate approximations to
many special functions.
- SpecialMath() - Constructor for class jsat.math.SpecialMath
-
- splitList(List<T>, int) - Static method in class jsat.utils.ListUtils
-
This method takes a list and breaks it into count lists backed by the original
list, with elements being equally spaced among the lists.
- splitListIndex(List<Pair<Double, Integer>>) - Method in class jsat.linear.vectorcollection.VPTree
-
Determines which index to use as the splitting index for the VP radius
- splitListIndex(List<Pair<Double, Integer>>) - Method in class jsat.linear.vectorcollection.VPTreeMV
-
- sqrdFunc - Static variable in class jsat.math.MathTricks
-
Convenience object for taking the squared value
of the first index
- sqrtFunc - Static variable in class jsat.math.MathTricks
-
Convenience object for taking the
square root
of the first index
- SquaredEuclideanDistance - Class in jsat.linear.distancemetrics
-
- SquaredEuclideanDistance() - Constructor for class jsat.linear.distancemetrics.SquaredEuclideanDistance
-
- SquaredLoss - Class in jsat.lossfunctions
-
The SquaredLoss loss function for regression L(x, y) = (x-y)2.
- SquaredLoss() - Constructor for class jsat.lossfunctions.SquaredLoss
-
- Stacking - Class in jsat.classifiers.boosting
-
This provides an implementation of the Stacking ensemble method.
- Stacking(int, Classifier, List<Classifier>) - Constructor for class jsat.classifiers.boosting.Stacking
-
Creates a new Stacking classifier
- Stacking(int, Classifier, Classifier...) - Constructor for class jsat.classifiers.boosting.Stacking
-
Creates a new Stacking classifier
- Stacking(Classifier, List<Classifier>) - Constructor for class jsat.classifiers.boosting.Stacking
-
Creates a new Stacking classifier that uses
3 folds of cross validation
- Stacking(Classifier, Classifier...) - Constructor for class jsat.classifiers.boosting.Stacking
-
Creates a new Stacking classifier that uses
3 folds of cross validation
- Stacking(int, Regressor, List<Regressor>) - Constructor for class jsat.classifiers.boosting.Stacking
-
Creates a new Stacking regressor
- Stacking(int, Regressor, Regressor...) - Constructor for class jsat.classifiers.boosting.Stacking
-
Creates a new Stacking regressor
- Stacking(Regressor, List<Regressor>) - Constructor for class jsat.classifiers.boosting.Stacking
-
Creates a new Stacking regressor that uses
3 folds of cross validation
- Stacking(Regressor, Regressor...) - Constructor for class jsat.classifiers.boosting.Stacking
-
Creates a new Stacking regressor that uses
3 folds of cross validation
- Stacking(Stacking) - Constructor for class jsat.classifiers.boosting.Stacking
-
Copy constructor
- standardDeviation() - Method in class jsat.distributions.Cauchy
-
The Cauchy distribution is unique in that it does not have a standard deviation value (undefined).
- standardDeviation() - Method in class jsat.distributions.Distribution
-
Computes the standard deviation of the distribution.
- standardDeviation() - Method in class jsat.distributions.Levy
-
- standardDeviation() - Method in class jsat.distributions.Normal
-
- standardDeviation() - Method in class jsat.linear.ConstantVector
-
- standardDeviation() - Method in class jsat.linear.ScaledVector
-
- standardDeviation() - Method in class jsat.linear.ShiftedVec
-
- standardDeviation() - Method in class jsat.linear.Vec
-
Computes the standard deviation of the values in this vector
- standardDeviation() - Method in class jsat.linear.VecPaired
-
- StandardizeTransform - Class in jsat.datatransform
-
This transform performs standardization of the data, which makes each column
have a mean of zero and a variance of one.
- StandardizeTransform() - Constructor for class jsat.datatransform.StandardizeTransform
-
Creates a new object for Standardizing datasets
- StandardizeTransform(DataSet) - Constructor for class jsat.datatransform.StandardizeTransform
-
Creates a new object for standaidizing datasets fit to the given dataset
- StandardizeTransform(StandardizeTransform) - Constructor for class jsat.datatransform.StandardizeTransform
-
Copy constructor
- StatisticTest - Interface in jsat.testing
-
- StatisticTest.H1 - Enum in jsat.testing
-
- stem(String) - Method in class jsat.text.stemming.LovinsStemmer
-
- stem(String) - Method in class jsat.text.stemming.PaiceHuskStemmer
-
- stem(String) - Method in class jsat.text.stemming.PorterStemmer
-
- stem(String) - Method in class jsat.text.stemming.Stemmer
-
Reduce the given input to its stem word
- stem(String) - Method in class jsat.text.stemming.VoidStemmer
-
- Stemmer - Class in jsat.text.stemming
-
Stemmers are algorithms that attempt reduce strings to their common stem or
root word.
- Stemmer() - Constructor for class jsat.text.stemming.Stemmer
-
- StemmingTokenizer - Class in jsat.text.tokenizer
-
- StemmingTokenizer(Stemmer, Tokenizer) - Constructor for class jsat.text.tokenizer.StemmingTokenizer
-
- STGD - Class in jsat.classifiers.linear
-
This provides an implementation of Sparse Truncated Gradient Descent for
L1 regularized linear classification and regression on sparse data
sets.
- STGD(int, double, double, double) - Constructor for class jsat.classifiers.linear.STGD
-
Creates a new STGD learner
- STGD(STGD) - Constructor for class jsat.classifiers.linear.STGD
-
Copy constructor
- StochasticGradientBoosting - Class in jsat.regression
-
An implementation of Stochastic Gradient Boosting (SGB) for the Squared Error
loss.
- StochasticGradientBoosting(Regressor, Regressor, int, double, double) - Constructor for class jsat.regression.StochasticGradientBoosting
-
Creates a new initialized SGB learner.
- StochasticGradientBoosting(Regressor, int, double, double) - Constructor for class jsat.regression.StochasticGradientBoosting
-
Creates a new SGB learner that is initialized using the weak learner.
- StochasticGradientBoosting(Regressor, int, double) - Constructor for class jsat.regression.StochasticGradientBoosting
-
Creates a new SGB learner that is initialized using the weak learner.
- StochasticGradientBoosting(Regressor, int) - Constructor for class jsat.regression.StochasticGradientBoosting
-
Creates a new SGB learner that is initialized using the weak learner.
- StochasticMultinomialLogisticRegression - Class in jsat.classifiers.linear
-
This is a Stochastic implementation of Multinomial Logistic Regression.
- StochasticMultinomialLogisticRegression(double, int, double, StochasticMultinomialLogisticRegression.Prior) - Constructor for class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Creates a new Stochastic Multinomial Logistic Regression object
- StochasticMultinomialLogisticRegression(double, int) - Constructor for class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
- StochasticMultinomialLogisticRegression() - Constructor for class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
- StochasticMultinomialLogisticRegression(StochasticMultinomialLogisticRegression) - Constructor for class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
Copy constructor
- StochasticMultinomialLogisticRegression.Prior - Enum in jsat.classifiers.linear
-
Represents a prior of the coefficients that can be applied to perform
regularization.
- StochasticRidgeRegression - Class in jsat.regression
-
A Stochastic implementation of Ridge Regression.
- StochasticRidgeRegression(double, int, int, double) - Constructor for class jsat.regression.StochasticRidgeRegression
-
Creates a new stochastic Ridge Regression learner that does not use a
decay rate
- StochasticRidgeRegression(double, int, int, double, DecayRate) - Constructor for class jsat.regression.StochasticRidgeRegression
-
Creates a new stochastic Ridge Regression learner
- StochasticSTLinearL1 - Class in jsat.classifiers.linear
-
This base class provides shared functionality and variables used by two
different training algorithms for L1 regularized linear models.
- StochasticSTLinearL1() - Constructor for class jsat.classifiers.linear.StochasticSTLinearL1
-
- StochasticSTLinearL1.Loss - Enum in jsat.classifiers.linear
-
- StopWordTokenizer - Class in jsat.text.tokenizer
-
This tokenizer wraps another such that any stop words that would have been
returned by the base tokenizer are removed.
- StopWordTokenizer(Tokenizer, Collection<String>) - Constructor for class jsat.text.tokenizer.StopWordTokenizer
-
Creates a new Stop Word tokenizer
- StopWordTokenizer(Tokenizer, String...) - Constructor for class jsat.text.tokenizer.StopWordTokenizer
-
Creates a new Stop Word tokenizer
- storageSpace - Variable in class jsat.text.HashedTextDataLoader
-
Temporary storage space to use for tokenization
- storageSpace - Variable in class jsat.text.TextDataLoader
-
Temporary storage space to use for tokenization
- storeMeans - Variable in class jsat.clustering.kmeans.KMeans
-
Indicates whether or not the means from the clustering should be saved
- storeMedoids - Variable in class jsat.clustering.PAM
-
- stratSet(int, Random) - Method in class jsat.classifiers.ClassificationDataSet
-
- STRING_ENCODING_ASCII - Static variable in class jsat.io.JSATData
-
- STRING_ENCODING_UTF_16 - Static variable in class jsat.io.JSATData
-
- StringUtils - Class in jsat.utils
-
- StringUtils() - Constructor for class jsat.utils.StringUtils
-
- StudentT - Class in jsat.distributions
-
- StudentT(double) - Constructor for class jsat.distributions.StudentT
-
- StudentT(double, double, double) - Constructor for class jsat.distributions.StudentT
-
- stump - Variable in class jsat.classifiers.trees.DecisionTree.Node
-
- SubMatrix - Class in jsat.linear
-
This class allows for the selection of an area of a matrix to operate on independently.
- SubMatrix(Matrix, int, int, int, int) - Constructor for class jsat.linear.SubMatrix
-
Creates a new matrix that is a sub view of the given base matrix.
- submit(Callable<T>) - Method in class jsat.utils.FakeExecutor
-
- submit(Runnable, T) - Method in class jsat.utils.FakeExecutor
-
- submit(Runnable) - Method in class jsat.utils.FakeExecutor
-
- subSet(Integer, Integer) - Method in class jsat.utils.IntSortedSet
-
- subtract(Matrix) - Method in class jsat.linear.Matrix
-
Creates a new Matrix that stores the result of A-B
- subtract(Matrix, ExecutorService) - Method in class jsat.linear.Matrix
-
Creates a new Matrix that stores the result of A-B
- subtract(double) - Method in class jsat.linear.Matrix
-
Creates a new Matrix that stores the result of A-c
- subtract(double, ExecutorService) - Method in class jsat.linear.Matrix
-
Creates a new Matrix that stores the result of A-c
- subtract(double) - Method in class jsat.linear.Vec
-
Returns a new vector that is the result of this - c
- subtract(Vec) - Method in class jsat.linear.Vec
-
Returns a new vector that is the result of this - b
- subtract(Vec) - Method in class jsat.linear.VecPaired
-
- subtract(Complex) - Method in class jsat.math.Complex
-
Creates a new complex number containing the resulting subtracting another from this one
- SubVector - Class in jsat.linear
-
SubVector takes an already existing vector and creates a new one that is a
subset of and backed by the original one.
- SubVector(int, int, Vec) - Constructor for class jsat.linear.SubVector
-
Creates a new sub vector of the input vector
- sum() - Method in class jsat.linear.ConstantVector
-
- sum() - Method in class jsat.linear.DenseVector
-
- sum() - Method in class jsat.linear.ScaledVector
-
- sum() - Method in class jsat.linear.SparseVector
-
- sum() - Method in class jsat.linear.Vec
-
Computes the sum of the values in this vector
- sum() - Method in class jsat.linear.VecPaired
-
- summaryStats(Vec, Vec) - Static method in class jsat.math.DescriptiveStatistics
-
Computes several summary statistics from the two data sets.
- SumOfSqrdPairwiseDistances - Class in jsat.clustering.evaluation.intra
-
Evaluates a cluster's validity by computing the normalized sum of pairwise
distances for all points in the cluster.
- SumOfSqrdPairwiseDistances() - Constructor for class jsat.clustering.evaluation.intra.SumOfSqrdPairwiseDistances
-
Creates a new evaluator that uses the Euclidean distance
- SumOfSqrdPairwiseDistances(DistanceMetric) - Constructor for class jsat.clustering.evaluation.intra.SumOfSqrdPairwiseDistances
-
Creates a new cluster evaluator using the given distance metric
- SumOfSqrdPairwiseDistances(SumOfSqrdPairwiseDistances) - Constructor for class jsat.clustering.evaluation.intra.SumOfSqrdPairwiseDistances
-
Copy constructor
- supportsAcceleration() - Method in class jsat.distributions.kernels.BaseKernelTrick
-
- supportsAcceleration() - Method in class jsat.distributions.kernels.BaseL2Kernel
-
- supportsAcceleration() - Method in class jsat.distributions.kernels.DistanceMetricBasedKernel
-
- supportsAcceleration() - Method in interface jsat.distributions.kernels.KernelTrick
-
- supportsAcceleration() - Method in class jsat.distributions.kernels.NormalizedKernel
-
- supportsAcceleration() - Method in class jsat.linear.distancemetrics.ChebyshevDistance
-
- supportsAcceleration() - Method in class jsat.linear.distancemetrics.CosineDistance
-
- supportsAcceleration() - Method in class jsat.linear.distancemetrics.CosineDistanceNormalized
-
- supportsAcceleration() - Method in class jsat.linear.distancemetrics.DistanceCounter
-
- supportsAcceleration() - Method in interface jsat.linear.distancemetrics.DistanceMetric
-
- supportsAcceleration() - Method in class jsat.linear.distancemetrics.EuclideanDistance
-
- supportsAcceleration() - Method in class jsat.linear.distancemetrics.KernelDistance
-
- supportsAcceleration() - Method in class jsat.linear.distancemetrics.MahalanobisDistance
-
- supportsAcceleration() - Method in class jsat.linear.distancemetrics.ManhattanDistance
-
- supportsAcceleration() - Method in class jsat.linear.distancemetrics.MinkowskiDistance
-
- supportsAcceleration() - Method in class jsat.linear.distancemetrics.NormalizedEuclideanDistance
-
- supportsAcceleration() - Method in class jsat.linear.distancemetrics.PearsonDistance
-
- supportsAcceleration() - Method in class jsat.linear.distancemetrics.SquaredEuclideanDistance
-
- supportsAcceleration() - Method in class jsat.linear.distancemetrics.WeightedEuclideanDistance
-
- supportsClassificationTraining() - Method in class jsat.linear.distancemetrics.MahalanobisDistance
-
- supportsClassificationTraining() - Method in class jsat.linear.distancemetrics.NormalizedEuclideanDistance
-
- supportsClassificationTraining() - Method in class jsat.linear.distancemetrics.TrainableDistanceMetric
-
Some metrics might be special purpose, and not trainable for all types of data sets or tasks.
- supportsRegressionTraining() - Method in class jsat.linear.distancemetrics.MahalanobisDistance
-
- supportsRegressionTraining() - Method in class jsat.linear.distancemetrics.NormalizedEuclideanDistance
-
- supportsRegressionTraining() - Method in class jsat.linear.distancemetrics.TrainableDistanceMetric
-
Some metrics might be special purpose, and not trainable for all types of data sets tasks.
- supportsWeightedData() - Method in class jsat.classifiers.bayesian.AODE
-
- supportsWeightedData() - Method in class jsat.classifiers.bayesian.BestClassDistribution
-
- supportsWeightedData() - Method in class jsat.classifiers.bayesian.ConditionalProbabilityTable
-
- supportsWeightedData() - Method in class jsat.classifiers.bayesian.graphicalmodel.DiscreteBayesNetwork
-
- supportsWeightedData() - Method in class jsat.classifiers.bayesian.MultinomialNaiveBayes
-
- supportsWeightedData() - Method in class jsat.classifiers.bayesian.MultivariateNormals
-
- supportsWeightedData() - Method in class jsat.classifiers.bayesian.NaiveBayes
-
- supportsWeightedData() - Method in class jsat.classifiers.bayesian.NaiveBayesUpdateable
-
- supportsWeightedData() - Method in class jsat.classifiers.bayesian.ODE
-
- supportsWeightedData() - Method in class jsat.classifiers.boosting.AdaBoostM1
-
- supportsWeightedData() - Method in class jsat.classifiers.boosting.ArcX4
-
- supportsWeightedData() - Method in class jsat.classifiers.boosting.Bagging
-
- supportsWeightedData() - Method in class jsat.classifiers.boosting.EmphasisBoost
-
- supportsWeightedData() - Method in class jsat.classifiers.boosting.LogitBoost
-
- supportsWeightedData() - Method in class jsat.classifiers.boosting.ModestAdaBoost
-
- supportsWeightedData() - Method in class jsat.classifiers.boosting.SAMME
-
- supportsWeightedData() - Method in class jsat.classifiers.boosting.Stacking
-
- supportsWeightedData() - Method in class jsat.classifiers.boosting.UpdatableStacking
-
- supportsWeightedData() - Method in class jsat.classifiers.boosting.Wagging
-
- supportsWeightedData() - Method in class jsat.classifiers.calibration.IsotonicCalibration
-
- supportsWeightedData() - Method in class jsat.classifiers.calibration.PlattCalibration
-
- supportsWeightedData() - Method in interface jsat.classifiers.Classifier
-
Indicates whether the model knows how to train using weighted data points.
- supportsWeightedData() - Method in class jsat.classifiers.knn.DANN
-
- supportsWeightedData() - Method in class jsat.classifiers.knn.LWL
-
- supportsWeightedData() - Method in class jsat.classifiers.knn.NearestNeighbour
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.ALMA2
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.AROW
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.BBR
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.kernelized.ALMA2K
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.kernelized.BOGD
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.kernelized.CSKLR
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.kernelized.CSKLRBatch
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.kernelized.DUOL
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.kernelized.Forgetron
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.kernelized.KernelSGD
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.kernelized.OSKL
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.kernelized.Projectron
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.LinearBatch
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.LinearL1SCD
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.LinearSGD
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.LogisticRegressionDCD
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.NewGLMNET
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.NHERD
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.PassiveAggressive
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.ROMMA
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.SCD
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.SCW
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.SMIDAS
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.SPA
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.STGD
-
- supportsWeightedData() - Method in class jsat.classifiers.linear.StochasticMultinomialLogisticRegression
-
- supportsWeightedData() - Method in class jsat.classifiers.MajorityVote
-
- supportsWeightedData() - Method in class jsat.classifiers.MultinomialLogisticRegression
-
- supportsWeightedData() - Method in class jsat.classifiers.neuralnetwork.BackPropagationNet
-
- supportsWeightedData() - Method in class jsat.classifiers.neuralnetwork.DReDNetSimple
-
- supportsWeightedData() - Method in class jsat.classifiers.neuralnetwork.LVQ
-
- supportsWeightedData() - Method in class jsat.classifiers.neuralnetwork.Perceptron
-
- supportsWeightedData() - Method in class jsat.classifiers.neuralnetwork.RBFNet
-
- supportsWeightedData() - Method in class jsat.classifiers.neuralnetwork.SOM
-
- supportsWeightedData() - Method in class jsat.classifiers.OneVSAll
-
- supportsWeightedData() - Method in class jsat.classifiers.OneVSOne
-
- supportsWeightedData() - Method in class jsat.classifiers.PriorClassifier
-
- supportsWeightedData() - Method in class jsat.classifiers.RegressorToClassifier
-
- supportsWeightedData() - Method in class jsat.classifiers.Rocchio
-
- supportsWeightedData() - Method in class jsat.classifiers.svm.DCD
-
- supportsWeightedData() - Method in class jsat.classifiers.svm.DCDs
-
- supportsWeightedData() - Method in class jsat.classifiers.svm.DCSVM
-
- supportsWeightedData() - Method in class jsat.classifiers.svm.extended.CPM
-
- supportsWeightedData() - Method in class jsat.classifiers.svm.extended.OnlineAMM
-
- supportsWeightedData() - Method in class jsat.classifiers.svm.LSSVM
-
- supportsWeightedData() - Method in class jsat.classifiers.svm.Pegasos
-
- supportsWeightedData() - Method in class jsat.classifiers.svm.PegasosK
-
- supportsWeightedData() - Method in class jsat.classifiers.svm.PlattSMO
-
- supportsWeightedData() - Method in class jsat.classifiers.svm.SBP
-
- supportsWeightedData() - Method in class jsat.classifiers.svm.SVMnoBias
-
- supportsWeightedData() - Method in class jsat.classifiers.trees.DecisionStump
-
- supportsWeightedData() - Method in class jsat.classifiers.trees.DecisionTree
-
- supportsWeightedData() - Method in class jsat.classifiers.trees.ERTrees
-
- supportsWeightedData() - Method in class jsat.classifiers.trees.ExtraTree
-
- supportsWeightedData() - Method in class jsat.classifiers.trees.ID3
-
- supportsWeightedData() - Method in class jsat.classifiers.trees.RandomForest
-
- supportsWeightedData() - Method in interface jsat.clustering.Clusterer
-
Indicates whether the model knows how to cluster using weighted data
points.
- supportsWeightedData() - Method in class jsat.clustering.ClustererBase
-
- supportsWeightedData() - Method in class jsat.clustering.kmeans.KernelKMeans
-
- supportsWeightedData() - Method in class jsat.clustering.kmeans.KMeans
-
- supportsWeightedData() - Method in class jsat.datatransform.DataModelPipeline
-
- supportsWeightedData() - Method in class jsat.parameters.ModelSearch
-
- supportsWeightedData() - Method in class jsat.regression.AveragedRegressor
-
- supportsWeightedData() - Method in class jsat.regression.KernelRidgeRegression
-
- supportsWeightedData() - Method in class jsat.regression.KernelRLS
-
- supportsWeightedData() - Method in class jsat.regression.LogisticRegression
-
- supportsWeightedData() - Method in class jsat.regression.MultipleLinearRegression
-
- supportsWeightedData() - Method in class jsat.regression.NadarayaWatson
-
- supportsWeightedData() - Method in class jsat.regression.OrdinaryKriging
-
- supportsWeightedData() - Method in class jsat.regression.RANSAC
-
- supportsWeightedData() - Method in interface jsat.regression.Regressor
-
- supportsWeightedData() - Method in class jsat.regression.RidgeRegression
-
- supportsWeightedData() - Method in class jsat.regression.StochasticGradientBoosting
-
- supportsWeightedData() - Method in class jsat.regression.StochasticRidgeRegression
-
- SupportVectorLearner - Class in jsat.classifiers.svm
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Base class for support vector style learners.
- SupportVectorLearner() - Constructor for class jsat.classifiers.svm.SupportVectorLearner
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This constructor is meant manly for Serialization to work.
- SupportVectorLearner(KernelTrick, SupportVectorLearner.CacheMode) - Constructor for class jsat.classifiers.svm.SupportVectorLearner
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Creates a new Support Vector Learner
- SupportVectorLearner(SupportVectorLearner) - Constructor for class jsat.classifiers.svm.SupportVectorLearner
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Copy constructor
- SupportVectorLearner.CacheMode - Enum in jsat.classifiers.svm
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Determines how the final kernel values are cached.
- SVMnoBias - Class in jsat.classifiers.svm
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This class implements a version of the Support Vector Machine without a bias
term.
- SVMnoBias(KernelTrick) - Constructor for class jsat.classifiers.svm.SVMnoBias
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Creates a new SVM object that uses no cache mode.
- SVMnoBias(SVMnoBias) - Constructor for class jsat.classifiers.svm.SVMnoBias
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- swap(int[], int, int) - Static method in class jsat.utils.ArrayUtils
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Swaps two indices in the given array
- swap(int, int) - Method in class jsat.utils.IndexTable
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Swaps the given indices in the index table.
- swap(List, int, int) - Static method in class jsat.utils.ListUtils
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Swaps the values in the list at the given positions
- swap(double[], int, int) - Static method in class jsat.utils.QuickSort
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- swap(float[], int, int) - Static method in class jsat.utils.QuickSort
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- swap(double[], int, int, Collection<List<?>>) - Static method in class jsat.utils.QuickSort
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- swap(float[], int, int, Collection<List<?>>) - Static method in class jsat.utils.QuickSort
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- swapC(double[], int, int) - Static method in class jsat.utils.QuickSort
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Conditional swap, only swaps the values if array[i] > array[j]
- swapCol(Matrix, int, int, int, int) - Static method in class jsat.linear.RowColumnOps
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Swaps the columns j and k in the given matrix.
- swapCol(Matrix, int, int) - Static method in class jsat.linear.RowColumnOps
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Swaps the columns j and k in the given matrix.
- swapRow(Matrix, int, int, int, int) - Static method in class jsat.linear.RowColumnOps
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Swaps the rows j and k in the given matrix.
- swapRow(Matrix, int, int) - Static method in class jsat.linear.RowColumnOps
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Swaps the columns j and k in the given matrix.
- swapRows(int, int) - Method in class jsat.linear.DenseMatrix
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- swapRows(int, int) - Method in class jsat.linear.GenericMatrix
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- swapRows(int, int) - Method in class jsat.linear.Matrix
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Alters the current matrix by swapping the values stored in two different
rows.
- swapRows(int, int) - Method in class jsat.linear.SparseMatrix
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- SymmetricDirichlet - Class in jsat.distributions.multivariate
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The Symmetric Dirichlet Distribution is a special case of the
Dirichlet distribution, and occurs when all alphas have the same value.
- SymmetricDirichlet(double, int) - Constructor for class jsat.distributions.multivariate.SymmetricDirichlet
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Creates a new Symmetric Dirichlet distribution.
- SystemInfo - Class in jsat.utils
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This class provides Static system information that may be useful for
algorithms that want to adjust their behavior based on the system's
hardware information.
- SystemInfo() - Constructor for class jsat.utils.SystemInfo
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