Index

$ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form

$

$ - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
Punctuation $
$(String) - Static method in interface smile.data.formula.Terms
Creates a variable.

A

a - Variable in class smile.validation.metric.ContingencyTable
The row sum of contingency table.
aat() - Method in class smile.math.matrix.BigMatrix
Returns A * A'.
aat() - Method in class smile.math.matrix.fp32.Matrix
Returns A * A'.
aat() - Method in class smile.math.matrix.fp32.SparseMatrix
Returns A * A'.
aat() - Method in class smile.math.matrix.Matrix
Returns A * A'.
aat() - Method in class smile.math.matrix.SparseMatrix
Returns A * A'.
Abbreviations - Interface in smile.nlp.dictionary
A dictionary interface for abbreviations.
abs() - Method in class smile.math.Complex
Returns the abs/modulus/magnitude.
abs(String) - Static method in interface smile.data.formula.Terms
The abs(x) term.
abs(Term) - Static method in interface smile.data.formula.Terms
The abs(x) term.
Abs - Class in smile.data.formula
The term of abs function.
Abs(Term) - Constructor for class smile.data.formula.Abs
Constructor.
AbstractBiFunction - Class in smile.data.formula
This class provides a skeletal implementation of the bi-function term.
AbstractBiFunction(String, Term, Term) - Constructor for class smile.data.formula.AbstractBiFunction
Constructor.
AbstractClassifier<T> - Class in smile.classification
Abstract base class of classifiers.
AbstractClassifier(int[]) - Constructor for class smile.classification.AbstractClassifier
Constructor.
AbstractClassifier(BaseVector<?, ?, ?>) - Constructor for class smile.classification.AbstractClassifier
Constructor.
AbstractClassifier(IntSet) - Constructor for class smile.classification.AbstractClassifier
Constructor.
AbstractDistribution - Class in smile.stat.distribution
The base class of univariate distributions.
AbstractDistribution() - Constructor for class smile.stat.distribution.AbstractDistribution
 
AbstractFunction - Class in smile.data.formula
This class provides a skeletal implementation of the function term.
AbstractFunction(String, Term) - Constructor for class smile.data.formula.AbstractFunction
Constructor.
AbstractInterpolation - Class in smile.interpolation
Abstract base class of one-dimensional interpolation methods.
AbstractInterpolation(double[], double[]) - Constructor for class smile.interpolation.AbstractInterpolation
Constructor.
AbstractTuple - Class in smile.data
Abstract tuple base class.
AbstractTuple() - Constructor for class smile.data.AbstractTuple
 
accept(int, int, double) - Method in interface smile.math.matrix.DoubleConsumer
Accepts one matrix element and performs the operation on the given arguments.
accept(int, int, float) - Method in interface smile.math.matrix.fp32.FloatConsumer
Accepts one matrix element and performs the operation on the given arguments.
accuracy - Variable in class smile.validation.ClassificationMetrics
The accuracy on validation data.
Accuracy - Class in smile.validation.metric
The accuracy is the proportion of true results (both true positives and true negatives) in the population.
Accuracy() - Constructor for class smile.validation.metric.Accuracy
 
acf(double[], int) - Static method in interface smile.timeseries.TimeSeries
Autocorrelation function.
acos(String) - Static method in interface smile.data.formula.Terms
The acos(x) term.
acos(Term) - Static method in interface smile.data.formula.Terms
The acos(x) term.
ActivationFunction - Interface in smile.base.mlp
The activation function in hidden layers.
ActivationFunction - Interface in smile.deep.activation
The activation function.
AdaBoost - Class in smile.classification
AdaBoost (Adaptive Boosting) classifier with decision trees.
AdaBoost(Formula, int, DecisionTree[], double[], double[], double[]) - Constructor for class smile.classification.AdaBoost
Constructor.
AdaBoost(Formula, int, DecisionTree[], double[], double[], double[], IntSet) - Constructor for class smile.classification.AdaBoost
Constructor.
Adam - Class in smile.deep.optimizer
Adaptive Moment optimizer.
Adam() - Constructor for class smile.deep.optimizer.Adam
Constructor.
Adam(TimeFunction) - Constructor for class smile.deep.optimizer.Adam
Constructor.
Adam(TimeFunction, double, double) - Constructor for class smile.deep.optimizer.Adam
Constructor.
Adam(TimeFunction, double, double, double) - Constructor for class smile.deep.optimizer.Adam
Constructor.
adb(Transpose, BigMatrix, double[], Transpose, BigMatrix) - Static method in class smile.math.matrix.BigMatrix
Returns A * D * B, where D is a diagonal matrix.
adb(Transpose, Matrix, float[], Transpose, Matrix) - Static method in class smile.math.matrix.fp32.Matrix
Returns A * D * B, where D is a diagonal matrix.
adb(Transpose, Matrix, double[], Transpose, Matrix) - Static method in class smile.math.matrix.Matrix
Returns A * D * B, where D is a diagonal matrix.
add(double) - Method in class smile.math.matrix.BigMatrix
A += b
add(double) - Method in class smile.math.matrix.Matrix
A += b
add(double) - Method in class smile.sort.DoubleHeapSelect
Assimilate a new value from the stream.
add(double) - Method in class smile.sort.IQAgent
Assimilate a new value from the stream.
add(double) - Method in class smile.util.Array2D
A += x.
add(double) - Method in class smile.util.DoubleArrayList
Appends the specified value to the end of this list.
add(double[]) - Method in class smile.util.DoubleArrayList
Appends an array to the end of this list.
add(double[], double[]) - Static method in class smile.math.MathEx
Element-wise sum of two arrays y = x + y.
add(double, double[], double[]) - Method in class smile.math.matrix.BigMatrix
Rank-1 update A += alpha * x * y'
add(double, double[], double[]) - Method in class smile.math.matrix.Matrix
Rank-1 update A += alpha * x * y'
add(double, double, BigMatrix) - Method in class smile.math.matrix.BigMatrix
Element-wise addition A = alpha * A + beta * B
add(double, double, Matrix) - Method in class smile.math.matrix.Matrix
Element-wise addition A = alpha * A + beta * B
add(double, BigMatrix) - Method in class smile.math.matrix.BigMatrix
Element-wise addition A += beta * B
add(double, BigMatrix, double, BigMatrix) - Method in class smile.math.matrix.BigMatrix
Element-wise addition C = alpha * A + beta * B
add(double, Matrix) - Method in class smile.math.matrix.Matrix
Element-wise addition A += beta * B
add(double, Matrix, double, Matrix) - Method in class smile.math.matrix.Matrix
Element-wise addition C = alpha * A + beta * B
add(float) - Method in class smile.math.matrix.fp32.Matrix
A += b
add(float) - Method in class smile.sort.FloatHeapSelect
Assimilate a new value from the stream.
add(float, float[], float[]) - Method in class smile.math.matrix.fp32.Matrix
Rank-1 update A += alpha * x * y'
add(float, float, Matrix) - Method in class smile.math.matrix.fp32.Matrix
Element-wise addition A = alpha * A + beta * B
add(float, Matrix) - Method in class smile.math.matrix.fp32.Matrix
Element-wise addition A += beta * B
add(float, Matrix, float, Matrix) - Method in class smile.math.matrix.fp32.Matrix
Element-wise addition C = alpha * A + beta * B
add(int) - Method in class smile.neighbor.lsh.Bucket
Adds a point to bucket.
add(int) - Method in class smile.sort.IntHeapSelect
Assimilate a new value from the stream.
add(int) - Method in class smile.util.IntArray2D
A += x.
add(int) - Method in class smile.util.IntArrayList
Appends the specified value to the end of this list.
add(int) - Method in class smile.util.IntHashSet
Adds the specified element to this set if it is not already present.
add(int[]) - Method in class smile.util.IntArrayList
Appends an array to the end of this list.
add(int, double[]) - Method in class smile.neighbor.lsh.Hash
Insert an item into the hash table.
add(int, double[]) - Method in class smile.neighbor.lsh.MultiProbeHash
 
add(int, int, double) - Method in class smile.math.matrix.BigMatrix
A[i,j] += b
add(int, int, double) - Method in class smile.math.matrix.Matrix
A[i,j] += b
add(int, int, double) - Method in class smile.util.Array2D
A[i, j] += x.
add(int, int, float) - Method in class smile.math.matrix.fp32.Matrix
A[i,j] += b
add(int, int, int) - Method in class smile.util.IntArray2D
A[i, j] += x.
add(String, double) - Method in class smile.hpo.Hyperparameters
Adds a parameter.
add(String, double[]) - Method in class smile.hpo.Hyperparameters
Adds a parameter.
add(String, double, double) - Method in class smile.hpo.Hyperparameters
Adds a parameter.
add(String, double, double, double) - Method in class smile.hpo.Hyperparameters
Adds a parameter.
add(String, int) - Method in class smile.hpo.Hyperparameters
Adds a parameter.
add(String, int[]) - Method in class smile.hpo.Hyperparameters
Adds a parameter.
add(String, int, int) - Method in class smile.hpo.Hyperparameters
Adds a parameter.
add(String, int, int, int) - Method in class smile.hpo.Hyperparameters
Adds a parameter.
add(String, String) - Static method in interface smile.data.formula.Terms
Adds two terms.
add(String, String) - Method in class smile.hpo.Hyperparameters
Adds a parameter.
add(String, String[]) - Method in class smile.hpo.Hyperparameters
Adds a parameter.
add(String, Term) - Static method in interface smile.data.formula.Terms
Adds two terms.
add(String, T) - Method in class smile.hash.PerfectMap.Builder
Add a new key-value pair.
add(Map<K, V>) - Method in class smile.neighbor.BKTree
Adds a dataset into BK-tree.
add(K, V) - Method in class smile.neighbor.BKTree
Adds a datum into the BK-tree.
add(Term, String) - Static method in interface smile.data.formula.Terms
Adds two terms.
add(Term, Term) - Static method in interface smile.data.formula.Terms
Adds two terms.
add(Complex) - Method in class smile.math.Complex
Returns this + b.
add(BigMatrix) - Method in class smile.math.matrix.BigMatrix
Element-wise addition A += B
add(Matrix) - Method in class smile.math.matrix.fp32.Matrix
Element-wise addition A += B
add(Matrix) - Method in class smile.math.matrix.Matrix
Element-wise addition A += B
add(Text) - Method in class smile.nlp.SimpleCorpus
Adds a document to the corpus.
add(Array2D) - Method in class smile.util.Array2D
A += B.
add(IntArray2D) - Method in class smile.util.IntArray2D
A += B.
add(IntArrayList) - Method in class smile.util.IntArrayList
Appends an array to the end of this list.
add(T) - Method in class smile.sort.HeapSelect
Assimilate a new value from the stream.
Add - Class in smile.data.formula
The term of a + b expression.
Add(Term, Term) - Constructor for class smile.data.formula.Add
Constructor.
add2(double, double, BigMatrix) - Method in class smile.math.matrix.BigMatrix
Element-wise addition A = alpha * A + beta * B^2
add2(double, double, Matrix) - Method in class smile.math.matrix.Matrix
Element-wise addition A = alpha * A + beta * B^2
add2(float, float, Matrix) - Method in class smile.math.matrix.fp32.Matrix
Element-wise addition A = alpha * A + beta * B^2
addAnchor(String) - Method in interface smile.nlp.AnchorText
Adds a link label to the anchor text.
addAnchor(String) - Method in class smile.nlp.SimpleText
 
addChild(String) - Method in class smile.taxonomy.Concept
Adds a child to this node.
addChild(K[], V, int) - Method in class smile.nlp.Trie.Node
Adds a child.
addChild(Concept) - Method in class smile.taxonomy.Concept
Adds a child to this node.
addDiag(double) - Method in class smile.math.matrix.BigMatrix
A[i, i] += b
addDiag(double) - Method in class smile.math.matrix.Matrix
A[i, i] += b
addDiag(double[]) - Method in class smile.math.matrix.BigMatrix
A[i, i] += b[i]
addDiag(double[]) - Method in class smile.math.matrix.Matrix
A[i, i] += b[i]
addDiag(float) - Method in class smile.math.matrix.fp32.Matrix
A[i, i] += b
addDiag(float[]) - Method in class smile.math.matrix.fp32.Matrix
A[i, i] += b[i]
addEdge(int, int) - Method in class smile.graph.AdjacencyList
 
addEdge(int, int) - Method in class smile.graph.AdjacencyMatrix
 
addEdge(int, int) - Method in interface smile.graph.Graph
Creates a new edge in this graph, going from the source vertex to the target vertex, and returns the created edge.
addEdge(int, int, double) - Method in class smile.graph.AdjacencyList
 
addEdge(int, int, double) - Method in class smile.graph.AdjacencyMatrix
 
addEdge(int, int, double) - Method in interface smile.graph.Graph
Creates a new edge in this graph, going from the source vertex to the target vertex, and returns the created edge.
addEdge(Neuron) - Method in class smile.vq.hebb.Neuron
Adds an edge.
addEdge(Neuron, int) - Method in class smile.vq.hebb.Neuron
Adds an edge.
addKeywords(String...) - Method in class smile.taxonomy.Concept
Adds a list of synomym to the concept synset.
AdjacencyList - Class in smile.graph
An adjacency list representation of a graph.
AdjacencyList(int) - Constructor for class smile.graph.AdjacencyList
Constructor.
AdjacencyList(int, boolean) - Constructor for class smile.graph.AdjacencyList
Constructor.
AdjacencyMatrix - Class in smile.graph
An adjacency matrix representation of a graph.
AdjacencyMatrix(int) - Constructor for class smile.graph.AdjacencyMatrix
Constructor.
AdjacencyMatrix(int, boolean) - Constructor for class smile.graph.AdjacencyMatrix
Constructor.
AdjustedMutualInformation - Class in smile.validation.metric
Adjusted Mutual Information (AMI) for comparing clustering.
AdjustedMutualInformation(AdjustedMutualInformation.Method) - Constructor for class smile.validation.metric.AdjustedMutualInformation
Constructor.
AdjustedMutualInformation.Method - Enum Class in smile.validation.metric
The normalization method.
adjustedR2() - Method in class smile.timeseries.AR
Returns adjusted R2 statistic.
adjustedR2() - Method in class smile.timeseries.ARMA
Returns adjusted R2 statistic.
AdjustedRandIndex - Class in smile.validation.metric
Adjusted Rand Index.
AdjustedRandIndex() - Constructor for class smile.validation.metric.AdjustedRandIndex
 
adjustedRSquared() - Method in class smile.regression.LinearModel
Returns adjusted R2 statistic.
age - Variable in class smile.vq.hebb.Edge
The age of the edges.
age() - Method in class smile.vq.hebb.Neuron
Increments the age of all edges emanating from the neuron.
AIC() - Method in class smile.classification.LogisticRegression
Returns the AIC score.
AIC() - Method in class smile.classification.Maxent
Returns the AIC score.
AIC() - Method in class smile.classification.SparseLogisticRegression
Returns the AIC score.
AIC() - Method in class smile.glm.GLM
Returns the AIC score.
AIC(double, int) - Static method in interface smile.validation.ModelSelection
Akaike information criterion.
ALL - Enum constant in enum class smile.math.blas.EigenRange
All eigenvalues will be found.
ALL - Enum constant in enum class smile.math.blas.SVDJob
All left (or right) singular vectors are returned in supplied matrix U (or Vt).
allocate(long) - Static method in class smile.io.Arrow
Creates the root allocator.
alpha - Variable in class smile.stat.distribution.BetaDistribution
The shape parameter.
alpha() - Method in class smile.stat.distribution.BetaDistribution
Returns the shape parameter alpha.
AnchorText - Interface in smile.nlp
The anchor text is the visible, clickable text in a hyperlink.
andThen(Transform) - Method in interface smile.data.transform.Transform
Returns a composed function that first applies this function to its input, and then applies the after function to the result.
antecedent - Variable in class smile.association.AssociationRule
Antecedent itemset.
anyNull() - Method in interface smile.data.Tuple
Returns true if there are any NULL values in this tuple.
anyNull() - Method in interface smile.data.vector.Vector
Returns true if there are any NULL values in this row.
append(int, double) - Method in class smile.util.SparseArray
Append an entry to the array, optimizing for the case where the index is greater than all existing indices in the array.
apply(double) - Method in interface smile.math.Function
Computes the value of the function at x.
apply(double) - Method in interface smile.math.kernel.DotProductKernel
Computes the kernel function.
apply(double) - Method in interface smile.math.kernel.IsotropicKernel
Computes the kernel function.
apply(double[]) - Method in class smile.feature.extraction.KernelPCA
 
apply(double[]) - Method in class smile.feature.extraction.Projection
Project a data point to the feature space.
apply(double...) - Method in interface smile.math.MultivariateFunction
Computes the value of the function at x.
apply(double[][]) - Method in class smile.feature.extraction.Projection
Project a set of data to the feature space.
apply(double, FPTree) - Static method in class smile.association.ARM
Mines the association rules.
apply(int) - Method in interface smile.data.Dataset
Returns the element at the specified position in this dataset.
apply(int) - Method in interface smile.data.Tuple
Returns the value at position i.
apply(int) - Method in interface smile.data.vector.BaseVector
Returns the value at position i, which may be null.
apply(int) - Method in class smile.math.Complex.Array
Returns the i-th element.
apply(int) - Method in interface smile.math.IntFunction
Computes the value of the function at x.
apply(int) - Method in interface smile.math.TimeFunction
Returns the function value at time step t.
apply(int...) - Method in interface smile.data.vector.BaseVector
Returns a new vector with selected entries.
apply(int, int) - Method in class smile.math.matrix.fp32.IMatrix
Returns A[i,j].
apply(int, int) - Method in class smile.math.matrix.IMatrix
Returns A[i,j] for Scala users.
apply(int, int) - Method in class smile.util.Array2D
Returns A[i, j].
apply(int, int) - Method in class smile.util.IntArray2D
Returns A[i, j].
apply(int, int, int, Fitness<BitString>) - Method in class smile.feature.selection.GAFE
Genetic algorithm based feature selection for classification.
apply(Enum<?>) - Method in interface smile.data.DataFrame
Selects column using an enum value.
apply(String) - Method in interface smile.data.DataFrame
Selects column based on the column name and return it as a Column.
apply(String) - Method in interface smile.data.Tuple
Returns the value by field name.
apply(String) - Method in class smile.feature.extraction.BagOfWords
Returns the bag-of-words features of a document.
apply(String) - Method in class smile.feature.extraction.HashEncoder
Returns the bag-of-words features of a document.
apply(String) - Method in class smile.nlp.embedding.Word2Vec
Returns the embedding vector of a word.
apply(String) - Method in interface smile.nlp.tokenizer.Tokenizer
 
apply(FPTree) - Static method in class smile.association.FPGrowth
Mines the frequent item sets.
apply(DataFrame) - Method in interface smile.data.formula.Feature
Applies the term on a data frame.
apply(DataFrame) - Method in class smile.data.transform.ColumnTransform
 
apply(DataFrame) - Method in interface smile.data.transform.Transform
Applies this transform to the given argument.
apply(DataFrame) - Method in class smile.feature.extraction.BinaryEncoder
Generates the compact representation of sparse binary features for a data frame.
apply(DataFrame) - Method in class smile.feature.extraction.Projection
 
apply(DataFrame) - Method in class smile.feature.extraction.SparseEncoder
Generates the sparse representation of a data frame.
apply(DataFrame) - Method in class smile.feature.imputation.SimpleImputer
 
apply(Tuple) - Method in interface smile.data.formula.Feature
Applies the term on a tuple.
apply(Tuple) - Method in class smile.data.formula.Formula
Apply the formula on a tuple to generate the model data.
apply(Tuple) - Method in class smile.data.transform.ColumnTransform
 
apply(Tuple) - Method in class smile.feature.extraction.BagOfWords
 
apply(Tuple) - Method in class smile.feature.extraction.BinaryEncoder
Generates the compact representation of sparse binary features for given object.
apply(Tuple) - Method in class smile.feature.extraction.Projection
 
apply(Tuple) - Method in class smile.feature.extraction.SparseEncoder
Generates the sparse representation of given object.
apply(Tuple) - Method in class smile.feature.imputation.KMedoidsImputer
 
apply(Tuple) - Method in class smile.feature.imputation.KNNImputer
 
apply(Tuple) - Method in class smile.feature.imputation.SimpleImputer
 
apply(Tuple) - Method in class smile.feature.transform.Normalizer
 
apply(BitString, BitString) - Method in enum class smile.gap.Crossover
Returns a pair of offsprings by crossovering parent chromosomes.
apply(T) - Method in class smile.manifold.KPCA
 
apply(T[]) - Method in interface smile.gap.Selection
Select a chromosome with replacement from the population based on their fitness.
apply(T[]) - Method in class smile.manifold.KPCA
Project a set of data to the feature space.
apply(T, T) - Method in interface smile.math.distance.Distance
Returns the distance measure between two objects.
apply(T, T) - Method in interface smile.math.kernel.MercerKernel
Kernel function.
applyAsBoolean(Tuple) - Method in interface smile.data.formula.Feature
Applies the term on a data object and produces an boolean-valued result.
applyAsByte(Tuple) - Method in interface smile.data.formula.Feature
Applies the term on a data object and produces an byte-valued result.
applyAsChar(Tuple) - Method in interface smile.data.formula.Feature
Applies the term on a data object and produces an char-valued result.
applyAsDouble(double[]) - Method in interface smile.math.MultivariateFunction
 
applyAsDouble(Tuple) - Method in interface smile.data.formula.Feature
Applies the term on a data object and produces an double-valued result.
applyAsDouble(T) - Method in interface smile.classification.Classifier
 
applyAsDouble(T) - Method in interface smile.regression.Regression
 
applyAsDouble(T, T) - Method in interface smile.math.distance.Distance
 
applyAsDouble(T, T) - Method in interface smile.math.kernel.MercerKernel
 
applyAsFloat(Tuple) - Method in interface smile.data.formula.Feature
Applies the term on a data object and produces an float-valued result.
applyAsFloat(T) - Method in interface smile.util.ToFloatFunction
Applies this function to the given argument.
applyAsInt(Tuple) - Method in interface smile.data.formula.Feature
Applies the term on a data object and produces an int-valued result.
applyAsInt(T) - Method in interface smile.classification.Classifier
 
applyAsLong(Tuple) - Method in interface smile.data.formula.Feature
Applies the term on a data object and produces an long-valued result.
applyAsShort(Tuple) - Method in interface smile.data.formula.Feature
Applies the term on a data object and produces an short-valued result.
ar() - Method in class smile.timeseries.AR
Returns the linear coefficients of AR (without intercept).
ar() - Method in class smile.timeseries.ARMA
Returns the linear coefficients of AR(p).
AR - Class in smile.timeseries
Autoregressive model.
AR(double[], double[], double, AR.Method) - Constructor for class smile.timeseries.AR
Constructor.
AR.Method - Enum Class in smile.timeseries
The fitting method.
arff(String) - Static method in interface smile.io.Read
Reads an ARFF file.
arff(Path) - Static method in interface smile.io.Read
Reads an ARFF file.
arff(DataFrame, Path, String) - Static method in interface smile.io.Write
Writes the data frame to an ARFF file.
Arff - Class in smile.io
Weka ARFF (attribute relation file format) is an ASCII text file format that is essentially a CSV file with a header that describes the meta-data.
Arff(Reader) - Constructor for class smile.io.Arff
Constructor.
Arff(String) - Constructor for class smile.io.Arff
Constructor.
Arff(String, Charset) - Constructor for class smile.io.Arff
Constructor.
Arff(Path) - Constructor for class smile.io.Arff
Constructor.
Arff(Path, Charset) - Constructor for class smile.io.Arff
Constructor.
ARM - Class in smile.association
Association Rule Mining.
ARMA - Class in smile.timeseries
Autoregressive moving-average model.
ARMA(double[], double[], double[], double, double[], double[]) - Constructor for class smile.timeseries.ARMA
Constructor.
ARPACK - Class in smile.math.matrix
ARPACK is a collection of Fortran77 subroutines designed to solve large scale eigenvalue problems.
ARPACK - Class in smile.math.matrix.fp32
ARPACK is a collection of Fortran77 subroutines designed to solve large scale eigenvalue problems.
ARPACK.AsymmOption - Enum Class in smile.math.matrix
Which eigenvalues of asymmetric matrix to compute.
ARPACK.AsymmOption - Enum Class in smile.math.matrix.fp32
Which eigenvalues of asymmetric matrix to compute.
ARPACK.SymmOption - Enum Class in smile.math.matrix
Which eigenvalues of symmetric matrix to compute.
ARPACK.SymmOption - Enum Class in smile.math.matrix.fp32
Which eigenvalues of symmetric matrix to compute.
array() - Method in interface smile.data.vector.BaseVector
Returns the array that backs this vector.
array() - Method in interface smile.data.vector.BooleanVector
 
array() - Method in interface smile.data.vector.ByteVector
 
array() - Method in interface smile.data.vector.CharVector
 
array() - Method in interface smile.data.vector.DoubleVector
 
array() - Method in interface smile.data.vector.FloatVector
 
array() - Method in interface smile.data.vector.IntVector
 
array() - Method in interface smile.data.vector.LongVector
 
array() - Method in interface smile.data.vector.ShortVector
 
array(DataType) - Static method in class smile.data.type.DataTypes
Creates an array data type.
Array - Enum constant in enum class smile.data.type.DataType.ID
Array type ID.
Array(int) - Constructor for class smile.math.Complex.Array
Constructor.
Array2D - Class in smile.util
2-dimensional array of doubles.
Array2D(double[][]) - Constructor for class smile.util.Array2D
Constructor.
Array2D(int, int) - Constructor for class smile.util.Array2D
Constructor of all-zero matrix.
Array2D(int, int, double) - Constructor for class smile.util.Array2D
Constructor.
Array2D(int, int, double[]) - Constructor for class smile.util.Array2D
Constructor.
ArrayType - Class in smile.data.type
Array of primitive data type.
arrow(String) - Static method in interface smile.io.Read
Reads an Apache Arrow file.
arrow(Path) - Static method in interface smile.io.Read
Reads an Apache Arrow file.
arrow(DataFrame, Path) - Static method in interface smile.io.Write
Writes an Apache Arrow file.
Arrow - Class in smile.io
Apache Arrow is a cross-language development platform for in-memory data.
Arrow() - Constructor for class smile.io.Arrow
Constructor.
Arrow(int) - Constructor for class smile.io.Arrow
Constructor.
asin(String) - Static method in interface smile.data.formula.Terms
The asin(x) term.
asin(Term) - Static method in interface smile.data.formula.Terms
The asin(x) term.
asolve(double[], double[]) - Method in interface smile.math.matrix.IMatrix.Preconditioner
Solve P * x = b for the preconditioner matrix P.
asolve(float[], float[]) - Method in interface smile.math.matrix.fp32.IMatrix.Preconditioner
Solve P * x = b for the preconditioner matrix P.
AssociationRule - Class in smile.association
Association rule object.
AssociationRule(int[], int[], double, double, double, double) - Constructor for class smile.association.AssociationRule
Constructor.
asum(double[]) - Method in interface smile.math.blas.BLAS
Sums the absolute values of the elements of a vector.
asum(float[]) - Method in interface smile.math.blas.BLAS
Sums the absolute values of the elements of a vector.
asum(int, double[], int) - Method in interface smile.math.blas.BLAS
Sums the absolute values of the elements of a vector.
asum(int, double[], int) - Method in class smile.math.blas.mkl.MKL
 
asum(int, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
asum(int, float[], int) - Method in interface smile.math.blas.BLAS
Sums the absolute values of the elements of a vector.
asum(int, float[], int) - Method in class smile.math.blas.mkl.MKL
 
asum(int, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
ata() - Method in class smile.math.matrix.BigMatrix
Returns A' * A.
ata() - Method in class smile.math.matrix.fp32.Matrix
Returns A' * A.
ata() - Method in class smile.math.matrix.fp32.SparseMatrix
Returns A' * A.
ata() - Method in class smile.math.matrix.Matrix
Returns A' * A.
ata() - Method in class smile.math.matrix.SparseMatrix
Returns A' * A.
atan(String) - Static method in interface smile.data.formula.Terms
The atan(x) term.
atan(Term) - Static method in interface smile.data.formula.Terms
The atan(x) term.
attractors - Variable in class smile.clustering.DENCLUE
The density attractor of each observation.
auc - Variable in class smile.validation.ClassificationMetrics
The AUC on validation data.
AUC - Class in smile.validation.metric
The area under the curve (AUC).
AUC() - Constructor for class smile.validation.metric.AUC
 
avg - Variable in class smile.validation.ClassificationValidations
The average of metrics.
avg - Variable in class smile.validation.RegressionValidations
The average of metrics.
avgDocSize() - Method in interface smile.nlp.Corpus
Returns the average size of documents in the corpus.
avgDocSize() - Method in class smile.nlp.SimpleCorpus
 
avro(String, InputStream) - Static method in interface smile.io.Read
Reads an Apache Avro file.
avro(String, String) - Static method in interface smile.io.Read
Reads an Apache Avro file.
avro(Path, InputStream) - Static method in interface smile.io.Read
Reads an Apache Avro file.
avro(Path, Path) - Static method in interface smile.io.Read
Reads an Apache Avro file.
Avro - Class in smile.io
Apache Avro is a data serialization system.
Avro(InputStream) - Constructor for class smile.io.Avro
Constructor.
Avro(Path) - Constructor for class smile.io.Avro
Constructor.
Avro(Schema) - Constructor for class smile.io.Avro
Constructor.
axpy(double, double[], double[]) - Method in interface smile.math.blas.BLAS
Computes a constant alpha times a vector x plus a vector y.
axpy(double, double[], double[]) - Static method in class smile.math.MathEx
Update an array by adding a multiple of another array y = a * x + y.
axpy(float, float[], float[]) - Method in interface smile.math.blas.BLAS
Computes a constant alpha times a vector x plus a vector y.
axpy(int, double, double[], int, double[], int) - Method in interface smile.math.blas.BLAS
Computes a constant alpha times a vector x plus a vector y.
axpy(int, double, double[], int, double[], int) - Method in class smile.math.blas.mkl.MKL
 
axpy(int, double, double[], int, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
axpy(int, float, float[], int, float[], int) - Method in interface smile.math.blas.BLAS
Computes a constant alpha times a vector x plus a vector y.
axpy(int, float, float[], int, float[], int) - Method in class smile.math.blas.mkl.MKL
 
axpy(int, float, float[], int, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 

B

b - Variable in class smile.validation.metric.ContingencyTable
The column sum of contingency table.
B - Variable in class smile.vq.BIRCH
The branching factor of non-leaf nodes.
backpopagateDropout() - Method in class smile.base.mlp.Layer
Propagates the errors back through the (implicit) dropout layer.
backpropagate(boolean) - Method in class smile.base.mlp.MultilayerPerceptron
Propagates the errors back through the network.
backpropagate(double[]) - Method in class smile.base.mlp.HiddenLayer
 
backpropagate(double[]) - Method in class smile.base.mlp.InputLayer
 
backpropagate(double[]) - Method in class smile.base.mlp.Layer
Propagates the errors back to a lower layer.
backpropagate(double[]) - Method in class smile.base.mlp.OutputLayer
 
Bag - Class in smile.validation
A bag of random selected samples.
Bag(int[], int[]) - Constructor for class smile.validation.Bag
Constructor.
BagOfWords - Class in smile.feature.extraction
The bag-of-words feature of text used in natural language processing and information retrieval.
BagOfWords(String[], Function<String, String[]>, String[], boolean) - Constructor for class smile.feature.extraction.BagOfWords
Constructor.
BagOfWords(Function<String, String[]>, String[]) - Constructor for class smile.feature.extraction.BagOfWords
Constructor.
BandMatrix - Class in smile.math.matrix
A band matrix is a sparse matrix, whose non-zero entries are confined to a diagonal band, comprising the main diagonal and zero or more diagonals on either side.
BandMatrix - Class in smile.math.matrix.fp32
A band matrix is a sparse matrix, whose non-zero entries are confined to a diagonal band, comprising the main diagonal and zero or more diagonals on either side.
BandMatrix(int, int, int, int) - Constructor for class smile.math.matrix.BandMatrix
Constructor.
BandMatrix(int, int, int, int) - Constructor for class smile.math.matrix.fp32.BandMatrix
Constructor.
BandMatrix(int, int, int, int, double[][]) - Constructor for class smile.math.matrix.BandMatrix
Constructor.
BandMatrix(int, int, int, int, float[][]) - Constructor for class smile.math.matrix.fp32.BandMatrix
Constructor.
BandMatrix.Cholesky - Class in smile.math.matrix
The Cholesky decomposition of a symmetric, positive-definite matrix.
BandMatrix.Cholesky - Class in smile.math.matrix.fp32
The Cholesky decomposition of a symmetric, positive-definite matrix.
BandMatrix.LU - Class in smile.math.matrix
The LU decomposition.
BandMatrix.LU - Class in smile.math.matrix.fp32
The LU decomposition.
bandwidth() - Method in class smile.stat.distribution.KernelDensity
Returns the bandwidth of kernel.
BaseVector<T,TS,S extends BaseStream<TS,S>> - Interface in smile.data.vector
Base interface for immutable named vectors, which are sequences of elements supporting random access and sequential stream operations.
BBDTree - Class in smile.clustering
Balanced Box-Decomposition Tree.
BBDTree(double[][]) - Constructor for class smile.clustering.BBDTree
Constructs a tree out of the given n data points living in R^d.
BE - Enum constant in enum class smile.math.matrix.ARPACK.SymmOption
Computes nev eigenvalues, half from each end of the spectrum.
BE - Enum constant in enum class smile.math.matrix.fp32.ARPACK.SymmOption
Computes nev eigenvalues, half from each end of the spectrum.
Bernoulli - Interface in smile.glm.model
The response variable is of Bernoulli distribution.
BERNOULLI - Enum constant in enum class smile.classification.DiscreteNaiveBayes.Model
The document Bernoulli model generates an indicator for each term of the vocabulary, either indicating presence of the term in the document or indicating absence.
BernoulliDistribution - Class in smile.stat.distribution
Bernoulli distribution is a discrete probability distribution, which takes value 1 with success probability p and value 0 with failure probability q = 1 - p.
BernoulliDistribution(boolean[]) - Constructor for class smile.stat.distribution.BernoulliDistribution
Construct an Bernoulli from the given samples.
BernoulliDistribution(double) - Constructor for class smile.stat.distribution.BernoulliDistribution
Constructor.
BERT - Class in smile.deep.model.bert
Bidirectional Encoder Representations from Transformers (BERT).
BERT() - Constructor for class smile.deep.model.bert.BERT
 
BestLocalizedWavelet - Class in smile.wavelet
Best localized wavelets.
BestLocalizedWavelet(int) - Constructor for class smile.wavelet.BestLocalizedWavelet
Constructor.
beta - Variable in class smile.glm.GLM
The linear weights.
beta - Variable in class smile.stat.distribution.BetaDistribution
The shape parameter.
beta() - Method in class smile.stat.distribution.BetaDistribution
Returns the shape parameter beta.
beta(double, double) - Static method in class smile.math.special.Beta
Beta function, also called the Euler integral of the first kind.
Beta - Class in smile.math.special
The beta function, also called the Euler integral of the first kind.
BetaDistribution - Class in smile.stat.distribution
The beta distribution is defined on the interval [0, 1] parameterized by two positive shape parameters, typically denoted by α and β.
BetaDistribution(double, double) - Constructor for class smile.stat.distribution.BetaDistribution
Constructor.
BFGS - Class in smile.math
The Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is an iterative method for solving unconstrained nonlinear optimization problems.
BFGS() - Constructor for class smile.math.BFGS
 
bfs() - Method in class smile.graph.AdjacencyList
 
bfs() - Method in class smile.graph.AdjacencyMatrix
 
bfs() - Method in interface smile.graph.Graph
Breadth-first search connected components of graph.
bfs(Visitor) - Method in class smile.graph.AdjacencyList
 
bfs(Visitor) - Method in class smile.graph.AdjacencyMatrix
 
bfs(Visitor) - Method in interface smile.graph.Graph
BFS search on graph and performs some operation defined in visitor on each vertex during traveling.
bias - Variable in class smile.base.mlp.Layer
The bias.
biasGradient - Variable in class smile.base.mlp.Layer
The bias gradient.
biasGradientMoment1 - Variable in class smile.base.mlp.Layer
The first moment of bias gradient.
biasGradientMoment2 - Variable in class smile.base.mlp.Layer
The second moment of bias gradient.
biasUpdate - Variable in class smile.base.mlp.Layer
The bias update.
bic - Variable in class smile.stat.distribution.DiscreteExponentialFamilyMixture
The BIC score when the distribution is fit on a sample data.
bic - Variable in class smile.stat.distribution.ExponentialFamilyMixture
The BIC score when the distribution is fit on a sample data.
bic - Variable in class smile.stat.distribution.MultivariateExponentialFamilyMixture
The BIC score when the distribution is fit on a sample data.
bic(double[]) - Method in class smile.stat.distribution.DiscreteMixture
Returns the BIC score.
bic(double[]) - Method in class smile.stat.distribution.Mixture
Returns the BIC score.
bic(double[][]) - Method in class smile.stat.distribution.MultivariateMixture
Returns the BIC score.
BIC() - Method in class smile.glm.GLM
Returns the BIC score.
BIC(double, int, int) - Static method in interface smile.validation.ModelSelection
Bayesian information criterion.
BicubicInterpolation - Class in smile.interpolation
Bicubic interpolation in a two-dimensional regular grid.
BicubicInterpolation(double[], double[], double[][]) - Constructor for class smile.interpolation.BicubicInterpolation
Constructor.
BigMatrix - Class in smile.math.matrix
Big dense matrix of double precision values for more than 2 billion elements.
BigMatrix(int, int) - Constructor for class smile.math.matrix.BigMatrix
Constructor of zero matrix.
BigMatrix(int, int, double) - Constructor for class smile.math.matrix.BigMatrix
Constructor.
BigMatrix(int, int, int, DoublePointer) - Constructor for class smile.math.matrix.BigMatrix
Constructor.
BigMatrix.Cholesky - Class in smile.math.matrix
The Cholesky decomposition of a symmetric, positive-definite matrix.
BigMatrix.EVD - Class in smile.math.matrix
Eigenvalue decomposition.
BigMatrix.LU - Class in smile.math.matrix
The LU decomposition.
BigMatrix.QR - Class in smile.math.matrix
The QR decomposition.
BigMatrix.SVD - Class in smile.math.matrix
Singular Value Decomposition.
Bigram - Class in smile.nlp
Bigrams or digrams are groups of two words, and are very commonly used as the basis for simple statistical analysis of text.
Bigram - Class in smile.nlp.collocation
Collocations are expressions of multiple words which commonly co-occur.
Bigram(String, String) - Constructor for class smile.nlp.Bigram
Constructor.
Bigram(String, String, int, double) - Constructor for class smile.nlp.collocation.Bigram
Constructor.
bigrams() - Method in interface smile.nlp.Corpus
Returns the iterator over the bigrams in the corpus.
bigrams() - Method in class smile.nlp.SimpleCorpus
 
BilinearInterpolation - Class in smile.interpolation
Bilinear interpolation in a two-dimensional regular grid.
BilinearInterpolation(double[], double[], double[][]) - Constructor for class smile.interpolation.BilinearInterpolation
Constructor.
binary(int, KernelMachine<int[]>) - Static method in class smile.base.svm.LinearKernelMachine
Creates a linear kernel machine.
binary(String) - Static method in interface smile.math.kernel.MercerKernel
Returns a binary sparse kernel function.
BinaryEncoder - Class in smile.feature.extraction
Encodes categorical features using sparse one-hot scheme.
BinaryEncoder(StructType, String...) - Constructor for class smile.feature.extraction.BinaryEncoder
Constructor.
BinarySparseDataset - Interface in smile.data
Binary sparse dataset.
BinarySparseGaussianKernel - Class in smile.math.kernel
Gaussian kernel, also referred as RBF kernel or squared exponential kernel.
BinarySparseGaussianKernel(double) - Constructor for class smile.math.kernel.BinarySparseGaussianKernel
Constructor.
BinarySparseGaussianKernel(double, double, double) - Constructor for class smile.math.kernel.BinarySparseGaussianKernel
Constructor.
BinarySparseHyperbolicTangentKernel - Class in smile.math.kernel
The hyperbolic tangent kernel on binary sparse data.
BinarySparseHyperbolicTangentKernel() - Constructor for class smile.math.kernel.BinarySparseHyperbolicTangentKernel
Constructor with scale 1.0 and offset 0.0.
BinarySparseHyperbolicTangentKernel(double, double) - Constructor for class smile.math.kernel.BinarySparseHyperbolicTangentKernel
Constructor.
BinarySparseHyperbolicTangentKernel(double, double, double[], double[]) - Constructor for class smile.math.kernel.BinarySparseHyperbolicTangentKernel
Constructor.
BinarySparseLaplacianKernel - Class in smile.math.kernel
Laplacian kernel, also referred as exponential kernel.
BinarySparseLaplacianKernel(double) - Constructor for class smile.math.kernel.BinarySparseLaplacianKernel
Constructor.
BinarySparseLaplacianKernel(double, double, double) - Constructor for class smile.math.kernel.BinarySparseLaplacianKernel
Constructor.
BinarySparseLinearKernel - Class in smile.math.kernel
The linear dot product kernel on sparse binary arrays in int[], which are the indices of nonzero elements.
BinarySparseLinearKernel() - Constructor for class smile.math.kernel.BinarySparseLinearKernel
Constructor.
BinarySparseMaternKernel - Class in smile.math.kernel
The class of Matérn kernels is a generalization of the Gaussian/RBF.
BinarySparseMaternKernel(double, double) - Constructor for class smile.math.kernel.BinarySparseMaternKernel
Constructor.
BinarySparseMaternKernel(double, double, double, double) - Constructor for class smile.math.kernel.BinarySparseMaternKernel
Constructor.
BinarySparsePolynomialKernel - Class in smile.math.kernel
The polynomial kernel on binary sparse data.
BinarySparsePolynomialKernel(int) - Constructor for class smile.math.kernel.BinarySparsePolynomialKernel
Constructor with scale 1 and offset 0.
BinarySparsePolynomialKernel(int, double, double) - Constructor for class smile.math.kernel.BinarySparsePolynomialKernel
Constructor.
BinarySparsePolynomialKernel(int, double, double, double[], double[]) - Constructor for class smile.math.kernel.BinarySparsePolynomialKernel
Constructor.
BinarySparseThinPlateSplineKernel - Class in smile.math.kernel
The Thin Plate Spline kernel on binary sparse data.
BinarySparseThinPlateSplineKernel(double) - Constructor for class smile.math.kernel.BinarySparseThinPlateSplineKernel
Constructor.
BinarySparseThinPlateSplineKernel(double, double, double) - Constructor for class smile.math.kernel.BinarySparseThinPlateSplineKernel
Constructor.
bind(StructType) - Method in class smile.data.formula.Abs
 
bind(StructType) - Method in class smile.data.formula.Add
 
bind(StructType) - Method in class smile.data.formula.Date
 
bind(StructType) - Method in class smile.data.formula.Div
 
bind(StructType) - Method in class smile.data.formula.DoubleFunction
 
bind(StructType) - Method in class smile.data.formula.FactorCrossing
 
bind(StructType) - Method in class smile.data.formula.FactorInteraction
 
bind(StructType) - Method in class smile.data.formula.Formula
Binds the formula to a schema and returns the schema of predictors.
bind(StructType) - Method in class smile.data.formula.IntFunction
 
bind(StructType) - Method in class smile.data.formula.Mul
 
bind(StructType) - Method in class smile.data.formula.Sub
 
bind(StructType) - Method in interface smile.data.formula.Term
Binds the term to a schema.
binomial(double[][], int[]) - Static method in class smile.classification.LogisticRegression
Fits binomial logistic regression.
binomial(double[][], int[], double, double, int) - Static method in class smile.classification.LogisticRegression
Fits binomial logistic regression.
binomial(double[][], int[], Properties) - Static method in class smile.classification.LogisticRegression
Fits binomial logistic regression.
binomial(int, int[][], int[]) - Static method in class smile.classification.Maxent
Fits maximum entropy classifier.
binomial(int, int[][], int[], double, double, int) - Static method in class smile.classification.Maxent
Fits maximum entropy classifier.
binomial(int, int[][], int[], Properties) - Static method in class smile.classification.Maxent
Fits maximum entropy classifier.
binomial(SparseDataset, int[]) - Static method in class smile.classification.SparseLogisticRegression
Fits binomial logistic regression.
binomial(SparseDataset, int[], double, double, int) - Static method in class smile.classification.SparseLogisticRegression
Fits binomial logistic regression.
binomial(SparseDataset, int[], Properties) - Static method in class smile.classification.SparseLogisticRegression
Fits binomial logistic regression.
Binomial - Interface in smile.glm.model
The response variable is of Binomial distribution.
Binomial(double[], double, double, IntSet) - Constructor for class smile.classification.LogisticRegression.Binomial
Constructor.
Binomial(double[], double, double, IntSet) - Constructor for class smile.classification.Maxent.Binomial
Constructor.
Binomial(double[], double, double, IntSet) - Constructor for class smile.classification.SparseLogisticRegression.Binomial
Constructor.
BinomialDistribution - Class in smile.stat.distribution
The binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p.
BinomialDistribution(int, double) - Constructor for class smile.stat.distribution.BinomialDistribution
Constructor.
bins(double[], double) - Static method in interface smile.math.Histogram
Returns the number of bins for a data based on a suggested bin width h.
bins(int) - Static method in interface smile.math.Histogram
Returns the number of bins by square-root rule, which takes the square root of the number of data points in the sample (used by Excel histograms and many others).
BIRCH - Class in smile.vq
Balanced Iterative Reducing and Clustering using Hierarchies.
BIRCH(int, int, int, double) - Constructor for class smile.vq.BIRCH
Constructor.
bits() - Method in class smile.gap.BitString
Returns the bit string of chromosome.
BitString - Class in smile.gap
The standard bit string representation of the solution domain.
BitString(byte[], Fitness<BitString>) - Constructor for class smile.gap.BitString
Constructor.
BitString(byte[], Fitness<BitString>, Crossover, double, double) - Constructor for class smile.gap.BitString
Constructor.
BitString(int, Fitness<BitString>) - Constructor for class smile.gap.BitString
Constructor.
BitString(int, Fitness<BitString>, Crossover, double, double) - Constructor for class smile.gap.BitString
Constructor.
bk() - Method in class smile.math.matrix.fp32.SymmMatrix
Bunch-Kaufman decomposition.
bk() - Method in class smile.math.matrix.SymmMatrix
Bunch-Kaufman decomposition.
BKTree<K,V> - Class in smile.neighbor
A BK-tree is a metric tree specifically adapted to discrete metric spaces.
BKTree(Metric<K>) - Constructor for class smile.neighbor.BKTree
Constructor.
blas() - Method in enum class smile.math.blas.Diag
Returns the int value for BLAS.
blas() - Method in enum class smile.math.blas.Layout
Returns the int value for BLAS.
blas() - Method in enum class smile.math.blas.Side
Returns the int value for BLAS.
blas() - Method in enum class smile.math.blas.Transpose
Returns the int value for BLAS.
blas() - Method in enum class smile.math.blas.UPLO
Returns the int value for BLAS.
BLAS - Interface in smile.math.blas
Basic Linear Algebra Subprograms.
BM25 - Class in smile.nlp.relevance
The BM25 weighting scheme, often called Okapi weighting, after the system in which it was first implemented, was developed as a way of building a probabilistic model sensitive to term frequency and document length while not introducing too many additional parameters into the model.
BM25() - Constructor for class smile.nlp.relevance.BM25
Default constructor with k1 = 1.2, b = 0.75, delta = 1.0.
BM25(double, double, double) - Constructor for class smile.nlp.relevance.BM25
Constructor.
body - Variable in class smile.nlp.Text
The text body.
Boolean - Enum constant in enum class smile.data.type.DataType.ID
Boolean type ID.
BOOLEAN - Static variable in interface smile.util.Regex
Boolean regular expression pattern.
BOOLEAN_REGEX - Static variable in interface smile.util.Regex
Boolean regular expression.
BooleanArrayType - Static variable in class smile.data.type.DataTypes
Boolean Array data type.
BooleanObjectType - Static variable in class smile.data.type.DataTypes
Boolean Object data type.
BooleanType - Class in smile.data.type
Boolean data type.
BooleanType - Static variable in class smile.data.type.DataTypes
Boolean data type.
booleanVector(int) - Method in interface smile.data.DataFrame
Selects column based on the column index.
booleanVector(int) - Method in class smile.data.IndexDataFrame
 
booleanVector(Enum<?>) - Method in interface smile.data.DataFrame
Selects column using an enum value.
booleanVector(String) - Method in interface smile.data.DataFrame
Selects column based on the column name.
BooleanVector - Interface in smile.data.vector
An immutable boolean vector.
Bootstrap - Interface in smile.validation
The bootstrap is a general tool for assessing statistical accuracy.
Box_Pierce - Enum constant in enum class smile.timeseries.BoxTest.Type
Box-Pierce test.
boxed() - Method in interface smile.data.type.DataType
Returns the boxed data type if this is a primitive type.
boxed(Collection<Tuple>) - Method in class smile.data.type.StructType
Updates the field type to the boxed one if the field has null/missing values in the data.
BoxTest - Class in smile.timeseries
Portmanteau test jointly that several autocorrelations of time series are zero.
BoxTest.Type - Enum Class in smile.timeseries
The type of test.
branch(Tuple) - Method in class smile.base.cart.InternalNode
Returns true if the instance goes to the true branch.
branch(Tuple) - Method in class smile.base.cart.NominalNode
 
branch(Tuple) - Method in class smile.base.cart.OrdinalNode
 
BreakIteratorSentenceSplitter - Class in smile.nlp.tokenizer
A sentence splitter based on the java.text.BreakIterator, which supports multiple natural languages (selected by locale setting).
BreakIteratorSentenceSplitter() - Constructor for class smile.nlp.tokenizer.BreakIteratorSentenceSplitter
Constructor for the default locale.
BreakIteratorSentenceSplitter(Locale) - Constructor for class smile.nlp.tokenizer.BreakIteratorSentenceSplitter
Constructor for the given locale.
BreakIteratorTokenizer - Class in smile.nlp.tokenizer
A word tokenizer based on the java.text.BreakIterator, which supports multiple natural languages (selected by locale setting).
BreakIteratorTokenizer() - Constructor for class smile.nlp.tokenizer.BreakIteratorTokenizer
Constructor for the default locale.
BreakIteratorTokenizer(Locale) - Constructor for class smile.nlp.tokenizer.BreakIteratorTokenizer
Constructor for the given locale.
breaks - Variable in class smile.feature.selection.InformationValue
Breakpoints of intervals for numerical variables.
breaks(double[], double) - Static method in interface smile.math.Histogram
Returns the breakpoints between histogram cells for a dataset based on a suggested bin width h.
breaks(double[], int) - Static method in interface smile.math.Histogram
Returns the breakpoints between histogram cells for a dataset.
breaks(double, double, double) - Static method in interface smile.math.Histogram
Returns the breakpoints between histogram cells for a given range based on a suggested bin width h.
breaks(double, double, int) - Static method in interface smile.math.Histogram
Returns the breakpoints between histogram cells for a given range.
bubble(int) - Static method in interface smile.vq.Neighborhood
Returns the bubble neighborhood function.
bucket - Variable in class smile.neighbor.lsh.Bucket
The bucket id is given by the universal bucket hashing.
Bucket - Class in smile.neighbor.lsh
A bucket is a container for points that all have the same value for hash function g (function g is a vector of k LSH functions).
Bucket(int) - Constructor for class smile.neighbor.lsh.Bucket
Constructor.
build() - Method in class smile.hash.PerfectMap.Builder
Builds the perfect map.
build(int) - Method in class smile.base.mlp.HiddenLayerBuilder
 
build(int) - Method in class smile.base.mlp.LayerBuilder
Builds a layer.
build(int) - Method in class smile.base.mlp.OutputLayerBuilder
 
builder(String, int, double, double) - Static method in class smile.base.mlp.Layer
Returns a hidden layer.
Builder() - Constructor for class smile.hash.PerfectMap.Builder
Constructor.
Builder(Map<String, T>) - Constructor for class smile.hash.PerfectMap.Builder
Constructor.
BunchKaufman(SymmMatrix, int[], int) - Constructor for class smile.math.matrix.fp32.SymmMatrix.BunchKaufman
Constructor.
BunchKaufman(SymmMatrix, int[], int) - Constructor for class smile.math.matrix.SymmMatrix.BunchKaufman
Constructor.
Byte - Enum constant in enum class smile.data.type.DataType.ID
Byte type ID.
ByteArrayType - Static variable in class smile.data.type.DataTypes
Byte Array data type.
ByteObjectType - Static variable in class smile.data.type.DataTypes
Byte Object data type.
ByteType - Class in smile.data.type
Byte data type.
ByteType - Static variable in class smile.data.type.DataTypes
Byte data type.
byteVector(int) - Method in interface smile.data.DataFrame
Selects column based on the column index.
byteVector(int) - Method in class smile.data.IndexDataFrame
 
byteVector(Enum<?>) - Method in interface smile.data.DataFrame
Selects column using an enum value.
byteVector(String) - Method in interface smile.data.DataFrame
Selects column based on the column name.
ByteVector - Interface in smile.data.vector
An immutable byte vector.

C

c(double...) - Static method in class smile.math.MathEx
Combines the arguments to form a vector.
c(double[]...) - Static method in class smile.math.MathEx
Concatenates multiple vectors into one.
c(float...) - Static method in class smile.math.MathEx
Combines the arguments to form a vector.
c(float[]...) - Static method in class smile.math.MathEx
Concatenates multiple vectors into one.
c(int...) - Static method in class smile.math.MathEx
Combines the arguments to form a vector.
c(int[]...) - Static method in class smile.math.MathEx
Concatenates multiple vectors into one.
c(String...) - Static method in class smile.math.MathEx
Combines the arguments to form a vector.
c(String[]...) - Static method in class smile.math.MathEx
Concatenates multiple vectors into one array of strings.
CARDINAL_NUMBER - Static variable in interface smile.util.Regex
Cardinal numbers.
CARDINAL_NUMBER_WITH_COMMA - Static variable in interface smile.util.Regex
Cardinal numbers, optionally thousands are separated by comma.
CART - Class in smile.base.cart
Classification and regression tree.
CART(DataFrame, StructField, int, int, int, int, int[], int[][]) - Constructor for class smile.base.cart.CART
Constructor.
CART(Formula, StructType, StructField, Node, double[]) - Constructor for class smile.base.cart.CART
Constructor.
CategoricalEncoder - Enum Class in smile.data
Categorical variable encoder.
CategoricalMeasure - Class in smile.data.measure
Categorical data can be stored into groups or categories with the aid of names or labels.
CategoricalMeasure(int[]) - Constructor for class smile.data.measure.CategoricalMeasure
Constructor.
CategoricalMeasure(int[], String[]) - Constructor for class smile.data.measure.CategoricalMeasure
Constructor.
CategoricalMeasure(String...) - Constructor for class smile.data.measure.CategoricalMeasure
Constructor.
CategoricalMeasure(List<String>) - Constructor for class smile.data.measure.CategoricalMeasure
Constructor.
cbind(double[]...) - Static method in class smile.math.MathEx
Concatenates vectors by columns.
cbind(float[]...) - Static method in class smile.math.MathEx
Concatenates vectors by columns.
cbind(int[]...) - Static method in class smile.math.MathEx
Concatenates vectors by columns.
cbind(String[]...) - Static method in class smile.math.MathEx
Concatenates vectors by columns.
cbrt(String) - Static method in interface smile.data.formula.Terms
The cbrt(x) term.
cbrt(Term) - Static method in interface smile.data.formula.Terms
The cbrt(x) term.
CC - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
Coordinating conjunction.
CD - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
Cardinal number.
cdf(double) - Method in class smile.stat.distribution.BernoulliDistribution
 
cdf(double) - Method in class smile.stat.distribution.BetaDistribution
 
cdf(double) - Method in class smile.stat.distribution.BinomialDistribution
 
cdf(double) - Method in class smile.stat.distribution.ChiSquareDistribution
 
cdf(double) - Method in class smile.stat.distribution.DiscreteMixture
 
cdf(double) - Method in interface smile.stat.distribution.Distribution
Cumulative distribution function.
cdf(double) - Method in class smile.stat.distribution.EmpiricalDistribution
 
cdf(double) - Method in class smile.stat.distribution.ExponentialDistribution
 
cdf(double) - Method in class smile.stat.distribution.FDistribution
 
cdf(double) - Method in class smile.stat.distribution.GammaDistribution
 
cdf(double) - Method in class smile.stat.distribution.GaussianDistribution
 
cdf(double) - Method in class smile.stat.distribution.GeometricDistribution
 
cdf(double) - Method in class smile.stat.distribution.HyperGeometricDistribution
 
cdf(double) - Method in class smile.stat.distribution.KernelDensity
Cumulative distribution function.
cdf(double) - Method in class smile.stat.distribution.LogisticDistribution
 
cdf(double) - Method in class smile.stat.distribution.LogNormalDistribution
 
cdf(double) - Method in class smile.stat.distribution.Mixture
 
cdf(double) - Method in class smile.stat.distribution.NegativeBinomialDistribution
 
cdf(double) - Method in class smile.stat.distribution.PoissonDistribution
 
cdf(double) - Method in class smile.stat.distribution.ShiftedGeometricDistribution
 
cdf(double) - Method in class smile.stat.distribution.TDistribution
 
cdf(double) - Method in class smile.stat.distribution.WeibullDistribution
 
cdf(double[]) - Method in interface smile.stat.distribution.MultivariateDistribution
Cumulative distribution function.
cdf(double[]) - Method in class smile.stat.distribution.MultivariateGaussianDistribution
Algorithm from Alan Genz (1992) Numerical Computation of Multivariate Normal Probabilities, Journal of Computational and Graphical Statistics, pp.
cdf(double[]) - Method in class smile.stat.distribution.MultivariateMixture
 
cdf2tailed(double) - Method in class smile.stat.distribution.TDistribution
Two-tailed cdf.
ceil(String) - Static method in interface smile.data.formula.Terms
The ceil(x) term.
ceil(Term) - Static method in interface smile.data.formula.Terms
The ceil(x) term.
center() - Method in class smile.feature.extraction.PCA
Returns the center of data.
center() - Method in class smile.feature.extraction.ProbabilisticPCA
Returns the center of data.
CentroidClustering<T,U> - Class in smile.clustering
In centroid-based clustering, clusters are represented by a central vector, which may not necessarily be a member of the data set.
CentroidClustering(double, T[], int[]) - Constructor for class smile.clustering.CentroidClustering
Constructor.
centroids - Variable in class smile.clustering.CentroidClustering
The centroids of each cluster.
centroids() - Method in class smile.vq.BIRCH
Returns the cluster centroids of leaf nodes.
change(int) - Method in class smile.util.PriorityQueue
The priority of item k has changed.
Char - Enum constant in enum class smile.data.type.DataType.ID
Char type ID.
CharArrayType - Static variable in class smile.data.type.DataTypes
Char Array data type.
CharObjectType - Static variable in class smile.data.type.DataTypes
Char Object data type.
charset(Charset) - Method in class smile.io.CSV
Sets the charset.
charset(Charset) - Method in class smile.io.JSON
Sets the charset.
CharType - Class in smile.data.type
Char data type.
CharType - Static variable in class smile.data.type.DataTypes
Char data type.
charVector(int) - Method in interface smile.data.DataFrame
Selects column based on the column index.
charVector(int) - Method in class smile.data.IndexDataFrame
 
charVector(Enum<?>) - Method in interface smile.data.DataFrame
Selects column using an enum value.
charVector(String) - Method in interface smile.data.DataFrame
Selects column based on the column name.
CharVector - Interface in smile.data.vector
An immutable char vector.
ChebyshevDistance - Class in smile.math.distance
Chebyshev distance (or Tchebychev distance), or L metric is a metric defined on a vector space where the distance between two vectors is the greatest of their differences along any coordinate dimension.
ChebyshevDistance() - Constructor for class smile.math.distance.ChebyshevDistance
Constructor.
children() - Method in class smile.taxonomy.Concept
Gets all children concepts.
chisq - Variable in class smile.stat.hypothesis.ChiSqTest
chi-square statistic
ChiSqTest - Class in smile.stat.hypothesis
Pearson's chi-square test, also known as the chi-square goodness-of-fit test or chi-square test for independence.
ChiSqTest(String, double, double, double) - Constructor for class smile.stat.hypothesis.ChiSqTest
Constructor.
ChiSqTest(String, double, double, double, double) - Constructor for class smile.stat.hypothesis.ChiSqTest
Constructor.
ChiSquareDistribution - Class in smile.stat.distribution
Chi-square (or chi-squared) distribution with k degrees of freedom is the distribution of a sum of the squares of k independent standard normal random variables.
ChiSquareDistribution(int) - Constructor for class smile.stat.distribution.ChiSquareDistribution
Constructor.
cholesky() - Method in class smile.math.matrix.BandMatrix
Cholesky decomposition for symmetric and positive definite matrix.
cholesky() - Method in class smile.math.matrix.BigMatrix
Cholesky decomposition for symmetric and positive definite matrix.
cholesky() - Method in class smile.math.matrix.fp32.BandMatrix
Cholesky decomposition for symmetric and positive definite matrix.
cholesky() - Method in class smile.math.matrix.fp32.Matrix
Cholesky decomposition for symmetric and positive definite matrix.
cholesky() - Method in class smile.math.matrix.fp32.SymmMatrix
Cholesky decomposition for symmetric and positive definite matrix.
cholesky() - Method in class smile.math.matrix.Matrix
Cholesky decomposition for symmetric and positive definite matrix.
cholesky() - Method in class smile.math.matrix.SymmMatrix
Cholesky decomposition for symmetric and positive definite matrix.
cholesky(boolean) - Method in class smile.math.matrix.BigMatrix
Cholesky decomposition for symmetric and positive definite matrix.
cholesky(boolean) - Method in class smile.math.matrix.fp32.Matrix
Cholesky decomposition for symmetric and positive definite matrix.
cholesky(boolean) - Method in class smile.math.matrix.Matrix
Cholesky decomposition for symmetric and positive definite matrix.
Cholesky(BandMatrix) - Constructor for class smile.math.matrix.BandMatrix.Cholesky
Constructor.
Cholesky(BigMatrix) - Constructor for class smile.math.matrix.BigMatrix.Cholesky
Constructor.
Cholesky(BandMatrix) - Constructor for class smile.math.matrix.fp32.BandMatrix.Cholesky
Constructor.
Cholesky(Matrix) - Constructor for class smile.math.matrix.fp32.Matrix.Cholesky
Constructor.
Cholesky(SymmMatrix) - Constructor for class smile.math.matrix.fp32.SymmMatrix.Cholesky
Constructor.
Cholesky(Matrix) - Constructor for class smile.math.matrix.Matrix.Cholesky
Constructor.
Cholesky(SymmMatrix) - Constructor for class smile.math.matrix.SymmMatrix.Cholesky
Constructor.
CholeskyOfAtA() - Method in class smile.math.matrix.BigMatrix.QR
Returns the Cholesky decomposition of A'A.
CholeskyOfAtA() - Method in class smile.math.matrix.fp32.Matrix.QR
Returns the Cholesky decomposition of A'A.
CholeskyOfAtA() - Method in class smile.math.matrix.Matrix.QR
Returns the Cholesky decomposition of A'A.
choose(int, int) - Static method in class smile.math.MathEx
The n choose k.
Chromosome - Interface in smile.gap
Artificial chromosomes in genetic algorithm/programming encoding candidate solutions to an optimization problem.
CLARANS<T> - Class in smile.clustering
Clustering Large Applications based upon RANdomized Search.
CLARANS(double, T[], int[], Distance<T>) - Constructor for class smile.clustering.CLARANS
Constructor.
classes - Variable in class smile.classification.AbstractClassifier
The class labels.
classes - Variable in class smile.classification.ClassLabels
The class labels.
classes() - Method in class smile.classification.AbstractClassifier
 
classes() - Method in interface smile.classification.Classifier
Returns the class labels.
classes() - Method in class smile.classification.DecisionTree
 
classes() - Method in class smile.classification.MLP
 
classes() - Method in class smile.classification.SVM
 
classification(int, int, Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in interface smile.validation.CrossValidation
Repeated cross validation of classification.
classification(int, int, T[], int[], BiFunction<T[], int[], M>) - Static method in interface smile.validation.CrossValidation
Repeated cross validation of classification.
classification(int, Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in interface smile.validation.Bootstrap
Runs classification bootstrap validation.
classification(int, Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in interface smile.validation.CrossValidation
Cross validation of classification.
classification(int, T[], int[], BiFunction<T[], int[], M>) - Static method in interface smile.validation.Bootstrap
Runs classification bootstrap validation.
classification(int, T[], int[], BiFunction<T[], int[], M>) - Static method in interface smile.validation.CrossValidation
Cross validation of classification.
classification(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in interface smile.validation.LOOCV
Runs leave-one-out cross validation tests.
classification(T[], int[], BiFunction<T[], int[], M>) - Static method in interface smile.validation.LOOCV
Runs leave-one-out cross validation tests.
CLASSIFICATION_ERROR - Enum constant in enum class smile.base.cart.SplitRule
Classification error.
ClassificationMetric - Interface in smile.validation.metric
An abstract interface to measure the classification performance.
ClassificationMetrics - Class in smile.validation
The classification validation metrics.
ClassificationMetrics(double, double, int, int, double) - Constructor for class smile.validation.ClassificationMetrics
Constructor.
ClassificationMetrics(double, double, int, int, double, double) - Constructor for class smile.validation.ClassificationMetrics
Constructor of multiclass soft classifier validation.
ClassificationMetrics(double, double, int, int, double, double, double, double, double, double) - Constructor for class smile.validation.ClassificationMetrics
Constructor of binary classifier validation.
ClassificationMetrics(double, double, int, int, double, double, double, double, double, double, double, double) - Constructor for class smile.validation.ClassificationMetrics
Constructor of binary soft classifier validation.
ClassificationMetrics(double, double, int, int, double, double, double, double, double, double, double, double, double) - Constructor for class smile.validation.ClassificationMetrics
Constructor.
ClassificationValidation<M> - Class in smile.validation
Classification model validation results.
ClassificationValidation(M, double, double, int[], int[]) - Constructor for class smile.validation.ClassificationValidation
Constructor.
ClassificationValidation(M, double, double, int[], int[], double[][]) - Constructor for class smile.validation.ClassificationValidation
Constructor of soft classifier validation.
ClassificationValidations<M> - Class in smile.validation
Classification model validation results.
ClassificationValidations(List<ClassificationValidation<M>>) - Constructor for class smile.validation.ClassificationValidations
Constructor.
Classifier<T> - Interface in smile.classification
A classifier assigns an input object into one of a given number of categories.
Classifier.Trainer<T,M extends Classifier<T>> - Interface in smile.classification
The classifier trainer.
ClassLabels - Class in smile.classification
Map arbitrary class labels to [0, k), where k is the number of classes.
ClassLabels(int, int[], IntSet) - Constructor for class smile.classification.ClassLabels
Constructor.
clear() - Method in class smile.base.cart.CART
Clear the workspace of building tree.
clear() - Method in class smile.util.DoubleArrayList
Removes all of the values from this list.
clear() - Method in class smile.util.IntArrayList
Removes all of the value from this list.
clear(double) - Method in class smile.vq.NeuralMap
Removes staled neurons and the edges beyond lifetime.
clipNorm - Variable in class smile.base.mlp.MultilayerPerceptron
The gradient clipping norm.
clipValue - Variable in class smile.base.mlp.MultilayerPerceptron
The gradient clipping value.
clone() - Method in class smile.math.matrix.BandMatrix
 
clone() - Method in class smile.math.matrix.BigMatrix
Returns a deep copy of matrix.
clone() - Method in class smile.math.matrix.fp32.BandMatrix
 
clone() - Method in class smile.math.matrix.fp32.Matrix
Returns a deep copy of matrix.
clone() - Method in class smile.math.matrix.fp32.SparseMatrix
 
clone() - Method in class smile.math.matrix.fp32.SymmMatrix
 
clone() - Method in class smile.math.matrix.Matrix
Returns a deep copy of matrix.
clone() - Method in class smile.math.matrix.SparseMatrix
 
clone() - Method in class smile.math.matrix.SymmMatrix
 
clone() - Method in class smile.neighbor.lsh.Probe
 
clone(double[][]) - Static method in class smile.math.MathEx
Deep clone a two-dimensional array.
clone(float[][]) - Static method in class smile.math.MathEx
Deep clone a two-dimensional array.
clone(int[][]) - Static method in class smile.math.MathEx
Deep clone a two-dimensional array.
close() - Method in class smile.io.Arff
 
CLOSING_PARENTHESIS - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
Punctuation ) ] }
CLOSING_QUOTATION - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
Punctuation ' or ''
clustering(double[][], double[][], int[], int[]) - Method in class smile.clustering.BBDTree
Given k cluster centroids, this method assigns data to nearest centroids.
ClusteringMetric - Interface in smile.validation.metric
An abstract interface to measure the clustering performance.
CNB - Enum constant in enum class smile.classification.DiscreteNaiveBayes.Model
Complement Naive Bayes.
coefficients() - Method in class smile.classification.LogisticRegression.Binomial
Returns an array of size (p+1) containing the linear weights of binary logistic regression, where p is the dimension of feature vectors.
coefficients() - Method in class smile.classification.LogisticRegression.Multinomial
Returns a 2d-array of size (k-1) x (p+1), containing the linear weights of multi-class logistic regression, where k is the number of classes and p is the dimension of feature vectors.
coefficients() - Method in class smile.classification.Maxent.Binomial
Returns an array of size (p+1) containing the linear weights of binary logistic regression, where p is the dimension of feature vectors.
coefficients() - Method in class smile.classification.Maxent.Multinomial
Returns a 2d-array of size (k-1) x (p+1), containing the linear weights of multi-class logistic regression, where k is the number of classes and p is the dimension of feature vectors.
coefficients() - Method in class smile.classification.SparseLogisticRegression.Binomial
Returns an array of size (p+1) containing the linear weights of binary logistic regression, where p is the dimension of feature vectors.
coefficients() - Method in class smile.classification.SparseLogisticRegression.Multinomial
Returns a 2d-array of size (k-1) x (p+1), containing the linear weights of multi-class logistic regression, where k is the number of classes and p is the dimension of feature vectors.
coefficients() - Method in class smile.glm.GLM
Returns an array of size (p+1) containing the linear weights of binary logistic regression, where p is the dimension of feature vectors.
coefficients() - Method in class smile.regression.LinearModel
Returns the linear coefficients without intercept.
coerce(DataType, DataType) - Static method in interface smile.data.type.DataType
Returns the common type.
CoifletWavelet - Class in smile.wavelet
Coiflet wavelets.
CoifletWavelet(int) - Constructor for class smile.wavelet.CoifletWavelet
Constructor.
col(int) - Method in class smile.math.matrix.BigMatrix
Returns the j-th column.
col(int) - Method in class smile.math.matrix.fp32.Matrix
Returns the j-th column.
col(int) - Method in class smile.math.matrix.Matrix
Returns the j-th column.
col(int...) - Method in class smile.math.matrix.BigMatrix
Returns the matrix of selected columns.
COL_MAJOR - Enum constant in enum class smile.math.blas.Layout
Column major layout.
collect() - Static method in interface smile.data.DataFrame.Collectors
Returns a stream collector that accumulates tuples into a DataFrame.
collect(Class<T>) - Static method in interface smile.data.DataFrame.Collectors
Returns a stream collector that accumulates objects into a DataFrame.
colMax(double[][]) - Static method in class smile.math.MathEx
Returns the column maximum of a matrix.
colMax(int[][]) - Static method in class smile.math.MathEx
Returns the column maximum of a matrix.
colMeans() - Method in class smile.math.matrix.BigMatrix
Returns the mean of each column.
colMeans() - Method in class smile.math.matrix.fp32.Matrix
Returns the mean of each column.
colMeans() - Method in class smile.math.matrix.Matrix
Returns the mean of each column.
colMeans(double[][]) - Static method in class smile.math.MathEx
Returns the column means of a matrix.
colMin(double[][]) - Static method in class smile.math.MathEx
Returns the column minimum of a matrix.
colMin(int[][]) - Static method in class smile.math.MathEx
Returns the column minimum of a matrix.
colName(int) - Method in class smile.math.matrix.fp32.IMatrix
Returns the name of i-th column.
colName(int) - Method in class smile.math.matrix.IMatrix
Returns the name of i-th column.
colNames() - Method in class smile.math.matrix.fp32.IMatrix
Returns the column names.
colNames() - Method in class smile.math.matrix.IMatrix
Returns the column names.
colNames(String[]) - Method in class smile.math.matrix.fp32.IMatrix
Sets the column names.
colNames(String[]) - Method in class smile.math.matrix.IMatrix
Sets the column names.
COLON - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
Punctuation ; : ...
cols(int...) - Method in class smile.math.matrix.fp32.Matrix
Returns the matrix of selected columns.
cols(int...) - Method in class smile.math.matrix.Matrix
Returns the matrix of selected columns.
colSds() - Method in class smile.math.matrix.BigMatrix
Returns the standard deviations of each column.
colSds() - Method in class smile.math.matrix.fp32.Matrix
Returns the standard deviations of each column.
colSds() - Method in class smile.math.matrix.Matrix
Returns the standard deviations of each column.
colSds(double[][]) - Static method in class smile.math.MathEx
Returns the column standard deviations of a matrix.
colSums() - Method in class smile.math.matrix.BigMatrix
Returns the sum of each column.
colSums() - Method in class smile.math.matrix.fp32.Matrix
Returns the sum of each column.
colSums() - Method in class smile.math.matrix.Matrix
Returns the sum of each column.
colSums(double[][]) - Static method in class smile.math.MathEx
Returns the column sums of a matrix.
colSums(int[][]) - Static method in class smile.math.MathEx
Returns the column sums of a matrix.
column(double[]) - Static method in class smile.math.matrix.BigMatrix
Returns a column vector/matrix.
column(double[]) - Static method in class smile.math.matrix.fp32.Matrix
Returns a column vector/matrix.
column(double[]) - Static method in class smile.math.matrix.Matrix
Returns a column vector/matrix.
column(double[], int, int) - Static method in class smile.math.matrix.BigMatrix
Returns a column vector/matrix.
column(double[], int, int) - Static method in class smile.math.matrix.fp32.Matrix
Returns a column vector/matrix.
column(double[], int, int) - Static method in class smile.math.matrix.Matrix
Returns a column vector/matrix.
column(float[]) - Static method in class smile.math.matrix.fp32.Matrix
Returns a column vector/matrix.
column(float[], int, int) - Static method in class smile.math.matrix.fp32.Matrix
Returns a column vector/matrix.
column(int) - Method in interface smile.data.DataFrame
Selects column based on the column index.
column(int) - Method in class smile.data.IndexDataFrame
 
column(Enum<?>) - Method in interface smile.data.DataFrame
Selects column using an enum value.
column(String) - Method in interface smile.data.DataFrame
Selects column based on the column name.
columns - Variable in class smile.feature.extraction.Projection
The fields of input space.
ColumnTransform - Class in smile.data.transform
Column-wise data transformation.
ColumnTransform(String, Map<String, Function>) - Constructor for class smile.data.transform.ColumnTransform
Constructor.
COMMA - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
Punctuation ,
COMPACT - Enum constant in enum class smile.math.blas.SVDJob
The first min(m, n) singular vectors are returned in supplied matrix U (or Vt).
comparator - Static variable in class smile.base.cart.Split
The comparator on the split score.
compareTo(CentroidClustering<T, U>) - Method in class smile.clustering.CentroidClustering
 
compareTo(MEC<T>) - Method in class smile.clustering.MEC
 
compareTo(InformationValue) - Method in class smile.feature.selection.InformationValue
 
compareTo(SignalNoiseRatio) - Method in class smile.feature.selection.SignalNoiseRatio
 
compareTo(SumSquaresRatio) - Method in class smile.feature.selection.SumSquaresRatio
 
compareTo(Chromosome) - Method in class smile.gap.BitString
 
compareTo(PrH) - Method in class smile.neighbor.lsh.PrH
 
compareTo(Probe) - Method in class smile.neighbor.lsh.Probe
 
compareTo(PrZ) - Method in class smile.neighbor.lsh.PrZ
 
compareTo(Neighbor<K, V>) - Method in class smile.neighbor.Neighbor
 
compareTo(Bigram) - Method in class smile.nlp.collocation.Bigram
 
compareTo(NGram) - Method in class smile.nlp.collocation.NGram
 
compareTo(Relevance) - Method in class smile.nlp.relevance.Relevance
 
compareTo(Neuron) - Method in class smile.vq.hebb.Neuron
 
CompleteLinkage - Class in smile.clustering.linkage
Complete linkage.
CompleteLinkage(double[][]) - Constructor for class smile.clustering.linkage.CompleteLinkage
Constructor.
CompleteLinkage(int, float[]) - Constructor for class smile.clustering.linkage.CompleteLinkage
Constructor.
Complex - Class in smile.math
Complex number.
Complex(double, double) - Constructor for class smile.math.Complex
Constructor.
Complex.Array - Class in smile.math
Packed array of complex numbers for better memory efficiency.
Component(double, DiscreteDistribution) - Constructor for class smile.stat.distribution.DiscreteMixture.Component
Constructor.
Component(double, Distribution) - Constructor for class smile.stat.distribution.Mixture.Component
Constructor.
Component(double, MultivariateDistribution) - Constructor for class smile.stat.distribution.MultivariateMixture.Component
Constructor.
components - Variable in class smile.ica.ICA
The independent components (row-wise).
components - Variable in class smile.stat.distribution.DiscreteMixture
The components of finite mixture model.
components - Variable in class smile.stat.distribution.Mixture
The components of finite mixture model.
components - Variable in class smile.stat.distribution.MultivariateMixture
The components of finite mixture model.
compose(Transform) - Method in interface smile.data.transform.Transform
Returns a composed function that first applies the before function to its input, and then applies this function to the result.
COMPREHENSIVE - Enum constant in enum class smile.nlp.dictionary.EnglishStopWords
A very long list of stop words.
computeGradient(double[]) - Method in class smile.base.mlp.InputLayer
 
computeGradient(double[]) - Method in class smile.base.mlp.Layer
Computes the parameter gradient for a sample of (mini-)batch.
computeGradientUpdate(double[], double, double, double) - Method in class smile.base.mlp.InputLayer
 
computeGradientUpdate(double[], double, double, double) - Method in class smile.base.mlp.Layer
Computes the parameter gradient and update the weights.
computeOutputGradient(double[], double) - Method in class smile.base.mlp.OutputLayer
Compute the network output gradient.
Concept - Class in smile.taxonomy
Concept is a set of synonyms, i.e.
Concept(Concept, String...) - Constructor for class smile.taxonomy.Concept
Constructor.
CONCISE - Enum constant in enum class smile.nlp.dictionary.EnglishDictionary
A concise dictionary of common terms in English.
condition() - Method in class smile.math.matrix.BigMatrix.SVD
Returns the L2 norm condition number, which is max(S) / min(S).
condition() - Method in class smile.math.matrix.fp32.Matrix.SVD
Returns the L2 norm condition number, which is max(S) / min(S).
condition() - Method in class smile.math.matrix.Matrix.SVD
Returns the L2 norm condition number, which is max(S) / min(S).
confidence - Variable in class smile.association.AssociationRule
The confidence value.
confusion - Variable in class smile.validation.ClassificationValidation
The confusion matrix.
ConfusionMatrix - Class in smile.validation.metric
The confusion matrix of truth and predictions.
ConfusionMatrix(int[][]) - Constructor for class smile.validation.metric.ConfusionMatrix
Constructor.
conjugate() - Method in class smile.math.Complex
Returns the conjugate.
CONJUGATE_TRANSPOSE - Enum constant in enum class smile.math.blas.Transpose
Conjugate transpose operation on the matrix.
consequent - Variable in class smile.association.AssociationRule
Consequent itemset.
constant(double) - Static method in interface smile.math.TimeFunction
Returns the constant learning rate.
Constant - Class in smile.data.formula
A constant value in the formula.
Constant() - Constructor for class smile.data.formula.Constant
 
contains(double[][], double[]) - Static method in class smile.math.MathEx
Determines if the polygon contains the point.
contains(double[][], double, double) - Static method in class smile.math.MathEx
Determines if the polygon contains the point.
contains(int) - Method in class smile.util.IntHashSet
Returns true if this set contains the specified element.
contains(String) - Method in interface smile.nlp.dictionary.Dictionary
Returns true if this dictionary contains the specified word.
contains(String) - Method in enum class smile.nlp.dictionary.EnglishDictionary
 
contains(String) - Method in class smile.nlp.dictionary.EnglishPunctuations
 
contains(String) - Method in enum class smile.nlp.dictionary.EnglishStopWords
 
contains(String) - Method in class smile.nlp.dictionary.SimpleDictionary
 
ContingencyTable - Class in smile.validation.metric
The contingency table.
ContingencyTable(int[], int[]) - Constructor for class smile.validation.metric.ContingencyTable
Constructor.
CooccurrenceKeywords - Interface in smile.nlp.keyword
Keyword extraction from a single document using word co-occurrence statistical information.
coordinates - Variable in class smile.manifold.IsoMap
The coordinate matrix in embedding space.
coordinates - Variable in class smile.manifold.IsotonicMDS
The coordinates.
coordinates - Variable in class smile.manifold.LaplacianEigenmap
The coordinate matrix in embedding space.
coordinates - Variable in class smile.manifold.LLE
The coordinate matrix in embedding space.
coordinates - Variable in class smile.manifold.MDS
The principal coordinates.
coordinates - Variable in class smile.manifold.SammonMapping
The coordinates.
coordinates - Variable in class smile.manifold.TSNE
The coordinate matrix in embedding space.
coordinates - Variable in class smile.manifold.UMAP
The coordinate matrix in embedding space.
coordinates() - Method in class smile.manifold.KPCA
Returns the nonlinear principal component scores, i.e., the representation of learning data in the nonlinear principal component space.
copy(double[][], double[][]) - Static method in class smile.math.MathEx
Deep copy x into y.
copy(float[][], float[][]) - Static method in class smile.math.MathEx
Deep copy x into y.
copy(int[][], int[][]) - Static method in class smile.math.MathEx
Copy x into y.
cor - Variable in class smile.stat.hypothesis.CorTest
The correlation coefficient.
cor(double[][]) - Static method in class smile.math.MathEx
Returns the sample correlation matrix.
cor(double[][], double[]) - Static method in class smile.math.MathEx
Returns the sample correlation matrix.
cor(double[][], String...) - Static method in class smile.feature.extraction.PCA
Fits principal component analysis with correlation matrix.
cor(double[], double[]) - Static method in class smile.math.MathEx
Returns the correlation coefficient between two vectors.
cor(float[], float[]) - Static method in class smile.math.MathEx
Returns the correlation coefficient between two vectors.
cor(int[], int[]) - Static method in class smile.math.MathEx
Returns the correlation coefficient between two vectors.
cor(DataFrame, String...) - Static method in class smile.feature.extraction.PCA
Fits principal component analysis with correlation matrix.
Corpus - Interface in smile.nlp
A corpus is a collection of documents.
CorrelationDistance - Class in smile.math.distance
Correlation distance is defined as 1 - correlation coefficient.
CorrelationDistance() - Constructor for class smile.math.distance.CorrelationDistance
Constructor of Pearson correlation distance.
CorrelationDistance(String) - Constructor for class smile.math.distance.CorrelationDistance
Constructor.
CorTest - Class in smile.stat.hypothesis
Correlation test.
CorTest(String, double, double, double, double) - Constructor for class smile.stat.hypothesis.CorTest
Constructor.
cos() - Method in class smile.math.Complex
Returns the complex cosine.
cos(double[], double[]) - Static method in class smile.math.MathEx
Returns the cosine similarity.
cos(float[], float[]) - Static method in class smile.math.MathEx
Returns the cosine similarity.
cos(String) - Static method in interface smile.data.formula.Terms
The cos(x) term.
cos(Term) - Static method in interface smile.data.formula.Terms
The cos(x) term.
cosh(String) - Static method in interface smile.data.formula.Terms
The cosh(x) term.
cosh(Term) - Static method in interface smile.data.formula.Terms
The cosh(x) term.
cost() - Method in class smile.base.mlp.OutputLayer
Returns the cost function of neural network.
cost() - Method in class smile.manifold.TSNE
Returns the cost function value.
Cost - Enum Class in smile.base.mlp
Neural network cost function.
count - Variable in class smile.nlp.collocation.Bigram
The frequency of bigram in the corpus.
count - Variable in class smile.nlp.collocation.NGram
The frequency of n-gram in the corpus.
count() - Method in class smile.base.cart.DecisionNode
Returns the number of node samples in each class.
count(String) - Method in interface smile.nlp.Corpus
Returns the total frequency of the term in the corpus.
count(String) - Method in class smile.nlp.SimpleCorpus
 
count(Bigram) - Method in interface smile.nlp.Corpus
Returns the total frequency of the bigram in the corpus.
count(Bigram) - Method in class smile.nlp.SimpleCorpus
 
counter - Variable in class smile.vq.hebb.Neuron
The local counter variable (e.g.
cov - Variable in class smile.regression.GaussianProcessRegression.JointPrediction
The covariance matrix of joint predictive distribution at query points.
cov() - Method in interface smile.stat.distribution.MultivariateDistribution
The covariance matrix of distribution.
cov() - Method in class smile.stat.distribution.MultivariateGaussianDistribution
 
cov() - Method in class smile.stat.distribution.MultivariateMixture
 
cov(double[][]) - Static method in class smile.math.MathEx
Returns the sample covariance matrix.
cov(double[][], double[]) - Static method in class smile.math.MathEx
Returns the sample covariance matrix.
cov(double[], double[]) - Static method in class smile.math.MathEx
Returns the covariance between two vectors.
cov(double[], int) - Static method in interface smile.timeseries.TimeSeries
Autocovariance function.
cov(float[], float[]) - Static method in class smile.math.MathEx
Returns the covariance between two vectors.
cov(int[], int[]) - Static method in class smile.math.MathEx
Returns the covariance between two vectors.
CoverTree<K,V> - Class in smile.neighbor
Cover tree is a data structure for generic nearest neighbor search, which is especially efficient in spaces with small intrinsic dimension.
CoverTree(List<K>, List<V>, Metric<K>) - Constructor for class smile.neighbor.CoverTree
Constructor.
CoverTree(List<K>, List<V>, Metric<K>, double) - Constructor for class smile.neighbor.CoverTree
Constructor.
CoverTree(K[], V[], Metric<K>) - Constructor for class smile.neighbor.CoverTree
Constructor.
CoverTree(K[], V[], Metric<K>, double) - Constructor for class smile.neighbor.CoverTree
Constructor.
CramerV - Variable in class smile.stat.hypothesis.ChiSqTest
Cramér's V is a measure of association between two nominal variables, giving a value between 0 and 1 (inclusive).
CRF - Class in smile.sequence
First-order linear conditional random field.
CRF(StructType, RegressionTree[][], double) - Constructor for class smile.sequence.CRF
Constructor.
CRFLabeler<T> - Class in smile.sequence
First-order CRF sequence labeler.
CRFLabeler(CRF, Function<T, Tuple>) - Constructor for class smile.sequence.CRFLabeler
Constructor.
cross(int, String...) - Static method in interface smile.data.formula.Terms
Factor crossing of two or more factors.
cross(String...) - Static method in interface smile.data.formula.Terms
Factor crossing of two or more factors.
crossentropy - Variable in class smile.validation.ClassificationMetrics
The cross entropy on validation data.
CrossEntropy - Interface in smile.validation.metric
Cross entropy generalizes the log loss metric to multiclass problems.
crossover(Chromosome) - Method in class smile.gap.BitString
 
crossover(Chromosome) - Method in interface smile.gap.Chromosome
Returns a pair of offsprings by crossovering this one with another one according to the crossover rate, which determines how often will be crossover performed.
Crossover - Enum Class in smile.gap
The types of crossover operation.
CrossValidation - Interface in smile.validation
Cross-validation is a technique for assessing how the results of a statistical analysis will generalize to an independent data set.
csv(String) - Static method in interface smile.io.Read
Reads a CSV file.
csv(String, String) - Static method in interface smile.io.Read
Reads a CSV file.
csv(String, CSVFormat) - Static method in interface smile.io.Read
Reads a CSV file.
csv(String, CSVFormat, StructType) - Static method in interface smile.io.Read
Reads a CSV file.
csv(Path) - Static method in interface smile.io.Read
Reads a CSV file.
csv(Path, CSVFormat) - Static method in interface smile.io.Read
Reads a CSV file.
csv(Path, CSVFormat, StructType) - Static method in interface smile.io.Read
Reads a CSV file.
csv(DataFrame, Path) - Static method in interface smile.io.Write
Writes a CSV file.
csv(DataFrame, Path, CSVFormat) - Static method in interface smile.io.Write
Writes a CSV file.
CSV - Class in smile.io
Reads and writes files in variations of the Comma Separated Value (CSV) format.
CSV() - Constructor for class smile.io.CSV
Constructor.
CSV(CSVFormat) - Constructor for class smile.io.CSV
Constructor.
CubicSplineInterpolation1D - Class in smile.interpolation
Cubic spline interpolation.
CubicSplineInterpolation1D(double[], double[]) - Constructor for class smile.interpolation.CubicSplineInterpolation1D
Constructor.
CubicSplineInterpolation2D - Class in smile.interpolation
Cubic spline interpolation in a two-dimensional regular grid.
CubicSplineInterpolation2D(double[], double[], double[][]) - Constructor for class smile.interpolation.CubicSplineInterpolation2D
Constructor.
cumulativeVarianceProportion() - Method in class smile.feature.extraction.PCA
Returns the cumulative proportion of variance contained in principal components, ordered from largest to smallest.
Currency - Static variable in interface smile.data.measure.Measure
Currency.

D

d - Variable in class smile.stat.hypothesis.KSTest
Kolmogorov-Smirnov statistic.
d - Variable in class smile.vq.BIRCH
The dimensionality of data.
d(byte[], byte[]) - Static method in class smile.math.distance.HammingDistance
Returns Hamming distance between the two byte arrays.
d(byte, byte) - Static method in class smile.math.distance.HammingDistance
Returns Hamming distance between the two bytes.
d(char[], char[]) - Method in class smile.math.distance.EditDistance
Edit distance between two strings.
d(double[], double[]) - Method in class smile.math.distance.ChebyshevDistance
Chebyshev distance between the two arrays of type double.
d(double[], double[]) - Method in class smile.math.distance.CorrelationDistance
Pearson correlation distance between the two arrays of type double.
d(double[], double[]) - Static method in class smile.math.distance.DynamicTimeWarping
Dynamic time warping without path constraints.
d(double[], double[]) - Method in class smile.math.distance.EuclideanDistance
Euclidean distance between the two arrays of type double.
d(double[], double[]) - Method in class smile.math.distance.JensenShannonDistance
 
d(double[], double[]) - Method in class smile.math.distance.MahalanobisDistance
 
d(double[], double[]) - Method in class smile.math.distance.ManhattanDistance
Manhattan distance between two arrays of type double.
d(double[], double[]) - Method in class smile.math.distance.MinkowskiDistance
Minkowski distance between the two arrays of type double.
d(double[], double[], int) - Static method in class smile.math.distance.DynamicTimeWarping
Dynamic time warping with Sakoe-Chiba band, which primarily to prevent unreasonable warping and also improve computational cost.
d(float[], float[]) - Static method in class smile.math.distance.ChebyshevDistance
Chebyshev distance between the two arrays of type float.
d(float[], float[]) - Static method in class smile.math.distance.DynamicTimeWarping
Dynamic time warping without path constraints.
d(float[], float[]) - Method in class smile.math.distance.EuclideanDistance
Euclidean distance between the two arrays of type float.
d(float[], float[]) - Method in class smile.math.distance.ManhattanDistance
Manhattan distance between two arrays of type float.
d(float[], float[]) - Method in class smile.math.distance.MinkowskiDistance
Minkowski distance between the two arrays of type float.
d(float[], float[], int) - Static method in class smile.math.distance.DynamicTimeWarping
Dynamic time warping with Sakoe-Chiba band, which primarily to prevent unreasonable warping and also improve computational cost.
d(int[], int[]) - Static method in class smile.math.distance.ChebyshevDistance
Chebyshev distance between the two arrays of type integer.
d(int[], int[]) - Static method in class smile.math.distance.DynamicTimeWarping
Dynamic time warping without path constraints.
d(int[], int[]) - Method in class smile.math.distance.EuclideanDistance
Euclidean distance between the two arrays of type integer.
d(int[], int[]) - Static method in class smile.math.distance.HammingDistance
Returns Hamming distance between the two integer arrays.
d(int[], int[]) - Method in class smile.math.distance.LeeDistance
 
d(int[], int[]) - Method in class smile.math.distance.ManhattanDistance
Manhattan distance between two arrays of type integer.
d(int[], int[]) - Method in class smile.math.distance.MinkowskiDistance
Minkowski distance between the two arrays of type integer.
d(int[], int[], int) - Static method in class smile.math.distance.DynamicTimeWarping
Dynamic time warping with Sakoe-Chiba band, which primarily to prevent unreasonable warping and also improve computational cost.
d(int, int) - Method in class smile.clustering.linkage.Linkage
Returns the distance/dissimilarity between two clusters/objects, which are indexed by integers.
d(int, int) - Static method in class smile.math.distance.HammingDistance
Returns Hamming distance between the two integers.
d(long, long) - Static method in class smile.math.distance.HammingDistance
Returns Hamming distance between the two long integers.
d(short[], short[]) - Static method in class smile.math.distance.HammingDistance
Returns Hamming distance between the two short arrays.
d(short, short) - Static method in class smile.math.distance.HammingDistance
Returns Hamming distance between the two shorts.
d(String, String) - Method in class smile.math.distance.EditDistance
Edit distance between two strings.
d(String, String) - Method in class smile.taxonomy.TaxonomicDistance
Computes the distance between two concepts in a taxonomy.
d(BitSet, BitSet) - Method in class smile.math.distance.HammingDistance
 
d(Set<T>, Set<T>) - Static method in class smile.math.distance.JaccardDistance
Returns the Jaccard distance between sets.
d(Concept, Concept) - Method in class smile.taxonomy.TaxonomicDistance
Computes the distance between two concepts in a taxonomy.
d(SparseArray, SparseArray) - Method in class smile.math.distance.SparseChebyshevDistance
 
d(SparseArray, SparseArray) - Method in class smile.math.distance.SparseEuclideanDistance
 
d(SparseArray, SparseArray) - Method in class smile.math.distance.SparseManhattanDistance
 
d(SparseArray, SparseArray) - Method in class smile.math.distance.SparseMinkowskiDistance
 
d(T[], T[]) - Method in class smile.math.distance.DynamicTimeWarping
 
d(T[], T[]) - Method in class smile.math.distance.JaccardDistance
 
d(T, T) - Method in interface smile.math.distance.Distance
Returns the distance measure between two objects.
D(T[]) - Method in interface smile.math.distance.Distance
Returns the pairwise distance matrix.
D(T[], T[]) - Method in interface smile.math.distance.Distance
Returns the pairwise distance matrix.
D4Wavelet - Class in smile.wavelet
The simplest and most localized wavelet, Daubechies wavelet of 4 coefficients.
D4Wavelet() - Constructor for class smile.wavelet.D4Wavelet
Constructor.
damerau(char[], char[]) - Static method in class smile.math.distance.EditDistance
Damerau-Levenshtein distance between two strings allows insertion, deletion, substitution, or transposition of characters.
damerau(String, String) - Static method in class smile.math.distance.EditDistance
Damerau-Levenshtein distance between two strings allows insertion, deletion, substitution, or transposition of characters.
DASH - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
Punctuation -
data - Variable in class smile.neighbor.LSH
The data objects.
data(String) - Static method in interface smile.io.Read
Reads a data file.
data(String, String) - Static method in interface smile.io.Read
Reads a data file.
DataFrame - Interface in smile.data
An immutable collection of data organized into named columns.
DataFrame.Collectors - Interface in smile.data
Stream collectors.
DataFrameClassifier - Interface in smile.classification
Classification trait on DataFrame.
DataFrameClassifier.Trainer<M extends DataFrameClassifier> - Interface in smile.classification
The classifier trainer.
DataFrameRegression - Interface in smile.regression
Regression trait on DataFrame.
DataFrameRegression.Trainer<M extends DataFrameRegression> - Interface in smile.regression
The regression trainer.
Dataset<T> - Interface in smile.data
An immutable collection of data objects.
Dataset.Collectors - Interface in smile.data
Stream collectors.
DataType - Interface in smile.data.type
The interface of data types.
DataType.ID - Enum Class in smile.data.type
Data type ID.
DataTypes - Class in smile.data.type
To get a specific data type, users should use singleton objects and factory methods in this class.
DataTypes() - Constructor for class smile.data.type.DataTypes
 
date(String) - Static method in class smile.data.type.DataTypes
Date data type with customized format.
date(String, DateFeature...) - Static method in interface smile.data.formula.Terms
Extracts date/time features.
Date - Class in smile.data.formula
Date/time feature extractor.
Date - Enum constant in enum class smile.data.type.DataType.ID
Date type ID.
Date(String, DateFeature...) - Constructor for class smile.data.formula.Date
Constructor.
DATE - Static variable in interface smile.util.Regex
Date regular expression pattern.
DateFeature - Enum Class in smile.data.formula
The date/time features.
datetime(String) - Static method in class smile.data.type.DataTypes
DateTime data type with customized format.
DateTime - Enum constant in enum class smile.data.type.DataType.ID
DateTime type ID.
DATETIME - Static variable in interface smile.util.Regex
Datetime regular expression pattern.
DateTimeType - Class in smile.data.type
DateTime data type.
DateTimeType - Static variable in class smile.data.type.DataTypes
DateTime data type with ISO format.
DateTimeType(String) - Constructor for class smile.data.type.DateTimeType
Constructor.
DateType - Class in smile.data.type
Date data type.
DateType - Static variable in class smile.data.type.DataTypes
Date data type with ISO format.
DateType(String) - Constructor for class smile.data.type.DateType
Constructor.
DaubechiesWavelet - Class in smile.wavelet
Daubechies wavelets.
DaubechiesWavelet(int) - Constructor for class smile.wavelet.DaubechiesWavelet
Constructor.
DAY_OF_MONTH - Enum constant in enum class smile.data.formula.DateFeature
The day of month represented by an integer from 1 to 31 in the usual manner.
DAY_OF_WEEK - Enum constant in enum class smile.data.formula.DateFeature
The day of week represented by an integer from 1 to 7; 1 is Monday, 2 is Tuesday, and so forth; thus 7 is Sunday.
DAY_OF_YEAR - Enum constant in enum class smile.data.formula.DateFeature
The day of year represented by an integer from 1 to 365, or 366 in a leap year.
DBSCAN<T> - Class in smile.clustering
Density-Based Spatial Clustering of Applications with Noise.
DBSCAN(int, double, RNNSearch<T, T>, int, int[], boolean[]) - Constructor for class smile.clustering.DBSCAN
Constructor.
Decimal - Enum constant in enum class smile.data.type.DataType.ID
Decimal type ID.
DECIMAL_FORMAT - Static variable in interface smile.util.Strings
Decimal format for floating numbers.
DecimalType - Class in smile.data.type
Arbitrary-precision decimal data type.
DecimalType - Static variable in class smile.data.type.DataTypes
Decimal data type.
DecisionNode - Class in smile.base.cart
A leaf node in decision tree.
DecisionNode(int[]) - Constructor for class smile.base.cart.DecisionNode
Constructor.
DecisionTree - Class in smile.classification
Decision tree.
DecisionTree(DataFrame, int[], StructField, int, SplitRule, int, int, int, int, int[], int[][]) - Constructor for class smile.classification.DecisionTree
Constructor.
decrement() - Method in class smile.util.MutableInt
Decrement by one.
decrement(int) - Method in class smile.util.MutableInt
Decrement.
DEFAULT - Enum constant in enum class smile.nlp.dictionary.EnglishStopWords
Default stop words list.
degree() - Method in class smile.math.kernel.Polynomial
Returns the degree of polynomial.
delete(String) - Static method in interface smile.data.formula.Terms
Deletes a variable or the intercept ("1") from the formula.
delete(Term) - Static method in interface smile.data.formula.Terms
Deletes a term from the formula.
DENCLUE - Class in smile.clustering
DENsity CLUstering.
DENCLUE(int, double[][], double[], double[][], double, int[], double) - Constructor for class smile.clustering.DENCLUE
Constructor.
denoise(double[], Wavelet) - Static method in interface smile.wavelet.WaveletShrinkage
Adaptive hard-thresholding denoising a time series with given wavelet.
denoise(double[], Wavelet, boolean) - Static method in interface smile.wavelet.WaveletShrinkage
Adaptive denoising a time series with given wavelet.
depth() - Method in class smile.base.cart.InternalNode
 
depth() - Method in class smile.base.cart.LeafNode
 
depth() - Method in interface smile.base.cart.Node
Returns the maximum depth of the tree -- the number of nodes along the longest path from this node down to the farthest leaf node.
det() - Method in class smile.math.matrix.BandMatrix.Cholesky
Returns the matrix determinant.
det() - Method in class smile.math.matrix.BandMatrix.LU
Returns the matrix determinant.
det() - Method in class smile.math.matrix.BigMatrix.Cholesky
Returns the matrix determinant.
det() - Method in class smile.math.matrix.BigMatrix.LU
Returns the matrix determinant.
det() - Method in class smile.math.matrix.fp32.BandMatrix.Cholesky
Returns the matrix determinant.
det() - Method in class smile.math.matrix.fp32.BandMatrix.LU
Returns the matrix determinant.
det() - Method in class smile.math.matrix.fp32.Matrix.Cholesky
Returns the matrix determinant.
det() - Method in class smile.math.matrix.fp32.Matrix.LU
Returns the matrix determinant.
det() - Method in class smile.math.matrix.fp32.SymmMatrix.BunchKaufman
Returns the matrix determinant.
det() - Method in class smile.math.matrix.fp32.SymmMatrix.Cholesky
Returns the matrix determinant.
det() - Method in class smile.math.matrix.Matrix.Cholesky
Returns the matrix determinant.
det() - Method in class smile.math.matrix.Matrix.LU
Returns the matrix determinant.
det() - Method in class smile.math.matrix.SymmMatrix.BunchKaufman
Returns the matrix determinant.
det() - Method in class smile.math.matrix.SymmMatrix.Cholesky
Returns the matrix determinant.
DeterministicAnnealing - Class in smile.clustering
Deterministic annealing clustering.
DeterministicAnnealing(double, double[][], int[]) - Constructor for class smile.clustering.DeterministicAnnealing
Constructor.
deviance - Variable in class smile.glm.GLM
The deviance = 2 * (LogLikelihood(Saturated Model) - LogLikelihood(Proposed Model)).
deviance() - Method in class smile.base.cart.DecisionNode
 
deviance() - Method in class smile.base.cart.InternalNode
 
deviance() - Method in interface smile.base.cart.Node
Returns the deviance of node.
deviance() - Method in class smile.base.cart.RegressionNode
 
deviance() - Method in class smile.glm.GLM
Returns the deviance of model.
deviance(double[], double[], double[]) - Method in interface smile.glm.model.Model
The deviance function.
deviance(int[], double[]) - Static method in class smile.base.cart.DecisionNode
Returns the deviance of node.
devianceResiduals - Variable in class smile.glm.GLM
The deviance residuals.
devianceResiduals() - Method in class smile.glm.GLM
Returns the deviance residuals.
df - Variable in class smile.glm.GLM
The degrees of freedom of the residual deviance.
df - Variable in class smile.stat.hypothesis.ChiSqTest
The degree of freedom of chi-square statistic.
df - Variable in class smile.stat.hypothesis.CorTest
The degree of freedom of test statistic.
df - Variable in class smile.stat.hypothesis.TTest
The degree of freedom of t-statistic.
df - Variable in class smile.timeseries.BoxTest
The degree of freedom.
df() - Method in class smile.regression.LinearModel
Returns the degree-of-freedom of residual standard error.
df() - Method in class smile.timeseries.AR
Returns the degree-of-freedom of residual standard error.
df() - Method in class smile.timeseries.ARMA
Returns the degree-of-freedom of residual standard error.
df1 - Variable in class smile.stat.hypothesis.FTest
The degree of freedom of F-statistic.
df2 - Variable in class smile.stat.hypothesis.FTest
The degree of freedom of F-statistic.
dfs() - Method in class smile.graph.AdjacencyList
 
dfs() - Method in class smile.graph.AdjacencyMatrix
 
dfs() - Method in interface smile.graph.Graph
Depth-first search connected components of graph.
dfs(Visitor) - Method in class smile.graph.AdjacencyList
 
dfs(Visitor) - Method in class smile.graph.AdjacencyMatrix
 
dfs(Visitor) - Method in interface smile.graph.Graph
DFS search on graph and performs some operation defined in visitor on each vertex during traveling.
diag() - Method in class smile.math.matrix.BigMatrix.EVD
Returns the block diagonal eigenvalue matrix whose diagonal are the real part of eigenvalues, lower subdiagonal are positive imaginary parts, and upper subdiagonal are negative imaginary parts.
diag() - Method in class smile.math.matrix.BigMatrix.SVD
Returns the diagonal matrix of singular values.
diag() - Method in class smile.math.matrix.fp32.IMatrix
Returns the diagonal elements.
diag() - Method in class smile.math.matrix.fp32.Matrix.EVD
Returns the block diagonal eigenvalue matrix whose diagonal are the real part of eigenvalues, lower subdiagonal are positive imaginary parts, and upper subdiagonal are negative imaginary parts.
diag() - Method in class smile.math.matrix.fp32.Matrix.SVD
Returns the diagonal matrix of singular values.
diag() - Method in class smile.math.matrix.fp32.SparseMatrix
 
diag() - Method in class smile.math.matrix.IMatrix
Returns the diagonal elements.
diag() - Method in class smile.math.matrix.Matrix.EVD
Returns the block diagonal eigenvalue matrix whose diagonal are the real part of eigenvalues, lower subdiagonal are positive imaginary parts, and upper subdiagonal are negative imaginary parts.
diag() - Method in class smile.math.matrix.Matrix.SVD
Returns the diagonal matrix of singular values.
diag() - Method in class smile.math.matrix.SparseMatrix
 
diag(double[]) - Static method in class smile.math.matrix.BigMatrix
Returns a square diagonal matrix.
diag(double[]) - Static method in class smile.math.matrix.Matrix
Returns a square diagonal matrix.
diag(float[]) - Static method in class smile.math.matrix.fp32.Matrix
Returns a square diagonal matrix.
diag(int, double) - Static method in class smile.math.matrix.BigMatrix
Returns a square diagonal matrix.
diag(int, double) - Static method in class smile.math.matrix.Matrix
Returns a square diagonal matrix.
diag(int, float) - Static method in class smile.math.matrix.fp32.Matrix
Returns a square diagonal matrix.
diag(int, int, double) - Static method in class smile.math.matrix.BigMatrix
Returns an m-by-n diagonal matrix.
diag(int, int, double) - Static method in class smile.math.matrix.Matrix
Returns an m-by-n diagonal matrix.
diag(int, int, float) - Static method in class smile.math.matrix.fp32.Matrix
Returns an m-by-n diagonal matrix.
diag(DoublePointer) - Static method in class smile.math.matrix.BigMatrix
Returns a square diagonal matrix.
Diag - Enum Class in smile.math.blas
The flag if a triangular matrix has unit diagonal elements.
diagonal - Variable in class smile.stat.distribution.MultivariateGaussianDistribution
True if the covariance matrix is diagonal.
Dictionary - Interface in smile.nlp.dictionary
A dictionary is a set of words in some natural language.
diff(double[], int) - Static method in interface smile.timeseries.TimeSeries
Returns the first-differencing of time series.
diff(double[], int, int) - Static method in interface smile.timeseries.TimeSeries
Returns the differencing of time series.
DifferentiableFunction - Interface in smile.math
A differentiable function is a function whose derivative exists at each point in its domain.
DifferentiableMultivariateFunction - Interface in smile.math
A differentiable function is a function whose derivative exists at each point in its domain.
digamma(double) - Static method in class smile.math.special.Gamma
The digamma function is defined as the logarithmic derivative of the gamma function.
DIGITS - Static variable in class smile.math.MathEx
The number of digits (in radix base) in the mantissa.
dijkstra() - Method in interface smile.graph.Graph
Calculates the all pair shortest path by Dijkstra algorithm.
dijkstra(int) - Method in class smile.graph.AdjacencyList
 
dijkstra(int) - Method in class smile.graph.AdjacencyMatrix
 
dijkstra(int) - Method in interface smile.graph.Graph
Calculate the shortest path from a source to all other vertices in the graph by Dijkstra algorithm.
dijkstra(int, boolean) - Method in class smile.graph.AdjacencyMatrix
Calculates the shortest path by Dijkstra algorithm.
dimension() - Method in class smile.classification.Maxent
Returns the dimension of input space.
dimension() - Method in class smile.nlp.embedding.Word2Vec
Returns the dimension of embedding vector space.
DiscreteDistribution - Class in smile.stat.distribution
Univariate discrete distributions.
DiscreteDistribution() - Constructor for class smile.stat.distribution.DiscreteDistribution
 
DiscreteExponentialFamily - Interface in smile.stat.distribution
The purpose of this interface is mainly to define the method M that is the Maximization step in the EM algorithm.
DiscreteExponentialFamilyMixture - Class in smile.stat.distribution
The finite mixture of distributions from discrete exponential family.
DiscreteExponentialFamilyMixture(DiscreteMixture.Component...) - Constructor for class smile.stat.distribution.DiscreteExponentialFamilyMixture
Constructor.
DiscreteMixture - Class in smile.stat.distribution
The finite mixture of discrete distributions.
DiscreteMixture(DiscreteMixture.Component...) - Constructor for class smile.stat.distribution.DiscreteMixture
Constructor.
DiscreteMixture.Component - Class in smile.stat.distribution
A component in the mixture distribution is defined by a distribution and its weight in the mixture.
DiscreteNaiveBayes - Class in smile.classification
Naive Bayes classifier for document classification in NLP.
DiscreteNaiveBayes(DiscreteNaiveBayes.Model, double[], int) - Constructor for class smile.classification.DiscreteNaiveBayes
Constructor of naive Bayes classifier for document classification.
DiscreteNaiveBayes(DiscreteNaiveBayes.Model, double[], int, double, IntSet) - Constructor for class smile.classification.DiscreteNaiveBayes
Constructor of naive Bayes classifier for document classification.
DiscreteNaiveBayes(DiscreteNaiveBayes.Model, int, int) - Constructor for class smile.classification.DiscreteNaiveBayes
Constructor of naive Bayes classifier for document classification.
DiscreteNaiveBayes(DiscreteNaiveBayes.Model, int, int, double, IntSet) - Constructor for class smile.classification.DiscreteNaiveBayes
Constructor of naive Bayes classifier for document classification.
DiscreteNaiveBayes.Model - Enum Class in smile.classification
The generation models of naive Bayes classifier.
distance - Variable in class smile.neighbor.Neighbor
The distance between the query and the neighbor.
distance - Variable in class smile.vq.hebb.Neuron
The distance between the neuron and an input signal.
distance(double[]) - Method in class smile.vq.hebb.Neuron
Computes the distance between the neuron and a signal.
distance(double[], double[]) - Method in class smile.clustering.DeterministicAnnealing
 
distance(double[], double[]) - Method in class smile.clustering.GMeans
 
distance(double[], double[]) - Method in class smile.clustering.KMeans
 
distance(double[], double[]) - Method in class smile.clustering.XMeans
 
distance(double[], double[]) - Static method in class smile.math.MathEx
The Euclidean distance.
distance(double[], SparseArray) - Method in class smile.clustering.SIB
 
distance(float[], float[]) - Static method in class smile.math.MathEx
The Euclidean distance.
distance(int[], int[]) - Method in class smile.clustering.KModes
 
distance(int[], int[]) - Static method in class smile.math.MathEx
The Euclidean distance on binary sparse arrays, which are the indices of nonzero elements in ascending order.
distance(SparseArray, SparseArray) - Static method in class smile.math.MathEx
The Euclidean distance.
distance(T, T) - Method in class smile.clustering.CLARANS
 
distance(T, U) - Method in class smile.clustering.CentroidClustering
The distance function.
Distance<T> - Interface in smile.math.distance
An interface to calculate a distance measure between two objects.
distinct() - Method in interface smile.data.vector.Vector
Returns the distinct values.
distortion - Variable in class smile.clustering.CentroidClustering
The total distortion.
distortion - Variable in class smile.clustering.SpectralClustering
The distortion in feature space.
distributed() - Method in interface smile.data.Dataset
Returns true if the dataset is distributed over multiple machines.
distribution - Variable in class smile.stat.distribution.DiscreteMixture.Component
The distribution of component.
distribution - Variable in class smile.stat.distribution.Mixture.Component
The distribution of component.
distribution - Variable in class smile.stat.distribution.MultivariateMixture.Component
The distribution of component.
Distribution - Interface in smile.stat.distribution
Probability distribution of univariate random variable.
div(double) - Method in class smile.math.matrix.BigMatrix
A /= b
div(double) - Method in class smile.math.matrix.Matrix
A /= b
div(double) - Method in class smile.util.Array2D
A /= x.
div(float) - Method in class smile.math.matrix.fp32.Matrix
A /= b
div(int) - Method in class smile.util.IntArray2D
A /= x.
div(int, int, double) - Method in class smile.math.matrix.BigMatrix
A[i,j] /= b
div(int, int, double) - Method in class smile.math.matrix.Matrix
A[i,j] /= b
div(int, int, double) - Method in class smile.util.Array2D
A[i, j] /= x.
div(int, int, float) - Method in class smile.math.matrix.fp32.Matrix
A[i,j] /= b
div(int, int, int) - Method in class smile.util.IntArray2D
A[i, j] /= x.
div(String, String) - Static method in interface smile.data.formula.Terms
Divides two terms.
div(String, Term) - Static method in interface smile.data.formula.Terms
Divides two terms.
div(Term, String) - Static method in interface smile.data.formula.Terms
Divides two terms.
div(Term, Term) - Static method in interface smile.data.formula.Terms
Divides two terms.
div(Complex) - Method in class smile.math.Complex
Returns a / b.
div(BigMatrix) - Method in class smile.math.matrix.BigMatrix
Element-wise division A /= B
div(Matrix) - Method in class smile.math.matrix.fp32.Matrix
Element-wise division A /= B
div(Matrix) - Method in class smile.math.matrix.Matrix
Element-wise division A /= B
div(Array2D) - Method in class smile.util.Array2D
A /= B.
div(IntArray2D) - Method in class smile.util.IntArray2D
A /= B.
Div - Class in smile.data.formula
The term of a / b expression.
Div(Term, Term) - Constructor for class smile.data.formula.Div
Constructor.
dlink(double) - Method in interface smile.glm.model.Model
The derivative of link function.
dot() - Method in class smile.base.cart.CART
Returns the graphic representation in Graphviz dot format.
dot() - Static method in interface smile.data.formula.Terms
Returns the special term "." that means all columns not otherwise in the formula in the context of a data frame.
dot(double[], double[]) - Method in interface smile.math.blas.BLAS
Computes the dot product of two vectors.
dot(double[], double[]) - Static method in class smile.math.MathEx
Returns the dot product between two vectors.
dot(float[], float[]) - Method in interface smile.math.blas.BLAS
Computes the dot product of two vectors.
dot(float[], float[]) - Static method in class smile.math.MathEx
Returns the dot product between two vectors.
dot(int[], int[]) - Static method in class smile.math.MathEx
Returns the dot product between two binary sparse arrays, which are the indices of nonzero elements in ascending order.
dot(int, double[], int, double[], int) - Method in interface smile.math.blas.BLAS
Computes the dot product of two vectors.
dot(int, double[], int, double[], int) - Method in class smile.math.blas.mkl.MKL
 
dot(int, double[], int, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
dot(int, float[], int, float[], int) - Method in interface smile.math.blas.BLAS
Computes the dot product of two vectors.
dot(int, float[], int, float[], int) - Method in class smile.math.blas.mkl.MKL
 
dot(int, float[], int, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
dot(StructType, StructField, int) - Method in class smile.base.cart.DecisionNode
 
dot(StructType, StructField, int) - Method in interface smile.base.cart.Node
Returns the dot representation of node.
dot(StructType, StructField, int) - Method in class smile.base.cart.NominalNode
 
dot(StructType, StructField, int) - Method in class smile.base.cart.OrdinalNode
 
dot(StructType, StructField, int) - Method in class smile.base.cart.RegressionNode
 
dot(SparseArray, SparseArray) - Static method in class smile.math.MathEx
Returns the dot product between two sparse arrays.
DotProductKernel - Interface in smile.math.kernel
Dot product kernel depends only on the dot product of x and y.
Double - Enum constant in enum class smile.data.type.DataType.ID
Double type ID.
DOUBLE - Static variable in interface smile.util.Regex
Double regular expression pattern.
DOUBLE_REGEX - Static variable in interface smile.util.Regex
Double regular expression.
DoubleArrayList - Class in smile.util
A resizeable, array-backed list of double primitives.
DoubleArrayList() - Constructor for class smile.util.DoubleArrayList
Constructs an empty list.
DoubleArrayList(double[]) - Constructor for class smile.util.DoubleArrayList
Constructs a list containing the values of the specified array.
DoubleArrayList(int) - Constructor for class smile.util.DoubleArrayList
Constructs an empty list with the specified initial capacity.
DoubleArrayType - Static variable in class smile.data.type.DataTypes
Double Array data type.
DoubleConsumer - Interface in smile.math.matrix
Double precision matrix element stream consumer.
DoubleFunction - Class in smile.data.formula
The generic term of applying a double function.
DoubleFunction(String, Term, Function) - Constructor for class smile.data.formula.DoubleFunction
Constructor.
DoubleHeapSelect - Class in smile.sort
This class tracks the smallest values seen thus far in a stream of values.
DoubleHeapSelect(double[]) - Constructor for class smile.sort.DoubleHeapSelect
Constructor.
DoubleHeapSelect(int) - Constructor for class smile.sort.DoubleHeapSelect
Constructor.
DoubleObjectType - Static variable in class smile.data.type.DataTypes
Double Object data type.
DoubleType - Class in smile.data.type
Double data type.
DoubleType - Static variable in class smile.data.type.DataTypes
Double data type.
doubleVector(int) - Method in interface smile.data.DataFrame
Selects column based on the column index.
doubleVector(int) - Method in class smile.data.IndexDataFrame
 
doubleVector(Enum<?>) - Method in interface smile.data.DataFrame
Selects column using an enum value.
doubleVector(String) - Method in interface smile.data.DataFrame
Selects column based on the column name.
DoubleVector - Interface in smile.data.vector
An immutable double vector.
drop(int...) - Method in interface smile.data.DataFrame
Returns a new DataFrame without selected columns.
drop(int...) - Method in class smile.data.IndexDataFrame
 
drop(String...) - Method in interface smile.data.DataFrame
Returns a new DataFrame without selected columns.
dropout - Variable in class smile.base.mlp.Layer
The dropout rate.
dropout - Variable in class smile.base.mlp.LayerBuilder
The dropout rate.
DT - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
Determiner.
DUMMY - Enum constant in enum class smile.data.CategoricalEncoder
Dummy encoding.
DynamicTimeWarping<T> - Class in smile.math.distance
Dynamic time warping is an algorithm for measuring similarity between two sequences which may vary in time or speed.
DynamicTimeWarping(Distance<T>) - Constructor for class smile.math.distance.DynamicTimeWarping
Constructor.
DynamicTimeWarping(Distance<T>, double) - Constructor for class smile.math.distance.DynamicTimeWarping
Dynamic time warping with Sakoe-Chiba band, which primarily to prevent unreasonable warping and also improve computational cost.

E

Edge - Class in smile.vq.hebb
The connection between neurons.
Edge(int, int, double) - Constructor for class smile.graph.Graph.Edge
Constructor.
Edge(Neuron) - Constructor for class smile.vq.hebb.Edge
Constructor.
Edge(Neuron, int) - Constructor for class smile.vq.hebb.Edge
Constructor.
edges - Variable in class smile.vq.hebb.Neuron
The direct connected neighbors.
EditDistance - Class in smile.math.distance
The Edit distance between two strings is a metric for measuring the amount of difference between two sequences.
EditDistance() - Constructor for class smile.math.distance.EditDistance
Constructor.
EditDistance(boolean) - Constructor for class smile.math.distance.EditDistance
Constructor.
EditDistance(int) - Constructor for class smile.math.distance.EditDistance
Constructor.
EditDistance(int[][]) - Constructor for class smile.math.distance.EditDistance
Constructor.
EditDistance(int[][], double) - Constructor for class smile.math.distance.EditDistance
Constructor.
EditDistance(int, boolean) - Constructor for class smile.math.distance.EditDistance
Constructor.
eigen() - Method in class smile.math.matrix.BigMatrix
Eigenvalue Decomposition.
eigen() - Method in class smile.math.matrix.fp32.Matrix
Eigenvalue Decomposition.
eigen() - Method in class smile.math.matrix.Matrix
Eigenvalue Decomposition.
eigen(boolean, boolean, boolean) - Method in class smile.math.matrix.BigMatrix
Eigenvalue Decomposition.
eigen(boolean, boolean, boolean) - Method in class smile.math.matrix.fp32.Matrix
Eigenvalue Decomposition.
eigen(boolean, boolean, boolean) - Method in class smile.math.matrix.Matrix
Eigenvalue Decomposition.
eigen(double[]) - Method in class smile.math.matrix.IMatrix
Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
eigen(double[], double, double, int) - Method in class smile.math.matrix.IMatrix
Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
eigen(float[]) - Method in class smile.math.matrix.fp32.IMatrix
Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
eigen(float[], float, float, int) - Method in class smile.math.matrix.fp32.IMatrix
Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
eigen(IMatrix, ARPACK.AsymmOption, int) - Static method in class smile.math.matrix.fp32.ARPACK
Computes NEV eigenvalues of an asymmetric single precision matrix.
eigen(IMatrix, ARPACK.AsymmOption, int, int, float) - Static method in class smile.math.matrix.fp32.ARPACK
Computes NEV eigenvalues of an asymmetric single precision matrix.
eigen(IMatrix, int) - Static method in class smile.math.matrix.Lanczos
Find k largest approximate eigen pairs of a symmetric matrix by the Lanczos algorithm.
eigen(IMatrix, int, double, int) - Static method in class smile.math.matrix.Lanczos
Find k largest approximate eigen pairs of a symmetric matrix by the Lanczos algorithm.
eigen(IMatrix, ARPACK.AsymmOption, int) - Static method in class smile.math.matrix.ARPACK
Computes NEV eigenvalues of an asymmetric double precision matrix.
eigen(IMatrix, ARPACK.AsymmOption, int, int, double) - Static method in class smile.math.matrix.ARPACK
Computes NEV eigenvalues of an asymmetric double precision matrix.
EigenRange - Enum Class in smile.math.blas
THe option of eigenvalue range.
ElasticNet - Class in smile.regression
Elastic Net regularization.
ElasticNet() - Constructor for class smile.regression.ElasticNet
 
EMAIL_ADDRESS - Static variable in interface smile.util.Regex
Email address.
EmpiricalDistribution - Class in smile.stat.distribution
An empirical distribution function or empirical cdf, is a cumulative probability distribution function that concentrates probability 1/n at each of the n numbers in a sample.
EmpiricalDistribution(double[]) - Constructor for class smile.stat.distribution.EmpiricalDistribution
Constructor.
EmpiricalDistribution(double[], IntSet) - Constructor for class smile.stat.distribution.EmpiricalDistribution
Constructor.
engine - Static variable in interface smile.math.blas.BLAS
The default BLAS engine.
engine - Static variable in interface smile.math.blas.LAPACK
The default LAPACK engine.
EnglishDictionary - Enum Class in smile.nlp.dictionary
A concise dictionary of common terms in English.
EnglishPOSLexicon - Class in smile.nlp.pos
An English lexicon with part-of-speech tags.
EnglishPunctuations - Class in smile.nlp.dictionary
Punctuation marks in English.
EnglishStopWords - Enum Class in smile.nlp.dictionary
Several sets of English stop words.
ensemble(Classifier<T>...) - Static method in interface smile.classification.Classifier
Return an ensemble of multiple base models to obtain better predictive performance.
ensemble(DataFrameClassifier...) - Static method in interface smile.classification.DataFrameClassifier
Return an ensemble of multiple base models to obtain better predictive performance.
ensemble(DataFrameRegression...) - Static method in interface smile.regression.DataFrameRegression
Return an ensemble of multiple base models to obtain better predictive performance.
ensemble(Regression<T>...) - Static method in interface smile.regression.Regression
Return an ensemble of multiple base models to obtain better predictive performance.
ensureCapacity(int) - Method in class smile.util.DoubleArrayList
Increases the capacity, if necessary, to ensure that it can hold at least the number of values specified by the minimum capacity argument.
ensureCapacity(int) - Method in class smile.util.IntArrayList
Increases the capacity, if necessary, to ensure that it can hold at least the number of values specified by the minimum capacity argument.
entropy - Variable in class smile.clustering.MEC
The conditional entropy as the objective function.
entropy() - Method in class smile.stat.distribution.BernoulliDistribution
 
entropy() - Method in class smile.stat.distribution.BetaDistribution
 
entropy() - Method in class smile.stat.distribution.BinomialDistribution
 
entropy() - Method in class smile.stat.distribution.ChiSquareDistribution
 
entropy() - Method in class smile.stat.distribution.DiscreteMixture
Shannon entropy.
entropy() - Method in interface smile.stat.distribution.Distribution
Shannon entropy of the distribution.
entropy() - Method in class smile.stat.distribution.EmpiricalDistribution
 
entropy() - Method in class smile.stat.distribution.ExponentialDistribution
 
entropy() - Method in class smile.stat.distribution.FDistribution
Shannon entropy.
entropy() - Method in class smile.stat.distribution.GammaDistribution
 
entropy() - Method in class smile.stat.distribution.GaussianDistribution
 
entropy() - Method in class smile.stat.distribution.GeometricDistribution
Shannon entropy.
entropy() - Method in class smile.stat.distribution.HyperGeometricDistribution
 
entropy() - Method in class smile.stat.distribution.KernelDensity
Shannon entropy.
entropy() - Method in class smile.stat.distribution.LogisticDistribution
 
entropy() - Method in class smile.stat.distribution.LogNormalDistribution
 
entropy() - Method in class smile.stat.distribution.Mixture
Shannon entropy.
entropy() - Method in interface smile.stat.distribution.MultivariateDistribution
Shannon entropy of the distribution.
entropy() - Method in class smile.stat.distribution.MultivariateGaussianDistribution
 
entropy() - Method in class smile.stat.distribution.MultivariateMixture
Shannon entropy.
entropy() - Method in class smile.stat.distribution.NegativeBinomialDistribution
Shannon entropy.
entropy() - Method in class smile.stat.distribution.PoissonDistribution
 
entropy() - Method in class smile.stat.distribution.ShiftedGeometricDistribution
 
entropy() - Method in class smile.stat.distribution.TDistribution
 
entropy() - Method in class smile.stat.distribution.WeibullDistribution
 
entropy(double[]) - Static method in class smile.math.MathEx
Shannon's entropy.
ENTROPY - Enum constant in enum class smile.base.cart.SplitRule
Used by the ID3, C4.5 and C5.0 tree generation algorithms.
entry - Variable in class smile.neighbor.lsh.Bucket
The indices of points that all have the same value for hash function g.
epsilon - Variable in class smile.base.mlp.MultilayerPerceptron
A small constant for numerical stability in RMSProp.
EPSILON - Static variable in interface smile.math.DifferentiableMultivariateFunction
A number close to zero, between machine epsilon and its square root.
EPSILON - Static variable in class smile.math.MathEx
The machine precision for the double type, which is the difference between 1 and the smallest value greater than 1 that is representable for the double type.
equals(double[][], double[][]) - Static method in class smile.math.MathEx
Check if x element-wisely equals y with default epsilon 1E-10.
equals(double[][], double[][], double) - Static method in class smile.math.MathEx
Check if x element-wisely equals y in given precision.
equals(double[], double[]) - Static method in class smile.math.MathEx
Check if x element-wisely equals y with default epsilon 1E-10.
equals(double[], double[], double) - Static method in class smile.math.MathEx
Check if x element-wisely equals y in given precision.
equals(double, double) - Static method in class smile.math.MathEx
Returns true if two double values equals to each other in the system precision.
equals(float[][], float[][]) - Static method in class smile.math.MathEx
Check if x element-wisely equals y with default epsilon 1E-7.
equals(float[][], float[][], float) - Static method in class smile.math.MathEx
Check if x element-wisely equals y in given precision.
equals(float[], float[]) - Static method in class smile.math.MathEx
Check if x element-wisely equals y with default epsilon 1E-7.
equals(float[], float[], float) - Static method in class smile.math.MathEx
Check if x element-wisely equals y in given precision.
equals(Object) - Method in class smile.association.AssociationRule
 
equals(Object) - Method in class smile.association.ItemSet
 
equals(Object) - Method in class smile.base.cart.DecisionNode
 
equals(Object) - Method in class smile.base.cart.RegressionNode
 
equals(Object) - Method in class smile.data.formula.Formula
 
equals(Object) - Method in class smile.data.measure.CategoricalMeasure
 
equals(Object) - Method in class smile.data.measure.NominalScale
 
equals(Object) - Method in class smile.data.measure.OrdinalScale
 
equals(Object) - Method in class smile.data.type.ArrayType
 
equals(Object) - Method in class smile.data.type.BooleanType
 
equals(Object) - Method in class smile.data.type.ByteType
 
equals(Object) - Method in class smile.data.type.CharType
 
equals(Object) - Method in class smile.data.type.DateTimeType
 
equals(Object) - Method in class smile.data.type.DateType
 
equals(Object) - Method in class smile.data.type.DecimalType
 
equals(Object) - Method in class smile.data.type.DoubleType
 
equals(Object) - Method in class smile.data.type.FloatType
 
equals(Object) - Method in class smile.data.type.IntegerType
 
equals(Object) - Method in class smile.data.type.LongType
 
equals(Object) - Method in class smile.data.type.ObjectType
 
equals(Object) - Method in class smile.data.type.ShortType
 
equals(Object) - Method in class smile.data.type.StringType
 
equals(Object) - Method in class smile.data.type.StructField
 
equals(Object) - Method in class smile.data.type.StructType
 
equals(Object) - Method in class smile.data.type.TimeType
 
equals(Object) - Method in class smile.math.Complex
 
equals(Object) - Method in class smile.math.matrix.BandMatrix
 
equals(Object) - Method in class smile.math.matrix.BigMatrix
 
equals(Object) - Method in class smile.math.matrix.fp32.BandMatrix
 
equals(Object) - Method in class smile.math.matrix.fp32.Matrix
 
equals(Object) - Method in class smile.math.matrix.fp32.SymmMatrix
 
equals(Object) - Method in class smile.math.matrix.Matrix
 
equals(Object) - Method in class smile.math.matrix.SymmMatrix
 
equals(Object) - Method in class smile.nlp.Bigram
 
equals(Object) - Method in class smile.nlp.NGram
 
equals(Object) - Method in class smile.nlp.SimpleText
 
equals(Object) - Method in class smile.util.IntPair
 
equals(BandMatrix, double) - Method in class smile.math.matrix.BandMatrix
Returns true if two matrices equal in given precision.
equals(BigMatrix, double) - Method in class smile.math.matrix.BigMatrix
Returns true if two matrices equal in given precision.
equals(BandMatrix, float) - Method in class smile.math.matrix.fp32.BandMatrix
Returns true if two matrices equal in given precision.
equals(Matrix, float) - Method in class smile.math.matrix.fp32.Matrix
Returns true if two matrices equal in given precision.
equals(SymmMatrix, float) - Method in class smile.math.matrix.fp32.SymmMatrix
Returns true if two matrices equal in given precision.
equals(Matrix, double) - Method in class smile.math.matrix.Matrix
Returns true if two matrices equal in given precision.
equals(SymmMatrix, double) - Method in class smile.math.matrix.SymmMatrix
Returns true if two matrices equal in given precision.
erf(double) - Static method in class smile.math.special.Erf
The Gauss error function.
Erf - Class in smile.math.special
The error function.
erfc(double) - Static method in class smile.math.special.Erf
The complementary error function.
erfcc(double) - Static method in class smile.math.special.Erf
The complementary error function with fractional error everywhere less than 1.2 × 10-7.
error - Variable in class smile.validation.ClassificationMetrics
The number of errors.
error() - Method in class smile.regression.LinearModel
Returns the residual standard error.
Error - Class in smile.validation.metric
The number of errors in the population.
Error() - Constructor for class smile.validation.metric.Error
 
estimate(int, double) - Method in class smile.neighbor.lsh.HashValueParzenModel
Given a hash value h, estimate the Gaussian model (mean and variance) of neighbors existing in the corresponding bucket.
EuclideanDistance - Class in smile.math.distance
Euclidean distance.
EuclideanDistance() - Constructor for class smile.math.distance.EuclideanDistance
Constructor.
EuclideanDistance(double[]) - Constructor for class smile.math.distance.EuclideanDistance
Constructor with a given weight vector.
EVD(double[], double[], Matrix, Matrix) - Constructor for class smile.math.matrix.Matrix.EVD
Constructor.
EVD(double[], Matrix) - Constructor for class smile.math.matrix.Matrix.EVD
Constructor.
EVD(float[], float[], Matrix, Matrix) - Constructor for class smile.math.matrix.fp32.Matrix.EVD
Constructor.
EVD(float[], Matrix) - Constructor for class smile.math.matrix.fp32.Matrix.EVD
Constructor.
EVD(DoublePointer, DoublePointer, BigMatrix, BigMatrix) - Constructor for class smile.math.matrix.BigMatrix.EVD
Constructor.
EVD(DoublePointer, BigMatrix) - Constructor for class smile.math.matrix.BigMatrix.EVD
Constructor.
EVDJob - Enum Class in smile.math.blas
The option if computing eigen vectors.
evolve() - Method in interface smile.gap.LamarckianChromosome
Performs a step of (hill-climbing) local search to evolve this chromosome.
evolve(int) - Method in class smile.gap.GeneticAlgorithm
Performs genetic algorithm for a given number of generations.
evolve(int, double) - Method in class smile.gap.GeneticAlgorithm
Performs genetic algorithm until the given number of generations is reached or the best fitness is larger than the given threshold.
EX - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
Existential there.
exp() - Method in class smile.math.Complex
Returns the complex exponential.
exp(double, double) - Static method in interface smile.math.TimeFunction
Returns the exponential decay function.
exp(double, double, double) - Static method in interface smile.math.TimeFunction
Returns the exponential decay function.
exp(double, double, double, boolean) - Static method in interface smile.math.TimeFunction
Returns the exponential decay function.
exp(String) - Static method in interface smile.data.formula.Terms
The exp(x) term.
exp(Term) - Static method in interface smile.data.formula.Terms
The exp(x) term.
Exp - Class in smile.ica
The contrast function when the independent components are highly super-Gaussian, or when robustness is very important.
Exp() - Constructor for class smile.ica.Exp
 
expand() - Method in class smile.data.formula.FactorCrossing
 
expand() - Method in interface smile.data.formula.Term
Expands the term (e.g.
expand() - Method in class smile.neighbor.lsh.Probe
This operation sets to one the component following the last nonzero component if it is not the last one.
expand(StructType) - Method in class smile.data.formula.Formula
Expands the Dot and FactorCrossing terms on the given schema.
expm1(String) - Static method in interface smile.data.formula.Terms
The exp(x) - 1 term.
expm1(Term) - Static method in interface smile.data.formula.Terms
The exp(x) - 1 term.
ExponentialDistribution - Class in smile.stat.distribution
An exponential distribution describes the times between events in a Poisson process, in which events occur continuously and independently at a constant average rate.
ExponentialDistribution(double) - Constructor for class smile.stat.distribution.ExponentialDistribution
Constructor.
ExponentialFamily - Interface in smile.stat.distribution
The exponential family is a class of probability distributions sharing a certain form.
ExponentialFamilyMixture - Class in smile.stat.distribution
The finite mixture of distributions from exponential family.
ExponentialFamilyMixture(Mixture.Component...) - Constructor for class smile.stat.distribution.ExponentialFamilyMixture
Constructor.
ExponentialVariogram - Class in smile.interpolation.variogram
Exponential variogram.
ExponentialVariogram(double, double) - Constructor for class smile.interpolation.variogram.ExponentialVariogram
Constructor.
ExponentialVariogram(double, double, double) - Constructor for class smile.interpolation.variogram.ExponentialVariogram
Constructor.
extend() - Method in class smile.neighbor.lsh.Probe
This operation adds one to the last nonzero component.
eye(int) - Static method in class smile.math.matrix.BigMatrix
Returns an identity matrix.
eye(int) - Static method in class smile.math.matrix.fp32.Matrix
Returns an identity matrix.
eye(int) - Static method in class smile.math.matrix.Matrix
Returns an identity matrix.
eye(int, int) - Static method in class smile.math.matrix.BigMatrix
Returns an m-by-n identity matrix.
eye(int, int) - Static method in class smile.math.matrix.fp32.Matrix
Returns an m-by-n identity matrix.
eye(int, int) - Static method in class smile.math.matrix.Matrix
Returns an m-by-n identity matrix.

F

f - Variable in class smile.stat.hypothesis.FTest
F-statistic.
f(double) - Method in class smile.ica.Exp
 
f(double) - Method in class smile.ica.Kurtosis
 
f(double) - Method in class smile.ica.LogCosh
 
f(double) - Method in class smile.interpolation.variogram.ExponentialVariogram
 
f(double) - Method in class smile.interpolation.variogram.GaussianVariogram
 
f(double) - Method in class smile.interpolation.variogram.PowerVariogram
 
f(double) - Method in class smile.interpolation.variogram.SphericalVariogram
 
f(double) - Method in interface smile.math.Function
Computes the value of the function at x.
f(double) - Method in interface smile.math.kernel.DotProductKernel
 
f(double) - Method in interface smile.math.kernel.IsotropicKernel
 
f(double) - Method in class smile.math.kernel.Matern
 
f(double) - Method in class smile.math.rbf.GaussianRadialBasis
 
f(double) - Method in class smile.math.rbf.InverseMultiquadricRadialBasis
 
f(double) - Method in class smile.math.rbf.MultiquadricRadialBasis
 
f(double) - Method in class smile.math.rbf.ThinPlateRadialBasis
 
f(double) - Method in class smile.math.Scaler
 
f(double[]) - Method in interface smile.base.mlp.ActivationFunction
The output function.
f(double[]) - Method in enum class smile.base.mlp.OutputFunction
The output function.
f(double[]) - Method in class smile.base.svm.LinearKernelMachine
Returns the value of decision function.
f(double[]) - Method in interface smile.deep.activation.ActivationFunction
The output function.
f(double[]) - Method in class smile.deep.activation.LeakyReLU
 
f(double[]) - Method in class smile.deep.activation.ReLU
 
f(double[]) - Method in class smile.deep.activation.Sigmoid
 
f(double[]) - Method in class smile.deep.activation.Softmax
 
f(double[]) - Method in class smile.deep.activation.Tanh
 
f(double[]) - Method in interface smile.math.MultivariateFunction
Computes the value of the function at x.
f(int) - Method in interface smile.math.IntFunction
Computes the value of the function at x.
f(int[]) - Method in class smile.base.svm.LinearKernelMachine
Returns the value of decision function.
f(SparseArray) - Method in class smile.base.svm.LinearKernelMachine
Returns the value of decision function.
f(T) - Method in class smile.base.rbf.RBF
The activation function.
f1 - Variable in class smile.validation.ClassificationMetrics
The F-1 score on validation data.
F1 - Static variable in class smile.validation.metric.FScore
The F_1 score, the harmonic mean of precision and recall.
F2 - Static variable in class smile.validation.metric.FScore
The F_2 score, which weighs recall higher than precision.
factor(int) - Method in class smile.data.measure.CategoricalMeasure
Returns the factor value (in range [0, size)) of level.
FactorCrossing - Class in smile.data.formula
Factor crossing.
FactorCrossing(int, String...) - Constructor for class smile.data.formula.FactorCrossing
Constructor.
FactorCrossing(String...) - Constructor for class smile.data.formula.FactorCrossing
Constructor.
factorial(int) - Static method in class smile.math.MathEx
The factorial of n.
FactorInteraction - Class in smile.data.formula
The interaction of all the factors appearing in the term.
FactorInteraction(String...) - Constructor for class smile.data.formula.FactorInteraction
Constructor.
factorize(String...) - Method in interface smile.data.DataFrame
Returns a new DataFrame with given columns converted to nominal.
factorize(CategoricalMeasure) - Method in interface smile.data.vector.StringVector
Converts strings to discrete measured values.
Fallout - Class in smile.validation.metric
Fall-out, false alarm rate, or false positive rate (FPR)
Fallout() - Constructor for class smile.validation.metric.Fallout
 
falseChild() - Method in class smile.base.cart.InternalNode
Returns the false branch child.
FDistribution - Class in smile.stat.distribution
F-distribution arises in the testing of whether two observed samples have the same variance.
FDistribution(int, int) - Constructor for class smile.stat.distribution.FDistribution
Constructor.
FDR - Class in smile.validation.metric
The false discovery rate (FDR) is ratio of false positives to combined true and false positives, which is actually 1 - precision.
FDR() - Constructor for class smile.validation.metric.FDR
 
feature - Variable in class smile.feature.selection.InformationValue
The feature name.
feature - Variable in class smile.feature.selection.SignalNoiseRatio
The feature name.
feature - Variable in class smile.feature.selection.SumSquaresRatio
The feature name.
feature() - Method in class smile.base.cart.InternalNode
Returns the split feature.
Feature - Interface in smile.data.formula
A feature in the formula once bound to a schema.
features - Variable in class smile.sequence.CRFLabeler
The feature function.
features() - Method in class smile.feature.extraction.BagOfWords
Returns the feature words.
FHalf - Static variable in class smile.validation.metric.FScore
The F_0.5 score, which weighs recall lower than precision.
field() - Method in interface smile.data.formula.Feature
Returns the meta data of feature.
field() - Method in interface smile.data.vector.BaseVector
Returns a struct field corresponding to this vector.
field(int) - Method in class smile.data.type.StructType
Return the field at position i.
field(String) - Method in class smile.data.type.StructType
Return the field of given name.
fields() - Method in class smile.data.type.StructType
Returns the fields.
file(String) - Static method in interface smile.io.HadoopInput
Returns the Parquet's InputFile instance of a file path or URI.
fill(char, int) - Static method in interface smile.util.Strings
Returns the string with a single repeated character to a specific length.
fill(double) - Method in class smile.math.matrix.BigMatrix
Fill the matrix with a value.
fill(double) - Method in class smile.math.matrix.Matrix
Fills the matrix with a value.
fill(float) - Method in class smile.math.matrix.fp32.Matrix
Fills the matrix with a value.
find(DifferentiableFunction, double, double, double, int) - Static method in class smile.math.Root
Newton's method (also known as the Newton–Raphson method).
find(Function, double, double, double, int) - Static method in class smile.math.Root
Brent's method for root-finding.
findBestSplit(LeafNode, int, double, int, int) - Method in class smile.base.cart.CART
Finds the best split for given column.
findBestSplit(LeafNode, int, double, int, int) - Method in class smile.classification.DecisionTree
 
findBestSplit(LeafNode, int, double, int, int) - Method in class smile.regression.RegressionTree
 
findBestSplit(LeafNode, int, int, boolean[]) - Method in class smile.base.cart.CART
Finds the best attribute to split on a set of samples.
fit(double[]) - Static method in class smile.stat.distribution.BetaDistribution
Estimates the distribution parameters by the moment method.
fit(double[]) - Static method in class smile.stat.distribution.ExponentialDistribution
Estimates the distribution parameters by MLE.
fit(double[]) - Static method in class smile.stat.distribution.GammaDistribution
Estimates the distribution parameters by (approximate) MLE.
fit(double[]) - Static method in class smile.stat.distribution.GaussianDistribution
Estimates the distribution parameters by MLE.
fit(double[]) - Static method in class smile.stat.distribution.GaussianMixture
Fits the Gaussian mixture model with the EM algorithm.
fit(double[]) - Static method in class smile.stat.distribution.LogNormalDistribution
Estimates the distribution parameters by MLE.
fit(double[][]) - Static method in class smile.anomaly.IsolationForest
Fits an isolation forest.
fit(double[][]) - Static method in class smile.stat.distribution.MultivariateGaussianDistribution
Estimates the mean and diagonal covariance by MLE.
fit(double[][]) - Static method in class smile.stat.distribution.MultivariateGaussianMixture
Fits the Gaussian mixture model with the EM algorithm.
fit(double[][], boolean) - Static method in class smile.stat.distribution.MultivariateGaussianDistribution
Estimates the mean and covariance by MLE.
fit(double[][], boolean) - Static method in class smile.stat.distribution.MultivariateGaussianMixture
Fits the Gaussian mixture model with the EM algorithm.
fit(double[][], double[], double, double, double) - Static method in class smile.regression.SVM
Fits a linear epsilon-SVR.
fit(double[][], double[], Properties) - Static method in class smile.regression.GaussianProcessRegression
Fits a regular Gaussian process model.
fit(double[][], double[], Properties) - Static method in class smile.regression.MLP
Fits a MLP model.
fit(double[][], double[], Properties) - Static method in class smile.regression.RBFNetwork
Fits a RBF network.
fit(double[][], double[], Properties) - Static method in class smile.regression.SVM
Fits an epsilon-SVR.
fit(double[][], double, int) - Static method in class smile.clustering.DENCLUE
Clustering data.
fit(double[][], double, int, double, int) - Static method in class smile.clustering.DENCLUE
Clustering data.
fit(double[][], int) - Static method in class smile.base.rbf.RBF
Fits Gaussian RBF function and centers on data.
fit(double[][], int) - Static method in class smile.clustering.DeterministicAnnealing
Clustering data into k clusters.
fit(double[][], int) - Static method in class smile.clustering.GMeans
Clustering data with the number of clusters determined by G-Means algorithm automatically.
fit(double[][], int) - Static method in class smile.clustering.KMeans
Partitions data into k clusters up to 100 iterations.
fit(double[][], int) - Static method in class smile.clustering.XMeans
Clustering data with the number of clusters determined by X-Means algorithm automatically.
fit(double[][], int) - Static method in class smile.ica.ICA
Fits independent component analysis.
fit(double[][], int[]) - Static method in class smile.classification.FLD
Fits Fisher's linear discriminant.
fit(double[][], int[]) - Static method in class smile.classification.KNN
Fits the 1-NN classifier.
fit(double[][], int[]) - Static method in class smile.classification.LDA
Fits linear discriminant analysis.
fit(double[][], int[]) - Static method in class smile.classification.LogisticRegression
Fits logistic regression.
fit(double[][], int[]) - Static method in class smile.classification.QDA
Fits quadratic discriminant analysis.
fit(double[][], int[], double) - Static method in class smile.classification.RDA
Fits regularized discriminant analysis.
fit(double[][], int[], double[], double) - Static method in class smile.classification.LDA
Fits linear discriminant analysis.
fit(double[][], int[], double[], double) - Static method in class smile.classification.QDA
Fits quadratic discriminant analysis.
fit(double[][], int[], double, double) - Static method in class smile.classification.SVM
Fits a binary linear SVM.
fit(double[][], int[], double, double[], double) - Static method in class smile.classification.RDA
Fits regularized discriminant analysis.
fit(double[][], int[], double, double, int) - Static method in class smile.classification.LogisticRegression
Fits logistic regression.
fit(double[][], int[], double, double, int) - Static method in class smile.classification.SVM
Fits a binary linear SVM.
fit(double[][], int[], int) - Static method in class smile.classification.KNN
Fits the K-NN classifier.
fit(double[][], int[], int, double) - Static method in class smile.classification.FLD
Fits Fisher's linear discriminant.
fit(double[][], int[], Properties) - Static method in class smile.classification.FLD
Fits Fisher's linear discriminant.
fit(double[][], int[], Properties) - Static method in class smile.classification.LDA
Fits linear discriminant analysis.
fit(double[][], int[], Properties) - Static method in class smile.classification.LogisticRegression
Fits logistic regression.
fit(double[][], int[], Properties) - Static method in class smile.classification.MLP
Fits a MLP model.
fit(double[][], int[], Properties) - Static method in class smile.classification.QDA
Fits quadratic discriminant analysis.
fit(double[][], int[], Properties) - Static method in class smile.classification.RBFNetwork
Fits a RBF network.
fit(double[][], int[], Properties) - Static method in class smile.classification.RDA
Fits regularized discriminant analysis.
fit(double[][], int[], Properties) - Static method in class smile.classification.SVM
Fits a binary or multiclass SVM.
fit(double[][], int, double) - Static method in class smile.base.rbf.RBF
Fits Gaussian RBF function and centers on data.
fit(double[][], int, double) - Static method in class smile.clustering.DBSCAN
Clustering the data with KD-tree.
fit(double[][], int, double) - Static method in class smile.clustering.SpectralClustering
Spectral clustering the data.
fit(double[][], int, double, int, double) - Static method in class smile.clustering.SpectralClustering
Spectral clustering the data.
fit(double[][], int, double, int, double, double) - Static method in class smile.clustering.DeterministicAnnealing
Clustering data into k clusters.
fit(double[][], int, int) - Static method in class smile.base.rbf.RBF
Fits Gaussian RBF function and centers on data.
fit(double[][], int, int, double) - Static method in class smile.clustering.GMeans
Clustering data with the number of clusters determined by G-Means algorithm automatically.
fit(double[][], int, int, double) - Static method in class smile.clustering.KMeans
Partitions data into k clusters up to 100 iterations.
fit(double[][], int, int, double) - Static method in class smile.clustering.SpectralClustering
Spectral clustering with Nystrom approximation.
fit(double[][], int, int, double) - Static method in class smile.clustering.XMeans
Clustering data with the number of clusters determined by X-Means algorithm automatically.
fit(double[][], int, int, double, int) - Static method in class smile.anomaly.IsolationForest
Fits a random forest for classification.
fit(double[][], int, int, double, int, double) - Static method in class smile.clustering.SpectralClustering
Spectral clustering with Nystrom approximation.
fit(double[][], int, String...) - Static method in class smile.feature.extraction.ProbabilisticPCA
Fits probabilistic principal component analysis.
fit(double[][], int, Properties) - Static method in class smile.ica.ICA
Fits independent component analysis.
fit(double[][], int, DifferentiableFunction, double, int) - Static method in class smile.ica.ICA
Fits independent component analysis.
fit(double[][], String...) - Static method in class smile.feature.extraction.PCA
Fits principal component analysis with covariance matrix.
fit(double[][], Properties) - Static method in class smile.anomaly.IsolationForest
Fits a random forest for classification.
fit(double[][], MultivariateMixture.Component...) - Static method in class smile.stat.distribution.MultivariateExponentialFamilyMixture
Fits the mixture model with the EM algorithm.
fit(double[][], MultivariateMixture.Component[], double, int, double) - Static method in class smile.stat.distribution.MultivariateExponentialFamilyMixture
Fits the mixture model with the EM algorithm.
fit(double[], int) - Static method in class smile.timeseries.AR
Fits an autoregressive model with Yule-Walker procedure.
fit(double[], int[]) - Static method in class smile.classification.IsotonicRegressionScaling
Trains the Isotonic Regression scaling.
fit(double[], int[]) - Static method in class smile.classification.PlattScaling
Trains the Platt scaling.
fit(double[], int[], int) - Static method in class smile.classification.PlattScaling
Trains the Platt scaling.
fit(double[], int, int) - Static method in class smile.timeseries.ARMA
Fits an ARMA model with Hannan-Rissanen algorithm.
fit(double[], Mixture.Component...) - Static method in class smile.stat.distribution.ExponentialFamilyMixture
Fits the mixture model with the EM algorithm.
fit(double[], Mixture.Component[], double, int, double) - Static method in class smile.stat.distribution.ExponentialFamilyMixture
Fits the mixture model with the EM algorithm.
fit(int[]) - Static method in class smile.classification.ClassLabels
Fits the class label mapping.
fit(int[]) - Static method in class smile.stat.distribution.BernoulliDistribution
Estimates the distribution parameters by MLE.
fit(int[]) - Static method in class smile.stat.distribution.EmpiricalDistribution
Estimates the distribution.
fit(int[]) - Static method in class smile.stat.distribution.GeometricDistribution
Estimates the distribution parameters by MLE.
fit(int[]) - Static method in class smile.stat.distribution.PoissonDistribution
Estimates the distribution parameters by MLE.
fit(int[]) - Static method in class smile.stat.distribution.ShiftedGeometricDistribution
Estimates the distribution parameters by MLE.
fit(int[][], double[], int, double, double, double) - Static method in class smile.regression.SVM
Fits a linear epsilon-SVR of binary sparse data.
fit(int[][], int) - Static method in class smile.clustering.KModes
Fits k-modes clustering.
fit(int[][], int[][]) - Static method in class smile.sequence.HMM
Fits an HMM by maximum likelihood estimation.
fit(int[][], int[], int, double, double) - Static method in class smile.classification.SVM
Fits a binary linear SVM of binary sparse data.
fit(int[][], int[], int, double, double, int) - Static method in class smile.classification.SVM
Fits a binary linear SVM of binary sparse data.
fit(int[][], int, int) - Static method in class smile.clustering.KModes
Fits k-modes clustering.
fit(int[], DiscreteMixture.Component...) - Static method in class smile.stat.distribution.DiscreteExponentialFamilyMixture
Fits the mixture model with the EM algorithm.
fit(int[], DiscreteMixture.Component[], double, int, double) - Static method in class smile.stat.distribution.DiscreteExponentialFamilyMixture
Fits the mixture model with the EM algorithm.
fit(int[], IntSet) - Static method in class smile.stat.distribution.EmpiricalDistribution
Estimates the distribution.
fit(int, double[]) - Static method in class smile.stat.distribution.GaussianMixture
Fits the Gaussian mixture model with the EM algorithm.
fit(int, double[][]) - Static method in class smile.stat.distribution.MultivariateGaussianMixture
Fits the Gaussian mixture model with the EM algorithm.
fit(int, double[][], boolean) - Static method in class smile.stat.distribution.MultivariateGaussianMixture
Fits the Gaussian mixture model with the EM algorithm.
fit(int, int[][], int[]) - Static method in class smile.classification.Maxent
Fits maximum entropy classifier.
fit(int, int[][], int[], double, double, int) - Static method in class smile.classification.Maxent
Fits maximum entropy classifier.
fit(int, int[][], int[], Properties) - Static method in class smile.classification.Maxent
Fits maximum entropy classifier.
fit(String[][], PennTreebankPOS[][]) - Static method in class smile.nlp.pos.HMMPOSTagger
Fits an HMM POS tagger by maximum likelihood estimation.
fit(Classifier<T>, T[], int[]) - Static method in class smile.classification.PlattScaling
Fits Platt Scaling to estimate posteriori probabilities.
fit(BBDTree, double[][], int, int, double) - Static method in class smile.clustering.KMeans
Partitions data into k clusters.
fit(Linkage) - Static method in class smile.clustering.HierarchicalClustering
Fits the Agglomerative Hierarchical Clustering with given linkage method, which includes proximity matrix.
fit(DataFrame) - Static method in class smile.feature.transform.WinsorScaler
Fits the data transformation with 5% lower limit and 95% upper limit.
fit(DataFrame, double, double, String...) - Static method in class smile.feature.imputation.SimpleImputer
Fits the missing value imputation values.
fit(DataFrame, double, double, String...) - Static method in class smile.feature.transform.WinsorScaler
Fits the data transformation.
fit(DataFrame, int, String...) - Static method in class smile.feature.extraction.ProbabilisticPCA
Fits probabilistic principal component analysis.
fit(DataFrame, String) - Static method in class smile.feature.selection.InformationValue
Calculates the information value.
fit(DataFrame, String) - Static method in class smile.feature.selection.SignalNoiseRatio
Calculates the signal noise ratio of numeric variables.
fit(DataFrame, String) - Static method in class smile.feature.selection.SumSquaresRatio
Calculates the sum squares ratio of numeric variables.
fit(DataFrame, String...) - Static method in class smile.feature.extraction.PCA
Fits principal component analysis with covariance matrix.
fit(DataFrame, String...) - Static method in class smile.feature.imputation.SimpleImputer
Fits the missing value imputation values.
fit(DataFrame, String...) - Static method in class smile.feature.transform.MaxAbsScaler
Fits the data transformation.
fit(DataFrame, String...) - Static method in class smile.feature.transform.RobustStandardizer
Fits the data transformation.
fit(DataFrame, String...) - Static method in class smile.feature.transform.Scaler
Fits the data transformation.
fit(DataFrame, String...) - Static method in class smile.feature.transform.Standardizer
Fits the data transformation.
fit(DataFrame, String, int) - Static method in class smile.feature.selection.InformationValue
Calculates the information value.
fit(DataFrame, Function<String, String[]>, int, String...) - Static method in class smile.feature.extraction.BagOfWords
Learns a vocabulary dictionary of top-k frequent tokens in the raw documents.
fit(DataFrame, Function<DataFrame, Transform>...) - Static method in interface smile.data.transform.Transform
Fits a pipeline of data transforms.
fit(DataFrame, Distance<Tuple>, int) - Static method in class smile.feature.imputation.KMedoidsImputer
Fits the missing value imputation values.
fit(DataFrame, MercerKernel<double[]>, int, double, String...) - Static method in class smile.feature.extraction.KernelPCA
Fits kernel principal component analysis.
fit(DataFrame, MercerKernel<double[]>, int, String...) - Static method in class smile.feature.extraction.KernelPCA
Fits kernel principal component analysis.
fit(Formula, DataFrame) - Static method in class smile.classification.AdaBoost
Fits a AdaBoost model.
fit(Formula, DataFrame) - Method in interface smile.classification.DataFrameClassifier.Trainer
Fits a classification model with the default hyper-parameters.
fit(Formula, DataFrame) - Static method in class smile.classification.DecisionTree
Fits a classification tree.
fit(Formula, DataFrame) - Static method in class smile.classification.GradientTreeBoost
Fits a gradient tree boosting for classification.
fit(Formula, DataFrame) - Static method in class smile.classification.RandomForest
Fits a random forest for classification.
fit(Formula, DataFrame) - Method in interface smile.regression.DataFrameRegression.Trainer
Fits a regression model with the default hyper-parameters.
fit(Formula, DataFrame) - Static method in class smile.regression.GradientTreeBoost
Fits a gradient tree boosting for regression.
fit(Formula, DataFrame) - Static method in class smile.regression.LASSO
Fits a L1-regularized least squares model.
fit(Formula, DataFrame) - Static method in class smile.regression.OLS
Fits an ordinary least squares model.
fit(Formula, DataFrame) - Static method in class smile.regression.RandomForest
Fits a random forest for regression.
fit(Formula, DataFrame) - Static method in class smile.regression.RegressionTree
Fits a regression tree.
fit(Formula, DataFrame) - Static method in class smile.regression.RidgeRegression
Fits a ridge regression model.
fit(Formula, DataFrame, double) - Static method in class smile.regression.LASSO
Fits a L1-regularized least squares model.
fit(Formula, DataFrame, double) - Static method in class smile.regression.RidgeRegression
Fits a ridge regression model.
fit(Formula, DataFrame, double[], double[], double[]) - Static method in class smile.regression.RidgeRegression
Fits a generalized ridge regression model that minimizes a weighted least squares criterion augmented with a generalized ridge penalty:
fit(Formula, DataFrame, double, double) - Static method in class smile.regression.ElasticNet
Fits an Elastic Net model.
fit(Formula, DataFrame, double, double, double, int) - Static method in class smile.regression.ElasticNet
Fits an Elastic Net model.
fit(Formula, DataFrame, double, double, int) - Static method in class smile.regression.LASSO
Fits a L1-regularized least squares model.
fit(Formula, DataFrame, int, int, int) - Static method in class smile.regression.RegressionTree
Fits a regression tree.
fit(Formula, DataFrame, int, int, int, int) - Static method in class smile.classification.AdaBoost
Fits a AdaBoost model.
fit(Formula, DataFrame, int, int, int, int, double, double) - Static method in class smile.classification.GradientTreeBoost
Fits a gradient tree boosting for classification.
fit(Formula, DataFrame, int, int, int, int, int, double) - Static method in class smile.regression.RandomForest
Fits a random forest for regression.
fit(Formula, DataFrame, int, int, int, int, int, double, LongStream) - Static method in class smile.regression.RandomForest
Fits a random forest for regression.
fit(Formula, DataFrame, int, int, SplitRule, int, int, int, double) - Static method in class smile.classification.RandomForest
Fits a random forest for classification.
fit(Formula, DataFrame, int, int, SplitRule, int, int, int, double, int[]) - Static method in class smile.classification.RandomForest
Fits a random forest for regression.
fit(Formula, DataFrame, int, int, SplitRule, int, int, int, double, int[], LongStream) - Static method in class smile.classification.RandomForest
Fits a random forest for classification.
fit(Formula, DataFrame, String, boolean, boolean) - Static method in class smile.regression.OLS
Fits an ordinary least squares model.
fit(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in class smile.classification.OneVersusOne
Fits a multi-class model with binary data frame classifiers.
fit(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in class smile.classification.OneVersusRest
Fits a multi-class model with binary data frame classifiers.
fit(Formula, DataFrame, Properties) - Static method in class smile.classification.AdaBoost
Fits a AdaBoost model.
fit(Formula, DataFrame, Properties) - Method in interface smile.classification.DataFrameClassifier.Trainer
Fits a classification model.
fit(Formula, DataFrame, Properties) - Static method in class smile.classification.DecisionTree
Fits a classification tree.
fit(Formula, DataFrame, Properties) - Static method in class smile.classification.GradientTreeBoost
Fits a gradient tree boosting for classification.
fit(Formula, DataFrame, Properties) - Static method in class smile.classification.RandomForest
Fits a random forest for classification.
fit(Formula, DataFrame, Properties) - Method in interface smile.regression.DataFrameRegression.Trainer
Fits a regression model.
fit(Formula, DataFrame, Properties) - Static method in class smile.regression.ElasticNet
Fits an Elastic Net model.
fit(Formula, DataFrame, Properties) - Static method in class smile.regression.GradientTreeBoost
Fits a gradient tree boosting for regression.
fit(Formula, DataFrame, Properties) - Static method in class smile.regression.LASSO
Fits a L1-regularized least squares model.
fit(Formula, DataFrame, Properties) - Static method in class smile.regression.OLS
Fits an ordinary least squares model.
fit(Formula, DataFrame, Properties) - Static method in class smile.regression.RandomForest
Fits a random forest for regression.
fit(Formula, DataFrame, Properties) - Static method in class smile.regression.RegressionTree
Fits a regression tree.
fit(Formula, DataFrame, Properties) - Static method in class smile.regression.RidgeRegression
Fits a ridge regression model.
fit(Formula, DataFrame, Loss, int, int, int, int, double, double) - Static method in class smile.regression.GradientTreeBoost
Fits a gradient tree boosting for regression.
fit(Formula, DataFrame, SplitRule, int, int, int) - Static method in class smile.classification.DecisionTree
Fits a classification tree.
fit(Formula, DataFrame, Model) - Static method in class smile.glm.GLM
Fits the generalized linear model with IWLS (iteratively reweighted least squares).
fit(Formula, DataFrame, Model, double, int) - Static method in class smile.glm.GLM
Fits the generalized linear model with IWLS (iteratively reweighted least squares).
fit(Formula, DataFrame, Model, Properties) - Static method in class smile.glm.GLM
Fits the generalized linear model with IWLS (iteratively reweighted least squares).
fit(SparseDataset, int[]) - Static method in class smile.classification.SparseLogisticRegression
Fits logistic regression.
fit(SparseDataset, int[], double, double, int) - Static method in class smile.classification.SparseLogisticRegression
Fits logistic regression.
fit(SparseDataset, int[], Properties) - Static method in class smile.classification.SparseLogisticRegression
Fits logistic regression.
fit(Tuple[][], int[][]) - Static method in class smile.sequence.CRF
Fits a CRF model.
fit(Tuple[][], int[][], int, int, int, int, double) - Static method in class smile.sequence.CRF
Fits a CRF model.
fit(Tuple[][], int[][], Properties) - Static method in class smile.sequence.CRF
Fits a CRF model.
fit(BaseVector<?, ?, ?>) - Static method in class smile.classification.ClassLabels
Fits the class label mapping.
fit(DifferentiableMultivariateFunction, double[][], double[], double[]) - Static method in class smile.math.LevenbergMarquardt
Fits the nonlinear least squares.
fit(DifferentiableMultivariateFunction, double[][], double[], double[], double, int) - Static method in class smile.math.LevenbergMarquardt
Fits the nonlinear least squares.
fit(DifferentiableMultivariateFunction, double[], double[], double[]) - Static method in class smile.math.LevenbergMarquardt
Fits the nonlinear least squares.
fit(DifferentiableMultivariateFunction, double[], double[], double[], double, int) - Static method in class smile.math.LevenbergMarquardt
Fits the nonlinear least squares.
fit(Matrix, int) - Static method in class smile.clustering.SpectralClustering
Spectral graph clustering.
fit(Matrix, int, int, double) - Static method in class smile.clustering.SpectralClustering
Spectral graph clustering.
fit(RNNSearch<double[], double[]>, double[][], double) - Method in class smile.neighbor.MPLSH
Fits the posteriori multiple probe algorithm.
fit(RNNSearch<double[], double[]>, double[][], double, int) - Method in class smile.neighbor.MPLSH
Fits the posteriori multiple probe algorithm.
fit(RNNSearch<double[], double[]>, double[][], double, int, double) - Method in class smile.neighbor.MPLSH
Train the posteriori multiple probe algorithm.
fit(SparseArray[], double[], int, double, double, double) - Static method in class smile.regression.SVM
Fits a linear epsilon-SVR of sparse data.
fit(SparseArray[], int) - Static method in class smile.clustering.SIB
Clustering data into k clusters up to 100 iterations.
fit(SparseArray[], int[], int, double, double) - Static method in class smile.classification.SVM
Fits a binary linear SVM.
fit(SparseArray[], int[], int, double, double, int) - Static method in class smile.classification.SVM
Fits a binary linear SVM.
fit(SparseArray[], int, int) - Static method in class smile.clustering.SIB
Clustering data into k clusters.
fit(T[]) - Method in class smile.base.svm.OCSVM
Fits an one-class support vector machine.
fit(T[][], int[][], Function<T, Tuple>) - Static method in class smile.sequence.CRFLabeler
Fits a CRF model.
fit(T[][], int[][], Function<T, Tuple>, int, int, int, int, double) - Static method in class smile.sequence.CRFLabeler
Fits a CRF.
fit(T[][], int[][], Function<T, Tuple>, Properties) - Static method in class smile.sequence.CRFLabeler
Fits a CRF model.
fit(T[][], int[][], ToIntFunction<T>) - Static method in class smile.sequence.HMM
Fits an HMM by maximum likelihood estimation.
fit(T[][], int[][], ToIntFunction<T>) - Static method in class smile.sequence.HMMLabeler
Fits an HMM by maximum likelihood estimation.
fit(T[], double[]) - Method in class smile.base.svm.SVR
Fits an epsilon support vector regression model.
fit(T[], double[]) - Method in interface smile.regression.Regression.Trainer
Fits a regression model with the default hyper-parameters.
fit(T[], double[], Properties) - Method in interface smile.regression.Regression.Trainer
Fits a regression model.
fit(T[], double[], RBF<T>[]) - Static method in class smile.regression.RBFNetwork
Fits a RBF network.
fit(T[], double[], RBF<T>[], boolean) - Static method in class smile.regression.RBFNetwork
Fits a RBF network.
fit(T[], double[], MercerKernel<T>, double) - Static method in class smile.regression.GaussianProcessRegression
Fits a regular Gaussian process model by the method of subset of regressors.
fit(T[], double[], MercerKernel<T>, double, boolean, double, int) - Static method in class smile.regression.GaussianProcessRegression
Fits a regular Gaussian process model.
fit(T[], double[], MercerKernel<T>, double, double, double) - Static method in class smile.regression.SVM
Fits an epsilon-SVR.
fit(T[], double[], MercerKernel<T>, Properties) - Static method in class smile.regression.GaussianProcessRegression
Fits a regular Gaussian process model.
fit(T[], double[], T[], MercerKernel<T>, double) - Static method in class smile.regression.GaussianProcessRegression
Fits an approximate Gaussian process model by the method of subset of regressors.
fit(T[], double[], T[], MercerKernel<T>, double, boolean) - Static method in class smile.regression.GaussianProcessRegression
Fits an approximate Gaussian process model by the method of subset of regressors.
fit(T[], double[], T[], MercerKernel<T>, Properties) - Static method in class smile.regression.GaussianProcessRegression
Fits an approximate Gaussian process model by the method of subset of regressors.
fit(T[], int[]) - Method in interface smile.classification.Classifier.Trainer
Fits a classification model with the default hyper-parameters.
fit(T[], int[], int) - Method in class smile.base.svm.LASVM
Trains the model.
fit(T[], int[], int, int, BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusOne
Fits a multi-class model with binary classifiers.
fit(T[], int[], int, int, BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusRest
Fits a multi-class model with binary classifiers.
fit(T[], int[], int, Distance<T>) - Static method in class smile.classification.KNN
Fits the K-NN classifier.
fit(T[], int[], BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusOne
Fits a multi-class model with binary classifiers.
fit(T[], int[], BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusRest
Fits a multi-class model with binary classifiers.
fit(T[], int[], Properties) - Method in interface smile.classification.Classifier.Trainer
Fits a classification model.
fit(T[], int[], RBF<T>[]) - Static method in class smile.classification.RBFNetwork
Fits a RBF network.
fit(T[], int[], RBF<T>[], boolean) - Static method in class smile.classification.RBFNetwork
Fits a RBF network.
fit(T[], int[], Distance<T>) - Static method in class smile.classification.KNN
Fits the 1-NN classifier.
fit(T[], int[], MercerKernel<T>, double, double) - Static method in class smile.classification.SVM
Fits a binary SVM.
fit(T[], int[], MercerKernel<T>, double, double, int) - Static method in class smile.classification.SVM
Fits a binary SVM.
fit(T[], Distance<T>, int) - Static method in class smile.clustering.CLARANS
Clustering data into k clusters.
fit(T[], Distance<T>, int, double) - Static method in class smile.clustering.DBSCAN
Clustering the data.
fit(T[], Distance<T>, int, double) - Static method in class smile.clustering.MEC
Clustering the data.
fit(T[], Distance<T>, int, int) - Static method in class smile.clustering.CLARANS
Constructor.
fit(T[], Metric<T>, int) - Static method in class smile.base.rbf.RBF
Fits Gaussian RBF function and centers on data.
fit(T[], Metric<T>, int, double) - Static method in class smile.base.rbf.RBF
Fits Gaussian RBF function and centers on data.
fit(T[], Metric<T>, int, int) - Static method in class smile.base.rbf.RBF
Fits Gaussian RBF function and centers on data.
fit(T[], MercerKernel<T>) - Static method in class smile.anomaly.SVM
Fits an one-class SVM.
fit(T[], MercerKernel<T>, double, double) - Static method in class smile.anomaly.SVM
Fits an one-class SVM.
fit(T[], MercerKernel<T>, int) - Static method in class smile.manifold.KPCA
Fits kernel principal component analysis.
fit(T[], MercerKernel<T>, int, double) - Static method in class smile.manifold.KPCA
Fits kernel principal component analysis.
fit(T[], RNNSearch<T, T>, int, double) - Static method in class smile.clustering.DBSCAN
Clustering the data.
fit(T[], RNNSearch<T, T>, int, double, int[], double) - Static method in class smile.clustering.MEC
Clustering the data.
fitness() - Method in class smile.gap.BitString
 
fitness() - Method in interface smile.gap.Chromosome
Returns the fitness of chromosome.
fitness(double[][], double[], double[][], double[], RegressionMetric, BiFunction<double[][], double[], Regression<double[]>>) - Static method in class smile.feature.selection.GAFE
Returns the fitness of the regression model.
fitness(double[][], int[], double[][], int[], ClassificationMetric, BiFunction<double[][], int[], Classifier<double[]>>) - Static method in class smile.feature.selection.GAFE
Returns the fitness of the classification model.
fitness(String, DataFrame, DataFrame, ClassificationMetric, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in class smile.feature.selection.GAFE
Returns the fitness of the classification model.
fitness(String, DataFrame, DataFrame, RegressionMetric, BiFunction<Formula, DataFrame, DataFrameRegression>) - Static method in class smile.feature.selection.GAFE
Returns the fitness of the regression model.
Fitness<T extends Chromosome> - Interface in smile.gap
A measure to evaluate the fitness of chromosomes.
fittedValues - Variable in class smile.math.LevenbergMarquardt
The fitted values.
fittedValues() - Method in class smile.glm.GLM
Returns the fitted mean values.
fittedValues() - Method in class smile.regression.LinearModel
Returns the fitted values.
fittedValues() - Method in class smile.timeseries.AR
Returns the fitted values.
fittedValues() - Method in class smile.timeseries.ARMA
Returns the fitted values.
fitTime - Variable in class smile.validation.ClassificationMetrics
The time in milliseconds of fitting the model.
fitTime - Variable in class smile.validation.RegressionMetrics
The time in milliseconds of fitting the model.
FLD - Class in smile.classification
Fisher's linear discriminant.
FLD(double[], double[][], Matrix) - Constructor for class smile.classification.FLD
Constructor.
FLD(double[], double[][], Matrix, IntSet) - Constructor for class smile.classification.FLD
Constructor.
Float - Enum constant in enum class smile.data.type.DataType.ID
Float type ID.
FLOAT_DIGITS - Static variable in class smile.math.MathEx
The number of digits (in radix base) in the mantissa.
FLOAT_EPSILON - Static variable in class smile.math.MathEx
The machine precision for the float type, which is the difference between 1 and the smallest value greater than 1 that is representable for the float type.
FLOAT_MACHEP - Static variable in class smile.math.MathEx
The largest negative integer such that 1.0 + RADIXMACHEP ≠ 1.0, except that machep is bounded below by -(DIGITS+3)
FLOAT_NEGEP - Static variable in class smile.math.MathEx
The largest negative integer such that 1.0 - RADIXNEGEP ≠ 1.0, except that negeps is bounded below by -(DIGITS+3)
FloatArrayType - Static variable in class smile.data.type.DataTypes
Float Array data type.
FloatConsumer - Interface in smile.math.matrix.fp32
Single precision matrix element stream consumer.
FloatHeapSelect - Class in smile.sort
This class tracks the smallest values seen thus far in a stream of values.
FloatHeapSelect(float[]) - Constructor for class smile.sort.FloatHeapSelect
Constructor.
FloatHeapSelect(int) - Constructor for class smile.sort.FloatHeapSelect
Constructor.
FloatObjectType - Static variable in class smile.data.type.DataTypes
Float Object data type.
FloatType - Class in smile.data.type
Float data type.
FloatType - Static variable in class smile.data.type.DataTypes
Float data type.
floatVector(int) - Method in interface smile.data.DataFrame
Selects column based on the column index.
floatVector(int) - Method in class smile.data.IndexDataFrame
 
floatVector(Enum<?>) - Method in interface smile.data.DataFrame
Selects column using an enum value.
floatVector(String) - Method in interface smile.data.DataFrame
Selects column based on the column name.
FloatVector - Interface in smile.data.vector
An immutable float vector.
floor(String) - Static method in interface smile.data.formula.Terms
The floor(x) term.
floor(Term) - Static method in interface smile.data.formula.Terms
The floor(x) term.
forEachNonZero(int, int, DoubleConsumer) - Method in class smile.math.matrix.SparseMatrix
For each loop on non-zero elements.
forEachNonZero(int, int, FloatConsumer) - Method in class smile.math.matrix.fp32.SparseMatrix
For each loop on non-zero elements.
forEachNonZero(DoubleConsumer) - Method in class smile.math.matrix.SparseMatrix
For each loop on non-zero elements.
forecast() - Method in class smile.timeseries.AR
Returns 1-step ahead forecast.
forecast() - Method in class smile.timeseries.ARMA
Returns 1-step ahead forecast.
forecast(int) - Method in class smile.timeseries.AR
Returns l-step ahead forecast.
forecast(int) - Method in class smile.timeseries.ARMA
Returns l-step ahead forecast.
format(double) - Static method in interface smile.util.Strings
Returns the string representation of a floating number without trailing zeros.
format(double, boolean) - Static method in interface smile.util.Strings
Returns the string representation of a floating number.
format(float) - Static method in interface smile.util.Strings
Returns the string representation of a floating number without trailing zeros.
format(float, boolean) - Static method in interface smile.util.Strings
Returns the string representation of a floating number.
formula - Variable in class smile.base.cart.CART
The model formula.
formula - Variable in class smile.glm.GLM
The symbolic description of the model to be fitted.
formula() - Method in class smile.classification.AdaBoost
 
formula() - Method in interface smile.classification.DataFrameClassifier
Returns the formula associated with the model.
formula() - Method in class smile.classification.DecisionTree
Returns null if the tree is part of ensemble algorithm.
formula() - Method in class smile.classification.GradientTreeBoost
 
formula() - Method in class smile.classification.RandomForest
 
formula() - Method in interface smile.feature.importance.TreeSHAP
Returns the formula associated with the model.
formula() - Method in interface smile.regression.DataFrameRegression
Returns the model formula.
formula() - Method in class smile.regression.GradientTreeBoost
 
formula() - Method in class smile.regression.LinearModel
 
formula() - Method in class smile.regression.RandomForest
 
formula() - Method in class smile.regression.RegressionTree
Returns null if the tree is part of ensemble algorithm.
Formula - Class in smile.data.formula
The model fitting formula in a compact symbolic form.
Formula(Term, Term...) - Constructor for class smile.data.formula.Formula
Constructor.
FPGrowth - Class in smile.association
Frequent item set mining based on the FP-growth (frequent pattern growth) algorithm, which employs an extended prefix-tree (FP-tree) structure to store the database in a compressed form.
FPTree - Class in smile.association
FP-tree data structure used in FP-growth (frequent pattern growth) algorithm for frequent item set mining.
frame(DataFrame) - Method in class smile.data.formula.Formula
Returns a data frame of predictors and optionally response variable (if input data frame has the related variable(s)).
from(Path) - Static method in interface smile.data.BinarySparseDataset
Parse a binary sparse dataset from a file, of which each line is a data item which are the indices of nonzero elements.
from(Path) - Static method in interface smile.data.SparseDataset
Parses spare dataset in coordinate triple tuple list format.
from(Path, int) - Static method in interface smile.data.SparseDataset
Reads spare dataset in coordinate triple tuple list format.
FScore - Class in smile.validation.metric
The F-score (or F-measure) considers both the precision and the recall of the test to compute the score.
FScore() - Constructor for class smile.validation.metric.FScore
Constructor of F1 score.
FScore(double) - Constructor for class smile.validation.metric.FScore
Constructor of general F-score.
ftest() - Method in class smile.regression.LinearModel
Returns the F-statistic of goodness-of-fit.
FTest - Class in smile.stat.hypothesis
F test of the hypothesis that two independent samples come from normal distributions with the same variance, against the alternative that they come from normal distributions with different variances.
FTest(double, int, int, double) - Constructor for class smile.stat.hypothesis.FTest
Constructor.
Function - Interface in smile.math
An interface representing a univariate real function.
FW - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
Foreign word.

G

g(double) - Method in class smile.ica.Exp
 
g(double) - Method in class smile.ica.Kurtosis
 
g(double) - Method in class smile.ica.LogCosh
 
g(double) - Method in interface smile.math.DifferentiableFunction
Computes the gradient/derivative at x.
g(double[], double[]) - Method in interface smile.base.mlp.ActivationFunction
The gradient function.
g(double[], double[]) - Method in interface smile.deep.activation.ActivationFunction
The gradient function.
g(double[], double[]) - Method in class smile.deep.activation.LeakyReLU
 
g(double[], double[]) - Method in class smile.deep.activation.ReLU
 
g(double[], double[]) - Method in class smile.deep.activation.Sigmoid
 
g(double[], double[]) - Method in class smile.deep.activation.Softmax
 
g(double[], double[]) - Method in class smile.deep.activation.Tanh
 
g(double[], double[]) - Method in interface smile.math.DifferentiableMultivariateFunction
Computes the value and gradient at x.
g(Cost, double[], double[]) - Method in enum class smile.base.mlp.OutputFunction
The gradient function.
g2(double) - Method in class smile.ica.Exp
 
g2(double) - Method in class smile.ica.Kurtosis
 
g2(double) - Method in class smile.ica.LogCosh
 
g2(double) - Method in interface smile.math.DifferentiableFunction
Compute the second-order derivative at x.
GAFE - Class in smile.feature.selection
Genetic algorithm based feature selection.
GAFE() - Constructor for class smile.feature.selection.GAFE
Constructor.
GAFE(Selection, int, Crossover, double, double) - Constructor for class smile.feature.selection.GAFE
Constructor.
gamma(double) - Static method in class smile.math.special.Gamma
Gamma function.
Gamma - Class in smile.math.special
The gamma, digamma, and incomplete gamma functions.
GammaDistribution - Class in smile.stat.distribution
The Gamma distribution is a continuous probability distributions with a scale parameter θ and a shape parameter k.
GammaDistribution(double, double) - Constructor for class smile.stat.distribution.GammaDistribution
Constructor.
Gaussian - Class in smile.math.kernel
Gaussian kernel, also referred as RBF kernel or squared exponential kernel.
Gaussian(double, double, double) - Constructor for class smile.math.kernel.Gaussian
Constructor.
Gaussian(double, double) - Static method in interface smile.vq.Neighborhood
Returns Gaussian neighborhood function.
GaussianDistribution - Class in smile.stat.distribution
The normal distribution or Gaussian distribution is a continuous probability distribution that describes data that clusters around a mean.
GaussianDistribution(double, double) - Constructor for class smile.stat.distribution.GaussianDistribution
Constructor
GaussianKernel - Class in smile.math.kernel
Gaussian kernel, also referred as RBF kernel or squared exponential kernel.
GaussianKernel(double) - Constructor for class smile.math.kernel.GaussianKernel
Constructor.
GaussianKernel(double, double, double) - Constructor for class smile.math.kernel.GaussianKernel
Constructor.
GaussianMixture - Class in smile.stat.distribution
Finite univariate Gaussian mixture.
GaussianMixture(Mixture.Component...) - Constructor for class smile.stat.distribution.GaussianMixture
Constructor.
GaussianProcessRegression<T> - Class in smile.regression
Gaussian Process for Regression.
GaussianProcessRegression(MercerKernel<T>, T[], double[], double) - Constructor for class smile.regression.GaussianProcessRegression
Constructor.
GaussianProcessRegression(MercerKernel<T>, T[], double[], double, double, double) - Constructor for class smile.regression.GaussianProcessRegression
Constructor.
GaussianProcessRegression(MercerKernel<T>, T[], double[], double, double, double, Matrix.Cholesky, double) - Constructor for class smile.regression.GaussianProcessRegression
Constructor.
GaussianProcessRegression.JointPrediction - Class in smile.regression
The joint prediction of multiple data points.
GaussianRadialBasis - Class in smile.math.rbf
Gaussian RBF.
GaussianRadialBasis() - Constructor for class smile.math.rbf.GaussianRadialBasis
Constructor.
GaussianRadialBasis(double) - Constructor for class smile.math.rbf.GaussianRadialBasis
Constructor.
GaussianVariogram - Class in smile.interpolation.variogram
Gaussian variogram.
GaussianVariogram(double, double) - Constructor for class smile.interpolation.variogram.GaussianVariogram
Constructor.
GaussianVariogram(double, double, double) - Constructor for class smile.interpolation.variogram.GaussianVariogram
Constructor.
gbmv(Layout, Transpose, int, int, int, int, double, double[], int, double[], int, double, double[], int) - Method in interface smile.math.blas.BLAS
Performs the matrix-vector operation using a band matrix.
gbmv(Layout, Transpose, int, int, int, int, double, double[], int, double[], int, double, double[], int) - Method in class smile.math.blas.mkl.MKL
 
gbmv(Layout, Transpose, int, int, int, int, double, double[], int, double[], int, double, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gbmv(Layout, Transpose, int, int, int, int, double, DoubleBuffer, int, DoubleBuffer, int, double, DoubleBuffer, int) - Method in interface smile.math.blas.BLAS
Performs the matrix-vector operation using a band matrix.
gbmv(Layout, Transpose, int, int, int, int, double, DoubleBuffer, int, DoubleBuffer, int, double, DoubleBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
gbmv(Layout, Transpose, int, int, int, int, double, DoubleBuffer, int, DoubleBuffer, int, double, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gbmv(Layout, Transpose, int, int, int, int, float, float[], int, float[], int, float, float[], int) - Method in interface smile.math.blas.BLAS
Performs the matrix-vector operation using a band matrix.
gbmv(Layout, Transpose, int, int, int, int, float, float[], int, float[], int, float, float[], int) - Method in class smile.math.blas.mkl.MKL
 
gbmv(Layout, Transpose, int, int, int, int, float, float[], int, float[], int, float, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gbmv(Layout, Transpose, int, int, int, int, float, FloatBuffer, int, FloatBuffer, int, float, FloatBuffer, int) - Method in interface smile.math.blas.BLAS
Performs the matrix-vector operation using a band matrix.
gbmv(Layout, Transpose, int, int, int, int, float, FloatBuffer, int, FloatBuffer, int, float, FloatBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
gbmv(Layout, Transpose, int, int, int, int, float, FloatBuffer, int, FloatBuffer, int, float, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gbsv(Layout, int, int, int, int, double[], int, int[], double[], int) - Method in interface smile.math.blas.LAPACK
Solves a real system of linear equations.
gbsv(Layout, int, int, int, int, double[], int, int[], double[], int) - Method in class smile.math.blas.mkl.MKL
 
gbsv(Layout, int, int, int, int, double[], int, int[], double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gbsv(Layout, int, int, int, int, float[], int, int[], float[], int) - Method in interface smile.math.blas.LAPACK
Solves a real system of linear equations.
gbsv(Layout, int, int, int, int, float[], int, int[], float[], int) - Method in class smile.math.blas.mkl.MKL
 
gbsv(Layout, int, int, int, int, float[], int, int[], float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gbsv(Layout, int, int, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in interface smile.math.blas.LAPACK
Solves a real system of linear equations.
gbsv(Layout, int, int, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
gbsv(Layout, int, int, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gbsv(Layout, int, int, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in interface smile.math.blas.LAPACK
Solves a real system of linear equations.
gbsv(Layout, int, int, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
gbsv(Layout, int, int, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gbtrf(Layout, int, int, int, int, double[], int, int[]) - Method in interface smile.math.blas.LAPACK
Computes an LU factorization of a band matrix A using partial pivoting with row interchanges.
gbtrf(Layout, int, int, int, int, double[], int, int[]) - Method in class smile.math.blas.mkl.MKL
 
gbtrf(Layout, int, int, int, int, double[], int, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
gbtrf(Layout, int, int, int, int, float[], int, int[]) - Method in interface smile.math.blas.LAPACK
Computes an LU factorization of a band matrix A using partial pivoting with row interchanges.
gbtrf(Layout, int, int, int, int, float[], int, int[]) - Method in class smile.math.blas.mkl.MKL
 
gbtrf(Layout, int, int, int, int, float[], int, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
gbtrf(Layout, int, int, int, int, DoubleBuffer, int, IntBuffer) - Method in interface smile.math.blas.LAPACK
Computes an LU factorization of a band matrix A using partial pivoting with row interchanges.
gbtrf(Layout, int, int, int, int, DoubleBuffer, int, IntBuffer) - Method in class smile.math.blas.mkl.MKL
 
gbtrf(Layout, int, int, int, int, DoubleBuffer, int, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
gbtrf(Layout, int, int, int, int, FloatBuffer, int, IntBuffer) - Method in interface smile.math.blas.LAPACK
Computes an LU factorization of a band matrix A using partial pivoting with row interchanges.
gbtrf(Layout, int, int, int, int, FloatBuffer, int, IntBuffer) - Method in class smile.math.blas.mkl.MKL
 
gbtrf(Layout, int, int, int, int, FloatBuffer, int, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
gbtrs(Layout, Transpose, int, int, int, int, double[], int, int[], double[], int) - Method in interface smile.math.blas.LAPACK
Solves a system of linear equations
gbtrs(Layout, Transpose, int, int, int, int, double[], int, int[], double[], int) - Method in class smile.math.blas.mkl.MKL
 
gbtrs(Layout, Transpose, int, int, int, int, double[], int, int[], double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gbtrs(Layout, Transpose, int, int, int, int, float[], int, int[], float[], int) - Method in interface smile.math.blas.LAPACK
Solves a system of linear equations
gbtrs(Layout, Transpose, int, int, int, int, float[], int, int[], float[], int) - Method in class smile.math.blas.mkl.MKL
 
gbtrs(Layout, Transpose, int, int, int, int, float[], int, int[], float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gbtrs(Layout, Transpose, int, int, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in interface smile.math.blas.LAPACK
Solves a system of linear equations
gbtrs(Layout, Transpose, int, int, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
gbtrs(Layout, Transpose, int, int, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gbtrs(Layout, Transpose, int, int, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in interface smile.math.blas.LAPACK
Solves a system of linear equations
gbtrs(Layout, Transpose, int, int, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
gbtrs(Layout, Transpose, int, int, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
geev(Layout, EVDJob, EVDJob, int, double[], int, double[], double[], double[], int, double[], int) - Method in interface smile.math.blas.LAPACK
Computes the eigenvalues and, optionally, the left and/or right eigenvectors.
geev(Layout, EVDJob, EVDJob, int, double[], int, double[], double[], double[], int, double[], int) - Method in class smile.math.blas.mkl.MKL
 
geev(Layout, EVDJob, EVDJob, int, double[], int, double[], double[], double[], int, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
geev(Layout, EVDJob, EVDJob, int, float[], int, float[], float[], float[], int, float[], int) - Method in interface smile.math.blas.LAPACK
Computes the eigenvalues and, optionally, the left and/or right eigenvectors.
geev(Layout, EVDJob, EVDJob, int, float[], int, float[], float[], float[], int, float[], int) - Method in class smile.math.blas.mkl.MKL
 
geev(Layout, EVDJob, EVDJob, int, float[], int, float[], float[], float[], int, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
geev(Layout, EVDJob, EVDJob, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int) - Method in interface smile.math.blas.LAPACK
Computes the eigenvalues and, optionally, the left and/or right eigenvectors.
geev(Layout, EVDJob, EVDJob, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
geev(Layout, EVDJob, EVDJob, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
geev(Layout, EVDJob, EVDJob, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer, int, FloatBuffer, int) - Method in interface smile.math.blas.LAPACK
Computes the eigenvalues and, optionally, the left and/or right eigenvectors.
geev(Layout, EVDJob, EVDJob, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer, int, FloatBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
geev(Layout, EVDJob, EVDJob, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer, int, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
geev(Layout, EVDJob, EVDJob, int, DoublePointer, int, DoublePointer, DoublePointer, DoublePointer, int, DoublePointer, int) - Method in interface smile.math.blas.LAPACK
Computes the eigenvalues and, optionally, the left and/or right eigenvectors.
geev(Layout, EVDJob, EVDJob, int, DoublePointer, int, DoublePointer, DoublePointer, DoublePointer, int, DoublePointer, int) - Method in class smile.math.blas.mkl.MKL
 
geev(Layout, EVDJob, EVDJob, int, DoublePointer, int, DoublePointer, DoublePointer, DoublePointer, int, DoublePointer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gels(Layout, Transpose, int, int, int, double[], int, double[], int) - Method in interface smile.math.blas.LAPACK
Solves an overdetermined or underdetermined system, using a QR or LQ factorization of A.
gels(Layout, Transpose, int, int, int, double[], int, double[], int) - Method in class smile.math.blas.mkl.MKL
 
gels(Layout, Transpose, int, int, int, double[], int, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gels(Layout, Transpose, int, int, int, float[], int, float[], int) - Method in interface smile.math.blas.LAPACK
Solves an overdetermined or underdetermined system, using a QR or LQ factorization of A.
gels(Layout, Transpose, int, int, int, float[], int, float[], int) - Method in class smile.math.blas.mkl.MKL
 
gels(Layout, Transpose, int, int, int, float[], int, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gels(Layout, Transpose, int, int, int, DoubleBuffer, int, DoubleBuffer, int) - Method in interface smile.math.blas.LAPACK
Solves an overdetermined or underdetermined system, using a QR or LQ factorization of A.
gels(Layout, Transpose, int, int, int, DoubleBuffer, int, DoubleBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
gels(Layout, Transpose, int, int, int, DoubleBuffer, int, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gels(Layout, Transpose, int, int, int, FloatBuffer, int, FloatBuffer, int) - Method in interface smile.math.blas.LAPACK
Solves an overdetermined or underdetermined system, using a QR or LQ factorization of A.
gels(Layout, Transpose, int, int, int, FloatBuffer, int, FloatBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
gels(Layout, Transpose, int, int, int, FloatBuffer, int, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gelsd(Layout, int, int, int, double[], int, double[], int, double[], double, int[]) - Method in interface smile.math.blas.LAPACK
Solves an overdetermined or underdetermined system, using a divide and conquer algorithm with the singular value decomposition (SVD) of A.
gelsd(Layout, int, int, int, double[], int, double[], int, double[], double, int[]) - Method in class smile.math.blas.mkl.MKL
 
gelsd(Layout, int, int, int, double[], int, double[], int, double[], double, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
gelsd(Layout, int, int, int, float[], int, float[], int, float[], float, int[]) - Method in interface smile.math.blas.LAPACK
Solves an overdetermined or underdetermined system, using a divide and conquer algorithm with the singular value decomposition (SVD) of A.
gelsd(Layout, int, int, int, float[], int, float[], int, float[], float, int[]) - Method in class smile.math.blas.mkl.MKL
 
gelsd(Layout, int, int, int, float[], int, float[], int, float[], float, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
gelsd(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, double, IntBuffer) - Method in interface smile.math.blas.LAPACK
Solves an overdetermined or underdetermined system, using a divide and conquer algorithm with the singular value decomposition (SVD) of A.
gelsd(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, double, IntBuffer) - Method in class smile.math.blas.mkl.MKL
 
gelsd(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, double, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
gelsd(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, float, IntBuffer) - Method in interface smile.math.blas.LAPACK
Solves an overdetermined or underdetermined system, using a divide and conquer algorithm with the singular value decomposition (SVD) of A.
gelsd(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, float, IntBuffer) - Method in class smile.math.blas.mkl.MKL
 
gelsd(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, float, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
gelss(Layout, int, int, int, double[], int, double[], int, double[], double, int[]) - Method in interface smile.math.blas.LAPACK
Solves an overdetermined or underdetermined system, using the singular value decomposition (SVD) of A.
gelss(Layout, int, int, int, double[], int, double[], int, double[], double, int[]) - Method in class smile.math.blas.mkl.MKL
 
gelss(Layout, int, int, int, double[], int, double[], int, double[], double, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
gelss(Layout, int, int, int, float[], int, float[], int, float[], float, int[]) - Method in interface smile.math.blas.LAPACK
Solves an overdetermined or underdetermined system, using the singular value decomposition (SVD) of A.
gelss(Layout, int, int, int, float[], int, float[], int, float[], float, int[]) - Method in class smile.math.blas.mkl.MKL
 
gelss(Layout, int, int, int, float[], int, float[], int, float[], float, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
gelss(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, double, IntBuffer) - Method in interface smile.math.blas.LAPACK
Solves an overdetermined or underdetermined system, using the singular value decomposition (SVD) of A.
gelss(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, double, IntBuffer) - Method in class smile.math.blas.mkl.MKL
 
gelss(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, double, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
gelss(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, float, IntBuffer) - Method in interface smile.math.blas.LAPACK
Solves an overdetermined or underdetermined system, using the singular value decomposition (SVD) of A.
gelss(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, float, IntBuffer) - Method in class smile.math.blas.mkl.MKL
 
gelss(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, float, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
gelsy(Layout, int, int, int, double[], int, double[], int, int[], double, int[]) - Method in interface smile.math.blas.LAPACK
Solves an overdetermined or underdetermined system, using a complete orthogonal factorization of A.
gelsy(Layout, int, int, int, double[], int, double[], int, int[], double, int[]) - Method in class smile.math.blas.mkl.MKL
 
gelsy(Layout, int, int, int, double[], int, double[], int, int[], double, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
gelsy(Layout, int, int, int, float[], int, float[], int, int[], float, int[]) - Method in interface smile.math.blas.LAPACK
Solves an overdetermined or underdetermined system, using a complete orthogonal factorization of A.
gelsy(Layout, int, int, int, float[], int, float[], int, int[], float, int[]) - Method in class smile.math.blas.mkl.MKL
 
gelsy(Layout, int, int, int, float[], int, float[], int, int[], float, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
gelsy(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, IntBuffer, double, IntBuffer) - Method in interface smile.math.blas.LAPACK
Solves an overdetermined or underdetermined system, using a complete orthogonal factorization of A.
gelsy(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, IntBuffer, double, IntBuffer) - Method in class smile.math.blas.mkl.MKL
 
gelsy(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, IntBuffer, double, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
gelsy(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, IntBuffer, float, IntBuffer) - Method in interface smile.math.blas.LAPACK
Solves an overdetermined or underdetermined system, using a complete orthogonal factorization of A.
gelsy(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, IntBuffer, float, IntBuffer) - Method in class smile.math.blas.mkl.MKL
 
gelsy(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, IntBuffer, float, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
gemm(Layout, Transpose, Transpose, int, int, int, double, double[], int, double[], int, double, double[], int) - Method in interface smile.math.blas.BLAS
Performs the matrix-matrix operation.
gemm(Layout, Transpose, Transpose, int, int, int, double, double[], int, double[], int, double, double[], int) - Method in class smile.math.blas.mkl.MKL
 
gemm(Layout, Transpose, Transpose, int, int, int, double, double[], int, double[], int, double, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gemm(Layout, Transpose, Transpose, int, int, int, double, DoubleBuffer, int, DoubleBuffer, int, double, DoubleBuffer, int) - Method in interface smile.math.blas.BLAS
Performs the matrix-matrix operation.
gemm(Layout, Transpose, Transpose, int, int, int, double, DoubleBuffer, int, DoubleBuffer, int, double, DoubleBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
gemm(Layout, Transpose, Transpose, int, int, int, double, DoubleBuffer, int, DoubleBuffer, int, double, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gemm(Layout, Transpose, Transpose, int, int, int, double, DoublePointer, int, DoublePointer, int, double, DoublePointer, int) - Method in interface smile.math.blas.BLAS
Performs the matrix-matrix operation.
gemm(Layout, Transpose, Transpose, int, int, int, double, DoublePointer, int, DoublePointer, int, double, DoublePointer, int) - Method in class smile.math.blas.mkl.MKL
 
gemm(Layout, Transpose, Transpose, int, int, int, double, DoublePointer, int, DoublePointer, int, double, DoublePointer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gemm(Layout, Transpose, Transpose, int, int, int, float, float[], int, float[], int, float, float[], int) - Method in interface smile.math.blas.BLAS
Performs the matrix-matrix operation.
gemm(Layout, Transpose, Transpose, int, int, int, float, float[], int, float[], int, float, float[], int) - Method in class smile.math.blas.mkl.MKL
 
gemm(Layout, Transpose, Transpose, int, int, int, float, float[], int, float[], int, float, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gemm(Layout, Transpose, Transpose, int, int, int, float, FloatBuffer, int, FloatBuffer, int, float, FloatBuffer, int) - Method in interface smile.math.blas.BLAS
Performs the matrix-matrix operation.
gemm(Layout, Transpose, Transpose, int, int, int, float, FloatBuffer, int, FloatBuffer, int, float, FloatBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
gemm(Layout, Transpose, Transpose, int, int, int, float, FloatBuffer, int, FloatBuffer, int, float, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gemv(Layout, Transpose, int, int, double, double[], int, double[], int, double, double[], int) - Method in interface smile.math.blas.BLAS
Performs the matrix-vector operation.
gemv(Layout, Transpose, int, int, double, double[], int, double[], int, double, double[], int) - Method in class smile.math.blas.mkl.MKL
 
gemv(Layout, Transpose, int, int, double, double[], int, double[], int, double, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gemv(Layout, Transpose, int, int, double, DoubleBuffer, int, DoubleBuffer, int, double, DoubleBuffer, int) - Method in interface smile.math.blas.BLAS
Performs the matrix-vector operation.
gemv(Layout, Transpose, int, int, double, DoubleBuffer, int, DoubleBuffer, int, double, DoubleBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
gemv(Layout, Transpose, int, int, double, DoubleBuffer, int, DoubleBuffer, int, double, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gemv(Layout, Transpose, int, int, double, DoublePointer, int, DoublePointer, int, double, DoublePointer, int) - Method in interface smile.math.blas.BLAS
Performs the matrix-vector operation.
gemv(Layout, Transpose, int, int, double, DoublePointer, int, DoublePointer, int, double, DoublePointer, int) - Method in class smile.math.blas.mkl.MKL
 
gemv(Layout, Transpose, int, int, double, DoublePointer, int, DoublePointer, int, double, DoublePointer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gemv(Layout, Transpose, int, int, float, float[], int, float[], int, float, float[], int) - Method in interface smile.math.blas.BLAS
Performs the matrix-vector operation.
gemv(Layout, Transpose, int, int, float, float[], int, float[], int, float, float[], int) - Method in class smile.math.blas.mkl.MKL
 
gemv(Layout, Transpose, int, int, float, float[], int, float[], int, float, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gemv(Layout, Transpose, int, int, float, FloatBuffer, int, FloatBuffer, int, float, FloatBuffer, int) - Method in interface smile.math.blas.BLAS
Performs the matrix-vector operation.
gemv(Layout, Transpose, int, int, float, FloatBuffer, int, FloatBuffer, int, float, FloatBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
gemv(Layout, Transpose, int, int, float, FloatBuffer, int, FloatBuffer, int, float, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
generateSeed() - Static method in class smile.math.MathEx
Returns a random number to seed other random number generators.
generateSeed(int) - Static method in class smile.math.MathEx
Returns the given number of random bytes to seed other random number generators.
GeneticAlgorithm<T extends Chromosome> - Class in smile.gap
A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution.
GeneticAlgorithm(T[]) - Constructor for class smile.gap.GeneticAlgorithm
Constructor.
GeneticAlgorithm(T[], Selection, int) - Constructor for class smile.gap.GeneticAlgorithm
Constructor.
GeometricDistribution - Class in smile.stat.distribution
The geometric distribution is a discrete probability distribution of the number X of Bernoulli trials needed to get one success, supported on the set {1, 2, 3, …}.
GeometricDistribution(double) - Constructor for class smile.stat.distribution.GeometricDistribution
Constructor.
geqrf(Layout, int, int, double[], int, double[]) - Method in interface smile.math.blas.LAPACK
Computes a QR factorization of a general M-by-N matrix A.
geqrf(Layout, int, int, double[], int, double[]) - Method in class smile.math.blas.mkl.MKL
 
geqrf(Layout, int, int, double[], int, double[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
geqrf(Layout, int, int, float[], int, float[]) - Method in interface smile.math.blas.LAPACK
Computes a QR factorization of a general M-by-N matrix A.
geqrf(Layout, int, int, float[], int, float[]) - Method in class smile.math.blas.mkl.MKL
 
geqrf(Layout, int, int, float[], int, float[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
geqrf(Layout, int, int, DoubleBuffer, int, DoubleBuffer) - Method in interface smile.math.blas.LAPACK
Computes a QR factorization of a general M-by-N matrix A.
geqrf(Layout, int, int, DoubleBuffer, int, DoubleBuffer) - Method in class smile.math.blas.mkl.MKL
 
geqrf(Layout, int, int, DoubleBuffer, int, DoubleBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
geqrf(Layout, int, int, FloatBuffer, int, FloatBuffer) - Method in interface smile.math.blas.LAPACK
Computes a QR factorization of a general M-by-N matrix A.
geqrf(Layout, int, int, FloatBuffer, int, FloatBuffer) - Method in class smile.math.blas.mkl.MKL
 
geqrf(Layout, int, int, FloatBuffer, int, FloatBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
geqrf(Layout, int, int, DoublePointer, int, DoublePointer) - Method in interface smile.math.blas.LAPACK
Computes a QR factorization of a general M-by-N matrix A.
geqrf(Layout, int, int, DoublePointer, int, DoublePointer) - Method in class smile.math.blas.mkl.MKL
 
geqrf(Layout, int, int, DoublePointer, int, DoublePointer) - Method in class smile.math.blas.openblas.OpenBLAS
 
ger(Layout, int, int, double, double[], int, double[], int, double[], int) - Method in interface smile.math.blas.BLAS
Performs the rank-1 update operation.
ger(Layout, int, int, double, double[], int, double[], int, double[], int) - Method in class smile.math.blas.mkl.MKL
 
ger(Layout, int, int, double, double[], int, double[], int, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
ger(Layout, int, int, double, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, int) - Method in interface smile.math.blas.BLAS
Performs the rank-1 update operation.
ger(Layout, int, int, double, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
ger(Layout, int, int, double, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
ger(Layout, int, int, double, DoublePointer, int, DoublePointer, int, DoublePointer, int) - Method in interface smile.math.blas.BLAS
Performs the rank-1 update operation.
ger(Layout, int, int, double, DoublePointer, int, DoublePointer, int, DoublePointer, int) - Method in class smile.math.blas.mkl.MKL
 
ger(Layout, int, int, double, DoublePointer, int, DoublePointer, int, DoublePointer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
ger(Layout, int, int, float, float[], int, float[], int, float[], int) - Method in interface smile.math.blas.BLAS
Performs the rank-1 update operation.
ger(Layout, int, int, float, float[], int, float[], int, float[], int) - Method in class smile.math.blas.mkl.MKL
 
ger(Layout, int, int, float, float[], int, float[], int, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
ger(Layout, int, int, float, FloatBuffer, int, FloatBuffer, int, FloatBuffer, int) - Method in interface smile.math.blas.BLAS
Performs the rank-1 update operation.
ger(Layout, int, int, float, FloatBuffer, int, FloatBuffer, int, FloatBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
ger(Layout, int, int, float, FloatBuffer, int, FloatBuffer, int, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gesdd(Layout, SVDJob, int, int, double[], int, double[], double[], int, double[], int) - Method in interface smile.math.blas.LAPACK
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
gesdd(Layout, SVDJob, int, int, double[], int, double[], double[], int, double[], int) - Method in class smile.math.blas.mkl.MKL
 
gesdd(Layout, SVDJob, int, int, double[], int, double[], double[], int, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gesdd(Layout, SVDJob, int, int, float[], int, float[], float[], int, float[], int) - Method in interface smile.math.blas.LAPACK
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
gesdd(Layout, SVDJob, int, int, float[], int, float[], float[], int, float[], int) - Method in class smile.math.blas.mkl.MKL
 
gesdd(Layout, SVDJob, int, int, float[], int, float[], float[], int, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gesdd(Layout, SVDJob, int, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int) - Method in interface smile.math.blas.LAPACK
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
gesdd(Layout, SVDJob, int, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
gesdd(Layout, SVDJob, int, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gesdd(Layout, SVDJob, int, int, FloatBuffer, int, FloatBuffer, FloatBuffer, int, FloatBuffer, int) - Method in interface smile.math.blas.LAPACK
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
gesdd(Layout, SVDJob, int, int, FloatBuffer, int, FloatBuffer, FloatBuffer, int, FloatBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
gesdd(Layout, SVDJob, int, int, FloatBuffer, int, FloatBuffer, FloatBuffer, int, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gesdd(Layout, SVDJob, int, int, DoublePointer, int, DoublePointer, DoublePointer, int, DoublePointer, int) - Method in interface smile.math.blas.LAPACK
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
gesdd(Layout, SVDJob, int, int, DoublePointer, int, DoublePointer, DoublePointer, int, DoublePointer, int) - Method in class smile.math.blas.mkl.MKL
 
gesdd(Layout, SVDJob, int, int, DoublePointer, int, DoublePointer, DoublePointer, int, DoublePointer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gesv(Layout, int, int, double[], int, int[], double[], int) - Method in interface smile.math.blas.LAPACK
Solves a real system of linear equations.
gesv(Layout, int, int, double[], int, int[], double[], int) - Method in class smile.math.blas.mkl.MKL
 
gesv(Layout, int, int, double[], int, int[], double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gesv(Layout, int, int, float[], int, int[], float[], int) - Method in interface smile.math.blas.LAPACK
Solves a real system of linear equations.
gesv(Layout, int, int, float[], int, int[], float[], int) - Method in class smile.math.blas.mkl.MKL
 
gesv(Layout, int, int, float[], int, int[], float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gesv(Layout, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in interface smile.math.blas.LAPACK
Solves a real system of linear equations.
gesv(Layout, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
gesv(Layout, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gesv(Layout, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in interface smile.math.blas.LAPACK
Solves a real system of linear equations.
gesv(Layout, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
gesv(Layout, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gesv(Layout, int, int, DoublePointer, int, IntPointer, DoublePointer, int) - Method in interface smile.math.blas.LAPACK
Solves a real system of linear equations.
gesv(Layout, int, int, DoublePointer, int, IntPointer, DoublePointer, int) - Method in class smile.math.blas.mkl.MKL
 
gesv(Layout, int, int, DoublePointer, int, IntPointer, DoublePointer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
gesvd(Layout, SVDJob, SVDJob, int, int, double[], int, double[], double[], int, double[], int, double[]) - Method in interface smile.math.blas.LAPACK
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
gesvd(Layout, SVDJob, SVDJob, int, int, double[], int, double[], double[], int, double[], int, double[]) - Method in class smile.math.blas.mkl.MKL
 
gesvd(Layout, SVDJob, SVDJob, int, int, double[], int, double[], double[], int, double[], int, double[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
gesvd(Layout, SVDJob, SVDJob, int, int, float[], int, float[], float[], int, float[], int, float[]) - Method in interface smile.math.blas.LAPACK
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
gesvd(Layout, SVDJob, SVDJob, int, int, float[], int, float[], float[], int, float[], int, float[]) - Method in class smile.math.blas.mkl.MKL
 
gesvd(Layout, SVDJob, SVDJob, int, int, float[], int, float[], float[], int, float[], int, float[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
gesvd(Layout, SVDJob, SVDJob, int, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer) - Method in interface smile.math.blas.LAPACK
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
gesvd(Layout, SVDJob, SVDJob, int, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer) - Method in class smile.math.blas.mkl.MKL
 
gesvd(Layout, SVDJob, SVDJob, int, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
gesvd(Layout, SVDJob, SVDJob, int, int, FloatBuffer, int, FloatBuffer, FloatBuffer, int, FloatBuffer, int, FloatBuffer) - Method in interface smile.math.blas.LAPACK
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
gesvd(Layout, SVDJob, SVDJob, int, int, FloatBuffer, int, FloatBuffer, FloatBuffer, int, FloatBuffer, int, FloatBuffer) - Method in class smile.math.blas.mkl.MKL
 
gesvd(Layout, SVDJob, SVDJob, int, int, FloatBuffer, int, FloatBuffer, FloatBuffer, int, FloatBuffer, int, FloatBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
get(double[]) - Method in class smile.neighbor.lsh.Hash
Returns the bucket entry for the given point.
get(int) - Method in interface smile.data.Dataset
Returns the element at the specified position in this dataset.
get(int) - Method in class smile.data.IndexDataFrame
 
get(int) - Method in interface smile.data.Tuple
Returns the value at position i.
get(int) - Method in interface smile.data.vector.BaseVector
Returns the value at position i, which may be null.
get(int) - Method in class smile.math.Complex.Array
Returns the i-th element.
get(int) - Method in class smile.math.matrix.fp32.SparseMatrix
Returns the element at the storage index.
get(int) - Method in class smile.math.matrix.SparseMatrix
Returns the element at the storage index.
get(int) - Method in class smile.neighbor.lsh.Hash
Returns the bucket entry for the given hash value.
get(int) - Method in class smile.sort.DoubleHeapSelect
Returns the i-th smallest value seen so far.
get(int) - Method in class smile.sort.FloatHeapSelect
Returns the i-th smallest value seen so far.
get(int) - Method in class smile.sort.HeapSelect
Returns the i-th smallest value seen so far.
get(int) - Method in class smile.sort.IntHeapSelect
Returns the i-th smallest value seen so far.
get(int) - Method in class smile.util.DoubleArrayList
Returns the value at the specified position in this list.
get(int) - Method in class smile.util.IntArrayList
Returns the value at the specified position in this list.
get(int) - Method in class smile.util.IntDoubleHashMap
Returns the value to which the specified key is mapped, or Double.NaN if this map contains no mapping for the key.
get(int) - Method in class smile.util.SparseArray
Returns the value of i-th entry.
get(int...) - Method in interface smile.data.vector.BaseVector
Returns a new vector with selected entries.
get(int...) - Method in interface smile.data.vector.BooleanVector
 
get(int...) - Method in interface smile.data.vector.ByteVector
 
get(int...) - Method in interface smile.data.vector.CharVector
 
get(int...) - Method in interface smile.data.vector.DoubleVector
 
get(int...) - Method in interface smile.data.vector.FloatVector
 
get(int...) - Method in interface smile.data.vector.IntVector
 
get(int...) - Method in interface smile.data.vector.LongVector
 
get(int...) - Method in interface smile.data.vector.ShortVector
 
get(int...) - Method in interface smile.data.vector.StringVector
 
get(int...) - Method in interface smile.data.vector.Vector
 
get(int[], int[]) - Method in class smile.math.matrix.BigMatrix
Returns the matrix of selected rows and columns.
get(int[], int[]) - Method in class smile.math.matrix.fp32.Matrix
Returns the matrix of selected rows and columns.
get(int[], int[]) - Method in class smile.math.matrix.Matrix
Returns the matrix of selected rows and columns.
get(int, int) - Method in interface smile.data.BinarySparseDataset
Returns the binary value at entry (i, j) by binary search.
get(int, int) - Method in interface smile.data.DataFrame
Returns the cell at (i, j).
get(int, int) - Method in class smile.data.IndexDataFrame
 
get(int, int) - Method in interface smile.data.SparseDataset
Returns the value at entry (i, j).
get(int, int) - Method in class smile.math.matrix.BandMatrix
 
get(int, int) - Method in class smile.math.matrix.BigMatrix
 
get(int, int) - Method in class smile.math.matrix.fp32.BandMatrix
 
get(int, int) - Method in class smile.math.matrix.fp32.IMatrix
Returns A[i,j].
get(int, int) - Method in class smile.math.matrix.fp32.Matrix
 
get(int, int) - Method in class smile.math.matrix.fp32.SparseMatrix
 
get(int, int) - Method in class smile.math.matrix.fp32.SymmMatrix
 
get(int, int) - Method in class smile.math.matrix.IMatrix
Returns A[i,j].
get(int, int) - Method in class smile.math.matrix.Matrix
 
get(int, int) - Method in class smile.math.matrix.SparseMatrix
 
get(int, int) - Method in class smile.math.matrix.SymmMatrix
 
get(int, int) - Method in class smile.util.Array2D
Returns A[i, j].
get(int, int) - Method in class smile.util.IntArray2D
Returns A[i, j].
get(int, String) - Method in interface smile.data.DataFrame
Returns the cell at (i, j).
get(String) - Method in interface smile.data.Tuple
Returns the value by field name.
get(String) - Method in class smile.hash.PerfectHash
Returns the index of a keyword.
get(String) - Method in class smile.hash.PerfectMap
Returns the value associated with the key.
get(String) - Method in class smile.nlp.embedding.Word2Vec
Returns the embedding vector of a word.
get(String) - Static method in class smile.nlp.pos.EnglishPOSLexicon
Returns the part-of-speech tags for given word, or null if the word does not exist in the dictionary.
get(K) - Method in class smile.nlp.Trie
Returns the node of a given key.
get(K[]) - Method in class smile.nlp.Trie
Returns the associated value of a given key.
getAbbreviation(String) - Method in interface smile.nlp.dictionary.Abbreviations
Returns the abbreviation for a word.
getAnchor() - Method in interface smile.nlp.AnchorText
Returns the anchor text if any.
getAnchor() - Method in class smile.nlp.SimpleText
Returns the anchor text if any.
getArray(int) - Method in interface smile.data.Tuple
Returns the value at position i of array type.
getArray(int, int) - Method in interface smile.data.DataFrame
Returns the value at position (i, j) of array type.
getArray(int, String) - Method in interface smile.data.DataFrame
Returns the field value of array type.
getArray(String) - Method in interface smile.data.Tuple
Returns the field value of array type.
getAs(int) - Method in interface smile.data.Tuple
Returns the value at position i.
getAs(String) - Method in interface smile.data.Tuple
Returns the value of a given fieldName.
getBoolean(int) - Method in interface smile.data.Tuple
Returns the value at position i as a primitive boolean.
getBoolean(int) - Method in interface smile.data.vector.BooleanVector
Returns the value at position i.
getBoolean(int, int) - Method in interface smile.data.DataFrame
Returns the value at position (i, j) as a primitive boolean.
getBoolean(int, String) - Method in interface smile.data.DataFrame
Returns the field value as a primitive boolean.
getBoolean(String) - Method in interface smile.data.Tuple
Returns the field value as a primitive boolean.
getByte(int) - Method in interface smile.data.Tuple
Returns the value at position i as a primitive byte.
getByte(int) - Method in interface smile.data.vector.BaseVector
Returns the byte value at position i.
getByte(int) - Method in interface smile.data.vector.BooleanVector
 
getByte(int) - Method in interface smile.data.vector.CharVector
 
getByte(int) - Method in interface smile.data.vector.DoubleVector
 
getByte(int) - Method in interface smile.data.vector.FloatVector
 
getByte(int) - Method in interface smile.data.vector.IntVector
 
getByte(int) - Method in interface smile.data.vector.LongVector
 
getByte(int) - Method in interface smile.data.vector.ShortVector
 
getByte(int) - Method in interface smile.data.vector.Vector
 
getByte(int, int) - Method in interface smile.data.DataFrame
Returns the value at position (i, j) as a primitive byte.
getByte(int, String) - Method in interface smile.data.DataFrame
Returns the field value as a primitive byte.
getByte(String) - Method in interface smile.data.Tuple
Returns the field value as a primitive byte.
getChar(int) - Method in interface smile.data.Tuple
Returns the value at position i as a primitive byte.
getChar(int) - Method in interface smile.data.vector.CharVector
Returns the value at position i.
getChar(int, int) - Method in interface smile.data.DataFrame
Returns the value at position (i, j) as a primitive byte.
getChar(int, String) - Method in interface smile.data.DataFrame
Returns the field value as a primitive byte.
getChar(String) - Method in interface smile.data.Tuple
Returns the field value as a primitive byte.
getChild(K) - Method in class smile.nlp.Trie.Node
Returns the child with the key.
getChild(K[], int) - Method in class smile.nlp.Trie.Node
Returns the value matching the key sequence.
getClipNorm() - Method in class smile.base.mlp.MultilayerPerceptron
Returns the gradient clipping norm.
getClipValue() - Method in class smile.base.mlp.MultilayerPerceptron
Returns the gradient clipping value.
getComponentType() - Method in class smile.data.type.ArrayType
Returns the type of array elements.
getConcept(String) - Method in class smile.taxonomy.Taxonomy
Returns the concept node which synset contains the keyword.
getConcepts() - Method in class smile.taxonomy.Taxonomy
Returns all named concepts in the taxonomy.
getDate(int) - Method in interface smile.data.Tuple
Returns the value at position i of date type as java.time.LocalDate.
getDate(int, int) - Method in interface smile.data.DataFrame
Returns the value at position (i, j) of date type as java.time.LocalDate.
getDate(int, String) - Method in interface smile.data.DataFrame
Returns the field value of date type as java.time.LocalDate.
getDate(String) - Method in interface smile.data.Tuple
Returns the field value of date type as java.time.LocalDate.
getDateTime(int) - Method in interface smile.data.Tuple
Returns the value at position i of date type as java.time.LocalDateTime.
getDateTime(int, int) - Method in interface smile.data.DataFrame
Returns the value at position (i, j) as java.time.LocalDateTime.
getDateTime(int, String) - Method in interface smile.data.DataFrame
Returns the field value as java.time.LocalDateTime.
getDateTime(String) - Method in interface smile.data.Tuple
Returns the field value of date type as java.time.LocalDateTime.
getDecimal(int) - Method in interface smile.data.Tuple
Returns the value at position i of decimal type as java.math.BigDecimal.
getDecimal(int, int) - Method in interface smile.data.DataFrame
Returns the value at position (i, j) of decimal type as java.math.BigDecimal.
getDecimal(int, String) - Method in interface smile.data.DataFrame
Returns the field value of decimal type as java.math.BigDecimal.
getDecimal(String) - Method in interface smile.data.Tuple
Returns the field value of decimal type as java.math.BigDecimal.
getDefault() - Static method in class smile.nlp.pos.HMMPOSTagger
Returns the default English POS tagger.
getDegree(int) - Method in class smile.graph.AdjacencyList
 
getDegree(int) - Method in class smile.graph.AdjacencyMatrix
 
getDegree(int) - Method in interface smile.graph.Graph
Returns the degree of the specified vertex.
getDouble(int) - Method in interface smile.data.Tuple
Returns the value at position i as a primitive double.
getDouble(int) - Method in interface smile.data.vector.BaseVector
Returns the double value at position i.
getDouble(int) - Method in interface smile.data.vector.BooleanVector
 
getDouble(int) - Method in interface smile.data.vector.ByteVector
 
getDouble(int) - Method in interface smile.data.vector.CharVector
 
getDouble(int) - Method in interface smile.data.vector.FloatVector
 
getDouble(int) - Method in interface smile.data.vector.IntVector
 
getDouble(int) - Method in interface smile.data.vector.LongVector
 
getDouble(int) - Method in interface smile.data.vector.ShortVector
 
getDouble(int) - Method in interface smile.data.vector.Vector
 
getDouble(int, int) - Method in interface smile.data.DataFrame
Returns the value at position (i, j) as a primitive double.
getDouble(int, String) - Method in interface smile.data.DataFrame
Returns the field value as a primitive double.
getDouble(String) - Method in interface smile.data.Tuple
Returns the field value as a primitive double.
getEdge(int, int) - Method in class smile.graph.AdjacencyList
 
getEdge(int, int) - Method in class smile.graph.AdjacencyMatrix
 
getEdge(int, int) - Method in interface smile.graph.Graph
Returns an edge connecting source vertex to target vertex if such edge exist in this graph.
getEdges() - Method in class smile.graph.AdjacencyList
 
getEdges() - Method in class smile.graph.AdjacencyMatrix
 
getEdges() - Method in interface smile.graph.Graph
Returns the edges in this graph.
getEdges(int) - Method in class smile.graph.AdjacencyList
 
getEdges(int) - Method in class smile.graph.AdjacencyMatrix
 
getEdges(int) - Method in interface smile.graph.Graph
Returns the edges from the specified vertex.
getEdges(int, int) - Method in class smile.graph.AdjacencyList
 
getEdges(int, int) - Method in class smile.graph.AdjacencyMatrix
 
getEdges(int, int) - Method in interface smile.graph.Graph
Returns the edges connecting source vertex to target vertex if such vertices exist in this graph.
getExtensionLevel() - Method in class smile.anomaly.IsolationForest
Returns the extension level.
getFloat(int) - Method in interface smile.data.Tuple
Returns the value at position i as a primitive float.
getFloat(int) - Method in interface smile.data.vector.BaseVector
Returns the float value at position i.
getFloat(int) - Method in interface smile.data.vector.BooleanVector
 
getFloat(int) - Method in interface smile.data.vector.ByteVector
 
getFloat(int) - Method in interface smile.data.vector.CharVector
 
getFloat(int) - Method in interface smile.data.vector.DoubleVector
 
getFloat(int) - Method in interface smile.data.vector.IntVector
 
getFloat(int) - Method in interface smile.data.vector.LongVector
 
getFloat(int) - Method in interface smile.data.vector.ShortVector
 
getFloat(int) - Method in interface smile.data.vector.Vector
 
getFloat(int, int) - Method in interface smile.data.DataFrame
Returns the value at position (i, j) as a primitive float.
getFloat(int, String) - Method in interface smile.data.DataFrame
Returns the field value as a primitive float.
getFloat(String) - Method in interface smile.data.Tuple
Returns the field value as a primitive float.
getFull(String) - Method in interface smile.nlp.dictionary.Abbreviations
Returns the full word of an abbreviation.
getIndegree(int) - Method in class smile.graph.AdjacencyList
 
getIndegree(int) - Method in class smile.graph.AdjacencyMatrix
 
getIndegree(int) - Method in interface smile.graph.Graph
Returns the in-degree of the specified vertex.
getInitialStateProbabilities() - Method in class smile.sequence.HMM
Returns the initial state probabilities.
getInputSize() - Method in class smile.base.mlp.Layer
Returns the dimension of input vector (not including bias value).
getInstance() - Static method in interface smile.math.blas.BLAS
Creates an instance.
getInstance() - Static method in interface smile.math.blas.LAPACK
Creates an instance.
getInstance() - Static method in class smile.nlp.dictionary.EnglishPunctuations
Returns the singleton instance.
getInstance() - Static method in class smile.nlp.normalizer.SimpleNormalizer
Returns the singleton instance.
getInstance() - Static method in class smile.nlp.tokenizer.PennTreebankTokenizer
Returns the singleton instance.
getInstance() - Static method in class smile.nlp.tokenizer.SimpleParagraphSplitter
Returns the singleton instance.
getInstance() - Static method in class smile.nlp.tokenizer.SimpleSentenceSplitter
Returns the singleton instance.
getInstance() - Static method in class smile.stat.distribution.GaussianDistribution
Returns the standard normal distribution.
getInt(int) - Method in interface smile.data.Tuple
Returns the value at position i as a primitive int.
getInt(int) - Method in interface smile.data.vector.BaseVector
Returns the integer value at position i.
getInt(int) - Method in interface smile.data.vector.BooleanVector
 
getInt(int) - Method in interface smile.data.vector.ByteVector
 
getInt(int) - Method in interface smile.data.vector.CharVector
 
getInt(int) - Method in interface smile.data.vector.DoubleVector
 
getInt(int) - Method in interface smile.data.vector.FloatVector
 
getInt(int) - Method in interface smile.data.vector.LongVector
 
getInt(int) - Method in interface smile.data.vector.ShortVector
 
getInt(int) - Method in interface smile.data.vector.Vector
 
getInt(int, int) - Method in interface smile.data.DataFrame
Returns the value at position (i, j) as a primitive int.
getInt(int, String) - Method in interface smile.data.DataFrame
Returns the field value as a primitive int.
getInt(String) - Method in interface smile.data.Tuple
Returns the field value as a primitive int.
getKey() - Method in class smile.nlp.Trie.Node
Returns the key.
getLearningRate() - Method in class smile.base.mlp.MultilayerPerceptron
Returns the learning rate.
getLearningRate() - Method in class smile.classification.LogisticRegression
Returns the learning rate of stochastic gradient descent.
getLearningRate() - Method in class smile.classification.Maxent
Returns the learning rate of stochastic gradient descent.
getLearningRate() - Method in class smile.classification.SparseLogisticRegression
Returns the learning rate of stochastic gradient descent.
getLocalSearchSteps() - Method in class smile.gap.GeneticAlgorithm
Gets the number of iterations of local search in Lamarckian algorithm.
getLong(int) - Method in interface smile.data.Tuple
Returns the value at position i as a primitive long.
getLong(int) - Method in interface smile.data.vector.BaseVector
Returns the long value at position i.
getLong(int) - Method in interface smile.data.vector.BooleanVector
 
getLong(int) - Method in interface smile.data.vector.ByteVector
 
getLong(int) - Method in interface smile.data.vector.CharVector
 
getLong(int) - Method in interface smile.data.vector.DoubleVector
 
getLong(int) - Method in interface smile.data.vector.FloatVector
 
getLong(int) - Method in interface smile.data.vector.IntVector
 
getLong(int) - Method in interface smile.data.vector.ShortVector
 
getLong(int) - Method in interface smile.data.vector.Vector
 
getLong(int, int) - Method in interface smile.data.DataFrame
Returns the value at position (i, j) as a primitive long.
getLong(int, String) - Method in interface smile.data.DataFrame
Returns the field value as a primitive long.
getLong(String) - Method in interface smile.data.Tuple
Returns the field value as a primitive long.
getMomentum() - Method in class smile.base.mlp.MultilayerPerceptron
Returns the momentum factor.
getNumVertices() - Method in class smile.graph.AdjacencyList
 
getNumVertices() - Method in class smile.graph.AdjacencyMatrix
 
getNumVertices() - Method in interface smile.graph.Graph
Returns the number of vertices.
getObjectClass() - Method in class smile.data.type.ObjectType
Returns the class of objects.
getOutdegree(int) - Method in class smile.graph.AdjacencyList
 
getOutdegree(int) - Method in class smile.graph.AdjacencyMatrix
 
getOutdegree(int) - Method in interface smile.graph.Graph
Returns the out-degree of the specified vertex.
getOutputSize() - Method in class smile.base.mlp.Layer
Returns the dimension of output vector.
getPathFromRoot() - Method in class smile.taxonomy.Concept
Returns the path from root to this node.
getPathToRoot() - Method in class smile.taxonomy.Concept
Returns the path from this node to the root.
getProbeSequence(double[], double, int) - Method in class smile.neighbor.lsh.PosterioriModel
Generate query-directed probes.
getProjection() - Method in class smile.classification.FLD
Returns the projection matrix W.
getProjection(double) - Method in class smile.feature.extraction.PCA
Returns the projection with top principal components that contain (more than) the given percentage of variance.
getProjection(int) - Method in class smile.feature.extraction.PCA
Returns the projection with given number of principal components.
getrf(Layout, int, int, double[], int, int[]) - Method in interface smile.math.blas.LAPACK
Computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges.
getrf(Layout, int, int, double[], int, int[]) - Method in class smile.math.blas.mkl.MKL
 
getrf(Layout, int, int, double[], int, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
getrf(Layout, int, int, float[], int, int[]) - Method in interface smile.math.blas.LAPACK
Computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges.
getrf(Layout, int, int, float[], int, int[]) - Method in class smile.math.blas.mkl.MKL
 
getrf(Layout, int, int, float[], int, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
getrf(Layout, int, int, DoubleBuffer, int, IntBuffer) - Method in interface smile.math.blas.LAPACK
Computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges.
getrf(Layout, int, int, DoubleBuffer, int, IntBuffer) - Method in class smile.math.blas.mkl.MKL
 
getrf(Layout, int, int, DoubleBuffer, int, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
getrf(Layout, int, int, FloatBuffer, int, IntBuffer) - Method in interface smile.math.blas.LAPACK
Computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges.
getrf(Layout, int, int, FloatBuffer, int, IntBuffer) - Method in class smile.math.blas.mkl.MKL
 
getrf(Layout, int, int, FloatBuffer, int, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
getrf(Layout, int, int, DoublePointer, int, IntPointer) - Method in interface smile.math.blas.LAPACK
Computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges.
getrf(Layout, int, int, DoublePointer, int, IntPointer) - Method in class smile.math.blas.mkl.MKL
 
getrf(Layout, int, int, DoublePointer, int, IntPointer) - Method in class smile.math.blas.openblas.OpenBLAS
 
getrf2(Layout, int, int, double[], int, int[]) - Method in interface smile.math.blas.LAPACK
Computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges.
getrf2(Layout, int, int, double[], int, int[]) - Method in class smile.math.blas.mkl.MKL
 
getrf2(Layout, int, int, double[], int, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
getrf2(Layout, int, int, float[], int, int[]) - Method in interface smile.math.blas.LAPACK
Computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges.
getrf2(Layout, int, int, float[], int, int[]) - Method in class smile.math.blas.mkl.MKL
 
getrf2(Layout, int, int, float[], int, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
getrf2(Layout, int, int, DoubleBuffer, int, IntBuffer) - Method in interface smile.math.blas.LAPACK
Computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges.
getrf2(Layout, int, int, DoubleBuffer, int, IntBuffer) - Method in class smile.math.blas.mkl.MKL
 
getrf2(Layout, int, int, DoubleBuffer, int, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
getrf2(Layout, int, int, FloatBuffer, int, IntBuffer) - Method in interface smile.math.blas.LAPACK
Computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges.
getrf2(Layout, int, int, FloatBuffer, int, IntBuffer) - Method in class smile.math.blas.mkl.MKL
 
getrf2(Layout, int, int, FloatBuffer, int, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
getRoot() - Method in class smile.taxonomy.Taxonomy
Returns the root node of taxonomy tree.
getrs(Layout, Transpose, int, int, double[], int, int[], double[], int) - Method in interface smile.math.blas.LAPACK
Solves a system of linear equations
getrs(Layout, Transpose, int, int, double[], int, int[], double[], int) - Method in class smile.math.blas.mkl.MKL
 
getrs(Layout, Transpose, int, int, double[], int, int[], double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
getrs(Layout, Transpose, int, int, float[], int, int[], float[], int) - Method in interface smile.math.blas.LAPACK
Solves a system of linear equations
getrs(Layout, Transpose, int, int, float[], int, int[], float[], int) - Method in class smile.math.blas.mkl.MKL
 
getrs(Layout, Transpose, int, int, float[], int, int[], float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
getrs(Layout, Transpose, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in interface smile.math.blas.LAPACK
Solves a system of linear equations
getrs(Layout, Transpose, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
getrs(Layout, Transpose, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
getrs(Layout, Transpose, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in interface smile.math.blas.LAPACK
Solves a system of linear equations
getrs(Layout, Transpose, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in class smile.math.blas.mkl.MKL
 
getrs(Layout, Transpose, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
getrs(Layout, Transpose, int, int, DoublePointer, int, IntPointer, DoublePointer, int) - Method in interface smile.math.blas.LAPACK
Solves a system of linear equations
getrs(Layout, Transpose, int, int, DoublePointer, int, IntPointer, DoublePointer, int) - Method in class smile.math.blas.mkl.MKL
 
getrs(Layout, Transpose, int, int, DoublePointer, int, IntPointer, DoublePointer, int) - Method in class smile.math.blas.openblas.OpenBLAS
 
getScale(int) - Method in interface smile.data.Tuple
Returns the value at position i of NominalScale or OrdinalScale.
getScale(int, int) - Method in interface smile.data.DataFrame
Returns the value at position (i, j) of NominalScale or OrdinalScale.
getScale(int, String) - Method in interface smile.data.DataFrame
Returns the field value of NominalScale or OrdinalScale.
getScale(String) - Method in interface smile.data.Tuple
Returns the field value of NominalScale or OrdinalScale.
getShort(int) - Method in interface smile.data.Tuple
Returns the value at position i as a primitive short.
getShort(int) - Method in interface smile.data.vector.BaseVector
Returns the short value at position i.
getShort(int) - Method in interface smile.data.vector.BooleanVector
 
getShort(int) - Method in interface smile.data.vector.ByteVector
 
getShort(int) - Method in interface smile.data.vector.CharVector
 
getShort(int) - Method in interface smile.data.vector.DoubleVector
 
getShort(int) - Method in interface smile.data.vector.FloatVector
 
getShort(int) - Method in interface smile.data.vector.IntVector
 
getShort(int) - Method in interface smile.data.vector.LongVector
 
getShort(int) - Method in interface smile.data.vector.Vector
 
getShort(int, int) - Method in interface smile.data.DataFrame
Returns the value at position (i, j) as a primitive short.
getShort(int, String) - Method in interface smile.data.DataFrame
Returns the field value as a primitive short.
getShort(String) - Method in interface smile.data.Tuple
Returns the field value as a primitive short.
getStateTransitionProbabilities() - Method in class smile.sequence.HMM
Returns the state transition probabilities.
getString(int) - Method in interface smile.data.Tuple
Returns the value at position i as a String object.
getString(int, int) - Method in interface smile.data.DataFrame
Returns the value at position (i, j) as a String object.
getString(int, String) - Method in interface smile.data.DataFrame
Returns the field value as a String object.
getString(String) - Method in interface smile.data.Tuple
Returns the field value as a String object.
getStruct(int) - Method in interface smile.data.Tuple
Returns the value at position i of struct type.
getStruct(int, int) - Method in interface smile.data.DataFrame
Returns the value at position (i, j) of struct type.
getStruct(int, String) - Method in interface smile.data.DataFrame
Returns the field value of struct type.
getStruct(String) - Method in interface smile.data.Tuple
Returns the field value of struct type.
getSymbolEmissionProbabilities() - Method in class smile.sequence.HMM
Returns the symbol emission probabilities.
getTestData(String...) - Static method in interface smile.util.Paths
Get the file path of a test sample dataset.
getTestDataLines(String...) - Static method in interface smile.util.Paths
Returns the reader of a test data.
getTestDataReader(String...) - Static method in interface smile.util.Paths
Returns the reader of a test data.
getTime(int) - Method in interface smile.data.Tuple
Returns the value at position i of date type as java.time.LocalTime.
getTime(int, int) - Method in interface smile.data.DataFrame
Returns the value at position (i, j) of date type as java.time.LocalTime.
getTime(int, String) - Method in interface smile.data.DataFrame
Returns the field value of date type as java.time.LocalTime.
getTime(String) - Method in interface smile.data.Tuple
Returns the field value of date type as java.time.LocalTime.
getValue() - Method in class smile.nlp.Trie.Node
Returns the value.
getValue(String) - Static method in enum class smile.nlp.pos.PennTreebankPOS
Returns an enum value from a string.
getWeight(int, int) - Method in class smile.graph.AdjacencyList
 
getWeight(int, int) - Method in class smile.graph.AdjacencyMatrix
 
getWeight(int, int) - Method in interface smile.graph.Graph
Returns the weight assigned to a given edge.
getWeightDecay() - Method in class smile.base.mlp.MultilayerPerceptron
Returns the weight decay factor.
ggglm(Layout, int, int, int, double[], int, double[], int, double[], double[], double[]) - Method in interface smile.math.blas.LAPACK
Solves a general Gauss-Markov linear model (GLM) problem.
ggglm(Layout, int, int, int, double[], int, double[], int, double[], double[], double[]) - Method in class smile.math.blas.mkl.MKL
 
ggglm(Layout, int, int, int, double[], int, double[], int, double[], double[], double[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
ggglm(Layout, int, int, int, float[], int, float[], int, float[], float[], float[]) - Method in interface smile.math.blas.LAPACK
Solves a general Gauss-Markov linear model (GLM) problem.
ggglm(Layout, int, int, int, float[], int, float[], int, float[], float[], float[]) - Method in class smile.math.blas.mkl.MKL
 
ggglm(Layout, int, int, int, float[], int, float[], int, float[], float[], float[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
ggglm(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer) - Method in interface smile.math.blas.LAPACK
Solves a general Gauss-Markov linear model (GLM) problem.
ggglm(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer) - Method in class smile.math.blas.mkl.MKL
 
ggglm(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
ggglm(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer) - Method in interface smile.math.blas.LAPACK
Solves a general Gauss-Markov linear model (GLM) problem.
ggglm(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer) - Method in class smile.math.blas.mkl.MKL
 
ggglm(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
gglse(Layout, int, int, int, double[], int, double[], int, double[], double[], double[]) - Method in interface smile.math.blas.LAPACK
Solves a linear equality-constrained least squares (LSE) problem.
gglse(Layout, int, int, int, double[], int, double[], int, double[], double[], double[]) - Method in class smile.math.blas.mkl.MKL
 
gglse(Layout, int, int, int, double[], int, double[], int, double[], double[], double[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
gglse(Layout, int, int, int, float[], int, float[], int, float[], float[], float[]) - Method in interface smile.math.blas.LAPACK
Solves a linear equality-constrained least squares (LSE) problem.
gglse(Layout, int, int, int, float[], int, float[], int, float[], float[], float[]) - Method in class smile.math.blas.mkl.MKL
 
gglse(Layout, int, int, int, float[], int, float[], int, float[], float[], float[]) - Method in class smile.math.blas.openblas.OpenBLAS
 
gglse(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer) - Method in interface smile.math.blas.LAPACK
Solves a linear equality-constrained least squares (LSE) problem.
gglse(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer) - Method in class smile.math.blas.mkl.MKL
 
gglse(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
gglse(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer) - Method in interface smile.math.blas.LAPACK
Solves a linear equality-constrained least squares (LSE) problem.
gglse(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer) - Method in class smile.math.blas.mkl.MKL
 
gglse(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
 
GHA - Class in smile.feature.extraction
Generalized Hebbian Algorithm.
GHA(double[][], TimeFunction, String...) - Constructor for class smile.feature.extraction.GHA
Constructor.
GHA(int, int, TimeFunction, String...) - Constructor for class smile.feature.extraction.GHA
Constructor.
GINI - Enum constant in enum class smile.base.cart.SplitRule
Used by the CART algorithm, Gini impurity is a measure of how often a randomly chosen element from the set would be incorrectly labeled if it were randomly labeled according to the distribution of labels in the subset.
GLM - Class in smile.glm
Generalized linear models.
GLM(Formula, String[], Model, double[], double, double, double, double[], double[], double[][]) - Constructor for class smile.glm.GLM
Constructor.
GloVe - Class in smile.nlp.embedding
Global Vectors for Word Representation.
GloVe() - Constructor for class smile.nlp.embedding.GloVe
 
GMeans - Class in smile.clustering
G-Means clustering algorithm, an extended K-Means which tries to automatically determine the number of clusters by normality test.
GMeans(double, double[][], int[]) - Constructor for class smile.clustering.GMeans
Constructor.
GoodTuring - Class in smile.stat
Good–Turing frequency estimation.
GOOGLE - Enum constant in enum class smile.nlp.dictionary.EnglishStopWords
The stop words list used by Google.
gradient() - Method in class smile.base.mlp.Layer
Returns the output gradient vector.
GradientTreeBoost - Class in smile.classification
Gradient boosting for classification.
GradientTreeBoost - Class in smile.regression
Gradient boosting for regression.
GradientTreeBoost(Formula, RegressionTree[][], double, double[]) - Constructor for class smile.classification.GradientTreeBoost
Constructor of multi-class.
GradientTreeBoost(Formula, RegressionTree[][], double, double[], IntSet) - Constructor for class smile.classification.GradientTreeBoost
Constructor of multi-class.
GradientTreeBoost(Formula, RegressionTree[], double, double, double[]) - Constructor for class smile.classification.GradientTreeBoost
Constructor of binary class.
GradientTreeBoost(Formula, RegressionTree[], double, double, double[]) - Constructor for class smile.regression.GradientTreeBoost
Constructor.
GradientTreeBoost(Formula, RegressionTree[], double, double, double[], IntSet) - Constructor for class smile.classification.GradientTreeBoost
Constructor of binary class.
graph - Variable in class smile.manifold.IsoMap
The nearest neighbor graph.
graph - Variable in class smile.manifold.LaplacianEigenmap
Nearest neighbor graph.
graph - Variable in class smile.manifold.LLE
Nearest neighbor graph.
graph - Variable in class smile.manifold.UMAP
The nearest neighbor graph.
Graph - Interface in smile.graph
A graph is an abstract representation of a set of objects where some pairs of the objects are connected by links.
Graph.Edge - Class in smile.graph
Graph edge.
grid() - Method in class smile.hpo.Hyperparameters
Generates a stream of hyperparameters for grid search.
GrowingNeuralGas - Class in smile.vq
Growing Neural Gas.
GrowingNeuralGas(int) - Constructor for class smile.vq.GrowingNeuralGas
Constructor.
GrowingNeuralGas(int, double, double, int, int, double, double) - Constructor for class smile.vq.GrowingNeuralGas
Constructor.

H

H - Variable in class smile.neighbor.LSH
The size of hash table.
H - Variable in class smile.neighbor.lsh.NeighborHashValueModel
The hash values of query object.
HaarWavelet - Class in smile.wavelet
Haar wavelet.
HaarWavelet() - Constructor for class smile.wavelet.HaarWavelet
Constructor.
HadoopInput - Interface in smile.io
Static methods that return the InputStream/Reader of a HDFS/S3.
HammingDistance - Class in smile.math.distance
In information theory, the Hamming distance between two strings of equal length is the number of positions for which the corresponding symbols are different.
HammingDistance() - Constructor for class smile.math.distance.HammingDistance
Constructor.
harwell(Path) - Static method in class smile.math.matrix.fp32.SparseMatrix
Reads a sparse matrix from a Harwell-Boeing Exchange Format file.
harwell(Path) - Static method in class smile.math.matrix.SparseMatrix
Reads a sparse matrix from a Harwell-Boeing Exchange Format file.
hasEdge(int, int) - Method in class smile.graph.AdjacencyList
 
hasEdge(int, int) - Method in class smile.graph.AdjacencyMatrix
 
hasEdge(int, int) - Method in interface smile.graph.Graph
Returns true if and only if this graph contains an edge going from the source vertex to the target vertex.
hash - Variable in class smile.neighbor.LSH
Hash functions.
hash(double[]) - Method in class smile.neighbor.lsh.Hash
Apply hash functions on given vector x.
hash(Hash, PrZ[]) - Method in class smile.neighbor.lsh.Probe
Returns the bucket number of the probe.
hash(T) - Method in interface smile.hash.SimHash
Return the hash code.
Hash - Class in smile.neighbor.lsh
The hash function for Euclidean spaces.
Hash(int, int, double, int) - Constructor for class smile.neighbor.lsh.Hash
Constructor.
hash128(ByteBuffer, int, int, long, long[]) - Static method in class smile.hash.MurmurHash3
128-bit MurmurHash3 for x64.
hash32(byte[], int, int, int) - Static method in class smile.hash.MurmurHash3
32-bit MurmurHash3.
hash32(String, int) - Static method in class smile.hash.MurmurHash3
32-bit MurmurHash3.
hash32(ByteBuffer, int, int, int) - Static method in interface smile.hash.MurmurHash2
32-bit MurmurHash.
hash64(ByteBuffer, int, int, long) - Static method in interface smile.hash.MurmurHash2
64-bit MurmurHash.
hashCode() - Method in class smile.association.AssociationRule
 
hashCode() - Method in class smile.association.ItemSet
 
hashCode() - Method in class smile.math.Complex
 
hashCode() - Method in class smile.nlp.Bigram
 
hashCode() - Method in class smile.nlp.NGram
 
hashCode() - Method in class smile.nlp.SimpleText
 
hashCode() - Method in class smile.util.IntPair
 
HashEncoder - Class in smile.feature.extraction
Feature hashing, also known as the hashing trick, is a fast and space-efficient way of vectorizing features, i.e.
HashEncoder(Function<String, String[]>, int) - Constructor for class smile.feature.extraction.HashEncoder
Constructor.
HashEncoder(Function<String, String[]>, int, boolean) - Constructor for class smile.feature.extraction.HashEncoder
Constructor.
HashValueParzenModel - Class in smile.neighbor.lsh
Hash value Parzen model for multi-probe hash.
HashValueParzenModel(MultiProbeHash, MultiProbeSample[], double) - Constructor for class smile.neighbor.lsh.HashValueParzenModel
Constructor.
hasMissing(Tuple) - Static method in class smile.feature.imputation.SimpleImputer
Return true if the tuple x has missing values.
hasNull() - Method in interface smile.data.Tuple
Returns true if the tuple has null/missing values.
heapify() - Method in class smile.sort.HeapSelect
In case of avoiding creating new objects frequently, one may check and update the peek object directly and call this method to sort the internal array in heap order.
HeapSelect<T extends Comparable<? super T>> - Class in smile.sort
This class tracks the smallest values seen thus far in a stream of values.
HeapSelect(Class<?>, int) - Constructor for class smile.sort.HeapSelect
Constructor.
HeapSelect(T[]) - Constructor for class smile.sort.HeapSelect
Constructor.
HeapSort - Interface in smile.sort
Heapsort is a comparison-based sorting algorithm, and is part of the selection sort family.
height() - Method in class smile.clustering.HierarchicalClustering
Returns a set of n-1 non-decreasing real values, which are the clustering height, i.e., the value of the criterion associated with the clustering method for the particular agglomeration.
HellingerKernel - Class in smile.math.kernel
The Hellinger kernel.
HellingerKernel() - Constructor for class smile.math.kernel.HellingerKernel
Constructor.
hi() - Method in class smile.math.kernel.BinarySparseGaussianKernel
 
hi() - Method in class smile.math.kernel.BinarySparseHyperbolicTangentKernel
 
hi() - Method in class smile.math.kernel.BinarySparseLaplacianKernel
 
hi() - Method in class smile.math.kernel.BinarySparseLinearKernel
 
hi() - Method in class smile.math.kernel.BinarySparseMaternKernel
 
hi() - Method in class smile.math.kernel.BinarySparsePolynomialKernel
 
hi() - Method in class smile.math.kernel.BinarySparseThinPlateSplineKernel
 
hi() - Method in class smile.math.kernel.GaussianKernel
 
hi() - Method in class smile.math.kernel.HellingerKernel
 
hi() - Method in class smile.math.kernel.HyperbolicTangentKernel
 
hi() - Method in class smile.math.kernel.LaplacianKernel
 
hi() - Method in class smile.math.kernel.LinearKernel
 
hi() - Method in class smile.math.kernel.MaternKernel
 
hi() - Method in interface smile.math.kernel.MercerKernel
Returns the upper bound of hyperparameters (in hyperparameter tuning).
hi() - Method in class smile.math.kernel.PearsonKernel
 
hi() - Method in class smile.math.kernel.PolynomialKernel
 
hi() - Method in class smile.math.kernel.ProductKernel
 
hi() - Method in class smile.math.kernel.SparseGaussianKernel
 
hi() - Method in class smile.math.kernel.SparseHyperbolicTangentKernel
 
hi() - Method in class smile.math.kernel.SparseLaplacianKernel
 
hi() - Method in class smile.math.kernel.SparseLinearKernel
 
hi() - Method in class smile.math.kernel.SparseMaternKernel
 
hi() - Method in class smile.math.kernel.SparsePolynomialKernel
 
hi() - Method in class smile.math.kernel.SparseThinPlateSplineKernel
 
hi() - Method in class smile.math.kernel.SumKernel
 
hi() - Method in class smile.math.kernel.ThinPlateSplineKernel
 
HiddenLayer - Class in smile.base.mlp
A hidden layer in the neural network.
HiddenLayer(int, int, ActivationFunction) - Constructor for class smile.base.mlp.HiddenLayer
Constructor.
HiddenLayerBuilder - Class in smile.base.mlp
The builder of hidden layers.
HiddenLayerBuilder(int, double, ActivationFunction) - Constructor for class smile.base.mlp.HiddenLayerBuilder
Constructor.
HierarchicalClustering - Class in smile.clustering
Agglomerative Hierarchical Clustering.
HierarchicalClustering(int[][], double[]) - Constructor for class smile.clustering.HierarchicalClustering
Constructor.
Histogram - Interface in smile.math
Histogram utilities.
HMM - Class in smile.sequence
First-order Hidden Markov Model.
HMM(double[], Matrix, Matrix) - Constructor for class smile.sequence.HMM
Constructor.
HMMLabeler<T> - Class in smile.sequence
First-order Hidden Markov Model sequence labeler.
HMMLabeler(HMM, ToIntFunction<T>) - Constructor for class smile.sequence.HMMLabeler
Constructor.
HMMPOSTagger - Class in smile.nlp.pos
Part-of-speech tagging with hidden Markov model.
HMMPOSTagger() - Constructor for class smile.nlp.pos.HMMPOSTagger
Constructor.
home - Static variable in interface smile.util.Paths
Smile home directory.
HOUR - Enum constant in enum class smile.data.formula.DateFeature
The hours represented by an integer from 0 to 23.
huber(double) - Static method in interface smile.base.cart.Loss
Huber loss function for M-regression, which attempts resistance to long-tailed error distributions and outliers while maintaining high efficiency for normally distributed errors.
Huber - Enum constant in enum class smile.base.cart.Loss.Type
Huber loss function for M-regression, which attempts resistance to long-tailed error distributions and outliers while maintaining high efficiency for normally distributed errors.
HyperbolicTangent - Class in smile.math.kernel
The hyperbolic tangent kernel.
HyperbolicTangent(double, double, double[], double[]) - Constructor for class smile.math.kernel.HyperbolicTangent
Constructor.
HyperbolicTangentKernel - Class in smile.math.kernel
The hyperbolic tangent kernel.
HyperbolicTangentKernel() - Constructor for class smile.math.kernel.HyperbolicTangentKernel
Constructor with scale 1.0 and offset 0.0.
HyperbolicTangentKernel(double, double) - Constructor for class smile.math.kernel.HyperbolicTangentKernel
Constructor.
HyperbolicTangentKernel(double, double, double[], double[]) - Constructor for class smile.math.kernel.HyperbolicTangentKernel
Constructor.
HyperGeometricDistribution - Class in smile.stat.distribution
The hypergeometric distribution is a discrete probability distribution that describes the number of successes in a sequence of n draws from a finite population without replacement, just as the binomial distribution describes the number of successes for draws with replacement.
HyperGeometricDistribution(int, int, int) - Constructor for class smile.stat.distribution.HyperGeometricDistribution
Constructor.
hyperparameters() - Method in class smile.math.kernel.BinarySparseGaussianKernel
 
hyperparameters() - Method in class smile.math.kernel.BinarySparseHyperbolicTangentKernel
 
hyperparameters() - Method in class smile.math.kernel.BinarySparseLaplacianKernel
 
hyperparameters() - Method in class smile.math.kernel.BinarySparseLinearKernel
 
hyperparameters() - Method in class smile.math.kernel.BinarySparseMaternKernel
 
hyperparameters() - Method in class smile.math.kernel.BinarySparsePolynomialKernel
 
hyperparameters() - Method in class smile.math.kernel.BinarySparseThinPlateSplineKernel
 
hyperparameters() - Method in class smile.math.kernel.GaussianKernel
 
hyperparameters() - Method in class smile.math.kernel.HellingerKernel
 
hyperparameters() - Method in class smile.math.kernel.HyperbolicTangentKernel
 
hyperparameters() - Method in class smile.math.kernel.LaplacianKernel
 
hyperparameters() - Method in class smile.math.kernel.LinearKernel
 
hyperparameters() - Method in class smile.math.kernel.MaternKernel
 
hyperparameters() - Method in interface smile.math.kernel.MercerKernel
Returns the hyperparameters of kernel.
hyperparameters() - Method in class smile.math.kernel.PearsonKernel
 
hyperparameters() - Method in class smile.math.kernel.PolynomialKernel
 
hyperparameters() - Method in class smile.math.kernel.ProductKernel
 
hyperparameters() - Method in class smile.math.kernel.SparseGaussianKernel
 
hyperparameters() - Method in class smile.math.kernel.SparseHyperbolicTangentKernel
 
hyperparameters() - Method in class smile.math.kernel.SparseLaplacianKernel
 
hyperparameters() - Method in class smile.math.kernel.SparseLinearKernel
 
hyperparameters() - Method in class smile.math.kernel.SparseMaternKernel
 
hyperparameters() - Method in class smile.math.kernel.SparsePolynomialKernel
 
hyperparameters() - Method in class smile.math.kernel.SparseThinPlateSplineKernel
 
hyperparameters() - Method in class smile.math.kernel.SumKernel
 
hyperparameters() - Method in class smile.math.kernel.ThinPlateSplineKernel
 
Hyperparameters - Class in smile.hpo
Hyperparameter configuration.
Hyperparameters() - Constructor for class smile.hpo.Hyperparameters
Constructor.
Hypothesis - Interface in smile.stat
Hypothesis test functions.
Hypothesis.chisq - Interface in smile.stat
Chi-square test.
Hypothesis.cor - Interface in smile.stat
Correlation test.
Hypothesis.F - Interface in smile.stat
F-test.
Hypothesis.KS - Interface in smile.stat
The Kolmogorov-Smirnov test (K-S test).
Hypothesis.t - Interface in smile.stat
t-test.

I

i - Variable in class smile.math.matrix.fp32.SparseMatrix.Entry
The row index.
i - Variable in class smile.math.matrix.SparseMatrix.Entry
The row index.
i - Variable in class smile.util.IntPair
The first integer.
i - Variable in class smile.util.SparseArray.Entry
The index of entry.
iamax(double[]) - Method in interface smile.math.blas.BLAS
Searches a vector for the first occurrence of the the maximum absolute value.
iamax(float[]) - Method in interface smile.math.blas.BLAS
Searches a vector for the first occurrence of the the maximum absolute value.
iamax(int, double[], int) - Method in interface smile.math.blas.BLAS
Searches a vector for the first occurrence of the the maximum absolute value.
iamax(int, double[], int) - Method in class smile.math.blas.mkl.MKL
 
iamax(int, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
iamax(int, float[], int) - Method in interface smile.math.blas.BLAS
Searches a vector for the first occurrence of the the maximum absolute value.
iamax(int, float[], int) - Method in class smile.math.blas.mkl.MKL
 
iamax(int, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
 
ICA - Class in smile.ica
Independent Component Analysis (ICA) is a computational method for separating a multivariate signal into additive components.
id - Variable in class smile.nlp.Text
The id of document in the corpus.
id() - Method in class smile.data.type.ArrayType
 
id() - Method in class smile.data.type.BooleanType
 
id() - Method in class smile.data.type.ByteType
 
id() - Method in class smile.data.type.CharType
 
id() - Method in interface smile.data.type.DataType
Returns the type ID enum.
id() - Method in class smile.data.type.DateTimeType
 
id() - Method in class smile.data.type.DateType
 
id() - Method in class smile.data.type.DecimalType
 
id() - Method in class smile.data.type.DoubleType
 
id() - Method in class smile.data.type.FloatType
 
id() - Method in class smile.data.type.IntegerType
 
id() - Method in class smile.data.type.LongType
 
id() - Method in class smile.data.type.ObjectType
 
id() - Method in class smile.data.type.ShortType
 
id() - Method in class smile.data.type.StringType
 
id() - Method in class smile.data.type.StructType
 
id() - Method in class smile.data.type.TimeType
 
im - Variable in class smile.math.Complex
The imaginary part.
IMatrix - Class in smile.math.matrix.fp32
Matrix base class.
IMatrix - Class in smile.math.matrix
Matrix base class.
IMatrix() - Constructor for class smile.math.matrix.fp32.IMatrix
 
IMatrix() - Constructor for class smile.math.matrix.IMatrix
 
IMatrix.Preconditioner - Interface in smile.math.matrix.fp32
The preconditioner matrix.
IMatrix.Preconditioner - Interface in smile.math.matrix
The preconditioner matrix.
importance - Variable in class smile.base.cart.CART
Variable importance.
importance() - Method in class smile.base.cart.CART
Returns the variable importance.
importance() - Method in class smile.classification.AdaBoost
Returns the variable importance.
importance() - Method in class smile.classification.GradientTreeBoost
Returns the variable importance.
importance() - Method in class smile.classification.RandomForest
Returns the variable importance.
importance() - Method in class smile.regression.GradientTreeBoost
Returns the variable importance.
importance() - Method in class smile.regression.RandomForest
Returns the variable importance.
impurity() - Method in class smile.base.cart.RegressionNode
Returns the residual sum of squares.
impurity(LeafNode) - Method in class smile.base.cart.CART
Returns the impurity of node.
impurity(LeafNode) - Method in class smile.classification.DecisionTree
 
impurity(LeafNode) - Method in class smile.regression.RegressionTree
 
impurity(SplitRule) - Method in class smile.base.cart.DecisionNode
Returns the impurity of node.
impurity(SplitRule, int, int[]) - Static method in class smile.base.cart.DecisionNode
Returns the impurity of samples.
impute(double[][]) - Static method in class smile.feature.imputation.SimpleImputer
Impute the missing values with column averages.
impute(double[][], int, int) - Static method in interface smile.feature.imputation.SVDImputer
Impute missing values in the dataset.
IN - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
Preposition or subordinating conjunction.
increment() - Method in class smile.util.MutableInt
Increment by one.
increment(int) - Method in class smile.util.MutableInt
Increment.
index - Variable in class smile.base.cart.CART
An index of samples to their original locations in training dataset.
index - Variable in class smile.manifold.IsoMap
The original sample index.
index - Variable in class smile.manifold.LaplacianEigenmap
The original sample index.
index - Variable in class smile.manifold.LLE
The original sample index.
index - Variable in class smile.manifold.UMAP
The original sample index.
index - Variable in class smile.math.matrix.fp32.SparseMatrix.Entry
The index to the matrix storage.
index - Variable in class smile.math.matrix.SparseMatrix.Entry
The index to the matrix storage.
index - Variable in class smile.neighbor.Neighbor
The index of neighbor object in the dataset.
index - Variable in class smile.util.IntSet
Map of values to index.
index(int, int) - Method in class smile.math.matrix.BigMatrix
Returns the linearized index of matrix element.
index(int, int) - Method in class smile.math.matrix.fp32.Matrix
Returns the linearized index of matrix element.
index(int, int) - Method in class smile.math.matrix.Matrix
Returns the linearized index of matrix element.
INDEX - Enum constant in enum class smile.math.blas.EigenRange
The IL-th through IU-th eigenvalues will be found.
IndexDataFrame - Class in smile.data
A data frame with a new index instead of the default [0, n) row index.
IndexDataFrame(DataFrame, int[]) - Constructor for class smile.data.IndexDataFrame
Constructor.
indexOf(int) - Method in class smile.util.IntSet
Maps the value to index.
indexOf(int[]) - Method in class smile.classification.ClassLabels
Maps the class labels to index.
indexOf(String) - Method in interface smile.data.DataFrame
Returns the index of a given column name.
indexOf(String) - Method in class smile.data.IndexDataFrame
 
indexOf(String) - Method in interface smile.data.Tuple
Returns the index of a given field name.
indexOf(String) - Method in class smile.data.type.StructType
Returns the index of a field.
infer(String) - Static method in interface smile.data.type.DataType
Infers the type of a string.
inferSchema(BufferedReader, int) - Method in class smile.io.JSON
Infer the schema from the top n rows.
inferSchema(Reader, int) - Method in class smile.io.CSV
Infer the schema from the top n rows.
info - Variable in class smile.math.matrix.BandMatrix.LU
If info = 0, the LU decomposition was successful.
info - Variable in class smile.math.matrix.BigMatrix.LU
If info = 0, the LU decomposition was successful.
info - Variable in class smile.math.matrix.fp32.BandMatrix.LU
If info = 0, the LU decomposition was successful.
info - Variable in class smile.math.matrix.fp32.Matrix.LU
If info = 0, the LU decomposition was successful.
info - Variable in class smile.math.matrix.fp32.SymmMatrix.BunchKaufman
If info = 0, the LU decomposition was successful.
info - Variable in class smile.math.matrix.Matrix.LU
If info = 0, the LU decomposition was successful.
info - Variable in class smile.math.matrix.SymmMatrix.BunchKaufman
If info = 0, the LU decomposition was successful.
InformationValue - Class in smile.feature.selection
Information Value (IV) measures the predictive strength of a feature for a binary dependent variable.
InformationValue(String, double, double[], double[]) - Constructor for class smile.feature.selection.InformationValue
Constructor.
initHashTable(int, int, int, double, int) - Method in class smile.neighbor.LSH
Initialize the hash tables.
initHashTable(int, int, int, double, int) - Method in class smile.neighbor.MPLSH
 
input(int) - Static method in class smile.base.mlp.Layer
Returns an input layer.
input(int, double) - Static method in class smile.base.mlp.Layer
Returns an input layer.
Input - Interface in smile.io
Static methods that return the InputStream/Reader of a file or URL.
InputLayer - Class in smile.base.mlp
An input layer in the neural network.
InputLayer(int) - Constructor for class smile.base.mlp.InputLayer
Constructor.
InputLayer(int, double) - Constructor for class smile.base.mlp.InputLayer
Constructor.
insert(int) - Method in class smile.util.PriorityQueue
Insert a new item into queue.
instance - Static variable in class smile.validation.metric.Accuracy
Default instance.
instance - Static variable in class smile.validation.metric.AdjustedRandIndex
Default instance.
instance - Static variable in class smile.validation.metric.AUC
Default instance.
instance - Static variable in class smile.validation.metric.Error
Default instance.
instance - Static variable in class smile.validation.metric.Fallout
Default instance.
instance - Static variable in class smile.validation.metric.FDR
Default instance.
instance - Static variable in class smile.validation.metric.LogLoss
Default instance.
instance - Static variable in class smile.validation.metric.MAD
Default instance.
instance - Static variable in class smile.validation.metric.MatthewsCorrelation
Default instance.
instance - Static variable in class smile.validation.metric.MSE
Default instance.
instance - Static variable in class smile.validation.metric.MutualInformation
Default instance.
instance - Static variable in class smile.validation.metric.Precision
Default instance.
instance - Static variable in class smile.validation.metric.R2
Default instance.
instance - Static variable in class smile.validation.metric.RandIndex
Default instance.
instance - Static variable in class smile.validation.metric.Recall
Default instance.
instance - Static variable in class smile.validation.metric.RMSE
Default instance.
instance - Static variable in class smile.validation.metric.RSS
Default instance.
instance - Static variable in class smile.validation.metric.Sensitivity
Default instance.
instance - Static variable in class smile.validation.metric.Specificity
Default instance.
Instance<T> - Interface in smile.data
An immutable instance.
IntArray2D - Class in smile.util
2-dimensional array of integers.
IntArray2D(int[][]) - Constructor for class smile.util.IntArray2D
Constructor.
IntArray2D(int, int) - Constructor for class smile.util.IntArray2D
Constructor of all-zero matrix.
IntArray2D(int, int, int) - Constructor for class smile.util.IntArray2D
Constructor.
IntArray2D(int, int, int[]) - Constructor for class smile.util.IntArray2D
Constructor.
IntArrayList - Class in smile.util
A resizeable, array-backed list of integer primitives.
IntArrayList() - Constructor for class smile.util.IntArrayList
Constructs an empty list.
IntArrayList(int) - Constructor for class smile.util.IntArrayList
Constructs an empty list with the specified initial capacity.
IntArrayList(int[]) - Constructor for class smile.util.IntArrayList
Constructs a list containing the values of the specified array.
IntDoubleHashMap - Class in smile.util
HashMap<int, double> for primitive types.
IntDoubleHashMap() - Constructor for class smile.util.IntDoubleHashMap
Constructs an empty HashMap with the default initial capacity (16) and the default load factor (0.75).
IntDoubleHashMap(int, float) - Constructor for class smile.util.IntDoubleHashMap
Constructor.
Integer - Enum constant in enum class smile.data.type.DataType.ID
Integer type ID.
INTEGER - Static variable in interface smile.util.Regex
Integer regular expression pattern.
INTEGER_REGEX - Static variable in interface smile.util.Regex
Integer regular expression.
IntegerArrayType - Static variable in class smile.data.type.DataTypes
Integer Array data type.
IntegerObjectType - Static variable in class smile.data.type.DataTypes
Integer Object data type.
IntegerType - Class in smile.data.type
Integer data type.
IntegerType - Static variable in class smile.data.type.DataTypes
Integer data type.
interact(String...) - Static method in interface smile.data.formula.Terms
Factor interaction of two or more factors.
intercept() - Method in class smile.base.svm.KernelMachine
Returns the intercept.
intercept() - Method in class smile.regression.LinearModel
Returns the intercept.
intercept() - Method in class smile.timeseries.AR
Returns the intercept.
intercept() - Method in class smile.timeseries.ARMA
Returns the intercept.
intercept(double[]) - Method in interface smile.base.cart.Loss
Returns the intercept of model.
InternalNode - Class in smile.base.cart
An internal node in CART.
InternalNode(int, double, double, Node, Node) - Constructor for class smile.base.cart.InternalNode
Constructor.
interpolate(double) - Method in class smile.interpolation.AbstractInterpolation
 
interpolate(double) - Method in interface smile.interpolation.Interpolation
Given a value x, return an interpolated value.
interpolate(double) - Method in class smile.interpolation.KrigingInterpolation1D
 
interpolate(double) - Method in class smile.interpolation.RBFInterpolation1D
 
interpolate(double) - Method in class smile.interpolation.ShepardInterpolation1D
 
interpolate(double...) - Method in class smile.interpolation.KrigingInterpolation
Interpolate the function at given point.
interpolate(double...) - Method in class smile.interpolation.RBFInterpolation
Interpolate the function at given point.
interpolate(double...) - Method in class smile.interpolation.ShepardInterpolation
Interpolate the function at given point.
interpolate(double[][]) - Static method in class smile.interpolation.LaplaceInterpolation
Laplace interpolation.
interpolate(double[][], double) - Static method in class smile.interpolation.LaplaceInterpolation
Laplace interpolation.
interpolate(double[][], double, int) - Static method in class smile.interpolation.LaplaceInterpolation
Laplace interpolation.
interpolate(double, double) - Method in class smile.interpolation.BicubicInterpolation
 
interpolate(double, double) - Method in class smile.interpolation.BilinearInterpolation
 
interpolate(double, double) - Method in class smile.interpolation.CubicSplineInterpolation2D
 
interpolate(double, double) - Method in interface smile.interpolation.Interpolation2D
Interpolate the data at a given 2-dimensional point.
interpolate(double, double) - Method in class smile.interpolation.KrigingInterpolation2D
 
interpolate(double, double) - Method in class smile.interpolation.RBFInterpolation2D
 
interpolate(double, double) - Method in class smile.interpolation.ShepardInterpolation2D
 
Interpolation - Interface in smile.interpolation
In numerical analysis, interpolation is a method of constructing new data points within the range of a discrete set of known data points.
Interpolation2D - Interface in smile.interpolation
Interpolation of 2-dimensional data.
IntervalScale - Class in smile.data.measure
The interval scale allows for the degree of difference between items, but not the ratio between them.
IntervalScale(NumberFormat) - Constructor for class smile.data.measure.IntervalScale
Constructor.
IntFunction - Class in smile.data.formula
The generic term of applying an integer function.
IntFunction - Interface in smile.math
An interface representing a univariate int function.
IntFunction(String, Term, IntFunction) - Constructor for class smile.data.formula.IntFunction
Constructor.
IntHashSet - Class in smile.util
HashSet<int> for primitive types.
IntHashSet() - Constructor for class smile.util.IntHashSet
Constructs an empty HashSet with the default initial capacity (16) and the default load factor (0.75).
IntHashSet(int, float) - Constructor for class smile.util.IntHashSet
Constructor.
IntHeapSelect - Class in smile.sort
This class tracks the smallest values seen thus far in a stream of values.
IntHeapSelect(int) - Constructor for class smile.sort.IntHeapSelect
Constructor.
IntHeapSelect(int[]) - Constructor for class smile.sort.IntHeapSelect
Constructor.
IntPair - Class in smile.util
A pair of integer.
IntPair(int, int) - Constructor for class smile.util.IntPair
Constructor.
IntSet - Class in smile.util
A set of integers.
IntSet(int[]) - Constructor for class smile.util.IntSet
Constructor.
intVector(int) - Method in interface smile.data.DataFrame
Selects column based on the column index.
intVector(int) - Method in class smile.data.IndexDataFrame
 
intVector(Enum<?>) - Method in interface smile.data.DataFrame
Selects column using an enum value.
intVector(String) - Method in interface smile.data.DataFrame
Selects column based on the column name.
IntVector - Interface in smile.data.vector
An immutable integer vector.
inv(double) - Method in interface smile.math.Function
Computes the value of the inverse function at x.
inv(double) - Method in class smile.math.Scaler
 
inverf(double) - Static method in class smile.math.special.Erf
The inverse error function.
inverfc(double) - Static method in class smile.math.special.Erf
The inverse complementary error function.
inverse() - Method in class smile.math.matrix.BandMatrix.Cholesky
Returns the inverse of matrix.
inverse() - Method in class smile.math.matrix.BandMatrix.LU
Returns the inverse of matrix.
inverse() - Method in class smile.math.matrix.BigMatrix.Cholesky
Returns the inverse of matrix.
inverse() - Method in class smile.math.matrix.BigMatrix
Returns the inverse of matrix.
inverse() - Method in class smile.math.matrix.BigMatrix.LU
Returns the inverse of matrix.
inverse() - Method in class smile.math.matrix.fp32.BandMatrix.Cholesky
Returns the inverse of matrix.
inverse() - Method in class smile.math.matrix.fp32.BandMatrix.LU
Returns the inverse of matrix.
inverse() - Method in class smile.math.matrix.fp32.Matrix.Cholesky
Returns the inverse of matrix.
inverse() - Method in class smile.math.matrix.fp32.Matrix
Returns the inverse of matrix.
inverse() - Method in class smile.math.matrix.fp32.Matrix.LU
Returns the inverse of matrix.
inverse() - Method in class smile.math.matrix.fp32.SymmMatrix.BunchKaufman
Returns the inverse of matrix.
inverse() - Method in class smile.math.matrix.fp32.SymmMatrix.Cholesky
Returns the inverse of matrix.
inverse() - Method in class smile.math.matrix.Matrix.Cholesky
Returns the inverse of matrix.
inverse() - Method in class smile.math.matrix.Matrix
Returns the inverse of matrix.
inverse() - Method in class smile.math.matrix.Matrix.LU
Returns the inverse of matrix.
inverse() - Method in class smile.math.matrix.SymmMatrix.BunchKaufman
Returns the inverse of matrix.
inverse() - Method in class smile.math.matrix.SymmMatrix.Cholesky
Returns the inverse of matrix.
inverse(double[]) - Method in class smile.wavelet.Wavelet
Inverse discrete wavelet transform.
inverse(double, double) - Static method in interface smile.math.TimeFunction
Returns the inverse decay function.
inverse(double, double, double) - Static method in interface smile.math.TimeFunction
Returns the inverse decay function.
inverse(double, double, double, boolean) - Static method in interface smile.math.TimeFunction
Returns the inverse decay function.
inverseCDF() - Method in class smile.stat.distribution.GaussianDistribution
Generates a Gaussian random number with the inverse CDF method.
InverseMultiquadricRadialBasis - Class in smile.math.rbf
Inverse multiquadric RBF.
InverseMultiquadricRadialBasis() - Constructor for class smile.math.rbf.InverseMultiquadricRadialBasis
Constructor.
InverseMultiquadricRadialBasis(double) - Constructor for class smile.math.rbf.InverseMultiquadricRadialBasis
Constructor.
inverseRegularizedIncompleteBetaFunction(double, double, double) - Static method in class smile.math.special.Beta
Inverse of regularized incomplete beta function.
inverseRegularizedIncompleteGamma(double, double) - Static method in class smile.math.special.Gamma
The inverse of regularized incomplete gamma function.
inverseTransformSampling() - Method in class smile.stat.distribution.AbstractDistribution
Use inverse transform sampling (also known as the inverse probability integral transform or inverse transformation method or Smirnov transform) to draw a sample from the given distribution.
invert(DataFrame) - Method in class smile.data.transform.InvertibleColumnTransform
 
invert(DataFrame) - Method in interface smile.data.transform.InvertibleTransform
Inverse transform a data frame.
invert(Tuple) - Method in class smile.data.transform.InvertibleColumnTransform
 
invert(Tuple) - Method in interface smile.data.transform.InvertibleTransform
Inverse transform a tuple.
InvertibleColumnTransform - Class in smile.data.transform
Invertible column-wise transformation.
InvertibleColumnTransform(String, Map<String, Function>, Map<String, Function>) - Constructor for class smile.data.transform.InvertibleColumnTransform
Constructor.
InvertibleTransform - Interface in smile.data.transform
Invertible data transformation.
invlink(double) - Method in interface smile.glm.model.Model
The inverse of link function (aka the mean function).
ipiv - Variable in class smile.math.matrix.BandMatrix.LU
The pivot vector.
ipiv - Variable in class smile.math.matrix.BigMatrix.LU
The pivot vector.
ipiv - Variable in class smile.math.matrix.fp32.BandMatrix.LU
The pivot vector.
ipiv - Variable in class smile.math.matrix.fp32.Matrix.LU
The pivot vector.
ipiv - Variable in class smile.math.matrix.fp32.SymmMatrix.BunchKaufman
The pivot vector.
ipiv - Variable in class smile.math.matrix.Matrix.LU
The pivot vector.
ipiv - Variable in class smile.math.matrix.SymmMatrix.BunchKaufman
The pivot vector.
IQAgent - Class in smile.sort
Incremental quantile estimation.
IQAgent() - Constructor for class smile.sort.IQAgent
Constructor.
IQAgent(int) - Constructor for class smile.sort.IQAgent
Constructor.
isAncestorOf(Concept) - Method in class smile.taxonomy.Concept
Returns true if this concept is an ancestor of the given concept.
isBoolean() - Method in class smile.data.type.BooleanType
 
isBoolean() - Method in interface smile.data.type.DataType
Returns true if the type is boolean or Boolean.
isBoolean() - Method in class smile.data.type.ObjectType
 
isByte() - Method in class smile.data.type.ByteType
 
isByte() - Method in interface smile.data.type.DataType
Returns true if the type is byte or Byte.
isByte() - Method in class smile.data.type.ObjectType
 
isChar() - Method in class smile.data.type.CharType
 
isChar() - Method in interface smile.data.type.DataType
Returns true if the type is char or Char.
isChar() - Method in class smile.data.type.ObjectType
 
isDouble() - Method in interface smile.data.type.DataType
Returns true if the type is double or Double.
isDouble() - Method in class smile.data.type.DoubleType
 
isDouble() - Method in class smile.data.type.ObjectType
 
isDouble(DataType) - Static method in interface smile.data.type.DataType
Returns true if the given type is of double, either primitive or boxed.
isEmpty() - Method in interface smile.data.Dataset
Returns true if the dataset is empty.
isEmpty() - Method in class smile.util.DoubleArrayList
Returns true if this list contains no values.
isEmpty() - Method in class smile.util.IntArrayList
Returns true if this list contains no values.
isEmpty() - Method in class smile.util.PriorityQueue
Returns true if the queue is empty.
isEmpty() - Method in class smile.util.SparseArray
Returns true if the array is empty.
isExpandable() - Method in class smile.neighbor.lsh.Probe
Returns true if the probe is expandable.
isExtendable() - Method in class smile.neighbor.lsh.Probe
Returns true if the probe is extendable.
isFloat() - Method in interface smile.data.type.DataType
Returns true if the type is float or Float.
isFloat() - Method in class smile.data.type.FloatType
 
isFloat() - Method in class smile.data.type.ObjectType
 
isFloat(DataType) - Static method in interface smile.data.type.DataType
Returns true if the given type is of float, either primitive or boxed.
isFloating() - Method in interface smile.data.type.DataType
Returns true if the type is float or double.
isInt() - Method in interface smile.data.type.DataType
Returns true if the type is int or Integer.
isInt() - Method in class smile.data.type.IntegerType
 
isInt() - Method in class smile.data.type.ObjectType
 
isInt(double) - Static method in class smile.math.MathEx
Returns true if x is an integer.
isInt(float) - Static method in class smile.math.MathEx
Returns true if x is an integer.
isInt(DataType) - Static method in interface smile.data.type.DataType
Returns true if the given type is of int, short, byte, char, either primitive or boxed.
isIntegral() - Method in interface smile.data.type.DataType
Returns true if the type is int, long, short or byte.
isLeaf() - Method in class smile.taxonomy.Concept
Check if a node is a leaf in the taxonomy tree.
isLong() - Method in interface smile.data.type.DataType
Returns true if the type is long or Long.
isLong() - Method in class smile.data.type.LongType
 
isLong() - Method in class smile.data.type.ObjectType
 
isLong(DataType) - Static method in interface smile.data.type.DataType
Returns true if the given type is of long, either primitive or boxed.
isNormalized() - Method in class smile.classification.RBFNetwork
Returns true if the model is normalized.
isNormalized() - Method in class smile.regression.RBFNetwork
Returns true if the model is normalized.
isNullAt(int) - Method in interface smile.data.Tuple
Checks whether the value at position i is null.
isNullAt(int) - Method in interface smile.data.vector.Vector
Checks if the value at position i is null.
isNullAt(int, int) - Method in interface smile.data.DataFrame
Checks whether the value at position (i, j) is null.
isNullAt(int, String) - Method in interface smile.data.DataFrame
Checks whether the field value is null.
isNullAt(String) - Method in interface smile.data.Tuple
Checks whether the field value is null.
isNullOrEmpty(String) - Static method in interface smile.util.Strings
Returns true if the string is null or empty.
isNumeric() - Method in interface smile.data.type.DataType
Returns true if the type is numeric (integral or floating).
isNumeric() - Method in class smile.data.type.StructField
Returns true if the field is of integer or floating but not nominal scale.
isObject() - Method in interface smile.data.type.DataType
Returns true if the type is ObjectType.
isObject() - Method in class smile.data.type.ObjectType
 
isObject() - Method in class smile.data.type.StringType
 
IsolationForest - Class in smile.anomaly
Isolation forest is an unsupervised learning algorithm for anomaly detection that works on the principle of isolating anomalies.
IsolationForest(int, int, IsolationTree...) - Constructor for class smile.anomaly.IsolationForest
Constructor.
IsolationTree - Class in smile.anomaly
Isolation tree.
IsolationTree(List<double[]>, int, int) - Constructor for class smile.anomaly.IsolationTree
Constructor.
IsoMap - Class in smile.manifold
Isometric feature mapping.
IsoMap(int[], double[][], AdjacencyList) - Constructor for class smile.manifold.IsoMap
Constructor.
IsotonicMDS - Class in smile.manifold
Kruskal's non-metric MDS.
IsotonicMDS(double, double[][]) - Constructor for class smile.manifold.IsotonicMDS
Constructor.
IsotonicRegressionScaling - Class in smile.classification
A method to calibrate decision function value to probability.
IsotonicRegressionScaling(double[], double[]) - Constructor for class smile.classification.IsotonicRegressionScaling
Constructor.
IsotropicKernel - Interface in smile.math.kernel
Isotropic kernel.
isPower2(int) - Static method in class smile.math.MathEx
Returns true if x is a power of 2.
isPrimitive() - Method in interface smile.data.type.DataType
Returns true if this is a primitive data type.
isProbablePrime(long, int) - Static method in class smile.math.MathEx
Returns true if n is probably prime, false if it's definitely composite.
isShiftable() - Method in class smile.neighbor.lsh.Probe
Returns true if the probe is shiftable.
isShort() - Method in interface smile.data.type.DataType
Returns true if the type is short or Short.
isShort() - Method in class smile.data.type.ObjectType
 
isShort() - Method in class smile.data.type.ShortType
 
isSingular() - Method in class smile.math.matrix.BandMatrix.LU
Returns true if the matrix is singular.
isSingular() - Method in class smile.math.matrix.BigMatrix.LU
Returns true if the matrix is singular.
isSingular() - Method in class smile.math.matrix.fp32.BandMatrix.LU
Returns true if the matrix is singular.
isSingular() - Method in class smile.math.matrix.fp32.Matrix.LU
Returns true if the matrix is singular.
isSingular() - Method in class smile.math.matrix.fp32.SymmMatrix.BunchKaufman
Returns true if the matrix is singular.
isSingular() - Method in class smile.math.matrix.Matrix.LU
Returns true if the matrix is singular.
isSingular() - Method in class smile.math.matrix.SymmMatrix.BunchKaufman
Returns true if the matrix is singular.
isString() - Method in interface smile.data.type.DataType
Returns true if the type is String.
isString() - Method in class smile.data.type.StringType
 
isSymmetric() - Method in class smile.math.matrix.BandMatrix
Return true if the matrix is symmetric (uplo != null).
isSymmetric() - Method in class smile.math.matrix.BigMatrix
Return true if the matrix is symmetric (uplo != null && diag == null).
isSymmetric() - Method in class smile.math.matrix.fp32.BandMatrix
Return true if the matrix is symmetric (uplo != null).
isSymmetric() - Method in class smile.math.matrix.fp32.Matrix
Return true if the matrix is symmetric (uplo != null && diag == null).
isSymmetric() - Method in class smile.math.matrix.Matrix
Return true if the matrix is symmetric (uplo != null && diag == null).
isVariable() - Method in interface smile.data.formula.Feature
Returns true if the term represents a plain variable/column in the data frame.
isZero(double) - Static method in class smile.math.MathEx
Tests if a floating number is zero in machine precision.
isZero(double, double) - Static method in class smile.math.MathEx
Tests if a floating number is zero in given precision.
isZero(float) - Static method in class smile.math.MathEx
Tests if a floating number is zero in machine precision.
isZero(float, float) - Static method in class smile.math.MathEx
Tests if a floating number is zero in given precision.
items - Variable in class smile.association.ItemSet
The set of items.
ItemSet - Class in smile.association
A set of items.
ItemSet(int[], int) - Constructor for class smile.association.ItemSet
Constructor.
iterator() - Method in class smile.association.ARM
 
iterator() - Method in class smile.association.FPGrowth
 
iterator() - Method in class smile.data.IndexDataFrame
 
iterator() - Method in class smile.math.matrix.fp32.SparseMatrix
Returns the iterator of nonzero entries.
iterator() - Method in class smile.math.matrix.SparseMatrix
Returns the iterator of nonzero entries.
iterator() - Method in interface smile.nlp.dictionary.Dictionary
Returns an iterator over the words in this dictionary.
iterator() - Method in enum class smile.nlp.dictionary.EnglishDictionary
 
iterator() - Method in class smile.nlp.dictionary.EnglishPunctuations
 
iterator() - Method in enum class smile.nlp.dictionary.EnglishStopWords
 
iterator() - Method in class smile.nlp.dictionary.SimpleDictionary
 
iterator() - Method in class smile.util.SparseArray
 
iterator(int, int) - Method in class smile.math.matrix.fp32.SparseMatrix
Returns the iterator of nonzero entries.
iterator(int, int) - Method in class smile.math.matrix.SparseMatrix
Returns the iterator of nonzero entries.
iv - Variable in class smile.feature.selection.InformationValue
Information value.

J

j - Variable in class smile.math.matrix.fp32.SparseMatrix.Entry
The column index.
j - Variable in class smile.math.matrix.SparseMatrix.Entry
The column index.
j - Variable in class smile.util.IntPair
The second integer.
JaccardDistance<T> - Class in smile.math.distance
The Jaccard index, also known as the Jaccard similarity coefficient is a statistic used for comparing the similarity and diversity of sample sets.
JaccardDistance() - Constructor for class smile.math.distance.JaccardDistance
Constructor.
Jacobi() - Method in class smile.math.matrix.fp32.IMatrix
Returns a simple Jacobi preconditioner matrix that is the trivial diagonal part of A in some cases.
Jacobi() - Method in class smile.math.matrix.IMatrix
Returns a simple Jacobi preconditioner matrix that is the trivial diagonal part of A in some cases.
JensenShannonDistance - Class in smile.math.distance
The Jensen-Shannon divergence is a popular method of measuring the similarity between two probability distributions.
JensenShannonDistance() - Constructor for class smile.math.distance.JensenShannonDistance
Constructor.
JensenShannonDivergence(double[], double[]) - Static method in class smile.math.MathEx
Jensen-Shannon divergence JS(P||Q) = (KL(P||M) + KL(Q||M)) / 2, where M = (P+Q)/2.
JensenShannonDivergence(double[], SparseArray) - Static method in class smile.math.MathEx
Jensen-Shannon divergence JS(P||Q) = (KL(P||M) + KL(Q||M)) / 2, where M = (P+Q)/2.
JensenShannonDivergence(SparseArray, double[]) - Static method in class smile.math.MathEx
Jensen-Shannon divergence JS(P||Q) = (KL(P||M) + KL(Q||M)) / 2, where M = (P+Q)/2.
JensenShannonDivergence(SparseArray, SparseArray) - Static method in class smile.math.MathEx
Jensen-Shannon divergence JS(P||Q) = (KL(P||M) + KL(Q||M)) / 2, where M = (P+Q)/2.
JJ - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
Adjective.
JJR - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
Adjective, comparative.
JJS - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
Adjective, superlative.
joint(int[], int[]) - Static method in class smile.validation.metric.NormalizedMutualInformation
Calculates the normalized mutual information of I(y1, y2) / H(y1, y2).
JOINT - Enum constant in enum class smile.validation.metric.NormalizedMutualInformation.Method
I(y1, y2) / H(y1, y2)
JOINT - Static variable in class smile.validation.metric.NormalizedMutualInformation
Default instance with max normalization.
JointPrediction(T[], double[], double[], Matrix) - Constructor for class smile.regression.GaussianProcessRegression.JointPrediction
Constructor.
json(String) - Static method in interface smile.io.Read
Reads a JSON file.
json(String, JSON.Mode, StructType) - Static method in interface smile.io.Read
Reads a JSON file.
json(Path) - Static method in interface smile.io.Read
Reads a JSON file.
json(Path, JSON.Mode, StructType) - Static method in interface smile.io.Read
Reads a JSON file.
JSON - Class in smile.io
Reads JSON datasets.
JSON() - Constructor for class smile.io.JSON
Constructor.
JSON.Mode - Enum Class in smile.io
JSON files in single-line or multi-line mode.

K

k - Variable in class smile.classification.ClassLabels
The number of classes.
k - Variable in class smile.clustering.PartitionClustering
The number of clusters.
k - Variable in class smile.neighbor.LSH
The number of random projections per hash value.
k - Variable in class smile.stat.distribution.GammaDistribution
The shape parameter.
k - Variable in class smile.stat.distribution.WeibullDistribution
The shape parameter.
k(double) - Method in class smile.math.kernel.BinarySparseLinearKernel
 
k(double) - Method in interface smile.math.kernel.DotProductKernel
Computes the dot product kernel function.
k(double) - Method in class smile.math.kernel.Gaussian
 
k(double) - Method in class smile.math.kernel.HyperbolicTangent
 
k(double) - Method in interface smile.math.kernel.IsotropicKernel
Computes the isotropic kernel function.
k(double) - Method in class smile.math.kernel.Laplacian
 
k(double) - Method in class smile.math.kernel.LinearKernel
 
k(double) - Method in class smile.math.kernel.Matern
 
k(double) - Method in class smile.math.kernel.Polynomial
 
k(double) - Method in class smile.math.kernel.SparseLinearKernel
 
k(double) - Method in class smile.math.kernel.ThinPlateSpline
 
k(double[], double[]) - Method in class smile.math.kernel.GaussianKernel
 
k(double[], double[]) - Method in class smile.math.kernel.HellingerKernel
 
k(double[], double[]) - Method in class smile.math.kernel.HyperbolicTangentKernel
 
k(double[], double[]) - Method in class smile.math.kernel.LaplacianKernel
 
k(double[], double[]) - Method in class smile.math.kernel.LinearKernel
 
k(double[], double[]) - Method in class smile.math.kernel.MaternKernel
 
k(double[], double[]) - Method in class smile.math.kernel.PearsonKernel
 
k(double[], double[]) - Method in class smile.math.kernel.PolynomialKernel
 
k(double[], double[]) - Method in class smile.math.kernel.ThinPlateSplineKernel
 
k(int[], int[]) - Method in class smile.math.kernel.BinarySparseGaussianKernel
 
k(int[], int[]) - Method in class smile.math.kernel.BinarySparseHyperbolicTangentKernel
 
k(int[], int[]) - Method in class smile.math.kernel.BinarySparseLaplacianKernel
 
k(int[], int[]) - Method in class smile.math.kernel.BinarySparseLinearKernel
 
k(int[], int[]) - Method in class smile.math.kernel.BinarySparseMaternKernel
 
k(int[], int[]) - Method in class smile.math.kernel.BinarySparsePolynomialKernel
 
k(int[], int[]) - Method in class smile.math.kernel.BinarySparseThinPlateSplineKernel
 
k(SparseArray, SparseArray) - Method in class smile.math.kernel.SparseGaussianKernel
 
k(SparseArray, SparseArray) - Method in class smile.math.kernel.SparseHyperbolicTangentKernel
 
k(SparseArray, SparseArray) - Method in class smile.math.kernel.SparseLaplacianKernel
 
k(SparseArray, SparseArray) - Method in class smile.math.kernel.SparseLinearKernel
 
k(SparseArray, SparseArray) - Method in class smile.math.kernel.SparseMaternKernel
 
k(SparseArray, SparseArray) - Method in class smile.math.kernel.SparsePolynomialKernel
 
k(SparseArray, SparseArray) - Method in class smile.math.kernel.SparseThinPlateSplineKernel
 
k(T, T) - Method in interface smile.math.kernel.MercerKernel
Kernel function.
k(T, T) - Method in class smile.math.kernel.ProductKernel
 
k(T, T) - Method in class smile.math.kernel.SumKernel
 
K(Matrix) - Method in interface smile.math.kernel.DotProductKernel
Computes the kernel matrix.
K(Matrix) - Method in interface smile.math.kernel.IsotropicKernel
Computes the kernel matrix.
K(T[]) - Method in interface smile.math.kernel.MercerKernel
Computes the kernel matrix.
K(T[], T[]) - Method in interface smile.math.kernel.MercerKernel
Returns the kernel matrix.
KDTree<E> - Class in smile.neighbor
A KD-tree (short for k-dimensional tree) is a space-partitioning dataset structure for organizing points in a k-dimensional space.
KDTree(double[][], E[]) - Constructor for class smile.neighbor.KDTree
Constructor.
kendall(double[], double[]) - Static method in class smile.math.MathEx
The Kendall Tau Rank Correlation Coefficient is used to measure the degree of correspondence between sets of rankings where the measures are not equidistant.
kendall(double[], double[]) - Static method in class smile.stat.hypothesis.CorTest
Kendall rank correlation test.
kendall(float[], float[]) - Static method in class smile.math.MathEx
The Kendall Tau Rank Correlation Coefficient is used to measure the degree of correspondence between sets of rankings where the measures are not equidistant.
kendall(int[], int[]) - Static method in class smile.math.MathEx
The Kendall Tau Rank Correlation Coefficient is used to measure the degree of correspondence between sets of rankings where the measures are not equidistant.
kernel - Variable in class smile.regression.GaussianProcessRegression
The covariance/kernel function.
kernel() - Method in class smile.base.svm.KernelMachine
Returns the kernel function.
KernelDensity - Class in smile.stat.distribution
Kernel density estimation is a non-parametric way of estimating the probability density function of a random variable.
KernelDensity(double[]) - Constructor for class smile.stat.distribution.KernelDensity
Constructor.
KernelDensity(double[], double) - Constructor for class smile.stat.distribution.KernelDensity
Constructor.
KernelMachine<T> - Class in smile.base.svm
Kernel machines.
KernelMachine<T> - Class in smile.regression
The learning methods building on kernels.
KernelMachine(MercerKernel<T>, T[], double[]) - Constructor for class smile.base.svm.KernelMachine
Constructor.
KernelMachine(MercerKernel<T>, T[], double[]) - Constructor for class smile.regression.KernelMachine
Constructor.
KernelMachine(MercerKernel<T>, T[], double[], double) - Constructor for class smile.base.svm.KernelMachine
Constructor.
KernelMachine(MercerKernel<T>, T[], double[], double) - Constructor for class smile.regression.KernelMachine
Constructor.
KernelPCA - Class in smile.feature.extraction
Kernel PCA transform.
KernelPCA(KPCA<double[]>, String...) - Constructor for class smile.feature.extraction.KernelPCA
Constructor.
key - Variable in class smile.neighbor.Neighbor
The key of neighbor.
keys - Variable in class smile.neighbor.LSH
The object keys.
keys() - Method in class smile.neighbor.MutableLSH
Returns the keys.
keywords() - Method in class smile.taxonomy.Concept
Returns the concept synonym set.
kg(double) - Method in class smile.math.kernel.BinarySparseLinearKernel
 
kg(double) - Method in interface smile.math.kernel.DotProductKernel
Computes the dot product kernel function and its gradient over hyperparameters..
kg(double) - Method in class smile.math.kernel.Gaussian
 
kg(double) - Method in class smile.math.kernel.HyperbolicTangent
 
kg(double) - Method in interface smile.math.kernel.IsotropicKernel
Computes the isotropic kernel function and its gradient over hyperparameters..
kg(double) - Method in class smile.math.kernel.Laplacian
 
kg(double) - Method in class smile.math.kernel.LinearKernel
 
kg(double) - Method in class smile.math.kernel.Matern
 
kg(double) - Method in class smile.math.kernel.Polynomial
 
kg(double) - Method in class smile.math.kernel.SparseLinearKernel
 
kg(double) - Method in class smile.math.kernel.ThinPlateSpline
 
kg(double[], double[]) - Method in class smile.math.kernel.GaussianKernel
 
kg(double[], double[]) - Method in class smile.math.kernel.HellingerKernel
 
kg(double[], double[]) - Method in class smile.math.kernel.HyperbolicTangentKernel
 
kg(double[], double[]) - Method in class smile.math.kernel.LaplacianKernel
 
kg(double[], double[]) - Method in class smile.math.kernel.LinearKernel
 
kg(double[], double[]) - Method in class smile.math.kernel.MaternKernel
 
kg(double[], double[]) - Method in class smile.math.kernel.PearsonKernel
 
kg(double[], double[]) - Method in class smile.math.kernel.PolynomialKernel
 
kg(double[], double[]) - Method in class smile.math.kernel.ThinPlateSplineKernel
 
kg(int[], int[]) - Method in class smile.math.kernel.BinarySparseGaussianKernel
 
kg(int[], int[]) - Method in class smile.math.kernel.BinarySparseHyperbolicTangentKernel
 
kg(int[], int[]) - Method in class smile.math.kernel.BinarySparseLaplacianKernel
 
kg(int[], int[]) - Method in class smile.math.kernel.BinarySparseLinearKernel
 
kg(int[], int[]) - Method in class smile.math.kernel.BinarySparseMaternKernel
 
kg(int[], int[]) - Method in class smile.math.kernel.BinarySparsePolynomialKernel
 
kg(int[], int[]) - Method in class smile.math.kernel.BinarySparseThinPlateSplineKernel
 
kg(SparseArray, SparseArray) - Method in class smile.math.kernel.SparseGaussianKernel
 
kg(SparseArray, SparseArray) - Method in class smile.math.kernel.SparseHyperbolicTangentKernel
 
kg(SparseArray, SparseArray) - Method in class smile.math.kernel.SparseLaplacianKernel
 
kg(SparseArray, SparseArray) - Method in class smile.math.kernel.SparseLinearKernel
 
kg(SparseArray, SparseArray) - Method in class smile.math.kernel.SparseMaternKernel
 
kg(SparseArray, SparseArray) - Method in class smile.math.kernel.SparsePolynomialKernel
 
kg(SparseArray, SparseArray) - Method in class smile.math.kernel.SparseThinPlateSplineKernel
 
kg(T, T) - Method in interface smile.math.kernel.MercerKernel
Computes the kernel and its gradient over hyperparameters.
kg(T, T) - Method in class smile.math.kernel.ProductKernel
 
kg(T, T) - Method in class smile.math.kernel.SumKernel
 
KG(Matrix) - Method in interface smile.math.kernel.IsotropicKernel
Computes the kernel and gradient matrices.
KG(T[]) - Method in interface smile.math.kernel.MercerKernel
Computes the kernel and gradient matrices.
kl() - Method in class smile.math.matrix.BandMatrix
Returns the number of subdiagonals.
kl() - Method in class smile.math.matrix.fp32.BandMatrix
Returns the number of subdiagonals.
KMeans - Class in smile.clustering
K-Means clustering.
KMeans(double, double[][], int[]) - Constructor for class smile.clustering.KMeans
Constructor.
KMedoidsImputer - Class in smile.feature.imputation
Missing value imputation by K-Medoids clustering.
KMedoidsImputer(CLARANS<Tuple>) - Constructor for class smile.feature.imputation.KMedoidsImputer
Constructor.
KModes - Class in smile.clustering
K-Modes clustering.
KModes(double, int[][], int[]) - Constructor for class smile.clustering.KModes
Constructor.
KNN<T> - Class in smile.classification
K-nearest neighbor classifier.
KNN(KNNSearch<T, T>, int[], int) - Constructor for class smile.classification.KNN
Constructor.
KNNImputer - Class in smile.feature.imputation
Missing value imputation with k-nearest neighbors.
KNNImputer(DataFrame, int, String...) - Constructor for class smile.feature.imputation.KNNImputer
Constructor with Euclidean distance on selected columns.
KNNImputer(DataFrame, int, Distance<Tuple>) - Constructor for class smile.feature.imputation.KNNImputer
Constructor.
KNNSearch<K,V> - Interface in smile.neighbor
Retrieves the top k nearest neighbors to the query.
kpca - Variable in class smile.feature.extraction.KernelPCA
Kernel PCA.
KPCA<T> - Class in smile.manifold
Kernel principal component analysis.
KPCA(T[], MercerKernel<T>, double[], double, double[][], double[], Matrix) - Constructor for class smile.manifold.KPCA
Constructor.
KrigingInterpolation - Class in smile.interpolation
Kriging interpolation for the data points irregularly distributed in space.
KrigingInterpolation(double[][], double[]) - Constructor for class smile.interpolation.KrigingInterpolation
Constructor.
KrigingInterpolation(double[][], double[], Variogram, double[]) - Constructor for class smile.interpolation.KrigingInterpolation
Constructor.
KrigingInterpolation1D - Class in smile.interpolation
Kriging interpolation for the data points irregularly distributed in space.
KrigingInterpolation1D(double[], double[]) - Constructor for class smile.interpolation.KrigingInterpolation1D
Constructor.
KrigingInterpolation1D(double[], double[], double) - Constructor for class smile.interpolation.KrigingInterpolation1D
Constructor.
KrigingInterpolation2D - Class in smile.interpolation
Kriging interpolation for the data points irregularly distributed in space.
KrigingInterpolation2D(double[], double[], double[]) - Constructor for class smile.interpolation.KrigingInterpolation2D
Constructor.
KrigingInterpolation2D(double[], double[], double[], double) - Constructor for class smile.interpolation.KrigingInterpolation2D
Constructor.
KSTest - Class in smile.stat.hypothesis
The Kolmogorov-Smirnov test (K-S test) is a form of minimum distance estimation used as a non-parametric test of equality of one-dimensional probability distributions.
KSTest(String, double, double) - Constructor for class smile.stat.hypothesis.KSTest
Constructor.
ku() - Method in class smile.math.matrix.BandMatrix
Returns the number of superdiagonals.
ku() - Method in class smile.math.matrix.fp32.BandMatrix
Returns the number of superdiagonals.
KullbackLeiblerDivergence(double[], double[]) - Static method in class smile.math.MathEx
Kullback-Leibler divergence.
KullbackLeiblerDivergence(double[], SparseArray) - Static method in class smile.math.MathEx
Kullback-Leibler divergence.
KullbackLeiblerDivergence(SparseArray, double[]) - Static method in class smile.math.MathEx
Kullback-Leibler divergence.
KullbackLeiblerDivergence(SparseArray, SparseArray) - Static method in class smile.math.MathEx
Kullback-Leibler divergence.
Kurtosis - Class in smile.ica
The kurtosis of the probability density function of a signal.
Kurtosis() - Constructor for class smile.ica.Kurtosis
 

L

L - Variable in class smile.regression.GaussianProcessRegression
The log marginal likelihood, which may be not available (NaN) when the model is fit with approximate methods.
L - Variable in class smile.stat.distribution.DiscreteExponentialFamilyMixture
The log-likelihood when the distribution is fit on a sample data.
L - Variable in class smile.stat.distribution.ExponentialFamilyMixture
The log-likelihood when the distribution is fit on a sample data.
L - Variable in class smile.stat.distribution.MultivariateExponentialFamilyMixture
The log-likelihood when the distribution is fit on a sample data.
L - Variable in class smile.vq.BIRCH
The number of CF entries in the leaf nodes.
L_INF - Enum constant in enum class smile.feature.transform.Normalizer.Norm
Normalize L-infinity vector norm.
L1 - Enum constant in enum class smile.feature.transform.Normalizer.Norm
Normalize L1 vector norm.
L2 - Enum constant in enum class smile.feature.transform.Normalizer.Norm
Normalize L2 vector norm.
LA - Enum constant in enum class smile.math.matrix.ARPACK.SymmOption
The largest algebraic eigenvalues.
LA - Enum constant in enum class smile.math.matrix.fp32.ARPACK.SymmOption
The largest algebraic eigenvalues.
label() - Method in interface smile.data.Instance
Returns the class label of instance.
lad() - Static method in interface smile.base.cart.Loss
Least absolute deviation regression loss.
LamarckianChromosome - Interface in smile.gap
Artificial chromosomes used in Lamarckian algorithm that is a hybrid of of evolutionary computation and a local improver such as hill-climbing.
lambda - Variable in class smile.base.mlp.MultilayerPerceptron
The L2 regularization factor, which is also the weight decay factor.
lambda - Variable in class smile.stat.distribution.ExponentialDistribution
The rate parameter.
lambda - Variable in class smile.stat.distribution.PoissonDistribution
The average number of events per interval.
lambda - Variable in class smile.stat.distribution.WeibullDistribution
The scale parameter.
LancasterStemmer - Class in smile.nlp.stemmer
The Paice/Husk Lancaster stemming algorithm.
LancasterStemmer() - Constructor for class smile.nlp.stemmer.LancasterStemmer
Constructor with default rules.
LancasterStemmer(boolean) - Constructor for class smile.nlp.stemmer.LancasterStemmer
Constructor with default rules.
LancasterStemmer(InputStream) - Constructor for class smile.nlp.stemmer.LancasterStemmer
Constructor with customized rules.
LancasterStemmer(InputStream, boolean) - Constructor for class smile.nlp.stemmer.LancasterStemmer
Constructor with customized rules.
Lanczos - Class in smile.math.matrix
The Lanczos algorithm is a direct algorithm devised by Cornelius Lanczos that is an adaptation of power methods to find the most useful eigenvalues and eigenvectors of an nth order linear system with a limited number of operations, m, where m is much smaller than n.
Lanczos() - Constructor for class smile.math.matrix.Lanczos
 
lapack() - Method in enum class smile.math.blas.Diag
Returns the value for LAPACK.
lapack() - Method in enum class smile.math.blas.EigenRange
Returns the byte value for LAPACK.
lapack() - Method in enum class smile.math.blas.EVDJob
Returns the byte value for LAPACK.
lapack() - Method in enum class smile.math.blas.Layout
Returns the byte value for LAPACK.
lapack() - Method in enum class smile.math.blas.Side
Returns the byte value for LAPACK.
lapack() - Method in enum class smile.math.blas.SVDJob
Returns the byte value for LAPACK.
lapack() - Method in enum class smile.math.blas.Transpose
Returns the byte value for LAPACK.
lapack() - Method in enum class smile.math.blas.UPLO
Returns the byte value for LAPACK.
LAPACK - Interface in smile.math.blas
Linear Algebra Package.
LaplaceInterpolation - Class in smile.interpolation
Laplace interpolation to restore missing or unmeasured values on a 2-dimensional evenly spaced regular grid.
LaplaceInterpolation() - Constructor for class smile.interpolation.LaplaceInterpolation
 
Laplacian - Class in smile.math.kernel
Laplacian kernel, also referred as exponential kernel.
Laplacian(double, double, double) - Constructor for class smile.math.kernel.Laplacian
Constructor.
LaplacianEigenmap - Class in smile.manifold
Laplacian Eigenmap.
LaplacianEigenmap(double, int[], double[][], AdjacencyList) - Constructor for class smile.manifold.LaplacianEigenmap
Constructor with Gaussian kernel.
LaplacianEigenmap(int[], double[][], AdjacencyList) - Constructor for class smile.manifold.LaplacianEigenmap
Constructor with discrete weights.
LaplacianKernel - Class in smile.math.kernel