- aat() - Method in class smile.math.matrix.FloatMatrix
-
Returns A * A'
- aat() - Method in class smile.math.matrix.FloatSparseMatrix
-
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 - Class in smile.data.formula
-
The term of abs function.
- Abs(Term) - Constructor for class smile.data.formula.Abs
-
Constructor.
- abs(String) - Static method in interface smile.data.formula.Terms
-
Applies Math.abs.
- abs(Term) - Static method in interface smile.data.formula.Terms
-
Applies Math.abs.
- abs() - Method in class smile.math.Complex
-
Returns abs/modulus/magnitude.
- 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.
- AbstractDifferentiableMultivariateFunction - Class in smile.math
-
An abstract implementation that uses finite differences to calculate the
partial derivatives instead of providing them analytically.
- AbstractDifferentiableMultivariateFunction() - Constructor for class smile.math.AbstractDifferentiableMultivariateFunction
-
- 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.FloatConsumer
-
Accepts one matrix element and performs the operation
on the given arguments.
- accept(File) - Method in class smile.swing.FileChooser.SimpleFileFilter
-
- 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
-
Applies Math.acos.
- acos(Term) - Static method in interface smile.data.formula.Terms
-
Applies Math.acos.
- actionPerformed(ActionEvent) - Method in class smile.plot.swing.PlotGrid
-
- actionPerformed(ActionEvent) - Method in class smile.swing.table.ButtonCellRenderer
-
- actionPerformed(ActionEvent) - Method in class smile.swing.table.ColorCellEditor
-
- actionPerformed(ActionEvent) - Method in class smile.swing.table.FontCellEditor
-
- actionPerformed(ActionEvent) - Method in class smile.swing.table.TableCopyPasteAdapter
-
This method is activated on the Keystrokes we are listening to in this
implementation.
- ActivationFunction - Interface in smile.base.mlp
-
The activation function in hidden layers.
- 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.
- adb(Transpose, Transpose, FloatMatrix, float[]) - Method in class smile.math.matrix.FloatMatrix
-
Returns A * D * B, where D is a diagonal matrix.
- adb(Transpose, Transpose, Matrix, double[]) - Method in class smile.math.matrix.Matrix
-
Returns A * D * B, where D is a diagonal matrix.
- Add - Class in smile.data.formula
-
The term of a + b expression.
- Add(Term, Term) - Constructor for class smile.data.formula.Add
-
Constructor.
- add(Term, Term) - Static method in interface smile.data.formula.Terms
-
Adds two terms.
- add(String, String) - Static method in interface smile.data.formula.Terms
-
Adds two terms.
- add(Term, String) - Static method in interface smile.data.formula.Terms
-
Adds two terms.
- 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(Complex) - Method in class smile.math.Complex
-
Returns this + b.
- add(double[], double[]) - Static method in class smile.math.MathEx
-
Element-wise sum of two arrays y = x + y.
- add(int, int, float) - Method in class smile.math.matrix.FloatMatrix
-
A[i,j] += b
- add(float) - Method in class smile.math.matrix.FloatMatrix
-
A += b
- add(int, int, float, FloatMatrix) - Method in class smile.math.matrix.FloatMatrix
-
Element-wise submatrix addition A[i, j] += alpha * B
- add(FloatMatrix) - Method in class smile.math.matrix.FloatMatrix
-
Element-wise addition A += B
- add(float, FloatMatrix) - Method in class smile.math.matrix.FloatMatrix
-
Element-wise addition A += alpha * B
- add(float, FloatMatrix, float, FloatMatrix) - Method in class smile.math.matrix.FloatMatrix
-
Element-wise addition C = alpha * A + beta * B
- add(int, int, float, float) - Method in class smile.math.matrix.FloatMatrix
-
A[i,j] = alpha * A[i,j] + beta
- add(int, int, float, float, FloatMatrix) - Method in class smile.math.matrix.FloatMatrix
-
Element-wise submatrix addition A[i, j] = alpha * A[i, j] + beta * B
- add(float, float, FloatMatrix) - Method in class smile.math.matrix.FloatMatrix
-
Element-wise addition A = alpha * A + beta * B
- add(float, float[], float[]) - Method in class smile.math.matrix.FloatMatrix
-
Rank-1 update A += alpha * x * y'
- add(int, int, double) - Method in class smile.math.matrix.Matrix
-
A[i,j] += b
- add(double) - Method in class smile.math.matrix.Matrix
-
A += b
- add(int, int, double, Matrix) - Method in class smile.math.matrix.Matrix
-
Element-wise submatrix addition A[i, j] += alpha * B
- add(Matrix) - Method in class smile.math.matrix.Matrix
-
Element-wise addition A += B
- add(double, Matrix) - Method in class smile.math.matrix.Matrix
-
Element-wise addition A += alpha * B
- add(double, Matrix, double, Matrix) - Method in class smile.math.matrix.Matrix
-
Element-wise addition C = alpha * A + beta * B
- add(int, int, double, double) - Method in class smile.math.matrix.Matrix
-
A[i,j] = alpha * A[i,j] + beta
- add(int, int, double, double, Matrix) - Method in class smile.math.matrix.Matrix
-
Element-wise submatrix addition A[i, j] = alpha * A[i, j] + beta * B
- add(double, double, Matrix) - Method in class smile.math.matrix.Matrix
-
Element-wise addition A = alpha * A + beta * B
- add(double, double[], double[]) - Method in class smile.math.matrix.Matrix
-
Rank-1 update A += alpha * x * y'
- add(E[]) - Method in class smile.neighbor.BKTree
-
Add a dataset into BK-tree.
- add(Collection<E>) - Method in class smile.neighbor.BKTree
-
Add a dataset into BK-tree.
- add(E) - Method in class smile.neighbor.BKTree
-
Add a datum into the BK-tree.
- add(int) - Method in class smile.neighbor.lsh.Bucket
-
Adds a point to bucket.
- 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(Text) - Method in class smile.nlp.SimpleCorpus
-
Add a document to the corpus.
- add(Shape) - Method in class smile.plot.swing.Canvas
-
Add a graphical shape to the canvas.
- add(Plot) - Method in class smile.plot.swing.Canvas
-
Add a graphical shape to the canvas.
- add(double...) - Method in class smile.plot.swing.Isoline
-
Add a point to the contour line.
- add(PlotPanel) - Method in class smile.plot.swing.PlotGrid
-
Add a plot into the frame.
- add(double) - Method in class smile.sort.DoubleHeapSelect
-
Assimilate a new value from the stream.
- add(float) - Method in class smile.sort.FloatHeapSelect
-
Assimilate a new value from the stream.
- add(T) - Method in class smile.sort.HeapSelect
-
Assimilate a new value from the stream.
- add(int) - Method in class smile.sort.IntHeapSelect
-
Assimilate a new value from the stream.
- add(double) - Method in class smile.sort.IQAgent
-
Assimilate a new value from the stream.
- add(int, int, double) - Method in class smile.util.Array2D
-
- add(Array2D) - Method in class smile.util.Array2D
-
- add(double) - Method in class smile.util.Array2D
-
- 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(int, int, int) - Method in class smile.util.IntArray2D
-
- add(IntArray2D) - Method in class smile.util.IntArray2D
-
- add(int) - Method in class smile.util.IntArray2D
-
- add(int) - Method in class smile.util.IntArrayList
-
Appends the specified value to the end of this list.
- add(IntArrayList) - Method in class smile.util.IntArrayList
-
Appends an array to the end of this list.
- add(int[]) - Method in class smile.util.IntArrayList
-
Appends an array 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(String, int) - Method in class smile.validation.Hyperparameters
-
Adds a parameter.
- add(String, double) - Method in class smile.validation.Hyperparameters
-
Adds a parameter.
- add(String, String) - Method in class smile.validation.Hyperparameters
-
Adds a parameter.
- add(String, int[]) - Method in class smile.validation.Hyperparameters
-
Adds a parameter.
- add(String, double[]) - Method in class smile.validation.Hyperparameters
-
Adds a parameter.
- add(String, String[]) - Method in class smile.validation.Hyperparameters
-
Adds a parameter.
- add(String, int, int) - Method in class smile.validation.Hyperparameters
-
Adds a parameter.
- add(String, int, int, int) - Method in class smile.validation.Hyperparameters
-
Adds a parameter.
- add(String, double, double) - Method in class smile.validation.Hyperparameters
-
Adds a parameter.
- add(String, double, double, double) - Method in class smile.validation.Hyperparameters
-
Adds a parameter.
- addAnchor(String) - Method in interface smile.nlp.AnchorText
-
Add a link label to the anchor text.
- addAnchor(String) - Method in class smile.nlp.SimpleText
-
- addChild(K[], V, int) - Method in class smile.nlp.Trie.Node
-
- addChild(String) - Method in class smile.taxonomy.Concept
-
Add a child to this node
- addChild(Concept) - Method in class smile.taxonomy.Concept
-
Add a child to this node
- addEdge(int, int) - Method in class smile.graph.AdjacencyList
-
- addEdge(int, int, double) - Method in class smile.graph.AdjacencyList
-
- addEdge(int, int) - Method in class smile.graph.AdjacencyMatrix
-
- addEdge(int, int, double) - 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 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.
- addExtension(String) - Method in class smile.swing.FileChooser.SimpleFileFilter
-
Adds a file type "dot" extension to filter against.
- addKeywords(String...) - Method in class smile.taxonomy.Concept
-
Add a list of synomym to the concept synset.
- addNotify() - Method in class smile.swing.Table.RowHeader
-
- addPropertyChangeListener(PropertyChangeListener) - Method in class smile.plot.swing.Canvas
-
Add a PropertyChangeListener to the listener list.
- 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 in smile.validation.metric
-
The normalization method.
- 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.
- adjustedRSquared() - Method in class smile.timeseries.AR
-
Returns adjusted R2 statistic.
- adjustedRSquared() - Method in class smile.timeseries.ARMA
-
Returns adjusted R2 statistic.
- age - Variable in class smile.vq.hebb.Edge
-
The age of this 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(boolean[]) - Static method in class smile.math.MathEx
-
Given a set of boolean values, are all of the values true?
- allocate(long) - Static method in class smile.io.Arrow
-
Creates the root allocator.
- alpha - Variable in class smile.stat.distribution.BetaDistribution
-
The shape parameter.
- AlphaIcon - Class in smile.swing
-
An Icon wrapper that paints the contained icon with a specified transparency.
- AlphaIcon(Icon, float) - Constructor for class smile.swing.AlphaIcon
-
Creates an
AlphaIcon with the specified icon and opacity.
- AnchorText - Interface in smile.nlp
-
The anchor text is the visible, clickable text in a hyperlink.
- antecedent - Variable in class smile.association.AssociationRule
-
Antecedent itemset.
- any(boolean[]) - Static method in class smile.math.MathEx
-
Given a set of boolean values, is at least one of the values true?
- 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, FPTree) - Static method in class smile.association.ARM
-
Mines the association rules.
- apply(FPTree) - Static method in class smile.association.FPGrowth
-
Mines the frequent item sets.
- apply(String) - Method in interface smile.data.DataFrame
-
Selects column based on the column name and return it as a Column.
- apply(Enum<?>) - Method in interface smile.data.DataFrame
-
Selects column using an enum value.
- apply(int) - Method in interface smile.data.Dataset
-
Returns the element at the specified position in this dataset.
- apply(Tuple) - Method in interface smile.data.formula.Feature
-
Applies the term on a data object.
- apply(DataFrame) - Method in interface smile.data.formula.Feature
-
- apply(Tuple) - Method in class smile.data.formula.Formula
-
Apply the formula on a tuple to generate the model data.
- apply(int) - Method in interface smile.data.Tuple
-
Returns the value at position i.
- apply(String) - Method in interface smile.data.Tuple
-
Returns the value by field name.
- apply(int) - Method in interface smile.data.vector.BaseVector
-
Returns the value at position i, which may be null.
- apply(int...) - Method in interface smile.data.vector.BaseVector
-
Returns a new vector with selected entries.
- apply(String[]) - Method in class smile.feature.Bag
-
Returns the bag-of-words features of a document.
- apply(int, int, int, Fitness<BitString>) - Method in class smile.feature.GAFE
-
Genetic algorithm based feature selection for classification.
- apply(Tuple) - Method in class smile.feature.SparseOneHotEncoder
-
Generates the compact representation of sparse binary features for given object.
- apply(DataFrame) - Method in class smile.feature.SparseOneHotEncoder
-
Generates the compact representation of sparse binary features for a data frame.
- apply(BitString, BitString) - Method in enum smile.gap.Crossover
-
Returns a pair of offsprings by crossovering parent chromosomes.
- apply(T[]) - Method in interface smile.gap.Selection
-
Select a chromosome with replacement from the population based on their
fitness.
- apply(int) - Method in class smile.math.Complex.Array
-
Returns the i-th element.
- apply(T, T) - Method in interface smile.math.distance.Distance
-
Returns the distance measure between two objects.
- apply(double) - Method in interface smile.math.Function
-
Computes the value of the function at x.
- apply(int) - Method in interface smile.math.IntFunction
-
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(T, T) - Method in interface smile.math.kernel.MercerKernel
-
Kernel function.
- apply(int, int) - Method in class smile.math.matrix.DMatrix
-
Returns A[i, j] for Scala users.
- apply(int, int) - Method in class smile.math.matrix.SMatrix
-
Returns A[i, j].
- apply(double...) - Method in interface smile.math.MultivariateFunction
-
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(String) - Method in class smile.nlp.embedding.Word2Vec
-
Returns the vector embedding of a word.
- apply(JTable) - Method in class smile.swing.table.TableColumnSettings
-
Apply this column settings to given table.
- apply(JTable) - Static method in class smile.swing.table.TableCopyPasteAdapter
-
- 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).
- 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(T) - Method in interface smile.classification.Classifier
-
- applyAsDouble(Tuple) - Method in interface smile.data.formula.Feature
-
Applies the term on a data object and produces an double-valued result.
- applyAsDouble(T, T) - Method in interface smile.math.distance.Distance
-
- applyAsDouble(T, T) - Method in interface smile.math.kernel.MercerKernel
-
- applyAsDouble(double[]) - Method in interface smile.math.MultivariateFunction
-
- applyAsDouble(T) - Method in interface smile.regression.Regression
-
- 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(T) - Method in interface smile.classification.Classifier
-
- applyAsInt(Tuple) - Method in interface smile.data.formula.Feature
-
Applies the term on a data object and produces an int-valued result.
- 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 - Class in smile.timeseries
-
Autoregressive model.
- AR(double[], double[], double, AR.Method) - Constructor for class smile.timeseries.AR
-
Constructor.
- 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.Method - Enum in smile.timeseries
-
The fitting method.
- 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(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.
- Arff(Reader) - Constructor for class smile.io.Arff
-
Constructor.
- 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.
- 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 - Interface in smile.math.matrix
-
ARPACK is a collection of Fortran77 subroutines designed to
solve large scale eigenvalue problems.
- ARPACK.AsymmOption - Enum in smile.math.matrix
-
Which eigenvalues of asymmetric matrix to compute.
- ARPACK.SymmOption - Enum in smile.math.matrix
-
Which eigenvalues of symmetric matrix to compute.
- array(DataType) - Static method in class smile.data.type.DataTypes
-
Creates an array data type.
- 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(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 - 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.
- 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.
- asin(String) - Static method in interface smile.data.formula.Terms
-
Applies Math.asin.
- asin(Term) - Static method in interface smile.data.formula.Terms
-
Applies Math.asin.
- AssociationRule - Class in smile.association
-
Association rule object.
- AssociationRule(int[], int[], double, double, double, double) - Constructor for class smile.association.AssociationRule
-
Constructor.
- asum(int, double[], int) - Method in interface smile.math.blas.BLAS
-
Sums the absolute values of the elements of a vector.
- asum(int, float[], int) - Method in interface smile.math.blas.BLAS
-
Sums the absolute values of the elements of a vector.
- 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 class smile.math.blas.mkl.MKL
-
- asum(int, float[], 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 class smile.math.blas.openblas.OpenBLAS
-
- ata() - Method in class smile.math.matrix.FloatMatrix
-
Returns A' * A
- ata() - Method in class smile.math.matrix.FloatSparseMatrix
-
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
-
Applies Math.atan.
- atan(Term) - Static method in interface smile.data.formula.Terms
-
Applies Math.atan.
- 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
-
- AverageImputation - Class in smile.imputation
-
Impute missing values with the average of other attributes in the instance.
- AverageImputation() - Constructor for class smile.imputation.AverageImputation
-
Constructor.
- avg - Variable in class smile.validation.ClassificationValidations
-
The average of metrics.
- avg - Variable in class smile.validation.RegressionValidations
-
The average of metrics.
- Avro - Class in smile.io
-
Apache Avro is a data serialization system.
- Avro(Schema) - Constructor for class smile.io.Avro
-
Constructor.
- Avro(InputStream) - Constructor for class smile.io.Avro
-
Constructor.
- Avro(Path) - Constructor for class smile.io.Avro
-
Constructor.
- 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.
- Axis - Class in smile.plot.swing
-
This class describes an axis of a coordinate system.
- Axis(Base, int) - Constructor for class smile.plot.swing.Axis
-
Constructor.
- 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, 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(double, double[], double[]) - Method in interface smile.math.blas.BLAS
-
Computes a constant alpha times a vector x plus a vector 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 class smile.math.blas.mkl.MKL
-
- axpy(int, float, float[], int, float[], 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 class smile.math.blas.openblas.OpenBLAS
-
- 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.
- B - Variable in class smile.vq.BIRCH
-
The branching factor of non-leaf nodes.
- backpropagate(double[]) - Method in class smile.base.mlp.HiddenLayer
-
- backpropagate(double[]) - Method in class smile.base.mlp.Layer
-
Propagates the errors back to a lower layer.
- backpropagate(double[], boolean) - Method in class smile.base.mlp.MultilayerPerceptron
-
Propagates the errors back through the network.
- backpropagate(double[]) - Method in class smile.base.mlp.OutputLayer
-
- Bag - Class in smile.feature
-
The bag-of-words feature of text used in natural language
processing and information retrieval.
- Bag(String[]) - Constructor for class smile.feature.Bag
-
Constructor.
- Bag(String[], boolean) - Constructor for class smile.feature.Bag
-
Constructor.
- Bag - Class in smile.validation
-
A bag of random selected samples.
- Bag(int[], int[]) - Constructor for class smile.validation.Bag
-
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(int, int, int, int) - Constructor for class smile.math.matrix.BandMatrix
-
Constructor.
- BandMatrix(int, int, int, int, double[][]) - Constructor for class smile.math.matrix.BandMatrix
-
Constructor.
- BandMatrix.Cholesky - Class in smile.math.matrix
-
The Cholesky decomposition of a symmetric, positive-definite matrix.
- BandMatrix.LU - Class in smile.math.matrix
-
The LU decomposition.
- bandwidth() - Method in class smile.stat.distribution.KernelDensity
-
Returns the bandwidth of kernel.
- Bar - Class in smile.plot.swing
-
Bars with heights proportional to the value.
- Bar(double[][], double, Color) - Constructor for class smile.plot.swing.Bar
-
Constructor.
- BarPlot - Class in smile.plot.swing
-
A barplot draws bars with heights proportional to the value.
- BarPlot(Bar...) - Constructor for class smile.plot.swing.BarPlot
-
Constructor.
- BarPlot(Bar[], Legend[]) - Constructor for class smile.plot.swing.BarPlot
-
Constructor.
- Base - Class in smile.plot.swing
-
The coordinate base of PlotCanvas.
- Base(double[], double[]) - Constructor for class smile.plot.swing.Base
-
Constructor.
- Base(double[], double[], boolean) - Constructor for class smile.plot.swing.Base
-
Constructor.
- BaseVector<T,TS,S extends java.util.stream.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 data living in R^d.
- Bernoulli - Interface in smile.glm.model
-
The response variable is of Bernoulli distribution.
- 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(double) - Constructor for class smile.stat.distribution.BernoulliDistribution
-
Constructor.
- BernoulliDistribution(boolean[]) - Constructor for class smile.stat.distribution.BernoulliDistribution
-
Construct an Bernoulli from the given samples.
- 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 - Class in smile.math.special
-
The beta function, also called the Euler integral of the first kind.
- beta(double, double) - Static method in class smile.math.special.Beta
-
Beta function, also called the Euler integral of the first kind.
- beta - Variable in class smile.stat.distribution.BetaDistribution
-
The shape parameter.
- 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(Visitor) - Method in class smile.graph.AdjacencyList
-
- bfs() - Method in class smile.graph.AdjacencyMatrix
-
- bfs(Visitor) - Method in class smile.graph.AdjacencyMatrix
-
- bfs() - Method in interface smile.graph.Graph
-
Breadth-first search connected components of graph.
- 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.
- biasUpdate - Variable in class smile.base.mlp.Layer
-
The bias update.
- BIC() - Method in class smile.glm.GLM
-
Returns the BIC score.
- bic - Variable in class smile.stat.distribution.DiscreteExponentialFamilyMixture
-
The BIC score when the distribution is fit on a sample data.
- bic(double[]) - Method in class smile.stat.distribution.DiscreteMixture
-
BIC score of the mixture for given data.
- bic - Variable in class smile.stat.distribution.ExponentialFamilyMixture
-
The BIC score when the distribution is fit on a sample data.
- bic(double[]) - Method in class smile.stat.distribution.Mixture
-
The BIC score of the mixture for given 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.MultivariateMixture
-
BIC score of the mixture for given data.
- BIC(double, int, int) - Static method in interface smile.validation.ModelSelection
-
Bayesian information criterion.
- BiconjugateGradient - Class in smile.math.matrix
-
The biconjugate gradient method is an algorithm to
solve systems of linear equations.
- BiconjugateGradient() - Constructor for class smile.math.matrix.BiconjugateGradient
-
- BicubicInterpolation - Class in smile.interpolation
-
Bicubic interpolation in a two-dimensional regular grid.
- BicubicInterpolation(double[], double[], double[][]) - Constructor for class smile.interpolation.BicubicInterpolation
-
Constructor.
- 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(String, String) - Constructor for class smile.nlp.Bigram
-
Constructor.
- Bigram - Class in smile.nlp.collocation
-
Collocations are expressions of multiple words which commonly co-occur.
- Bigram(String, String, int, double) - Constructor for class smile.nlp.collocation.Bigram
-
Constructor.
- 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.
- 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.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(Formula, DataFrame) - Static method in class smile.classification.LogisticRegression
-
Fits binomial logistic regression.
- binomial(Formula, DataFrame, Properties) - Static method in class smile.classification.LogisticRegression
-
Fits binomial logistic regression.
- binomial(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(double[][], int[], double, double, int) - Static method in class smile.classification.LogisticRegression
-
Fits binomial logistic regression.
- Binomial(double[], double, double, IntSet) - Constructor for class smile.classification.LogisticRegression.Binomial
-
Constructor.
- binomial(int, int[][], int[]) - Static method in class smile.classification.Maxent
-
Learn maximum entropy classifier.
- binomial(int, int[][], int[], Properties) - Static method in class smile.classification.Maxent
-
Learn maximum entropy classifier.
- binomial(int, int[][], int[], double, double, int) - Static method in class smile.classification.Maxent
-
Learn maximum entropy classifier.
- Binomial(double[], double, double, IntSet) - Constructor for class smile.classification.Maxent.Binomial
-
Constructor.
- binomial(SparseDataset, 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(SparseDataset, int[], double, double, int) - Static method in class smile.classification.SparseLogisticRegression
-
Fits binomial logistic regression.
- Binomial(double[], double, double, IntSet) - Constructor for class smile.classification.SparseLogisticRegression.Binomial
-
Constructor.
- Binomial - Interface in smile.glm.model
-
The response variable is of Binomial distribution.
- 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(int, Fitness<BitString>) - Constructor for class smile.gap.BitString
-
Constructor.
- BitString(int, Fitness<BitString>, Crossover, double, double) - Constructor for class smile.gap.BitString
-
Constructor.
- 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.
- bk() - Method in class smile.math.matrix.FloatSymmMatrix
-
Bunch-Kaufman decomposition.
- bk() - Method in class smile.math.matrix.SymmMatrix
-
Bunch-Kaufman decomposition.
- BKTree<E> - Class in smile.neighbor
-
A BK-tree is a metric tree specifically adapted to discrete metric spaces.
- BKTree(Metric<E>) - Constructor for class smile.neighbor.BKTree
-
Constructor.
- BLACK - Static variable in interface smile.plot.swing.Palette
-
- BLAS - Interface in smile.math.blas
-
Basic Linear Algebra Subprograms.
- blas() - Method in enum smile.math.blas.Diag
-
Returns the byte value for BLAS.
- blas() - Method in enum smile.math.blas.Layout
-
Returns the byte value for BLAS.
- blas() - Method in enum smile.math.blas.Side
-
Returns the byte value for BLAS.
- blas() - Method in enum smile.math.blas.Transpose
-
Returns the byte value for BLAS.
- blas() - Method in enum smile.math.blas.UPLO
-
Returns the byte value for BLAS.
- BLUE - Static variable in interface smile.plot.swing.Palette
-
- 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.
- 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.
- BooleanPattern - Static variable in interface smile.data.type.DataType
-
Regex for boolean.
- 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(String) - Method in interface smile.data.DataFrame
-
Selects column based on the column name.
- booleanVector(Enum<?>) - Method in interface smile.data.DataFrame
-
Selects column using an enum value.
- booleanVector(int) - Method in class smile.data.IndexDataFrame
-
- 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.
- 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.
- BoxPlot - Class in smile.plot.swing
-
A boxplot is a convenient way of graphically depicting groups of numerical
data through their five-number summaries (the smallest observation
(sample minimum), lower quartile (Q1), median (Q2), upper quartile (Q3),
and largest observation (sample maximum).
- BoxPlot(double[][], String[]) - Constructor for class smile.plot.swing.BoxPlot
-
Constructor.
- BoxTest - Class in smile.timeseries
-
Portmanteau test jointly that several autocorrelations of time series
are zero.
- BoxTest.Type - Enum 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(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, 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[], int) - Static method in interface smile.math.Histogram
-
Returns the breakpoints between histogram cells for a dataset.
- breaks(double, double, int) - Static method in interface smile.math.Histogram
-
Returns the breakpoints between histogram cells for a given range.
- BROWN - Static variable in interface smile.plot.swing.Palette
-
- bubble(int) - Static method in interface smile.vq.Neighborhood
-
Returns the bubble neighborhood function.
- 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.
- bucket - Variable in class smile.neighbor.lsh.Bucket
-
The bucket id is given by the universal bucket hashing.
- build(int) - Method in class smile.base.mlp.HiddenLayerBuilder
-
- build(int) - Method in class smile.base.mlp.LayerBuilder
-
Creates a hidden layer.
- build(int) - Method in class smile.base.mlp.OutputLayerBuilder
-
- build() - Method in class smile.hash.PerfectMap.Builder
-
Builds the perfect map.
- Builder() - Constructor for class smile.hash.PerfectMap.Builder
-
Constructor.
- Builder(Map<String, T>) - Constructor for class smile.hash.PerfectMap.Builder
-
Constructor.
- BunchKaufman(FloatSymmMatrix, int[], int) - Constructor for class smile.math.matrix.FloatSymmMatrix.BunchKaufman
-
Constructor.
- BunchKaufman(SymmMatrix, int[], int) - Constructor for class smile.math.matrix.SymmMatrix.BunchKaufman
-
Constructor.
- BURGUNDY - Static variable in interface smile.plot.swing.Palette
-
- Button - Class in smile.swing
-
Action initialized JButton.
- Button(Action) - Constructor for class smile.swing.Button
-
Constructor.
- ButtonCellRenderer - Class in smile.swing.table
-
The ButtonCellRenderer class provides a renderer and an editor that looks
like a JButton.
- ButtonCellRenderer(JTable, Action, int) - Constructor for class smile.swing.table.ButtonCellRenderer
-
Create the ButtonCellRenderer to be used as a renderer and editor.
- ByteArrayCellRenderer - Class in smile.swing.table
-
Byte array renderer in JTable.
- ByteArrayCellRenderer() - Constructor for class smile.swing.table.ByteArrayCellRenderer
-
Constructor.
- 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(String) - Method in interface smile.data.DataFrame
-
Selects column based on the column name.
- byteVector(Enum<?>) - Method in interface smile.data.DataFrame
-
Selects column using an enum value.
- byteVector(int) - Method in class smile.data.IndexDataFrame
-
- ByteVector - Interface in smile.data.vector
-
An immutable byte vector.
- c(int...) - Static method in class smile.math.MathEx
-
Combines the arguments to form a vector.
- c(float...) - Static method in class smile.math.MathEx
-
Combines the arguments to form a vector.
- c(double...) - Static method in class smile.math.MathEx
-
Combines the arguments to form a vector.
- c(String...) - Static method in class smile.math.MathEx
-
Combines the arguments to form a vector.
- c(int[]...) - Static method in class smile.math.MathEx
-
Merges multiple vectors into one.
- c(float[]...) - Static method in class smile.math.MathEx
-
Merges multiple vectors into one.
- c(double[]...) - Static method in class smile.math.MathEx
-
Merges multiple vectors into one.
- c(String[]...) - Static method in class smile.math.MathEx
-
Concatenates multiple vectors into one array of strings.
- CADET_BLUE - Static variable in interface smile.plot.swing.Palette
-
- CANCEL_OPTION - Static variable in class smile.swing.FontChooser
-
Return value from showDialog().
- canvas() - Method in class smile.plot.swing.BarPlot
-
- canvas() - Method in class smile.plot.swing.BoxPlot
-
- Canvas - Class in smile.plot.swing
-
Canvas for mathematical plots.
- Canvas(double[], double[]) - Constructor for class smile.plot.swing.Canvas
-
Constructor
- Canvas(double[], double[], boolean) - Constructor for class smile.plot.swing.Canvas
-
Constructor
- canvas() - Method in class smile.plot.swing.Contour
-
- canvas() - Method in class smile.plot.swing.Dendrogram
-
- canvas() - Method in class smile.plot.swing.Heatmap
-
- canvas() - Method in class smile.plot.swing.Hexmap
-
- canvas() - Method in class smile.plot.swing.LinePlot
-
- canvas() - Method in class smile.plot.swing.Plot
-
Returns a canvas of the plot.
- canvas() - Method in class smile.plot.swing.ScreePlot
-
- canvas() - Method in class smile.plot.swing.SparseMatrixPlot
-
- canvas() - Method in class smile.plot.swing.StaircasePlot
-
- CART - Class in smile.base.cart
-
Classification and regression tree.
- CART(Formula, StructType, StructField, Node, double[]) - Constructor for class smile.base.cart.CART
-
Constructor.
- CART(DataFrame, StructField, int, int, int, int, int[], int[][]) - Constructor for class smile.base.cart.CART
-
Constructor.
- CategoricalEncoder - Enum 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(String...) - Constructor for class smile.data.measure.CategoricalMeasure
-
Constructor.
- CategoricalMeasure(List<String>) - Constructor for class smile.data.measure.CategoricalMeasure
-
Constructor.
- CategoricalMeasure(int[]) - Constructor for class smile.data.measure.CategoricalMeasure
-
Constructor.
- CategoricalMeasure(int[], String[]) - Constructor for class smile.data.measure.CategoricalMeasure
-
Constructor.
- cbind(int[]...) - Static method in class smile.math.MathEx
-
Take a sequence of vector arguments and combine by columns.
- cbind(float[]...) - Static method in class smile.math.MathEx
-
Take a sequence of vector arguments and combine by columns.
- cbind(double[]...) - Static method in class smile.math.MathEx
-
Take a sequence of vector arguments and combine by columns.
- cbind(String[]...) - Static method in class smile.math.MathEx
-
Take a sequence of vector arguments and combine by columns.
- cbrt(String) - Static method in interface smile.data.formula.Terms
-
Applies Math.cbrt.
- cbrt(Term) - Static method in interface smile.data.formula.Terms
-
Applies Math.cbrt.
- 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 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
-
- 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
-
- cdf2tiled(double) - Method in class smile.stat.distribution.TDistribution
-
Two-tailed cdf.
- ceil(String) - Static method in interface smile.data.formula.Terms
-
Applies Math.ceil.
- ceil(Term) - Static method in interface smile.data.formula.Terms
-
Applies Math.ceil.
- 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.
- 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(String) - Method in interface smile.data.DataFrame
-
Selects column based on the column name.
- charVector(Enum<?>) - Method in interface smile.data.DataFrame
-
Selects column using an enum value.
- charVector(int) - Method in class smile.data.IndexDataFrame
-
- 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.
- 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.
- 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(BandMatrix) - Constructor for class smile.math.matrix.BandMatrix.Cholesky
-
Constructor.
- cholesky() - Method in class smile.math.matrix.FloatBandMatrix
-
Cholesky decomposition for symmetric and positive definite matrix.
- Cholesky(FloatBandMatrix) - Constructor for class smile.math.matrix.FloatBandMatrix.Cholesky
-
Constructor.
- cholesky() - Method in class smile.math.matrix.FloatMatrix
-
Cholesky decomposition for symmetric and positive definite matrix.
- cholesky(boolean) - Method in class smile.math.matrix.FloatMatrix
-
Cholesky decomposition for symmetric and positive definite matrix.
- Cholesky(FloatMatrix) - Constructor for class smile.math.matrix.FloatMatrix.Cholesky
-
Constructor.
- cholesky() - Method in class smile.math.matrix.FloatSymmMatrix
-
Cholesky decomposition for symmetric and positive definite matrix.
- Cholesky(FloatSymmMatrix) - Constructor for class smile.math.matrix.FloatSymmMatrix.Cholesky
-
Constructor.
- cholesky() - Method in class smile.math.matrix.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(Matrix) - Constructor for class smile.math.matrix.Matrix.Cholesky
-
Constructor.
- cholesky() - Method in class smile.math.matrix.SymmMatrix
-
Cholesky decomposition for symmetric and positive definite matrix.
- Cholesky(SymmMatrix) - Constructor for class smile.math.matrix.SymmMatrix.Cholesky
-
Constructor.
- CholeskyOfAtA() - Method in class smile.math.matrix.FloatMatrix.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.
- classification(int, T[], int[], BiFunction<T[], int[], 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.Bootstrap
-
Runs classification bootstrap validation.
- classification(int, T[], int[], BiFunction<T[], int[], M>) - Static method in interface smile.validation.CrossValidation
-
Runs classification cross validation.
- classification(int, Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in interface smile.validation.CrossValidation
-
Runs classification cross validation.
- classification(T[], int[], BiFunction<T[], int[], M>) - Static method in interface smile.validation.LOOCV
-
Runs leave-one-out cross validation tests.
- classification(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in interface smile.validation.LOOCV
-
Runs leave-one-out cross validation tests.
- 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, int[], int[], double, double) - Constructor for class smile.validation.ClassificationValidation
-
Constructor.
- ClassificationValidation(M, int[], int[], double[][], double, 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.
- 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.plot.swing.Canvas
-
Remove all graphic plots from the canvas.
- 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.
- clearClip() - Method in class smile.plot.swing.Graphics
-
Clear the restriction of the draw area.
- clip() - Method in class smile.plot.swing.Graphics
-
Restrict the draw area to the valid base coordinate space.
- clone(int[][]) - 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(double[][]) - Static method in class smile.math.MathEx
-
Deep clone a two-dimensional array.
- clone() - Method in class smile.math.matrix.BandMatrix
-
- clone() - Method in class smile.math.matrix.FloatBandMatrix
-
- clone() - Method in class smile.math.matrix.FloatMatrix
-
Returns a deep copy of matrix.
- clone() - Method in class smile.math.matrix.FloatSparseMatrix
-
- clone() - Method in class smile.math.matrix.FloatSymmMatrix
-
- 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
-
- close() - Method in class smile.io.Arff
-
- 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.
- 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.FloatMatrix
-
Returns the j-th column.
- col(int...) - Method in class smile.math.matrix.FloatMatrix
-
Returns the matrix of selected columns.
- col(int) - Method in class smile.math.matrix.Matrix
-
Returns the j-th column.
- col(int...) - Method in class smile.math.matrix.Matrix
-
Returns the matrix of selected columns.
- collect(Class<T>) - Static method in interface smile.data.DataFrame
-
Returns a stream collector that accumulates objects into a DataFrame.
- collect() - Static method in interface smile.data.DataFrame
-
Returns a stream collector that accumulates tuples into a DataFrame.
- colMax(int[][]) - Static method in class smile.math.MathEx
-
Returns the column maximum for a matrix.
- colMax(double[][]) - Static method in class smile.math.MathEx
-
Returns the column maximum for a matrix.
- colMeans(double[][]) - Static method in class smile.math.MathEx
-
Returns the column means for a matrix.
- colMeans() - Method in class smile.math.matrix.FloatMatrix
-
Returns the mean of each column.
- colMeans() - Method in class smile.math.matrix.Matrix
-
Returns the mean of each column.
- colMin(int[][]) - Static method in class smile.math.MathEx
-
Returns the column minimum for a matrix.
- colMin(double[][]) - Static method in class smile.math.MathEx
-
Returns the column minimum for a matrix.
- colName(int) - Method in class smile.math.matrix.IMatrix
-
Returns the name of i-th column.
- colNames() - Method in class smile.math.matrix.IMatrix
-
Returns the column names.
- colNames(String[]) - Method in class smile.math.matrix.IMatrix
-
Sets the column names.
- ColorCellEditor - Class in smile.swing.table
-
Color editor in JTable.
- ColorCellEditor() - Constructor for class smile.swing.table.ColorCellEditor
-
Constructor.
- ColorCellRenderer - Class in smile.swing.table
-
Color renderer in JTable.
- ColorCellRenderer() - Constructor for class smile.swing.table.ColorCellRenderer
-
Constructor.
- ColorCellRenderer(boolean) - Constructor for class smile.swing.table.ColorCellRenderer
-
Constructor.
- COLORS - Static variable in interface smile.plot.swing.Palette
-
- colSds(double[][]) - Static method in class smile.math.MathEx
-
Returns the column deviations for a matrix.
- colSds() - Method in class smile.math.matrix.FloatMatrix
-
Returns the standard deviations of each column.
- colSds() - Method in class smile.math.matrix.Matrix
-
Returns the standard deviations of each column.
- colSums(int[][]) - Static method in class smile.math.MathEx
-
Returns the column sums for a matrix.
- colSums(double[][]) - Static method in class smile.math.MathEx
-
Returns the column sums for a matrix.
- colSums() - Method in class smile.math.matrix.FloatMatrix
-
Returns the sum of each column.
- colSums() - Method in class smile.math.matrix.Matrix
-
Returns the sum of each column.
- column(int) - Method in interface smile.data.DataFrame
-
Selects column based on the column index.
- column(String) - Method in interface smile.data.DataFrame
-
Selects column based on the column name.
- column(Enum<?>) - Method in interface smile.data.DataFrame
-
Selects column using an enum value.
- column(int) - Method in class smile.data.IndexDataFrame
-
- columnAdded(TableColumnModelEvent) - Method in class smile.swing.table.TableColumnSettings
-
- columnIndex(String) - Method in interface smile.data.DataFrame
-
Returns the index of a given column name.
- columnIndex(String) - Method in class smile.data.IndexDataFrame
-
- columnMarginChanged(ChangeEvent) - Method in class smile.swing.table.TableColumnSettings
-
- columnMoved(TableColumnModelEvent) - Method in class smile.swing.table.TableColumnSettings
-
- columnRemoved(TableColumnModelEvent) - Method in class smile.swing.table.TableColumnSettings
-
- columnSelectionChanged(ListSelectionEvent) - Method in class smile.swing.table.TableColumnSettings
-
- comparator - Static variable in class smile.base.cart.Split
-
- compareTo(CentroidClustering<T, U>) - Method in class smile.clustering.CentroidClustering
-
- compareTo(MEC<T>) - Method in class smile.clustering.MEC
-
- 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.projection.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.
- 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.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.
- condition() - Method in class smile.math.matrix.FloatMatrix.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.
- consequent - Variable in class smile.association.AssociationRule
-
Consequent itemset.
- Constant - Class in smile.data.formula
-
A constant value in the formula.
- Constant() - Constructor for class smile.data.formula.Constant
-
- constant(double) - Static method in interface smile.math.TimeFunction
-
Returns the constant learning rate.
- contains(double[][], double[]) - Static method in class smile.math.MathEx
-
Determines if the polygon contains the specified coordinates.
- contains(double[][], double, double) - Static method in class smile.math.MathEx
-
Determines if the polygon contains the specified coordinates.
- contains(String) - Method in interface smile.nlp.dictionary.Dictionary
-
Returns true if this dictionary contains the specified word.
- contains(String) - Method in enum smile.nlp.dictionary.EnglishDictionary
-
- contains(String) - Method in class smile.nlp.dictionary.EnglishPunctuations
-
- contains(String) - Method in enum smile.nlp.dictionary.EnglishStopWords
-
- contains(String) - Method in class smile.nlp.dictionary.SimpleDictionary
-
- contains(int) - Method in class smile.util.IntHashSet
-
Returns true if this set contains the specified element.
- Contour - Class in smile.plot.swing
-
A contour plot is a graphical technique for representing a 3-dimensional
surface by plotting constant z slices, called contours, on a 2-dimensional
format.
- Contour(double[][], int, boolean) - Constructor for class smile.plot.swing.Contour
-
Constructor.
- Contour(double[][], double[]) - Constructor for class smile.plot.swing.Contour
-
Constructor.
- Contour(double[], double[], double[][], int, boolean) - Constructor for class smile.plot.swing.Contour
-
Constructor.
- Contour(double[], double[], double[][], double[]) - Constructor for class smile.plot.swing.Contour
-
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.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.TSNE
-
The coordinate matrix in embedding space.
- coordinates - Variable in class smile.manifold.UMAP
-
The coordinate matrix in embedding space.
- coordinates - Variable in class smile.mds.IsotonicMDS
-
The coordinates.
- coordinates - Variable in class smile.mds.MDS
-
The principal coordinates.
- coordinates - Variable in class smile.mds.SammonMapping
-
The coordinates.
- copy(int[], int[]) - Static method in class smile.math.MathEx
-
Copy x into y.
- copy(float[], float[]) - Static method in class smile.math.MathEx
-
Copy x into y.
- copy(double[], double[]) - Static method in class smile.math.MathEx
-
Copy x into y.
- copy(int[][], int[][]) - Static method in class smile.math.MathEx
-
Copy x into y.
- copy(float[][], float[][]) - Static method in class smile.math.MathEx
-
Copy x into y.
- copy(double[][], double[][]) - Static method in class smile.math.MathEx
-
Copy x into y.
- cor(int[], int[]) - 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(double[], double[]) - Static method in class smile.math.MathEx
-
Returns the correlation coefficient between two vectors.
- 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[][]) - Static method in class smile.projection.PCA
-
Fits principal component analysis with correlation matrix.
- cor - Variable in class smile.stat.hypothesis.CorTest
-
Correlation coefficient
- 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.
- cos(String) - Static method in interface smile.data.formula.Terms
-
Applies Math.cos.
- cos(Term) - Static method in interface smile.data.formula.Terms
-
Applies Math.cos.
- cos() - Method in class smile.math.Complex
-
Returns the complex cosine.
- cos(float[], float[]) - Static method in class smile.math.MathEx
-
Returns the cosine similarity.
- cos(double[], double[]) - Static method in class smile.math.MathEx
-
Returns the cosine similarity.
- cosh(String) - Static method in interface smile.data.formula.Terms
-
Applies Math.cosh.
- cosh(Term) - Static method in interface smile.data.formula.Terms
-
Applies Math.cosh.
- Cost - Enum in smile.base.mlp
-
Neural network cost function.
- cost() - Method in class smile.base.mlp.OutputLayer
-
Returns the cost function of neural network.
- count() - Method in class smile.base.cart.DecisionNode
-
Returns the number of node samples in each class.
- 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.
- counter - Variable in class smile.vq.hebb.Neuron
-
The local counter variable (e.g.
- cov(int[], int[]) - Static method in class smile.math.MathEx
-
Returns the covariance between two vectors.
- cov(float[], float[]) - Static method in class smile.math.MathEx
-
Returns the covariance between two vectors.
- cov(double[], double[]) - Static method in class smile.math.MathEx
-
Returns the covariance between two vectors.
- 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 - 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[], int) - Static method in interface smile.timeseries.TimeSeries
-
Autocovariance function.
- CoverTree<E> - 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(E[], Metric<E>) - Constructor for class smile.neighbor.CoverTree
-
Constructor.
- CoverTree(E[], Metric<E>, double) - Constructor for class smile.neighbor.CoverTree
-
Constructor.
- 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(String...) - Static method in interface smile.data.formula.Terms
-
Factor crossing of two or more factors.
- cross(int, 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 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 - 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.
- csv(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.
- 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.
- Currency - Static variable in interface smile.data.measure.Measure
-
Currency.
- CURRENCY - Static variable in class smile.swing.table.NumberCellRenderer
-
- CYAN - Static variable in interface smile.plot.swing.Palette
-
- 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.ChebyshevDistance
-
Chebyshev distance between the two arrays of type integer.
- d(float[], float[]) - Static method in class smile.math.distance.ChebyshevDistance
-
Chebyshev distance between the two arrays of type float.
- 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(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.
- d(T[], T[]) - Method in class smile.math.distance.DynamicTimeWarping
-
- d(int[], int[]) - Static method in class smile.math.distance.DynamicTimeWarping
-
Dynamic time warping without path constraints.
- 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(float[], float[]) - Static method in class smile.math.distance.DynamicTimeWarping
-
Dynamic time warping without path constraints.
- 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(double[], double[]) - Static method in class smile.math.distance.DynamicTimeWarping
-
Dynamic time warping without path constraints.
- 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(String, String) - Method in class smile.math.distance.EditDistance
-
Edit distance between two strings.
- d(char[], char[]) - Method in class smile.math.distance.EditDistance
-
Edit distance between two strings.
- d(int[], int[]) - Method in class smile.math.distance.EuclideanDistance
-
Euclidean distance between the two arrays of type integer.
- d(float[], float[]) - Method in class smile.math.distance.EuclideanDistance
-
Euclidean distance between the two arrays of type float.
- d(double[], double[]) - Method in class smile.math.distance.EuclideanDistance
-
Euclidean distance between the two arrays of type double.
- d(BitSet, BitSet) - Method in class smile.math.distance.HammingDistance
-
- d(byte, byte) - Static method in class smile.math.distance.HammingDistance
-
Returns Hamming distance between the two bytes.
- d(short, short) - Static method in class smile.math.distance.HammingDistance
-
Returns Hamming distance between the two shorts.
- 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(byte[], byte[]) - Static method in class smile.math.distance.HammingDistance
-
Returns Hamming distance between the two byte arrays.
- d(short[], short[]) - Static method in class smile.math.distance.HammingDistance
-
Returns Hamming distance between the two short arrays.
- d(int[], int[]) - Static method in class smile.math.distance.HammingDistance
-
Returns Hamming distance between the two integer arrays.
- d(T[], T[]) - Method in class smile.math.distance.JaccardDistance
-
- d(Set<T>, Set<T>) - Static method in class smile.math.distance.JaccardDistance
-
Returns the Jaccard distance between sets.
- d(double[], double[]) - Method in class smile.math.distance.JensenShannonDistance
-
- d(int[], int[]) - Method in class smile.math.distance.LeeDistance
-
- d(double[], double[]) - Method in class smile.math.distance.MahalanobisDistance
-
- d(int[], int[]) - Method in class smile.math.distance.ManhattanDistance
-
Manhattan distance between two arrays of type integer.
- d(float[], float[]) - Method in class smile.math.distance.ManhattanDistance
-
Manhattan distance between two arrays of type float.
- d(double[], double[]) - Method in class smile.math.distance.ManhattanDistance
-
Manhattan distance between two arrays of type double.
- d(int[], int[]) - Method in class smile.math.distance.MinkowskiDistance
-
Minkowski distance between the two arrays of type integer.
- d(float[], float[]) - Method in class smile.math.distance.MinkowskiDistance
-
Minkowski distance between the two arrays of type float.
- d(double[], double[]) - Method in class smile.math.distance.MinkowskiDistance
-
Minkowski distance between the two arrays of type double.
- 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 - Variable in class smile.stat.hypothesis.KSTest
-
Kolmogorov-Smirnov statistic
- d(String, String) - Method in class smile.taxonomy.TaxonomicDistance
-
Compute the distance between two concepts in a taxonomy.
- d(Concept, Concept) - Method in class smile.taxonomy.TaxonomicDistance
-
Compute the distance between two concepts in a taxonomy.
- d - Variable in class smile.vq.BIRCH
-
The dimensionality of data.
- 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(String, String) - Static method in class smile.math.distance.EditDistance
-
Damerau-Levenshtein distance between two strings allows insertion,
deletion, substitution, or transposition of characters.
- 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.
- DARK_BLUE - Static variable in interface smile.plot.swing.Palette
-
- DARK_CYAN - Static variable in interface smile.plot.swing.Palette
-
- DARK_GRAY - Static variable in interface smile.plot.swing.Palette
-
- DARK_GREEN - Static variable in interface smile.plot.swing.Palette
-
- DARK_MAGENTA - Static variable in interface smile.plot.swing.Palette
-
- DARK_PURPLE - Static variable in interface smile.plot.swing.Palette
-
- DARK_RED - Static variable in interface smile.plot.swing.Palette
-
- DARK_SLATE_GRAY - Static variable in interface smile.plot.swing.Palette
-
- data - Variable in class smile.neighbor.LSH
-
The data objects.
- DataFrame - Interface in smile.data
-
An immutable collection of data organized into named columns.
- DataFrameClassifier - Interface in smile.classification
-
Classification trait on DataFrame.
- DataFrameRegression - Interface in smile.regression
-
Regression trait on DataFrame.
- Dataset<T> - Interface in smile.data
-
An immutable collection of data objects.
- DataType - Interface in smile.data.type
-
The interface of data types.
- DataType.ID - Enum 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 - Class in smile.data.formula
-
Date/time feature extractor.
- Date(String, DateFeature...) - Constructor for class smile.data.formula.Date
-
Constructor.
- date(String, DateFeature...) - Static method in interface smile.data.formula.Terms
-
Extracts date/time features.
- date(String) - Static method in class smile.data.type.DataTypes
-
Date data type with customized format.
- DateCellEditor - Class in smile.swing.table
-
Implements a cell editor that uses a formatted text field
to edit Date values.
- DateCellEditor(String) - Constructor for class smile.swing.table.DateCellEditor
-
Constructor.
- DateCellEditor(DateFormat) - Constructor for class smile.swing.table.DateCellEditor
-
Constructor.
- DateCellRenderer - Class in smile.swing.table
-
Date cell renderer.
- DateCellRenderer(String) - Constructor for class smile.swing.table.DateCellRenderer
-
- DateCellRenderer(DateFormat) - Constructor for class smile.swing.table.DateCellRenderer
-
- DateFeature - Enum in smile.data.formula
-
The date/time features.
- DatePattern - Static variable in interface smile.data.type.DataType
-
Regex for date.
- datetime(String) - Static method in class smile.data.type.DataTypes
-
DateTime data type with customized format.
- DateTimePattern - Static variable in interface smile.data.type.DataType
-
Regex for datetime.
- DateTimeType - Static variable in class smile.data.type.DataTypes
-
DateTime data type with ISO format.
- DateTimeType - Class in smile.data.type
-
DateTime data type.
- DateTimeType(String) - Constructor for class smile.data.type.DateTimeType
-
Constructor.
- DateType - Static variable in class smile.data.type.DataTypes
-
Date data type with ISO format.
- DateType - Class in smile.data.type
-
Date data type.
- 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.
- DBSCAN<T> - Class in smile.clustering
-
Density-Based Spatial Clustering of Applications with Noise.
- DBSCAN(int, double, RNNSearch<T, T>, int, int[]) - Constructor for class smile.clustering.DBSCAN
-
Constructor.
- decimal - Static variable in interface smile.util.Strings
-
Decimal format for floating numbers.
- DecimalType - Static variable in class smile.data.type.DataTypes
-
Decimal data type.
- DecimalType - Class in smile.data.type
-
Arbitrary-precision 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.
- DefaultTableHeaderCellRenderer - Class in smile.swing.table
-
A default cell renderer for a JTableHeader.
- DefaultTableHeaderCellRenderer() - Constructor for class smile.swing.table.DefaultTableHeaderCellRenderer
-
Constructs a
DefaultTableHeaderCellRenderer.
- degree() - Method in class smile.math.kernel.Polynomial
-
Returns the degree of kernel.
- 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.
- Dendrogram - Class in smile.plot.swing
-
A dendrogram is a tree diagram frequently used to illustrate the arrangement
of the clusters produced by hierarchical clustering.
- Dendrogram(int[][], double[]) - Constructor for class smile.plot.swing.Dendrogram
-
Constructor.
- Dendrogram(int[][], double[], Color) - Constructor for class smile.plot.swing.Dendrogram
-
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.FloatBandMatrix.Cholesky
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.FloatBandMatrix.LU
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.FloatMatrix.Cholesky
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.FloatMatrix.LU
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.FloatSymmMatrix.BunchKaufman
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.FloatSymmMatrix.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() - Method in class smile.base.cart.DecisionNode
-
- deviance(int[], double[]) - Static method in class smile.base.cart.DecisionNode
-
Returns the deviance of node.
- 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 - Variable in class smile.glm.GLM
-
The deviance = 2 * (LogLikelihood(Saturated Model) - LogLikelihood(Proposed Model)).
- 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.
- 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() - Method in class smile.regression.LinearModel
-
Returns the degree-of-freedom of residual standard error.
- df - Variable in class smile.stat.hypothesis.ChiSqTest
-
The degree of freedom of chisq-statistic.
- df - Variable in class smile.stat.hypothesis.CorTest
-
Degree of freedom
- df - Variable in class smile.stat.hypothesis.TTest
-
The degree of freedom of t-statistic.
- 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.
- df - Variable in class smile.timeseries.BoxTest
-
The degree of freedom.
- 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(Visitor) - Method in class smile.graph.AdjacencyList
-
- dfs() - Method in class smile.graph.AdjacencyMatrix
-
- dfs(Visitor) - Method in class smile.graph.AdjacencyMatrix
-
- dfs() - Method in interface smile.graph.Graph
-
Depth-first search connected components of graph.
- 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 - Enum in smile.math.blas
-
The flag if a triangular matrix has unit diagonal elements.
- diag() - Method in class smile.math.matrix.DMatrix
-
Returns the diagonal elements.
- diag(float[]) - Static method in class smile.math.matrix.FloatMatrix
-
Returns a square diagonal matrix with the elements of vector
v on the main diagonal.
- diag() - Method in class smile.math.matrix.FloatMatrix.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.FloatMatrix.SVD
-
Returns the diagonal matrix of singular values.
- diag() - Method in class smile.math.matrix.FloatSparseMatrix
-
- diag(double[]) - Static method in class smile.math.matrix.Matrix
-
Returns a square diagonal matrix with the elements of vector
v on the main diagonal.
- 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.SMatrix
-
Returns the diagonal elements.
- diag() - Method in class smile.math.matrix.SparseMatrix
-
- diagonal - Variable in class smile.stat.distribution.MultivariateGaussianDistribution
-
True if the covariance matrix is diagonal.
- dialogResultValue - Variable in class smile.swing.FontChooser
-
- 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(int) - Method in class smile.graph.AdjacencyList
-
- dijkstra(int) - Method in class smile.graph.AdjacencyMatrix
-
- dijkstra(int, boolean) - Method in class smile.graph.AdjacencyMatrix
-
Calculates the shortest path by Dijkstra algorithm.
- 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() - Method in interface smile.graph.Graph
-
Calculates the all pair 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 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, 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(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.Model - Enum in smile.classification
-
The generation models of naive Bayes classifier.
- distance(T, U) - Method in class smile.clustering.CentroidClustering
-
The distance function.
- distance(T, T) - Method in class smile.clustering.CLARANS
-
- 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(int[], int[]) - Method in class smile.clustering.KModes
-
- distance(double[], SparseArray) - Method in class smile.clustering.SIB
-
- distance(double[], double[]) - Method in class smile.clustering.XMeans
-
- Distance<T> - Interface in smile.math.distance
-
An interface to calculate a distance measure between two objects.
- 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(float[], float[]) - Static method in class smile.math.MathEx
-
The Euclidean distance.
- distance(double[], double[]) - Static method in class smile.math.MathEx
-
The Euclidean distance.
- distance(SparseArray, SparseArray) - Static method in class smile.math.MathEx
-
The Euclidean distance.
- 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.
- 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 - Interface in smile.stat.distribution
-
Probability distribution of univariate random variable.
- 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.
- Div - Class in smile.data.formula
-
The term of a / b expression.
- Div(Term, Term) - Constructor for class smile.data.formula.Div
-
Constructor.
- div(Term, Term) - Static method in interface smile.data.formula.Terms
-
Divides two terms.
- div(String, String) - 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(String, Term) - Static method in interface smile.data.formula.Terms
-
Divides two terms.
- div(Complex) - Method in class smile.math.Complex
-
Returns a / b.
- div(int, int, float) - Method in class smile.math.matrix.FloatMatrix
-
A[i,j] /= b
- div(float) - Method in class smile.math.matrix.FloatMatrix
-
A /= b
- div(int, int, float, FloatMatrix) - Method in class smile.math.matrix.FloatMatrix
-
Element-wise submatrix division A[i, j] /= alpha * B
- div(FloatMatrix) - Method in class smile.math.matrix.FloatMatrix
-
Element-wise division A /= B
- div(float, FloatMatrix) - Method in class smile.math.matrix.FloatMatrix
-
Element-wise division A /= alpha * B
- div(float, FloatMatrix, FloatMatrix) - Method in class smile.math.matrix.FloatMatrix
-
Element-wise division C = alpha * A / B
- div(int, int, double) - Method in class smile.math.matrix.Matrix
-
A[i,j] /= b
- div(double) - Method in class smile.math.matrix.Matrix
-
A /= b
- div(int, int, double, Matrix) - Method in class smile.math.matrix.Matrix
-
Element-wise submatrix division A[i, j] /= alpha * B
- div(Matrix) - Method in class smile.math.matrix.Matrix
-
Element-wise division A /= B
- div(double, Matrix) - Method in class smile.math.matrix.Matrix
-
Element-wise division A /= alpha * B
- div(double, Matrix, Matrix) - Method in class smile.math.matrix.Matrix
-
Element-wise division C = alpha * A / B
- div(int, int, double) - Method in class smile.util.Array2D
-
- div(Array2D) - Method in class smile.util.Array2D
-
- div(double) - Method in class smile.util.Array2D
-
- div(int, int, int) - Method in class smile.util.IntArray2D
-
- div(IntArray2D) - Method in class smile.util.IntArray2D
-
- div(int) - Method in class smile.util.IntArray2D
-
- dlink(double) - Method in interface smile.glm.model.Model
-
The derivative of link function.
- DMatrix - Class in smile.math.matrix
-
Double precision matrix base class.
- DMatrix() - Constructor for class smile.math.matrix.DMatrix
-
- dot() - Method in class smile.base.cart.CART
-
Returns the graphic representation in Graphviz dot format.
- dot(StructType, StructField, int) - Method in class smile.base.cart.DecisionNode
-
- dot(StructType, StructField, int) - Method in interface smile.base.cart.Node
-
Returns a dot representation for visualization.
- 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() - 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(int, double[], int, double[], int) - Method in interface smile.math.blas.BLAS
-
Computes the dot product of two vectors.
- dot(int, float[], int, float[], int) - Method in interface smile.math.blas.BLAS
-
Computes the dot product of two vectors.
- dot(double[], double[]) - Method in interface smile.math.blas.BLAS
-
Computes the dot product of two vectors.
- dot(float[], float[]) - 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, float[], int, float[], 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 class smile.math.blas.openblas.OpenBLAS
-
- 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(float[], float[]) - Static method in class smile.math.MathEx
-
Returns the dot product between two vectors.
- dot(double[], double[]) - Static method in class smile.math.MathEx
-
Returns the dot product between two vectors.
- 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.
- DoubleArrayCellEditor - Class in smile.swing.table
-
Implements a cell editor that uses a formatted text field
to edit double[] values.
- DoubleArrayCellEditor() - Constructor for class smile.swing.table.DoubleArrayCellEditor
-
Constructor.
- DoubleArrayCellRenderer - Class in smile.swing.table
-
Double array renderer in JTable.
- DoubleArrayCellRenderer() - Constructor for class smile.swing.table.DoubleArrayCellRenderer
-
Constructor.
- 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(int) - Constructor for class smile.util.DoubleArrayList
-
Constructs an empty list with the specified initial capacity.
- DoubleArrayList(double[]) - Constructor for class smile.util.DoubleArrayList
-
Constructs a list containing the values of the specified array.
- DoubleArrayType - Static variable in class smile.data.type.DataTypes
-
Double Array data type.
- DoubleCellEditor - Class in smile.swing.table
-
Implements a cell editor that uses a formatted text field
to edit Double values.
- DoubleCellEditor() - Constructor for class smile.swing.table.DoubleCellEditor
-
Constructor.
- DoubleCellEditor(double, double) - Constructor for class smile.swing.table.DoubleCellEditor
-
Constructor.
- 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(int) - Constructor for class smile.sort.DoubleHeapSelect
-
Constructor.
- DoubleHeapSelect(double[]) - Constructor for class smile.sort.DoubleHeapSelect
-
Constructor.
- DoubleObjectType - Static variable in class smile.data.type.DataTypes
-
Double Object data type.
- DoublePattern - Static variable in interface smile.data.type.DataType
-
Regex for double.
- DoubleType - Static variable in class smile.data.type.DataTypes
-
Double data type.
- DoubleType - Class in smile.data.type
-
Double data type.
- doubleVector(int) - Method in interface smile.data.DataFrame
-
Selects column based on the column index.
- doubleVector(String) - Method in interface smile.data.DataFrame
-
Selects column based on the column name.
- doubleVector(Enum<?>) - Method in interface smile.data.DataFrame
-
Selects column using an enum value.
- doubleVector(int) - Method in class smile.data.IndexDataFrame
-
- DoubleVector - Interface in smile.data.vector
-
An immutable double vector.
- drawLine(double[]...) - Method in class smile.plot.swing.Graphics
-
Draw poly line.
- drawLineBaseRatio(double[]...) - Method in class smile.plot.swing.Graphics
-
Draw poly line.
- drawPoint(double...) - Method in class smile.plot.swing.Graphics
-
Draw a dot.
- drawPoint(char, double...) - Method in class smile.plot.swing.Graphics
-
Draw a dot with given pattern.
- drawPolygon(double[]...) - Method in class smile.plot.swing.Graphics
-
Draw polygon.
- drawRect(double[], double[]) - Method in class smile.plot.swing.Graphics
-
Draw the outline of the specified rectangle.
- drawRectBaseRatio(double[], double[]) - Method in class smile.plot.swing.Graphics
-
Draw the outline of the specified rectangle.
- drawText(String, double[]) - Method in class smile.plot.swing.Graphics
-
Draw a string.
- drawText(String, double[], double) - Method in class smile.plot.swing.Graphics
-
Draw a string with given rotation angle.
- drawText(String, double[], double, double) - Method in class smile.plot.swing.Graphics
-
Draw a string with given reference point.
- drawText(String, double[], double, double, double) - Method in class smile.plot.swing.Graphics
-
Draw a string with given reference point and rotation angle.
- drawTextBaseRatio(String, double[]) - Method in class smile.plot.swing.Graphics
-
Draw a string with given rotation angle.
- drawTextBaseRatio(String, double[], double) - Method in class smile.plot.swing.Graphics
-
Draw a string with given rotation angle.
- drawTextBaseRatio(String, double[], double, double) - Method in class smile.plot.swing.Graphics
-
Draw a string with given reference point.
- drawTextBaseRatio(String, double[], double, double, double) - Method in class smile.plot.swing.Graphics
-
Draw a string with given reference point and rotation angle.
- drop(int...) - Method in interface smile.data.DataFrame
-
Returns a new DataFrame without given column indices.
- drop(String...) - Method in interface smile.data.DataFrame
-
Returns a new DataFrame without given column names.
- drop(int...) - Method in class smile.data.IndexDataFrame
-
- 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.
- Edge(int, int, double) - Constructor for class smile.graph.Graph.Edge
-
Constructor.
- Edge - Class in smile.vq.hebb
-
The connection between neurons.
- 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, boolean) - 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.
- eigen(DMatrix, ARPACK.AsymmOption, int) - Static method in interface smile.math.matrix.ARPACK
-
Computes NEV eigenvalues of an asymmetric double precision matrix.
- eigen(DMatrix, ARPACK.AsymmOption, int, int, double) - Static method in interface smile.math.matrix.ARPACK
-
Computes NEV eigenvalues of an asymmetric double precision matrix.
- eigen(SMatrix, ARPACK.AsymmOption, int) - Static method in interface smile.math.matrix.ARPACK
-
Computes NEV eigenvalues of an asymmetric single precision matrix.
- eigen(SMatrix, ARPACK.AsymmOption, int, int, float) - Static method in interface smile.math.matrix.ARPACK
-
Computes NEV eigenvalues of an asymmetric single precision matrix.
- eigen() - Method in class smile.math.matrix.FloatMatrix
-
Eigenvalue Decomposition.
- eigen(boolean, boolean, boolean) - Method in class smile.math.matrix.FloatMatrix
-
Eigenvalue Decomposition.
- eigen(DMatrix, int) - Static method in class smile.math.matrix.Lanczos
-
Find k largest approximate eigen pairs of a symmetric matrix by the
Lanczos algorithm.
- eigen(DMatrix, 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() - Method in class smile.math.matrix.Matrix
-
Eigenvalue Decomposition.
- eigen(boolean, boolean, boolean) - Method in class smile.math.matrix.Matrix
-
Eigenvalue Decomposition.
- eigen(DMatrix, double[]) - Static method in class smile.math.matrix.PowerIteration
-
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(DMatrix, double[], double, double, int) - Static method in class smile.math.matrix.PowerIteration
-
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.
- EigenRange - Enum in smile.math.blas
-
THe option of eigenvalue range.
- ElasticNet - Class in smile.regression
-
Elastic Net regularization.
- ElasticNet() - Constructor for class smile.regression.ElasticNet
-
- 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.
- empty() - Method in class smile.util.PriorityQueue
-
Returns true if the queue is empty.
- 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 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 in smile.nlp.dictionary
-
Several sets of English stop words.
- 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(double[]) - Static method in class smile.math.MathEx
-
Shannon's entropy.
- 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
-
- 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 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(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(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.
- 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.
- 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.
- 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.
- equals(Object) - Method in class smile.math.matrix.BandMatrix
-
- equals(BandMatrix, double) - Method in class smile.math.matrix.BandMatrix
-
Returns if two matrices equals given an error margin.
- equals(Object) - Method in class smile.math.matrix.FloatBandMatrix
-
- equals(FloatBandMatrix, float) - Method in class smile.math.matrix.FloatBandMatrix
-
Returns if two matrices equals given an error margin.
- equals(Object) - Method in class smile.math.matrix.FloatMatrix
-
- equals(FloatMatrix, float) - Method in class smile.math.matrix.FloatMatrix
-
Returns if two matrices equals given an error margin.
- equals(Object) - Method in class smile.math.matrix.FloatSymmMatrix
-
- equals(FloatSymmMatrix, float) - Method in class smile.math.matrix.FloatSymmMatrix
-
Returns if two matrices equals given an error margin.
- equals(Object) - Method in class smile.math.matrix.Matrix
-
- equals(Matrix, double) - Method in class smile.math.matrix.Matrix
-
Returns if two matrices equals given an error margin.
- equals(Object) - Method in class smile.math.matrix.SymmMatrix
-
- equals(SymmMatrix, double) - Method in class smile.math.matrix.SymmMatrix
-
Returns if two matrices equals given an error margin.
- 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
-
- Erf - Class in smile.math.special
-
The error function.
- erf(double) - Static method in class smile.math.special.Erf
-
The Gauss 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() - Method in class smile.regression.LinearModel
-
Returns the residual standard error.
- error - Variable in class smile.validation.ClassificationMetrics
-
The number of errors.
- Error - Class in smile.validation.metric
-
The number of errors in the population.
- Error() - Constructor for class smile.validation.metric.Error
-
- ERROR_OPTION - Static variable in class smile.swing.FontChooser
-
Return value from showDialog().
- 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(float[], FloatMatrix) - Constructor for class smile.math.matrix.FloatMatrix.EVD
-
Constructor.
- EVD(float[], float[], FloatMatrix, FloatMatrix) - Constructor for class smile.math.matrix.FloatMatrix.EVD
-
Constructor.
- EVD(double[], Matrix) - Constructor for class smile.math.matrix.Matrix.EVD
-
Constructor.
- EVD(double[], double[], Matrix, Matrix) - Constructor for class smile.math.matrix.Matrix.EVD
-
Constructor.
- EVDJob - Enum in smile.math.blas
-
The option if computing eigen vectors.
- 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.
- evolve() - Method in interface smile.gap.LamarckianChromosome
-
Performs a step of (hill-climbing) local search to evolve this chromosome.
- exp(String) - Static method in interface smile.data.formula.Terms
-
Applies Math.exp.
- exp(Term) - Static method in interface smile.data.formula.Terms
-
Applies Math.exp.
- 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
initLearningRate * exp(-t / decaySteps).
- exp(double, double, double) - Static method in interface smile.math.TimeFunction
-
Returns the exponential decay function
initLearningRate * pow(endLearningRate / initLearningRate, min(t, decaySteps) / decaySteps).
- exp(double, double, double, boolean) - Static method in interface smile.math.TimeFunction
-
Returns the exponential decay function
initLearningRate * pow(decayRate, t / decaySteps).
- Exp - Class in smile.projection.ica
-
The contrast function when the independent components are highly
super-Gaussian, or when robustness is very important.
- Exp() - Constructor for class smile.projection.ica.Exp
-
- expand(StructType) - Method in class smile.data.formula.Formula
-
Expands the Dot and FactorCrossing terms on the given schema.
- 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.
- expm1(String) - Static method in interface smile.data.formula.Terms
-
Applies Math.expm1.
- expm1(Term) - Static method in interface smile.data.formula.Terms
-
Applies Math.expm1.
- 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.
- extendBound(int) - Method in class smile.plot.swing.Base
-
Rounds the bounds for axis i.
- extendBound(double[], double[]) - Method in class smile.plot.swing.Base
-
Extend lower and upper bounds.
- extendBound(double[], double[]) - Method in class smile.plot.swing.Canvas
-
Extend lower and upper bounds.
- extendLowerBound(double[]) - Method in class smile.plot.swing.Base
-
Extend lower bounds.
- extendLowerBound(double[]) - Method in class smile.plot.swing.Canvas
-
Extend lower bounds.
- extendUpperBound(double[]) - Method in class smile.plot.swing.Base
-
Extend upper bounds.
- extendUpperBound(double[]) - Method in class smile.plot.swing.Canvas
-
Extend upper bounds.
- eye(int) - Static method in class smile.math.matrix.FloatMatrix
-
Returns an n-by-n identity matrix.
- eye(int, int) - Static method in class smile.math.matrix.FloatMatrix
-
Returns an m-by-n identity matrix.
- eye(int) - Static method in class smile.math.matrix.Matrix
-
Returns an n-by-n identity matrix.
- eye(int, int) - Static method in class smile.math.matrix.Matrix
-
Returns an m-by-n identity matrix.
- f(double[]) - Method in interface smile.base.mlp.ActivationFunction
-
The output function.
- f(double[]) - Method in class smile.base.mlp.HiddenLayer
-
- f(double[]) - Method in class smile.base.mlp.Layer
-
The activation or output function.
- f(double[]) - Method in enum smile.base.mlp.OutputFunction
-
The output function.
- f(double[]) - Method in class smile.base.mlp.OutputLayer
-
- f(T) - Method in class smile.base.rbf.RBF
-
The activation function.
- f(double[]) - Method in class smile.base.svm.LinearKernelMachine
-
Returns the value of decision function.
- 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(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(int) - Method in interface smile.math.IntFunction
-
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 interface smile.math.MultivariateFunction
-
Computes the value of the function at x.
- 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.projection.ica.Exp
-
- f(double) - Method in class smile.projection.ica.Kurtosis
-
- f(double) - Method in class smile.projection.ica.LogCosh
-
- f - Variable in class smile.stat.hypothesis.FTest
-
f-statistic
- 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.
- 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() - 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.
- FeatureRanking - Interface in smile.feature
-
Univariate feature ranking metric.
- features - Variable in class smile.sequence.CRFLabeler
-
The feature function.
- FeatureTransform - Interface in smile.feature
-
Feature transformation.
- 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(String) - Method in class smile.data.type.StructType
-
Return the field of given name.
- field(int) - Method in class smile.data.type.StructType
-
Return the field at position i.
- field() - Method in interface smile.data.vector.BaseVector
-
Returns a struct field corresponding to this vector.
- fieldIndex(String) - Method in interface smile.data.Tuple
-
Returns the index of a given field name.
- fieldIndex(String) - Method in class smile.data.type.StructType
-
Returns the index of a field.
- fieldName(int) - Method in class smile.data.type.StructType
-
Returns the name of a field.
- 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.
- FileChooser - Class in smile.swing
-
File chooser for with file/images preview.
- FileChooser() - Constructor for class smile.swing.FileChooser
-
Constructor.
- FileChooser.SimpleFileFilter - Class in smile.swing
-
A simple extension-based file filter.
- fill(float) - Method in class smile.math.matrix.FloatMatrix
-
Fill the matrix with a value.
- fill(double) - Method in class smile.math.matrix.Matrix
-
Fill the matrix with a value.
- fill(char, int) - Static method in interface smile.util.Strings
-
Returns a string with a single repeated character to a specific length.
- fillPolygon(double[]...) - Method in class smile.plot.swing.Graphics
-
Fill polygon.
- fillPolygon(float, double[]...) - Method in class smile.plot.swing.Graphics
-
Fill polygon.
- fillRect(double[], double[]) - Method in class smile.plot.swing.Graphics
-
Fill the specified rectangle.
- fillRectBaseRatio(double[], double[]) - Method in class smile.plot.swing.Graphics
-
Fill the specified rectangle.
- find(Function, double, double, double, int) - Static method in interface smile.math.Root
-
Brent's method for root-finding.
- find(DifferentiableFunction, double, double, double, int) - Static method in interface smile.math.Root
-
Newton's method (also known as the Newton–Raphson method).
- findBestSplit(LeafNode, int, int, boolean[]) - Method in class smile.base.cart.CART
-
Finds the best attribute to split on a set of samples.
- 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
-
- fit(double[][], int) - Static method in class smile.base.rbf.RBF
-
Learns Gaussian RBF function and centers from data.
- fit(double[][], int, int) - Static method in class smile.base.rbf.RBF
-
Learns Gaussian RBF function and centers from data.
- fit(double[][], int, double) - Static method in class smile.base.rbf.RBF
-
Learns Gaussian RBF function and centers from data.
- fit(T[], Metric<T>, int) - Static method in class smile.base.rbf.RBF
-
Learns Gaussian RBF function and centers from data.
- fit(T[], Metric<T>, int, int) - Static method in class smile.base.rbf.RBF
-
Learns Gaussian RBF function and centers from data.
- fit(T[], Metric<T>, int, double) - Static method in class smile.base.rbf.RBF
-
Learns Gaussian RBF function and centers from data.
- fit(T[], int[]) - Method in class smile.base.svm.LASVM
-
Trains the model.
- fit(T[], int[], int) - Method in class smile.base.svm.LASVM
-
Trains the model.
- fit(T[], double[]) - Method in class smile.base.svm.SVR
-
Fits a epsilon support vector regression model.
- fit(Formula, DataFrame) - Static method in class smile.classification.AdaBoost
-
Fits a AdaBoost model.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.AdaBoost
-
Fits a AdaBoost model.
- fit(Formula, DataFrame, int, int, int, int) - Static method in class smile.classification.AdaBoost
-
Fits a AdaBoost model.
- fit(int[]) - Static method in class smile.classification.ClassLabels
-
Learns the class label mapping from samples.
- fit(BaseVector) - Static method in class smile.classification.ClassLabels
-
Learns the class label mapping from samples.
- fit(Formula, DataFrame) - Static method in class smile.classification.DecisionTree
-
Learns a classification tree.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.DecisionTree
-
Learns a classification tree.
- fit(Formula, DataFrame, SplitRule, int, int, int) - Static method in class smile.classification.DecisionTree
-
Learns a classification tree.
- fit(Formula, DataFrame) - Static method in class smile.classification.FLD
-
Learn Fisher's linear discriminant.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.FLD
-
Learn Fisher's linear discriminant.
- fit(double[][], int[]) - Static method in class smile.classification.FLD
-
Learn Fisher's linear discriminant.
- fit(double[][], int[], int, double) - Static method in class smile.classification.FLD
-
Learn Fisher's linear discriminant.
- fit(Formula, DataFrame) - Static method in class smile.classification.GradientTreeBoost
-
Fits a gradient tree boosting for classification.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.GradientTreeBoost
-
Fits a gradient tree boosting for classification.
- fit(Formula, DataFrame, int, int, int, int, double, double) - Static method in class smile.classification.GradientTreeBoost
-
Fits a gradient tree boosting for classification.
- fit(double[], int[]) - Static method in class smile.classification.IsotonicRegressionScaling
-
Trains the Isotonic Regression scaling.
- fit(T[], int[], Distance<T>) - Static method in class smile.classification.KNN
-
Learn the 1-NN classifier.
- fit(T[], int[], int, Distance<T>) - Static method in class smile.classification.KNN
-
Learn the K-NN classifier.
- fit(double[][], int[]) - Static method in class smile.classification.KNN
-
Learn the 1-NN classifier.
- fit(double[][], int[], int) - Static method in class smile.classification.KNN
-
Learn the K-NN classifier.
- fit(Formula, DataFrame) - Static method in class smile.classification.LDA
-
Learns linear discriminant analysis.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.LDA
-
Learns linear discriminant analysis.
- fit(double[][], int[]) - Static method in class smile.classification.LDA
-
Learns linear discriminant analysis.
- fit(double[][], int[], Properties) - Static method in class smile.classification.LDA
-
Learns linear discriminant analysis.
- fit(double[][], int[], double[], double) - Static method in class smile.classification.LDA
-
Learns linear discriminant analysis.
- fit(Formula, DataFrame) - Static method in class smile.classification.LogisticRegression
-
Fits logistic regression.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.LogisticRegression
-
Fits logistic regression.
- fit(double[][], int[]) - Static method in class smile.classification.LogisticRegression
-
Fits logistic regression.
- fit(double[][], int[], Properties) - Static method in class smile.classification.LogisticRegression
-
Fits logistic regression.
- fit(double[][], int[], double, double, int) - Static method in class smile.classification.LogisticRegression
-
Fits logistic regression.
- fit(int, int[][], int[]) - Static method in class smile.classification.Maxent
-
Learn maximum entropy classifier.
- fit(int, int[][], int[], Properties) - Static method in class smile.classification.Maxent
-
Learn maximum entropy classifier.
- fit(int, int[][], int[], double, double, int) - Static method in class smile.classification.Maxent
-
Learn maximum entropy 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[], int, int, BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusOne
-
Fits a multi-class model with binary classifiers.
- 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(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[], int, int, BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusRest
-
Fits a multi-class model with binary 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(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(Classifier<T>, T[], int[]) - Static method in class smile.classification.PlattScaling
-
Fits Platt Scaling to estimate posteriori probabilities.
- fit(Formula, DataFrame) - Static method in class smile.classification.QDA
-
Learns quadratic discriminant analysis.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.QDA
-
Learns quadratic discriminant analysis.
- fit(double[][], int[]) - Static method in class smile.classification.QDA
-
Learn quadratic discriminant analysis.
- fit(double[][], int[], Properties) - Static method in class smile.classification.QDA
-
Learns quadratic discriminant analysis.
- fit(double[][], int[], double[], double) - Static method in class smile.classification.QDA
-
Learn quadratic discriminant analysis.
- fit(Formula, DataFrame) - Static method in class smile.classification.RandomForest
-
Fits a random forest for classification.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.RandomForest
-
Fits a random forest for classification.
- 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(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(Formula, DataFrame) - Static method in class smile.classification.RDA
-
Learns regularized discriminant analysis.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.RDA
-
Learns regularized discriminant analysis.
- fit(double[][], int[], Properties) - Static method in class smile.classification.RDA
-
Learns regularized discriminant analysis.
- fit(double[][], int[], double) - Static method in class smile.classification.RDA
-
Learn regularized discriminant analysis.
- fit(double[][], int[], double, double[], double) - Static method in class smile.classification.RDA
-
Learn regularized discriminant analysis.
- fit(SparseDataset, 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(SparseDataset, int[], double, double, int) - Static method in class smile.classification.SparseLogisticRegression
-
Fits logistic regression.
- fit(double[][], int[], double, double) - Static method in class smile.classification.SVM
-
Fits a binary-class linear SVM.
- fit(int[][], int[], int, double, double) - Static method in class smile.classification.SVM
-
Fits a binary-class linear SVM of binary sparse data.
- fit(SparseArray[], int[], int, double, double) - Static method in class smile.classification.SVM
-
Fits a binary-class linear SVM.
- fit(T[], int[], MercerKernel<T>, double, double) - Static method in class smile.classification.SVM
-
Fits a binary-class SVM.
- fit(T[], Distance<T>, int) - Static method in class smile.clustering.CLARANS
-
Clustering data into k clusters.
- fit(T[], Distance<T>, int, int) - Static method in class smile.clustering.CLARANS
-
Constructor.
- fit(double[][], int, double) - Static method in class smile.clustering.DBSCAN
-
Clustering the data with KD-tree.
- fit(T[], Distance<T>, int, double) - Static method in class smile.clustering.DBSCAN
-
Clustering the data.
- fit(T[], RNNSearch<T, T>, int, double) - Static method in class smile.clustering.DBSCAN
-
Clustering the data.
- 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.clustering.DeterministicAnnealing
-
Clustering data into k clusters.
- fit(double[][], int, double, int, double, double) - 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, int, double) - Static method in class smile.clustering.GMeans
-
Clustering data with the number of clusters
determined by G-Means algorithm automatically.
- fit(Linkage) - Static method in class smile.clustering.HierarchicalClustering
-
Fits the Agglomerative Hierarchical Clustering with given linkage
method, which includes proximity matrix.
- fit(double[][], int) - 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.KMeans
-
Partitions data into k clusters up to 100 iterations.
- fit(BBDTree, double[][], int, int, double) - Static method in class smile.clustering.KMeans
-
Partitions data into k clusters.
- fit(int[][], int) - Static method in class smile.clustering.KModes
-
Fits k-modes clustering.
- fit(int[][], int, int) - Static method in class smile.clustering.KModes
-
Fits k-modes clustering.
- fit(T[], Distance<T>, int, double) - Static method in class smile.clustering.MEC
-
Clustering the data.
- fit(T[], RNNSearch<T, T>, int, double, int[], double) - Static method in class smile.clustering.MEC
-
Clustering the data.
- fit(SparseArray[], int) - Static method in class smile.clustering.SIB
-
Clustering data into k clusters up to 100 iterations.
- fit(SparseArray[], int, int) - Static method in class smile.clustering.SIB
-
Clustering data into k clusters.
- 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(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, int, double) - Static method in class smile.clustering.SpectralClustering
-
Spectral clustering with Nystrom approximation.
- fit(double[][], int, int, double, int, double) - Static method in class smile.clustering.SpectralClustering
-
Spectral clustering with Nystrom approximation.
- 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, int, double) - Static method in class smile.clustering.XMeans
-
Clustering data with the number of clusters
determined by X-Means algorithm automatically.
- fit(DataFrame) - Static method in class smile.feature.MaxAbsScaler
-
Learns transformation parameters from a dataset.
- fit(double[][]) - Static method in class smile.feature.MaxAbsScaler
-
Learns transformation parameters from a dataset.
- fit(DataFrame) - Static method in class smile.feature.RobustStandardizer
-
Learns transformation parameters from a dataset.
- fit(double[][]) - Static method in class smile.feature.RobustStandardizer
-
Learns transformation parameters from a dataset.
- fit(DataFrame) - Static method in class smile.feature.Scaler
-
Learns transformation parameters from a dataset.
- fit(double[][]) - Static method in class smile.feature.Scaler
-
Learns transformation parameters from a dataset.
- fit(DataFrame) - Static method in class smile.feature.Standardizer
-
Learns transformation parameters from a dataset.
- fit(double[][]) - Static method in class smile.feature.Standardizer
-
Learns transformation parameters from a dataset.
- fit(DataFrame) - Static method in class smile.feature.WinsorScaler
-
Learns transformation parameters from a dataset with 5% lower limit
and 95% upper limit.
- fit(DataFrame, double, double) - Static method in class smile.feature.WinsorScaler
-
Learns transformation parameters from a dataset.
- fit(double[][]) - Static method in class smile.feature.WinsorScaler
-
Learns transformation parameters from a dataset.
- fit(double[][], double, double) - Static method in class smile.feature.WinsorScaler
-
Learns transformation parameters from a dataset.
- 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, Properties) - 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(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(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(String[][], PennTreebankPOS[][]) - Static method in class smile.nlp.pos.HMMPOSTagger
-
Fits an HMM POS tagger by maximum likelihood estimation.
- fit(double[][], int) - Static method in class smile.projection.ICA
-
Fits independent component analysis.
- fit(double[][], int, Properties) - Static method in class smile.projection.ICA
-
Fits independent component analysis.
- fit(double[][], int, DifferentiableFunction, double, int) - Static method in class smile.projection.ICA
-
Fits independent component analysis.
- fit(T[], MercerKernel<T>, int) - Static method in class smile.projection.KPCA
-
Fits kernel principal component analysis.
- fit(T[], MercerKernel<T>, int, double) - Static method in class smile.projection.KPCA
-
Fits kernel principal component analysis.
- fit(double[][]) - Static method in class smile.projection.PCA
-
Fits principal component analysis with covariance matrix.
- fit(double[][], int) - Static method in class smile.projection.ProbabilisticPCA
-
Fits probabilistic principal component analysis.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.ElasticNet
-
Fit an Elastic Net model.
- fit(Formula, DataFrame, double, double) - Static method in class smile.regression.ElasticNet
-
Fit an Elastic Net model.
- fit(Formula, DataFrame, double, double, double, int) - Static method in class smile.regression.ElasticNet
-
Fit an Elastic Net model.
- fit(T[], double[], MercerKernel<T>, Properties) - Static method in class smile.regression.GaussianProcessRegression
-
Fits a regular Gaussian process model.
- 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[], 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[], 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(Formula, DataFrame) - Static method in class smile.regression.GradientTreeBoost
-
Fits a gradient tree boosting for regression.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.GradientTreeBoost
-
Fits a gradient tree boosting for regression.
- 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) - Static method in class smile.regression.LASSO
-
Fits a L1-regularized least squares model.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.LASSO
-
Fits a L1-regularized least squares model.
- fit(Formula, DataFrame, double) - Static method in class smile.regression.LASSO
-
Fits a L1-regularized least squares model.
- fit(Formula, DataFrame, double, double, int) - 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, Properties) - Static method in class smile.regression.OLS
-
Fits an ordinary least squares model.
- fit(Formula, DataFrame, String, boolean, boolean) - Static method in class smile.regression.OLS
-
Fits an ordinary least squares model.
- fit(Formula, DataFrame) - Static method in class smile.regression.RandomForest
-
Learns a random forest for regression.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.RandomForest
-
Learns a random forest for regression.
- fit(Formula, DataFrame, int, int, int, int, int, double) - Static method in class smile.regression.RandomForest
-
Learns a random forest for regression.
- fit(Formula, DataFrame, int, int, int, int, int, double, LongStream) - Static method in class smile.regression.RandomForest
-
Learns a random forest for regression.
- 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(Formula, DataFrame) - Static method in class smile.regression.RegressionTree
-
Learns a regression tree.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.RegressionTree
-
Learns a regression tree.
- fit(Formula, DataFrame, int, int, int) - Static method in class smile.regression.RegressionTree
-
Learns a regression tree.
- fit(Formula, DataFrame) - Static method in class smile.regression.RidgeRegression
-
Fits a ridge regression model.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.RidgeRegression
-
Fits a ridge regression 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(double[][], double[], double, double, double) - Static method in class smile.regression.SVR
-
Fits a linear epsilon-SVR.
- fit(int[][], double[], int, double, double, double) - Static method in class smile.regression.SVR
-
Fits a linear epsilon-SVR of binary sparse data.
- fit(SparseArray[], double[], int, double, double, double) - Static method in class smile.regression.SVR
-
Fits a linear epsilon-SVR of sparse data.
- fit(T[], double[], MercerKernel<T>, double, double, double) - Static method in class smile.regression.SVR
-
Fits a epsilon-SVR.
- fit(Tuple[][], int[][]) - 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(Tuple[][], int[][], int, int, int, int, double) - Static method in class smile.sequence.CRF
-
Fits a CRF model.
- fit(T[][], int[][], Function<T, Tuple>) - Static method in class smile.sequence.CRFLabeler
-
Fits a CRF model.
- fit(T[][], int[][], Function<T, Tuple>, Properties) - 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(int[][], int[][]) - 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.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(int[]) - Static method in class smile.stat.distribution.BernoulliDistribution
-
Estimates the distribution parameters by MLE.
- fit(double[]) - Static method in class smile.stat.distribution.BetaDistribution
-
Estimates the distribution parameters by the moment method.
- 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[]) - Static method in class smile.stat.distribution.EmpiricalDistribution
-
Estimates the distribution.
- fit(int[], IntSet) - Static method in class smile.stat.distribution.EmpiricalDistribution
-
Estimates the distribution.
- fit(double[]) - Static method in class smile.stat.distribution.ExponentialDistribution
-
Estimates the distribution parameters by MLE.
- 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(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(int, 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.GaussianMixture
-
Fits the Gaussian mixture model with the EM algorithm.
- fit(int[]) - Static method in class smile.stat.distribution.GeometricDistribution
-
Estimates the distribution parameters by MLE.
- fit(double[]) - Static method in class smile.stat.distribution.LogNormalDistribution
-
Estimates the distribution parameters by MLE.
- 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[][]) - Static method in class smile.stat.distribution.MultivariateGaussianDistribution
-
Estimates the mean and diagonal covariance by MLE.
- fit(double[][], boolean) - Static method in class smile.stat.distribution.MultivariateGaussianDistribution
-
Estimates the mean and covariance by MLE.
- 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(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.MultivariateGaussianMixture
-
Fits the Gaussian mixture model with the EM algorithm.
- 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(double[], int) - Static method in class smile.timeseries.AR
-
Fits an autoregressive model with Yule-Walker procedure.
- fit(double[], int, int) - Static method in class smile.timeseries.ARMA
-
Fits an ARMA model with Hannan-Rissanen algorithm.
- fitness(double[][], int[], double[][], int[], ClassificationMetric, BiFunction<double[][], int[], Classifier<double[]>>) - Static method in class smile.feature.GAFE
-
Returns a classification fitness measure.
- fitness(double[][], double[], double[][], double[], RegressionMetric, BiFunction<double[][], double[], Regression<double[]>>) - Static method in class smile.feature.GAFE
-
Returns a regression fitness function.
- fitness(String, DataFrame, DataFrame, ClassificationMetric, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in class smile.feature.GAFE
-
Returns a classification fitness function.
- fitness(String, DataFrame, DataFrame, RegressionMetric, BiFunction<Formula, DataFrame, DataFrameRegression>) - Static method in class smile.feature.GAFE
-
Returns a regression fitness function.
- fitness() - Method in class smile.gap.BitString
-
- fitness() - Method in interface smile.gap.Chromosome
-
Returns the fitness of chromosome.
- Fitness<T extends Chromosome> - Interface in smile.gap
-
A measure to evaluate the fitness of chromosomes.
- fittedValues() - Method in class smile.glm.GLM
-
Returns the fitted mean values.
- fittedValues - Variable in class smile.math.LevenbergMarquardt
-
The fitted values.
- fittedValues() - Method in class smile.regression.LinearModel
-
Returns the fitted values.
- fittedValues - Variable in class smile.regression.Regression.Metric
-
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_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)
- FloatArrayCellRenderer - Class in smile.swing.table
-
Float array renderer in JTable.
- FloatArrayCellRenderer() - Constructor for class smile.swing.table.FloatArrayCellRenderer
-
Constructor.
- FloatArrayType - Static variable in class smile.data.type.DataTypes
-
Float Array data type.
- FloatBandMatrix - 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.
- FloatBandMatrix(int, int, int, int) - Constructor for class smile.math.matrix.FloatBandMatrix
-
Constructor.
- FloatBandMatrix(int, int, int, int, float[][]) - Constructor for class smile.math.matrix.FloatBandMatrix
-
Constructor.
- FloatBandMatrix.Cholesky - Class in smile.math.matrix
-
The Cholesky decomposition of a symmetric, positive-definite matrix.
- FloatBandMatrix.LU - Class in smile.math.matrix
-
The LU decomposition.
- FloatConsumer - Interface in smile.math.matrix
-
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(int) - Constructor for class smile.sort.FloatHeapSelect
-
Constructor.
- FloatHeapSelect(float[]) - Constructor for class smile.sort.FloatHeapSelect
-
Constructor.
- FloatMatrix - Class in smile.math.matrix
-
- FloatMatrix(int, int) - Constructor for class smile.math.matrix.FloatMatrix
-
Constructor of zero matrix.
- FloatMatrix(int, int, float) - Constructor for class smile.math.matrix.FloatMatrix
-
Constructor.
- FloatMatrix(int, int, float[][]) - Constructor for class smile.math.matrix.FloatMatrix
-
Constructor.
- FloatMatrix(float[][]) - Constructor for class smile.math.matrix.FloatMatrix
-
Constructor.
- FloatMatrix(float[]) - Constructor for class smile.math.matrix.FloatMatrix
-
Constructor of a column vector/matrix with given array as the internal storage.
- FloatMatrix(float[], int, int) - Constructor for class smile.math.matrix.FloatMatrix
-
Constructor of a column vector/matrix with given array as the internal storage.
- FloatMatrix(int, int, int, FloatBuffer) - Constructor for class smile.math.matrix.FloatMatrix
-
Constructor.
- FloatMatrix.Cholesky - Class in smile.math.matrix
-
The Cholesky decomposition of a symmetric, positive-definite matrix.
- FloatMatrix.EVD - Class in smile.math.matrix
-
Eigenvalue decomposition.
- FloatMatrix.LU - Class in smile.math.matrix
-
The LU decomposition.
- FloatMatrix.QR - Class in smile.math.matrix
-
The QR decomposition.
- FloatMatrix.SVD - Class in smile.math.matrix
-
Singular Value Decomposition.
- FloatObjectType - Static variable in class smile.data.type.DataTypes
-
Float Object data type.
- FloatSparseMatrix - Class in smile.math.matrix
-
A sparse matrix is a matrix populated primarily with zeros.
- FloatSparseMatrix(int, int, float[], int[], int[]) - Constructor for class smile.math.matrix.FloatSparseMatrix
-
Constructor.
- FloatSparseMatrix(float[][]) - Constructor for class smile.math.matrix.FloatSparseMatrix
-
Constructor.
- FloatSparseMatrix(float[][], float) - Constructor for class smile.math.matrix.FloatSparseMatrix
-
Constructor.
- FloatSparseMatrix.Entry - Class in smile.math.matrix
-
Encapsulates an entry in a matrix for use in streaming.
- FloatSymmMatrix - Class in smile.math.matrix
-
They symmetric matrix in packed storage.
- FloatSymmMatrix(UPLO, int) - Constructor for class smile.math.matrix.FloatSymmMatrix
-
Constructor.
- FloatSymmMatrix(UPLO, float[][]) - Constructor for class smile.math.matrix.FloatSymmMatrix
-
Constructor.
- FloatSymmMatrix.BunchKaufman - Class in smile.math.matrix
-
The LU decomposition.
- FloatSymmMatrix.Cholesky - Class in smile.math.matrix
-
The Cholesky decomposition of a symmetric, positive-definite matrix.
- FloatType - Static variable in class smile.data.type.DataTypes
-
Float data type.
- FloatType - Class in smile.data.type
-
Float data type.
- floatVector(int) - Method in interface smile.data.DataFrame
-
Selects column based on the column index.
- floatVector(String) - Method in interface smile.data.DataFrame
-
Selects column based on the column name.
- floatVector(Enum<?>) - Method in interface smile.data.DataFrame
-
Selects column using an enum value.
- floatVector(int) - Method in class smile.data.IndexDataFrame
-
- FloatVector - Interface in smile.data.vector
-
An immutable float vector.
- floor(String) - Static method in interface smile.data.formula.Terms
-
Applies Math.floor.
- floor(Term) - Static method in interface smile.data.formula.Terms
-
Applies Math.floor.
- FontCellEditor - Class in smile.swing.table
-
Font editor in JTable.
- FontCellEditor() - Constructor for class smile.swing.table.FontCellEditor
-
Constructor.
- FontCellRenderer - Class in smile.swing.table
-
Font renderer in JTable.
- FontCellRenderer() - Constructor for class smile.swing.table.FontCellRenderer
-
Constructor.
- FontCellRenderer(String) - Constructor for class smile.swing.table.FontCellRenderer
-
Constructor.
- FontChooser - Class in smile.swing
-
The FontChooser class is a swing component
for font selection with JFileChooser-like APIs.
- FontChooser() - Constructor for class smile.swing.FontChooser
-
Constructs a FontChooser object.
- FontChooser(String[]) - Constructor for class smile.swing.FontChooser
-
Constructs a FontChooser object using the given font size array.
- forEachNonZero(DoubleConsumer) - Method in class smile.math.matrix.FloatSparseMatrix
-
For each loop on non-zero elements.
- forEachNonZero(int, int, FloatConsumer) - Method in class smile.math.matrix.FloatSparseMatrix
-
For each loop on non-zero elements.
- forEachNonZero(DoubleConsumer) - Method in class smile.math.matrix.SparseMatrix
-
For each loop on non-zero elements.
- forEachNonZero(int, int, 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(int) - Method in class smile.timeseries.AR
-
Returns l-step ahead forecast.
- forecast() - Method in class smile.timeseries.ARMA
-
Returns 1-step ahead forecast.
- forecast(int) - Method in class smile.timeseries.ARMA
-
Returns l-step ahead forecast.
- FOREST_GREEN - Static variable in interface smile.plot.swing.Palette
-
- 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.
- 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.
- formula - Variable in class smile.base.cart.CART
-
The model formula.
- 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 - 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.
- formula() - Method in interface smile.feature.TreeSHAP
-
Returns the formula associated with the model.
- formula - Variable in class smile.glm.GLM
-
The symbolic description of the model to be fitted.
- formula() - Method in interface smile.regression.DataFrameRegression
-
Returns the formula associated with the model.
- 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.
- 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.
- Function - Interface in smile.math
-
An interface representing a univariate real function.
- g(double[], double[]) - Method in interface smile.base.mlp.ActivationFunction
-
The gradient function.
- g(Cost, double[], double[]) - Method in enum smile.base.mlp.OutputFunction
-
The gradient function.
- g(double[], double[]) - Method in class smile.math.AbstractDifferentiableMultivariateFunction
-
- g(double) - Method in interface smile.math.DifferentiableFunction
-
Computes the gradient/derivative at x.
- g(double[], double[]) - Method in interface smile.math.DifferentiableMultivariateFunction
-
Computes the value and gradient at x.
- g(double) - Method in class smile.projection.ica.Exp
-
- g(double) - Method in class smile.projection.ica.Kurtosis
-
- g(double) - Method in class smile.projection.ica.LogCosh
-
- g2(double) - Method in interface smile.math.DifferentiableFunction
-
Compute the second-order derivative at x.
- g2(double) - Method in class smile.projection.ica.Exp
-
- g2(double) - Method in class smile.projection.ica.Kurtosis
-
- g2(double) - Method in class smile.projection.ica.LogCosh
-
- GAFE - Class in smile.feature
-
Genetic algorithm based feature selection.
- GAFE() - Constructor for class smile.feature.GAFE
-
Constructor.
- GAFE(Selection, int, Crossover, double, double) - Constructor for class smile.feature.GAFE
-
Constructor.
- Gamma - Class in smile.math.special
-
The gamma, digamma, and incomplete gamma functions.
- gamma(double) - Static method in class smile.math.special.Gamma
-
Gamma function.
- 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, 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, 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, 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, double, double[], int, double[], int, double, double[], 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.mkl.MKL
-
- 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, FloatBuffer, int, FloatBuffer, int, float, FloatBuffer, 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 class smile.math.blas.openblas.OpenBLAS
-
- 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 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, 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, 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, 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, double[], int, int[], double[], 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.mkl.MKL
-
- 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, FloatBuffer, int, IntBuffer, FloatBuffer, 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, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
-
- 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, 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, 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, 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, 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, double[], int, int[]) - Method in class smile.math.blas.mkl.MKL
-
- gbtrf(Layout, int, int, int, int, DoubleBuffer, int, IntBuffer) - Method in class smile.math.blas.mkl.MKL
-
- gbtrf(Layout, int, int, int, int, float[], int, int[]) - Method in class smile.math.blas.mkl.MKL
-
- gbtrf(Layout, int, int, int, int, FloatBuffer, int, IntBuffer) - 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, DoubleBuffer, int, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
-
- gbtrf(Layout, int, int, int, int, float[], int, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
-
- 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, 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, 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, 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, double[], int, int[], double[], 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.mkl.MKL
-
- 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, FloatBuffer, int, IntBuffer, FloatBuffer, 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, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
-
- 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, 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, 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, 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, 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, double[], int, double[], double[], double[], int, double[], 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.mkl.MKL
-
- 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, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer, int, FloatBuffer, 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, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
-
- 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, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer, int, FloatBuffer, 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, 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, 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, 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, double[], int, double[], 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.mkl.MKL
-
- gels(Layout, Transpose, int, int, int, float[], int, float[], 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.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, DoubleBuffer, int, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
-
- gels(Layout, Transpose, int, int, int, float[], int, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
-
- 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, 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, 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, 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, double[], int, double[], int, double[], double, int[]) - 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.mkl.MKL
-
- 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, FloatBuffer, int, FloatBuffer, int, FloatBuffer, float, IntBuffer) - 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, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, double, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
-
- 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, 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, 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, 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, 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, double[], int, double[], int, double[], double, int[]) - 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.mkl.MKL
-
- 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, FloatBuffer, int, FloatBuffer, int, FloatBuffer, float, IntBuffer) - 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, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, double, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
-
- 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, 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, 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, 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, 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, double[], int, double[], int, int[], double, int[]) - 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.mkl.MKL
-
- 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, FloatBuffer, int, FloatBuffer, int, IntBuffer, float, IntBuffer) - 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, DoubleBuffer, int, DoubleBuffer, int, IntBuffer, double, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
-
- 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, 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, 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, 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, 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, double, double[], int, double[], int, double, double[], 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.mkl.MKL
-
- 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, FloatBuffer, int, FloatBuffer, int, float, FloatBuffer, 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 class smile.math.blas.openblas.OpenBLAS
-
- 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 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, DoubleBuffer, int, DoubleBuffer, int, double, DoubleBuffer, 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 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 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, DoubleBuffer, int, DoubleBuffer, int, double, DoubleBuffer, 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.mkl.MKL
-
- 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, 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 class smile.math.blas.openblas.OpenBLAS
-
- 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 class smile.math.blas.openblas.OpenBLAS
-
- 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, 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, 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, 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, double[], int, double[]) - Method in class smile.math.blas.mkl.MKL
-
- geqrf(Layout, int, int, DoubleBuffer, int, DoubleBuffer) - Method in class smile.math.blas.mkl.MKL
-
- geqrf(Layout, int, int, float[], int, float[]) - Method in class smile.math.blas.mkl.MKL
-
- geqrf(Layout, int, int, FloatBuffer, int, FloatBuffer) - 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, DoubleBuffer, int, DoubleBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
-
- geqrf(Layout, int, int, float[], int, float[]) - Method in class smile.math.blas.openblas.OpenBLAS
-
- geqrf(Layout, int, int, FloatBuffer, int, FloatBuffer) - 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, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, 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 interface smile.math.blas.BLAS
-
Performs the rank-1 update operation.
- 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, double, double[], int, double[], int, double[], 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.mkl.MKL
-
- ger(Layout, int, int, float, float[], int, float[], int, float[], 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.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 class smile.math.blas.openblas.OpenBLAS
-
- 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 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, 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, 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, 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, double[], int, double[], double[], int, double[], 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.mkl.MKL
-
- 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, FloatBuffer, int, FloatBuffer, FloatBuffer, int, FloatBuffer, 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, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
-
- 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, FloatBuffer, int, FloatBuffer, FloatBuffer, int, FloatBuffer, 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, DoubleBuffer, int, IntBuffer, DoubleBuffer, 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 interface smile.math.blas.LAPACK
-
Solves a real system of linear equations.
- 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, double[], int, int[], double[], int) - Method in class smile.math.blas.mkl.MKL
-
- gesv(Layout, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in class smile.math.blas.mkl.MKL
-
- gesv(Layout, int, int, float[], int, int[], float[], int) - Method in class smile.math.blas.mkl.MKL
-
- gesv(Layout, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, 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, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
-
- gesv(Layout, int, int, float[], int, int[], float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
-
- gesv(Layout, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, 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, 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, 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, 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, double[], int, double[], double[], int, double[], int, double[]) - 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.mkl.MKL
-
- 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, FloatBuffer, int, FloatBuffer, FloatBuffer, int, FloatBuffer, int, FloatBuffer) - 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, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
-
- 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, FloatBuffer, int, FloatBuffer, FloatBuffer, int, FloatBuffer, int, FloatBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
-
- 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, String) - Method in interface smile.data.DataFrame
-
Returns the cell at (i, j).
- get(int) - Method in interface smile.data.Dataset
-
Returns the element at the specified position in this dataset.
- get(int, int) - Method in class smile.data.IndexDataFrame
-
- get(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) - Method in interface smile.data.Tuple
-
Returns the value at position i.
- get(String) - Method in interface smile.data.Tuple
-
Returns the value by field name.
- get(int) - Method in interface smile.data.vector.BaseVector
-
Returns the value at position i, which may be null.
- 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(String) - Method in class smile.hash.PerfectHash
-
Returns the index of a string.
- get(String) - Method in class smile.hash.PerfectMap
-
Returns the value associated with the key.
- get(int) - Method in class smile.math.Complex.Array
-
Returns the i-th element.
- get(int, int) - Method in class smile.math.matrix.BandMatrix
-
- get(int, int) - Method in class smile.math.matrix.DMatrix
-
Returns A[i, j].
- get(int, int) - Method in class smile.math.matrix.FloatBandMatrix
-
- get(int, int) - Method in class smile.math.matrix.FloatMatrix
-
- get(int) - Method in class smile.math.matrix.FloatSparseMatrix
-
Returns the element at the storage index.
- get(int, int) - Method in class smile.math.matrix.FloatSparseMatrix
-
- get(int, int) - Method in class smile.math.matrix.FloatSymmMatrix
-
- get(int, int) - Method in class smile.math.matrix.Matrix
-
- get(int, int) - Method in class smile.math.matrix.SMatrix
-
Returns A[i, j].
- get(int) - Method in class smile.math.matrix.SparseMatrix
-
Returns the element at the storage index.
- get(int, int) - Method in class smile.math.matrix.SparseMatrix
-
- get(int, int) - Method in class smile.math.matrix.SymmMatrix
-
- get(int) - Method in class smile.neighbor.lsh.Hash
-
Returns the bucket entry for the given hash value.
- get(double[]) - Method in class smile.neighbor.lsh.Hash
-
Returns the bucket entry for the given point.
- get(String) - Method in class smile.nlp.embedding.Word2Vec
-
Returns the vector embedding of a word.
- get(String) - Static method in class smile.nlp.pos.EnglishPOSLexicon
-
Returns 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 associated value of a given key.
- get(K) - Method in class smile.nlp.Trie
-
Returns the node of a given key.
- get(int, int) - Method in interface smile.plot.swing.Hexmap.Tooltip
-
Gets the tooltip of cell at (i, j).
- 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, int) - Method in class smile.util.Array2D
-
Returns A(i, j).
- get(int) - Method in class smile.util.DoubleArrayList
-
Returns the value at the specified position in this list.
- get(int, int) - Method in class smile.util.IntArray2D
-
Returns A(i, j).
- 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.
- getAbbreviation(String) - Method in interface smile.nlp.dictionary.Abbreviations
-
Returns the abbreviation for a word.
- getAlpha() - Method in class smile.stat.distribution.BetaDistribution
-
Returns the shape parameter alpha.
- getAlpha() - Method in class smile.swing.AlphaIcon
-
Gets this
AlphaIcon's opacity
- 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, 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(int) - Method in interface smile.data.Tuple
-
Returns the value at position i 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.
- getAverageDocumentSize() - Method in interface smile.nlp.Corpus
-
Returns the average size of documents in the corpus.
- getAverageDocumentSize() - Method in class smile.nlp.SimpleCorpus
-
- getAxis(int) - Method in class smile.plot.swing.Canvas
-
Returns the i-th axis.
- getAxisLabel(int) - Method in class smile.plot.swing.Canvas
-
Returns the label/legend of an axis.
- getAxisLabels() - Method in class smile.plot.swing.Canvas
-
Returns the labels/legends of axes.
- getBeta() - Method in class smile.stat.distribution.BetaDistribution
-
Returns the shape parameter beta.
- getBigramFrequency(Bigram) - Method in interface smile.nlp.Corpus
-
Returns the total frequency of the bigram in the corpus.
- getBigramFrequency(Bigram) - Method in class smile.nlp.SimpleCorpus
-
- getBigrams() - Method in interface smile.nlp.Corpus
-
Returns an iterator over the bigrams in the corpus.
- getBigrams() - Method in class smile.nlp.SimpleCorpus
-
- 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(int) - Method in interface smile.data.Tuple
-
Returns the value at position i as a primitive boolean.
- getBoolean(String) - Method in interface smile.data.Tuple
-
Returns the field value as a primitive boolean.
- getBoolean(int) - Method in interface smile.data.vector.BooleanVector
-
Returns the value at position i.
- 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(int) - Method in interface smile.data.Tuple
-
Returns the value at position i as a primitive byte.
- getByte(String) - Method in interface smile.data.Tuple
-
Returns the field value 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
-
- getCellEditor(int, int) - Method in class smile.swing.Table
-
- getCellEditorValue() - Method in class smile.swing.table.ButtonCellRenderer
-
- getCellEditorValue() - Method in class smile.swing.table.ColorCellEditor
-
- getCellEditorValue() - Method in class smile.swing.table.DateCellEditor
-
- getCellEditorValue() - Method in class smile.swing.table.DoubleArrayCellEditor
-
- getCellEditorValue() - Method in class smile.swing.table.DoubleCellEditor
-
- getCellEditorValue() - Method in class smile.swing.table.FontCellEditor
-
- getCellEditorValue() - Method in class smile.swing.table.IntegerArrayCellEditor
-
- getCellEditorValue() - Method in class smile.swing.table.IntegerCellEditor
-
- getCellRenderer(int, int) - Method in class smile.swing.Table
-
- getCenter() - Method in class smile.projection.PCA
-
Returns the center of data.
- getCenter() - Method in class smile.projection.ProbabilisticPCA
-
Returns the center of data.
- 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(int) - Method in interface smile.data.Tuple
-
Returns the value at position i as a primitive byte.
- getChar(String) - Method in interface smile.data.Tuple
-
Returns the field value as a primitive byte.
- getChar(int) - Method in interface smile.data.vector.CharVector
-
Returns the value at position i.
- getChild(K[], int) - Method in class smile.nlp.Trie.Node
-
- getChild(K) - Method in class smile.nlp.Trie.Node
-
- getChildren() - Method in class smile.taxonomy.Concept
-
Get all children concepts.
- getColor() - Method in class smile.plot.swing.Graphics
-
Get the current color.
- getComponentType() - Method in class smile.data.type.ArrayType
-
Returns the type of array elements.
- getConcept(String) - Method in class smile.taxonomy.Taxonomy
-
Returns a concept node which synset contains the keyword.
- getConcepts() - Method in class smile.taxonomy.Taxonomy
-
Returns all named concepts from this taxonomy
- getCoordinates() - Method in class smile.projection.KPCA
-
Returns the nonlinear principal component scores, i.e., the representation
of learning data in the nonlinear principal component space.
- getCoordinateSpace() - Method in class smile.plot.swing.Base
-
Returns the coordinates.
- getCumulativeVarianceProportion() - Method in class smile.projection.PCA
-
Returns the cumulative proportion of variance contained in principal components,
ordered from largest to smallest.
- 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(int) - Method in interface smile.data.Tuple
-
Returns the value at position i 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, 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(int) - Method in interface smile.data.Tuple
-
Returns the value at position i of date type 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, 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(int) - Method in interface smile.data.Tuple
-
Returns the value at position i 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.
- getDescription() - Method in class smile.swing.FileChooser.SimpleFileFilter
-
Returns the human readable description of this filter.
- getDimension() - Method in class smile.plot.swing.Base
-
Returns the dimensionality of coordinates.
- 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(int) - Method in interface smile.data.Tuple
-
Returns the value at position i as a primitive double.
- getDouble(String) - Method in interface smile.data.Tuple
-
Returns the field value 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
-
- 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(int) - Method in class smile.graph.AdjacencyList
-
- getEdges(int, int) - Method in class smile.graph.AdjacencyList
-
- getEdges() - Method in class smile.graph.AdjacencyMatrix
-
- getEdges(int) - Method in class smile.graph.AdjacencyMatrix
-
- getEdges(int, int) - Method in class smile.graph.AdjacencyMatrix
-
- getEdges() - Method in interface smile.graph.Graph
-
Returns a set of the edges contained in this graph.
- getEdges(int) - Method in interface smile.graph.Graph
-
Returns a set of all edges from the specified vertex.
- getEdges(int, int) - Method in interface smile.graph.Graph
-
Returns a set of all edges connecting source vertex to target vertex if
such vertices exist in this graph.
- getExtension(File) - Static method in class smile.swing.FileChooser
-
Returns the file name extension in lower case.
- 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(int) - Method in interface smile.data.Tuple
-
Returns the value at position i as a primitive float.
- getFloat(String) - Method in interface smile.data.Tuple
-
Returns the field value 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
-
- getFocusBorder() - Method in class smile.swing.table.ButtonCellRenderer
-
Get foreground color of the button when the cell has focus
- getFont() - Method in class smile.plot.swing.Graphics
-
Get the current font.
- getFull(String) - Method in interface smile.nlp.dictionary.Abbreviations
-
Returns the full word for a given abbreviation.
- getGraphics() - Method in class smile.plot.swing.Graphics
-
Returns the Java2D graphics object.
- getHeight() - 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.
- getHeight() - Method in class smile.plot.swing.Dendrogram
-
Returns the height of tree.
- getIcon() - Method in class smile.swing.AlphaIcon
-
Gets the icon wrapped by this
AlphaIcon
- getIcon(JTable, int) - Method in class smile.swing.table.DefaultTableHeaderCellRenderer
-
Overloaded to return an icon suitable to the primary sorted column, or
null if the column is not the primary sort key.
- getIcon(JTable, int) - Method in class smile.swing.table.MultiColumnSortTableHeaderCellRenderer
-
Overridden to return an icon suitable to a sorted column, or null if the
column is unsorted.
- getIconHeight() - Method in class smile.swing.AlphaIcon
-
Gets the height of the bounding rectangle of this
AlphaIcon.
- getIconWidth() - Method in class smile.swing.AlphaIcon
-
Gets the width of the bounding rectangle of this
AlphaIcon.
- 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
-
- getInstance() - Static method in class smile.swing.FileChooser
-
Returns the shared file chooser instance.
- getInstance() - Static method in class smile.swing.FontChooser
-
Returns the shared font chooser instance.
- getInstance() - Static method in class smile.swing.table.DoubleCellEditor
-
Returns the default instance.
- 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(int) - Method in interface smile.data.Tuple
-
Returns the value at position i as a primitive int.
- getInt(String) - Method in interface smile.data.Tuple
-
Returns the field value 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
-
- getKey() - Method in class smile.nlp.Trie.Node
-
- getKeywords() - Method in class smile.taxonomy.Concept
-
Returns the concept synonym set.
- getLabel() - Method in class smile.plot.swing.Axis
-
Returns the label of the axis.
- 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.
- getLearningRate() - Method in class smile.projection.GHA
-
Returns the learning rate.
- getLoadings() - Method in class smile.projection.PCA
-
Returns the variable loading matrix, ordered from largest to smallest
by corresponding eigenvalues.
- getLoadings() - Method in class smile.projection.ProbabilisticPCA
-
Returns the variable loading matrix, ordered from largest to smallest
by corresponding eigenvalues.
- getLocalSearchSteps() - Method in class smile.gap.GeneticAlgorithm
-
Gets the number of iterations of local search for Lamarckian algorithm.
- 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(int) - Method in interface smile.data.Tuple
-
Returns the value at position i as a primitive long.
- getLong(String) - Method in interface smile.data.Tuple
-
Returns the field value 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
-
- getLowerBound() - Method in class smile.plot.swing.BarPlot
-
- getLowerBound() - Method in class smile.plot.swing.BoxPlot
-
- getLowerBound() - Method in class smile.plot.swing.Contour
-
- getLowerBound() - Method in class smile.plot.swing.Dendrogram
-
- getLowerBound() - Method in class smile.plot.swing.Graphics
-
Returns the lower bounds of coordinate space.
- getLowerBound() - Method in class smile.plot.swing.Grid
-
- getLowerBound() - Method in class smile.plot.swing.Heatmap
-
- getLowerBound() - Method in class smile.plot.swing.Hexmap
-
- getLowerBound() - Method in class smile.plot.swing.Histogram3D
-
- getLowerBound() - Method in class smile.plot.swing.LinePlot
-
- getLowerBound() - Method in class smile.plot.swing.Plot
-
Returns the lower bound of data.
- getLowerBound() - Method in class smile.plot.swing.QQPlot
-
- getLowerBound() - Method in class smile.plot.swing.ScatterPlot
-
- getLowerBound() - Method in class smile.plot.swing.ScreePlot
-
- getLowerBound() - Method in class smile.plot.swing.SparseMatrixPlot
-
- getLowerBound() - Method in class smile.plot.swing.StaircasePlot
-
- getLowerBound() - Method in class smile.plot.swing.Surface
-
- getLowerBound() - Method in class smile.plot.swing.TextPlot
-
- getLowerBound() - Method in class smile.plot.swing.Wireframe
-
- getLowerBounds() - Method in class smile.plot.swing.Base
-
Returns the lower bounds.
- getLowerBounds() - Method in class smile.plot.swing.Canvas
-
Returns the lower bounds.
- getMargin() - Method in class smile.plot.swing.Canvas
-
Returns the size of margin, which is not used as plot area.
- getMessage(String) - Method in class smile.swing.FontChooser
-
- getMnemonic() - Method in class smile.swing.table.ButtonCellRenderer
-
- getMomentum() - Method in class smile.base.mlp.MultilayerPerceptron
-
Returns the momentum factor.
- getNoiseVariance() - Method in class smile.projection.ProbabilisticPCA
-
Returns the variance of noise.
- getNumberFormat() - Method in class smile.swing.table.NumberCellRenderer
-
Returns the number format used for rendering.
- getNumBigrams() - Method in interface smile.nlp.Corpus
-
Returns the number of bigrams in the corpus.
- getNumBigrams() - Method in class smile.nlp.SimpleCorpus
-
- getNumDocuments() - Method in interface smile.nlp.Corpus
-
Returns the number of documents in the corpus.
- getNumDocuments() - Method in class smile.nlp.SimpleCorpus
-
- getNumTerms() - Method in interface smile.nlp.Corpus
-
Returns the number of unique terms in the corpus.
- getNumTerms() - Method in class smile.nlp.SimpleCorpus
-
- getNumVertices() - Method in class smile.graph.AdjacencyList
-
- getNumVertices() - Method in class smile.graph.AdjacencyMatrix
-
- getNumVertices() - Method in interface smile.graph.Graph
-
Returns the number 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.
- getPage() - Method in class smile.swing.table.PageTableModel
-
Returns the current page.
- getPageCount() - Method in class smile.swing.table.PageTableModel
-
Returns the number of pages.
- getPageSize() - Method in class smile.swing.table.PageTableModel
-
Returns the page size.
- getPaint() - Method in class smile.plot.swing.Graphics
-
Get the current paint object.
- getPathFromRoot() - Method in class smile.taxonomy.Concept
-
Returns the path from root to the given node.
- getPathToRoot() - Method in class smile.taxonomy.Concept
-
Returns the path from the given node to the root.
- getPrecisionDigits() - Method in class smile.plot.swing.Base
-
Returns the precision unit digits of axes.
- getPrecisionUnit() - Method in class smile.plot.swing.Base
-
Returns the precision units of axes.
- getPrinter() - Static method in class smile.swing.Printer
-
Returns the printer controller object.
- 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() - Method in class smile.plot.swing.Graphics
-
Returns the projection object.
- getProjection() - Method in class smile.projection.GHA
-
Returns the projection matrix.
- getProjection() - Method in class smile.projection.KPCA
-
Returns the projection matrix.
- getProjection() - Method in interface smile.projection.LinearProjection
-
Returns the projection matrix.
- getProjection() - Method in class smile.projection.PCA
-
- getProjection() - Method in class smile.projection.ProbabilisticPCA
-
Returns the projection matrix.
- getProjection() - Method in class smile.projection.RandomProjection
-
- getPropertyChangeListeners() - Method in class smile.plot.swing.Canvas
-
Returns an array of all the listeners that were added to
the PropertyChangeSupport object with addPropertyChangeListener().
- getReadableImageFilter() - Static method in class smile.swing.FileChooser.SimpleFileFilter
-
Returns the filter for readable images.
- getRealRow(int) - Method in class smile.swing.table.PageTableModel
-
Returns the row number of data given the row number of current page.
- getRealRowCount() - Method in class smile.swing.table.PageTableModel
-
The sub class should implement this method to return the real number
of rows in the model.
- 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, 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, 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, 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, DoubleBuffer, int, IntBuffer) - Method in class smile.math.blas.mkl.MKL
-
- getrf(Layout, int, int, double[], int, int[]) - Method in class smile.math.blas.mkl.MKL
-
- getrf(Layout, int, int, float[], int, int[]) - Method in class smile.math.blas.mkl.MKL
-
- getrf(Layout, int, int, FloatBuffer, 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, double[], int, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
-
- getrf(Layout, int, int, float[], int, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
-
- getrf(Layout, int, int, FloatBuffer, int, IntBuffer) - 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, 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, 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, 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, double[], int, int[]) - Method in class smile.math.blas.mkl.MKL
-
- getrf2(Layout, int, int, DoubleBuffer, int, IntBuffer) - Method in class smile.math.blas.mkl.MKL
-
- getrf2(Layout, int, int, float[], int, int[]) - Method in class smile.math.blas.mkl.MKL
-
- getrf2(Layout, int, int, FloatBuffer, int, IntBuffer) - 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, DoubleBuffer, int, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
-
- getrf2(Layout, int, int, float[], int, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
-
- 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.
- getRowCount() - Method in class smile.swing.table.PageTableModel
-
- getRowCount() - Method in class smile.swing.Table.RowHeader
-
Delegate method to main table
- getRowHeader() - Method in class smile.swing.Table
-
Returns a row header for this table.
- getRowHeight(int) - Method in class smile.swing.Table.RowHeader
-
- 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, DoubleBuffer, int, IntBuffer, DoubleBuffer, 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 interface smile.math.blas.LAPACK
-
Solves a system of linear equations
- 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, double[], int, int[], double[], 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.mkl.MKL
-
- getrs(Layout, Transpose, int, int, float[], int, int[], float[], 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.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, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
-
- getrs(Layout, Transpose, int, int, float[], int, int[], float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
-
- getrs(Layout, Transpose, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
-
- 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(int) - Method in interface smile.data.Tuple
-
Returns the value at position i of NominalScale or OrdinalScale.
- getScale(String) - Method in interface smile.data.Tuple
-
Returns the field value of NominalScale or OrdinalScale.
- getScrollableTracksViewportWidth() - Method in class smile.swing.Table
-
- getSelectedFont() - Method in class smile.swing.FontChooser
-
Get the selected font.
- getSelectedFontFamily() - Method in class smile.swing.FontChooser
-
Get the family name of the selected font.
- getSelectedFontSize() - Method in class smile.swing.FontChooser
-
Get the size of the selected font.
- getSelectedFontStyle() - Method in class smile.swing.FontChooser
-
Get the style of the selected font.
- getShapes() - Method in class smile.plot.swing.Canvas
-
Returns the list of shapes in the canvas.
- 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(int) - Method in interface smile.data.Tuple
-
Returns the value at position i as a primitive short.
- getShort(String) - Method in interface smile.data.Tuple
-
Returns the field value 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
-
- getSortKey(JTable, int) - Method in class smile.swing.table.DefaultTableHeaderCellRenderer
-
Returns the current sort key, or null if the column is unsorted.
- getStateTransitionProbabilities() - Method in class smile.sequence.HMM
-
Returns the state transition probabilities.
- 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(int) - Method in interface smile.data.Tuple
-
Returns the value at position i as a String object.
- getString(String) - Method in interface smile.data.Tuple
-
Returns the field value as a String object.
- getStroke() - Method in class smile.plot.swing.Graphics
-
Get the current stroke.
- 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(int) - Method in interface smile.data.Tuple
-
Returns the value at position i 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.
- getTableCellEditorComponent(JTable, Object, boolean, int, int) - Method in class smile.swing.table.ButtonCellRenderer
-
- getTableCellEditorComponent(JTable, Object, boolean, int, int) - Method in class smile.swing.table.ColorCellEditor
-
- getTableCellEditorComponent(JTable, Object, boolean, int, int) - Method in class smile.swing.table.DateCellEditor
-
- getTableCellEditorComponent(JTable, Object, boolean, int, int) - Method in class smile.swing.table.DoubleArrayCellEditor
-
- getTableCellEditorComponent(JTable, Object, boolean, int, int) - Method in class smile.swing.table.DoubleCellEditor
-
- getTableCellEditorComponent(JTable, Object, boolean, int, int) - Method in class smile.swing.table.FontCellEditor
-
- getTableCellEditorComponent(JTable, Object, boolean, int, int) - Method in class smile.swing.table.IntegerArrayCellEditor
-
- getTableCellEditorComponent(JTable, Object, boolean, int, int) - Method in class smile.swing.table.IntegerCellEditor
-
- getTableCellRendererComponent(JTable, Object, boolean, boolean, int, int) - Method in class smile.swing.table.ButtonCellRenderer
-
- getTableCellRendererComponent(JTable, Object, boolean, boolean, int, int) - Method in class smile.swing.table.ColorCellRenderer
-
- getTableCellRendererComponent(JTable, Object, boolean, boolean, int, int) - Method in class smile.swing.table.DefaultTableHeaderCellRenderer
-
Returns the default table header cell renderer.
- getTermFrequency(String) - Method in interface smile.nlp.Corpus
-
Returns the total frequency of the term in the corpus.
- getTermFrequency(String) - Method in class smile.nlp.SimpleCorpus
-
- getTerms() - Method in interface smile.nlp.Corpus
-
Returns an iterator over the terms in the corpus.
- getTerms() - Method in class smile.nlp.SimpleCorpus
-
- 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, 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(int) - Method in interface smile.data.Tuple
-
Returns the value at position i 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.
- getTitle() - Method in class smile.plot.swing.Canvas
-
Returns the main title of canvas.
- getTitleColor() - Method in class smile.plot.swing.Canvas
-
Returns the color for title.
- getTitleFont() - Method in class smile.plot.swing.Canvas
-
Returns the font for title.
- getToolbar() - Method in class smile.plot.swing.PlotPanel
-
Returns a tool bar to control the plot.
- getToolbar() - Method in class smile.swing.table.PageTableModel
-
Returns a tool bar to control the plot.
- getTree() - Method in class smile.clustering.HierarchicalClustering
-
Returns an n-1 by 2 matrix of which row i describes the merging of clusters at
step i of the clustering.
- getUpperBound() - Method in class smile.plot.swing.BarPlot
-
- getUpperBound() - Method in class smile.plot.swing.BoxPlot
-
- getUpperBound() - Method in class smile.plot.swing.Contour
-
- getUpperBound() - Method in class smile.plot.swing.Dendrogram
-
- getUpperBound() - Method in class smile.plot.swing.Graphics
-
Returns the upper bounds of coordinate space.
- getUpperBound() - Method in class smile.plot.swing.Grid
-
- getUpperBound() - Method in class smile.plot.swing.Heatmap
-
- getUpperBound() - Method in class smile.plot.swing.Hexmap
-
- getUpperBound() - Method in class smile.plot.swing.Histogram3D
-
- getUpperBound() - Method in class smile.plot.swing.LinePlot
-
- getUpperBound() - Method in class smile.plot.swing.Plot
-
Returns the upper bound of data.
- getUpperBound() - Method in class smile.plot.swing.QQPlot
-
- getUpperBound() - Method in class smile.plot.swing.ScatterPlot
-
- getUpperBound() - Method in class smile.plot.swing.ScreePlot
-
- getUpperBound() - Method in class smile.plot.swing.SparseMatrixPlot
-
- getUpperBound() - Method in class smile.plot.swing.StaircasePlot
-
- getUpperBound() - Method in class smile.plot.swing.Surface
-
- getUpperBound() - Method in class smile.plot.swing.TextPlot
-
- getUpperBound() - Method in class smile.plot.swing.Wireframe
-
- getUpperBounds() - Method in class smile.plot.swing.Base
-
Returns the upper bounds.
- getUpperBounds() - Method in class smile.plot.swing.Canvas
-
Returns the upper bounds.
- getValue(String) - Static method in enum smile.nlp.pos.PennTreebankPOS
-
Returns an enum value from a string.
- getValue() - Method in class smile.nlp.Trie.Node
-
- getValueAt(int, int) - Method in class smile.swing.table.PageTableModel
-
- getValueAt(int, int) - Method in class smile.swing.Table.RowHeader
-
This table does not use any data from the main TableModel,
so just return a value based on the row parameter.
- getValueAtRealRow(int, int) - Method in class smile.swing.table.PageTableModel
-
Returns the value for the cell at real row index.
- getVariance() - Method in class smile.projection.PCA
-
Returns the principal component variances, ordered from largest to smallest,
which are the eigenvalues of the covariance or correlation matrix of learning data.
- getVarianceProportion() - Method in class smile.projection.PCA
-
Returns the proportion of variance contained in each principal component,
ordered from largest to smallest.
- getVariances() - Method in class smile.projection.KPCA
-
Returns the eigenvalues of kernel principal components, ordered from largest to smallest.
- 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.
- getWritableImageFIlter() - Static method in class smile.swing.FileChooser.SimpleFileFilter
-
Returns the filter for writable images.
- 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, 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, 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, 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, double[], int, double[], int, double[], double[], double[]) - 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.mkl.MKL
-
- 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, FloatBuffer, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer) - 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, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
-
- 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, 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, 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, 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, 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, double[], int, double[], int, double[], double[], double[]) - 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.mkl.MKL
-
- 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, FloatBuffer, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer) - 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, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
-
- 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, FloatBuffer, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
-
- GHA - Class in smile.projection
-
Generalized Hebbian Algorithm.
- GHA(int, int, double) - Constructor for class smile.projection.GHA
-
Constructor.
- GHA(double[][], double) - Constructor for class smile.projection.GHA
-
Constructor.
- 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.
- GOLD - Static variable in interface smile.plot.swing.Palette
-
- GoodTuring - Class in smile.stat
-
Good–Turing frequency estimation.
- gradient() - Method in class smile.base.mlp.Layer
-
Returns the output gradient vector.
- GradientTreeBoost - Class in smile.classification
-
Gradient boosting for classification.
- GradientTreeBoost(Formula, RegressionTree[], double, double, double[]) - Constructor for class smile.classification.GradientTreeBoost
-
Constructor of binary class.
- GradientTreeBoost(Formula, RegressionTree[], double, double, double[], IntSet) - Constructor for class smile.classification.GradientTreeBoost
-
Constructor of binary class.
- 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 - Class in smile.regression
-
Gradient boosting for regression.
- GradientTreeBoost(Formula, RegressionTree[], double, double, double[]) - Constructor for class smile.regression.GradientTreeBoost
-
Constructor.
- 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 - 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.Edge - Class in smile.graph
-
Graph edge.
- Graphics - Class in smile.plot.swing
-
Graphics provides methods to draw graphical primitives in logical/mathematical
coordinates.
- Graphics(Projection) - Constructor for class smile.plot.swing.Graphics
-
Constructor.
- GRASS_GREEN - Static variable in interface smile.plot.swing.Palette
-
- GREEN - Static variable in interface smile.plot.swing.Palette
-
- Grid - Class in smile.plot.swing
-
A 2D grid plot.
- Grid(double[][][], Color) - Constructor for class smile.plot.swing.Grid
-
Constructor.
- grid() - Method in class smile.validation.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.
- i - Variable in class smile.math.matrix.FloatSparseMatrix.Entry
-
The row index.
- i - Variable in class smile.math.matrix.SparseMatrix.Entry
-
The row index.
- i - Variable in class smile.util.IntPair
-
- i - Variable in class smile.util.SparseArray.Entry
-
The index of entry.
- 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, float[], int) - Method in interface smile.math.blas.BLAS
-
Searches a vector for the first occurrence of the the maximum absolute
value.
- 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 class smile.math.blas.mkl.MKL
-
- iamax(int, float[], 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 class smile.math.blas.openblas.OpenBLAS
-
- ICA - Class in smile.projection
-
Independent Component Analysis (ICA) is a computational method for separating
a multivariate signal into additive components.
- ICA(double[][]) - Constructor for class smile.projection.ICA
-
Constructor.
- 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
-
- id - Variable in class smile.nlp.Text
-
The id of document in the corpus.
- im - Variable in class smile.math.Complex
-
The imaginary part.
- IMatrix<T> - Class in smile.math.matrix
-
An abstract interface of matrix.
- IMatrix() - Constructor for class smile.math.matrix.IMatrix
-
- 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(LeafNode) - Method in class smile.base.cart.CART
-
Returns the impurity of node.
- 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.
- impurity() - Method in class smile.base.cart.RegressionNode
-
Returns the residual sum of squares.
- impurity(LeafNode) - Method in class smile.classification.DecisionTree
-
- impurity(LeafNode) - Method in class smile.regression.RegressionTree
-
- impute(double[][]) - Method in class smile.imputation.AverageImputation
-
- impute(double[][]) - Method in class smile.imputation.KMeansImputation
-
- impute(double[][]) - Method in class smile.imputation.KNNImputation
-
- impute(double[][]) - Method in class smile.imputation.LLSImputation
-
- impute(double[][]) - Method in interface smile.imputation.MissingValueImputation
-
Impute missing values in the data.
- impute(double[][]) - Method in class smile.imputation.SVDImputation
-
- impute(double[][], int) - Method in class smile.imputation.SVDImputation
-
Impute missing values in the dataset.
- imputeWithColumnAverage(double[][]) - Static method in interface smile.imputation.MissingValueImputation
-
Impute the missing values with column averages.
- 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(int, int) - Method in class smile.math.matrix.FloatMatrix
-
Returns the linear index of matrix element.
- index - Variable in class smile.math.matrix.FloatSparseMatrix.Entry
-
The index to the matrix storage.
- index(int, int) - Method in class smile.math.matrix.Matrix
-
Returns the linear index of matrix element.
- 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.
- 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.classification.ClassLabels
-
Maps the class labels to index.
- indexOf(int) - Method in class smile.util.IntSet
-
Maps the value to index.
- infer(String) - Static method in interface smile.data.type.DataType
-
Infers the type of a string.
- inferSchema(Reader, int) - Method in class smile.io.CSV
-
Infer the schema from the top n rows.
- inferSchema(BufferedReader, int) - Method in class smile.io.JSON
-
Infer the schema from the top n objects.
- info - Variable in class smile.math.matrix.BandMatrix.LU
-
If info = 0, the LU decomposition was successful.
- info - Variable in class smile.math.matrix.FloatBandMatrix.LU
-
If info = 0, the LU decomposition was successful.
- info - Variable in class smile.math.matrix.FloatMatrix.LU
-
If info = 0, the LU decomposition was successful.
- info - Variable in class smile.math.matrix.FloatSymmMatrix.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.
- 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 - Interface in smile.io
-
Static methods that return the InputStream/Reader of a file or URL.
- insert(int) - Method in class smile.util.PriorityQueue
-
Insert a new item into queue.
- Instance<T> - Interface in smile.data
-
An immutable instance.
- instance - Static variable in class smile.feature.SignalNoiseRatio
-
- instance - Static variable in class smile.feature.SumSquaresRatio
-
- 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.
- instances() - Method in class smile.base.svm.KernelMachine
-
Returns the instances of kernel machines.
- 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 - Static variable in class smile.swing.table.NumberCellRenderer
-
- IntegerArrayCellEditor - Class in smile.swing.table
-
Implements a cell editor that uses a formatted text field
to edit int[] values.
- IntegerArrayCellEditor() - Constructor for class smile.swing.table.IntegerArrayCellEditor
-
Constructor.
- IntegerArrayCellRenderer - Class in smile.swing.table
-
Integer array renderer in JTable.
- IntegerArrayCellRenderer() - Constructor for class smile.swing.table.IntegerArrayCellRenderer
-
Constructor.
- IntegerArrayType - Static variable in class smile.data.type.DataTypes
-
Integer Array data type.
- IntegerCellEditor - Class in smile.swing.table
-
Implements a cell editor that uses a formatted text field
to edit Integer values.
- IntegerCellEditor() - Constructor for class smile.swing.table.IntegerCellEditor
-
Constructor.
- IntegerCellEditor(int, int) - Constructor for class smile.swing.table.IntegerCellEditor
-
Constructor.
- IntegerObjectType - Static variable in class smile.data.type.DataTypes
-
Integer Object data type.
- IntegerType - Static variable in class smile.data.type.DataTypes
-
Integer data type.
- IntegerType - Class in smile.data.type
-
Integer data type.
- interact(String...) - Static method in interface smile.data.formula.Terms
-
Factor interaction of two or more factors.
- intercept(double[]) - Method in interface smile.base.cart.Loss
-
Returns the intercept of model.
- 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.
- InternalNode - Class in smile.base.cart
-
An internal node in CART.
- InternalNode(int, double, double, Node, Node) - Constructor for class smile.base.cart.InternalNode
-
- interpolate(double) - Method in class smile.interpolation.AbstractInterpolation
-
- 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) - Method in interface smile.interpolation.Interpolation
-
Given a value x, return an interpolated value.
- interpolate(double, double) - Method in interface smile.interpolation.Interpolation2D
-
Interpolate the data at a given 2-dimensional point.
- interpolate(double...) - Method in class smile.interpolation.KrigingInterpolation
-
Interpolate the function at given point.
- interpolate(double) - Method in class smile.interpolation.KrigingInterpolation1D
-
- interpolate(double, double) - Method in class smile.interpolation.KrigingInterpolation2D
-
- 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...) - Method in class smile.interpolation.RBFInterpolation
-
Interpolate the function at given point.
- interpolate(double) - Method in class smile.interpolation.RBFInterpolation1D
-
- interpolate(double, double) - Method in class smile.interpolation.RBFInterpolation2D
-
- interpolate(double...) - Method in class smile.interpolation.ShepardInterpolation
-
Interpolate the function at given point.
- interpolate(double) - Method in class smile.interpolation.ShepardInterpolation1D
-
- 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(String, Term, IntFunction) - Constructor for class smile.data.formula.IntFunction
-
Constructor.
- IntFunction - Interface in smile.math
-
An interface representing a univariate int function.
- IntHashSet - Class in smile.util
-
HashSet 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.
- IntPattern - Static variable in interface smile.data.type.DataType
-
Regex for integer.
- 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(String) - Method in interface smile.data.DataFrame
-
Selects column based on the column name.
- intVector(Enum<?>) - Method in interface smile.data.DataFrame
-
Selects column using an enum value.
- intVector(int) - Method in class smile.data.IndexDataFrame
-
- IntVector - Interface in smile.data.vector
-
An immutable integer vector.
- 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 matrix inverse.
- inverse() - Method in class smile.math.matrix.BandMatrix.LU
-
Returns the matrix inverse.
- inverse() - Method in class smile.math.matrix.FloatBandMatrix.Cholesky
-
Returns the matrix inverse.
- inverse() - Method in class smile.math.matrix.FloatBandMatrix.LU
-
Returns the matrix inverse.
- inverse() - Method in class smile.math.matrix.FloatMatrix.Cholesky
-
Returns the matrix inverse.
- inverse() - Method in class smile.math.matrix.FloatMatrix
-
Returns the inverse matrix.
- inverse() - Method in class smile.math.matrix.FloatMatrix.LU
-
Returns the matrix inverse.
- inverse() - Method in class smile.math.matrix.FloatSymmMatrix.BunchKaufman
-
Returns the matrix inverse.
- inverse() - Method in class smile.math.matrix.FloatSymmMatrix.Cholesky
-
Returns the matrix inverse.
- inverse() - Method in class smile.math.matrix.Matrix.Cholesky
-
Returns the matrix inverse.
- inverse() - Method in class smile.math.matrix.Matrix
-
Returns the inverse matrix.
- inverse() - Method in class smile.math.matrix.Matrix.LU
-
Returns the matrix inverse.
- inverse() - Method in class smile.math.matrix.SymmMatrix.BunchKaufman
-
Returns the matrix inverse.
- inverse() - Method in class smile.math.matrix.SymmMatrix.Cholesky
-
Returns the matrix inverse.
- inverse(double, double) - Static method in interface smile.math.TimeFunction
-
Returns the inverse decay function
initLearningRate * decaySteps / (t + decaySteps).
- inverse(double, double, double) - Static method in interface smile.math.TimeFunction
-
Returns the inverse decay function
initLearningRate / (1 + decayRate * t / decaySteps).
- inverse(double, double, double, boolean) - Static method in interface smile.math.TimeFunction
-
Returns the inverse decay function
initLearningRate / (1 + decayRate * t / decaySteps).
- inverse(double[]) - Method in class smile.wavelet.Wavelet
-
Inverse discrete wavelet transform.
- 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
-
- InverseMultiquadricRadialBasis(double) - Constructor for class smile.math.rbf.InverseMultiquadricRadialBasis
-
- 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.
- invlink(double) - Method in interface smile.glm.model.Model
-
The inverse of link function.
- ipiv - Variable in class smile.math.matrix.BandMatrix.LU
-
The pivot vector.
- ipiv - Variable in class smile.math.matrix.FloatBandMatrix.LU
-
The pivot vector.
- ipiv - Variable in class smile.math.matrix.FloatMatrix.LU
-
The pivot vector.
- ipiv - Variable in class smile.math.matrix.FloatSymmMatrix.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
-
- isCellEditable(int, int) - Method in class smile.swing.Table.RowHeader
-
Don't edit data in the main TableModel by mistake
- 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(DataType) - Static method in interface smile.data.type.DataType
-
Returns true if the given type is of double,
either primitive or boxed.
- isDouble() - Method in class smile.data.type.DoubleType
-
- isDouble() - Method in class smile.data.type.ObjectType
-
- isEmpty() - Method in interface smile.data.Dataset
-
Returns true if the dataset is empty.
- isEmpty() - Method in class smile.plot.swing.Isoline
-
Returns true if the isoline doesn't have any points.
- 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.SparseArray
-
Returns true if the array is empty.
- isExpandable() - Method in class smile.neighbor.lsh.Probe
-
- isExtendable() - Method in class smile.neighbor.lsh.Probe
-
- isFloat() - Method in interface smile.data.type.DataType
-
Returns true if the type is float or Float.
- isFloat(DataType) - Static method in interface smile.data.type.DataType
-
Returns true if the given type is of float,
either primitive or boxed.
- isFloat() - Method in class smile.data.type.FloatType
-
- isFloat() - Method in class smile.data.type.ObjectType
-
- isFloating() - Method in interface smile.data.type.DataType
-
Returns true if the type is float or double.
- isFrameVisible() - Method in class smile.plot.swing.Axis
-
Returns the visibility of the frame grid lines and their labels.
- isGridVisible() - Method in class smile.plot.swing.Axis
-
Returns the visibility of the grid lines and their labels.
- isInt() - Method in interface smile.data.type.DataType
-
Returns true if the type is int or 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.
- isInt() - Method in class smile.data.type.IntegerType
-
- isInt() - Method in class smile.data.type.ObjectType
-
- isInt(float) - Static method in class smile.math.MathEx
-
Returns true if x is an integer.
- isInt(double) - Static method in class smile.math.MathEx
-
Returns true if x is an integer.
- 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.
- isLegendVisible() - Method in class smile.plot.swing.Canvas
-
Returns true if legends are visible.
- isLong() - Method in interface smile.data.type.DataType
-
Returns true if the type is long or Long.
- isLong(DataType) - Static method in interface smile.data.type.DataType
-
Returns true if the given type is of long,
either primitive or boxed.
- isLong() - Method in class smile.data.type.LongType
-
- isLong() - Method in class smile.data.type.ObjectType
-
- 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, 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(int) - Method in interface smile.data.Tuple
-
Checks whether the value at position i is null.
- isNullAt(String) - Method in interface smile.data.Tuple
-
Checks whether the field value is null.
- isNullAt(int) - Method in interface smile.data.vector.Vector
-
Checks whether the value at position i 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.
- ISO8601 - Static variable in class smile.swing.table.DateCellEditor
-
- ISO8601 - Static variable in class smile.swing.table.DateCellRenderer
-
- 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
-
- Isoline - Class in smile.plot.swing
-
Contour contains a list of segments.
- Isoline(double, boolean) - Constructor for class smile.plot.swing.Isoline
-
Constructor.
- IsoMap - Class in smile.manifold
-
Isometric feature mapping.
- IsoMap(int[], double[][], AdjacencyList) - Constructor for class smile.manifold.IsoMap
-
Constructor.
- IsotonicMDS - Class in smile.mds
-
Kruskal's nonmetric MDS.
- IsotonicMDS(double, double[][]) - Constructor for class smile.mds.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
-
- isShort() - Method in interface smile.data.type.DataType
-
Returns true if the type is short or Shorter.
- 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 if the matrix is singular.
- isSingular() - Method in class smile.math.matrix.FloatBandMatrix.LU
-
Returns if the matrix is singular.
- isSingular() - Method in class smile.math.matrix.FloatMatrix.LU
-
Returns if the matrix is singular.
- isSingular() - Method in class smile.math.matrix.FloatSymmMatrix.BunchKaufman
-
Returns if the matrix is singular.
- isSingular() - Method in class smile.math.matrix.Matrix.LU
-
Returns if the matrix is singular.
- isSingular() - Method in class smile.math.matrix.SymmMatrix.BunchKaufman
-
Returns 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
-
- isSubmatrix() - Method in class smile.math.matrix.FloatMatrix
-
Returns if the matrix is a submatrix.
- isSubmatrix() - Method in class smile.math.matrix.Matrix
-
Returns if the matrix is a submatrix.
- isSymmetric() - Method in class smile.math.matrix.BandMatrix
-
Return if the matrix is symmetric (uplo != null).
- isSymmetric() - Method in class smile.math.matrix.FloatBandMatrix
-
Return if the matrix is symmetric (uplo != null).
- isSymmetric() - Method in class smile.math.matrix.FloatMatrix
-
Return if the matrix is symmetric (uplo != null && diag == null).
- isSymmetric() - Method in class smile.math.matrix.Matrix
-
Return if the matrix is symmetric (uplo != null && diag == null).
- isTickVisible() - Method in class smile.plot.swing.Axis
-
Returns the visibility of the axis label.
- isVariable() - Method in interface smile.data.formula.Feature
-
Returns true if the term represents a plain variable/column in the data frame.
- isZero(float) - Static method in class smile.math.MathEx
-
Tests if a floating number is zero.
- isZero(float, float) - Static method in class smile.math.MathEx
-
Tests if a floating number is zero with given epsilon.
- isZero(double) - Static method in class smile.math.MathEx
-
Tests if a floating number is zero.
- isZero(double, double) - Static method in class smile.math.MathEx
-
Tests if a floating number is zero with given epsilon.
- 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.FloatSparseMatrix
-
Returns an iterator of nonzero entries.
- iterator(int, int) - Method in class smile.math.matrix.FloatSparseMatrix
-
Returns an iterator of nonzero entries.
- iterator() - Method in class smile.math.matrix.SparseMatrix
-
Returns an iterator of nonzero entries.
- iterator(int, int) - Method in class smile.math.matrix.SparseMatrix
-
Returns an iterator of nonzero entries.
- iterator() - Method in interface smile.nlp.dictionary.Dictionary
-
Returns an iterator over the elements in this dictionary.
- iterator() - Method in enum smile.nlp.dictionary.EnglishDictionary
-
- iterator() - Method in class smile.nlp.dictionary.EnglishPunctuations
-
- iterator() - Method in enum smile.nlp.dictionary.EnglishStopWords
-
- iterator() - Method in class smile.nlp.dictionary.SimpleDictionary
-
- iterator() - Method in class smile.util.SparseArray
-
Returns an iterator of nonzero entries.