Index
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form
$
- $ - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
-
Punctuation $
- $(String) - Static method in interface smile.data.formula.Terms
-
Creates a variable.
A
- a - Variable in class smile.validation.metric.ContingencyTable
-
The row sum of contingency table.
- aat() - Method in class smile.math.matrix.BigMatrix
-
Returns
A * A'
. - aat() - Method in class smile.math.matrix.fp32.Matrix
-
Returns
A * A'
. - aat() - Method in class smile.math.matrix.fp32.SparseMatrix
-
Returns
A * A'
. - aat() - Method in class smile.math.matrix.Matrix
-
Returns
A * A'
. - aat() - Method in class smile.math.matrix.SparseMatrix
-
Returns
A * A'
. - Abbreviations - Interface in smile.nlp.dictionary
-
A dictionary interface for abbreviations.
- abs() - Method in class smile.math.Complex
-
Returns the abs/modulus/magnitude.
- abs(String) - Static method in interface smile.data.formula.Terms
-
The
abs(x)
term. - abs(Term) - Static method in interface smile.data.formula.Terms
-
The
abs(x)
term. - Abs - Class in smile.data.formula
-
The term of abs function.
- Abs(Term) - Constructor for class smile.data.formula.Abs
-
Constructor.
- AbstractBiFunction - Class in smile.data.formula
-
This class provides a skeletal implementation of the bi-function term.
- AbstractBiFunction(String, Term, Term) - Constructor for class smile.data.formula.AbstractBiFunction
-
Constructor.
- AbstractClassifier<T> - Class in smile.classification
-
Abstract base class of classifiers.
- AbstractClassifier(int[]) - Constructor for class smile.classification.AbstractClassifier
-
Constructor.
- AbstractClassifier(BaseVector<?, ?, ?>) - Constructor for class smile.classification.AbstractClassifier
-
Constructor.
- AbstractClassifier(IntSet) - Constructor for class smile.classification.AbstractClassifier
-
Constructor.
- AbstractFunction - Class in smile.data.formula
-
This class provides a skeletal implementation of the function term.
- AbstractFunction(String, Term) - Constructor for class smile.data.formula.AbstractFunction
-
Constructor.
- AbstractInterpolation - Class in smile.interpolation
-
Abstract base class of one-dimensional interpolation methods.
- AbstractInterpolation(double[], double[]) - Constructor for class smile.interpolation.AbstractInterpolation
-
Constructor.
- AbstractTuple - Class in smile.data
-
Abstract tuple base class.
- AbstractTuple() - Constructor for class smile.data.AbstractTuple
- accept(int, int, double) - Method in interface smile.math.matrix.DoubleConsumer
-
Accepts one matrix element and performs the operation on the given arguments.
- accept(int, int, float) - Method in interface smile.math.matrix.fp32.FloatConsumer
-
Accepts one matrix element and performs the operation on the given arguments.
- 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.deep.metric
-
The accuracy is the proportion of true results (both true positives and true negatives) in the population.
- 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.deep.metric.Accuracy
-
Constructor.
- Accuracy() - Constructor for class smile.validation.metric.Accuracy
- Accuracy(double) - Constructor for class smile.deep.metric.Accuracy
-
Constructor.
- acf(double[], int) - Static method in interface smile.timeseries.TimeSeries
-
Autocorrelation function.
- acos() - Method in class smile.deep.tensor.Tensor
-
Returns a new tensor with the arccosine of the elements of input.
- acos(String) - Static method in interface smile.data.formula.Terms
-
The
acos(x)
term. - acos(Term) - Static method in interface smile.data.formula.Terms
-
The
acos(x)
term. - acos_() - Method in class smile.deep.tensor.Tensor
-
Computes the arccosine of the elements of input in place.
- 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.
- activation() - Method in record class smile.llm.Transformer.Options
-
Returns the value of the
activation
record component. - activation() - Method in record class smile.vision.layer.Conv2dNormActivation.Options
-
Returns the value of the
activation
record component. - ActivationFunction - Class in smile.deep.activation
-
The activation function.
- ActivationFunction - Interface in smile.base.mlp
-
The activation function in hidden layers.
- ActivationFunction(String, boolean) - Constructor for class smile.deep.activation.ActivationFunction
-
Constructor.
- AdaBoost - Class in smile.classification
-
AdaBoost (Adaptive Boosting) classifier with decision trees.
- AdaBoost(Formula, int, DecisionTree[], double[], double[], double[]) - Constructor for class smile.classification.AdaBoost
-
Constructor.
- AdaBoost(Formula, int, DecisionTree[], double[], double[], double[], IntSet) - Constructor for class smile.classification.AdaBoost
-
Constructor.
- Adam(Model, double) - Static method in class smile.deep.Optimizer
-
Returns an Adam optimizer.
- Adam(Model, double, double, double, double, double, boolean) - Static method in class smile.deep.Optimizer
-
Returns an Adam optimizer.
- AdamW(Model, double) - Static method in class smile.deep.Optimizer
-
Returns an AdamW optimizer.
- AdamW(Model, double, double, double, double, double, boolean) - Static method in class smile.deep.Optimizer
-
Returns an AdamW optimizer.
- adaptiveAvgPool2d(int) - Static method in interface smile.deep.layer.Layer
-
Returns an adaptive average pooling layer.
- AdaptiveAvgPool2dLayer - Class in smile.deep.layer
-
An adaptive average pooling that reduces a tensor by combining cells.
- AdaptiveAvgPool2dLayer(int) - Constructor for class smile.deep.layer.AdaptiveAvgPool2dLayer
-
Constructor.
- AdaptiveAvgPool2dLayer(int, int) - Constructor for class smile.deep.layer.AdaptiveAvgPool2dLayer
-
Constructor.
- adb(Transpose, BigMatrix, double[], Transpose, BigMatrix) - Static method in class smile.math.matrix.BigMatrix
-
Returns
A * D * B
, where D is a diagonal matrix. - adb(Transpose, Matrix, float[], Transpose, Matrix) - Static method in class smile.math.matrix.fp32.Matrix
-
Returns
A * D * B
, where D is a diagonal matrix. - adb(Transpose, Matrix, double[], Transpose, Matrix) - Static method in class smile.math.matrix.Matrix
-
Returns
A * D * B
, where D is a diagonal matrix. - add(double) - Method in class smile.deep.tensor.Tensor
-
Returns A + b.
- add(double) - Method in class smile.math.matrix.BigMatrix
-
A += b
- add(double) - Method in class smile.math.matrix.Matrix
-
A += b
- add(double) - Method in class smile.sort.DoubleHeapSelect
-
Assimilate a new value from the stream.
- add(double) - Method in class smile.sort.IQAgent
-
Assimilate a new value from the stream.
- add(double) - Method in class smile.util.Array2D
-
A += x.
- add(double) - Method in class smile.util.DoubleArrayList
-
Appends the specified value to the end of this list.
- add(double...) - Method in class smile.plot.swing.Isoline
-
Add a point to the contour line.
- add(double[]) - Method in class smile.util.DoubleArrayList
-
Appends an array to the end of this list.
- add(double[], double[]) - Static method in class smile.math.MathEx
-
Element-wise sum of two arrays y = x + y.
- add(double, double[], double[]) - Method in class smile.math.matrix.BigMatrix
-
Rank-1 update A += alpha * x * y'
- add(double, double[], double[]) - Method in class smile.math.matrix.Matrix
-
Rank-1 update A += alpha * x * y'
- add(double, double, BigMatrix) - Method in class smile.math.matrix.BigMatrix
-
Element-wise addition A = alpha * A + beta * B
- add(double, double, Matrix) - Method in class smile.math.matrix.Matrix
-
Element-wise addition A = alpha * A + beta * B
- add(double, BigMatrix) - Method in class smile.math.matrix.BigMatrix
-
Element-wise addition A += beta * B
- add(double, BigMatrix, double, BigMatrix) - Method in class smile.math.matrix.BigMatrix
-
Element-wise addition C = alpha * A + beta * B
- add(double, Matrix) - Method in class smile.math.matrix.Matrix
-
Element-wise addition A += beta * B
- add(double, Matrix, double, Matrix) - Method in class smile.math.matrix.Matrix
-
Element-wise addition C = alpha * A + beta * B
- add(float) - Method in class smile.deep.tensor.Tensor
-
Returns A + b.
- add(float) - Method in class smile.math.matrix.fp32.Matrix
-
A += b
- add(float) - Method in class smile.sort.FloatHeapSelect
-
Assimilate a new value from the stream.
- add(float, float[], float[]) - Method in class smile.math.matrix.fp32.Matrix
-
Rank-1 update A += alpha * x * y'
- add(float, float, Matrix) - Method in class smile.math.matrix.fp32.Matrix
-
Element-wise addition A = alpha * A + beta * B
- add(float, Matrix) - Method in class smile.math.matrix.fp32.Matrix
-
Element-wise addition A += beta * B
- add(float, Matrix, float, Matrix) - Method in class smile.math.matrix.fp32.Matrix
-
Element-wise addition C = alpha * A + beta * B
- add(int) - Method in class smile.neighbor.lsh.Bucket
-
Adds a point to bucket.
- add(int) - Method in class smile.sort.IntHeapSelect
-
Assimilate a new value from the stream.
- add(int) - Method in class smile.util.IntArray2D
-
A += x.
- add(int) - Method in class smile.util.IntArrayList
-
Appends the specified value to the end of this list.
- add(int) - Method in class smile.util.IntHashSet
-
Adds the specified element to this set if it is not already present.
- add(int...) - Method in class smile.util.IntArrayList
-
Appends an array to the end of this list.
- add(int, double[]) - Method in class smile.neighbor.lsh.Hash
-
Insert an item into the hash table.
- add(int, double[]) - Method in class smile.neighbor.lsh.MultiProbeHash
- add(int, int, double) - Method in class smile.math.matrix.BigMatrix
-
A[i,j] += b
- add(int, int, double) - Method in class smile.math.matrix.Matrix
-
A[i,j] += b
- add(int, int, double) - Method in class smile.util.Array2D
-
A[i, j] += x.
- add(int, int, float) - Method in class smile.math.matrix.fp32.Matrix
-
A[i,j] += b
- add(int, int, int) - Method in class smile.util.IntArray2D
-
A[i, j] += x.
- add(String, double) - Method in class smile.hpo.Hyperparameters
-
Adds a parameter.
- add(String, double[]) - Method in class smile.hpo.Hyperparameters
-
Adds a parameter.
- add(String, double, double) - Method in class smile.hpo.Hyperparameters
-
Adds a parameter.
- add(String, double, double, double) - Method in class smile.hpo.Hyperparameters
-
Adds a parameter.
- add(String, int) - Method in class smile.hpo.Hyperparameters
-
Adds a parameter.
- add(String, int[]) - Method in class smile.hpo.Hyperparameters
-
Adds a parameter.
- add(String, int, int) - Method in class smile.hpo.Hyperparameters
-
Adds a parameter.
- add(String, int, int, int) - Method in class smile.hpo.Hyperparameters
-
Adds a parameter.
- add(String, String) - Static method in interface smile.data.formula.Terms
-
Adds two terms.
- add(String, String) - Method in class smile.hpo.Hyperparameters
-
Adds a parameter.
- add(String, String[]) - Method in class smile.hpo.Hyperparameters
-
Adds a parameter.
- add(String, Module) - Method in class smile.deep.layer.LayerBlock
-
Adds a sub-layer.
- add(String, Term) - Static method in interface smile.data.formula.Terms
-
Adds two terms.
- add(String, Layer) - Method in class smile.deep.layer.LayerBlock
-
Adds a sub-layer.
- add(String, T) - Method in class smile.hash.PerfectMap.Builder
-
Add a new key-value pair.
- add(Map<K, V>) - Method in class smile.neighbor.BKTree
-
Adds a dataset into BK-tree.
- add(K, V) - Method in class smile.neighbor.BKTree
-
Adds a datum into the BK-tree.
- add(Term, String) - Static method in interface smile.data.formula.Terms
-
Adds two terms.
- add(Term, Term) - Static method in interface smile.data.formula.Terms
-
Adds two terms.
- add(Layer) - Method in class smile.deep.layer.SequentialBlock
-
Adds a layer to the sequential block.
- add(Tensor) - Method in class smile.deep.tensor.Tensor
-
Returns A + B.
- add(Tensor, double) - Method in class smile.deep.tensor.Tensor
-
Returns A + alpha * B.
- add(Complex) - Method in class smile.math.Complex
-
Returns this + b.
- add(BigMatrix) - Method in class smile.math.matrix.BigMatrix
-
Element-wise addition A += B
- add(Matrix) - Method in class smile.math.matrix.fp32.Matrix
-
Element-wise addition A += B
- add(Matrix) - Method in class smile.math.matrix.Matrix
-
Element-wise addition A += B
- add(Text) - Method in class smile.nlp.SimpleCorpus
-
Adds a document to the corpus.
- add(Plot) - Method in class smile.plot.swing.Canvas
-
Add a graphical shape to the canvas.
- add(PlotPanel) - Method in class smile.plot.swing.PlotGrid
-
Add a plot into the frame.
- add(Shape) - Method in class smile.plot.swing.Canvas
-
Add a graphical shape to the canvas.
- add(Array2D) - Method in class smile.util.Array2D
-
A += B.
- add(IntArray2D) - Method in class smile.util.IntArray2D
-
A += B.
- add(IntArrayList) - Method in class smile.util.IntArrayList
-
Appends an array to the end of this list.
- add(T) - Method in class smile.sort.HeapSelect
-
Assimilate a new value from the stream.
- Add - Class in smile.data.formula
-
The term of
a + b
expression. - Add(Term, Term) - Constructor for class smile.data.formula.Add
-
Constructor.
- add_(double) - Method in class smile.deep.tensor.Tensor
-
Returns A += b.
- add_(float) - Method in class smile.deep.tensor.Tensor
-
Returns A += b.
- add_(Tensor) - Method in class smile.deep.tensor.Tensor
-
Returns A += B.
- add_(Tensor, double) - Method in class smile.deep.tensor.Tensor
-
Returns A += alpha * B.
- add2(double, double, BigMatrix) - Method in class smile.math.matrix.BigMatrix
-
Element-wise addition A = alpha * A + beta * B^2
- add2(double, double, Matrix) - Method in class smile.math.matrix.Matrix
-
Element-wise addition A = alpha * A + beta * B^2
- add2(float, float, Matrix) - Method in class smile.math.matrix.fp32.Matrix
-
Element-wise addition A = alpha * A + beta * B^2
- addAnchor(String) - Method in interface smile.nlp.AnchorText
-
Adds a link label to the anchor text.
- addAnchor(String) - Method in class smile.nlp.SimpleText
- addChild(String) - Method in class smile.taxonomy.Concept
-
Adds a child to this node.
- addChild(K[], V, int) - Method in class smile.nlp.Trie.Node
-
Adds a child.
- addChild(Concept) - Method in class smile.taxonomy.Concept
-
Adds a child to this node.
- addDiag(double) - Method in class smile.math.matrix.BigMatrix
-
A[i, i] += b
- addDiag(double) - Method in class smile.math.matrix.Matrix
-
A[i, i] += b
- addDiag(double[]) - Method in class smile.math.matrix.BigMatrix
-
A[i, i] += b[i]
- addDiag(double[]) - Method in class smile.math.matrix.Matrix
-
A[i, i] += b[i]
- addDiag(float) - Method in class smile.math.matrix.fp32.Matrix
-
A[i, i] += b
- addDiag(float[]) - Method in class smile.math.matrix.fp32.Matrix
-
A[i, i] += b[i]
- addEdge(int, int) - Method in class smile.graph.AdjacencyList
- addEdge(int, int) - Method in class smile.graph.AdjacencyMatrix
- addEdge(int, int) - Method in interface smile.graph.Graph
-
Creates a new edge in this graph, going from the source vertex to the target vertex, and returns the created edge.
- addEdge(int, int, double) - Method in class smile.graph.AdjacencyList
- addEdge(int, int, double) - Method in class smile.graph.AdjacencyMatrix
- addEdge(int, int, double) - Method in interface smile.graph.Graph
-
Creates a new edge in this graph, going from the source vertex to the target vertex, and returns the created edge.
- addEdge(Neuron) - Method in class smile.vq.hebb.Neuron
-
Adds an edge.
- addEdge(Neuron, int) - Method in class smile.vq.hebb.Neuron
-
Adds an edge.
- 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
-
Adds 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 Class in smile.validation.metric
-
The normalization method.
- adjustedR2() - Method in class smile.timeseries.AR
-
Returns adjusted R2 statistic.
- adjustedR2() - Method in class smile.timeseries.ARMA
-
Returns adjusted R2 statistic.
- AdjustedRandIndex - Class in smile.validation.metric
-
Adjusted Rand Index.
- AdjustedRandIndex() - Constructor for class smile.validation.metric.AdjustedRandIndex
- adjustedRSquared() - Method in class smile.regression.LinearModel
-
Returns adjusted R2 statistic.
- age - Variable in class smile.vq.hebb.Edge
-
The age of the edges.
- age() - Method in class smile.vq.hebb.Neuron
-
Increments the age of all edges emanating from the neuron.
- aggregate(String) - Method in class smile.plot.vega.Field
-
Sets the aggregation function for the field (e.g., "mean", "sum", "median", "min", "max", "count").
- aggregate(String, String, String, String...) - Method in class smile.plot.vega.Transform
-
Aggregate summarizes a table as one record for each group.
- 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.
- align(String) - Method in class smile.plot.vega.Concat
- align(String) - Method in class smile.plot.vega.Facet
- align(String) - Method in class smile.plot.vega.FacetField
-
Sets the alignment to apply to row/column facet's subplot.
- align(String) - Method in class smile.plot.vega.Repeat
- align(String) - Method in interface smile.plot.vega.ViewLayoutComposition
-
Sets the alignment to apply to grid rows and columns.
- align(String, String) - Method in class smile.plot.vega.Concat
- align(String, String) - Method in class smile.plot.vega.Facet
- align(String, String) - Method in class smile.plot.vega.Repeat
- align(String, String) - Method in interface smile.plot.vega.ViewLayoutComposition
-
Sets different alignments for rows and columns.
- ALL - Enum constant in enum class smile.math.blas.EigenRange
-
All eigenvalues will be found.
- ALL - Enum constant in enum class smile.math.blas.SVDJob
-
All left (or right) singular vectors are returned in supplied matrix U (or Vt).
- allocate(long) - Static method in class smile.io.Arrow
-
Creates the root allocator.
- allowSpecialTokens(boolean) - Method in class smile.llm.tokenizer.Tiktoken
-
Sets how special tokens will be encoded.
- alpha - Variable in class smile.stat.distribution.BetaDistribution
-
The shape parameter.
- alpha() - Method in class smile.stat.distribution.BetaDistribution
-
Returns the shape parameter alpha.
- 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. - anchor(double) - Method in class smile.plot.vega.BinParams
-
Sets the value in the binned domain at which to anchor the bins, shifting the bin boundaries if necessary to ensure that a boundary aligns with the anchor value.
- AnchorText - Interface in smile.nlp
-
The anchor text is the visible, clickable text in a hyperlink.
- and(Tensor) - Method in class smile.deep.tensor.Tensor
-
Returns logical AND of two boolean tensors.
- and(Predicate...) - Static method in class smile.plot.vega.Predicate
-
Logical AND composition to combine predicates.
- and_(Tensor) - Method in class smile.deep.tensor.Tensor
-
Returns logical AND of two boolean tensors.
- andThen(Transform) - Method in interface smile.data.transform.Transform
-
Returns a composed function that first applies this function to its input, and then applies the
after
function to the result. - antecedent - Variable in class smile.association.AssociationRule
-
Antecedent itemset.
- anyNull() - Method in interface smile.data.Tuple
-
Returns true if there are any NULL values in this tuple.
- anyNull() - Method in interface smile.data.vector.Vector
-
Returns true if there are any NULL values in this row.
- append(int, double) - Method in class smile.util.SparseArray
-
Append an entry to the array, optimizing for the case where the index is greater than all existing indices in the array.
- apply(double) - Method in interface smile.math.Function
-
Computes the value of the function at x.
- apply(double) - Method in interface smile.math.kernel.DotProductKernel
-
Computes the kernel function.
- apply(double) - Method in interface smile.math.kernel.IsotropicKernel
-
Computes the kernel function.
- apply(double[]) - Method in class smile.feature.extraction.KernelPCA
- apply(double[]) - Method in class smile.feature.extraction.Projection
-
Project a data point to the feature space.
- apply(double...) - Method in interface smile.math.MultivariateFunction
-
Computes the value of the function at x.
- apply(double[][]) - Method in class smile.feature.extraction.Projection
-
Project a set of data to the feature space.
- apply(double, FPTree) - Static method in class smile.association.ARM
-
Mines the association rules.
- apply(int) - Method in interface smile.data.DataFrame
-
Returns the row at the specified index.
- apply(int) - Method in interface smile.data.Dataset
-
Returns the index at the specified index.
- apply(int) - Method in interface smile.data.Tuple
-
Returns the value at position i.
- apply(int) - Method in interface smile.data.vector.BaseVector
-
Returns the value at position i, which may be null.
- apply(int) - Method in class smile.math.Complex.Array
-
Returns the i-th element.
- apply(int) - Method in interface smile.math.IntFunction
-
Computes the value of the function at x.
- apply(int) - Method in interface smile.math.TimeFunction
-
Returns the function value at time step t.
- apply(int...) - Method in interface smile.data.vector.BaseVector
-
Returns a new vector with selected entries.
- apply(int, int) - Method in class smile.math.matrix.fp32.IMatrix
-
Returns
A[i,j]
. - apply(int, int) - Method in class smile.math.matrix.IMatrix
-
Returns
A[i,j]
for Scala users. - apply(int, int) - Method in class smile.util.Array2D
-
Returns A[i, j].
- apply(int, int) - Method in class smile.util.IntArray2D
-
Returns A[i, j].
- apply(int, int, int, Fitness<BitString>) - Method in class smile.feature.selection.GAFE
-
Genetic algorithm based feature selection for classification.
- apply(Enum<?>) - Method in interface smile.data.DataFrame
-
Selects column using an enum value.
- apply(String) - Method in interface smile.data.DataFrame
-
Selects column based on the column name and return it as a Column.
- apply(String) - Method in interface smile.data.Tuple
-
Returns the value by field name.
- apply(String) - Method in class smile.feature.extraction.BagOfWords
-
Returns the bag-of-words features of a document.
- apply(String) - Method in class smile.feature.extraction.HashEncoder
-
Returns the bag-of-words features of a document.
- apply(String) - Method in class smile.nlp.embedding.Word2Vec
-
Returns the embedding vector of a word.
- apply(String) - Method in interface smile.nlp.tokenizer.Tokenizer
- apply(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(FPTree) - Static method in class smile.association.FPGrowth
-
Mines the frequent item sets.
- apply(DataFrame) - Method in interface smile.data.formula.Feature
-
Applies the term on a data frame.
- apply(DataFrame) - Method in class smile.data.transform.ColumnTransform
- apply(DataFrame) - Method in interface smile.data.transform.Transform
-
Applies this transform to the given argument.
- apply(DataFrame) - Method in class smile.feature.extraction.BinaryEncoder
-
Generates the compact representation of sparse binary features for a data frame.
- apply(DataFrame) - Method in class smile.feature.extraction.Projection
- apply(DataFrame) - Method in class smile.feature.extraction.SparseEncoder
-
Generates the sparse representation of a data frame.
- apply(DataFrame) - Method in class smile.feature.imputation.SimpleImputer
- apply(Tuple) - Method in interface smile.data.formula.Feature
-
Applies the term on a tuple.
- apply(Tuple) - Method in class smile.data.formula.Formula
-
Apply the formula on a tuple to generate the model data.
- apply(Tuple) - Method in class smile.data.transform.ColumnTransform
- apply(Tuple) - Method in class smile.feature.extraction.BagOfWords
- apply(Tuple) - Method in class smile.feature.extraction.BinaryEncoder
-
Generates the compact representation of sparse binary features for given object.
- apply(Tuple) - Method in class smile.feature.extraction.Projection
- apply(Tuple) - Method in class smile.feature.extraction.SparseEncoder
-
Generates the sparse representation of given object.
- apply(Tuple) - Method in class smile.feature.imputation.KMedoidsImputer
- apply(Tuple) - Method in class smile.feature.imputation.KNNImputer
- apply(Tuple) - Method in class smile.feature.imputation.SimpleImputer
- apply(Tuple) - Method in class smile.feature.transform.Normalizer
- apply(Tensor) - Method in interface smile.deep.layer.Layer
- apply(Tensor) - Method in class smile.deep.Model
- apply(BitString, BitString) - Method in enum class smile.gap.Crossover
-
Returns a pair of offsprings by crossovering parent chromosomes.
- apply(T) - Method in class smile.manifold.KPCA
- apply(T[]) - Method in interface smile.gap.Selection
-
Select a chromosome with replacement from the population based on their fitness.
- apply(T[]) - Method in class smile.manifold.KPCA
-
Project a set of data to the feature space.
- apply(T, T) - Method in interface smile.math.distance.Distance
-
Returns the distance measure between two objects.
- apply(T, T) - Method in interface smile.math.kernel.MercerKernel
-
Kernel function.
- applyAsBoolean(Tuple) - Method in interface smile.data.formula.Feature
-
Applies the term on a data object and produces a boolean-valued result.
- applyAsByte(Tuple) - Method in interface smile.data.formula.Feature
-
Applies the term on a data object and produces a byte-valued result.
- applyAsChar(Tuple) - Method in interface smile.data.formula.Feature
-
Applies the term on a data object and produces a char-valued result.
- applyAsDouble(double[]) - Method in interface smile.math.MultivariateFunction
- applyAsDouble(Tuple) - Method in interface smile.data.formula.Feature
-
Applies the term on a data object and produces a double-valued result.
- applyAsDouble(T) - Method in interface smile.classification.Classifier
- applyAsDouble(T) - Method in interface smile.regression.Regression
- applyAsDouble(T, T) - Method in interface smile.math.distance.Distance
- applyAsDouble(T, T) - Method in interface smile.math.kernel.MercerKernel
- applyAsFloat(Tuple) - Method in interface smile.data.formula.Feature
-
Applies the term on a data object and produces a float-valued result.
- applyAsFloat(T) - Method in interface smile.util.ToFloatFunction
-
Applies this function to the given argument.
- applyAsInt(Tuple) - Method in interface smile.data.formula.Feature
-
Applies the term on a data object and produces an int-valued result.
- applyAsInt(T) - Method in interface smile.classification.Classifier
- applyAsLong(Tuple) - Method in interface smile.data.formula.Feature
-
Applies the term on a data object and produces a long-valued result.
- applyAsShort(Tuple) - Method in interface smile.data.formula.Feature
-
Applies the term on a data object and produces a short-valued result.
- ar() - Method in class smile.timeseries.AR
-
Returns the linear coefficients of AR (without intercept).
- ar() - Method in class smile.timeseries.ARMA
-
Returns the linear coefficients of AR(p).
- AR - Class in smile.timeseries
-
Autoregressive model.
- AR(double[], double[], double, AR.Method) - Constructor for class smile.timeseries.AR
-
Constructor.
- AR.Method - Enum Class in smile.timeseries
-
The fitting method.
- arange(double, double, double) - Static method in class smile.deep.tensor.Tensor
-
Returns a 1-D tensor of size (end - start) / step with values from the interval [start, end) taken with common difference step beginning from start.
- arange(float, float, float) - Static method in class smile.deep.tensor.Tensor
-
Returns a 1-D tensor of size (end - start) / step with values from the interval [start, end) taken with common difference step beginning from start.
- arange(int, int, int) - Static method in class smile.deep.tensor.Tensor
-
Returns a 1-D tensor of size (end - start) / step with values from the interval [start, end) taken with common difference step beginning from start.
- arange(long, long, long) - Static method in class smile.deep.tensor.Tensor
-
Returns a 1-D tensor of size (end - start) / step with values from the interval [start, end) taken with common difference step beginning from start.
- arff(String) - Static method in interface smile.io.Read
-
Reads an ARFF file.
- arff(Path) - Static method in interface smile.io.Read
-
Reads an ARFF file.
- arff(DataFrame, Path, String) - Static method in interface smile.io.Write
-
Writes the data frame to an ARFF file.
- Arff - Class in smile.io
-
Weka ARFF (attribute relation file format) is an ASCII text file format that is essentially a CSV file with a header that describes the meta-data.
- Arff(Reader) - Constructor for class smile.io.Arff
-
Constructor.
- Arff(String) - Constructor for class smile.io.Arff
-
Constructor.
- Arff(String, Charset) - Constructor for class smile.io.Arff
-
Constructor.
- Arff(Path) - Constructor for class smile.io.Arff
-
Constructor.
- Arff(Path, Charset) - Constructor for class smile.io.Arff
-
Constructor.
- argmax(int, boolean) - Method in class smile.deep.tensor.Tensor
-
Returns the indices of the maximum value of a tensor across a dimension.
- aria(boolean) - Method in class smile.plot.vega.Axis
-
Sets if ARIA attributes should be included (SVG output only).
- aria(boolean) - Method in class smile.plot.vega.Legend
-
Sets if ARIA attributes should be included (SVG output only).
- aria(boolean) - Method in class smile.plot.vega.Mark
-
Sets the aria.
- ARM - Class in smile.association
-
Association Rule Mining.
- ARMA - Class in smile.timeseries
-
Autoregressive moving-average model.
- ARMA(double[], double[], double[], double, double[], double[]) - Constructor for class smile.timeseries.ARMA
-
Constructor.
- ARPACK - Class in smile.math.matrix
-
ARPACK is a collection of Fortran77 subroutines designed to solve large scale eigenvalue problems.
- ARPACK - Class in smile.math.matrix.fp32
-
ARPACK is a collection of Fortran77 subroutines designed to solve large scale eigenvalue problems.
- ARPACK.AsymmOption - Enum Class in smile.math.matrix
-
Which eigenvalues of asymmetric matrix to compute.
- ARPACK.AsymmOption - Enum Class in smile.math.matrix.fp32
-
Which eigenvalues of asymmetric matrix to compute.
- ARPACK.SymmOption - Enum Class in smile.math.matrix
-
Which eigenvalues of symmetric matrix to compute.
- ARPACK.SymmOption - Enum Class in smile.math.matrix.fp32
-
Which eigenvalues of symmetric matrix to compute.
- array() - Method in interface smile.data.vector.BaseVector
-
Returns the array that backs this vector.
- array() - Method in interface smile.data.vector.BooleanVector
- array() - Method in interface smile.data.vector.ByteVector
- array() - Method in interface smile.data.vector.CharVector
- array() - Method in interface smile.data.vector.DoubleVector
- array() - Method in interface smile.data.vector.FloatVector
- array() - Method in interface smile.data.vector.IntVector
- array() - Method in interface smile.data.vector.LongVector
- array() - Method in interface smile.data.vector.ShortVector
- array() - Method in record class smile.util.Bytes
-
Returns the value of the
array
record component. - array(DataType) - Static method in class smile.data.type.DataTypes
-
Creates an array data type.
- Array - Enum constant in enum class smile.data.type.DataType.ID
-
Array type ID.
- Array(int) - Constructor for class smile.math.Complex.Array
-
Constructor.
- Array2D - Class in smile.util
-
2-dimensional array of doubles.
- Array2D(double[][]) - Constructor for class smile.util.Array2D
-
Constructor.
- Array2D(int, int) - Constructor for class smile.util.Array2D
-
Constructor of all-zero matrix.
- Array2D(int, int, double) - Constructor for class smile.util.Array2D
-
Constructor.
- Array2D(int, int, double[]) - Constructor for class smile.util.Array2D
-
Constructor.
- ArrayType - Class in smile.data.type
-
Array of primitive data type.
- arrow(String) - Static method in interface smile.io.Read
-
Reads an Apache Arrow file.
- arrow(Path) - Static method in interface smile.io.Read
-
Reads an Apache Arrow file.
- arrow(DataFrame, Path) - Static method in interface smile.io.Write
-
Writes an Apache Arrow file.
- Arrow - Class in smile.io
-
Apache Arrow is a cross-language development platform for in-memory data.
- Arrow() - Constructor for class smile.io.Arrow
-
Constructor.
- Arrow(int) - Constructor for class smile.io.Arrow
-
Constructor.
- as() - Method in record class smile.plot.vega.WindowTransformField
-
Returns the value of the
as
record component. - as(String...) - Method in class smile.plot.vega.DensityTransform
-
Sets the output fields for the sample value and corresponding density estimate.
- as(String...) - Method in class smile.plot.vega.LoessTransform
-
Sets the output field names for the smoothed points generated by the loess transform.
- as(String...) - Method in class smile.plot.vega.QuantileTransform
-
Sets the output field names for the probability and quantile values.
- as(String...) - Method in class smile.plot.vega.RegressionTransform
-
Sets the output field names for the smoothed points generated by the loess transform.
- asin() - Method in class smile.deep.tensor.Tensor
-
Returns a new tensor with the arcsine of the elements of input.
- asin(String) - Static method in interface smile.data.formula.Terms
-
The
asin(x)
term. - asin(Term) - Static method in interface smile.data.formula.Terms
-
The
asin(x)
term. - asin_() - Method in class smile.deep.tensor.Tensor
-
Computes the arcsine of the elements of input in place.
- asolve(double[], double[]) - Method in interface smile.math.matrix.IMatrix.Preconditioner
-
Solve P * x = b for the preconditioner matrix P.
- asolve(float[], float[]) - Method in interface smile.math.matrix.fp32.IMatrix.Preconditioner
-
Solve P * x = b for the preconditioner matrix P.
- assistant - Enum constant in enum class smile.llm.llama.Role
-
AI assistant.
- AssociationRule - Class in smile.association
-
Association rule object.
- AssociationRule(int[], int[], double, double, double, double) - Constructor for class smile.association.AssociationRule
-
Constructor.
- asTorch() - Method in class smile.deep.activation.ActivationFunction
- asTorch() - Method in class smile.deep.layer.AdaptiveAvgPool2dLayer
- asTorch() - Method in class smile.deep.layer.AvgPool2dLayer
- asTorch() - Method in class smile.deep.layer.BatchNorm1dLayer
- asTorch() - Method in class smile.deep.layer.BatchNorm2dLayer
- asTorch() - Method in class smile.deep.layer.Conv2dLayer
- asTorch() - Method in class smile.deep.layer.DropoutLayer
- asTorch() - Method in class smile.deep.layer.EmbeddingLayer
- asTorch() - Method in class smile.deep.layer.FullyConnectedLayer
- asTorch() - Method in class smile.deep.layer.GroupNormLayer
- asTorch() - Method in interface smile.deep.layer.Layer
-
Returns the PyTorch Module object.
- asTorch() - Method in class smile.deep.layer.LayerBlock
- asTorch() - Method in class smile.deep.layer.MaxPool2dLayer
- asTorch() - Method in class smile.deep.Model
-
Returns the PyTorch Module object.
- asTorch() - Method in class smile.deep.tensor.Device
-
Returns the PyTorch device object.
- asTorch() - Method in class smile.deep.tensor.Tensor
-
Returns the PyTorch tensor object.
- asTorch() - Method in class smile.llm.PositionalEncoding
- asTorch() - Method in class smile.vision.layer.StochasticDepth
- asum(double[]) - Method in interface smile.math.blas.BLAS
-
Sums the absolute values of the elements of a vector.
- asum(float[]) - Method in interface smile.math.blas.BLAS
-
Sums the absolute values of the elements of a vector.
- asum(int, double[], int) - Method in interface smile.math.blas.BLAS
-
Sums the absolute values of the elements of a vector.
- asum(int, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- asum(int, float[], int) - Method in interface smile.math.blas.BLAS
-
Sums the absolute values of the elements of a vector.
- asum(int, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- ata() - Method in class smile.math.matrix.BigMatrix
-
Returns
A' * A
. - ata() - Method in class smile.math.matrix.fp32.Matrix
-
Returns
A' * A
. - ata() - Method in class smile.math.matrix.fp32.SparseMatrix
-
Returns
A' * A
. - ata() - Method in class smile.math.matrix.Matrix
-
Returns
A' * A
. - ata() - Method in class smile.math.matrix.SparseMatrix
-
Returns
A' * A
. - atan(String) - Static method in interface smile.data.formula.Terms
-
The
atan(x)
term. - atan(Term) - Static method in interface smile.data.formula.Terms
-
The
atan(x)
term. - attach(AutoCloseable...) - Method in class smile.util.AutoScope
-
Attaches resources to this Scope.
- 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
- AutoScope - Class in smile.util
-
AutoScope allows for predictable, deterministic resource deallocation.
- AutoScope(AutoCloseable...) - Constructor for class smile.util.AutoScope
-
Constructors.
- autosize() - Method in class smile.plot.vega.Concat
- autosize() - Method in class smile.plot.vega.Config
-
Sets the overall size of the visualization.
- autosize() - Method in class smile.plot.vega.Facet
- autosize() - Method in class smile.plot.vega.Repeat
- autosize() - Method in class smile.plot.vega.VegaLite
-
Sets the overall size of the visualization.
- autosize() - Method in class smile.plot.vega.View
- autosize(String, boolean, String) - Method in class smile.plot.vega.Concat
- autosize(String, boolean, String) - Method in class smile.plot.vega.Config
-
Sets the overall size of the visualization.
- autosize(String, boolean, String) - Method in class smile.plot.vega.Facet
- autosize(String, boolean, String) - Method in class smile.plot.vega.Repeat
- autosize(String, boolean, String) - Method in class smile.plot.vega.VegaLite
-
Sets the overall size of the visualization.
- autosize(String, boolean, String) - Method in class smile.plot.vega.View
- Averaging - Enum Class in smile.deep.metric
-
The averaging strategy to aggregate binary performance metrics across multi-classes.
- avg - Variable in class smile.validation.ClassificationValidations
-
The average of metrics.
- avg - Variable in class smile.validation.RegressionValidations
-
The average of metrics.
- avgDocSize() - Method in interface smile.nlp.Corpus
-
Returns the average size of documents in the corpus.
- avgDocSize() - Method in class smile.nlp.SimpleCorpus
- avgPool2d(int) - Static method in interface smile.deep.layer.Layer
-
Returns an average pooling layer that reduces a tensor by combining cells, and assigning the average value of the input cells to the output cell.
- AvgPool2dLayer - Class in smile.deep.layer
-
An average pooling layer that reduces a tensor by combining cells, and assigning the average value of the input cells to the output cell.
- AvgPool2dLayer(int) - Constructor for class smile.deep.layer.AvgPool2dLayer
-
Constructor.
- AvgPool2dLayer(int, int) - Constructor for class smile.deep.layer.AvgPool2dLayer
-
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.
- Avro - Class in smile.io
-
Apache Avro is a data serialization system.
- Avro(InputStream) - Constructor for class smile.io.Avro
-
Constructor.
- Avro(Path) - Constructor for class smile.io.Avro
-
Constructor.
- Avro(Schema) - Constructor for class smile.io.Avro
-
Constructor.
- axis() - Method in class smile.plot.vega.Config
-
Returns the axis definition object.
- axis() - Method in class smile.plot.vega.Field
-
Returns the axis definition object.
- axis() - Method in class smile.plot.vega.ViewConfig
-
Returns the axis definition object.
- Axis - Class in smile.plot.swing
-
This class describes an axis of a coordinate system.
- Axis - Class in smile.plot.vega
-
Axes provide axis lines, ticks, and labels to convey how a positional range represents a data range.
- Axis(Base, int) - Constructor for class smile.plot.swing.Axis
-
Constructor.
- axpy(double, double[], double[]) - Method in interface smile.math.blas.BLAS
-
Computes a constant alpha times a vector x plus a vector y.
- axpy(double, double[], double[]) - Static method in class smile.math.MathEx
-
Update an array by adding a multiple of another array y = a * x + y.
- axpy(float, float[], float[]) - Method in interface smile.math.blas.BLAS
-
Computes a constant alpha times a vector x plus a vector y.
- axpy(int, double, double[], int, double[], int) - Method in interface smile.math.blas.BLAS
-
Computes a constant alpha times a vector x plus a vector y.
- axpy(int, double, double[], int, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- axpy(int, float, float[], int, float[], int) - Method in interface smile.math.blas.BLAS
-
Computes a constant alpha times a vector x plus a vector y.
- axpy(int, float, float[], int, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
B
- b - Variable in class smile.validation.metric.ContingencyTable
-
The column sum of contingency table.
- B - Variable in class smile.vq.BIRCH
-
The branching factor of non-leaf nodes.
- background() - Method in class smile.plot.vega.View
-
Returns the view background's fill and stroke object.
- background(String) - Method in class smile.plot.vega.Concat
- background(String) - Method in class smile.plot.vega.Config
-
Sets the background with CSS color property.
- background(String) - Method in class smile.plot.vega.Facet
- background(String) - Method in class smile.plot.vega.Repeat
- background(String) - Method in class smile.plot.vega.VegaLite
-
Sets the background of the entire view with CSS color property.
- background(String) - Method in class smile.plot.vega.View
- Background - Class in smile.plot.vega
-
The view background of a single-view or layer specification.
- backpopagateDropout() - Method in class smile.base.mlp.Layer
-
Propagates the errors back through the (implicit) dropout layer.
- backpropagate(boolean) - Method in class smile.base.mlp.MultilayerPerceptron
-
Propagates the errors back through the network.
- backpropagate(double[]) - Method in class smile.base.mlp.HiddenLayer
- backpropagate(double[]) - Method in class smile.base.mlp.InputLayer
- backpropagate(double[]) - Method in class smile.base.mlp.Layer
-
Propagates the errors back to a lower layer.
- backpropagate(double[]) - Method in class smile.base.mlp.OutputLayer
- backward() - Method in class smile.deep.tensor.Tensor
-
Computes the gradients.
- Bag - Class in smile.validation
-
A bag of random selected samples.
- Bag(int[], int[]) - Constructor for class smile.validation.Bag
-
Constructor.
- BagOfWords - Class in smile.feature.extraction
-
The bag-of-words feature of text used in natural language processing and information retrieval.
- BagOfWords(String[], Function<String, String[]>, String[], boolean) - Constructor for class smile.feature.extraction.BagOfWords
-
Constructor.
- BagOfWords(Function<String, String[]>, String[]) - Constructor for class smile.feature.extraction.BagOfWords
-
Constructor.
- BandMatrix - Class in smile.math.matrix
-
A band matrix is a sparse matrix, whose non-zero entries are confined to a diagonal band, comprising the main diagonal and zero or more diagonals on either side.
- BandMatrix - Class in smile.math.matrix.fp32
-
A band matrix is a sparse matrix, whose non-zero entries are confined to a diagonal band, comprising the main diagonal and zero or more diagonals on either side.
- BandMatrix(int, int, int, int) - Constructor for class smile.math.matrix.BandMatrix
-
Constructor.
- BandMatrix(int, int, int, int) - Constructor for class smile.math.matrix.fp32.BandMatrix
-
Constructor.
- BandMatrix(int, int, int, int, double[][]) - Constructor for class smile.math.matrix.BandMatrix
-
Constructor.
- BandMatrix(int, int, int, int, float[][]) - Constructor for class smile.math.matrix.fp32.BandMatrix
-
Constructor.
- BandMatrix.Cholesky - Class in smile.math.matrix
-
The Cholesky decomposition of a symmetric, positive-definite matrix.
- BandMatrix.Cholesky - Class in smile.math.matrix.fp32
-
The Cholesky decomposition of a symmetric, positive-definite matrix.
- BandMatrix.LU - Class in smile.math.matrix
-
The LU decomposition.
- BandMatrix.LU - Class in smile.math.matrix.fp32
-
The LU decomposition.
- bandPosition(double) - Method in class smile.plot.vega.Axis
-
For band scales, sets the interpolation fraction where axis ticks should be positioned.
- bandwidth() - Method in class smile.stat.distribution.KernelDensity
-
Returns the bandwidth of kernel.
- bandwidth(double) - Method in class smile.plot.vega.DensityTransform
-
Sets the bandwidth (standard deviation) of the Gaussian kernel.
- bandwidth(double) - Method in class smile.plot.vega.LoessTransform
-
Sets a bandwidth parameter in the range [0, 1] that determines the amount of smoothing.
- 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(int) - Method in class smile.plot.vega.BinParams
-
Sets the number base to use for automatic bin determination (default is base 10).
- 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, - Interface in smile.data.vectorS> -
Base interface for immutable named vectors, which are sequences of elements supporting random access and sequential stream operations.
- batch(int) - Method in interface smile.data.Dataset
-
Returns an iterator of mini-batches.
- batchNorm1d(int) - Static method in interface smile.deep.layer.Layer
-
Returns a normalization layer that re-centers and normalizes the output of one layer before feeding it to another.
- batchNorm1d(int, double, double, boolean) - Static method in interface smile.deep.layer.Layer
-
Returns a normalization layer that re-centers and normalizes the output of one layer before feeding it to another.
- BatchNorm1dLayer - Class in smile.deep.layer
-
A batch normalization layer that re-centers and normalizes the output of one layer before feeding it to another.
- BatchNorm1dLayer(int) - Constructor for class smile.deep.layer.BatchNorm1dLayer
-
Constructor.
- BatchNorm1dLayer(int, double, double, boolean) - Constructor for class smile.deep.layer.BatchNorm1dLayer
-
Constructor.
- batchNorm2d(int) - Static method in interface smile.deep.layer.Layer
-
Returns a normalization layer that re-centers and normalizes the output of one layer before feeding it to another.
- batchNorm2d(int, double, double, boolean) - Static method in interface smile.deep.layer.Layer
-
Returns a normalization layer that re-centers and normalizes the output of one layer before feeding it to another.
- BatchNorm2dLayer - Class in smile.deep.layer
-
A batch normalization layer that re-centers and normalizes the output of one layer before feeding it to another.
- BatchNorm2dLayer(int) - Constructor for class smile.deep.layer.BatchNorm2dLayer
-
Constructor.
- BatchNorm2dLayer(int, double, double, boolean) - Constructor for class smile.deep.layer.BatchNorm2dLayer
-
Constructor.
- BBDTree - Class in smile.clustering
-
Balanced Box-Decomposition Tree.
- BBDTree(double[][]) - Constructor for class smile.clustering.BBDTree
-
Constructs a tree out of the given n data points living in R^d.
- BE - Enum constant in enum class smile.math.matrix.ARPACK.SymmOption
-
Computes nev eigenvalues, half from each end of the spectrum.
- BE - Enum constant in enum class smile.math.matrix.fp32.ARPACK.SymmOption
-
Computes nev eigenvalues, half from each end of the spectrum.
- Bernoulli - Interface in smile.glm.model
-
The response variable is of Bernoulli distribution.
- BERNOULLI - Enum constant in enum class smile.classification.DiscreteNaiveBayes.Model
-
The document Bernoulli model generates an indicator for each term of the vocabulary, either indicating presence of the term in the document or indicating absence.
- bernoulli_(double) - Method in class smile.deep.tensor.Tensor
-
Draws binary random numbers (0 or 1) from a Bernoulli distribution.
- BernoulliDistribution - Class in smile.stat.distribution
-
Bernoulli's distribution is a discrete probability distribution, which takes value 1 with success probability p and value 0 with failure probability q = 1 - p.
- BernoulliDistribution(boolean[]) - Constructor for class smile.stat.distribution.BernoulliDistribution
-
Construct a Bernoulli from the given samples.
- BernoulliDistribution(double) - Constructor for class smile.stat.distribution.BernoulliDistribution
-
Constructor.
- BERT - Class in smile.llm
-
Bidirectional Encoder Representations from Transformers (BERT).
- BERT() - Constructor for class smile.llm.BERT
- BestLocalizedWavelet - Class in smile.wavelet
-
Best localized wavelets.
- BestLocalizedWavelet(int) - Constructor for class smile.wavelet.BestLocalizedWavelet
-
Constructor.
- beta - Variable in class smile.glm.GLM
-
The linear weights.
- beta - Variable in class smile.stat.distribution.BetaDistribution
-
The shape parameter.
- beta() - Method in class smile.stat.distribution.BetaDistribution
-
Returns the shape parameter beta.
- beta(double, double) - Static method in class smile.math.special.Beta
-
Beta function, also called the Euler integral of the first kind.
- Beta - Class in smile.math.special
-
The beta function, also called the Euler integral of the first kind.
- BetaDistribution - Class in smile.stat.distribution
-
The beta distribution is defined on the interval [0, 1] parameterized by two positive shape parameters, typically denoted by α and β.
- BetaDistribution(double, double) - Constructor for class smile.stat.distribution.BetaDistribution
-
Constructor.
- BFGS - Class in smile.math
-
The Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is an iterative method for solving unconstrained nonlinear optimization problems.
- BFGS() - Constructor for class smile.math.BFGS
- BFloat16 - Enum constant in enum class smile.deep.tensor.ScalarType
-
The bfloat16 (brain floating point) floating-point format occupies 16 bits.
- bfs() - Method in class smile.graph.AdjacencyList
- bfs() - Method in class smile.graph.AdjacencyMatrix
- bfs() - Method in interface smile.graph.Graph
-
Breadth-first search connected components of graph.
- bfs(Visitor) - Method in class smile.graph.AdjacencyList
- bfs(Visitor) - Method in class smile.graph.AdjacencyMatrix
- bfs(Visitor) - Method in interface smile.graph.Graph
-
BFS search on graph and performs some operation defined in visitor on each vertex during traveling.
- bias - Variable in class smile.base.mlp.Layer
-
The bias.
- biasGradient - Variable in class smile.base.mlp.Layer
-
The bias gradient.
- biasGradientMoment1 - Variable in class smile.base.mlp.Layer
-
The first moment of bias gradient.
- biasGradientMoment2 - Variable in class smile.base.mlp.Layer
-
The second moment of bias gradient.
- biasUpdate - Variable in class smile.base.mlp.Layer
-
The bias update.
- bic - Variable in class smile.stat.distribution.DiscreteExponentialFamilyMixture
-
The BIC score when the distribution is fit on a sample data.
- bic - Variable in class smile.stat.distribution.ExponentialFamilyMixture
-
The BIC score when the distribution is fit on a sample data.
- bic - Variable in class smile.stat.distribution.MultivariateExponentialFamilyMixture
-
The BIC score when the distribution is fit on a sample data.
- bic(double[]) - Method in class smile.stat.distribution.DiscreteMixture
-
Returns the BIC score.
- bic(double[]) - Method in class smile.stat.distribution.Mixture
-
Returns the BIC score.
- bic(double[][]) - Method in class smile.stat.distribution.MultivariateMixture
-
Returns the BIC score.
- BIC() - Method in class smile.glm.GLM
-
Returns the BIC score.
- BIC(double, int, int) - Static method in interface smile.validation.ModelSelection
-
Bayesian information criterion.
- BicubicInterpolation - Class in smile.interpolation
-
Bicubic interpolation in a two-dimensional regular grid.
- BicubicInterpolation(double[], double[], double[][]) - Constructor for class smile.interpolation.BicubicInterpolation
-
Constructor.
- BigMatrix - Class in smile.math.matrix
-
Big dense matrix of double precision values for more than 2 billion elements.
- BigMatrix(int, int) - Constructor for class smile.math.matrix.BigMatrix
-
Constructor of zero matrix.
- BigMatrix(int, int, double) - Constructor for class smile.math.matrix.BigMatrix
-
Constructor.
- BigMatrix(int, int, int, DoublePointer) - Constructor for class smile.math.matrix.BigMatrix
-
Constructor.
- BigMatrix.Cholesky - Class in smile.math.matrix
-
The Cholesky decomposition of a symmetric, positive-definite matrix.
- BigMatrix.EVD - Class in smile.math.matrix
-
Eigenvalue decomposition.
- BigMatrix.LU - Class in smile.math.matrix
-
The LU decomposition.
- BigMatrix.QR - Class in smile.math.matrix
-
The QR decomposition.
- BigMatrix.SVD - Class in smile.math.matrix
-
Singular Value Decomposition.
- Bigram - Class in smile.nlp
-
Bigrams or digrams are groups of two words, and are very commonly used as the basis for simple statistical analysis of text.
- Bigram - Class in smile.nlp.collocation
-
Collocations are expressions of multiple words which commonly co-occur.
- Bigram(String, String) - Constructor for class smile.nlp.Bigram
-
Constructor.
- Bigram(String, String, int, double) - Constructor for class smile.nlp.collocation.Bigram
-
Constructor.
- bigrams() - Method in interface smile.nlp.Corpus
-
Returns the iterator over the bigrams in the corpus.
- bigrams() - Method in class smile.nlp.SimpleCorpus
- BilinearInterpolation - Class in smile.interpolation
-
Bilinear interpolation in a two-dimensional regular grid.
- BilinearInterpolation(double[], double[], double[][]) - Constructor for class smile.interpolation.BilinearInterpolation
-
Constructor.
- bin(boolean) - Method in class smile.plot.vega.FacetField
-
Turns on/off binning a quantitative field.
- bin(boolean) - Method in class smile.plot.vega.Field
-
Turns on/off binning a quantitative field.
- bin(String) - Method in class smile.plot.vega.FacetField
-
Indicates that the data for x or y channel are binned before they are imported into Vega-Lite.
- bin(String) - Method in class smile.plot.vega.Field
-
Indicates that the data for x or y channel are binned before they are imported into Vega-Lite.
- bin(String, String) - Method in class smile.plot.vega.Transform
-
Adds a bin transformation.
- bin(BinParams) - Method in class smile.plot.vega.Field
-
Sets custom binning parameters.
- binary(int, KernelMachine<int[]>) - Static method in class smile.base.svm.LinearKernelMachine
-
Creates a linear kernel machine.
- binary(String) - Static method in interface smile.math.kernel.MercerKernel
-
Returns a binary sparse kernel function.
- BinaryEncoder - Class in smile.feature.extraction
-
Encodes categorical features using sparse one-hot scheme.
- BinaryEncoder(StructType, String...) - Constructor for class smile.feature.extraction.BinaryEncoder
-
Constructor.
- BinarySparseDataset<T> - Interface in smile.data
-
Binary sparse dataset.
- BinarySparseGaussianKernel - Class in smile.math.kernel
-
Gaussian kernel, also referred as RBF kernel or squared exponential kernel.
- BinarySparseGaussianKernel(double) - Constructor for class smile.math.kernel.BinarySparseGaussianKernel
-
Constructor.
- BinarySparseGaussianKernel(double, double, double) - Constructor for class smile.math.kernel.BinarySparseGaussianKernel
-
Constructor.
- BinarySparseHyperbolicTangentKernel - Class in smile.math.kernel
-
The hyperbolic tangent kernel on binary sparse data.
- BinarySparseHyperbolicTangentKernel() - Constructor for class smile.math.kernel.BinarySparseHyperbolicTangentKernel
-
Constructor with scale 1.0 and offset 0.0.
- BinarySparseHyperbolicTangentKernel(double, double) - Constructor for class smile.math.kernel.BinarySparseHyperbolicTangentKernel
-
Constructor.
- BinarySparseHyperbolicTangentKernel(double, double, double[], double[]) - Constructor for class smile.math.kernel.BinarySparseHyperbolicTangentKernel
-
Constructor.
- BinarySparseLaplacianKernel - Class in smile.math.kernel
-
Laplacian kernel, also referred as exponential kernel.
- BinarySparseLaplacianKernel(double) - Constructor for class smile.math.kernel.BinarySparseLaplacianKernel
-
Constructor.
- BinarySparseLaplacianKernel(double, double, double) - Constructor for class smile.math.kernel.BinarySparseLaplacianKernel
-
Constructor.
- BinarySparseLinearKernel - Class in smile.math.kernel
-
The linear dot product kernel on sparse binary arrays in
int[]
, which are the indices of nonzero elements. - BinarySparseLinearKernel() - Constructor for class smile.math.kernel.BinarySparseLinearKernel
-
Constructor.
- BinarySparseMaternKernel - Class in smile.math.kernel
-
The class of Matérn kernels is a generalization of the Gaussian/RBF.
- BinarySparseMaternKernel(double, double) - Constructor for class smile.math.kernel.BinarySparseMaternKernel
-
Constructor.
- BinarySparseMaternKernel(double, double, double, double) - Constructor for class smile.math.kernel.BinarySparseMaternKernel
-
Constructor.
- BinarySparsePolynomialKernel - Class in smile.math.kernel
-
The polynomial kernel on binary sparse data.
- BinarySparsePolynomialKernel(int) - Constructor for class smile.math.kernel.BinarySparsePolynomialKernel
-
Constructor with scale 1 and offset 0.
- BinarySparsePolynomialKernel(int, double, double) - Constructor for class smile.math.kernel.BinarySparsePolynomialKernel
-
Constructor.
- BinarySparsePolynomialKernel(int, double, double, double[], double[]) - Constructor for class smile.math.kernel.BinarySparsePolynomialKernel
-
Constructor.
- BinarySparseThinPlateSplineKernel - Class in smile.math.kernel
-
The Thin Plate Spline kernel on binary sparse data.
- BinarySparseThinPlateSplineKernel(double) - Constructor for class smile.math.kernel.BinarySparseThinPlateSplineKernel
-
Constructor.
- BinarySparseThinPlateSplineKernel(double, double, double) - Constructor for class smile.math.kernel.BinarySparseThinPlateSplineKernel
-
Constructor.
- bind(StructType) - Method in class smile.data.formula.Abs
- bind(StructType) - Method in class smile.data.formula.Add
- bind(StructType) - Method in class smile.data.formula.Date
- bind(StructType) - Method in class smile.data.formula.Div
- bind(StructType) - Method in class smile.data.formula.DoubleFunction
- bind(StructType) - Method in class smile.data.formula.FactorCrossing
- bind(StructType) - Method in class smile.data.formula.FactorInteraction
- bind(StructType) - Method in class smile.data.formula.Formula
-
Binds the formula to a schema and returns the schema of predictors.
- bind(StructType) - Method in class smile.data.formula.IntFunction
- bind(StructType) - Method in class smile.data.formula.Mul
- bind(StructType) - Method in class smile.data.formula.Sub
- bind(StructType) - Method in interface smile.data.formula.Term
-
Binds the term to a schema.
- binomial(double[][], int[]) - Static method in class smile.classification.LogisticRegression
-
Fits binomial logistic regression.
- binomial(double[][], int[], double, double, int) - Static method in class smile.classification.LogisticRegression
-
Fits binomial logistic regression.
- binomial(double[][], int[], Properties) - Static method in class smile.classification.LogisticRegression
-
Fits binomial logistic regression.
- binomial(int, int[][], int[]) - Static method in class smile.classification.Maxent
-
Fits maximum entropy classifier.
- binomial(int, int[][], int[], double, double, int) - Static method in class smile.classification.Maxent
-
Fits maximum entropy classifier.
- binomial(int, int[][], int[], Properties) - Static method in class smile.classification.Maxent
-
Fits maximum entropy classifier.
- binomial(SparseDataset<Integer>) - Static method in class smile.classification.SparseLogisticRegression
-
Fits binomial logistic regression.
- binomial(SparseDataset<Integer>, double, double, int) - Static method in class smile.classification.SparseLogisticRegression
-
Fits binomial logistic regression.
- binomial(SparseDataset<Integer>, Properties) - Static method in class smile.classification.SparseLogisticRegression
-
Fits binomial logistic regression.
- Binomial - Interface in smile.glm.model
-
The response variable is of Binomial distribution.
- Binomial(double[], double, double, IntSet) - Constructor for class smile.classification.LogisticRegression.Binomial
-
Constructor.
- Binomial(double[], double, double, IntSet) - Constructor for class smile.classification.Maxent.Binomial
-
Constructor.
- Binomial(double[], double, double, IntSet) - Constructor for class smile.classification.SparseLogisticRegression.Binomial
-
Constructor.
- BinomialDistribution - Class in smile.stat.distribution
-
The binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p.
- BinomialDistribution(int, double) - Constructor for class smile.stat.distribution.BinomialDistribution
-
Constructor.
- BinParams - Class in smile.plot.vega
-
To test a data point in a filter transform or a test property in conditional encoding, a predicate definition of the following forms must be specified: - a Vega expression string, where datum can be used to refer to the current data object.
- BinParams() - Constructor for class smile.plot.vega.BinParams
-
Constructor.
- bins(double[], double) - Static method in interface smile.math.Histogram
-
Returns the number of bins for a data based on a suggested bin width h.
- bins(int) - Static method in interface smile.math.Histogram
-
Returns the number of bins by square-root rule, which takes the square root of the number of data points in the sample (used by Excel histograms and many others).
- BIRCH - Class in smile.vq
-
Balanced Iterative Reducing and Clustering using Hierarchies.
- BIRCH(int, int, int, double) - Constructor for class smile.vq.BIRCH
-
Constructor.
- bits() - Method in class smile.gap.BitString
-
Returns the bit string of chromosome.
- BitString - Class in smile.gap
-
The standard bit string representation of the solution domain.
- BitString(byte[], Fitness<BitString>) - Constructor for class smile.gap.BitString
-
Constructor.
- BitString(byte[], Fitness<BitString>, Crossover, double, double) - Constructor for class smile.gap.BitString
-
Constructor.
- BitString(int, Fitness<BitString>) - Constructor for class smile.gap.BitString
-
Constructor.
- BitString(int, Fitness<BitString>, Crossover, double, double) - Constructor for class smile.gap.BitString
-
Constructor.
- bk() - Method in class smile.math.matrix.fp32.SymmMatrix
-
Bunch-Kaufman decomposition.
- bk() - Method in class smile.math.matrix.SymmMatrix
-
Bunch-Kaufman decomposition.
- BKTree<K,
V> - Class in smile.neighbor -
A BK-tree is a metric tree specifically adapted to discrete metric spaces.
- BKTree(Metric<K>) - Constructor for class smile.neighbor.BKTree
-
Constructor.
- BLACK - Static variable in interface smile.plot.swing.Palette
- blas() - Method in enum class smile.math.blas.Diag
-
Returns the int value for BLAS.
- blas() - Method in enum class smile.math.blas.Layout
-
Returns the int value for BLAS.
- blas() - Method in enum class smile.math.blas.Side
-
Returns the int value for BLAS.
- blas() - Method in enum class smile.math.blas.Transpose
-
Returns the int value for BLAS.
- blas() - Method in enum class smile.math.blas.UPLO
-
Returns the int value for BLAS.
- BLAS - Interface in smile.math.blas
-
Basic Linear Algebra Subprograms.
- blend(String) - Method in class smile.plot.vega.Mark
-
Sets the color blend mode for drawing an item on its current background.
- block() - Method in record class smile.vision.layer.MBConvConfig
-
Returns the value of the
block
record component. - 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.
- Boolean - Enum constant in enum class smile.data.type.DataType.ID
-
Boolean type ID.
- BOOLEAN - Static variable in interface smile.util.Regex
-
Boolean regular expression pattern.
- BOOLEAN_REGEX - Static variable in interface smile.util.Regex
-
Boolean regular expression.
- BooleanArrayType - Static variable in class smile.data.type.DataTypes
-
Boolean Array data type.
- BooleanObjectType - Static variable in class smile.data.type.DataTypes
-
Boolean Object data type.
- BooleanType - Class in smile.data.type
-
Boolean data type.
- BooleanType - Static variable in class smile.data.type.DataTypes
-
Boolean data type.
- booleanVector(int) - Method in interface smile.data.DataFrame
-
Selects column based on the column index.
- booleanVector(int) - Method in class smile.data.IndexDataFrame
- booleanVector(Enum<?>) - Method in interface smile.data.DataFrame
-
Selects column using an enum value.
- booleanVector(String) - Method in interface smile.data.DataFrame
-
Selects column based on the column name.
- BooleanVector - Interface in smile.data.vector
-
An immutable boolean vector.
- Bootstrap - Interface in smile.validation
-
The bootstrap is a general tool for assessing statistical accuracy.
- bounds(String) - Method in class smile.plot.vega.Concat
- bounds(String) - Method in class smile.plot.vega.Facet
- bounds(String) - Method in class smile.plot.vega.Repeat
- bounds(String) - Method in interface smile.plot.vega.ViewLayoutComposition
-
Sets the bounds calculation method to use for determining the extent of a sub-plot.
- Box_Pierce - Enum constant in enum class smile.timeseries.BoxTest.Type
-
Box-Pierce test.
- boxed() - Method in interface smile.data.type.DataType
-
Returns the boxed data type if this is a primitive type.
- boxed(Collection<Tuple>) - Method in class smile.data.type.StructType
-
Updates the field type to the boxed one if the field has null/missing values in the data.
- 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 Class in smile.timeseries
-
The type of test.
- branch(Tuple) - Method in class smile.base.cart.InternalNode
-
Returns true if the instance goes to the true branch.
- branch(Tuple) - Method in class smile.base.cart.NominalNode
- branch(Tuple) - Method in class smile.base.cart.OrdinalNode
- BreakIteratorSentenceSplitter - Class in smile.nlp.tokenizer
-
A sentence splitter based on the java.text.BreakIterator, which supports multiple natural languages (selected by locale setting).
- BreakIteratorSentenceSplitter() - Constructor for class smile.nlp.tokenizer.BreakIteratorSentenceSplitter
-
Constructor for the default locale.
- BreakIteratorSentenceSplitter(Locale) - Constructor for class smile.nlp.tokenizer.BreakIteratorSentenceSplitter
-
Constructor for the given locale.
- BreakIteratorTokenizer - Class in smile.nlp.tokenizer
-
A word tokenizer based on the java.text.BreakIterator, which supports multiple natural languages (selected by locale setting).
- BreakIteratorTokenizer() - Constructor for class smile.nlp.tokenizer.BreakIteratorTokenizer
-
Constructor for the default locale.
- BreakIteratorTokenizer(Locale) - Constructor for class smile.nlp.tokenizer.BreakIteratorTokenizer
-
Constructor for the given locale.
- breaks - Variable in class smile.feature.selection.InformationValue
-
Breakpoints of intervals for numerical variables.
- breaks(double[], double) - Static method in interface smile.math.Histogram
-
Returns the breakpoints between histogram cells for a dataset based on a suggested bin width h.
- breaks(double[], int) - Static method in interface smile.math.Histogram
-
Returns the breakpoints between histogram cells for a dataset.
- breaks(double, double, double) - Static method in interface smile.math.Histogram
-
Returns the breakpoints between histogram cells for a given range based on a suggested bin width h.
- breaks(double, double, int) - Static method in interface smile.math.Histogram
-
Returns the breakpoints between histogram cells for a given range.
- BROWN - Static variable in interface smile.plot.swing.Palette
- bubble(int) - Static method in interface smile.vq.Neighborhood
-
Returns the bubble neighborhood function.
- bucket - Variable in class smile.neighbor.lsh.Bucket
-
The bucket id is given by the universal bucket hashing.
- Bucket - Class in smile.neighbor.lsh
-
A bucket is a container for points that all have the same value for hash function g (function g is a vector of k LSH functions).
- Bucket(int) - Constructor for class smile.neighbor.lsh.Bucket
-
Constructor.
- build() - Method in class smile.hash.PerfectMap.Builder
-
Builds the perfect map.
- build(int) - Method in class smile.base.mlp.HiddenLayerBuilder
- build(int) - Method in class smile.base.mlp.LayerBuilder
-
Builds a layer.
- build(int) - Method in class smile.base.mlp.OutputLayerBuilder
- builder(String, int, double, double) - Static method in class smile.base.mlp.Layer
-
Returns a hidden layer.
- Builder() - Constructor for class smile.hash.PerfectMap.Builder
-
Constructor.
- Builder(Map<String, T>) - Constructor for class smile.hash.PerfectMap.Builder
-
Constructor.
- BunchKaufman(SymmMatrix, int[], int) - Constructor for class smile.math.matrix.fp32.SymmMatrix.BunchKaufman
-
Constructor.
- BunchKaufman(SymmMatrix, int[], int) - Constructor for class smile.math.matrix.SymmMatrix.BunchKaufman
-
Constructor.
- 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.
- Byte - Enum constant in enum class smile.data.type.DataType.ID
-
Byte type ID.
- 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.
- Bytes - Record Class in smile.util
-
Byte string.
- Bytes(byte[]) - Constructor for record class smile.util.Bytes
-
Creates an instance of a
Bytes
record class. - Bytes(String) - Constructor for record class smile.util.Bytes
-
Constructor with a string input.
- ByteType - Class in smile.data.type
-
Byte data type.
- ByteType - Static variable in class smile.data.type.DataTypes
-
Byte data type.
- byteValue() - Method in class smile.deep.tensor.Tensor
-
Returns the byte value when the tensor holds a single value.
- byteVector(int) - Method in interface smile.data.DataFrame
-
Selects column based on the column index.
- byteVector(int) - Method in class smile.data.IndexDataFrame
- byteVector(Enum<?>) - Method in interface smile.data.DataFrame
-
Selects column using an enum value.
- byteVector(String) - Method in interface smile.data.DataFrame
-
Selects column based on the column name.
- ByteVector - Interface in smile.data.vector
-
An immutable byte vector.
C
- c(double...) - Static method in class smile.math.MathEx
-
Combines the arguments to form a vector.
- c(double[]...) - Static method in class smile.math.MathEx
-
Concatenates multiple vectors into one.
- c(float...) - Static method in class smile.math.MathEx
-
Combines the arguments to form a vector.
- c(float[]...) - Static method in class smile.math.MathEx
-
Concatenates multiple vectors into one.
- c(int...) - Static method in class smile.math.MathEx
-
Combines the arguments to form a vector.
- c(int[]...) - Static method in class smile.math.MathEx
-
Concatenates multiple vectors into one.
- c(String...) - Static method in class smile.math.MathEx
-
Combines the arguments to form a vector.
- c(String[]...) - Static method in class smile.math.MathEx
-
Concatenates multiple vectors into one array of strings.
- CacheFiles - Interface in smile.util
-
Static methods that manage cache files.
- CADET_BLUE - Static variable in interface smile.plot.swing.Palette
- calculate(String, String) - Method in class smile.plot.vega.Transform
-
Adds a formula transform extends data objects with new fields (columns) according to an expression.
- 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() - 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
- 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
- CARDINAL_NUMBER - Static variable in interface smile.util.Regex
-
Cardinal numbers.
- CARDINAL_NUMBER_WITH_COMMA - Static variable in interface smile.util.Regex
-
Cardinal numbers, optionally thousands are separated by comma.
- CART - Class in smile.base.cart
-
Classification and regression tree.
- CART(DataFrame, StructField, int, int, int, int, int[], int[][]) - Constructor for class smile.base.cart.CART
-
Constructor.
- CART(Formula, StructType, StructField, Node, double[]) - Constructor for class smile.base.cart.CART
-
Constructor.
- CategoricalEncoder - Enum Class in smile.data
-
Categorical variable encoder.
- CategoricalMeasure - Class in smile.data.measure
-
Categorical data can be stored into groups or categories with the aid of names or labels.
- CategoricalMeasure(int[]) - Constructor for class smile.data.measure.CategoricalMeasure
-
Constructor.
- CategoricalMeasure(int[], String[]) - Constructor for class smile.data.measure.CategoricalMeasure
-
Constructor.
- CategoricalMeasure(String...) - Constructor for class smile.data.measure.CategoricalMeasure
-
Constructor.
- CategoricalMeasure(List<String>) - Constructor for class smile.data.measure.CategoricalMeasure
-
Constructor.
- cbind(double[]...) - Static method in class smile.math.MathEx
-
Concatenates vectors by columns.
- cbind(float[]...) - Static method in class smile.math.MathEx
-
Concatenates vectors by columns.
- cbind(int[]...) - Static method in class smile.math.MathEx
-
Concatenates vectors by columns.
- cbind(String[]...) - Static method in class smile.math.MathEx
-
Concatenates vectors by columns.
- cbrt(String) - Static method in interface smile.data.formula.Terms
-
The
cbrt(x)
term. - cbrt(Term) - Static method in interface smile.data.formula.Terms
-
The
cbrt(x)
term. - CC - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
-
Coordinating conjunction.
- CD - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
-
Cardinal number.
- cdf(double) - Method in class smile.stat.distribution.BernoulliDistribution
- cdf(double) - Method in class smile.stat.distribution.BetaDistribution
- cdf(double) - Method in class smile.stat.distribution.BinomialDistribution
- cdf(double) - Method in class smile.stat.distribution.ChiSquareDistribution
- cdf(double) - Method in class smile.stat.distribution.DiscreteMixture
- cdf(double) - Method in interface smile.stat.distribution.Distribution
-
Cumulative distribution function.
- cdf(double) - Method in class smile.stat.distribution.EmpiricalDistribution
- cdf(double) - Method in class smile.stat.distribution.ExponentialDistribution
- cdf(double) - Method in class smile.stat.distribution.FDistribution
- cdf(double) - Method in class smile.stat.distribution.GammaDistribution
- cdf(double) - Method in class smile.stat.distribution.GaussianDistribution
- cdf(double) - Method in class smile.stat.distribution.GeometricDistribution
- cdf(double) - Method in class smile.stat.distribution.HyperGeometricDistribution
- cdf(double) - Method in class smile.stat.distribution.KernelDensity
-
Cumulative distribution function.
- cdf(double) - Method in class smile.stat.distribution.LogisticDistribution
- cdf(double) - Method in class smile.stat.distribution.LogNormalDistribution
- cdf(double) - Method in class smile.stat.distribution.Mixture
- cdf(double) - Method in class smile.stat.distribution.NegativeBinomialDistribution
- cdf(double) - Method in class smile.stat.distribution.PoissonDistribution
- cdf(double) - Method in class smile.stat.distribution.ShiftedGeometricDistribution
- cdf(double) - Method in class smile.stat.distribution.TDistribution
- cdf(double) - Method in class smile.stat.distribution.WeibullDistribution
- cdf(double[]) - Method in interface smile.stat.distribution.MultivariateDistribution
-
Cumulative distribution function.
- cdf(double[]) - Method in class smile.stat.distribution.MultivariateGaussianDistribution
-
Algorithm from Alan Genz (1992) Numerical Computation of Multivariate Normal Probabilities, Journal of Computational and Graphical Statistics, pp.
- cdf(double[]) - Method in class smile.stat.distribution.MultivariateMixture
- cdf2tailed(double) - Method in class smile.stat.distribution.TDistribution
-
Two-tailed cdf.
- ceil(String) - Static method in interface smile.data.formula.Terms
-
The
ceil(x)
term. - ceil(Term) - Static method in interface smile.data.formula.Terms
-
The
ceil(x)
term. - center() - Method in class smile.feature.extraction.PCA
-
Returns the center of data.
- center() - Method in class smile.feature.extraction.ProbabilisticPCA
-
Returns the center of data.
- center(boolean) - Method in class smile.plot.vega.Concat
- center(boolean) - Method in class smile.plot.vega.Facet
- center(boolean) - Method in class smile.plot.vega.FacetField
-
Sets if facet's subviews should be centered relative to their respective rows or columns.
- center(boolean) - Method in class smile.plot.vega.Repeat
- center(boolean) - Method in interface smile.plot.vega.ViewLayoutComposition
-
Sets if subviews should be centered relative to their respective rows or columns.
- center(double, double) - Method in class smile.plot.vega.Projection
-
Sets the projection's center, a two-element array of longitude and latitude in degrees.
- center(int, int) - Method in class smile.plot.vega.Concat
- center(int, int) - Method in class smile.plot.vega.Facet
- center(int, int) - Method in class smile.plot.vega.Repeat
- center(int, int) - Method in interface smile.plot.vega.ViewLayoutComposition
-
Sets if subviews should be centered relative to their respective rows or columns.
- CentroidClustering<T,
U> - Class in smile.clustering -
In centroid-based clustering, clusters are represented by a central vector, which may not necessarily be a member of the data set.
- CentroidClustering(double, T[], int[]) - Constructor for class smile.clustering.CentroidClustering
-
Constructor.
- centroids - Variable in class smile.clustering.CentroidClustering
-
The centroids of each cluster.
- centroids() - Method in class smile.vq.BIRCH
-
Returns the cluster centroids of leaf nodes.
- change(int) - Method in class smile.util.PriorityQueue
-
The priority of item k has changed.
- Char - Enum constant in enum class smile.data.type.DataType.ID
-
Char type ID.
- CharArrayType - Static variable in class smile.data.type.DataTypes
-
Char Array data type.
- CharObjectType - Static variable in class smile.data.type.DataTypes
-
Char Object data type.
- charset(Charset) - Method in class smile.io.CSV
-
Sets the charset.
- charset(Charset) - Method in class smile.io.JSON
-
Sets the charset.
- CharType - Class in smile.data.type
-
Char data type.
- CharType - Static variable in class smile.data.type.DataTypes
-
Char data type.
- charVector(int) - Method in interface smile.data.DataFrame
-
Selects column based on the column index.
- charVector(int) - Method in class smile.data.IndexDataFrame
- charVector(Enum<?>) - Method in interface smile.data.DataFrame
-
Selects column using an enum value.
- charVector(String) - Method in interface smile.data.DataFrame
-
Selects column based on the column name.
- CharVector - Interface in smile.data.vector
-
An immutable char vector.
- ChebyshevDistance - Class in smile.math.distance
-
Chebyshev distance (or Tchebychev distance), or L∞ metric is a metric defined on a vector space where the distance between two vectors is the greatest of their differences along any coordinate dimension.
- ChebyshevDistance() - Constructor for class smile.math.distance.ChebyshevDistance
-
Constructor.
- children() - Method in class smile.taxonomy.Concept
-
Gets all children concepts.
- chisq - Variable in class smile.stat.hypothesis.ChiSqTest
-
chi-square statistic
- ChiSqTest - Class in smile.stat.hypothesis
-
Pearson's chi-square test, also known as the chi-square goodness-of-fit test or chi-square test for independence.
- ChiSqTest(String, double, double, double) - Constructor for class smile.stat.hypothesis.ChiSqTest
-
Constructor.
- ChiSqTest(String, double, double, double, double) - Constructor for class smile.stat.hypothesis.ChiSqTest
-
Constructor.
- ChiSquareDistribution - Class in smile.stat.distribution
-
Chi-square (or chi-squared) distribution with k degrees of freedom is the distribution of a sum of the squares of k independent standard normal random variables.
- ChiSquareDistribution(int) - Constructor for class smile.stat.distribution.ChiSquareDistribution
-
Constructor.
- cholesky() - Method in class smile.math.matrix.BandMatrix
-
Cholesky decomposition for symmetric and positive definite matrix.
- cholesky() - Method in class smile.math.matrix.BigMatrix
-
Cholesky decomposition for symmetric and positive definite matrix.
- cholesky() - Method in class smile.math.matrix.fp32.BandMatrix
-
Cholesky decomposition for symmetric and positive definite matrix.
- cholesky() - Method in class smile.math.matrix.fp32.Matrix
-
Cholesky decomposition for symmetric and positive definite matrix.
- cholesky() - Method in class smile.math.matrix.fp32.SymmMatrix
-
Cholesky decomposition for symmetric and positive definite matrix.
- cholesky() - Method in class smile.math.matrix.Matrix
-
Cholesky decomposition for symmetric and positive definite matrix.
- cholesky() - Method in class smile.math.matrix.SymmMatrix
-
Cholesky decomposition for symmetric and positive definite matrix.
- cholesky(boolean) - Method in class smile.math.matrix.BigMatrix
-
Cholesky decomposition for symmetric and positive definite matrix.
- cholesky(boolean) - Method in class smile.math.matrix.fp32.Matrix
-
Cholesky decomposition for symmetric and positive definite matrix.
- cholesky(boolean) - Method in class smile.math.matrix.Matrix
-
Cholesky decomposition for symmetric and positive definite matrix.
- Cholesky(BandMatrix) - Constructor for class smile.math.matrix.BandMatrix.Cholesky
-
Constructor.
- Cholesky(BigMatrix) - Constructor for class smile.math.matrix.BigMatrix.Cholesky
-
Constructor.
- Cholesky(BandMatrix) - Constructor for class smile.math.matrix.fp32.BandMatrix.Cholesky
-
Constructor.
- Cholesky(Matrix) - Constructor for class smile.math.matrix.fp32.Matrix.Cholesky
-
Constructor.
- Cholesky(SymmMatrix) - Constructor for class smile.math.matrix.fp32.SymmMatrix.Cholesky
-
Constructor.
- Cholesky(Matrix) - Constructor for class smile.math.matrix.Matrix.Cholesky
-
Constructor.
- Cholesky(SymmMatrix) - Constructor for class smile.math.matrix.SymmMatrix.Cholesky
-
Constructor.
- CholeskyOfAtA() - Method in class smile.math.matrix.BigMatrix.QR
-
Returns the Cholesky decomposition of A'A.
- CholeskyOfAtA() - Method in class smile.math.matrix.fp32.Matrix.QR
-
Returns the Cholesky decomposition of A'A.
- CholeskyOfAtA() - Method in class smile.math.matrix.Matrix.QR
-
Returns the Cholesky decomposition of A'A.
- choose(int, int) - Static method in class smile.math.MathEx
-
The n choose k.
- Chromosome - Interface in smile.gap
-
Artificial chromosomes in genetic algorithm/programming encoding candidate solutions to an optimization problem.
- CLARANS<T> - Class in smile.clustering
-
Clustering Large Applications based upon RANdomized Search.
- CLARANS(double, T[], int[], Distance<T>) - Constructor for class smile.clustering.CLARANS
-
Constructor.
- classes - Variable in class smile.classification.AbstractClassifier
-
The class labels.
- classes - Variable in class smile.classification.ClassLabels
-
The class labels.
- classes() - Method in class smile.classification.AbstractClassifier
- classes() - Method in interface smile.classification.Classifier
-
Returns the class labels.
- classes() - Method in class smile.classification.DecisionTree
- classes() - Method in class smile.classification.MLP
- classes() - Method in class smile.classification.SVM
- classification(int, int) - Static method in interface smile.vision.transform.Transform
-
Returns a transform for image classification.
- classification(int, int, float[], float[], int) - Static method in interface smile.vision.transform.Transform
-
Returns a transform for image classification.
- classification(int, int, Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in interface smile.validation.CrossValidation
-
Repeated cross validation of classification.
- classification(int, int, T[], int[], BiFunction<T[], int[], M>) - Static method in interface smile.validation.CrossValidation
-
Repeated cross validation of classification.
- classification(int, Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in interface smile.validation.Bootstrap
-
Runs classification bootstrap validation.
- classification(int, Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in interface smile.validation.CrossValidation
-
Cross validation of classification.
- classification(int, T[], int[], BiFunction<T[], int[], M>) - Static method in interface smile.validation.Bootstrap
-
Runs classification bootstrap validation.
- classification(int, T[], int[], BiFunction<T[], int[], M>) - Static method in interface smile.validation.CrossValidation
-
Cross validation of classification.
- classification(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in interface smile.validation.LOOCV
-
Runs leave-one-out cross validation tests.
- classification(T[], int[], BiFunction<T[], int[], M>) - Static method in interface smile.validation.LOOCV
-
Runs leave-one-out cross validation tests.
- CLASSIFICATION_ERROR - Enum constant in enum class smile.base.cart.SplitRule
-
Classification error.
- ClassificationMetric - Interface in smile.validation.metric
-
An abstract interface to measure the classification performance.
- ClassificationMetrics - Class in smile.validation
-
The classification validation metrics.
- ClassificationMetrics(double, double, int, int, double) - Constructor for class smile.validation.ClassificationMetrics
-
Constructor.
- ClassificationMetrics(double, double, int, int, double, double) - Constructor for class smile.validation.ClassificationMetrics
-
Constructor of multiclass soft classifier validation.
- ClassificationMetrics(double, double, int, int, double, double, double, double, double, double) - Constructor for class smile.validation.ClassificationMetrics
-
Constructor of binary classifier validation.
- ClassificationMetrics(double, double, int, int, double, double, double, double, double, double, double, double) - Constructor for class smile.validation.ClassificationMetrics
-
Constructor of binary soft classifier validation.
- ClassificationMetrics(double, double, int, int, double, double, double, double, double, double, double, double, double) - Constructor for class smile.validation.ClassificationMetrics
-
Constructor.
- ClassificationValidation<M> - Class in smile.validation
-
Classification model validation results.
- ClassificationValidation(M, double, double, int[], int[]) - Constructor for class smile.validation.ClassificationValidation
-
Constructor.
- ClassificationValidation(M, double, double, int[], int[], double[][]) - Constructor for class smile.validation.ClassificationValidation
-
Constructor of soft classifier validation.
- ClassificationValidations<M> - Class in smile.validation
-
Classification model validation results.
- ClassificationValidations(List<ClassificationValidation<M>>) - Constructor for class smile.validation.ClassificationValidations
-
Constructor.
- Classifier<T> - Interface in smile.classification
-
A classifier assigns an input object into one of a given number of categories.
- Classifier.Trainer<T,
M> - Interface in smile.classification -
The classifier trainer.
- ClassLabels - Class in smile.classification
-
Map arbitrary class labels to [0, k), where k is the number of classes.
- ClassLabels(int, int[], IntSet) - Constructor for class smile.classification.ClassLabels
-
Constructor.
- clean() - Static method in interface smile.util.CacheFiles
-
Cleans up the cache directory.
- 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 the values from this list.
- clear() - Method in class smile.util.IntArrayList
-
Removes all the values 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.
- clip(boolean) - Method in class smile.plot.vega.Mark
-
Sets whether a mark be clipped to the enclosing group's width and height.
- clipAngle(double) - Method in class smile.plot.vega.Projection
-
Sets the projection's clipping circle radius to the specified angle in degrees.
- clipExtent(double, double, double, double) - Method in class smile.plot.vega.Projection
-
Sets the projection's viewport clip extent to the specified bounds in pixels.
- clipHeight(double) - Method in class smile.plot.vega.Legend
-
Sets the height in pixels to clip symbol legend entries and limit their size.
- clipNorm - Variable in class smile.base.mlp.MultilayerPerceptron
-
The gradient clipping norm.
- clipValue - Variable in class smile.base.mlp.MultilayerPerceptron
-
The gradient clipping value.
- clone() - Method in class smile.deep.tensor.Tensor
- clone() - Method in class smile.math.matrix.BandMatrix
- clone() - Method in class smile.math.matrix.BigMatrix
-
Returns a deep copy of matrix.
- clone() - Method in class smile.math.matrix.fp32.BandMatrix
- clone() - Method in class smile.math.matrix.fp32.Matrix
-
Returns a deep copy of matrix.
- clone() - Method in class smile.math.matrix.fp32.SparseMatrix
- clone() - Method in class smile.math.matrix.fp32.SymmMatrix
- clone() - Method in class smile.math.matrix.Matrix
-
Returns a deep copy of matrix.
- clone() - Method in class smile.math.matrix.SparseMatrix
- clone() - Method in class smile.math.matrix.SymmMatrix
- clone() - Method in class smile.neighbor.lsh.Probe
- clone(double[][]) - Static method in class smile.math.MathEx
-
Deep clone a two-dimensional array.
- clone(float[][]) - Static method in class smile.math.MathEx
-
Deep clone a two-dimensional array.
- clone(int[][]) - Static method in class smile.math.MathEx
-
Deep clone a two-dimensional array.
- close() - Method in class smile.data.SQL
- close() - Method in record class smile.deep.SampleBatch
- close() - Method in class smile.deep.tensor.Tensor
- close() - Method in class smile.io.Arff
- close() - Method in class smile.util.AutoScope
- CLOSING_PARENTHESIS - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
-
Punctuation ) ] }
- CLOSING_QUOTATION - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
-
Punctuation ' or ''
- clustering(double[][], double[][], int[], int[]) - Method in class smile.clustering.BBDTree
-
Given k cluster centroids, this method assigns data to nearest centroids.
- ClusteringMetric - Interface in smile.validation.metric
-
An abstract interface to measure the clustering performance.
- CNB - Enum constant in enum class smile.classification.DiscreteNaiveBayes.Model
-
Complement Naive Bayes.
- coefficients() - Method in class smile.classification.LogisticRegression.Binomial
-
Returns an array of size (p+1) containing the linear weights of binary logistic regression, where p is the dimension of feature vectors.
- coefficients() - Method in class smile.classification.LogisticRegression.Multinomial
-
Returns a 2d-array of size (k-1) x (p+1), containing the linear weights of multi-class logistic regression, where k is the number of classes and p is the dimension of feature vectors.
- coefficients() - Method in class smile.classification.Maxent.Binomial
-
Returns an array of size (p+1) containing the linear weights of binary logistic regression, where p is the dimension of feature vectors.
- coefficients() - Method in class smile.classification.Maxent.Multinomial
-
Returns a 2d-array of size (k-1) x (p+1), containing the linear weights of multi-class logistic regression, where k is the number of classes and p is the dimension of feature vectors.
- coefficients() - Method in class smile.classification.SparseLogisticRegression.Binomial
-
Returns an array of size (p+1) containing the linear weights of binary logistic regression, where p is the dimension of feature vectors.
- coefficients() - Method in class smile.classification.SparseLogisticRegression.Multinomial
-
Returns a 2d-array of size (k-1) x (p+1), containing the linear weights of multi-class logistic regression, where k is the number of classes and p is the dimension of feature vectors.
- coefficients() - Method in class smile.glm.GLM
-
Returns an array of size (p+1) containing the linear weights of binary logistic regression, where p is the dimension of feature vectors.
- coefficients() - Method in class smile.regression.LinearModel
-
Returns the linear coefficients without intercept.
- coerce(DataType, DataType) - Static method in interface smile.data.type.DataType
-
Returns the common type.
- CoifletWavelet - Class in smile.wavelet
-
Coiflet wavelets.
- CoifletWavelet(int) - Constructor for class smile.wavelet.CoifletWavelet
-
Constructor.
- col(int) - Method in class smile.math.matrix.BigMatrix
-
Returns the j-th column.
- col(int) - Method in class smile.math.matrix.fp32.Matrix
-
Returns the j-th column.
- col(int) - Method in class smile.math.matrix.Matrix
-
Returns the j-th column.
- col(int...) - Method in class smile.math.matrix.BigMatrix
-
Returns the matrix of selected columns.
- COL_MAJOR - Enum constant in enum class smile.math.blas.Layout
-
Column major layout.
- collect() - Static method in interface smile.data.DataFrame.Collectors
-
Returns a stream collector that accumulates tuples into a DataFrame.
- collect(Class<T>) - Static method in interface smile.data.DataFrame.Collectors
-
Returns a stream collector that accumulates objects into a DataFrame.
- collector() - Static method in interface smile.data.Dataset
-
Returns a stream collector that accumulates elements into a Dataset.
- collector() - Static method in class smile.math.matrix.Matrix
-
Returns a stream collector that accumulates elements into a Matrix.
- colMax(double[][]) - Static method in class smile.math.MathEx
-
Returns the column maximum of a matrix.
- colMax(int[][]) - Static method in class smile.math.MathEx
-
Returns the column maximum of a matrix.
- colMeans() - Method in class smile.math.matrix.BigMatrix
-
Returns the mean of each column.
- colMeans() - Method in class smile.math.matrix.fp32.Matrix
-
Returns the mean of each column.
- colMeans() - Method in class smile.math.matrix.Matrix
-
Returns the mean of each column.
- colMeans(double[][]) - Static method in class smile.math.MathEx
-
Returns the column means of a matrix.
- colMin(double[][]) - Static method in class smile.math.MathEx
-
Returns the column minimum of a matrix.
- colMin(int[][]) - Static method in class smile.math.MathEx
-
Returns the column minimum of a matrix.
- colName(int) - Method in class smile.math.matrix.fp32.IMatrix
-
Returns the name of i-th column.
- colName(int) - Method in class smile.math.matrix.IMatrix
-
Returns the name of i-th column.
- colNames() - Method in class smile.math.matrix.fp32.IMatrix
-
Returns the column names.
- colNames() - Method in class smile.math.matrix.IMatrix
-
Returns the column names.
- colNames(String[]) - Method in class smile.math.matrix.fp32.IMatrix
-
Sets the column names.
- colNames(String[]) - Method in class smile.math.matrix.IMatrix
-
Sets the column names.
- Colon - Static variable in class smile.deep.tensor.Index
-
The colon (:) is used to slice all elements of a dimension.
- COLON - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
-
Punctuation ; : ...
- color(String) - Method in class smile.plot.vega.Mark
-
Sets the default color.
- 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
- cols(int...) - Method in class smile.math.matrix.fp32.Matrix
-
Returns the matrix of selected columns.
- cols(int...) - Method in class smile.math.matrix.Matrix
-
Returns the matrix of selected columns.
- colSds() - Method in class smile.math.matrix.BigMatrix
-
Returns the standard deviations of each column.
- colSds() - Method in class smile.math.matrix.fp32.Matrix
-
Returns the standard deviations of each column.
- colSds() - Method in class smile.math.matrix.Matrix
-
Returns the standard deviations of each column.
- colSds(double[][]) - Static method in class smile.math.MathEx
-
Returns the column standard deviations of a matrix.
- colSums() - Method in class smile.math.matrix.BigMatrix
-
Returns the sum of each column.
- colSums() - Method in class smile.math.matrix.fp32.Matrix
-
Returns the sum of each column.
- colSums() - Method in class smile.math.matrix.Matrix
-
Returns the sum of each column.
- colSums(double[][]) - Static method in class smile.math.MathEx
-
Returns the column sums of a matrix.
- colSums(int[][]) - Static method in class smile.math.MathEx
-
Returns the column sums of a matrix.
- column(double[]) - Static method in class smile.math.matrix.BigMatrix
-
Returns a column vector/matrix.
- column(double[]) - Static method in class smile.math.matrix.fp32.Matrix
-
Returns a column vector/matrix.
- column(double[]) - Static method in class smile.math.matrix.Matrix
-
Returns a column vector/matrix.
- column(double[], int, int) - Static method in class smile.math.matrix.BigMatrix
-
Returns a column vector/matrix.
- column(double[], int, int) - Static method in class smile.math.matrix.fp32.Matrix
-
Returns a column vector/matrix.
- column(double[], int, int) - Static method in class smile.math.matrix.Matrix
-
Returns a column vector/matrix.
- column(float[]) - Static method in class smile.math.matrix.fp32.Matrix
-
Returns a column vector/matrix.
- column(float[], int, int) - Static method in class smile.math.matrix.fp32.Matrix
-
Returns a column vector/matrix.
- column(int) - Method in interface smile.data.DataFrame
-
Selects column based on the column index.
- column(int) - Method in class smile.data.IndexDataFrame
- column(Enum<?>) - Method in interface smile.data.DataFrame
-
Selects column using an enum value.
- column(String) - Method in interface smile.data.DataFrame
-
Selects column based on the column name.
- column(String) - Method in class smile.plot.vega.Facet
-
Returns the field definition for the vertical facet of trellis plots.
- columnAdded(TableColumnModelEvent) - Method in class smile.swing.table.TableColumnSettings
- columnMarginChanged(ChangeEvent) - Method in class smile.swing.table.TableColumnSettings
- columnMoved(TableColumnModelEvent) - Method in class smile.swing.table.TableColumnSettings
- columnPadding(double) - Method in class smile.plot.vega.Legend
-
Sets the horizontal padding in pixels between symbol legend entries.
- columnRemoved(TableColumnModelEvent) - Method in class smile.swing.table.TableColumnSettings
- columns - Variable in class smile.feature.extraction.Projection
-
The fields of input space.
- columns(int) - Method in class smile.plot.vega.Facet
-
Sets the number of columns to include in the view composition layout.
- columns(int) - Method in class smile.plot.vega.Legend
-
Sets the number of columns in which to arrange symbol legend entries.
- columns(int) - Method in class smile.plot.vega.Repeat
-
Sets the number of columns to include in the view composition layout.
- columnSelectionChanged(ListSelectionEvent) - Method in class smile.swing.table.TableColumnSettings
- ColumnTransform - Class in smile.data.transform
-
Column-wise data transformation.
- ColumnTransform(String, Map<String, Function>) - Constructor for class smile.data.transform.ColumnTransform
-
Constructor.
- COMMA - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
-
Punctuation ,
- COMPACT - Enum constant in enum class smile.math.blas.SVDJob
-
The first min(m, n) singular vectors are returned in supplied matrix U (or Vt).
- comparator - Static variable in class smile.base.cart.Split
-
The comparator on the split score.
- compareTo(CentroidClustering<T, U>) - Method in class smile.clustering.CentroidClustering
- compareTo(MEC<T>) - Method in class smile.clustering.MEC
- compareTo(InformationValue) - Method in class smile.feature.selection.InformationValue
- compareTo(SignalNoiseRatio) - Method in class smile.feature.selection.SignalNoiseRatio
- compareTo(SumSquaresRatio) - Method in class smile.feature.selection.SumSquaresRatio
- compareTo(Chromosome) - Method in class smile.gap.BitString
- compareTo(PrH) - Method in class smile.neighbor.lsh.PrH
- compareTo(Probe) - Method in class smile.neighbor.lsh.Probe
- compareTo(PrZ) - Method in class smile.neighbor.lsh.PrZ
- compareTo(Neighbor<K, V>) - Method in class smile.neighbor.Neighbor
- compareTo(Bigram) - Method in class smile.nlp.collocation.Bigram
- compareTo(NGram) - Method in class smile.nlp.collocation.NGram
- compareTo(Relevance) - Method in class smile.nlp.relevance.Relevance
- compareTo(Neuron) - Method in class smile.vq.hebb.Neuron
- CompleteLinkage - Class in smile.clustering.linkage
-
Complete linkage.
- CompleteLinkage(double[][]) - Constructor for class smile.clustering.linkage.CompleteLinkage
-
Constructor.
- CompleteLinkage(int, float[]) - Constructor for class smile.clustering.linkage.CompleteLinkage
-
Constructor.
- Complex - Class in smile.math
-
Complex number.
- Complex(double, double) - Constructor for class smile.math.Complex
-
Constructor.
- Complex.Array - Class in smile.math
-
Packed array of complex numbers for better memory efficiency.
- Component(double, DiscreteDistribution) - Constructor for class smile.stat.distribution.DiscreteMixture.Component
-
Constructor.
- Component(double, Distribution) - Constructor for class smile.stat.distribution.Mixture.Component
-
Constructor.
- Component(double, MultivariateDistribution) - Constructor for class smile.stat.distribution.MultivariateMixture.Component
-
Constructor.
- components - Variable in class smile.ica.ICA
-
The independent components (row-wise).
- components - Variable in class smile.stat.distribution.DiscreteMixture
-
The components of finite mixture model.
- components - Variable in class smile.stat.distribution.Mixture
-
The components of finite mixture model.
- components - Variable in class smile.stat.distribution.MultivariateMixture
-
The components of finite mixture model.
- compose(Transform) - Method in interface smile.data.transform.Transform
-
Returns a composed function that first applies the
before
function to its input, and then applies this function to the result. - COMPREHENSIVE - Enum constant in enum class smile.nlp.dictionary.EnglishStopWords
-
A very long list of stop words.
- compute() - Method in class smile.deep.metric.Accuracy
- compute() - Method in interface smile.deep.metric.Metric
-
Computes the metric value from the metric state, which are updated by previous update() calls.
- compute() - Method in class smile.deep.metric.Precision
- compute() - Method in class smile.deep.metric.Recall
- computeGradient(double[]) - Method in class smile.base.mlp.InputLayer
- computeGradient(double[]) - Method in class smile.base.mlp.Layer
-
Computes the parameter gradient for a sample of (mini-)batch.
- computeGradientUpdate(double[], double, double, double) - Method in class smile.base.mlp.InputLayer
- computeGradientUpdate(double[], double, double, double) - Method in class smile.base.mlp.Layer
-
Computes the parameter gradient and update the weights.
- computeOutputGradient(double[], double) - Method in class smile.base.mlp.OutputLayer
-
Compute the network output gradient.
- Concat - Class in smile.plot.vega
-
Concatenating views.
- Concat(int, VegaLite...) - Constructor for class smile.plot.vega.Concat
-
Constructor to put multiple views into a flexible flow layout.
- Concept - Class in smile.taxonomy
-
Concept is a set of synonyms, i.e.
- Concept(Concept, String...) - Constructor for class smile.taxonomy.Concept
-
Constructor.
- CONCISE - Enum constant in enum class smile.nlp.dictionary.EnglishDictionary
-
A concise dictionary of common terms in English.
- condition() - Method in class smile.math.matrix.BigMatrix.SVD
-
Returns the L2 norm condition number, which is max(S) / min(S).
- condition() - Method in class smile.math.matrix.fp32.Matrix.SVD
-
Returns the L2 norm condition number, which is max(S) / min(S).
- condition() - Method in class smile.math.matrix.Matrix.SVD
-
Returns the L2 norm condition number, which is max(S) / min(S).
- confidence - Variable in class smile.association.AssociationRule
-
The confidence value.
- config() - Method in class smile.plot.vega.VegaLite
-
Returns the configuration object that lists properties of a visualization for creating a consistent theme.
- Config - Class in smile.plot.vega
-
Vega-Lite's config object lists configuration properties of a visualization for creating a consistent theme.
- confusion - Variable in class smile.validation.ClassificationValidation
-
The confusion matrix.
- ConfusionMatrix - Class in smile.validation.metric
-
The confusion matrix of truth and predictions.
- ConfusionMatrix(int[][]) - Constructor for class smile.validation.metric.ConfusionMatrix
-
Constructor.
- conjugate() - Method in class smile.math.Complex
-
Returns the conjugate.
- CONJUGATE_TRANSPOSE - Enum constant in enum class smile.math.blas.Transpose
-
Conjugate transpose operation on the matrix.
- consequent - Variable in class smile.association.AssociationRule
-
Consequent itemset.
- constant(double) - Static method in interface smile.math.TimeFunction
-
Returns the constant learning rate.
- Constant - Class in smile.data.formula
-
A constant value in the formula.
- Constant() - Constructor for class smile.data.formula.Constant
- contains(double[][], double[]) - Static method in class smile.math.MathEx
-
Determines if the polygon contains the point.
- contains(double[][], double, double) - Static method in class smile.math.MathEx
-
Determines if the polygon contains the point.
- contains(int) - Method in class smile.util.IntHashSet
-
Returns true if this set contains the specified element.
- contains(String) - Method in interface smile.nlp.dictionary.Dictionary
-
Returns true if this dictionary contains the specified word.
- contains(String) - Method in enum class smile.nlp.dictionary.EnglishDictionary
- contains(String) - Method in class smile.nlp.dictionary.EnglishPunctuations
- contains(String) - Method in enum class smile.nlp.dictionary.EnglishStopWords
- contains(String) - Method in class smile.nlp.dictionary.SimpleDictionary
- content() - Method in record class smile.llm.llama.Message
-
Returns the value of the
content
record component. - ContingencyTable - Class in smile.validation.metric
-
The contingency table.
- ContingencyTable(int[], int[]) - Constructor for class smile.validation.metric.ContingencyTable
-
Constructor.
- continuousHeight(int) - Method in class smile.plot.vega.ViewConfig
-
Sets the default height when the plot has a continuous field for y or latitude, or has arc marks.
- continuousWidth(int) - Method in class smile.plot.vega.ViewConfig
-
Sets the default width when the plot has a continuous field for x or longitude, or has arc marks.
- 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[][], double[]) - Constructor for class smile.plot.swing.Contour
-
Constructor.
- Contour(double[][], int, boolean) - Constructor for class smile.plot.swing.Contour
-
Constructor.
- Contour(double[], double[], double[][], double[]) - Constructor for class smile.plot.swing.Contour
-
Constructor.
- Contour(double[], double[], double[][], int, boolean) - Constructor for class smile.plot.swing.Contour
-
Constructor.
- conv2d(int, int, int) - Static method in interface smile.deep.layer.Layer
-
Returns a convolutional layer.
- conv2d(int, int, int, int, int, int, int, boolean, String) - Static method in interface smile.deep.layer.Layer
-
Returns a convolutional layer.
- conv2d(int, int, int, int, String, int, int, boolean, String) - Static method in interface smile.deep.layer.Layer
-
Returns a convolutional layer.
- Conv2dLayer - Class in smile.deep.layer
-
A convolutional layer.
- Conv2dLayer(int, int, int, int, int, int, int, boolean, String) - Constructor for class smile.deep.layer.Conv2dLayer
-
Constructor.
- Conv2dLayer(int, int, int, int, String, int, int, boolean, String) - Constructor for class smile.deep.layer.Conv2dLayer
-
Constructor.
- Conv2dNormActivation - Class in smile.vision.layer
-
Convolution2d-Normalization-Activation block.
- Conv2dNormActivation(Conv2dNormActivation.Options) - Constructor for class smile.vision.layer.Conv2dNormActivation
-
Constructor.
- Conv2dNormActivation.Options - Record Class in smile.vision.layer
-
Conv2dNormActivation configurations.
- CooccurrenceKeywords - Interface in smile.nlp.keyword
-
Keyword extraction from a single document using word co-occurrence statistical information.
- coordinates - Variable in class smile.manifold.IsoMap
-
The coordinate matrix in embedding space.
- coordinates - Variable in class smile.manifold.IsotonicMDS
-
The coordinates.
- coordinates - Variable in class smile.manifold.LaplacianEigenmap
-
The coordinate matrix in embedding space.
- coordinates - Variable in class smile.manifold.LLE
-
The coordinate matrix in embedding space.
- coordinates - Variable in class smile.manifold.MDS
-
The principal coordinates.
- coordinates - Variable in class smile.manifold.SammonMapping
-
The coordinates.
- coordinates - Variable in class smile.manifold.TSNE
-
The coordinate matrix in embedding space.
- coordinates - Variable in class smile.manifold.UMAP
-
The coordinate matrix in embedding space.
- coordinates() - Method in class smile.manifold.KPCA
-
Returns the nonlinear principal component scores, i.e., the representation of learning data in the nonlinear principal component space.
- copy(double[][], double[][]) - Static method in class smile.math.MathEx
-
Deep copy x into y.
- copy(float[][], float[][]) - Static method in class smile.math.MathEx
-
Deep copy x into y.
- copy(int[][], int[][]) - Static method in class smile.math.MathEx
-
Copy x into y.
- cor - Variable in class smile.stat.hypothesis.CorTest
-
The correlation coefficient.
- cor(double[][]) - Static method in class smile.math.MathEx
-
Returns the sample correlation matrix.
- cor(double[][], double[]) - Static method in class smile.math.MathEx
-
Returns the sample correlation matrix.
- cor(double[][], String...) - Static method in class smile.feature.extraction.PCA
-
Fits principal component analysis with correlation matrix.
- cor(double[], double[]) - Static method in class smile.math.MathEx
-
Returns the correlation coefficient between two vectors.
- cor(float[], float[]) - Static method in class smile.math.MathEx
-
Returns the correlation coefficient between two vectors.
- cor(int[], int[]) - Static method in class smile.math.MathEx
-
Returns the correlation coefficient between two vectors.
- cor(DataFrame, String...) - Static method in class smile.feature.extraction.PCA
-
Fits principal component analysis with correlation matrix.
- cornerRadius(double) - Method in class smile.plot.vega.Legend
-
Sets the corner radius for the full legend.
- cornerRadius(double) - Method in class smile.plot.vega.Mark
-
Sets the radius in pixels of rounded rectangles or arcs' corners.
- cornerRadius(int) - Method in class smile.plot.vega.Background
-
Sets the radius of corners.
- cornerRadius(int) - Method in class smile.plot.vega.ViewConfig
-
Sets the radius of corners.
- cornerRadiusBottomLeft(double) - Method in class smile.plot.vega.Mark
-
Sets the radius in pixels of rounded rectangles' bottom left corner.
- cornerRadiusBottomRight(double) - Method in class smile.plot.vega.Mark
-
Sets the radius in pixels of rounded rectangles' bottom right corner.
- cornerRadiusEnd(double) - Method in class smile.plot.vega.Mark
-
For vertical bars, sets the top-left and top-right corner radius.
- cornerRadiusTopLeft(double) - Method in class smile.plot.vega.Mark
-
Sets the radius in pixels of rounded rectangles' top left corner.
- cornerRadiusTopRight(double) - Method in class smile.plot.vega.Mark
-
Sets the radius in pixels of rounded rectangles' top right corner.
- Corpus - Interface in smile.nlp
-
A corpus is a collection of documents.
- CorrelationDistance - Class in smile.math.distance
-
Correlation distance is defined as 1 - correlation coefficient.
- CorrelationDistance() - Constructor for class smile.math.distance.CorrelationDistance
-
Constructor of Pearson correlation distance.
- CorrelationDistance(String) - Constructor for class smile.math.distance.CorrelationDistance
-
Constructor.
- CorTest - Class in smile.stat.hypothesis
-
Correlation test.
- CorTest(String, double, double, double, double) - Constructor for class smile.stat.hypothesis.CorTest
-
Constructor.
- cos() - Method in class smile.deep.tensor.Tensor
-
Returns a new tensor with the cosine of the elements of input.
- cos() - Method in class smile.math.Complex
-
Returns the complex cosine.
- cos(double[], double[]) - Static method in class smile.math.MathEx
-
Returns the cosine similarity.
- cos(float[], float[]) - Static method in class smile.math.MathEx
-
Returns the cosine similarity.
- cos(String) - Static method in interface smile.data.formula.Terms
-
The
cos(x)
term. - cos(Term) - Static method in interface smile.data.formula.Terms
-
The
cos(x)
term. - cos_() - Method in class smile.deep.tensor.Tensor
-
Computes the cosine of the elements of input in place.
- cosh(String) - Static method in interface smile.data.formula.Terms
-
The
cosh(x)
term. - cosh(Term) - Static method in interface smile.data.formula.Terms
-
The
cosh(x)
term. - cosine(double, double, double) - Static method in interface smile.math.TimeFunction
-
Returns the cosine annealing function.
- cost() - Method in class smile.base.mlp.OutputLayer
-
Returns the cost function of neural network.
- cost() - Method in class smile.manifold.TSNE
-
Returns the cost function value.
- Cost - Enum Class in smile.base.mlp
-
Neural network cost function.
- count - Variable in class smile.nlp.collocation.Bigram
-
The frequency of bigram in the corpus.
- count - Variable in class smile.nlp.collocation.NGram
-
The frequency of n-gram in the corpus.
- count() - Method in class smile.base.cart.DecisionNode
-
Returns the number of node samples in each class.
- count(String) - Method in interface smile.nlp.Corpus
-
Returns the total frequency of the term in the corpus.
- count(String) - Method in class smile.nlp.SimpleCorpus
- count(Bigram) - Method in interface smile.nlp.Corpus
-
Returns the total frequency of the bigram in the corpus.
- count(Bigram) - Method in class smile.nlp.SimpleCorpus
- counter - Variable in class smile.vq.hebb.Neuron
-
The local counter variable (e.g.
- counts(boolean) - Method in class smile.plot.vega.DensityTransform
-
Produces probability estimates or smoothed counts.
- countTitle(String) - Method in class smile.plot.vega.Config
-
Sets the default axis and legend title for count fields.
- cov - Variable in class smile.regression.GaussianProcessRegression.JointPrediction
-
The covariance matrix of joint predictive distribution at query points.
- cov() - Method in interface smile.stat.distribution.MultivariateDistribution
-
The covariance matrix of distribution.
- cov() - Method in class smile.stat.distribution.MultivariateGaussianDistribution
- cov() - Method in class smile.stat.distribution.MultivariateMixture
- cov(double[][]) - Static method in class smile.math.MathEx
-
Returns the sample covariance matrix.
- cov(double[][], double[]) - Static method in class smile.math.MathEx
-
Returns the sample covariance matrix.
- cov(double[], double[]) - Static method in class smile.math.MathEx
-
Returns the covariance between two vectors.
- cov(double[], int) - Static method in interface smile.timeseries.TimeSeries
-
Autocovariance function.
- cov(float[], float[]) - Static method in class smile.math.MathEx
-
Returns the covariance between two vectors.
- cov(int[], int[]) - Static method in class smile.math.MathEx
-
Returns the covariance between two vectors.
- CoverTree<K,
V> - Class in smile.neighbor -
Cover tree is a data structure for generic nearest neighbor search, which is especially efficient in spaces with small intrinsic dimension.
- CoverTree(List<K>, List<V>, Metric<K>) - Constructor for class smile.neighbor.CoverTree
-
Constructor.
- CoverTree(List<K>, List<V>, Metric<K>, double) - Constructor for class smile.neighbor.CoverTree
-
Constructor.
- CoverTree(K[], V[], Metric<K>) - Constructor for class smile.neighbor.CoverTree
-
Constructor.
- CoverTree(K[], V[], Metric<K>, double) - Constructor for class smile.neighbor.CoverTree
-
Constructor.
- CPU - Enum constant in enum class smile.deep.tensor.DeviceType
-
CPU
- CPU() - Static method in class smile.deep.tensor.Device
-
Returns the CPU device.
- CramerV - Variable in class smile.stat.hypothesis.ChiSqTest
-
Cramér's V is a measure of association between two nominal variables, giving a value between 0 and 1 (inclusive).
- CRF - Class in smile.sequence
-
First-order linear conditional random field.
- CRF(StructType, RegressionTree[][], double) - Constructor for class smile.sequence.CRF
-
Constructor.
- CRFLabeler<T> - Class in smile.sequence
-
First-order CRF sequence labeler.
- CRFLabeler(CRF, Function<T, Tuple>) - Constructor for class smile.sequence.CRFLabeler
-
Constructor.
- crop(BufferedImage, int, boolean) - Method in interface smile.vision.transform.Transform
-
Crops an image.
- crop(BufferedImage, int, int, boolean) - Method in interface smile.vision.transform.Transform
-
Crops an image.
- cross(int, String...) - Static method in interface smile.data.formula.Terms
-
Factor crossing of two or more factors.
- cross(String...) - Static method in interface smile.data.formula.Terms
-
Factor crossing of two or more factors.
- crossentropy - Variable in class smile.validation.ClassificationMetrics
-
The cross entropy on validation data.
- crossEntropy() - Static method in interface smile.deep.Loss
-
Cross Entropy Loss Function.
- CrossEntropy - Interface in smile.validation.metric
-
Cross entropy generalizes the log loss metric to multiclass problems.
- crossover(Chromosome) - Method in class smile.gap.BitString
- crossover(Chromosome) - Method in interface smile.gap.Chromosome
-
Returns a pair of offsprings by crossovering this one with another one according to the crossover rate, which determines how often will be crossover performed.
- Crossover - Enum Class in smile.gap
-
The types of crossover operation.
- CrossValidation - Interface in smile.validation
-
Cross-validation is a technique for assessing how the results of a statistical analysis will generalize to an independent data set.
- csv(String) - Static method in interface smile.io.Read
-
Reads a CSV file.
- csv(String, char, Map<String, String>, String...) - Method in class smile.data.SQL
-
Creates an in-memory table from csv files.
- csv(String, String) - Static method in interface smile.io.Read
-
Reads a CSV file.
- csv(String, String...) - Method in class smile.data.SQL
-
Creates an in-memory table from csv files.
- csv(String, Map<String, String>) - Method in class smile.plot.vega.Data
-
Loads a comma-separated values (CSV) file
- csv(String, CSVFormat) - Static method in interface smile.io.Read
-
Reads a CSV file.
- csv(String, CSVFormat, StructType) - Static method in interface smile.io.Read
-
Reads a CSV file.
- csv(Path) - Static method in interface smile.io.Read
-
Reads a CSV file.
- csv(Path, CSVFormat) - Static method in interface smile.io.Read
-
Reads a CSV file.
- csv(Path, CSVFormat, StructType) - Static method in interface smile.io.Read
-
Reads a CSV file.
- csv(DataFrame, Path) - Static method in interface smile.io.Write
-
Writes a CSV file.
- csv(DataFrame, Path, CSVFormat) - Static method in interface smile.io.Write
-
Writes a CSV file.
- CSV - Class in smile.io
-
Reads and writes files in variations of the Comma Separated Value (CSV) format.
- CSV() - Constructor for class smile.io.CSV
-
Constructor.
- CSV(CSVFormat) - Constructor for class smile.io.CSV
-
Constructor.
- CubicSplineInterpolation1D - Class in smile.interpolation
-
Cubic spline interpolation.
- CubicSplineInterpolation1D(double[], double[]) - Constructor for class smile.interpolation.CubicSplineInterpolation1D
-
Constructor.
- CubicSplineInterpolation2D - Class in smile.interpolation
-
Cubic spline interpolation in a two-dimensional regular grid.
- CubicSplineInterpolation2D(double[], double[], double[][]) - Constructor for class smile.interpolation.CubicSplineInterpolation2D
-
Constructor.
- CUDA - Enum constant in enum class smile.deep.tensor.DeviceType
-
NVIDIA GPU
- CUDA - Interface in smile.deep
-
NVIDIA CUDA helper functions.
- CUDA() - Static method in class smile.deep.tensor.Device
-
Returns the default NVIDIA CUDA device.
- CUDA(byte) - Static method in class smile.deep.tensor.Device
-
Returns the NVIDIA CUDA device.
- cumulative(boolean) - Method in class smile.plot.vega.DensityTransform
-
Produces density estimates or cumulative density estimates.
- cumulativeVarianceProportion() - Method in class smile.feature.extraction.PCA
-
Returns the cumulative proportion of variance contained in principal components, ordered from largest to smallest.
- Currency - Static variable in interface smile.data.measure.Measure
-
Currency.
- CURRENCY - Static variable in class smile.swing.table.NumberCellRenderer
- cursor(String) - Method in class smile.plot.vega.Background
-
Sets the mouse cursor used over the view.
- cursor(String) - Method in class smile.plot.vega.ViewConfig
-
Sets the mouse cursor used over the view.
- customFormatTypes(boolean) - Method in class smile.plot.vega.FormatConfig
-
Allow the formatType property for text marks and guides to accept a custom formatter function registered as a Vega expression.
- CYAN - Static variable in interface smile.plot.swing.Palette
D
- d - Variable in class smile.stat.hypothesis.KSTest
-
Kolmogorov-Smirnov statistic.
- d - Variable in class smile.vq.BIRCH
-
The dimensionality of data.
- d(byte[], byte[]) - Static method in class smile.math.distance.HammingDistance
-
Returns Hamming distance between the two byte arrays.
- d(byte, byte) - Static method in class smile.math.distance.HammingDistance
-
Returns Hamming distance between the two bytes.
- d(char[], char[]) - Method in class smile.math.distance.EditDistance
-
Edit distance between two strings.
- d(double[], double[]) - Method in class smile.math.distance.ChebyshevDistance
-
Chebyshev distance between the two arrays of type double.
- d(double[], double[]) - Method in class smile.math.distance.CorrelationDistance
-
Pearson correlation distance between the two arrays of type double.
- d(double[], double[]) - Static method in class smile.math.distance.DynamicTimeWarping
-
Dynamic time warping without path constraints.
- d(double[], double[]) - Method in class smile.math.distance.EuclideanDistance
-
Euclidean distance between the two arrays of type double.
- d(double[], double[]) - Method in class smile.math.distance.JensenShannonDistance
- d(double[], double[]) - Method in class smile.math.distance.MahalanobisDistance
- d(double[], double[]) - Method in class smile.math.distance.ManhattanDistance
-
Manhattan distance between two arrays of type double.
- d(double[], double[]) - Method in class smile.math.distance.MinkowskiDistance
-
Minkowski distance between the two arrays of type double.
- d(double[], double[], int) - Static method in class smile.math.distance.DynamicTimeWarping
-
Dynamic time warping with Sakoe-Chiba band, which primarily to prevent unreasonable warping and also improve computational cost.
- d(float[], float[]) - Static method in class smile.math.distance.ChebyshevDistance
-
Chebyshev distance between the two arrays of type float.
- d(float[], float[]) - Static method in class smile.math.distance.DynamicTimeWarping
-
Dynamic time warping without path constraints.
- d(float[], float[]) - Method in class smile.math.distance.EuclideanDistance
-
Euclidean distance between the two arrays of type float.
- d(float[], float[]) - Method in class smile.math.distance.ManhattanDistance
-
Manhattan distance between two arrays of type float.
- d(float[], float[]) - Method in class smile.math.distance.MinkowskiDistance
-
Minkowski distance between the two arrays of type float.
- d(float[], float[], int) - Static method in class smile.math.distance.DynamicTimeWarping
-
Dynamic time warping with Sakoe-Chiba band, which primarily to prevent unreasonable warping and also improve computational cost.
- d(int[], int[]) - Static method in class smile.math.distance.ChebyshevDistance
-
Chebyshev distance between the two arrays of type integer.
- d(int[], int[]) - Static method in class smile.math.distance.DynamicTimeWarping
-
Dynamic time warping without path constraints.
- d(int[], int[]) - Method in class smile.math.distance.EuclideanDistance
-
Euclidean distance between the two arrays of type integer.
- d(int[], int[]) - Static method in class smile.math.distance.HammingDistance
-
Returns Hamming distance between the two integer arrays.
- d(int[], int[]) - Method in class smile.math.distance.LeeDistance
- d(int[], int[]) - Method in class smile.math.distance.ManhattanDistance
-
Manhattan distance between two arrays of type integer.
- d(int[], int[]) - Method in class smile.math.distance.MinkowskiDistance
-
Minkowski distance between the two arrays of type integer.
- d(int[], int[], int) - Static method in class smile.math.distance.DynamicTimeWarping
-
Dynamic time warping with Sakoe-Chiba band, which primarily to prevent unreasonable warping and also improve computational cost.
- d(int, int) - Method in class smile.clustering.linkage.Linkage
-
Returns the distance/dissimilarity between two clusters/objects, which are indexed by integers.
- d(int, int) - Static method in class smile.math.distance.HammingDistance
-
Returns Hamming distance between the two integers.
- d(long, long) - Static method in class smile.math.distance.HammingDistance
-
Returns Hamming distance between the two long integers.
- d(short[], short[]) - Static method in class smile.math.distance.HammingDistance
-
Returns Hamming distance between the two short arrays.
- d(short, short) - Static method in class smile.math.distance.HammingDistance
-
Returns Hamming distance between the two shorts.
- d(String, String) - Method in class smile.math.distance.EditDistance
-
Edit distance between two strings.
- d(String, String) - Method in class smile.taxonomy.TaxonomicDistance
-
Computes the distance between two concepts in a taxonomy.
- d(BitSet, BitSet) - Method in class smile.math.distance.HammingDistance
- d(Set<T>, Set<T>) - Static method in class smile.math.distance.JaccardDistance
-
Returns the Jaccard distance between sets.
- d(Concept, Concept) - Method in class smile.taxonomy.TaxonomicDistance
-
Computes the distance between two concepts in a taxonomy.
- d(SparseArray, SparseArray) - Method in class smile.math.distance.SparseChebyshevDistance
- d(SparseArray, SparseArray) - Method in class smile.math.distance.SparseEuclideanDistance
- d(SparseArray, SparseArray) - Method in class smile.math.distance.SparseManhattanDistance
- d(SparseArray, SparseArray) - Method in class smile.math.distance.SparseMinkowskiDistance
- d(T[], T[]) - Method in class smile.math.distance.DynamicTimeWarping
- d(T[], T[]) - Method in class smile.math.distance.JaccardDistance
- d(T, T) - Method in interface smile.math.distance.Distance
-
Returns the distance measure between two objects.
- D(T[]) - Method in interface smile.math.distance.Distance
-
Returns the pairwise distance matrix.
- D(T[], T[]) - Method in interface smile.math.distance.Distance
-
Returns the pairwise distance matrix.
- D4Wavelet - Class in smile.wavelet
-
The simplest and most localized wavelet, Daubechies wavelet of 4 coefficients.
- D4Wavelet() - Constructor for class smile.wavelet.D4Wavelet
-
Constructor.
- damerau(char[], char[]) - Static method in class smile.math.distance.EditDistance
-
Damerau-Levenshtein distance between two strings allows insertion, deletion, substitution, or transposition of characters.
- damerau(String, String) - Static method in class smile.math.distance.EditDistance
-
Damerau-Levenshtein distance between two strings allows insertion, deletion, substitution, or transposition of characters.
- 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
- DASH - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
-
Punctuation -
- DASH - Enum constant in enum class smile.plot.swing.Line.Style
- data - Variable in class smile.neighbor.LSH
-
The data objects.
- data() - Method in record class smile.deep.SampleBatch
-
Returns the value of the
data
record component. - data() - Method in class smile.plot.vega.LookupData
-
Returns the secondary data source.
- data() - Method in class smile.plot.vega.VegaLite
-
Returns the data specification object.
- data(String) - Static method in interface smile.io.Read
-
Reads a data file.
- data(String, String) - Static method in interface smile.io.Read
-
Reads a data file.
- Data - Class in smile.plot.vega
-
The basic data model used by Vega-Lite is tabular data.
- DataFrame - Interface in smile.data
-
An immutable collection of data organized into named columns.
- DataFrame.Collectors - Interface in smile.data
-
Stream collectors.
- DataFrameClassifier - Interface in smile.classification
-
Classification trait on DataFrame.
- DataFrameClassifier.Trainer<M> - Interface in smile.classification
-
The classifier trainer.
- DataFrameRegression - Interface in smile.regression
-
Regression trait on DataFrame.
- DataFrameRegression.Trainer<M> - Interface in smile.regression
-
The regression trainer.
- Dataset<D,
T> - Interface in smile.data -
An immutable collection of data objects.
- Dataset - Interface in smile.deep
-
A dataset consists of data and an associated target (label) and can be iterated in mini-batches.
- DataType - Interface in smile.data.type
-
The interface of data types.
- DataType.ID - Enum Class in smile.data.type
-
Data type ID.
- DataTypes - Class in smile.data.type
-
To get a specific data type, users should use singleton objects and factory methods in this class.
- DataTypes() - Constructor for class smile.data.type.DataTypes
- date(String) - Static method in class smile.data.type.DataTypes
-
Date data type with customized format.
- date(String, DateFeature...) - Static method in interface smile.data.formula.Terms
-
Extracts date/time features.
- Date - Class in smile.data.formula
-
Date/time feature extractor.
- Date - Enum constant in enum class smile.data.type.DataType.ID
-
Date type ID.
- Date(String, DateFeature...) - Constructor for class smile.data.formula.Date
-
Constructor.
- DATE - Static variable in interface smile.util.Regex
-
Date regular expression pattern.
- 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 Class in smile.data.formula
-
The date/time features.
- datetime(String) - Static method in class smile.data.type.DataTypes
-
DateTime data type with customized format.
- DateTime - Enum constant in enum class smile.data.type.DataType.ID
-
DateTime type ID.
- DATETIME - Static variable in interface smile.util.Regex
-
Datetime regular expression pattern.
- DateTimeType - Class in smile.data.type
-
DateTime data type.
- DateTimeType - Static variable in class smile.data.type.DataTypes
-
DateTime data type with ISO format.
- DateTimeType(String) - Constructor for class smile.data.type.DateTimeType
-
Constructor.
- DateType - Class in smile.data.type
-
Date data type.
- DateType - Static variable in class smile.data.type.DataTypes
-
Date data type with ISO format.
- DateType(String) - Constructor for class smile.data.type.DateType
-
Constructor.
- DaubechiesWavelet - Class in smile.wavelet
-
Daubechies wavelets.
- DaubechiesWavelet(int) - Constructor for class smile.wavelet.DaubechiesWavelet
-
Constructor.
- DAY_OF_MONTH - Enum constant in enum class smile.data.formula.DateFeature
-
The day of month represented by an integer from 1 to 31 in the usual manner.
- DAY_OF_WEEK - Enum constant in enum class smile.data.formula.DateFeature
-
The day of week represented by an integer from 1 to 7; 1 is Monday, 2 is Tuesday, and so forth; thus 7 is Sunday.
- DAY_OF_YEAR - Enum constant in enum class smile.data.formula.DateFeature
-
The day of year represented by an integer from 1 to 365, or 366 in a leap year.
- DBSCAN<T> - Class in smile.clustering
-
Density-Based Spatial Clustering of Applications with Noise.
- DBSCAN(int, double, RNNSearch<T, T>, int, int[], boolean[]) - Constructor for class smile.clustering.DBSCAN
-
Constructor.
- Decimal - Enum constant in enum class smile.data.type.DataType.ID
-
Decimal type ID.
- DECIMAL_FORMAT - Static variable in interface smile.util.Strings
-
Decimal format for floating numbers.
- DecimalType - Class in smile.data.type
-
Arbitrary-precision decimal data type.
- DecimalType - Static variable in class smile.data.type.DataTypes
-
Decimal data type.
- DecisionNode - Class in smile.base.cart
-
A leaf node in decision tree.
- DecisionNode(int[]) - Constructor for class smile.base.cart.DecisionNode
-
Constructor.
- DecisionTree - Class in smile.classification
-
Decision tree.
- DecisionTree(DataFrame, int[], StructField, int, SplitRule, int, int, int, int, int[], int[][]) - Constructor for class smile.classification.DecisionTree
-
Constructor.
- decode(int[]) - Method in class smile.llm.tokenizer.SentencePiece
- decode(int[]) - Method in class smile.llm.tokenizer.Tiktoken
- decode(int[]) - Method in interface smile.llm.tokenizer.Tokenizer
-
Decodes a list of token IDs into a string.
- decrement() - Method in class smile.util.MutableInt
-
Decrement by one.
- decrement(int) - Method in class smile.util.MutableInt
-
Decrement.
- DEFAULT - Enum constant in enum class smile.nlp.dictionary.EnglishStopWords
-
Default stop words list.
- DEFAULT_MEAN - Static variable in interface smile.vision.transform.Transform
-
The default mean value of pixel RGB after normalized to [0, 1].
- DEFAULT_STD - Static variable in interface smile.vision.transform.Transform
-
The default standard deviation of pixel RGB after normalized to [0, 1].
- 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 polynomial.
- delete(String) - Static method in interface smile.data.formula.Terms
-
Deletes a variable or the intercept ("1") from the formula.
- delete(Term) - Static method in interface smile.data.formula.Terms
-
Deletes a term from the formula.
- DENCLUE - Class in smile.clustering
-
DENsity CLUstering.
- DENCLUE(int, double[][], double[], double[][], double, int[], double) - Constructor for class smile.clustering.DENCLUE
-
Constructor.
- 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.
- density(String, String...) - Method in class smile.plot.vega.Transform
-
Adds a density transformation.
- DensityTransform - Class in smile.plot.vega
-
The density transform performs one-dimensional kernel density estimation over an input data stream and generates a new data stream of samples of the estimated densities.
- 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.
- describe(String) - Method in class smile.data.SQL
-
Returns the columns in a table.
- description(String) - Method in class smile.plot.vega.Axis
-
Sets the text description of this axis for ARIA accessibility (SVG output only).
- description(String) - Method in class smile.plot.vega.Concat
- description(String) - Method in class smile.plot.vega.Facet
- description(String) - Method in class smile.plot.vega.Legend
-
Sets the text description of this legend for ARIA accessibility (SVG output only).
- description(String) - Method in class smile.plot.vega.Mark
-
Sets the description.
- description(String) - Method in class smile.plot.vega.Repeat
- description(String) - Method in class smile.plot.vega.VegaLite
-
Sets the description of this mark for commenting purpose.
- description(String) - Method in class smile.plot.vega.View
- det() - Method in class smile.math.matrix.BandMatrix.Cholesky
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.BandMatrix.LU
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.BigMatrix.Cholesky
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.BigMatrix.LU
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.fp32.BandMatrix.Cholesky
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.fp32.BandMatrix.LU
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.fp32.Matrix.Cholesky
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.fp32.Matrix.LU
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.fp32.SymmMatrix.BunchKaufman
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.fp32.SymmMatrix.Cholesky
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.Matrix.Cholesky
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.Matrix.LU
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.SymmMatrix.BunchKaufman
-
Returns the matrix determinant.
- det() - Method in class smile.math.matrix.SymmMatrix.Cholesky
-
Returns the matrix determinant.
- detach() - Method in class smile.deep.tensor.Tensor
-
Returns a new tensor, detached from the current graph.
- detach(AutoCloseable...) - Method in class smile.util.AutoScope
-
Detaches resources from this Scope.
- DeterministicAnnealing - Class in smile.clustering
-
Deterministic annealing clustering.
- DeterministicAnnealing(double, double[][], int[]) - Constructor for class smile.clustering.DeterministicAnnealing
-
Constructor.
- deviance - Variable in class smile.glm.GLM
-
The deviance = 2 * (LogLikelihood(Saturated Model) - LogLikelihood(Proposed Model)).
- deviance() - Method in class smile.base.cart.DecisionNode
- deviance() - Method in class smile.base.cart.InternalNode
- deviance() - Method in interface smile.base.cart.Node
-
Returns the deviance of node.
- deviance() - Method in class smile.base.cart.RegressionNode
- deviance() - Method in class smile.glm.GLM
-
Returns the deviance of model.
- deviance(double[], double[], double[]) - Method in interface smile.glm.model.Model
-
The deviance function.
- deviance(int[], double[]) - Static method in class smile.base.cart.DecisionNode
-
Returns the deviance of node.
- devianceResiduals - Variable in class smile.glm.GLM
-
The deviance residuals.
- devianceResiduals() - Method in class smile.glm.GLM
-
Returns the deviance residuals.
- device() - Static method in interface smile.deep.CUDA
-
Returns the default CUDA device.
- device() - Method in class smile.deep.Model
-
Returns the device on which the model is stored.
- device() - Method in class smile.deep.tensor.Tensor
-
Returns the device on which the tensor is.
- device(byte) - Static method in interface smile.deep.CUDA
-
Returns the CUDA device of given index.
- device(Device) - Method in class smile.deep.tensor.Tensor.Options
-
Sets a compute device on which a tensor is stored.
- Device - Class in smile.deep.tensor
-
The compute device on which a tensor is stored.
- Device(DeviceType) - Constructor for class smile.deep.tensor.Device
-
Constructor.
- Device(DeviceType, byte) - Constructor for class smile.deep.tensor.Device
-
Constructor.
- deviceCount() - Static method in interface smile.deep.CUDA
-
Returns the number of CUDA devices.
- DeviceType - Enum Class in smile.deep.tensor
-
The compute device type.
- df - Variable in class smile.glm.GLM
-
The degrees of freedom of the residual deviance.
- df - Variable in class smile.stat.hypothesis.ChiSqTest
-
The degree of freedom of chi-square statistic.
- df - Variable in class smile.stat.hypothesis.CorTest
-
The degree of freedom of test statistic.
- df - Variable in class smile.stat.hypothesis.TTest
-
The degree of freedom of t-statistic.
- df - Variable in class smile.timeseries.BoxTest
-
The degree of freedom.
- df() - Method in class smile.regression.LinearModel
-
Returns the degree-of-freedom of residual standard error.
- df() - Method in class smile.timeseries.AR
-
Returns the degree-of-freedom of residual standard error.
- df() - Method in class smile.timeseries.ARMA
-
Returns the degree-of-freedom of residual standard error.
- df1 - Variable in class smile.stat.hypothesis.FTest
-
The degree of freedom of F-statistic.
- df2 - Variable in class smile.stat.hypothesis.FTest
-
The degree of freedom of F-statistic.
- dfs() - Method in class smile.graph.AdjacencyList
- dfs() - Method in class smile.graph.AdjacencyMatrix
- dfs() - Method in interface smile.graph.Graph
-
Depth-first search connected components of graph.
- dfs(Visitor) - Method in class smile.graph.AdjacencyList
- dfs(Visitor) - Method in class smile.graph.AdjacencyMatrix
- dfs(Visitor) - Method in interface smile.graph.Graph
-
DFS search on graph and performs some operation defined in visitor on each vertex during traveling.
- diag() - Method in class smile.math.matrix.BigMatrix.EVD
-
Returns the block diagonal eigenvalue matrix whose diagonal are the real part of eigenvalues, lower subdiagonal are positive imaginary parts, and upper subdiagonal are negative imaginary parts.
- diag() - Method in class smile.math.matrix.BigMatrix.SVD
-
Returns the diagonal matrix of singular values.
- diag() - Method in class smile.math.matrix.fp32.IMatrix
-
Returns the diagonal elements.
- diag() - Method in class smile.math.matrix.fp32.Matrix.EVD
-
Returns the block diagonal eigenvalue matrix whose diagonal are the real part of eigenvalues, lower subdiagonal are positive imaginary parts, and upper subdiagonal are negative imaginary parts.
- diag() - Method in class smile.math.matrix.fp32.Matrix.SVD
-
Returns the diagonal matrix of singular values.
- diag() - Method in class smile.math.matrix.fp32.SparseMatrix
- diag() - Method in class smile.math.matrix.IMatrix
-
Returns the diagonal elements.
- diag() - Method in class smile.math.matrix.Matrix.EVD
-
Returns the block diagonal eigenvalue matrix whose diagonal are the real part of eigenvalues, lower subdiagonal are positive imaginary parts, and upper subdiagonal are negative imaginary parts.
- diag() - Method in class smile.math.matrix.Matrix.SVD
-
Returns the diagonal matrix of singular values.
- diag() - Method in class smile.math.matrix.SparseMatrix
- diag(double[]) - Static method in class smile.math.matrix.BigMatrix
-
Returns a square diagonal matrix.
- diag(double[]) - Static method in class smile.math.matrix.Matrix
-
Returns a square diagonal matrix.
- diag(float[]) - Static method in class smile.math.matrix.fp32.Matrix
-
Returns a square diagonal matrix.
- diag(int, double) - Static method in class smile.math.matrix.BigMatrix
-
Returns a square diagonal matrix.
- diag(int, double) - Static method in class smile.math.matrix.Matrix
-
Returns a square diagonal matrix.
- diag(int, float) - Static method in class smile.math.matrix.fp32.Matrix
-
Returns a square diagonal matrix.
- diag(int, int, double) - Static method in class smile.math.matrix.BigMatrix
-
Returns an m-by-n diagonal matrix.
- diag(int, int, double) - Static method in class smile.math.matrix.Matrix
-
Returns an m-by-n diagonal matrix.
- diag(int, int, float) - Static method in class smile.math.matrix.fp32.Matrix
-
Returns an m-by-n diagonal matrix.
- diag(DoublePointer) - Static method in class smile.math.matrix.BigMatrix
-
Returns a square diagonal matrix.
- Diag - Enum Class in smile.math.blas
-
The flag if a triangular matrix has unit diagonal elements.
- diagonal - Variable in class smile.stat.distribution.MultivariateGaussianDistribution
-
True if the covariance matrix is diagonal.
- 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() - Method in interface smile.graph.Graph
-
Calculates the all pair shortest path by Dijkstra algorithm.
- dijkstra(int) - Method in class smile.graph.AdjacencyList
- dijkstra(int) - Method in class smile.graph.AdjacencyMatrix
- dijkstra(int) - Method in interface smile.graph.Graph
-
Calculate the shortest path from a source to all other vertices in the graph by Dijkstra algorithm.
- dijkstra(int, boolean) - Method in class smile.graph.AdjacencyMatrix
-
Calculates the shortest path by Dijkstra algorithm.
- dilation() - Method in record class smile.vision.layer.Conv2dNormActivation.Options
-
Returns the value of the
dilation
record component. - dim() - Method in class smile.deep.tensor.Tensor
-
Returns the number of dimensions of tensor.
- dimension() - Method in class smile.classification.Maxent
-
Returns the dimension of input space.
- dimension() - Method in class smile.nlp.embedding.Word2Vec
-
Returns the dimension of embedding vector space.
- dimFeedForward() - Method in record class smile.llm.Transformer.Options
-
Returns the value of the
dimFeedForward
record component. - dir() - Static method in interface smile.util.CacheFiles
-
Returns the cache directory path.
- direction(String) - Method in class smile.plot.vega.Legend
-
Sets the direction of the legend, one of "vertical" or "horizontal".
- 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.
- discreteHeight(int) - Method in class smile.plot.vega.ViewConfig
-
Sets the default height when the plot has non arc marks and either a discrete y-field or no y-field.
- DiscreteMixture - Class in smile.stat.distribution
-
The finite mixture of discrete distributions.
- DiscreteMixture(DiscreteMixture.Component...) - Constructor for class smile.stat.distribution.DiscreteMixture
-
Constructor.
- DiscreteMixture.Component - Class in smile.stat.distribution
-
A component in the mixture distribution is defined by a distribution and its weight in the mixture.
- DiscreteNaiveBayes - Class in smile.classification
-
Naive Bayes classifier for document classification in NLP.
- DiscreteNaiveBayes(DiscreteNaiveBayes.Model, double[], int) - Constructor for class smile.classification.DiscreteNaiveBayes
-
Constructor of naive Bayes classifier for document classification.
- DiscreteNaiveBayes(DiscreteNaiveBayes.Model, double[], int, double, IntSet) - Constructor for class smile.classification.DiscreteNaiveBayes
-
Constructor of naive Bayes classifier for document classification.
- DiscreteNaiveBayes(DiscreteNaiveBayes.Model, int, int) - Constructor for class smile.classification.DiscreteNaiveBayes
-
Constructor of naive Bayes classifier for document classification.
- DiscreteNaiveBayes(DiscreteNaiveBayes.Model, int, int, double, IntSet) - Constructor for class smile.classification.DiscreteNaiveBayes
-
Constructor of naive Bayes classifier for document classification.
- DiscreteNaiveBayes.Model - Enum Class in smile.classification
-
The generation models of naive Bayes classifier.
- discreteWidth(int) - Method in class smile.plot.vega.ViewConfig
-
Sets the default width when the plot has non-arc marks and either a discrete x-field or no x-field.
- distance - Variable in class smile.neighbor.Neighbor
-
The distance between the query and the neighbor.
- distance - Variable in class smile.vq.hebb.Neuron
-
The distance between the neuron and an input signal.
- distance(double[]) - Method in class smile.vq.hebb.Neuron
-
Computes the distance between the neuron and a signal.
- distance(double[], double[]) - Method in class smile.clustering.DeterministicAnnealing
- distance(double[], double[]) - Method in class smile.clustering.GMeans
- distance(double[], double[]) - Method in class smile.clustering.KMeans
- distance(double[], double[]) - Method in class smile.clustering.XMeans
- distance(double[], double[]) - Static method in class smile.math.MathEx
-
The Euclidean distance.
- distance(double[], SparseArray) - Method in class smile.clustering.SIB
- distance(float[], float[]) - Static method in class smile.math.MathEx
-
The Euclidean distance.
- distance(int[], int[]) - Method in class smile.clustering.KModes
- distance(int[], int[]) - Static method in class smile.math.MathEx
-
The Euclidean distance on binary sparse arrays, which are the indices of nonzero elements in ascending order.
- distance(SparseArray, SparseArray) - Static method in class smile.math.MathEx
-
The Euclidean distance.
- distance(T, T) - Method in class smile.clustering.CLARANS
- distance(T, U) - Method in class smile.clustering.CentroidClustering
-
The distance function.
- Distance<T> - Interface in smile.math.distance
-
An interface to calculate a distance measure between two objects.
- distinct() - Method in interface smile.data.vector.Vector
-
Returns the distinct values.
- distortion - Variable in class smile.clustering.CentroidClustering
-
The total distortion.
- distortion - Variable in class smile.clustering.SpectralClustering
-
The distortion in feature space.
- distribution - Variable in class smile.stat.distribution.DiscreteMixture.Component
-
The distribution of component.
- distribution - Variable in class smile.stat.distribution.Mixture.Component
-
The distribution of component.
- distribution - Variable in class smile.stat.distribution.MultivariateMixture.Component
-
The distribution of component.
- Distribution - Interface in smile.stat.distribution
-
Probability distribution of univariate random variable.
- div(double) - Method in class smile.deep.tensor.Tensor
-
Returns A / b.
- div(double) - Method in class smile.math.matrix.BigMatrix
-
A /= b
- div(double) - Method in class smile.math.matrix.Matrix
-
A /= b
- div(double) - Method in class smile.util.Array2D
-
A /= x.
- div(float) - Method in class smile.deep.tensor.Tensor
-
Returns A / b.
- div(float) - Method in class smile.math.matrix.fp32.Matrix
-
A /= b
- div(int) - Method in class smile.util.IntArray2D
-
A /= x.
- div(int, int, double) - Method in class smile.math.matrix.BigMatrix
-
A[i,j] /= b
- div(int, int, double) - Method in class smile.math.matrix.Matrix
-
A[i,j] /= b
- div(int, int, double) - Method in class smile.util.Array2D
-
A[i, j] /= x.
- div(int, int, float) - Method in class smile.math.matrix.fp32.Matrix
-
A[i,j] /= b
- div(int, int, int) - Method in class smile.util.IntArray2D
-
A[i, j] /= x.
- div(String, String) - Static method in interface smile.data.formula.Terms
-
Divides two terms.
- div(String, Term) - Static method in interface smile.data.formula.Terms
-
Divides two terms.
- div(Term, String) - Static method in interface smile.data.formula.Terms
-
Divides two terms.
- div(Term, Term) - Static method in interface smile.data.formula.Terms
-
Divides two terms.
- div(Tensor) - Method in class smile.deep.tensor.Tensor
-
Returns A / B element wisely.
- div(Complex) - Method in class smile.math.Complex
-
Returns a / b.
- div(BigMatrix) - Method in class smile.math.matrix.BigMatrix
-
Element-wise division A /= B
- div(Matrix) - Method in class smile.math.matrix.fp32.Matrix
-
Element-wise division A /= B
- div(Matrix) - Method in class smile.math.matrix.Matrix
-
Element-wise division A /= B
- div(Array2D) - Method in class smile.util.Array2D
-
A /= B.
- div(IntArray2D) - Method in class smile.util.IntArray2D
-
A /= B.
- Div - Class in smile.data.formula
-
The term of
a / b
expression. - Div(Term, Term) - Constructor for class smile.data.formula.Div
-
Constructor.
- div_(double) - Method in class smile.deep.tensor.Tensor
-
Returns A /= b.
- div_(float) - Method in class smile.deep.tensor.Tensor
-
Returns A /= b.
- div_(Tensor) - Method in class smile.deep.tensor.Tensor
-
Returns A /= B element wisely.
- divide(int...) - Method in class smile.plot.vega.BinParams
-
Sets the scale factors indicating allowable subdivisions.
- dlink(double) - Method in interface smile.glm.model.Model
-
The derivative of link function.
- dModel() - Method in record class smile.llm.Transformer.Options
-
Returns the value of the
dModel
record component. - domain(boolean) - Method in class smile.plot.vega.Axis
-
Sets if the domain (the axis baseline) should be included as part of the axis.
- domain(double...) - Method in class smile.plot.vega.Field
-
Sets the customize domain values.
- domain(String...) - Method in class smile.plot.vega.Field
-
Sets the customize domain values.
- domainCap(String) - Method in class smile.plot.vega.Axis
-
Sets the stroke cap for the domain line's ending style.
- domainColor(String) - Method in class smile.plot.vega.Axis
-
Sets the color of axis domain line.
- domainDash(double, double) - Method in class smile.plot.vega.Axis
-
Sets the alternating [stroke, space] lengths for dashed domain lines.
- domainDashOffset(double) - Method in class smile.plot.vega.Axis
-
Sets the pixel offset at which to start drawing with the domain dash array.
- domainMax(double) - Method in class smile.plot.vega.Field
-
Sets the maximum value in the scale domain, overriding the domain property or the default domain.
- domainMax(String) - Method in class smile.plot.vega.Field
-
Sets the maximum value in the scale domain, overriding the domain property or the default domain.
- domainMin(double) - Method in class smile.plot.vega.Field
-
Sets the minimum value in the scale domain, overriding the domain property or the default domain.
- domainMin(String) - Method in class smile.plot.vega.Field
-
Sets the minimum value in the scale domain, overriding the domain property or the default domain.
- domainOpacity(double) - Method in class smile.plot.vega.Axis
-
Sets the opacity of the axis domain line.
- domainWidth(double) - Method in class smile.plot.vega.Axis
-
Sets the stroke width of axis domain line.
- dot() - Method in class smile.base.cart.CART
-
Returns the graphic representation in Graphviz dot format.
- dot() - Static method in interface smile.data.formula.Terms
-
Returns the special term "." that means all columns not otherwise in the formula in the context of a data frame.
- dot(double[], double[]) - Method in interface smile.math.blas.BLAS
-
Computes the dot product of two vectors.
- dot(double[], double[]) - Static method in class smile.math.MathEx
-
Returns the dot product between two vectors.
- dot(float[], float[]) - Method in interface smile.math.blas.BLAS
-
Computes the dot product of two vectors.
- dot(float[], float[]) - Static method in class smile.math.MathEx
-
Returns the dot product between two vectors.
- dot(int[], int[]) - Static method in class smile.math.MathEx
-
Returns the dot product between two binary sparse arrays, which are the indices of nonzero elements in ascending order.
- dot(int, double[], int, double[], int) - Method in interface smile.math.blas.BLAS
-
Computes the dot product of two vectors.
- dot(int, double[], int, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- dot(int, float[], int, float[], int) - Method in interface smile.math.blas.BLAS
-
Computes the dot product of two vectors.
- dot(int, float[], int, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- dot(StructType, StructField, int) - Method in class smile.base.cart.DecisionNode
- dot(StructType, StructField, int) - Method in interface smile.base.cart.Node
-
Returns the dot representation of node.
- dot(StructType, StructField, int) - Method in class smile.base.cart.NominalNode
- dot(StructType, StructField, int) - Method in class smile.base.cart.OrdinalNode
- dot(StructType, StructField, int) - Method in class smile.base.cart.RegressionNode
- dot(SparseArray, SparseArray) - Static method in class smile.math.MathEx
-
Returns the dot product between two sparse arrays.
- DOT - Enum constant in enum class smile.plot.swing.Line.Style
- DOT_DASH - Enum constant in enum class smile.plot.swing.Line.Style
- DotProductKernel - Interface in smile.math.kernel
-
Dot product kernel depends only on the dot product of x and y.
- Double - Enum constant in enum class smile.data.type.DataType.ID
-
Double type ID.
- DOUBLE - Static variable in interface smile.util.Regex
-
Double regular expression pattern.
- DOUBLE_REGEX - Static variable in interface smile.util.Regex
-
Double regular expression.
- 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(double[]) - Constructor for class smile.util.DoubleArrayList
-
Constructs a list containing the values of the specified array.
- DoubleArrayList(int) - Constructor for class smile.util.DoubleArrayList
-
Constructs an empty list with the specified initial capacity.
- DoubleArrayType - Static variable in class smile.data.type.DataTypes
-
Double Array data type.
- 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(double[]) - Constructor for class smile.sort.DoubleHeapSelect
-
Constructor.
- DoubleHeapSelect(int) - Constructor for class smile.sort.DoubleHeapSelect
-
Constructor.
- DoubleObjectType - Static variable in class smile.data.type.DataTypes
-
Double Object data type.
- DoubleType - Class in smile.data.type
-
Double data type.
- DoubleType - Static variable in class smile.data.type.DataTypes
-
Double data type.
- doubleValue() - Method in class smile.deep.tensor.Tensor
-
Returns the double value when the tensor holds a single value.
- doubleVector(int) - Method in interface smile.data.DataFrame
-
Selects column based on the column index.
- doubleVector(int) - Method in class smile.data.IndexDataFrame
- doubleVector(Enum<?>) - Method in interface smile.data.DataFrame
-
Selects column using an enum value.
- doubleVector(String) - Method in interface smile.data.DataFrame
-
Selects column based on the column name.
- DoubleVector - Interface in smile.data.vector
-
An immutable double vector.
- download(String) - Static method in interface smile.util.CacheFiles
-
Downloads a file and save to the cache directory.
- download(String, boolean) - Static method in interface smile.util.CacheFiles
-
Downloads a file and save to the cache directory.
- 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(char, double...) - Method in class smile.plot.swing.Graphics
-
Draw a dot with given pattern.
- drawPoint(double...) - Method in class smile.plot.swing.Graphics
-
Draw a dot.
- 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 selected columns.
- drop(int...) - Method in class smile.data.IndexDataFrame
- drop(String...) - Method in interface smile.data.DataFrame
-
Returns a new DataFrame without selected columns.
- dropout - Variable in class smile.base.mlp.Layer
-
The dropout rate.
- dropout - Variable in class smile.base.mlp.LayerBuilder
-
The dropout rate.
- dropout() - Method in record class smile.llm.Transformer.Options
-
Returns the value of the
dropout
record component. - dropout(double) - Static method in interface smile.deep.layer.Layer
-
Returns a dropout layer that randomly zeroes some of the elements of the input tensor with probability p during training.
- dropout(double) - Method in class smile.deep.tensor.Tensor
-
Randomly zeroes some elements of the input tensor with probability p.
- dropout_(double) - Method in class smile.deep.tensor.Tensor
-
Randomly zeroes some elements in place with probability p.
- DropoutLayer - Class in smile.deep.layer
-
A dropout layer that randomly zeroes some of the elements of the input tensor with probability p during training.
- DropoutLayer(double) - Constructor for class smile.deep.layer.DropoutLayer
-
Constructor.
- DropoutLayer(double, boolean) - Constructor for class smile.deep.layer.DropoutLayer
-
Constructor.
- dsv(String, String) - Method in class smile.plot.vega.Data
-
Loads a delimited text file with a custom delimiter.
- dsv(String, String, Map<String, String>) - Method in class smile.plot.vega.Data
-
Loads a delimited text file with a custom delimiter.
- DT - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
-
Determiner.
- dtype() - Method in class smile.deep.tensor.Tensor
-
Returns the element data type.
- dtype(ScalarType) - Method in class smile.deep.tensor.Tensor.Options
-
Sets the data type of the elements stored in the tensor.
- DUMMY - Enum constant in enum class smile.data.CategoricalEncoder
-
Dummy encoding.
- DynamicTimeWarping<T> - Class in smile.math.distance
-
Dynamic time warping is an algorithm for measuring similarity between two sequences which may vary in time or speed.
- DynamicTimeWarping(Distance<T>) - Constructor for class smile.math.distance.DynamicTimeWarping
-
Constructor.
- DynamicTimeWarping(Distance<T>, double) - Constructor for class smile.math.distance.DynamicTimeWarping
-
Dynamic time warping with Sakoe-Chiba band, which primarily to prevent unreasonable warping and also improve computational cost.
E
- Edge - Class in smile.vq.hebb
-
The connection between neurons.
- Edge(int, int, double) - Constructor for class smile.graph.Graph.Edge
-
Constructor.
- Edge(Neuron) - Constructor for class smile.vq.hebb.Edge
-
Constructor.
- Edge(Neuron, int) - Constructor for class smile.vq.hebb.Edge
-
Constructor.
- edges - Variable in class smile.vq.hebb.Neuron
-
The direct connected neighbors.
- EditDistance - Class in smile.math.distance
-
The Edit distance between two strings is a metric for measuring the amount of difference between two sequences.
- EditDistance() - Constructor for class smile.math.distance.EditDistance
-
Constructor.
- EditDistance(boolean) - Constructor for class smile.math.distance.EditDistance
-
Constructor.
- EditDistance(int) - Constructor for class smile.math.distance.EditDistance
-
Constructor.
- EditDistance(int[][]) - Constructor for class smile.math.distance.EditDistance
-
Constructor.
- EditDistance(int[][], double) - Constructor for class smile.math.distance.EditDistance
-
Constructor.
- EditDistance(int, boolean) - Constructor for class smile.math.distance.EditDistance
-
Constructor.
- EfficientNet - Class in smile.vision
-
EfficientNet is an image classification model family.
- EfficientNet(MBConvConfig[], double, double, int, int, IntFunction<Layer>) - Constructor for class smile.vision.EfficientNet
-
Constructor.
- eigen() - Method in class smile.math.matrix.BigMatrix
-
Eigenvalue Decomposition.
- eigen() - Method in class smile.math.matrix.fp32.Matrix
-
Eigenvalue Decomposition.
- eigen() - Method in class smile.math.matrix.Matrix
-
Eigenvalue Decomposition.
- eigen(boolean, boolean, boolean) - Method in class smile.math.matrix.BigMatrix
-
Eigenvalue Decomposition.
- eigen(boolean, boolean, boolean) - Method in class smile.math.matrix.fp32.Matrix
-
Eigenvalue Decomposition.
- eigen(boolean, boolean, boolean) - Method in class smile.math.matrix.Matrix
-
Eigenvalue Decomposition.
- eigen(double[]) - Method in class smile.math.matrix.IMatrix
-
Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
- eigen(double[], double, double, int) - Method in class smile.math.matrix.IMatrix
-
Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
- eigen(float[]) - Method in class smile.math.matrix.fp32.IMatrix
-
Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
- eigen(float[], float, float, int) - Method in class smile.math.matrix.fp32.IMatrix
-
Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
- eigen(IMatrix, ARPACK.AsymmOption, int) - Static method in class smile.math.matrix.fp32.ARPACK
-
Computes NEV eigenvalues of an asymmetric single precision matrix.
- eigen(IMatrix, ARPACK.AsymmOption, int, int, float) - Static method in class smile.math.matrix.fp32.ARPACK
-
Computes NEV eigenvalues of an asymmetric single precision matrix.
- eigen(IMatrix, int) - Static method in class smile.math.matrix.Lanczos
-
Find k-largest approximate eigen pairs of a symmetric matrix by the Lanczos algorithm.
- eigen(IMatrix, int, double, int) - Static method in class smile.math.matrix.Lanczos
-
Find k-largest approximate eigen pairs of a symmetric matrix by the Lanczos algorithm.
- eigen(IMatrix, ARPACK.AsymmOption, int) - Static method in class smile.math.matrix.ARPACK
-
Computes NEV eigenvalues of an asymmetric double precision matrix.
- eigen(IMatrix, ARPACK.AsymmOption, int, int, double) - Static method in class smile.math.matrix.ARPACK
-
Computes NEV eigenvalues of an asymmetric double precision matrix.
- EigenRange - Enum Class in smile.math.blas
-
THe option of eigenvalue range.
- ElasticNet - Class in smile.regression
-
Elastic Net regularization.
- ElasticNet() - Constructor for class smile.regression.ElasticNet
- Ellipsis - Static variable in class smile.deep.tensor.Index
-
The ellipsis (...) is used to slice higher-dimensional data structures as in numpy.
- EMAIL_ADDRESS - Static variable in interface smile.util.Regex
-
Email address.
- embedding(int, int) - Static method in interface smile.deep.layer.Layer
-
Returns an embedding layer that is a simple lookup table that stores embeddings of a fixed dictionary and size.
- embedding(int, int, double) - Static method in interface smile.deep.layer.Layer
-
Returns an embedding layer that is a simple lookup table that stores embeddings of a fixed dictionary and size.
- EmbeddingLayer - Class in smile.deep.layer
-
An embedding layer that is a simple lookup table that stores embeddings of a fixed dictionary and size.
- EmbeddingLayer(int, int) - Constructor for class smile.deep.layer.EmbeddingLayer
-
Constructor.
- EmbeddingLayer(int, int, double) - Constructor for class smile.deep.layer.EmbeddingLayer
-
Constructor.
- 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(long...) - Static method in class smile.deep.tensor.Tensor
-
Returns a tensor with uninitialized data.
- empty(Tensor.Options, long...) - Static method in class smile.deep.tensor.Tensor
-
Returns a tensor with uninitialized data.
- emptyCache() - Method in class smile.deep.tensor.Device
-
Releases all unoccupied cached memory.
- encode(String) - Method in class smile.llm.tokenizer.SentencePiece
- encode(String) - Method in class smile.llm.tokenizer.Tiktoken
- encode(String) - Method in interface smile.llm.tokenizer.Tokenizer
-
Encodes a string into a list of token IDs.
- encode(String, boolean, boolean) - Method in class smile.llm.tokenizer.SentencePiece
- encode(String, boolean, boolean) - Method in class smile.llm.tokenizer.Tiktoken
- encode(String, boolean, boolean) - Method in interface smile.llm.tokenizer.Tokenizer
-
Encodes a string into a list of token IDs.
- encode(String, String) - Method in class smile.plot.vega.View
-
Returns the field object for encoding a channel.
- encodeDatum(String, double) - Method in class smile.plot.vega.Layer
- encodeDatum(String, double) - Method in class smile.plot.vega.View
-
Sets a constant data value encoded via a scale.
- encodeDatum(String, int) - Method in class smile.plot.vega.Layer
- encodeDatum(String, int) - Method in class smile.plot.vega.View
-
Sets a constant data value encoded via a scale.
- encodeDatum(String, String) - Method in class smile.plot.vega.Layer
- encodeDatum(String, String) - Method in class smile.plot.vega.View
-
Sets a constant data value encoded via a scale.
- encodeDialog(Message...) - Method in class smile.llm.llama.Tokenizer
-
Encodes the messages of a dialog.
- encodeMessage(Message) - Method in class smile.llm.llama.Tokenizer
-
Encodes a message.
- encodeValue(String, double) - Method in class smile.plot.vega.Layer
- encodeValue(String, double) - Method in class smile.plot.vega.View
-
Sets an encoded constant visual value.
- encodeValue(String, int) - Method in class smile.plot.vega.Layer
- encodeValue(String, int) - Method in class smile.plot.vega.View
-
Sets an encoded constant visual value.
- encodeValue(String, String) - Method in class smile.plot.vega.Layer
- encodeValue(String, String) - Method in class smile.plot.vega.View
-
Sets an encoded constant visual value.
- engine - Static variable in interface smile.math.blas.BLAS
-
The default BLAS engine.
- engine - Static variable in interface smile.math.blas.LAPACK
-
The default LAPACK engine.
- EnglishDictionary - Enum Class in smile.nlp.dictionary
-
A concise dictionary of common terms in English.
- EnglishPOSLexicon - Class in smile.nlp.pos
-
An English lexicon with part-of-speech tags.
- EnglishPunctuations - Class in smile.nlp.dictionary
-
Punctuation marks in English.
- EnglishStopWords - Enum Class in smile.nlp.dictionary
-
Several sets of English stop words.
- ensemble(Classifier<T>...) - Static method in interface smile.classification.Classifier
-
Return an ensemble of multiple base models to obtain better predictive performance.
- ensemble(DataFrameClassifier...) - Static method in interface smile.classification.DataFrameClassifier
-
Return an ensemble of multiple base models to obtain better predictive performance.
- ensemble(DataFrameRegression...) - Static method in interface smile.regression.DataFrameRegression
-
Return an ensemble of multiple base models to obtain better predictive performance.
- ensemble(Regression<T>...) - Static method in interface smile.regression.Regression
-
Return an ensemble of multiple base models to obtain better predictive performance.
- ensureCapacity(int) - Method in class smile.util.DoubleArrayList
-
Increases the capacity, if necessary, to ensure that it can hold at least the number of values specified by the minimum capacity argument.
- ensureCapacity(int) - Method in class smile.util.IntArrayList
-
Increases the capacity, if necessary, to ensure that it can hold at least the number of values specified by the minimum capacity argument.
- entropy - Variable in class smile.clustering.MEC
-
The conditional entropy as the objective function.
- entropy() - Method in class smile.stat.distribution.BernoulliDistribution
- entropy() - Method in class smile.stat.distribution.BetaDistribution
- entropy() - Method in class smile.stat.distribution.BinomialDistribution
- entropy() - Method in class smile.stat.distribution.ChiSquareDistribution
- entropy() - Method in class smile.stat.distribution.DiscreteMixture
-
Shannon's entropy.
- entropy() - Method in interface smile.stat.distribution.Distribution
-
Returns 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's 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's entropy.
- entropy() - Method in class smile.stat.distribution.HyperGeometricDistribution
- entropy() - Method in class smile.stat.distribution.KernelDensity
-
Shannon's 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's entropy.
- entropy() - Method in interface smile.stat.distribution.MultivariateDistribution
-
Shannon's 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's entropy.
- entropy() - Method in class smile.stat.distribution.PoissonDistribution
- entropy() - Method in class smile.stat.distribution.ShiftedGeometricDistribution
- entropy() - Method in class smile.stat.distribution.TDistribution
- entropy() - Method in class smile.stat.distribution.WeibullDistribution
- entropy(double[]) - Static method in class smile.math.MathEx
-
Shannon's entropy.
- ENTROPY - Enum constant in enum class smile.base.cart.SplitRule
-
Used by the ID3, C4.5 and C5.0 tree generation algorithms.
- entry - Variable in class smile.neighbor.lsh.Bucket
-
The indices of points that all have the same value for hash function g.
- epsilon - Variable in class smile.base.mlp.MultilayerPerceptron
-
A small constant for numerical stability in RMSProp.
- EPSILON - Static variable in interface smile.math.DifferentiableMultivariateFunction
-
A number close to zero, between machine epsilon and its square root.
- EPSILON - Static variable in class smile.math.MathEx
-
The machine precision for the double type, which is the difference between 1 and the smallest value greater than 1 that is representable for the double type.
- eq(double) - Method in class smile.deep.tensor.Tensor
-
Computes element-wise equality.
- eq(int) - Method in class smile.deep.tensor.Tensor
-
Computes element-wise equality.
- eq(Tensor) - Method in class smile.deep.tensor.Tensor
-
Computes element-wise equality.
- equals(double[][], double[][]) - Static method in class smile.math.MathEx
-
Check if x element-wisely equals y with default epsilon 1E-10.
- equals(double[][], double[][], double) - Static method in class smile.math.MathEx
-
Check if x element-wisely equals y in given precision.
- equals(double[], double[]) - Static method in class smile.math.MathEx
-
Check if x element-wisely equals y with default epsilon 1E-10.
- equals(double[], double[], double) - Static method in class smile.math.MathEx
-
Check if x element-wisely equals y in given precision.
- equals(double, double) - Static method in class smile.math.MathEx
-
Returns true if two double values equals to each other in the system precision.
- equals(float[][], float[][]) - Static method in class smile.math.MathEx
-
Check if x element-wisely equals y with default epsilon 1E-7.
- equals(float[][], float[][], float) - Static method in class smile.math.MathEx
-
Check if x element-wisely equals y in given precision.
- equals(float[], float[]) - Static method in class smile.math.MathEx
-
Check if x element-wisely equals y with default epsilon 1E-7.
- equals(float[], float[], float) - Static method in class smile.math.MathEx
-
Check if x element-wisely equals y in given precision.
- equals(Object) - Method in class smile.association.AssociationRule
- equals(Object) - Method in class smile.association.ItemSet
- equals(Object) - Method in class smile.base.cart.DecisionNode
- equals(Object) - Method in class smile.base.cart.RegressionNode
- equals(Object) - Method in class smile.data.formula.Formula
- equals(Object) - Method in class smile.data.measure.CategoricalMeasure
- equals(Object) - Method in class smile.data.measure.NominalScale
- equals(Object) - Method in class smile.data.measure.OrdinalScale
- equals(Object) - Method in record class smile.data.SampleInstance
-
Indicates whether some other object is "equal to" this one.
- 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 record class smile.deep.SampleBatch
-
Indicates whether some other object is "equal to" this one.
- equals(Object) - Method in class smile.deep.tensor.Device
- equals(Object) - Method in class smile.deep.tensor.Tensor
- equals(Object) - Method in record class smile.llm.llama.Message
-
Indicates whether some other object is "equal to" this one.
- equals(Object) - Method in record class smile.llm.Transformer.Options
-
Indicates whether some other object is "equal to" this one.
- equals(Object) - Method in class smile.math.Complex
- equals(Object) - Method in class smile.math.matrix.BandMatrix
- equals(Object) - Method in class smile.math.matrix.BigMatrix
- equals(Object) - Method in class smile.math.matrix.fp32.BandMatrix
- equals(Object) - Method in class smile.math.matrix.fp32.Matrix
- equals(Object) - Method in class smile.math.matrix.fp32.SymmMatrix
- equals(Object) - Method in class smile.math.matrix.Matrix
- equals(Object) - Method in class smile.math.matrix.SymmMatrix
- equals(Object) - Method in class smile.nlp.Bigram
- equals(Object) - Method in class smile.nlp.NGram
- equals(Object) - Method in class smile.nlp.SimpleText
- equals(Object) - Method in record class smile.plot.vega.SortField
-
Indicates whether some other object is "equal to" this one.
- equals(Object) - Method in record class smile.plot.vega.WindowTransformField
-
Indicates whether some other object is "equal to" this one.
- equals(Object) - Method in record class smile.util.Bytes
-
Indicates whether some other object is "equal to" this one.
- equals(Object) - Method in record class smile.util.IntPair
-
Indicates whether some other object is "equal to" this one.
- equals(Object) - Method in record class smile.util.Tuple2
-
Indicates whether some other object is "equal to" this one.
- equals(Object) - Method in record class smile.vision.layer.Conv2dNormActivation.Options
-
Indicates whether some other object is "equal to" this one.
- equals(Object) - Method in record class smile.vision.layer.MBConvConfig
-
Indicates whether some other object is "equal to" this one.
- equals(BandMatrix, double) - Method in class smile.math.matrix.BandMatrix
-
Returns true if two matrices equal in given precision.
- equals(BigMatrix, double) - Method in class smile.math.matrix.BigMatrix
-
Returns true if two matrices equal in given precision.
- equals(BandMatrix, float) - Method in class smile.math.matrix.fp32.BandMatrix
-
Returns true if two matrices equal in given precision.
- equals(Matrix, float) - Method in class smile.math.matrix.fp32.Matrix
-
Returns true if two matrices equal in given precision.
- equals(SymmMatrix, float) - Method in class smile.math.matrix.fp32.SymmMatrix
-
Returns true if two matrices equal in given precision.
- equals(Matrix, double) - Method in class smile.math.matrix.Matrix
-
Returns true if two matrices equal in given precision.
- equals(SymmMatrix, double) - Method in class smile.math.matrix.SymmMatrix
-
Returns true if two matrices equal in given precision.
- erf(double) - Static method in class smile.math.special.Erf
-
The Gauss error function.
- Erf - Class in smile.math.special
-
The error function.
- erfc(double) - Static method in class smile.math.special.Erf
-
The complementary error function.
- erfcc(double) - Static method in class smile.math.special.Erf
-
The complementary error function with fractional error everywhere less than 1.2 × 10-7.
- error - Variable in class smile.validation.ClassificationMetrics
-
The number of errors.
- error() - Method in class smile.regression.LinearModel
-
Returns the residual standard error.
- Error - Class in smile.validation.metric
-
The number of errors in the population.
- Error() - Constructor for class smile.validation.metric.Error
- 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.
- eval() - Method in class smile.deep.Model
-
Sets the model in the evaluation/inference mode.
- eval(Dataset, Metric...) - Method in class smile.deep.Model
-
Evaluates the model accuracy on a test dataset.
- EVD(double[], double[], Matrix, Matrix) - Constructor for class smile.math.matrix.Matrix.EVD
-
Constructor.
- EVD(double[], Matrix) - Constructor for class smile.math.matrix.Matrix.EVD
-
Constructor.
- EVD(float[], float[], Matrix, Matrix) - Constructor for class smile.math.matrix.fp32.Matrix.EVD
-
Constructor.
- EVD(float[], Matrix) - Constructor for class smile.math.matrix.fp32.Matrix.EVD
-
Constructor.
- EVD(DoublePointer, DoublePointer, BigMatrix, BigMatrix) - Constructor for class smile.math.matrix.BigMatrix.EVD
-
Constructor.
- EVD(DoublePointer, BigMatrix) - Constructor for class smile.math.matrix.BigMatrix.EVD
-
Constructor.
- EVDJob - Enum Class in smile.math.blas
-
The option if computing eigen vectors.
- evolve() - Method in interface smile.gap.LamarckianChromosome
-
Performs a step of (hill-climbing) local search to evolve this chromosome.
- evolve(int) - Method in class smile.gap.GeneticAlgorithm
-
Performs genetic algorithm for a given number of generations.
- evolve(int, double) - Method in class smile.gap.GeneticAlgorithm
-
Performs genetic algorithm until the given number of generations is reached or the best fitness is larger than the given threshold.
- EX - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
-
Existential there.
- execute(String) - Method in class smile.data.SQL
-
Executes an SQL statement, which may return multiple results.
- exp() - Method in class smile.deep.tensor.Tensor
-
Returns the exponential of elements in the tensor.
- exp() - Method in class smile.math.Complex
-
Returns the complex exponential.
- exp(double, double) - Static method in interface smile.math.TimeFunction
-
Returns the exponential decay function.
- exp(double, double, double) - Static method in interface smile.math.TimeFunction
-
Returns the exponential decay function.
- exp(double, double, double, boolean) - Static method in interface smile.math.TimeFunction
-
Returns the exponential decay function.
- exp(String) - Static method in interface smile.data.formula.Terms
-
The
exp(x)
term. - exp(Term) - Static method in interface smile.data.formula.Terms
-
The
exp(x)
term. - Exp - Class in smile.ica
-
The contrast function when the independent components are highly super-Gaussian, or when robustness is very important.
- Exp() - Constructor for class smile.ica.Exp
- exp_() - Method in class smile.deep.tensor.Tensor
-
Returns the exponential of elements in the tensor in place.
- expand() - Method in class smile.data.formula.FactorCrossing
- expand() - Method in interface smile.data.formula.Term
-
Expands the term (e.g.
- expand() - Method in class smile.neighbor.lsh.Probe
-
This operation sets to one the component following the last nonzero component if it is not the last one.
- expand(StructType) - Method in class smile.data.formula.Formula
-
Expands the Dot and FactorCrossing terms on the given schema.
- expandRatio() - Method in record class smile.vision.layer.MBConvConfig
-
Returns the value of the
expandRatio
record component. - expm1(String) - Static method in interface smile.data.formula.Terms
-
The
exp(x) - 1
term. - expm1(Term) - Static method in interface smile.data.formula.Terms
-
The
exp(x) - 1
term. - ExponentialDistribution - Class in smile.stat.distribution
-
An exponential distribution describes the times between events in a Poisson process, in which events occur continuously and independently at a constant average rate.
- ExponentialDistribution(double) - Constructor for class smile.stat.distribution.ExponentialDistribution
-
Constructor.
- ExponentialFamily - Interface in smile.stat.distribution
-
The exponential family is a class of probability distributions sharing a certain form.
- ExponentialFamilyMixture - Class in smile.stat.distribution
-
The finite mixture of distributions from exponential family.
- ExponentialFamilyMixture(Mixture.Component...) - Constructor for class smile.stat.distribution.ExponentialFamilyMixture
-
Constructor.
- ExponentialVariogram - Class in smile.interpolation.variogram
-
Exponential variogram.
- ExponentialVariogram(double, double) - Constructor for class smile.interpolation.variogram.ExponentialVariogram
-
Constructor.
- ExponentialVariogram(double, double, double) - Constructor for class smile.interpolation.variogram.ExponentialVariogram
-
Constructor.
- extend() - Method in class smile.neighbor.lsh.Probe
-
This operation adds one to the last nonzero component.
- 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.
- extendBound(int) - Method in class smile.plot.swing.Base
-
Rounds the bounds for axis i.
- 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.
- extent(double, double) - Method in class smile.plot.vega.BinParams
-
Sets the range of desired bin values
- extent(double, double) - Method in class smile.plot.vega.DensityTransform
-
Sets a [min, max] domain from which to sample the distribution.
- extent(double, double) - Method in class smile.plot.vega.RegressionTransform
-
Sets a [min, max] domain over the independent (x) field for the starting and ending points of the generated trend line.
- extent(String) - Method in class smile.plot.vega.Mark
-
Sets the extent of the band.
- extent(String, String) - Method in class smile.plot.vega.Transform
-
Adds an extent transform.
- eye(int) - Static method in class smile.math.matrix.BigMatrix
-
Returns an identity matrix.
- eye(int) - Static method in class smile.math.matrix.fp32.Matrix
-
Returns an identity matrix.
- eye(int) - Static method in class smile.math.matrix.Matrix
-
Returns an identity matrix.
- eye(int, int) - Static method in class smile.math.matrix.BigMatrix
-
Returns an m-by-n identity matrix.
- eye(int, int) - Static method in class smile.math.matrix.fp32.Matrix
-
Returns an m-by-n identity matrix.
- eye(int, int) - Static method in class smile.math.matrix.Matrix
-
Returns an m-by-n identity matrix.
- eye(long) - Static method in class smile.deep.tensor.Tensor
-
Returns an identity matrix.
- eye(Tensor.Options, long) - Static method in class smile.deep.tensor.Tensor
-
Returns an identity matrix.
F
- f - Variable in class smile.stat.hypothesis.FTest
-
F-statistic.
- f(double) - Method in class smile.ica.Exp
- f(double) - Method in class smile.ica.Kurtosis
- f(double) - Method in class smile.ica.LogCosh
- f(double) - Method in class smile.interpolation.variogram.ExponentialVariogram
- f(double) - Method in class smile.interpolation.variogram.GaussianVariogram
- f(double) - Method in class smile.interpolation.variogram.PowerVariogram
- f(double) - Method in class smile.interpolation.variogram.SphericalVariogram
- f(double) - Method in interface smile.math.Function
-
Computes the value of the function at x.
- f(double) - Method in interface smile.math.kernel.DotProductKernel
- f(double) - Method in interface smile.math.kernel.IsotropicKernel
- f(double) - Method in class smile.math.kernel.Matern
- f(double) - Method in class smile.math.rbf.GaussianRadialBasis
- f(double) - Method in class smile.math.rbf.InverseMultiquadricRadialBasis
- f(double) - Method in class smile.math.rbf.MultiquadricRadialBasis
- f(double) - Method in class smile.math.rbf.ThinPlateRadialBasis
- f(double) - Method in class smile.math.Scaler
- f(double[]) - Method in interface smile.base.mlp.ActivationFunction
-
The output function.
- f(double[]) - Method in enum class smile.base.mlp.OutputFunction
-
The output function.
- f(double[]) - Method in class smile.base.svm.LinearKernelMachine
-
Returns the value of decision function.
- f(double[]) - Method in interface smile.math.MultivariateFunction
-
Computes the value of the function at x.
- f(int) - Method in interface smile.math.IntFunction
-
Computes the value of the function at x.
- f(int[]) - Method in class smile.base.svm.LinearKernelMachine
-
Returns the value of decision function.
- f(SparseArray) - Method in class smile.base.svm.LinearKernelMachine
-
Returns the value of decision function.
- f(T) - Method in class smile.base.rbf.RBF
-
The activation function.
- f1 - Variable in class smile.validation.ClassificationMetrics
-
The F-1 score on validation data.
- F1 - Static variable in class smile.validation.metric.FScore
-
The F_1 score, the harmonic mean of precision and recall.
- F2 - Static variable in class smile.validation.metric.FScore
-
The F_2 score, which weighs recall higher than precision.
- facet(String) - Method in class smile.plot.vega.Facet
-
Returns the field definition for faceting the plot by one field.
- Facet - Class in smile.plot.vega
-
A facet is a trellis plot (or small multiple) of a series of similar plots that displays different subsets of the same data, facilitating comparison across subsets.
- Facet(VegaLite) - Constructor for class smile.plot.vega.Facet
-
Constructor.
- FacetField - Class in smile.plot.vega
-
Facet field definition object.
- factor(int) - Method in class smile.data.measure.CategoricalMeasure
-
Returns the factor value (in range [0, size)) of level.
- FactorCrossing - Class in smile.data.formula
-
Factor crossing.
- FactorCrossing(int, String...) - Constructor for class smile.data.formula.FactorCrossing
-
Constructor.
- FactorCrossing(String...) - Constructor for class smile.data.formula.FactorCrossing
-
Constructor.
- factorial(int) - Static method in class smile.math.MathEx
-
The factorial of n.
- FactorInteraction - Class in smile.data.formula
-
The interaction of all the factors appearing in the term.
- FactorInteraction(String...) - Constructor for class smile.data.formula.FactorInteraction
-
Constructor.
- factorize(String...) - Method in interface smile.data.DataFrame
-
Returns a new DataFrame with given columns converted to nominal.
- factorize(CategoricalMeasure) - Method in interface smile.data.vector.StringVector
-
Converts strings to discrete measured values.
- Fallout - Class in smile.validation.metric
-
Fall-out, false alarm rate, or false positive rate (FPR)
- Fallout() - Constructor for class smile.validation.metric.Fallout
- falseChild() - Method in class smile.base.cart.InternalNode
-
Returns the false branch child.
- FDistribution - Class in smile.stat.distribution
-
F-distribution arises in the testing of whether two observed samples have the same variance.
- FDistribution(int, int) - Constructor for class smile.stat.distribution.FDistribution
-
Constructor.
- FDR - Class in smile.validation.metric
-
The false discovery rate (FDR) is ratio of false positives to combined true and false positives, which is actually 1 - precision.
- FDR() - Constructor for class smile.validation.metric.FDR
- feature - Variable in class smile.feature.selection.InformationValue
-
The feature name.
- feature - Variable in class smile.feature.selection.SignalNoiseRatio
-
The feature name.
- feature - Variable in class smile.feature.selection.SumSquaresRatio
-
The feature name.
- feature() - Method in class smile.base.cart.InternalNode
-
Returns the split feature.
- Feature - Interface in smile.data.formula
-
A feature in the formula once bound to a schema.
- features - Variable in class smile.sequence.CRFLabeler
-
The feature function.
- features() - Method in class smile.feature.extraction.BagOfWords
-
Returns the feature words.
- features() - Method in class smile.vision.EfficientNet
-
Returns the feature layer block.
- 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 metadata of feature.
- field() - Method in interface smile.data.vector.BaseVector
-
Returns a struct field corresponding to this vector.
- field() - Method in record class smile.plot.vega.SortField
-
Returns the value of the
field
record component. - field() - Method in record class smile.plot.vega.WindowTransformField
-
Returns the value of the
field
record component. - field(int) - Method in class smile.data.type.StructType
-
Return the field at position i.
- field(String) - Method in class smile.data.type.StructType
-
Return the field of given name.
- Field - Class in smile.plot.vega
-
Encoding field definition object.
- fields() - Method in class smile.data.type.StructType
-
Returns the fields.
- fields(String...) - Method in class smile.plot.vega.LookupData
-
Returns the fields in foreign data or selection to lookup.
- fieldTitle(String) - Method in class smile.plot.vega.Config
-
Defines how Vega-Lite generates title for 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(char, int) - Static method in interface smile.util.Strings
-
Returns the string with a single repeated character to a specific length.
- fill(double) - Method in class smile.math.matrix.BigMatrix
-
Fill the matrix with a value.
- fill(double) - Method in class smile.math.matrix.Matrix
-
Fills the matrix with a value.
- fill(float) - Method in class smile.math.matrix.fp32.Matrix
-
Fills the matrix with a value.
- fill(String) - Method in class smile.plot.vega.Background
-
Sets the fill color.
- fill(String) - Method in class smile.plot.vega.Mark
-
Sets the default fill color.
- fill(String) - Method in class smile.plot.vega.ViewConfig
-
Sets the fill color.
- fill_(double) - Method in class smile.deep.tensor.Tensor
-
Fills this tensor with the specified value.
- fill_(int) - Method in class smile.deep.tensor.Tensor
-
Fills this tensor with the specified value.
- fillColor(String) - Method in class smile.plot.vega.Legend
-
Sets the background fill color for the full legend.
- filled(boolean) - Method in class smile.plot.vega.Mark
-
Sets whether the mark's color should be used as fill color instead of stroke color.
- fillna(double) - Method in interface smile.data.DataFrame
-
Fills NaN/Inf values of floating number columns using the specified value.
- fillna(double) - Method in interface smile.data.vector.DoubleVector
-
Fills NaN/Inf values using the specified value.
- fillna(double) - Method in interface smile.data.vector.NumberVector
-
Fill null/NaN/Inf values using the specified value.
- fillna(float) - Method in interface smile.data.vector.FloatVector
-
Fills NaN/Inf values using the specified value.
- fillOpacity(double) - Method in class smile.plot.vega.Background
-
Sets the fill opacity.
- fillOpacity(double) - Method in class smile.plot.vega.Mark
-
Sets the fill opacity.
- fillOpacity(double) - Method in class smile.plot.vega.ViewConfig
-
Sets the fill opacity.
- 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.
- filter(String) - Method in class smile.plot.vega.Transform
-
Adds a filter transform.
- filter(Predicate) - Method in class smile.plot.vega.Transform
-
Adds a filter transform.
- find(DifferentiableFunction, double, double, double, int) - Static method in class smile.math.Root
-
Newton's method (also known as the Newton–Raphson method).
- find(Function, double, double, double, int) - Static method in class smile.math.Root
-
Brent's method for root-finding.
- findBestSplit(LeafNode, int, double, int, int) - Method in class smile.base.cart.CART
-
Finds the best split for given column.
- findBestSplit(LeafNode, int, double, int, int) - Method in class smile.classification.DecisionTree
- findBestSplit(LeafNode, int, double, int, int) - Method in class smile.regression.RegressionTree
- findBestSplit(LeafNode, int, int, boolean[]) - Method in class smile.base.cart.CART
-
Finds the best attribute to split on a set of samples.
- fit(double[]) - Static method in class smile.stat.distribution.BetaDistribution
-
Estimates the distribution parameters by the moment method.
- fit(double[]) - Static method in class smile.stat.distribution.ExponentialDistribution
-
Estimates the distribution parameters by MLE.
- fit(double[]) - Static method in class smile.stat.distribution.GammaDistribution
-
Estimates the distribution parameters by (approximate) MLE.
- fit(double[]) - Static method in class smile.stat.distribution.GaussianDistribution
-
Estimates the distribution parameters by MLE.
- fit(double[]) - Static method in class smile.stat.distribution.GaussianMixture
-
Fits the Gaussian mixture model with the EM algorithm.
- fit(double[]) - Static method in class smile.stat.distribution.LogNormalDistribution
-
Estimates the distribution parameters by MLE.
- fit(double[][]) - Static method in class smile.anomaly.IsolationForest
-
Fits an isolation forest.
- fit(double[][]) - Static method in class smile.stat.distribution.MultivariateGaussianDistribution
-
Estimates the mean and diagonal covariance by MLE.
- fit(double[][]) - Static method in class smile.stat.distribution.MultivariateGaussianMixture
-
Fits the Gaussian mixture model with the EM algorithm.
- fit(double[][], boolean) - Static method in class smile.stat.distribution.MultivariateGaussianDistribution
-
Estimates the mean and covariance by MLE.
- fit(double[][], boolean) - Static method in class smile.stat.distribution.MultivariateGaussianMixture
-
Fits the Gaussian mixture model with the EM algorithm.
- fit(double[][], double[], double, double, double) - Static method in class smile.regression.SVM
-
Fits a linear epsilon-SVR.
- fit(double[][], double[], Properties) - Static method in class smile.regression.GaussianProcessRegression
-
Fits a regular Gaussian process model.
- fit(double[][], double[], Properties) - Static method in class smile.regression.MLP
-
Fits a MLP model.
- fit(double[][], double[], Properties) - Static method in class smile.regression.RBFNetwork
-
Fits an RBF network.
- fit(double[][], double[], Properties) - Static method in class smile.regression.SVM
-
Fits an epsilon-SVR.
- fit(double[][], double, int) - Static method in class smile.clustering.DENCLUE
-
Clustering data.
- fit(double[][], double, int, double, int) - Static method in class smile.clustering.DENCLUE
-
Clustering data.
- fit(double[][], int) - Static method in class smile.base.rbf.RBF
-
Fits Gaussian RBF function and centers on data.
- fit(double[][], int) - Static method in class smile.clustering.DeterministicAnnealing
-
Clustering data into k clusters.
- fit(double[][], int) - Static method in class smile.clustering.GMeans
-
Clustering data with the number of clusters determined by G-Means algorithm automatically.
- fit(double[][], int) - Static method in class smile.clustering.KMeans
-
Partitions data into k clusters up to 100 iterations.
- fit(double[][], int) - Static method in class smile.clustering.XMeans
-
Clustering data with the number of clusters determined by X-Means algorithm automatically.
- fit(double[][], int) - Static method in class smile.ica.ICA
-
Fits independent component analysis.
- fit(double[][], int[]) - Static method in class smile.classification.FLD
-
Fits Fisher's linear discriminant.
- fit(double[][], int[]) - Static method in class smile.classification.KNN
-
Fits the 1-NN classifier.
- fit(double[][], int[]) - Static method in class smile.classification.LDA
-
Fits linear discriminant analysis.
- fit(double[][], int[]) - Static method in class smile.classification.LogisticRegression
-
Fits logistic regression.
- fit(double[][], int[]) - Static method in class smile.classification.QDA
-
Fits quadratic discriminant analysis.
- fit(double[][], int[], double) - Static method in class smile.classification.RDA
-
Fits regularized discriminant analysis.
- fit(double[][], int[], double[], double) - Static method in class smile.classification.LDA
-
Fits linear discriminant analysis.
- fit(double[][], int[], double[], double) - Static method in class smile.classification.QDA
-
Fits quadratic discriminant analysis.
- fit(double[][], int[], double, double) - Static method in class smile.classification.SVM
-
Fits a binary linear SVM.
- fit(double[][], int[], double, double[], double) - Static method in class smile.classification.RDA
-
Fits regularized discriminant analysis.
- fit(double[][], int[], double, double, int) - Static method in class smile.classification.LogisticRegression
-
Fits logistic regression.
- fit(double[][], int[], double, double, int) - Static method in class smile.classification.SVM
-
Fits a binary linear SVM.
- fit(double[][], int[], int) - Static method in class smile.classification.KNN
-
Fits the K-NN classifier.
- fit(double[][], int[], int, double) - Static method in class smile.classification.FLD
-
Fits Fisher's linear discriminant.
- fit(double[][], int[], Properties) - Static method in class smile.classification.FLD
-
Fits Fisher's linear discriminant.
- fit(double[][], int[], Properties) - Static method in class smile.classification.LDA
-
Fits linear discriminant analysis.
- fit(double[][], int[], Properties) - Static method in class smile.classification.LogisticRegression
-
Fits logistic regression.
- fit(double[][], int[], Properties) - Static method in class smile.classification.MLP
-
Fits a MLP model.
- fit(double[][], int[], Properties) - Static method in class smile.classification.QDA
-
Fits quadratic discriminant analysis.
- fit(double[][], int[], Properties) - Static method in class smile.classification.RBFNetwork
-
Fits an RBF network.
- fit(double[][], int[], Properties) - Static method in class smile.classification.RDA
-
Fits regularized discriminant analysis.
- fit(double[][], int[], Properties) - Static method in class smile.classification.SVM
-
Fits a binary or multiclass SVM.
- fit(double[][], int, double) - Static method in class smile.base.rbf.RBF
-
Fits Gaussian RBF function and centers on data.
- fit(double[][], int, double) - Static method in class smile.clustering.DBSCAN
-
Clustering the data with KD-tree.
- fit(double[][], int, double) - Static method in class smile.clustering.SpectralClustering
-
Spectral clustering the data.
- fit(double[][], int, double, int, double) - Static method in class smile.clustering.SpectralClustering
-
Spectral clustering the data.
- fit(double[][], int, double, int, double, double) - Static method in class smile.clustering.DeterministicAnnealing
-
Clustering data into k clusters.
- fit(double[][], int, int) - Static method in class smile.base.rbf.RBF
-
Fits Gaussian RBF function and centers on data.
- fit(double[][], int, int, double) - Static method in class smile.clustering.GMeans
-
Clustering data with the number of clusters determined by G-Means algorithm automatically.
- fit(double[][], int, int, double) - Static method in class smile.clustering.KMeans
-
Partitions data into k clusters up to 100 iterations.
- fit(double[][], int, int, double) - Static method in class smile.clustering.SpectralClustering
-
Spectral clustering with Nystrom approximation.
- fit(double[][], int, int, double) - Static method in class smile.clustering.XMeans
-
Clustering data with the number of clusters determined by X-Means algorithm automatically.
- fit(double[][], int, int, double, int) - Static method in class smile.anomaly.IsolationForest
-
Fits a random forest for classification.
- fit(double[][], int, int, double, int, double) - Static method in class smile.clustering.SpectralClustering
-
Spectral clustering with Nystrom approximation.
- fit(double[][], int, String...) - Static method in class smile.feature.extraction.ProbabilisticPCA
-
Fits probabilistic principal component analysis.
- fit(double[][], int, Properties) - Static method in class smile.ica.ICA
-
Fits independent component analysis.
- fit(double[][], int, DifferentiableFunction, double, int) - Static method in class smile.ica.ICA
-
Fits independent component analysis.
- fit(double[][], String...) - Static method in class smile.feature.extraction.PCA
-
Fits principal component analysis with covariance matrix.
- fit(double[][], Properties) - Static method in class smile.anomaly.IsolationForest
-
Fits a random forest for classification.
- fit(double[][], MultivariateMixture.Component...) - Static method in class smile.stat.distribution.MultivariateExponentialFamilyMixture
-
Fits the mixture model with the EM algorithm.
- fit(double[][], MultivariateMixture.Component[], double, int, double) - Static method in class smile.stat.distribution.MultivariateExponentialFamilyMixture
-
Fits the mixture model with the EM algorithm.
- fit(double[], int) - Static method in class smile.timeseries.AR
-
Fits an autoregressive model with Yule-Walker procedure.
- fit(double[], int[]) - Static method in class smile.classification.IsotonicRegressionScaling
-
Trains the Isotonic Regression scaling.
- fit(double[], int[]) - Static method in class smile.classification.PlattScaling
-
Trains the Platt scaling.
- fit(double[], int[], int) - Static method in class smile.classification.PlattScaling
-
Trains the Platt scaling.
- fit(double[], int, int) - Static method in class smile.timeseries.ARMA
-
Fits an ARMA model with Hannan-Rissanen algorithm.
- fit(double[], Mixture.Component...) - Static method in class smile.stat.distribution.ExponentialFamilyMixture
-
Fits the mixture model with the EM algorithm.
- fit(double[], Mixture.Component[], double, int, double) - Static method in class smile.stat.distribution.ExponentialFamilyMixture
-
Fits the mixture model with the EM algorithm.
- fit(int[]) - Static method in class smile.classification.ClassLabels
-
Fits the class label mapping.
- fit(int[]) - Static method in class smile.stat.distribution.BernoulliDistribution
-
Estimates the distribution parameters by MLE.
- fit(int[]) - Static method in class smile.stat.distribution.EmpiricalDistribution
-
Estimates the distribution.
- fit(int[]) - Static method in class smile.stat.distribution.GeometricDistribution
-
Estimates the distribution parameters by MLE.
- fit(int[]) - Static method in class smile.stat.distribution.PoissonDistribution
-
Estimates the distribution parameters by MLE.
- fit(int[]) - Static method in class smile.stat.distribution.ShiftedGeometricDistribution
-
Estimates the distribution parameters by MLE.
- fit(int[][], double[], int, double, double, double) - Static method in class smile.regression.SVM
-
Fits a linear epsilon-SVR of binary sparse data.
- fit(int[][], int) - Static method in class smile.clustering.KModes
-
Fits k-modes clustering.
- fit(int[][], int[][]) - Static method in class smile.sequence.HMM
-
Fits an HMM by maximum likelihood estimation.
- fit(int[][], int[], int, double, double) - Static method in class smile.classification.SVM
-
Fits a binary linear SVM of binary sparse data.
- fit(int[][], int[], int, double, double, int) - Static method in class smile.classification.SVM
-
Fits a binary linear SVM of binary sparse data.
- fit(int[][], int, int) - Static method in class smile.clustering.KModes
-
Fits k-modes clustering.
- fit(int[], DiscreteMixture.Component...) - Static method in class smile.stat.distribution.DiscreteExponentialFamilyMixture
-
Fits the mixture model with the EM algorithm.
- fit(int[], DiscreteMixture.Component[], double, int, double) - Static method in class smile.stat.distribution.DiscreteExponentialFamilyMixture
-
Fits the mixture model with the EM algorithm.
- fit(int[], IntSet) - Static method in class smile.stat.distribution.EmpiricalDistribution
-
Estimates the distribution.
- fit(int, double[]) - Static method in class smile.stat.distribution.GaussianMixture
-
Fits the Gaussian mixture model with the EM algorithm.
- fit(int, double[][]) - Static method in class smile.stat.distribution.MultivariateGaussianMixture
-
Fits the Gaussian mixture model with the EM algorithm.
- fit(int, double[][], boolean) - Static method in class smile.stat.distribution.MultivariateGaussianMixture
-
Fits the Gaussian mixture model with the EM algorithm.
- fit(int, int[][], int[]) - Static method in class smile.classification.Maxent
-
Fits maximum entropy classifier.
- fit(int, int[][], int[], double, double, int) - Static method in class smile.classification.Maxent
-
Fits maximum entropy classifier.
- fit(int, int[][], int[], Properties) - Static method in class smile.classification.Maxent
-
Fits maximum entropy classifier.
- fit(String[][], PennTreebankPOS[][]) - Static method in class smile.nlp.pos.HMMPOSTagger
-
Fits an HMM POS tagger by maximum likelihood estimation.
- fit(Classifier<T>, T[], int[]) - Static method in class smile.classification.PlattScaling
-
Fits Platt Scaling to estimate posteriori probabilities.
- fit(BBDTree, double[][], int, int, double) - Static method in class smile.clustering.KMeans
-
Partitions data into k clusters.
- fit(Linkage) - Static method in class smile.clustering.HierarchicalClustering
-
Fits the Agglomerative Hierarchical Clustering with given linkage method, which includes proximity matrix.
- fit(DataFrame) - Static method in class smile.feature.transform.WinsorScaler
-
Fits the data transformation with 5% lower limit and 95% upper limit.
- fit(DataFrame, double, double, String...) - Static method in class smile.feature.imputation.SimpleImputer
-
Fits the missing value imputation values.
- fit(DataFrame, double, double, String...) - Static method in class smile.feature.transform.WinsorScaler
-
Fits the data transformation.
- fit(DataFrame, int, String...) - Static method in class smile.feature.extraction.ProbabilisticPCA
-
Fits probabilistic principal component analysis.
- fit(DataFrame, String) - Static method in class smile.feature.selection.InformationValue
-
Calculates the information value.
- fit(DataFrame, String) - Static method in class smile.feature.selection.SignalNoiseRatio
-
Calculates the signal noise ratio of numeric variables.
- fit(DataFrame, String) - Static method in class smile.feature.selection.SumSquaresRatio
-
Calculates the sum squares ratio of numeric variables.
- fit(DataFrame, String...) - Static method in class smile.feature.extraction.PCA
-
Fits principal component analysis with covariance matrix.
- fit(DataFrame, String...) - Static method in class smile.feature.imputation.SimpleImputer
-
Fits the missing value imputation values.
- fit(DataFrame, String...) - Static method in class smile.feature.transform.MaxAbsScaler
-
Fits the data transformation.
- fit(DataFrame, String...) - Static method in class smile.feature.transform.RobustStandardizer
-
Fits the data transformation.
- fit(DataFrame, String...) - Static method in class smile.feature.transform.Scaler
-
Fits the data transformation.
- fit(DataFrame, String...) - Static method in class smile.feature.transform.Standardizer
-
Fits the data transformation.
- fit(DataFrame, String, int) - Static method in class smile.feature.selection.InformationValue
-
Calculates the information value.
- fit(DataFrame, Function<String, String[]>, int, String...) - Static method in class smile.feature.extraction.BagOfWords
-
Learns a vocabulary dictionary of top-k frequent tokens in the raw documents.
- fit(DataFrame, Function<DataFrame, Transform>...) - Static method in interface smile.data.transform.Transform
-
Fits a pipeline of data transforms.
- fit(DataFrame, Distance<Tuple>, int) - Static method in class smile.feature.imputation.KMedoidsImputer
-
Fits the missing value imputation values.
- fit(DataFrame, MercerKernel<double[]>, int, double, String...) - Static method in class smile.feature.extraction.KernelPCA
-
Fits kernel principal component analysis.
- fit(DataFrame, MercerKernel<double[]>, int, String...) - Static method in class smile.feature.extraction.KernelPCA
-
Fits kernel principal component analysis.
- fit(Dataset<?, Integer>) - Static method in class smile.classification.ClassLabels
-
Fits the class label mapping.
- fit(Formula, DataFrame) - Static method in class smile.classification.AdaBoost
-
Fits a AdaBoost model.
- fit(Formula, DataFrame) - Method in interface smile.classification.DataFrameClassifier.Trainer
-
Fits a classification model with the default hyper-parameters.
- fit(Formula, DataFrame) - Static method in class smile.classification.DecisionTree
-
Fits a classification tree.
- fit(Formula, DataFrame) - Static method in class smile.classification.GradientTreeBoost
-
Fits a gradient tree boosting for classification.
- fit(Formula, DataFrame) - Static method in class smile.classification.RandomForest
-
Fits a random forest for classification.
- fit(Formula, DataFrame) - Method in interface smile.regression.DataFrameRegression.Trainer
-
Fits a regression model with the default hyper-parameters.
- fit(Formula, DataFrame) - Static method in class smile.regression.GradientTreeBoost
-
Fits a gradient tree boosting for regression.
- fit(Formula, DataFrame) - Static method in class smile.regression.LASSO
-
Fits a L1-regularized least squares model.
- fit(Formula, DataFrame) - Static method in class smile.regression.OLS
-
Fits an ordinary least squares model.
- fit(Formula, DataFrame) - Static method in class smile.regression.RandomForest
-
Fits a random forest for regression.
- fit(Formula, DataFrame) - Static method in class smile.regression.RegressionTree
-
Fits a regression tree.
- fit(Formula, DataFrame) - Static method in class smile.regression.RidgeRegression
-
Fits a ridge regression model.
- fit(Formula, DataFrame, double) - Static method in class smile.regression.LASSO
-
Fits a L1-regularized least squares model.
- fit(Formula, DataFrame, double) - Static method in class smile.regression.RidgeRegression
-
Fits a ridge regression model.
- fit(Formula, DataFrame, double[], double[], double[]) - Static method in class smile.regression.RidgeRegression
-
Fits a generalized ridge regression model that minimizes a weighted least squares criterion augmented with a generalized ridge penalty:
- fit(Formula, DataFrame, double, double) - Static method in class smile.regression.ElasticNet
-
Fits an Elastic Net model.
- fit(Formula, DataFrame, double, double, double, int) - Static method in class smile.regression.ElasticNet
-
Fits an Elastic Net model.
- fit(Formula, DataFrame, double, double, int) - Static method in class smile.regression.LASSO
-
Fits a L1-regularized least squares model.
- fit(Formula, DataFrame, int, int, int) - Static method in class smile.regression.RegressionTree
-
Fits a regression tree.
- fit(Formula, DataFrame, int, int, int, int) - Static method in class smile.classification.AdaBoost
-
Fits a AdaBoost model.
- fit(Formula, DataFrame, int, int, int, int, double, double) - Static method in class smile.classification.GradientTreeBoost
-
Fits a gradient tree boosting for classification.
- fit(Formula, DataFrame, int, int, int, int, int, double) - Static method in class smile.regression.RandomForest
-
Fits a random forest for regression.
- fit(Formula, DataFrame, int, int, int, int, int, double, LongStream) - Static method in class smile.regression.RandomForest
-
Fits a random forest for regression.
- fit(Formula, DataFrame, int, int, SplitRule, int, int, int, double) - Static method in class smile.classification.RandomForest
-
Fits a random forest for classification.
- fit(Formula, DataFrame, int, int, SplitRule, int, int, int, double, int[]) - Static method in class smile.classification.RandomForest
-
Fits a random forest for regression.
- fit(Formula, DataFrame, int, int, SplitRule, int, int, int, double, int[], LongStream) - Static method in class smile.classification.RandomForest
-
Fits a random forest for classification.
- fit(Formula, DataFrame, String, boolean, boolean) - Static method in class smile.regression.OLS
-
Fits an ordinary least squares model.
- fit(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in class smile.classification.OneVersusOne
-
Fits a multi-class model with binary data frame classifiers.
- fit(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in class smile.classification.OneVersusRest
-
Fits a multi-class model with binary data frame classifiers.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.AdaBoost
-
Fits a AdaBoost model.
- fit(Formula, DataFrame, Properties) - Method in interface smile.classification.DataFrameClassifier.Trainer
-
Fits a classification model.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.DecisionTree
-
Fits a classification tree.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.GradientTreeBoost
-
Fits a gradient tree boosting for classification.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.RandomForest
-
Fits a random forest for classification.
- fit(Formula, DataFrame, Properties) - Method in interface smile.regression.DataFrameRegression.Trainer
-
Fits a regression model.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.ElasticNet
-
Fits an Elastic Net model.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.GradientTreeBoost
-
Fits a gradient tree boosting for regression.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.LASSO
-
Fits a L1-regularized least squares model.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.OLS
-
Fits an ordinary least squares model.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.RandomForest
-
Fits a random forest for regression.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.RegressionTree
-
Fits a regression tree.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.RidgeRegression
-
Fits a ridge regression model.
- fit(Formula, DataFrame, Loss, int, int, int, int, double, double) - Static method in class smile.regression.GradientTreeBoost
-
Fits a gradient tree boosting for regression.
- fit(Formula, DataFrame, SplitRule, int, int, int) - Static method in class smile.classification.DecisionTree
-
Fits a classification tree.
- fit(Formula, DataFrame, Model) - Static method in class smile.glm.GLM
-
Fits the generalized linear model with IWLS (iteratively reweighted least squares).
- fit(Formula, DataFrame, Model, double, int) - Static method in class smile.glm.GLM
-
Fits the generalized linear model with IWLS (iteratively reweighted least squares).
- fit(Formula, DataFrame, Model, Properties) - Static method in class smile.glm.GLM
-
Fits the generalized linear model with IWLS (iteratively reweighted least squares).
- fit(SparseDataset<Integer>) - Static method in class smile.classification.SparseLogisticRegression
-
Fits logistic regression.
- fit(SparseDataset<Integer>, double, double, int) - Static method in class smile.classification.SparseLogisticRegression
-
Fits logistic regression.
- fit(SparseDataset<Integer>, Properties) - Static method in class smile.classification.SparseLogisticRegression
-
Fits logistic regression.
- fit(Tuple[][], int[][]) - Static method in class smile.sequence.CRF
-
Fits a CRF model.
- fit(Tuple[][], int[][], int, int, int, int, double) - Static method in class smile.sequence.CRF
-
Fits a CRF model.
- fit(Tuple[][], int[][], Properties) - Static method in class smile.sequence.CRF
-
Fits a CRF model.
- fit(BaseVector<?, ?, ?>) - Static method in class smile.classification.ClassLabels
-
Fits the class label mapping.
- fit(DifferentiableMultivariateFunction, double[][], double[], double[]) - Static method in class smile.math.LevenbergMarquardt
-
Fits the nonlinear least squares.
- fit(DifferentiableMultivariateFunction, double[][], double[], double[], double, int) - Static method in class smile.math.LevenbergMarquardt
-
Fits the nonlinear least squares.
- fit(DifferentiableMultivariateFunction, double[], double[], double[]) - Static method in class smile.math.LevenbergMarquardt
-
Fits the nonlinear least squares.
- fit(DifferentiableMultivariateFunction, double[], double[], double[], double, int) - Static method in class smile.math.LevenbergMarquardt
-
Fits the nonlinear least squares.
- fit(Matrix, int) - Static method in class smile.clustering.SpectralClustering
-
Spectral graph clustering.
- fit(Matrix, int, int, double) - Static method in class smile.clustering.SpectralClustering
-
Spectral graph clustering.
- fit(RNNSearch<double[], double[]>, double[][], double) - Method in class smile.neighbor.MPLSH
-
Fits the posteriori multiple probe algorithm.
- fit(RNNSearch<double[], double[]>, double[][], double, int) - Method in class smile.neighbor.MPLSH
-
Fits the posteriori multiple probe algorithm.
- fit(RNNSearch<double[], double[]>, double[][], double, int, double) - Method in class smile.neighbor.MPLSH
-
Train the posteriori multiple probe algorithm.
- fit(SparseArray[], double[], int, double, double, double) - Static method in class smile.regression.SVM
-
Fits a linear epsilon-SVR of sparse data.
- fit(SparseArray[], int) - Static method in class smile.clustering.SIB
-
Clustering data into k clusters up to 100 iterations.
- fit(SparseArray[], int[], int, double, double) - Static method in class smile.classification.SVM
-
Fits a binary linear SVM.
- fit(SparseArray[], int[], int, double, double, int) - Static method in class smile.classification.SVM
-
Fits a binary linear SVM.
- fit(SparseArray[], int, int) - Static method in class smile.clustering.SIB
-
Clustering data into k clusters.
- fit(T[]) - Method in class smile.base.svm.OCSVM
-
Fits a one-class support vector machine.
- fit(T[][], int[][], Function<T, Tuple>) - Static method in class smile.sequence.CRFLabeler
-
Fits a CRF model.
- fit(T[][], int[][], Function<T, Tuple>, int, int, int, int, double) - Static method in class smile.sequence.CRFLabeler
-
Fits a CRF.
- fit(T[][], int[][], Function<T, Tuple>, Properties) - Static method in class smile.sequence.CRFLabeler
-
Fits a CRF model.
- fit(T[][], int[][], ToIntFunction<T>) - Static method in class smile.sequence.HMM
-
Fits an HMM by maximum likelihood estimation.
- fit(T[][], int[][], ToIntFunction<T>) - Static method in class smile.sequence.HMMLabeler
-
Fits an HMM by maximum likelihood estimation.
- fit(T[], double[]) - Method in class smile.base.svm.SVR
-
Fits an epsilon support vector regression model.
- fit(T[], double[]) - Method in interface smile.regression.Regression.Trainer
-
Fits a regression model with the default hyper-parameters.
- fit(T[], double[], Properties) - Method in interface smile.regression.Regression.Trainer
-
Fits a regression model.
- fit(T[], double[], RBF<T>[]) - Static method in class smile.regression.RBFNetwork
-
Fits an RBF network.
- fit(T[], double[], RBF<T>[], boolean) - Static method in class smile.regression.RBFNetwork
-
Fits an RBF network.
- fit(T[], double[], MercerKernel<T>, double) - Static method in class smile.regression.GaussianProcessRegression
-
Fits a regular Gaussian process model by the method of subset of regressors.
- fit(T[], double[], MercerKernel<T>, double, boolean, double, int) - Static method in class smile.regression.GaussianProcessRegression
-
Fits a regular Gaussian process model.
- fit(T[], double[], MercerKernel<T>, double, double, double) - Static method in class smile.regression.SVM
-
Fits an epsilon-SVR.
- fit(T[], double[], MercerKernel<T>, Properties) - Static method in class smile.regression.GaussianProcessRegression
-
Fits a regular Gaussian process model.
- fit(T[], double[], T[], MercerKernel<T>, double) - Static method in class smile.regression.GaussianProcessRegression
-
Fits an approximate Gaussian process model by the method of subset of regressors.
- fit(T[], double[], T[], MercerKernel<T>, double, boolean) - Static method in class smile.regression.GaussianProcessRegression
-
Fits an approximate Gaussian process model by the method of subset of regressors.
- fit(T[], double[], T[], MercerKernel<T>, Properties) - Static method in class smile.regression.GaussianProcessRegression
-
Fits an approximate Gaussian process model by the method of subset of regressors.
- fit(T[], int[]) - Method in interface smile.classification.Classifier.Trainer
-
Fits a classification model with the default hyper-parameters.
- fit(T[], int[], int) - Method in class smile.base.svm.LASVM
-
Trains the model.
- fit(T[], int[], int, int, BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusOne
-
Fits a multi-class model with binary classifiers.
- fit(T[], int[], int, int, BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusRest
-
Fits a multi-class model with binary classifiers.
- fit(T[], int[], int, Distance<T>) - Static method in class smile.classification.KNN
-
Fits the K-NN classifier.
- fit(T[], int[], BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusOne
-
Fits a multi-class model with binary classifiers.
- fit(T[], int[], BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusRest
-
Fits a multi-class model with binary classifiers.
- fit(T[], int[], Properties) - Method in interface smile.classification.Classifier.Trainer
-
Fits a classification model.
- fit(T[], int[], RBF<T>[]) - Static method in class smile.classification.RBFNetwork
-
Fits an RBF network.
- fit(T[], int[], RBF<T>[], boolean) - Static method in class smile.classification.RBFNetwork
-
Fits an RBF network.
- fit(T[], int[], Distance<T>) - Static method in class smile.classification.KNN
-
Fits the 1-NN classifier.
- fit(T[], int[], MercerKernel<T>, double, double) - Static method in class smile.classification.SVM
-
Fits a binary SVM.
- fit(T[], int[], MercerKernel<T>, double, double, int) - Static method in class smile.classification.SVM
-
Fits a binary SVM.
- fit(T[], Distance<T>, int) - Static method in class smile.clustering.CLARANS
-
Clustering data into k clusters.
- fit(T[], Distance<T>, int, double) - Static method in class smile.clustering.DBSCAN
-
Clustering the data.
- fit(T[], Distance<T>, int, double) - Static method in class smile.clustering.MEC
-
Clustering the data.
- fit(T[], Distance<T>, int, int) - Static method in class smile.clustering.CLARANS
-
Constructor.
- fit(T[], Metric<T>, int) - Static method in class smile.base.rbf.RBF
-
Fits Gaussian RBF function and centers on data.
- fit(T[], Metric<T>, int, double) - Static method in class smile.base.rbf.RBF
-
Fits Gaussian RBF function and centers on data.
- fit(T[], Metric<T>, int, int) - Static method in class smile.base.rbf.RBF
-
Fits Gaussian RBF function and centers on data.
- fit(T[], MercerKernel<T>) - Static method in class smile.anomaly.SVM
-
Fits a one-class SVM.
- fit(T[], MercerKernel<T>, double, double) - Static method in class smile.anomaly.SVM
-
Fits a one-class SVM.
- fit(T[], MercerKernel<T>, int) - Static method in class smile.manifold.KPCA
-
Fits kernel principal component analysis.
- fit(T[], MercerKernel<T>, int, double) - Static method in class smile.manifold.KPCA
-
Fits kernel principal component analysis.
- fit(T[], RNNSearch<T, T>, int, double) - Static method in class smile.clustering.DBSCAN
-
Clustering the data.
- fit(T[], RNNSearch<T, T>, int, double, int[], double) - Static method in class smile.clustering.MEC
-
Clustering the data.
- fitness() - Method in class smile.gap.BitString
- fitness() - Method in interface smile.gap.Chromosome
-
Returns the fitness of chromosome.
- fitness(double[][], double[], double[][], double[], RegressionMetric, BiFunction<double[][], double[], Regression<double[]>>) - Static method in class smile.feature.selection.GAFE
-
Returns the fitness of the regression model.
- fitness(double[][], int[], double[][], int[], ClassificationMetric, BiFunction<double[][], int[], Classifier<double[]>>) - Static method in class smile.feature.selection.GAFE
-
Returns the fitness of the classification model.
- fitness(String, DataFrame, DataFrame, ClassificationMetric, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in class smile.feature.selection.GAFE
-
Returns the fitness of the classification model.
- fitness(String, DataFrame, DataFrame, RegressionMetric, BiFunction<Formula, DataFrame, DataFrameRegression>) - Static method in class smile.feature.selection.GAFE
-
Returns the fitness of the regression model.
- Fitness<T> - Interface in smile.gap
-
A measure to evaluate the fitness of chromosomes.
- fittedValues - Variable in class smile.math.LevenbergMarquardt
-
The fitted values.
- fittedValues() - Method in class smile.glm.GLM
-
Returns the fitted mean values.
- fittedValues() - Method in class smile.regression.LinearModel
-
Returns the fitted values.
- fittedValues() - Method in class smile.timeseries.AR
-
Returns the fitted values.
- fittedValues() - Method in class smile.timeseries.ARMA
-
Returns the fitted values.
- fitTime - Variable in class smile.validation.ClassificationMetrics
-
The time in milliseconds of fitting the model.
- fitTime - Variable in class smile.validation.RegressionMetrics
-
The time in milliseconds of fitting the model.
- flatten() - Method in class smile.deep.tensor.Tensor
-
Flattens the tensor by reshaping it into a one-dimensional tensor.
- flatten(int) - Method in class smile.deep.tensor.Tensor
-
Flattens the tensor by reshaping it into a one-dimensional tensor.
- flatten(int, int) - Method in class smile.deep.tensor.Tensor
-
Flattens the tensor by reshaping it into a one-dimensional tensor.
- flatten(String[], String[]) - Method in class smile.plot.vega.Transform
-
Adds a flatten transform.
- FLD - Class in smile.classification
-
Fisher's linear discriminant.
- FLD(double[], double[][], Matrix) - Constructor for class smile.classification.FLD
-
Constructor.
- FLD(double[], double[][], Matrix, IntSet) - Constructor for class smile.classification.FLD
-
Constructor.
- Float - Enum constant in enum class smile.data.type.DataType.ID
-
Float type ID.
- FLOAT_DIGITS - Static variable in class smile.math.MathEx
-
The number of digits (in radix base) in the mantissa.
- FLOAT_EPSILON - Static variable in class smile.math.MathEx
-
The machine precision for the float type, which is the difference between 1 and the smallest value greater than 1 that is representable for the float type.
- FLOAT_MACHEP - Static variable in class smile.math.MathEx
-
The largest negative integer such that 1.0 + RADIXMACHEP ≠ 1.0, except that machep is bounded below by -(DIGITS+3)
- FLOAT_NEGEP - Static variable in class smile.math.MathEx
-
The largest negative integer such that 1.0 - RADIXNEGEP ≠ 1.0, except that negeps is bounded below by -(DIGITS+3)
- Float16 - Enum constant in enum class smile.deep.tensor.ScalarType
-
Half-precision floating-point number.
- Float32 - Enum constant in enum class smile.deep.tensor.ScalarType
-
Single-precision floating-point number.
- Float64 - Enum constant in enum class smile.deep.tensor.ScalarType
-
Double-precision floating-point number.
- 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.
- FloatConsumer - Interface in smile.math.matrix.fp32
-
Single precision matrix element stream consumer.
- FloatHeapSelect - Class in smile.sort
-
This class tracks the smallest values seen thus far in a stream of values.
- FloatHeapSelect(float[]) - Constructor for class smile.sort.FloatHeapSelect
-
Constructor.
- FloatHeapSelect(int) - Constructor for class smile.sort.FloatHeapSelect
-
Constructor.
- FloatObjectType - Static variable in class smile.data.type.DataTypes
-
Float Object data type.
- FloatType - Class in smile.data.type
-
Float data type.
- FloatType - Static variable in class smile.data.type.DataTypes
-
Float data type.
- floatValue() - Method in class smile.deep.tensor.Tensor
-
Returns the float value when the tensor holds a single value.
- floatVector(int) - Method in interface smile.data.DataFrame
-
Selects column based on the column index.
- floatVector(int) - Method in class smile.data.IndexDataFrame
- floatVector(Enum<?>) - Method in interface smile.data.DataFrame
-
Selects column using an enum value.
- floatVector(String) - Method in interface smile.data.DataFrame
-
Selects column based on the column name.
- FloatVector - Interface in smile.data.vector
-
An immutable float vector.
- floor(String) - Static method in interface smile.data.formula.Terms
-
The
floor(x)
term. - floor(Term) - Static method in interface smile.data.formula.Terms
-
The
floor(x)
term. - fold(String[], String[]) - Method in class smile.plot.vega.Transform
-
Adds a fold transform.
- folder2Id - Static variable in interface smile.vision.ImageNet
-
The map from folder name to class id.
- folder2Target - Static variable in interface smile.vision.ImageNet
-
The functor mapping folder name to class id.
- folders - Static variable in interface smile.vision.ImageNet
-
Folder names in the same order of labels.
- font(String) - Method in class smile.plot.vega.Config
-
Sets the default font for all text marks, titles, and labels.
- 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 withJFileChooser
-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(int, int, DoubleConsumer) - Method in class smile.math.matrix.SparseMatrix
-
For each loop on non-zero elements.
- forEachNonZero(int, int, FloatConsumer) - Method in class smile.math.matrix.fp32.SparseMatrix
-
For each loop on non-zero elements.
- forEachNonZero(DoubleConsumer) - Method in class smile.math.matrix.SparseMatrix
-
For each loop on non-zero elements.
- forecast() - Method in class smile.timeseries.AR
-
Returns 1-step ahead forecast.
- forecast() - Method in class smile.timeseries.ARMA
-
Returns 1-step ahead forecast.
- forecast(int) - Method in class smile.timeseries.AR
-
Returns l-step ahead forecast.
- forecast(int) - Method in class smile.timeseries.ARMA
-
Returns l-step ahead forecast.
- FOREST_GREEN - Static variable in interface smile.plot.swing.Palette
- format(double) - Static method in interface smile.util.Strings
-
Returns the string representation of a floating number without trailing zeros.
- format(double, boolean) - Static method in interface smile.util.Strings
-
Returns the string representation of a floating number.
- format(float) - Static method in interface smile.util.Strings
-
Returns the string representation of a floating number without trailing zeros.
- format(float, boolean) - Static method in interface smile.util.Strings
-
Returns the string representation of a floating number.
- format(String) - Method in class smile.plot.vega.Axis
-
Sets the text format.
- format(String) - Method in class smile.plot.vega.Data
-
Sets the format for parsing the data.
- format(String) - Method in class smile.plot.vega.Legend
-
Sets the text format.
- format(String, Map<String, String>) - Method in class smile.plot.vega.Data
-
Sets the format for parsing the data.
- FormatConfig - Class in smile.plot.vega
-
These config properties define the default number and time formats for text marks as well as axes, headers, tooltip, and legends.
- formatType(String) - Method in class smile.plot.vega.Axis
-
Sets the format type for labels.
- formatType(String) - Method in class smile.plot.vega.Legend
-
Sets the format type for labels.
- formula - Variable in class smile.base.cart.CART
-
The model formula.
- formula - Variable in class smile.glm.GLM
-
The symbolic description of the model to be fitted.
- formula() - Method in class smile.classification.AdaBoost
- formula() - Method in interface smile.classification.DataFrameClassifier
-
Returns the formula associated with the model.
- formula() - Method in class smile.classification.DecisionTree
-
Returns null if the tree is part of ensemble algorithm.
- formula() - Method in class smile.classification.GradientTreeBoost
- formula() - Method in class smile.classification.RandomForest
- formula() - Method in interface smile.feature.importance.TreeSHAP
-
Returns the formula associated with the model.
- formula() - Method in interface smile.regression.DataFrameRegression
-
Returns the model formula.
- formula() - Method in class smile.regression.GradientTreeBoost
- formula() - Method in class smile.regression.LinearModel
- formula() - Method in class smile.regression.RandomForest
- formula() - Method in class smile.regression.RegressionTree
-
Returns null if the tree is part of ensemble algorithm.
- Formula - Class in smile.data.formula
-
The model fitting formula in a compact symbolic form.
- Formula(Term, Term...) - Constructor for class smile.data.formula.Formula
-
Constructor.
- forward(BufferedImage...) - Method in interface smile.vision.transform.Transform
-
Transforms images to 4-D tensor with shape [samples, channels, height, width].
- forward(BufferedImage...) - Method in class smile.vision.VisionModel
-
Forward propagation (or forward pass) through the model.
- forward(Tensor) - Method in class smile.deep.activation.GELU
- forward(Tensor) - Method in class smile.deep.activation.GLU
- forward(Tensor) - Method in class smile.deep.activation.HardShrink
- forward(Tensor) - Method in class smile.deep.activation.LeakyReLU
- forward(Tensor) - Method in class smile.deep.activation.LogSigmoid
- forward(Tensor) - Method in class smile.deep.activation.LogSoftmax
- forward(Tensor) - Method in class smile.deep.activation.ReLU
- forward(Tensor) - Method in class smile.deep.activation.Sigmoid
- forward(Tensor) - Method in class smile.deep.activation.SiLU
- forward(Tensor) - Method in class smile.deep.activation.Softmax
- forward(Tensor) - Method in class smile.deep.activation.SoftShrink
- forward(Tensor) - Method in class smile.deep.activation.Tanh
- forward(Tensor) - Method in class smile.deep.activation.TanhShrink
- forward(Tensor) - Method in class smile.deep.layer.AdaptiveAvgPool2dLayer
- forward(Tensor) - Method in class smile.deep.layer.AvgPool2dLayer
- forward(Tensor) - Method in class smile.deep.layer.BatchNorm1dLayer
- forward(Tensor) - Method in class smile.deep.layer.BatchNorm2dLayer
- forward(Tensor) - Method in class smile.deep.layer.Conv2dLayer
- forward(Tensor) - Method in class smile.deep.layer.DropoutLayer
- forward(Tensor) - Method in class smile.deep.layer.EmbeddingLayer
- forward(Tensor) - Method in class smile.deep.layer.FullyConnectedLayer
- forward(Tensor) - Method in class smile.deep.layer.GroupNormLayer
- forward(Tensor) - Method in interface smile.deep.layer.Layer
-
Forward propagation (or forward pass) through the layer.
- forward(Tensor) - Method in class smile.deep.layer.MaxPool2dLayer
- forward(Tensor) - Method in class smile.deep.layer.SequentialBlock
- forward(Tensor) - Method in class smile.deep.Model
-
Forward propagation (or forward pass) through the model.
- forward(Tensor) - Method in class smile.llm.PositionalEncoding
- forward(Tensor) - Method in class smile.llm.Transformer
-
Forward propagation (or forward pass).
- forward(Tensor) - Method in class smile.vision.EfficientNet
- forward(Tensor) - Method in class smile.vision.layer.Conv2dNormActivation
- forward(Tensor) - Method in class smile.vision.layer.FusedMBConv
- forward(Tensor) - Method in class smile.vision.layer.MBConv
- forward(Tensor) - Method in class smile.vision.layer.SqueezeExcitation
- forward(Tensor) - Method in class smile.vision.layer.StochasticDepth
- 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(Integer, Integer) - Method in class smile.plot.vega.ImputeTransform
-
Sets the frame to control the window over which the specified method is applied.
- frame(Integer, Integer) - Method in class smile.plot.vega.WindowTransform
-
Sets the frame specification indicating how the sliding window should proceed.
- frame(DataFrame) - Method in class smile.data.formula.Formula
-
Returns a data frame of predictors and optionally response variable (if input data frame has the related variable(s)).
- from(Path) - Static method in interface smile.data.BinarySparseDataset
-
Parse a binary sparse dataset from a file, of which each line is a data item which are the indices of nonzero elements.
- from(Path) - Static method in interface smile.data.SparseDataset
-
Parses spare dataset in coordinate triple tuple list format.
- from(Path, int) - Static method in interface smile.data.SparseDataset
-
Reads spare dataset in coordinate triple tuple list format.
- FScore - Class in smile.validation.metric
-
The F-score (or F-measure) considers both the precision and the recall of the test to compute the score.
- FScore() - Constructor for class smile.validation.metric.FScore
-
Constructor of F1 score.
- FScore(double) - Constructor for class smile.validation.metric.FScore
-
Constructor of general F-score.
- ftest() - Method in class smile.regression.LinearModel
-
Returns the F-statistic of goodness-of-fit.
- FTest - Class in smile.stat.hypothesis
-
F test of the hypothesis that two independent samples come from normal distributions with the same variance, against the alternative that they come from normal distributions with different variances.
- FTest(double, int, int, double) - Constructor for class smile.stat.hypothesis.FTest
-
Constructor.
- FullyConnectedLayer - Class in smile.deep.layer
-
A fully connected layer with nonlinear activation function.
- FullyConnectedLayer(int, int) - Constructor for class smile.deep.layer.FullyConnectedLayer
-
Constructor.
- Function - Interface in smile.math
-
An interface representing a univariate real function.
- FusedMBConv - Class in smile.vision.layer
-
Fused-MBConv replaces the depthwise-conv3Ă—3 and expansion-conv1Ă—1 in MBConv with single regular conv3Ă—3.
- FusedMBConv(MBConvConfig, double, IntFunction<Layer>) - Constructor for class smile.vision.layer.FusedMBConv
-
Constructor.
- FusedMBConv(double, int, int, int, int, int) - Static method in record class smile.vision.layer.MBConvConfig
-
Returns the config for Fused-MBConv block.
- FW - Enum constant in enum class smile.nlp.pos.PennTreebankPOS
-
Foreign word.
G
- g(double) - Method in class smile.ica.Exp
- g(double) - Method in class smile.ica.Kurtosis
- g(double) - Method in class smile.ica.LogCosh
- g(double) - Method in interface smile.math.DifferentiableFunction
-
Computes the gradient/derivative at x.
- g(double[], double[]) - Method in interface smile.base.mlp.ActivationFunction
-
The gradient function.
- g(double[], double[]) - Method in interface smile.math.DifferentiableMultivariateFunction
-
Computes the value and gradient at x.
- g(Cost, double[], double[]) - Method in enum class smile.base.mlp.OutputFunction
-
The gradient function.
- g2(double) - Method in class smile.ica.Exp
- g2(double) - Method in class smile.ica.Kurtosis
- g2(double) - Method in class smile.ica.LogCosh
- g2(double) - Method in interface smile.math.DifferentiableFunction
-
Compute the second-order derivative at x.
- GAFE - Class in smile.feature.selection
-
Genetic algorithm based feature selection.
- GAFE() - Constructor for class smile.feature.selection.GAFE
-
Constructor.
- GAFE(Selection, int, Crossover, double, double) - Constructor for class smile.feature.selection.GAFE
-
Constructor.
- gamma(double) - Static method in class smile.math.special.Gamma
-
Gamma function.
- Gamma - Class in smile.math.special
-
The gamma, digamma, and incomplete gamma functions.
- GammaDistribution - Class in smile.stat.distribution
-
The Gamma distribution is a continuous probability distributions with a scale parameter θ and a shape parameter k.
- GammaDistribution(double, double) - Constructor for class smile.stat.distribution.GammaDistribution
-
Constructor.
- gather(int, Tensor) - Method in class smile.deep.tensor.Tensor
-
Gathers values along an axis specified by dim.
- Gaussian - Class in smile.math.kernel
-
Gaussian kernel, also referred as RBF kernel or squared exponential kernel.
- Gaussian(double, double, double) - Constructor for class smile.math.kernel.Gaussian
-
Constructor.
- Gaussian(double, double) - Static method in interface smile.vq.Neighborhood
-
Returns Gaussian neighborhood function.
- GaussianDistribution - Class in smile.stat.distribution
-
The normal distribution or Gaussian distribution is a continuous probability distribution that describes data that clusters around a mean.
- GaussianDistribution(double, double) - Constructor for class smile.stat.distribution.GaussianDistribution
-
Constructor
- GaussianKernel - Class in smile.math.kernel
-
Gaussian kernel, also referred as RBF kernel or squared exponential kernel.
- GaussianKernel(double) - Constructor for class smile.math.kernel.GaussianKernel
-
Constructor.
- GaussianKernel(double, double, double) - Constructor for class smile.math.kernel.GaussianKernel
-
Constructor.
- GaussianMixture - Class in smile.stat.distribution
-
Finite univariate Gaussian mixture.
- GaussianMixture(Mixture.Component...) - Constructor for class smile.stat.distribution.GaussianMixture
-
Constructor.
- GaussianProcessRegression<T> - Class in smile.regression
-
Gaussian Process for Regression.
- GaussianProcessRegression(MercerKernel<T>, T[], double[], double) - Constructor for class smile.regression.GaussianProcessRegression
-
Constructor.
- GaussianProcessRegression(MercerKernel<T>, T[], double[], double, double, double) - Constructor for class smile.regression.GaussianProcessRegression
-
Constructor.
- GaussianProcessRegression(MercerKernel<T>, T[], double[], double, double, double, Matrix.Cholesky, double) - Constructor for class smile.regression.GaussianProcessRegression
-
Constructor.
- GaussianProcessRegression.JointPrediction - Class in smile.regression
-
The joint prediction of multiple data points.
- GaussianRadialBasis - Class in smile.math.rbf
-
Gaussian RBF.
- GaussianRadialBasis() - Constructor for class smile.math.rbf.GaussianRadialBasis
-
Constructor.
- GaussianRadialBasis(double) - Constructor for class smile.math.rbf.GaussianRadialBasis
-
Constructor.
- GaussianVariogram - Class in smile.interpolation.variogram
-
Gaussian variogram.
- GaussianVariogram(double, double) - Constructor for class smile.interpolation.variogram.GaussianVariogram
-
Constructor.
- GaussianVariogram(double, double, double) - Constructor for class smile.interpolation.variogram.GaussianVariogram
-
Constructor.
- gbmv(Layout, Transpose, int, int, int, int, double, double[], int, double[], int, double, double[], int) - Method in interface smile.math.blas.BLAS
-
Performs the matrix-vector operation using a band matrix.
- gbmv(Layout, Transpose, int, int, int, int, double, double[], int, double[], int, double, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- gbmv(Layout, Transpose, int, int, int, int, double, DoubleBuffer, int, DoubleBuffer, int, double, DoubleBuffer, int) - Method in interface smile.math.blas.BLAS
-
Performs the matrix-vector operation using a band matrix.
- gbmv(Layout, Transpose, int, int, int, int, double, DoubleBuffer, int, DoubleBuffer, int, double, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gbmv(Layout, Transpose, int, int, int, int, float, float[], int, float[], int, float, float[], int) - Method in interface smile.math.blas.BLAS
-
Performs the matrix-vector operation using a band matrix.
- gbmv(Layout, Transpose, int, int, int, int, float, float[], int, float[], int, float, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- gbmv(Layout, Transpose, int, int, int, int, float, FloatBuffer, int, FloatBuffer, int, float, FloatBuffer, int) - Method in interface smile.math.blas.BLAS
-
Performs the matrix-vector operation using a band matrix.
- gbmv(Layout, Transpose, int, int, int, int, float, FloatBuffer, int, FloatBuffer, int, float, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gbsv(Layout, int, int, int, int, double[], int, int[], double[], int) - Method in interface smile.math.blas.LAPACK
-
Solves a real system of linear equations.
- gbsv(Layout, int, int, int, int, double[], int, int[], double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- gbsv(Layout, int, int, int, int, float[], int, int[], float[], int) - Method in interface smile.math.blas.LAPACK
-
Solves a real system of linear equations.
- gbsv(Layout, int, int, int, int, float[], int, int[], float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- gbsv(Layout, int, int, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in interface smile.math.blas.LAPACK
-
Solves a real system of linear equations.
- gbsv(Layout, int, int, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gbsv(Layout, int, int, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in interface smile.math.blas.LAPACK
-
Solves a real system of linear equations.
- gbsv(Layout, int, int, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gbtrf(Layout, int, int, int, int, double[], int, int[]) - Method in interface smile.math.blas.LAPACK
-
Computes an LU factorization of a band matrix A using partial pivoting with row interchanges.
- gbtrf(Layout, int, int, int, int, double[], int, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
- gbtrf(Layout, int, int, int, int, float[], int, int[]) - Method in interface smile.math.blas.LAPACK
-
Computes an LU factorization of a band matrix A using partial pivoting with row interchanges.
- gbtrf(Layout, int, int, int, int, float[], int, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
- gbtrf(Layout, int, int, int, int, DoubleBuffer, int, IntBuffer) - Method in interface smile.math.blas.LAPACK
-
Computes an LU factorization of a band matrix A using partial pivoting with row interchanges.
- gbtrf(Layout, int, int, int, int, DoubleBuffer, int, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- gbtrf(Layout, int, int, int, int, FloatBuffer, int, IntBuffer) - Method in interface smile.math.blas.LAPACK
-
Computes an LU factorization of a band matrix A using partial pivoting with row interchanges.
- gbtrf(Layout, int, int, int, int, FloatBuffer, int, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- gbtrs(Layout, Transpose, int, int, int, int, double[], int, int[], double[], int) - Method in interface smile.math.blas.LAPACK
-
Solves a system of linear equations
- gbtrs(Layout, Transpose, int, int, int, int, double[], int, int[], double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- gbtrs(Layout, Transpose, int, int, int, int, float[], int, int[], float[], int) - Method in interface smile.math.blas.LAPACK
-
Solves a system of linear equations
- gbtrs(Layout, Transpose, int, int, int, int, float[], int, int[], float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- gbtrs(Layout, Transpose, int, int, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in interface smile.math.blas.LAPACK
-
Solves a system of linear equations
- gbtrs(Layout, Transpose, int, int, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gbtrs(Layout, Transpose, int, int, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in interface smile.math.blas.LAPACK
-
Solves a system of linear equations
- gbtrs(Layout, Transpose, int, int, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- ge(double) - Method in class smile.deep.tensor.Tensor
-
Computes element-wise greater-than-or-equal-to comparison.
- ge(int) - Method in class smile.deep.tensor.Tensor
-
Computes element-wise greater-than-or-equal-to comparison.
- ge(Tensor) - Method in class smile.deep.tensor.Tensor
-
Computes element-wise greater-than-or-equal-to comparison.
- geev(Layout, EVDJob, EVDJob, int, double[], int, double[], double[], double[], int, double[], int) - Method in interface smile.math.blas.LAPACK
-
Computes the eigenvalues and, optionally, the left and/or right eigenvectors.
- geev(Layout, EVDJob, EVDJob, int, double[], int, double[], double[], double[], int, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- geev(Layout, EVDJob, EVDJob, int, float[], int, float[], float[], float[], int, float[], int) - Method in interface smile.math.blas.LAPACK
-
Computes the eigenvalues and, optionally, the left and/or right eigenvectors.
- geev(Layout, EVDJob, EVDJob, int, float[], int, float[], float[], float[], int, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- geev(Layout, EVDJob, EVDJob, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int) - Method in interface smile.math.blas.LAPACK
-
Computes the eigenvalues and, optionally, the left and/or right eigenvectors.
- geev(Layout, EVDJob, EVDJob, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- geev(Layout, EVDJob, EVDJob, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer, int, FloatBuffer, int) - Method in interface smile.math.blas.LAPACK
-
Computes the eigenvalues and, optionally, the left and/or right eigenvectors.
- geev(Layout, EVDJob, EVDJob, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer, int, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- geev(Layout, EVDJob, EVDJob, int, DoublePointer, int, DoublePointer, DoublePointer, DoublePointer, int, DoublePointer, int) - Method in interface smile.math.blas.LAPACK
-
Computes the eigenvalues and, optionally, the left and/or right eigenvectors.
- geev(Layout, EVDJob, EVDJob, int, DoublePointer, int, DoublePointer, DoublePointer, DoublePointer, int, DoublePointer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gels(Layout, Transpose, int, int, int, double[], int, double[], int) - Method in interface smile.math.blas.LAPACK
-
Solves an overdetermined or underdetermined system, using a QR or LQ factorization of A.
- gels(Layout, Transpose, int, int, int, double[], int, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- gels(Layout, Transpose, int, int, int, float[], int, float[], int) - Method in interface smile.math.blas.LAPACK
-
Solves an overdetermined or underdetermined system, using a QR or LQ factorization of A.
- gels(Layout, Transpose, int, int, int, float[], int, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- gels(Layout, Transpose, int, int, int, DoubleBuffer, int, DoubleBuffer, int) - Method in interface smile.math.blas.LAPACK
-
Solves an overdetermined or underdetermined system, using a QR or LQ factorization of A.
- gels(Layout, Transpose, int, int, int, DoubleBuffer, int, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gels(Layout, Transpose, int, int, int, FloatBuffer, int, FloatBuffer, int) - Method in interface smile.math.blas.LAPACK
-
Solves an overdetermined or underdetermined system, using a QR or LQ factorization of A.
- gels(Layout, Transpose, int, int, int, FloatBuffer, int, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gelsd(Layout, int, int, int, double[], int, double[], int, double[], double, int[]) - Method in interface smile.math.blas.LAPACK
-
Solves an overdetermined or underdetermined system, using a divide and conquer algorithm with the singular value decomposition (SVD) of A.
- gelsd(Layout, int, int, int, double[], int, double[], int, double[], double, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
- gelsd(Layout, int, int, int, float[], int, float[], int, float[], float, int[]) - Method in interface smile.math.blas.LAPACK
-
Solves an overdetermined or underdetermined system, using a divide and conquer algorithm with the singular value decomposition (SVD) of A.
- gelsd(Layout, int, int, int, float[], int, float[], int, float[], float, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
- gelsd(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, double, IntBuffer) - Method in interface smile.math.blas.LAPACK
-
Solves an overdetermined or underdetermined system, using a divide and conquer algorithm with the singular value decomposition (SVD) of A.
- gelsd(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, double, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- gelsd(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, float, IntBuffer) - Method in interface smile.math.blas.LAPACK
-
Solves an overdetermined or underdetermined system, using a divide and conquer algorithm with the singular value decomposition (SVD) of A.
- gelsd(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, float, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- gelss(Layout, int, int, int, double[], int, double[], int, double[], double, int[]) - Method in interface smile.math.blas.LAPACK
-
Solves an overdetermined or underdetermined system, using the singular value decomposition (SVD) of A.
- gelss(Layout, int, int, int, double[], int, double[], int, double[], double, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
- gelss(Layout, int, int, int, float[], int, float[], int, float[], float, int[]) - Method in interface smile.math.blas.LAPACK
-
Solves an overdetermined or underdetermined system, using the singular value decomposition (SVD) of A.
- gelss(Layout, int, int, int, float[], int, float[], int, float[], float, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
- gelss(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, double, IntBuffer) - Method in interface smile.math.blas.LAPACK
-
Solves an overdetermined or underdetermined system, using the singular value decomposition (SVD) of A.
- gelss(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, double, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- gelss(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, float, IntBuffer) - Method in interface smile.math.blas.LAPACK
-
Solves an overdetermined or underdetermined system, using the singular value decomposition (SVD) of A.
- gelss(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, float, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- gelsy(Layout, int, int, int, double[], int, double[], int, int[], double, int[]) - Method in interface smile.math.blas.LAPACK
-
Solves an overdetermined or underdetermined system, using a complete orthogonal factorization of A.
- gelsy(Layout, int, int, int, double[], int, double[], int, int[], double, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
- gelsy(Layout, int, int, int, float[], int, float[], int, int[], float, int[]) - Method in interface smile.math.blas.LAPACK
-
Solves an overdetermined or underdetermined system, using a complete orthogonal factorization of A.
- gelsy(Layout, int, int, int, float[], int, float[], int, int[], float, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
- gelsy(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, IntBuffer, double, IntBuffer) - Method in interface smile.math.blas.LAPACK
-
Solves an overdetermined or underdetermined system, using a complete orthogonal factorization of A.
- gelsy(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, IntBuffer, double, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- gelsy(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, IntBuffer, float, IntBuffer) - Method in interface smile.math.blas.LAPACK
-
Solves an overdetermined or underdetermined system, using a complete orthogonal factorization of A.
- gelsy(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, IntBuffer, float, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- gelu(int, int) - Static method in interface smile.deep.layer.Layer
-
Returns a fully connected layer with GELU activation function.
- gelu(int, int, double) - Static method in interface smile.deep.layer.Layer
-
Returns a fully connected layer with GELU activation function.
- GELU - Class in smile.deep.activation
-
Gaussian Error Linear Unit activation function.
- GELU(boolean) - Constructor for class smile.deep.activation.GELU
-
Constructor.
- gemm(Layout, Transpose, Transpose, int, int, int, double, double[], int, double[], int, double, double[], int) - Method in interface smile.math.blas.BLAS
-
Performs the matrix-matrix operation.
- gemm(Layout, Transpose, Transpose, int, int, int, double, double[], int, double[], int, double, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- gemm(Layout, Transpose, Transpose, int, int, int, double, DoubleBuffer, int, DoubleBuffer, int, double, DoubleBuffer, int) - Method in interface smile.math.blas.BLAS
-
Performs the matrix-matrix operation.
- gemm(Layout, Transpose, Transpose, int, int, int, double, DoubleBuffer, int, DoubleBuffer, int, double, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gemm(Layout, Transpose, Transpose, int, int, int, double, DoublePointer, int, DoublePointer, int, double, DoublePointer, int) - Method in interface smile.math.blas.BLAS
-
Performs the matrix-matrix operation.
- gemm(Layout, Transpose, Transpose, int, int, int, double, DoublePointer, int, DoublePointer, int, double, DoublePointer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gemm(Layout, Transpose, Transpose, int, int, int, float, float[], int, float[], int, float, float[], int) - Method in interface smile.math.blas.BLAS
-
Performs the matrix-matrix operation.
- gemm(Layout, Transpose, Transpose, int, int, int, float, float[], int, float[], int, float, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- gemm(Layout, Transpose, Transpose, int, int, int, float, FloatBuffer, int, FloatBuffer, int, float, FloatBuffer, int) - Method in interface smile.math.blas.BLAS
-
Performs the matrix-matrix operation.
- gemm(Layout, Transpose, Transpose, int, int, int, float, FloatBuffer, int, FloatBuffer, int, float, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gemv(Layout, Transpose, int, int, double, double[], int, double[], int, double, double[], int) - Method in interface smile.math.blas.BLAS
-
Performs the matrix-vector operation.
- gemv(Layout, Transpose, int, int, double, double[], int, double[], int, double, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- gemv(Layout, Transpose, int, int, double, DoubleBuffer, int, DoubleBuffer, int, double, DoubleBuffer, int) - Method in interface smile.math.blas.BLAS
-
Performs the matrix-vector operation.
- gemv(Layout, Transpose, int, int, double, DoubleBuffer, int, DoubleBuffer, int, double, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gemv(Layout, Transpose, int, int, double, DoublePointer, int, DoublePointer, int, double, DoublePointer, int) - Method in interface smile.math.blas.BLAS
-
Performs the matrix-vector operation.
- gemv(Layout, Transpose, int, int, double, DoublePointer, int, DoublePointer, int, double, DoublePointer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gemv(Layout, Transpose, int, int, float, float[], int, float[], int, float, float[], int) - Method in interface smile.math.blas.BLAS
-
Performs the matrix-vector operation.
- gemv(Layout, Transpose, int, int, float, float[], int, float[], int, float, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- gemv(Layout, Transpose, int, int, float, FloatBuffer, int, FloatBuffer, int, float, FloatBuffer, int) - Method in interface smile.math.blas.BLAS
-
Performs the matrix-vector operation.
- gemv(Layout, Transpose, int, int, float, FloatBuffer, int, FloatBuffer, int, float, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- generateSeed() - Static method in class smile.math.MathEx
-
Returns a random number to seed other random number generators.
- generateSeed(int) - Static method in class smile.math.MathEx
-
Returns the given number of random bytes to seed other random number generators.
- GeneticAlgorithm<T> - Class in smile.gap
-
A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution.
- GeneticAlgorithm(T[]) - Constructor for class smile.gap.GeneticAlgorithm
-
Constructor.
- GeneticAlgorithm(T[], Selection, int) - Constructor for class smile.gap.GeneticAlgorithm
-
Constructor.
- GeometricDistribution - Class in smile.stat.distribution
-
The geometric distribution is a discrete probability distribution of the number X of Bernoulli trials needed to get one success, supported on the set
{1, 2, 3, …}
. - GeometricDistribution(double) - Constructor for class smile.stat.distribution.GeometricDistribution
-
Constructor.
- geqrf(Layout, int, int, double[], int, double[]) - Method in interface smile.math.blas.LAPACK
-
Computes a QR factorization of a general M-by-N matrix A.
- geqrf(Layout, int, int, double[], int, double[]) - Method in class smile.math.blas.openblas.OpenBLAS
- geqrf(Layout, int, int, float[], int, float[]) - Method in interface smile.math.blas.LAPACK
-
Computes a QR factorization of a general M-by-N matrix A.
- geqrf(Layout, int, int, float[], int, float[]) - Method in class smile.math.blas.openblas.OpenBLAS
- geqrf(Layout, int, int, DoubleBuffer, int, DoubleBuffer) - Method in interface smile.math.blas.LAPACK
-
Computes a QR factorization of a general M-by-N matrix A.
- geqrf(Layout, int, int, DoubleBuffer, int, DoubleBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- geqrf(Layout, int, int, FloatBuffer, int, FloatBuffer) - Method in interface smile.math.blas.LAPACK
-
Computes a QR factorization of a general M-by-N matrix A.
- geqrf(Layout, int, int, FloatBuffer, int, FloatBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- geqrf(Layout, int, int, DoublePointer, int, DoublePointer) - Method in interface smile.math.blas.LAPACK
-
Computes a QR factorization of a general M-by-N matrix A.
- geqrf(Layout, int, int, DoublePointer, int, DoublePointer) - Method in class smile.math.blas.openblas.OpenBLAS
- ger(Layout, int, int, double, double[], int, double[], int, double[], int) - Method in interface smile.math.blas.BLAS
-
Performs the rank-1 update operation.
- ger(Layout, int, int, double, double[], int, double[], int, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- ger(Layout, int, int, double, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, int) - Method in interface smile.math.blas.BLAS
-
Performs the rank-1 update operation.
- ger(Layout, int, int, double, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- ger(Layout, int, int, double, DoublePointer, int, DoublePointer, int, DoublePointer, int) - Method in interface smile.math.blas.BLAS
-
Performs the rank-1 update operation.
- ger(Layout, int, int, double, DoublePointer, int, DoublePointer, int, DoublePointer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- ger(Layout, int, int, float, float[], int, float[], int, float[], int) - Method in interface smile.math.blas.BLAS
-
Performs the rank-1 update operation.
- ger(Layout, int, int, float, float[], int, float[], int, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- ger(Layout, int, int, float, FloatBuffer, int, FloatBuffer, int, FloatBuffer, int) - Method in interface smile.math.blas.BLAS
-
Performs the rank-1 update operation.
- ger(Layout, int, int, float, FloatBuffer, int, FloatBuffer, int, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gesdd(Layout, SVDJob, int, int, double[], int, double[], double[], int, double[], int) - Method in interface smile.math.blas.LAPACK
-
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
- gesdd(Layout, SVDJob, int, int, double[], int, double[], double[], int, double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- gesdd(Layout, SVDJob, int, int, float[], int, float[], float[], int, float[], int) - Method in interface smile.math.blas.LAPACK
-
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
- gesdd(Layout, SVDJob, int, int, float[], int, float[], float[], int, float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- gesdd(Layout, SVDJob, int, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int) - Method in interface smile.math.blas.LAPACK
-
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
- gesdd(Layout, SVDJob, int, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gesdd(Layout, SVDJob, int, int, FloatBuffer, int, FloatBuffer, FloatBuffer, int, FloatBuffer, int) - Method in interface smile.math.blas.LAPACK
-
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
- gesdd(Layout, SVDJob, int, int, FloatBuffer, int, FloatBuffer, FloatBuffer, int, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gesdd(Layout, SVDJob, int, int, DoublePointer, int, DoublePointer, DoublePointer, int, DoublePointer, int) - Method in interface smile.math.blas.LAPACK
-
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
- gesdd(Layout, SVDJob, int, int, DoublePointer, int, DoublePointer, DoublePointer, int, DoublePointer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gesv(Layout, int, int, double[], int, int[], double[], int) - Method in interface smile.math.blas.LAPACK
-
Solves a real system of linear equations.
- gesv(Layout, int, int, double[], int, int[], double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- gesv(Layout, int, int, float[], int, int[], float[], int) - Method in interface smile.math.blas.LAPACK
-
Solves a real system of linear equations.
- gesv(Layout, int, int, float[], int, int[], float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- gesv(Layout, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in interface smile.math.blas.LAPACK
-
Solves a real system of linear equations.
- gesv(Layout, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gesv(Layout, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in interface smile.math.blas.LAPACK
-
Solves a real system of linear equations.
- gesv(Layout, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gesv(Layout, int, int, DoublePointer, int, IntPointer, DoublePointer, int) - Method in interface smile.math.blas.LAPACK
-
Solves a real system of linear equations.
- gesv(Layout, int, int, DoublePointer, int, IntPointer, DoublePointer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- gesvd(Layout, SVDJob, SVDJob, int, int, double[], int, double[], double[], int, double[], int, double[]) - Method in interface smile.math.blas.LAPACK
-
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
- gesvd(Layout, SVDJob, SVDJob, int, int, double[], int, double[], double[], int, double[], int, double[]) - Method in class smile.math.blas.openblas.OpenBLAS
- gesvd(Layout, SVDJob, SVDJob, int, int, float[], int, float[], float[], int, float[], int, float[]) - Method in interface smile.math.blas.LAPACK
-
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
- gesvd(Layout, SVDJob, SVDJob, int, int, float[], int, float[], float[], int, float[], int, float[]) - Method in class smile.math.blas.openblas.OpenBLAS
- gesvd(Layout, SVDJob, SVDJob, int, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer) - Method in interface smile.math.blas.LAPACK
-
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
- gesvd(Layout, SVDJob, SVDJob, int, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- gesvd(Layout, SVDJob, SVDJob, int, int, FloatBuffer, int, FloatBuffer, FloatBuffer, int, FloatBuffer, int, FloatBuffer) - Method in interface smile.math.blas.LAPACK
-
Computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors.
- gesvd(Layout, SVDJob, SVDJob, int, int, FloatBuffer, int, FloatBuffer, FloatBuffer, int, FloatBuffer, int, FloatBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- get(double[]) - Method in class smile.neighbor.lsh.Hash
-
Returns the bucket entry for the given point.
- get(int) - Method in interface smile.data.DataFrame
-
Returns the row at the specified index.
- get(int) - Method in interface smile.data.Dataset
-
Returns the instance at the specified index.
- get(int) - Method in class smile.data.IndexDataFrame
- get(int) - Method in interface smile.data.Tuple
-
Returns the value at position i.
- get(int) - Method in interface smile.data.vector.BaseVector
-
Returns the value at position i, which may be null.
- get(int) - Method in class smile.math.Complex.Array
-
Returns the i-th element.
- get(int) - Method in class smile.math.matrix.fp32.SparseMatrix
-
Returns the element at the storage index.
- get(int) - Method in class smile.math.matrix.SparseMatrix
-
Returns the element at the storage index.
- get(int) - Method in class smile.neighbor.lsh.Hash
-
Returns the bucket entry for the given hash value.
- get(int) - Method in class smile.sort.DoubleHeapSelect
-
Returns the i-th smallest value seen so far.
- get(int) - Method in class smile.sort.FloatHeapSelect
-
Returns the i-th smallest value seen so far.
- get(int) - Method in class smile.sort.HeapSelect
-
Returns the i-th smallest value seen so far.
- get(int) - Method in class smile.sort.IntHeapSelect
-
Returns the i-th smallest value seen so far.
- get(int) - Method in class smile.util.DoubleArrayList
-
Returns the value at the specified position in this list.
- get(int) - Method in class smile.util.IntArrayList
-
Returns the value at the specified position in this list.
- get(int) - Method in class smile.util.IntDoubleHashMap
-
Returns the value to which the specified key is mapped, or Double.NaN if this map contains no mapping for the key.
- get(int) - Method in class smile.util.SparseArray
-
Returns the value of i-th entry.
- get(int...) - Method in interface smile.data.vector.BaseVector
-
Returns a new vector with selected entries.
- get(int...) - Method in interface smile.data.vector.BooleanVector
- get(int...) - Method in interface smile.data.vector.ByteVector
- get(int...) - Method in interface smile.data.vector.CharVector
- get(int...) - Method in interface smile.data.vector.DoubleVector
- get(int...) - Method in interface smile.data.vector.FloatVector
- get(int...) - Method in interface smile.data.vector.IntVector
- get(int...) - Method in interface smile.data.vector.LongVector
- get(int...) - Method in interface smile.data.vector.ShortVector
- get(int...) - Method in interface smile.data.vector.StringVector
- get(int...) - Method in interface smile.data.vector.Vector
- get(int...) - Method in class smile.deep.tensor.Tensor
-
Returns a portion of tensor given the indices.
- get(int[], int[]) - Method in class smile.math.matrix.BigMatrix
-
Returns the matrix of selected rows and columns.
- get(int[], int[]) - Method in class smile.math.matrix.fp32.Matrix
-
Returns the matrix of selected rows and columns.
- get(int[], int[]) - Method in class smile.math.matrix.Matrix
-
Returns the matrix of selected rows and columns.
- get(int, int) - Method in interface smile.data.BinarySparseDataset
-
Returns the binary value at entry (i, j) by binary search.
- get(int, int) - Method in interface smile.data.DataFrame
-
Returns the cell at (i, j).
- get(int, int) - Method in class smile.data.IndexDataFrame
- get(int, int) - Method in interface smile.data.SparseDataset
-
Returns the value at entry (i, j).
- get(int, int) - Method in class smile.math.matrix.BandMatrix
- get(int, int) - Method in class smile.math.matrix.BigMatrix
- get(int, int) - Method in class smile.math.matrix.fp32.BandMatrix
- get(int, int) - Method in class smile.math.matrix.fp32.IMatrix
-
Returns
A[i,j]
. - get(int, int) - Method in class smile.math.matrix.fp32.Matrix
- get(int, int) - Method in class smile.math.matrix.fp32.SparseMatrix
- get(int, int) - Method in class smile.math.matrix.fp32.SymmMatrix
- get(int, int) - Method in class smile.math.matrix.IMatrix
-
Returns
A[i,j]
. - get(int, int) - Method in class smile.math.matrix.Matrix
- get(int, int) - Method in class smile.math.matrix.SparseMatrix
- get(int, int) - Method in class smile.math.matrix.SymmMatrix
- get(int, int) - Method in interface smile.plot.swing.Hexmap.Tooltip
-
Gets the tooltip of cell at (i, j).
- get(int, int) - Method in class smile.util.Array2D
-
Returns A[i, j].
- get(int, int) - Method in class smile.util.IntArray2D
-
Returns A[i, j].
- get(int, String) - Method in interface smile.data.DataFrame
-
Returns the cell at (i, j).
- get(long...) - Method in class smile.deep.tensor.Tensor
-
Returns a portion of tensor given the indices.
- get(String) - Method in interface smile.data.Tuple
-
Returns the value by field name.
- get(String) - Method in class smile.hash.PerfectHash
-
Returns the index of a keyword.
- get(String) - Method in class smile.hash.PerfectMap
-
Returns the value associated with the key.
- get(String) - Method in class smile.nlp.embedding.Word2Vec
-
Returns the embedding vector of a word.
- get(String) - Static method in class smile.nlp.pos.EnglishPOSLexicon
-
Returns the part-of-speech tags for given word, or null if the word does not exist in the dictionary.
- get(K) - Method in class smile.nlp.Trie
-
Returns the node of a given key.
- get(K[]) - Method in class smile.nlp.Trie
-
Returns the associated value of a given key.
- get(Index...) - Method in class smile.deep.tensor.Tensor
-
Returns a portion of tensor given the indices.
- get(Tensor) - Method in class smile.deep.tensor.Tensor
-
Returns a portion of tensor given the indices.
- getAbbreviation(String) - Method in interface smile.nlp.dictionary.Abbreviations
-
Returns the abbreviation for a word.
- 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) - Method in interface smile.data.Tuple
-
Returns the value at position i of array type.
- getArray(int, int) - Method in interface smile.data.DataFrame
-
Returns the value at position (i, j) of array type.
- getArray(int, String) - Method in interface smile.data.DataFrame
-
Returns the field value of array type.
- getArray(String) - Method in interface smile.data.Tuple
-
Returns the field value of array type.
- getAs(int) - Method in interface smile.data.Tuple
-
Returns the value at position i.
- getAs(String) - Method in interface smile.data.Tuple
-
Returns the value of a given fieldName.
- 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.
- getBoolean(int) - Method in interface smile.data.Tuple
-
Returns the value at position i as a primitive boolean.
- getBoolean(int) - Method in interface smile.data.vector.BaseVector
-
Returns the boolean value at position i.
- getBoolean(int) - Method in interface smile.data.vector.ByteVector
- getBoolean(int) - Method in interface smile.data.vector.CharVector
- getBoolean(int) - Method in interface smile.data.vector.DoubleVector
- getBoolean(int) - Method in interface smile.data.vector.FloatVector
- getBoolean(int) - Method in interface smile.data.vector.IntVector
- getBoolean(int) - Method in interface smile.data.vector.LongVector
- getBoolean(int) - Method in interface smile.data.vector.ShortVector
- getBoolean(int) - Method in interface smile.data.vector.StringVector
- getBoolean(int) - Method in interface smile.data.vector.Vector
- getBoolean(int, int) - Method in interface smile.data.DataFrame
-
Returns the value at position (i, j) as a primitive boolean.
- getBoolean(int, String) - Method in interface smile.data.DataFrame
-
Returns the field value as a primitive boolean.
- getBoolean(String) - Method in interface smile.data.Tuple
-
Returns the field value as a primitive boolean.
- getByte(int) - Method in interface smile.data.Tuple
-
Returns the value at position i as a primitive byte.
- getByte(int) - Method in interface smile.data.vector.BaseVector
-
Returns the byte value at position i.
- getByte(int) - Method in interface smile.data.vector.BooleanVector
- getByte(int) - Method in interface smile.data.vector.CharVector
- getByte(int) - Method in interface smile.data.vector.DoubleVector
- getByte(int) - Method in interface smile.data.vector.FloatVector
- getByte(int) - Method in interface smile.data.vector.IntVector
- getByte(int) - Method in interface smile.data.vector.LongVector
- getByte(int) - Method in interface smile.data.vector.ShortVector
- getByte(int) - Method in interface smile.data.vector.StringVector
- getByte(int) - Method in interface smile.data.vector.Vector
- getByte(int...) - Method in class smile.deep.tensor.Tensor
-
Returns the byte value of element at given index.
- 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(long...) - Method in class smile.deep.tensor.Tensor
-
Returns the byte value of element at given index.
- getByte(String) - Method in interface smile.data.Tuple
-
Returns the field value as a primitive byte.
- 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
- getChar(int) - Method in interface smile.data.Tuple
-
Returns the value at position i as a primitive byte.
- getChar(int) - Method in interface smile.data.vector.BaseVector
-
Returns the character value at position i.
- getChar(int) - Method in interface smile.data.vector.BooleanVector
- getChar(int) - Method in interface smile.data.vector.ByteVector
- getChar(int) - Method in interface smile.data.vector.DoubleVector
- getChar(int) - Method in interface smile.data.vector.FloatVector
- getChar(int) - Method in interface smile.data.vector.IntVector
- getChar(int) - Method in interface smile.data.vector.LongVector
- getChar(int) - Method in interface smile.data.vector.ShortVector
- getChar(int) - Method in interface smile.data.vector.StringVector
- getChar(int) - Method in interface smile.data.vector.Vector
- getChar(int, int) - Method in interface smile.data.DataFrame
-
Returns the value at position (i, j) as a primitive byte.
- getChar(int, String) - Method in interface smile.data.DataFrame
-
Returns the field value as a primitive byte.
- getChar(String) - Method in interface smile.data.Tuple
-
Returns the field value as a primitive byte.
- getChild(K) - Method in class smile.nlp.Trie.Node
-
Returns the child with the key.
- getChild(K[], int) - Method in class smile.nlp.Trie.Node
-
Returns the value matching the key sequence.
- getClipNorm() - Method in class smile.base.mlp.MultilayerPerceptron
-
Returns the gradient clipping norm.
- getClipValue() - Method in class smile.base.mlp.MultilayerPerceptron
-
Returns the gradient clipping value.
- 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 the concept node which synset contains the keyword.
- getConcepts() - Method in class smile.taxonomy.Taxonomy
-
Returns all named concepts in the taxonomy.
- getCoordinateSpace() - Method in class smile.plot.swing.Base
-
Returns the coordinates.
- getDate(int) - Method in interface smile.data.Tuple
-
Returns the value at position i of date type as java.time.LocalDate.
- getDate(int, int) - Method in interface smile.data.DataFrame
-
Returns the value at position (i, j) of date type as java.time.LocalDate.
- getDate(int, String) - Method in interface smile.data.DataFrame
-
Returns the field value of date type as java.time.LocalDate.
- getDate(String) - Method in interface smile.data.Tuple
-
Returns the field value of date type as java.time.LocalDate.
- getDateTime(int) - Method in interface smile.data.Tuple
-
Returns the value at position i of date type as java.time.LocalDateTime.
- getDateTime(int, int) - Method in interface smile.data.DataFrame
-
Returns the value at position (i, j) as java.time.LocalDateTime.
- getDateTime(int, String) - Method in interface smile.data.DataFrame
-
Returns the field value as java.time.LocalDateTime.
- getDateTime(String) - Method in interface smile.data.Tuple
-
Returns the field value of date type as java.time.LocalDateTime.
- getDecimal(int) - Method in interface smile.data.Tuple
-
Returns the value at position i of decimal type as java.math.BigDecimal.
- getDecimal(int, int) - Method in interface smile.data.DataFrame
-
Returns the value at position (i, j) of decimal type as java.math.BigDecimal.
- getDecimal(int, String) - Method in interface smile.data.DataFrame
-
Returns the field value of decimal type as java.math.BigDecimal.
- getDecimal(String) - Method in interface smile.data.Tuple
-
Returns the field value of decimal type as java.math.BigDecimal.
- getDefault() - Static method in class smile.nlp.pos.HMMPOSTagger
-
Returns the default English POS tagger.
- getDegree(int) - Method in class smile.graph.AdjacencyList
- getDegree(int) - Method in class smile.graph.AdjacencyMatrix
- getDegree(int) - Method in interface smile.graph.Graph
-
Returns the degree of the specified vertex.
- 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) - Method in interface smile.data.Tuple
-
Returns the value at position i as a primitive double.
- getDouble(int) - Method in interface smile.data.vector.BaseVector
-
Returns the double value at position i.
- getDouble(int) - Method in interface smile.data.vector.BooleanVector
- getDouble(int) - Method in interface smile.data.vector.ByteVector
- getDouble(int) - Method in interface smile.data.vector.CharVector
- getDouble(int) - Method in interface smile.data.vector.FloatVector
- getDouble(int) - Method in interface smile.data.vector.IntVector
- getDouble(int) - Method in interface smile.data.vector.LongVector
- getDouble(int) - Method in interface smile.data.vector.ShortVector
- getDouble(int) - Method in interface smile.data.vector.StringVector
- getDouble(int) - Method in interface smile.data.vector.Vector
- getDouble(int...) - Method in class smile.deep.tensor.Tensor
-
Returns the double value of element at given index.
- 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(long...) - Method in class smile.deep.tensor.Tensor
-
Returns the double value of element at given index.
- getDouble(String) - Method in interface smile.data.Tuple
-
Returns the field value as a primitive double.
- getEdge(int, int) - Method in class smile.graph.AdjacencyList
- getEdge(int, int) - Method in class smile.graph.AdjacencyMatrix
- getEdge(int, int) - Method in interface smile.graph.Graph
-
Returns an edge connecting source vertex to target vertex if such edge exist in this graph.
- getEdges() - Method in class smile.graph.AdjacencyList
- getEdges() - Method in class smile.graph.AdjacencyMatrix
- getEdges() - Method in interface smile.graph.Graph
-
Returns the edges in this graph.
- getEdges(int) - Method in class smile.graph.AdjacencyList
- getEdges(int) - Method in class smile.graph.AdjacencyMatrix
- getEdges(int) - Method in interface smile.graph.Graph
-
Returns the edges from the specified vertex.
- getEdges(int, int) - Method in class smile.graph.AdjacencyList
- getEdges(int, int) - Method in class smile.graph.AdjacencyMatrix
- getEdges(int, int) - Method in interface smile.graph.Graph
-
Returns the edges connecting source vertex to target vertex if such vertices exist in this graph.
- getExtension(File) - Static method in class smile.swing.FileChooser
-
Returns the file name extension in lower case.
- getExtensionLevel() - Method in class smile.anomaly.IsolationForest
-
Returns the extension level.
- getFloat(int) - Method in interface smile.data.Tuple
-
Returns the value at position i as a primitive float.
- getFloat(int) - Method in interface smile.data.vector.BaseVector
-
Returns the float value at position i.
- getFloat(int) - Method in interface smile.data.vector.BooleanVector
- getFloat(int) - Method in interface smile.data.vector.ByteVector
- getFloat(int) - Method in interface smile.data.vector.CharVector
- getFloat(int) - Method in interface smile.data.vector.DoubleVector
- getFloat(int) - Method in interface smile.data.vector.IntVector
- getFloat(int) - Method in interface smile.data.vector.LongVector
- getFloat(int) - Method in interface smile.data.vector.ShortVector
- getFloat(int) - Method in interface smile.data.vector.StringVector
- getFloat(int) - Method in interface smile.data.vector.Vector
- getFloat(int...) - Method in class smile.deep.tensor.Tensor
-
Returns the float value of element at given index.
- 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(long...) - Method in class smile.deep.tensor.Tensor
-
Returns the float value of element at given index.
- getFloat(String) - Method in interface smile.data.Tuple
-
Returns the field value as a primitive float.
- 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 of an abbreviation.
- getGraphics() - Method in class smile.plot.swing.Graphics
-
Returns the Java2D graphics object.
- 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
-
Returns the standard normal distribution.
- 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) - Method in interface smile.data.Tuple
-
Returns the value at position i as a primitive int.
- getInt(int) - Method in interface smile.data.vector.BaseVector
-
Returns the integer value at position i.
- getInt(int) - Method in interface smile.data.vector.BooleanVector
- getInt(int) - Method in interface smile.data.vector.ByteVector
- getInt(int) - Method in interface smile.data.vector.CharVector
- getInt(int) - Method in interface smile.data.vector.DoubleVector
- getInt(int) - Method in interface smile.data.vector.FloatVector
- getInt(int) - Method in interface smile.data.vector.LongVector
- getInt(int) - Method in interface smile.data.vector.ShortVector
- getInt(int) - Method in interface smile.data.vector.StringVector
- getInt(int) - Method in interface smile.data.vector.Vector
- getInt(int...) - Method in class smile.deep.tensor.Tensor
-
Returns the int value of element at given index.
- 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(long...) - Method in class smile.deep.tensor.Tensor
-
Returns the int value of element at given index.
- getInt(String) - Method in interface smile.data.Tuple
-
Returns the field value as a primitive int.
- getKey() - Method in class smile.nlp.Trie.Node
-
Returns the key.
- 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.
- getLocalSearchSteps() - Method in class smile.gap.GeneticAlgorithm
-
Gets the number of iterations of local search in Lamarckian algorithm.
- getLong(int) - Method in interface smile.data.Tuple
-
Returns the value at position i as a primitive long.
- getLong(int) - Method in interface smile.data.vector.BaseVector
-
Returns the long value at position i.
- getLong(int) - Method in interface smile.data.vector.BooleanVector
- getLong(int) - Method in interface smile.data.vector.ByteVector
- getLong(int) - Method in interface smile.data.vector.CharVector
- getLong(int) - Method in interface smile.data.vector.DoubleVector
- getLong(int) - Method in interface smile.data.vector.FloatVector
- getLong(int) - Method in interface smile.data.vector.IntVector
- getLong(int) - Method in interface smile.data.vector.ShortVector
- getLong(int) - Method in interface smile.data.vector.StringVector
- getLong(int) - Method in interface smile.data.vector.Vector
- getLong(int...) - Method in class smile.deep.tensor.Tensor
-
Returns the long value of element at given index.
- 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(long...) - Method in class smile.deep.tensor.Tensor
-
Returns the long value of element at given index.
- getLong(String) - Method in interface smile.data.Tuple
-
Returns the field value as a primitive long.
- 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.
- getNumberFormat() - Method in class smile.swing.table.NumberCellRenderer
-
Returns the number format used for rendering.
- getNumVertices() - Method in class smile.graph.AdjacencyList
- getNumVertices() - Method in class smile.graph.AdjacencyMatrix
- getNumVertices() - Method in interface smile.graph.Graph
-
Returns the number of vertices.
- getObjectClass() - Method in class smile.data.type.ObjectType
-
Returns the class of objects.
- getOutdegree(int) - Method in class smile.graph.AdjacencyList
- getOutdegree(int) - Method in class smile.graph.AdjacencyMatrix
- getOutdegree(int) - Method in interface smile.graph.Graph
-
Returns the out-degree of the specified vertex.
- getOutputSize() - Method in class smile.base.mlp.Layer
-
Returns the dimension of output vector.
- 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 this node.
- getPathToRoot() - Method in class smile.taxonomy.Concept
-
Returns the path from this 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(double) - Method in class smile.feature.extraction.PCA
-
Returns the projection with top principal components that contain (more than) the given percentage of variance.
- getProjection(int) - Method in class smile.feature.extraction.PCA
-
Returns the projection with given number of principal components.
- 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 subclass 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, double[], int, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
- getrf(Layout, int, int, float[], int, int[]) - Method in interface smile.math.blas.LAPACK
-
Computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges.
- getrf(Layout, int, int, float[], int, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
- getrf(Layout, int, int, DoubleBuffer, int, IntBuffer) - Method in interface smile.math.blas.LAPACK
-
Computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges.
- getrf(Layout, int, int, DoubleBuffer, int, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- getrf(Layout, int, int, FloatBuffer, int, IntBuffer) - Method in interface smile.math.blas.LAPACK
-
Computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges.
- getrf(Layout, int, int, FloatBuffer, int, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- getrf(Layout, int, int, DoublePointer, int, IntPointer) - Method in interface smile.math.blas.LAPACK
-
Computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges.
- getrf(Layout, int, int, DoublePointer, int, IntPointer) - Method in class smile.math.blas.openblas.OpenBLAS
- getrf2(Layout, int, int, double[], int, int[]) - Method in interface smile.math.blas.LAPACK
-
Computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges.
- getrf2(Layout, int, int, double[], int, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
- getrf2(Layout, int, int, float[], int, int[]) - Method in interface smile.math.blas.LAPACK
-
Computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges.
- getrf2(Layout, int, int, float[], int, int[]) - Method in class smile.math.blas.openblas.OpenBLAS
- getrf2(Layout, int, int, DoubleBuffer, int, IntBuffer) - Method in interface smile.math.blas.LAPACK
-
Computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges.
- getrf2(Layout, int, int, DoubleBuffer, int, IntBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- getrf2(Layout, int, int, FloatBuffer, int, IntBuffer) - Method in interface smile.math.blas.LAPACK
-
Computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges.
- getrf2(Layout, int, int, FloatBuffer, int, IntBuffer) - Method in class smile.math.blas.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, double[], int, int[], double[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- getrs(Layout, Transpose, int, int, float[], int, int[], float[], int) - Method in interface smile.math.blas.LAPACK
-
Solves a system of linear equations
- getrs(Layout, Transpose, int, int, float[], int, int[], float[], int) - Method in class smile.math.blas.openblas.OpenBLAS
- getrs(Layout, Transpose, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in interface smile.math.blas.LAPACK
-
Solves a system of linear equations
- getrs(Layout, Transpose, int, int, DoubleBuffer, int, IntBuffer, DoubleBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- getrs(Layout, Transpose, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in interface smile.math.blas.LAPACK
-
Solves a system of linear equations
- getrs(Layout, Transpose, int, int, FloatBuffer, int, IntBuffer, FloatBuffer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- getrs(Layout, Transpose, int, int, DoublePointer, int, IntPointer, DoublePointer, int) - Method in interface smile.math.blas.LAPACK
-
Solves a system of linear equations
- getrs(Layout, Transpose, int, int, DoublePointer, int, IntPointer, DoublePointer, int) - Method in class smile.math.blas.openblas.OpenBLAS
- getScale(int) - Method in interface smile.data.Tuple
-
Returns the value at position i of NominalScale or OrdinalScale.
- getScale(int, int) - Method in interface smile.data.DataFrame
-
Returns the value at position (i, j) of NominalScale or OrdinalScale.
- getScale(int, String) - Method in interface smile.data.DataFrame
-
Returns the field value of NominalScale or OrdinalScale.
- getScale(String) - Method in interface smile.data.Tuple
-
Returns the field value of NominalScale or OrdinalScale.
- 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) - Method in interface smile.data.Tuple
-
Returns the value at position i as a primitive short.
- getShort(int) - Method in interface smile.data.vector.BaseVector
-
Returns the short value at position i.
- getShort(int) - Method in interface smile.data.vector.BooleanVector
- getShort(int) - Method in interface smile.data.vector.ByteVector
- getShort(int) - Method in interface smile.data.vector.CharVector
- getShort(int) - Method in interface smile.data.vector.DoubleVector
- getShort(int) - Method in interface smile.data.vector.FloatVector
- getShort(int) - Method in interface smile.data.vector.IntVector
- getShort(int) - Method in interface smile.data.vector.LongVector
- getShort(int) - Method in interface smile.data.vector.StringVector
- getShort(int) - Method in interface smile.data.vector.Vector
- getShort(int...) - Method in class smile.deep.tensor.Tensor
-
Returns the short value of element at given index.
- 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(long...) - Method in class smile.deep.tensor.Tensor
-
Returns the short value of element at given index.
- getShort(String) - Method in interface smile.data.Tuple
-
Returns the field value as a primitive short.
- 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) - Method in interface smile.data.Tuple
-
Returns the value at position i as a String object.
- getString(int, int) - Method in interface smile.data.DataFrame
-
Returns the value at position (i, j) as a String object.
- getString(int, String) - Method in interface smile.data.DataFrame
-
Returns the field value as a String object.
- getString(String) - Method in interface smile.data.Tuple
-
Returns the field value as a String object.
- getStroke() - Method in class smile.plot.swing.Graphics
-
Get the current stroke.
- getStruct(int) - Method in interface smile.data.Tuple
-
Returns the value at position i of struct type.
- getStruct(int, int) - Method in interface smile.data.DataFrame
-
Returns the value at position (i, j) of struct type.
- getStruct(int, String) - Method in interface smile.data.DataFrame
-
Returns the field value of struct type.
- getStruct(String) - Method in interface smile.data.Tuple
-
Returns the field value of struct type.
- getSymbolEmissionProbabilities() - Method in class smile.sequence.HMM
-
Returns the symbol emission probabilities.
- 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.
- getTestData(String...) - Static method in interface smile.util.Paths
-
Get the file path of a test sample dataset.
- getTestDataLines(String...) - Static method in interface smile.util.Paths
-
Returns the reader of a test data.
- getTestDataReader(String...) - Static method in interface smile.util.Paths
-
Returns the reader of a test data.
- getTime(int) - Method in interface smile.data.Tuple
-
Returns the value at position i of date type as java.time.LocalTime.
- getTime(int, int) - Method in interface smile.data.DataFrame
-
Returns the value at position (i, j) of date type as java.time.LocalTime.
- getTime(int, String) - Method in interface smile.data.DataFrame
-
Returns the field value of date type as java.time.LocalTime.
- getTime(String) - Method in interface smile.data.Tuple
-
Returns the field value of date type as java.time.LocalTime.
- 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 toolbar to control the plot.
- getToolbar() - Method in class smile.swing.table.PageTableModel
-
Returns a toolbar to control the plot.
- 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() - Method in class smile.nlp.Trie.Node
-
Returns the value.
- getValue(String) - Static method in enum class smile.nlp.pos.PennTreebankPOS
-
Returns an enum value from a string.
- 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.
- 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, double[], int, double[], int, double[], double[], double[]) - Method in class smile.math.blas.openblas.OpenBLAS
- ggglm(Layout, int, int, int, float[], int, float[], int, float[], float[], float[]) - Method in interface smile.math.blas.LAPACK
-
Solves a general Gauss-Markov linear model (GLM) problem.
- ggglm(Layout, int, int, int, float[], int, float[], int, float[], float[], float[]) - Method in class smile.math.blas.openblas.OpenBLAS
- ggglm(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer) - Method in interface smile.math.blas.LAPACK
-
Solves a general Gauss-Markov linear model (GLM) problem.
- ggglm(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- ggglm(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer) - Method in interface smile.math.blas.LAPACK
-
Solves a general Gauss-Markov linear model (GLM) problem.
- ggglm(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- gglse(Layout, int, int, int, double[], int, double[], int, double[], double[], double[]) - Method in interface smile.math.blas.LAPACK
-
Solves a linear equality-constrained least squares (LSE) problem.
- gglse(Layout, int, int, int, double[], int, double[], int, double[], double[], double[]) - Method in class smile.math.blas.openblas.OpenBLAS
- gglse(Layout, int, int, int, float[], int, float[], int, float[], float[], float[]) - Method in interface smile.math.blas.LAPACK
-
Solves a linear equality-constrained least squares (LSE) problem.
- gglse(Layout, int, int, int, float[], int, float[], int, float[], float[], float[]) - Method in class smile.math.blas.openblas.OpenBLAS
- gglse(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer) - Method in interface smile.math.blas.LAPACK
-
Solves a linear equality-constrained least squares (LSE) problem.
- gglse(Layout, int, int, int, DoubleBuffer, int, DoubleBuffer, int, DoubleBuffer, DoubleBuffer, DoubleBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- gglse(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer) - Method in interface smile.math.blas.LAPACK
-
Solves a linear equality-constrained least squares (LSE) problem.
- gglse(Layout, int, int, int, FloatBuffer, int, FloatBuffer, int, FloatBuffer, FloatBuffer, FloatBuffer) - Method in class smile.math.blas.openblas.OpenBLAS
- GHA - Class in smile.feature.extraction
-
Generalized Hebbian Algorithm.
- GHA(double[][], TimeFunction, String...) - Constructor for class smile.feature.extraction.GHA
-
Constructor.
- GHA(int, int, TimeFunction, String...) - Constructor for class smile.feature.extraction.GHA
-
Constructor.
- GINI - Enum constant in enum class smile.base.cart.SplitRule
-
Used by the CART algorithm, Gini impurity is a measure of how often a randomly chosen element from the set would be incorrectly labeled if it were randomly labeled according to the distribution of labels in the subset.
- GLM - Class in smile.glm
-
Generalized linear models.
- GLM(Formula, String[], Model, double[], double, double, double, double[], double[], double[][]) - Constructor for class smile.glm.GLM
-
Constructor.
- GloVe - Class in smile.nlp.embedding
-
Global Vectors for Word Representation.
- GloVe() - Constructor for class smile.nlp.embedding.GloVe
- GLU - Class in smile.deep.activation
-
Gated Linear Unit activation function.
- GLU() - Constructor for class smile.deep.activation.GLU
-
Constructor.
- 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.
- GOOGLE - Enum constant in enum class smile.nlp.dictionary.EnglishStopWords
-
The stop words list used by Google.
- gradient() - Method in class smile.base.mlp.Layer
-
Returns the output gradient vector.
- gradientLength(double) - Method in class smile.plot.vega.Legend
-
Sets the length in pixels of the primary axis of a color gradient.
- gradientOpacity(double) - Method in class smile.plot.vega.Legend
-
Sets the opacity of the color gradient.
- gradientStrokeColor(String) - Method in class smile.plot.vega.Legend
-
Sets the color of the gradient stroke.
- gradientStrokeWidth(double) - Method in class smile.plot.vega.Legend
-
Sets the width of the gradient stroke.
- gradientThickness(double) - Method in class smile.plot.vega.Legend
-
Sets the thickness in pixels of the color gradient.
- GradientTreeBoost - Class in smile.classification
-
Gradient boosting for classification.
- GradientTreeBoost - Class in smile.regression
-
Gradient boosting for regression.
- GradientTreeBoost(Formula, RegressionTree[][], double, double[]) - Constructor for class smile.classification.GradientTreeBoost
-
Constructor of multi-class.
- GradientTreeBoost(Formula, RegressionTree[][], double, double[], IntSet) - Constructor for class smile.classification.GradientTreeBoost
-
Constructor of multi-class.
- GradientTreeBoost(Formula, RegressionTree[], double, double, double[]) - Constructor for class smile.classification.GradientTreeBoost
-
Constructor of binary class.
- GradientTreeBoost(Formula, RegressionTree[], double, double, double[]) - Constructor for class smile.regression.GradientTreeBoost
-
Constructor.
- GradientTreeBoost(Formula, RegressionTree[], double, double, double[], IntSet) - Constructor for class smile.classification.GradientTreeBoost
-
Constructor of binary class.
- graph - Variable in class smile.manifold.IsoMap
-
The nearest neighbor graph.
- graph - Variable in class smile.manifold.LaplacianEigenmap
-
Nearest neighbor graph.
- graph - Variable in class smile.manifold.LLE
-
Nearest neighbor graph.
- graph - Variable in class smile.manifold.UMAP
-
The nearest neighbor graph.
- Graph - Interface in smile.graph
-
A graph is an abstract representation of a set of objects where some pairs of the objects are connected by links.
- Graph.Edge - Class in smile.graph
-
Graph edge.
- 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() - Method in class smile.hpo.Hyperparameters
-
Generates a stream of hyperparameters for grid search.
- grid(boolean) - Method in class smile.plot.vega.Axis
-
Sets if gridlines should be included as part of the axis.
- Grid - Class in smile.plot.swing
-
A 2D grid plot.
- Grid(double[][][], Color) - Constructor for class smile.plot.swing.Grid
-
Constructor.
- gridAlign(String) - Method in class smile.plot.vega.Legend
-
Sets the alignment to apply to symbol legends rows and columns.
- gridCap(String) - Method in class smile.plot.vega.Axis
-
Sets the stroke cap for gridlines' ending style.
- gridColor(String) - Method in class smile.plot.vega.Axis
-
Sets the color of gridlines.
- gridDash(double, double) - Method in class smile.plot.vega.Axis
-
Sets the alternating [stroke, space] lengths for dashed gridlines.
- gridOpacity(double) - Method in class smile.plot.vega.Axis
-
Sets the stroke opacity of grid.
- gridWidth(double) - Method in class smile.plot.vega.Axis
-
Sets the grid width.
- groupby(String...) - Method in class smile.plot.vega.ImputeTransform
-
Sets the data fields by which to group the values.
- groupby(String...) - Method in class smile.plot.vega.LoessTransform
-
Sets the data fields to group by.
- groupby(String...) - Method in class smile.plot.vega.PivotTransform
-
Sets the data fields to group by.
- groupby(String...) - Method in class smile.plot.vega.QuantileTransform
-
Sets the data fields to group by.
- groupby(String...) - Method in class smile.plot.vega.RegressionTransform
-
Sets the data fields to group by.
- groupby(String...) - Method in class smile.plot.vega.WindowTransform
-
Sets the data fields for partitioning the data objects into separate windows.
- GroupNormLayer - Class in smile.deep.layer
-
Group normalization.
- GroupNormLayer(int, int) - Constructor for class smile.deep.layer.GroupNormLayer
-
Constructor.
- GroupNormLayer(int, int, double, boolean) - Constructor for class smile.deep.layer.GroupNormLayer
-
Constructor.
- groups() - Method in record class smile.vision.layer.Conv2dNormActivation.Options
-
Returns the value of the
groups
record component. - 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.
- gt(double) - Method in class smile.deep.tensor.Tensor
-
Computes element-wise greater-than comparison.
- gt(int) - Method in class smile.deep.tensor.Tensor
-
Computes element-wise greater-than comparison.
- gt(Tensor) - Method in class smile.deep.tensor.Tensor
-
Computes element-wise greater-than comparison.
H
- H - Variable in class smile.neighbor.LSH
-
The size of hash table.
- H - Variable in class smile.neighbor.lsh.NeighborHashValueModel
-
The hash values of query object.
- HaarWavelet - Class in smile.wavelet
-
Haar wavelet.
- HaarWavelet() - Constructor for class smile.wavelet.HaarWavelet
-
Constructor.
- HadoopInput - Interface in smile.io
-
Static methods that return the InputStream/Reader of a HDFS/S3.
- HammingDistance - Class in smile.math.distance
-
In information theory, the Hamming distance between two strings of equal length is the number of positions for which the corresponding symbols are different.
- HammingDistance() - Constructor for class smile.math.distance.HammingDistance
-
Constructor.
- hardShrink(int, int) - Static method in interface smile.deep.layer.Layer
-
Returns a fully connected layer with hard shrink activation function.
- HardShrink - Class in smile.deep.activation
-
Hard Shrink activation function.
- HardShrink() - Constructor for class smile.deep.activation.HardShrink
-
Constructor.
- HardShrink(double) - Constructor for class smile.deep.activation.HardShrink
-
Constructor.
- harwell(Path) - Static method in class smile.math.matrix.fp32.SparseMatrix
-
Reads a sparse matrix from a Harwell-Boeing Exchange Format file.
- harwell(Path) - Static method in class smile.math.matrix.SparseMatrix
-
Reads a sparse matrix from a Harwell-Boeing Exchange Format file.
- hasEdge(int, int) - Method in class smile.graph.AdjacencyList
- hasEdge(int, int) - Method in class smile.graph.AdjacencyMatrix
- hasEdge(int, int) - Method in interface smile.graph.Graph
-
Returns true if and only if this graph contains an edge going from the source vertex to the target vertex.
- hash - Variable in class smile.neighbor.LSH
-
Hash functions.
- hash(double[]) - Method in class smile.neighbor.lsh.Hash
-
Apply hash functions on given vector x.
- hash(Hash, PrZ[]) - Method in class smile.neighbor.lsh.Probe
-
Returns the bucket number of the probe.
- hash(T) - Method in interface smile.hash.SimHash
-
Return the hash code.
- Hash - Class in smile.neighbor.lsh
-
The hash function for Euclidean spaces.
- Hash(int, int, double, int) - Constructor for class smile.neighbor.lsh.Hash
-
Constructor.
- hash128(ByteBuffer, int, int, long, long[]) - Static method in class smile.hash.MurmurHash3
-
128-bit MurmurHash3 for x64.
- hash32(byte[], int, int, int) - Static method in class smile.hash.MurmurHash3
-
32-bit MurmurHash3.
- hash32(String, int) - Static method in class smile.hash.MurmurHash3
-
32-bit MurmurHash3.
- hash32(ByteBuffer, int, int, int) - Static method in interface smile.hash.MurmurHash2
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32-bit MurmurHash.
- hash64(ByteBuffer, int, int, long) - Static method in interface smile.hash.MurmurHash2
-
64-bit MurmurHash.
- hashCode() - Method in class smile.association.AssociationRule
- hashCode() - Method in class smile.association.ItemSet
- hashCode() - Method in record class smile.data.SampleInstance
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Returns a hash code value for this object.
- hashCode() - Method in record class smile.deep.SampleBatch
-
Returns a hash code value for this object.
- hashCode() - Method in record class smile.llm.llama.Message