Serialized Form

Package smile.anomaly

Class smile.anomaly.IsolationForest

class IsolationForest extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

c

double c

The normalizing factor.

extensionLevel

int extensionLevel

The extension level, i.e. how many dimension are specified in the random slope. With 0 extension level, it is coincident with the standard Isolation Forest.

trees

IsolationTree[] trees

Forest of isolation trees.

Class smile.anomaly.IsolationTree

class IsolationTree extends Object implements Serializable

Serialized Fields

root

smile.anomaly.IsolationTree.Node root

Tree root node.

Class smile.anomaly.SVM

class SVM extends KernelMachine<T> implements Serializable

Package smile.base.cart

Class smile.base.cart.CART

class CART extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

formula

Formula formula

The model formula.

importance

double[] importance

Variable importance. Every time a split of a node is made on variable the (GINI, information gain, etc.) impurity criterion for the two descendent nodes is less than the parent node. Adding up the decreases for each individual variable over the tree gives a simple measure of variable importance.

maxDepth

int maxDepth

The maximum depth of the tree.

maxNodes

int maxNodes

The maximum number of leaf nodes in the tree.

mtry

int mtry

The number of input variables to be used to determine the decision at a node of the tree.

nodeSize

int nodeSize

The number of instances in a node below which the tree will not split, setting nodeSize = 5 generally gives good results.

response

StructField response

The schema of response variable.

root

Node root

The root of decision tree.

schema

StructType schema

The schema of predictors.

Class smile.base.cart.DecisionNode

class DecisionNode extends LeafNode implements Serializable

serialVersionUID:

2L

Serialized Fields

count

int[] count

The number of node samples in each class.

output

int output

The predicted output.

Class smile.base.cart.InternalNode

class InternalNode extends Object implements Serializable

Serialized Fields

deviance

double deviance

The deviance of node.

falseChild

Node falseChild

Children node.

feature

int feature

The split feature for this node.

score

double score

Reduction in impurity compared to parent.

size

int size

The number of samples in the node.

trueChild

Node trueChild

Children node.

Class smile.base.cart.LeafNode

class LeafNode extends Object implements Serializable

Serialized Fields

size

int size

The number of samples in the node.

Class smile.base.cart.NominalNode

class NominalNode extends InternalNode implements Serializable

serialVersionUID:

2L

Serialized Fields

value

int value

The split value.

Class smile.base.cart.OrdinalNode

class OrdinalNode extends InternalNode implements Serializable

serialVersionUID:

2L

Serialized Fields

value

double value

The split value.

Class smile.base.cart.RegressionNode

class RegressionNode extends LeafNode implements Serializable

serialVersionUID:

2L

Serialized Fields

mean

double mean

The mean of response variable.

output

double output

The predicted output. In standard regression tree, this is same as the mean. However, in gradient tree boosting, this may be different.

rss

double rss

The residual sum of squares.

Package smile.base.mlp

Class smile.base.mlp.HiddenLayer

class HiddenLayer extends Layer implements Serializable

serialVersionUID:

2L

Serialized Fields

activation

ActivationFunction activation

The activation function.

Class smile.base.mlp.InputLayer

class InputLayer extends Layer implements Serializable

serialVersionUID:

2L

Serialization Methods

readObject

private void readObject(ObjectInputStream in)
                 throws IOException,
ClassNotFoundException

Initializes the workspace when deserializing the object.

Parameters:

in - the input stream.

Throws:

IOException - when fails to read the stream.

ClassNotFoundException - when fails to load the class.

Class smile.base.mlp.Layer

class Layer extends Object implements Serializable

serialVersionUID:

2L

Serialization Methods

readObject

private void readObject(ObjectInputStream in)
                 throws IOException,
ClassNotFoundException

Initializes the workspace when deserializing the object.

Parameters:

in - the input stream.

Throws:

IOException - when fails to read the stream.

ClassNotFoundException - when fails to load the class.

Serialized Fields

bias

double[] bias

The bias.

dropout

double dropout

The dropout rate. Dropout randomly sets input units to 0 with this rate at each step during training time, which helps prevent overfitting.

n

int n

The number of neurons in this layer

p

int p

The number of input variables.

weight

Matrix weight

The affine transformation matrix.

Class smile.base.mlp.MultilayerPerceptron

class MultilayerPerceptron extends Object implements Serializable

serialVersionUID:

2L

Serialization Methods

readObject

private void readObject(ObjectInputStream in)
                 throws IOException,
ClassNotFoundException

Initializes the workspace when deserializing the object.

Parameters:

in - the input stream.

Throws:

IOException - when fails to read the stream.

ClassNotFoundException - when fails to load the class.

Serialized Fields

clipNorm

double clipNorm

The gradient clipping norm.

clipValue

double clipValue

The gradient clipping value.

epsilon

double epsilon

A small constant for numerical stability in RMSProp.

lambda

double lambda

The L2 regularization factor, which is also the weight decay factor.

learningRate

TimeFunction learningRate

The learning rate.

momentum

TimeFunction momentum

The momentum factor.

net

Layer[] net

The input and hidden layers.

output

OutputLayer output

The output layer.

p

int p

The dimensionality of input data.

rho

double rho

The discounting factor for the history/coming gradient in RMSProp.

t

int t

The training iterations.

Class smile.base.mlp.OutputLayer

class OutputLayer extends Layer implements Serializable

serialVersionUID:

2L

Serialized Fields

activation

OutputFunction activation

The output activation function.

cost

Cost cost

The cost function.

Package smile.base.rbf

Class smile.base.rbf.RBF

class RBF extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

center

T center

The center of neuron.

distance

Metric<T> distance

Metric distance.

rbf

RadialBasisFunction rbf

Radial basis function.

Package smile.base.svm

Class smile.base.svm.KernelMachine

class KernelMachine extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

b

double b

The intercept.

kernel

MercerKernel<T> kernel

The kernel function.

vectors

T[] vectors

The support vectors (or control points).

w

double[] w

The linear weights.

Class smile.base.svm.LASVM

class LASVM extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

b

double b

Threshold of decision function.

Cn

double Cn

The soft margin penalty parameter for negative samples.

Cp

double Cp

The soft margin penalty parameter for positive samples.

gmax

double gmax

The gradient of most violating pair.

gmin

double gmin

The gradient of most violating pair.

K

double[][] K

The kernel matrix.

kernel

MercerKernel<T> kernel

The kernel function.

minmaxflag

boolean minmaxflag

True if minmax() is already called after update.

svmax

SupportVector<T> svmax

The most violating pair.

svmin

SupportVector<T> svmin

The most violating pair.

tol

double tol

The tolerance of convergence test.

vectors

ArrayList<SupportVector<T>> vectors

Support vectors.

x

T[] x

The training samples.

Class smile.base.svm.LinearKernelMachine

class LinearKernelMachine extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

b

double b

The intercept.

w

double[] w

The weight vector.

Class smile.base.svm.SupportVector

class SupportVector extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

alpha

double alpha

Lagrangian multiplier of support vector.

cmax

double cmax

Upper bound of alpha.

cmin

double cmin

Lower bound of alpha.

g

double g

Gradient y - Kα.

i

int i

The index of support vector in training samples.

k

double k

Kernel value k(x, x)

x

T x

Support vector.

Package smile.classification

Class smile.classification.AbstractClassifier

class AbstractClassifier extends Object implements Serializable

Serialized Fields

classes

IntSet classes

The class labels.

Class smile.classification.AdaBoost

class AdaBoost extends AbstractClassifier<Tuple> implements Serializable

serialVersionUID:

2L

Serialized Fields

alpha

double[] alpha

The weight of each decision tree.

error

double[] error

The weighted error of each decision tree during training.

formula

Formula formula

The model formula.

importance

double[] importance

Variable importance. Every time a split of a node is made on variable the (GINI, information gain, etc.) impurity criterion for the two descendent nodes is less than the parent node. Adding up the decreases for each individual variable over all trees in the forest gives a fast variable importance that is often very consistent with the permutation importance measure.

k

int k

The number of classes.

trees

DecisionTree[] trees

Forest of decision trees.

Class smile.classification.ClassLabels

class ClassLabels extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

classes

IntSet classes

The class labels.

k

int k

The number of classes.

ni

int[] ni

The number of samples per classes.

priori

double[] priori

The estimated priori probabilities.

y

int[] y

The sample class id in [0, k).

Class smile.classification.DecisionTree

class DecisionTree extends CART implements Serializable

serialVersionUID:

2L

Serialized Fields

classes

IntSet classes

The class labels.

k

int k

The number of classes.

rule

SplitRule rule

The splitting rule.

Class smile.classification.DiscreteNaiveBayes

class DiscreteNaiveBayes extends AbstractClassifier<int[]> implements Serializable

serialVersionUID:

2L

Serialized Fields

fixedPriori

boolean fixedPriori

If true, don't update the priori during learning.

k

int k

The number of classes.

logcondprob

double[][] logcondprob

The log conditional probabilities for document classification.

model

DiscreteNaiveBayes.Model model

The generation model of naive Bayes.

n

int n

The total number of documents.

nc

int[] nc

The number of documents in each class.

nt

int[] nt

The number of terms per class.

ntc

int[][] ntc

The number of each term per class.

p

int p

The number of independent variables.

priori

double[] priori

The priori probability of each class.

sigma

double sigma

Amount of add-k smoothing of evidence. By default, we use add-one or Laplace smoothing, which simply adds one to each count to eliminate zeros. Add-one smoothing can be interpreted as a uniform prior (each term occurs once for each class) that is then updated as evidence from the training data comes in.

Class smile.classification.FLD

class FLD extends AbstractClassifier<double[]> implements Serializable

serialVersionUID:

2L

Serialized Fields

k

int k

The number of classes.

mean

double[] mean

Projected mean vector.

mu

double[][] mu

Projected class mean vectors.

p

int p

The dimensionality of data.

scaling

Matrix scaling

Project matrix.

Class smile.classification.GradientTreeBoost

class GradientTreeBoost extends AbstractClassifier<Tuple> implements Serializable

serialVersionUID:

2L

Serialized Fields

b

double b

The intercept for binary classification.

forest

RegressionTree[][] forest

Forest of regression trees for multi-class classification.

formula

Formula formula

The model formula.

importance

double[] importance

Variable importance. Every time a split of a node is made on variable the impurity criterion for the two descendent nodes is less than the parent node. Adding up the decreases for each individual variable over all trees in the forest gives a simple variable importance.

k

int k

The number of classes.

shrinkage

double shrinkage

The shrinkage parameter in (0, 1] controls the learning rate of procedure.

trees

RegressionTree[] trees

Forest of regression trees for binary classification.

Class smile.classification.IsotonicRegressionScaling

class IsotonicRegressionScaling extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

buckets

double[] buckets

The step-wise buckets of function values in ascending order.

prob

double[] prob

The probability of instances falling into the corresponding buckets.

Class smile.classification.KNN

class KNN extends AbstractClassifier<T> implements Serializable

serialVersionUID:

2L

Serialized Fields

k

int k

The number of neighbors for decision.

knn

KNNSearch<T,T> knn

The data structure for nearest neighbor search.

y

int[] y

The labels of training sample.

Class smile.classification.LDA

class LDA extends AbstractClassifier<double[]> implements Serializable

serialVersionUID:

2L

Serialized Fields

eigen

double[] eigen

The eigen values of common variance matrix.

k

int k

The number of classes.

logppriori

double[] logppriori

The constant term of discriminant function of each class.

mu

double[][] mu

THe mean vectors of each class.

p

int p

The dimensionality of data.

priori

double[] priori

The a priori probabilities of each class.

scaling

Matrix scaling

The eigen vectors of common covariance matrix, which transforms observations to discriminant functions, normalized so that common covariance matrix is spherical.

Class smile.classification.LogisticRegression

class LogisticRegression extends AbstractClassifier<double[]> implements Serializable

serialVersionUID:

2L

Serialized Fields

eta

double eta

learning rate for stochastic gradient descent.

k

int k

The number of classes.

L

double L

The log-likelihood of learned model.

lambda

double lambda

Regularization factor.

p

int p

The dimension of input space.

Class smile.classification.LogisticRegression.Binomial

class Binomial extends LogisticRegression implements Serializable

Serialized Fields

w

double[] w

The linear weights.

Class smile.classification.LogisticRegression.Multinomial

class Multinomial extends LogisticRegression implements Serializable

Serialized Fields

w

double[][] w

The linear weights.

Class smile.classification.Maxent

class Maxent extends AbstractClassifier<int[]> implements Serializable

serialVersionUID:

2L

Serialized Fields

eta

double eta

learning rate for stochastic gradient descent.

k

int k

The number of classes.

L

double L

The log-likelihood of learned model.

lambda

double lambda

Regularization factor.

p

int p

The dimension of input space.

Class smile.classification.Maxent.Binomial

class Binomial extends Maxent implements Serializable

Serialized Fields

w

double[] w

The linear weights.

Class smile.classification.Maxent.Multinomial

class Multinomial extends Maxent implements Serializable

Serialized Fields

w

double[][] w

The linear weights.

Class smile.classification.MLP

class MLP extends MultilayerPerceptron implements Serializable

serialVersionUID:

2L

Serialized Fields

classes

IntSet classes

The class label encoder.

k

int k

The number of classes.

Class smile.classification.NaiveBayes

class NaiveBayes extends AbstractClassifier<double[]> implements Serializable

serialVersionUID:

2L

Serialized Fields

k

int k

The number of classes.

p

int p

The number of independent variables.

priori

double[] priori

The priori probability of each class.

prob

Distribution[][] prob

The conditional distribution for general purpose naive Bayes classifier.

Class smile.classification.OneVersusOne

class OneVersusOne extends AbstractClassifier<T> implements Serializable

serialVersionUID:

2L

Serialized Fields

classifiers

Classifier<T>[][] classifiers

The binary classifier.

k

int k

The number of classes.

platt

PlattScaling[][] platt

The binary classifier.

Class smile.classification.OneVersusRest

class OneVersusRest extends AbstractClassifier<T> implements Serializable

serialVersionUID:

2L

Serialized Fields

classifiers

Classifier<T>[] classifiers

The binary classifier.

k

int k

The number of classes.

platt

PlattScaling[] platt

The probability estimation by Platt scaling.

Class smile.classification.PlattScaling

class PlattScaling extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

alpha

double alpha

The scaling parameter.

beta

double beta

The scaling parameter.

Class smile.classification.QDA

class QDA extends AbstractClassifier<double[]> implements Serializable

serialVersionUID:

2L

Serialized Fields

eigen

double[][] eigen

Eigen values of each covariance matrix.

k

int k

The number of classes.

logppriori

double[] logppriori

Constant term of discriminant function of each class.

mu

double[][] mu

Mean vectors of each class.

p

int p

The dimensionality of data.

priori

double[] priori

A priori probabilities of each class.

scaling

Matrix[] scaling

Eigen vectors of each covariance matrix, which transforms observations to discriminant functions, normalized so that within groups covariance matrix is spherical.

Class smile.classification.RandomForest

class RandomForest extends AbstractClassifier<Tuple> implements Serializable

serialVersionUID:

2L

Serialized Fields

formula

Formula formula

The model formula.

importance

double[] importance

Variable importance. Every time a split of a node is made on variable the (GINI, information gain, etc.) impurity criterion for the two descendent nodes is less than the parent node. Adding up the decreases for each individual variable over all trees in the forest gives a fast variable importance that is often very consistent with the permutation importance measure.

k

int k

The number of classes.

metrics

ClassificationMetrics metrics

The overall out-of-bag metrics, which are quite accurate given that enough trees have been grown (otherwise the OOB error estimate can bias upward).

models

RandomForest.Model[] models

Forest of decision trees. The second value is the accuracy of tree on the OOB samples, which can be used a weight when aggregating tree votes.

Class smile.classification.RandomForest.Model

class Model extends Object implements Serializable

Serialized Fields

metrics

ClassificationMetrics metrics

The performance metrics on out-of-bag samples.

tree

DecisionTree tree

The decision tree.

weight

double weight

The weight of tree, which can be used when aggregating tree votes.

Class smile.classification.RBFNetwork

class RBFNetwork extends AbstractClassifier<T> implements Serializable

serialVersionUID:

2L

Serialized Fields

k

int k

The number of classes.

normalized

boolean normalized

True to fit a normalized RBF network.

rbf

RBF<T>[] rbf

The radial basis function.

w

Matrix w

The linear weights.

Class smile.classification.RDA

class RDA extends QDA implements Serializable

serialVersionUID:

2L

Class smile.classification.SparseLogisticRegression

class SparseLogisticRegression extends AbstractClassifier<SparseArray> implements Serializable

serialVersionUID:

2L

Serialized Fields

eta

double eta

learning rate for stochastic gradient descent.

k

int k

The number of classes.

L

double L

The log-likelihood of learned model.

lambda

double lambda

Regularization factor.

p

int p

The dimension of input space.

Class smile.classification.SparseLogisticRegression.Binomial

class Binomial extends SparseLogisticRegression implements Serializable

Serialized Fields

w

double[] w

The linear weights.

Class smile.classification.SparseLogisticRegression.Multinomial

class Multinomial extends SparseLogisticRegression implements Serializable

Serialized Fields

w

double[][] w

The linear weights.

Class smile.classification.SVM

class SVM extends KernelMachine<T> implements Serializable

Package smile.clustering

Class smile.clustering.CentroidClustering

class CentroidClustering extends PartitionClustering implements Serializable

serialVersionUID:

2L

Serialized Fields

centroids

T[] centroids

The centroids of each cluster.

distortion

double distortion

The total distortion.

Class smile.clustering.CLARANS

class CLARANS extends CentroidClustering<T,T> implements Serializable

serialVersionUID:

2L

Serialized Fields

distance

Distance<T> distance

The lambda of distance measure.

Class smile.clustering.DBSCAN

class DBSCAN extends PartitionClustering implements Serializable

serialVersionUID:

2L

Serialized Fields

core

boolean[] core

The flag if the point is a core point (at least minPts points are within radius).

minPts

double minPts

The minimum number of points required to form a cluster

nns

RNNSearch<T,T> nns

Data structure for neighborhood search.

radius

double radius

The neighborhood radius.

Class smile.clustering.DENCLUE

class DENCLUE extends PartitionClustering implements Serializable

serialVersionUID:

2L

Serialized Fields

attractors

double[][] attractors

The density attractor of each observation.

radius

double[] radius

The radius of density attractor.

samples

double[][] samples

The samples decided by k-means used in the iterations of hill climbing.

sigma

double sigma

The smooth parameter in the Gaussian kernel.

tol

double tol

The tolerance of hill-climbing procedure.

Class smile.clustering.DeterministicAnnealing

class DeterministicAnnealing extends CentroidClustering<double[],double[]> implements Serializable

serialVersionUID:

2L

Class smile.clustering.GMeans

class GMeans extends CentroidClustering<double[],double[]> implements Serializable

serialVersionUID:

2L

Class smile.clustering.HierarchicalClustering

class HierarchicalClustering extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

height

double[] height

A set of n-1 non-decreasing real values, which are the clustering height, i.e., the value of the criterion associated with the clustering method for the particular agglomeration.

merge

int[][] merge

An n-1 by 2 matrix of which row i describes the merging of clusters at step i of the clustering. If an element j in the row is less than n, then observation j was merged at this stage. If j >= n then the merge was with the cluster formed at the (earlier) stage j-n of the algorithm.

Class smile.clustering.KMeans

class KMeans extends CentroidClustering<double[],double[]> implements Serializable

serialVersionUID:

2L

Class smile.clustering.KModes

class KModes extends CentroidClustering<int[],int[]> implements Serializable

serialVersionUID:

2L

Class smile.clustering.MEC

class MEC extends PartitionClustering implements Serializable

serialVersionUID:

2L

Serialized Fields

entropy

double entropy

The conditional entropy as the objective function.

nns

RNNSearch<T,T> nns

The neighborhood search data structure.

radius

double radius

The range of neighborhood.

Class smile.clustering.PartitionClustering

class PartitionClustering extends Object implements Serializable

Serialized Fields

k

int k

The number of clusters.

size

int[] size

The number of observations in each cluster.

y

int[] y

The cluster labels of data.

Class smile.clustering.SIB

class SIB extends CentroidClustering<double[],SparseArray> implements Serializable

serialVersionUID:

2L

Class smile.clustering.SpectralClustering

class SpectralClustering extends PartitionClustering implements Serializable

serialVersionUID:

2L

Serialized Fields

distortion

double distortion

The distortion in feature space.

Class smile.clustering.XMeans

class XMeans extends CentroidClustering<double[],double[]> implements Serializable

serialVersionUID:

2L

Package smile.data

Class smile.data.AbstractTuple

class AbstractTuple extends Object implements Serializable

Package smile.data.formula

Class smile.data.formula.Abs

class Abs extends AbstractFunction implements Serializable

Class smile.data.formula.AbstractBiFunction

class AbstractBiFunction extends Object implements Serializable

Serialized Fields

name

String name

The name of function.

x

Term x

The first parameter of function.

y

Term y

The second parameter of function.

Class smile.data.formula.AbstractFunction

class AbstractFunction extends Object implements Serializable

Serialized Fields

name

String name

The name of function.

x

Term x

The operand.

Class smile.data.formula.Add

class Add extends Operator implements Serializable

Class smile.data.formula.Constant

class Constant extends Object implements Serializable

Class smile.data.formula.Date

class Date extends Object implements Serializable

Serialized Fields

features

DateFeature[] features

The features to extract.

name

String name

The name of variable.

Class smile.data.formula.Delete

class Delete extends Object implements Serializable

Serialized Fields

x

Term x

The term to delete.

Class smile.data.formula.Div

class Div extends Operator implements Serializable

Class smile.data.formula.Dot

class Dot extends Object implements Serializable

Class smile.data.formula.DoubleFunction

class DoubleFunction extends AbstractFunction implements Serializable

Serialized Fields

lambda

Function lambda

The function on a double.

Class smile.data.formula.FactorCrossing

class FactorCrossing extends Object implements Serializable

Serialized Fields

factors

List<String> factors

The children factors.

order

int order

The order of interactions.

terms

List<Term> terms

The terms.

Class smile.data.formula.FactorInteraction

class FactorInteraction extends Object implements Serializable

Serialized Fields

factors

List<String> factors

The factors of interaction.

Class smile.data.formula.Formula

class Formula extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

predictors

Term[] predictors

The right-hand side of formula.

response

Term response

The left-hand side of formula.

Record Class smile.data.formula.Intercept

class Intercept extends Record implements Serializable

Serialized Fields

bias

boolean bias

The field for the bias record component.

Class smile.data.formula.IntFunction

class IntFunction extends AbstractFunction implements Serializable

Serialized Fields

lambda

IntFunction lambda

The function on an integer.

Class smile.data.formula.Mul

class Mul extends Operator implements Serializable

Class smile.data.formula.Operator

class Operator extends AbstractBiFunction implements Serializable

Class smile.data.formula.Round

class Round extends AbstractFunction implements Serializable

Class smile.data.formula.Sub

class Sub extends Operator implements Serializable

Record Class smile.data.formula.Variable

class Variable extends Record implements Serializable

Serialized Fields

name

String name

The field for the name record component.

Package smile.data.measure

Class smile.data.measure.CategoricalMeasure

class CategoricalMeasure extends Object implements Serializable

Serialized Fields

factor

boolean factor

The flag if the values are of standard factor [0, 1, 2, ..., k).

level2value

Map<String,Number> level2value

Map a string to an integer level.

levels

String[] levels

The levels of measurement.

value2level

Map<Number,String> value2level

Map an integer to string.

values

int[] values

The valid values.

Class smile.data.measure.IntervalScale

class IntervalScale extends NumericalMeasure implements Serializable

Class smile.data.measure.NominalScale

class NominalScale extends CategoricalMeasure implements Serializable

Class smile.data.measure.NumericalMeasure

class NumericalMeasure extends Object implements Serializable

Serialized Fields

format

NumberFormat format

For formatting and parsing numbers.

Class smile.data.measure.OrdinalScale

class OrdinalScale extends CategoricalMeasure implements Serializable

Class smile.data.measure.RatioScale

class RatioScale extends NumericalMeasure implements Serializable

Package smile.data.transform

Class smile.data.transform.ColumnTransform

class ColumnTransform extends Object implements Serializable

Serialized Fields

name

String name

The name of transformation.

transforms

Map<String,Function> transforms

The map of column name to transform lambda.

Class smile.data.transform.InvertibleColumnTransform

class InvertibleColumnTransform extends ColumnTransform implements Serializable

Serialized Fields

inverses

Map<String,Function> inverses

The map of column name to inverse transform lambda.

Package smile.data.type

Class smile.data.type.ArrayType

class ArrayType extends Object implements Serializable

Serialized Fields

type

DataType type

Element data type.

Class smile.data.type.BooleanType

class BooleanType extends Object implements Serializable

Class smile.data.type.ByteType

class ByteType extends Object implements Serializable

Class smile.data.type.CharType

class CharType extends Object implements Serializable

Class smile.data.type.DateTimeType

class DateTimeType extends Object implements Serializable

Serialized Fields

formatter

DateTimeFormatter formatter

Date formatter.

pattern

String pattern

Date format pattern.

Class smile.data.type.DateType

class DateType extends Object implements Serializable

Serialized Fields

formatter

DateTimeFormatter formatter

Date formatter.

pattern

String pattern

Date format pattern.

Class smile.data.type.DecimalType

class DecimalType extends Object implements Serializable

Class smile.data.type.DoubleType

class DoubleType extends Object implements Serializable

Class smile.data.type.FloatType

class FloatType extends Object implements Serializable

Class smile.data.type.IntegerType

class IntegerType extends Object implements Serializable

Class smile.data.type.LongType

class LongType extends Object implements Serializable

Class smile.data.type.ObjectType

class ObjectType extends Object implements Serializable

Serialized Fields

clazz

Class<?> clazz

Object Class.

Class smile.data.type.ShortType

class ShortType extends Object implements Serializable

Class smile.data.type.StringType

class StringType extends Object implements Serializable

Class smile.data.type.StructField

class StructField extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

measure

Measure measure

Optional levels of measurements.

name

String name

Field name.

type

DataType type

Field data type.

Class smile.data.type.StructType

class StructType extends Object implements Serializable

Serialized Fields

fields

StructField[] fields

Struct fields.

index

Map<String,Integer> index

Field name to index map.

Class smile.data.type.TimeType

class TimeType extends Object implements Serializable

Serialized Fields

formatter

DateTimeFormatter formatter

Date formatter.

pattern

String pattern

Date format pattern.

Package smile.data.vector

Package smile.deep.activation

Class smile.deep.activation.ActivationFunction

class ActivationFunction extends Object implements Serializable

Serialized Fields

inplace

boolean inplace

True if the operation executes in-place.

module

org.bytedeco.pytorch.Module module

The module of activation function.

name

String name

The function name.

Class smile.deep.activation.GELU

class GELU extends ActivationFunction implements Serializable

Class smile.deep.activation.GLU

class GLU extends ActivationFunction implements Serializable

Class smile.deep.activation.HardShrink

class HardShrink extends ActivationFunction implements Serializable

Serialized Fields

lambda

org.bytedeco.pytorch.Scalar lambda

The lambda value in the formulation.

Class smile.deep.activation.LeakyReLU

class LeakyReLU extends ActivationFunction implements Serializable

Serialized Fields

negativeSlope

org.bytedeco.pytorch.Scalar negativeSlope

Controls the angle of the negative slope.

Class smile.deep.activation.LogSigmoid

class LogSigmoid extends ActivationFunction implements Serializable

Class smile.deep.activation.LogSoftmax

class LogSoftmax extends ActivationFunction implements Serializable

Class smile.deep.activation.ReLU

class ReLU extends ActivationFunction implements Serializable

Class smile.deep.activation.Sigmoid

class Sigmoid extends ActivationFunction implements Serializable

Class smile.deep.activation.SiLU

class SiLU extends ActivationFunction implements Serializable

Class smile.deep.activation.Softmax

class Softmax extends ActivationFunction implements Serializable

Class smile.deep.activation.SoftShrink

class SoftShrink extends ActivationFunction implements Serializable

Serialized Fields

lambda

org.bytedeco.pytorch.Scalar lambda

The lambda value in the formulation.

Class smile.deep.activation.Tanh

class Tanh extends ActivationFunction implements Serializable

Class smile.deep.activation.TanhShrink

class TanhShrink extends ActivationFunction implements Serializable

Package smile.feature.extraction

Class smile.feature.extraction.BagOfWords

class BagOfWords extends Object implements Serializable

Serialized Fields

binary

boolean binary

True to check if feature words appear in a document instead of their frequencies.

columns

String[] columns

The input text fields.

featureIndex

Map<String,Integer> featureIndex

The mapping from feature words to indices.

schema

StructType schema

The schema of output space.

tokenizer

Function<String,String[]> tokenizer

The tokenizer of text, which may include additional processing such as filtering stop word, converting to lowercase, stemming, etc.

words

String[] words

The feature words.

Class smile.feature.extraction.GHA

class GHA extends Projection implements Serializable

serialVersionUID:

2L

Serialized Fields

n

int n

The dimension of input space.

p

int p

The dimension of feature space.

r

TimeFunction r

The learning rate;

t

int t

The training iterations.

wy

double[] wy

Workspace for W' * y.

y

double[] y

Workspace for W * x.

Class smile.feature.extraction.KernelPCA

class KernelPCA extends Projection implements Serializable

serialVersionUID:

2L

Serialized Fields

kpca

KPCA<double[]> kpca

Kernel PCA.

Class smile.feature.extraction.PCA

class PCA extends Projection implements Serializable

serialVersionUID:

2L

Serialized Fields

cumulativeProportion

double[] cumulativeProportion

The cumulative proportion of variance contained in principal components.

eigvalues

double[] eigvalues

Eigenvalues of principal components.

eigvectors

Matrix eigvectors

The matrix of variable loadings, whose columns contain the eigenvectors.

mu

double[] mu

The sample mean.

pmu

double[] pmu

The projected sample mean.

proportion

double[] proportion

The proportion of variance contained in each principal component.

Class smile.feature.extraction.ProbabilisticPCA

class ProbabilisticPCA extends Projection implements Serializable

serialVersionUID:

2L

Serialized Fields

loading

Matrix loading

The loading matrix.

mu

double[] mu

The sample mean.

noise

double noise

The variance of noise part.

pmu

double[] pmu

The projected sample mean.

Class smile.feature.extraction.Projection

class Projection extends Object implements Serializable

Serialized Fields

columns

String[] columns

The fields of input space.

projection

Matrix projection

The projection matrix. The dimension reduced data can be obtained by y = W * x.

schema

StructType schema

The schema of output space.

Class smile.feature.extraction.RandomProjection

class RandomProjection extends Projection implements Serializable

serialVersionUID:

2L

Package smile.feature.imputation

Class smile.feature.imputation.KMedoidsImputer

class KMedoidsImputer extends Object implements Serializable

Serialized Fields

kmedoids

CLARANS<Tuple> kmedoids

The K-Medoids clustering.

Class smile.feature.imputation.KNNImputer

class KNNImputer extends Object implements Serializable

Serialized Fields

k

int k

The number of nearest neighbors used for imputation.

knn

KNNSearch<Tuple,Tuple> knn

K-nearest neighbor search algorithm.

Class smile.feature.imputation.SimpleImputer

class SimpleImputer extends Object implements Serializable

Serialized Fields

values

Map<String,Object> values

The map of column name to the constant value.

Package smile.feature.transform

Class smile.feature.transform.Normalizer

class Normalizer extends Object implements Serializable

Serialized Fields

columns

Set<String> columns

The columns to transform.

norm

Normalizer.Norm norm

The vector norm.

Package smile.glm

Class smile.glm.GLM

class GLM extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

beta

double[] beta

The linear weights.

deviance

double deviance

The deviance = 2 * (LogLikelihood(Saturated Model) - LogLikelihood(Proposed Model)).

devianceResiduals

double[] devianceResiduals

The deviance residuals.

df

int df

The degrees of freedom of the residual deviance.

formula

Formula formula

The symbolic description of the model to be fitted.

logLikelihood

double logLikelihood

Log-likelihood.

model

Model model

The model specifications (link function, deviance, etc.).

mu

double[] mu

The fitted mean values.

nullDeviance

double nullDeviance

The null deviance = 2 * (LogLikelihood(Saturated Model) - LogLikelihood(Null Model)).

The saturated model, also referred to as the full model or maximal model, allows a different mean response for each group of replicates. One can think of the saturated model as having the most general possible mean structure for the data since the means are unconstrained.

The null model assumes that all observations have the same distribution with common parameter. Like the saturated model, the null model does not depend on predictor variables. While the saturated most is the most general model, the null model is the most restricted model.

predictors

String[] predictors

The predictors of design matrix.

ztest

double[][] ztest

The coefficients, their standard errors, z-scores, and p-values.

Package smile.glm.model

Package smile.graph

Class smile.graph.AdjacencyList

class AdjacencyList extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

digraph

boolean digraph

Is the graph directed?

graph

LinkedList<Graph.Edge>[] graph

Adjacency list. Non-zero values are the weights of edges.

Class smile.graph.AdjacencyMatrix

class AdjacencyMatrix extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

digraph

boolean digraph

Is the graph directed?

graph

double[][] graph

Adjacency matrix. Non-zero values are the weights of edges.

Package smile.hash

Class smile.hash.PerfectHash

class PerfectHash extends Object implements Serializable

Serialized Fields

keywords

String[] keywords

The keyword set.

kvals

int[] kvals

The k parameters to calculate the hash.

min

char min

The lowest character value shown in the keywords.

select

int[] select

The character positions in keywords used to calculate the hash.

table

int[] table

Hash table.

Package smile.ica

Class smile.ica.Exp

class Exp extends Object implements Serializable

Class smile.ica.ICA

class ICA extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

components

double[][] components

The independent components (row-wise).

Class smile.ica.Kurtosis

class Kurtosis extends Object implements Serializable

Class smile.ica.LogCosh

class LogCosh extends Object implements Serializable

Package smile.interpolation

Class smile.interpolation.AbstractInterpolation

class AbstractInterpolation extends Object implements Serializable

Serialized Fields

cor

boolean cor

The indicator if consecutive calls seem correlated. This variable is used by interpolate to decide if to use locate or hunt on the next call, which is invisible to the user.

dj

int dj

The factor min(1, pow(n, 0.25)) to check if consecutive calls seem correlated.

jsav

int jsav

The previous search location.

n

int n

The number of control points.

xx

double[] xx

The tabulated control points.

yy

double[] yy

The function values at control points.

Class smile.interpolation.BicubicInterpolation

class BicubicInterpolation extends Object implements Serializable

Serialized Fields

m

int m

The number of control points on the first dimension.

n

int n

The number of control points on the second dimension.

x1

double[] x1

The first dimension of tabulated control points.

x1terp

LinearInterpolation x1terp

To locate the control point in the first dimension.

x2

double[] x2

The second dimension of tabulated control points.

x2terp

LinearInterpolation x2terp

To locate the control point in the second dimension.

y

double[] y

The workspace of function values.

y1

double[] y1

The workspace of derivatives.

y12

double[] y12

The workspace of derivatives.

y2

double[] y2

The workspace of derivatives.

yv

double[][] yv

The function values at control points.

Class smile.interpolation.BilinearInterpolation

class BilinearInterpolation extends Object implements Serializable

Serialized Fields

x1terp

LinearInterpolation x1terp

To locate the control point in the first dimension.

x2terp

LinearInterpolation x2terp

To locate the control point in the second dimension.

y

double[][] y

The function values at xx.

Class smile.interpolation.CubicSplineInterpolation1D

class CubicSplineInterpolation1D extends AbstractInterpolation implements Serializable

Serialized Fields

y2

double[] y2

Second derivatives of the interpolating function at the tabulated points.

Class smile.interpolation.CubicSplineInterpolation2D

class CubicSplineInterpolation2D extends Object implements Serializable

Serialized Fields

m

int m

The number of control points on the first dimension.

srp

CubicSplineInterpolation1D[] srp

The interpolation along the second dimension on every control point of first dimension.

x1

double[] x1

The first dimension of tabulated control points.

yv

double[] yv

The function values at xx.

Class smile.interpolation.KrigingInterpolation1D

class KrigingInterpolation1D extends Object implements Serializable

Serialized Fields

alpha

double alpha

The parameter of power variogram.

beta

double beta

The parameter of power variogram.

x

double[] x

The control points.

yvi

double[] yvi

The linear weights.

Class smile.interpolation.KrigingInterpolation2D

class KrigingInterpolation2D extends Object implements Serializable

Serialized Fields

alpha

double alpha

The parameter of power variogram.

beta

double beta

The parameter of power variogram.

x1

double[] x1

The first dimension of tabulated control points.

x2

double[] x2

The second dimension of tabulated control points.

yvi

double[] yvi

The linear weights.

Class smile.interpolation.LinearInterpolation

class LinearInterpolation extends AbstractInterpolation implements Serializable

Class smile.interpolation.RBFInterpolation1D

class RBFInterpolation1D extends Object implements Serializable

Serialized Fields

normalized

boolean normalized

True to fit a normalized rbf interpolation.

rbf

RadialBasisFunction rbf

The radial basis function.

w

double[] w

The linear weights.

x

double[] x

The control points.

Class smile.interpolation.RBFInterpolation2D

class RBFInterpolation2D extends Object implements Serializable

Serialized Fields

normalized

boolean normalized

True to fit a normalized rbf interpolation.

rbf

RadialBasisFunction rbf

The radial basis function.

w

double[] w

The linear weights.

x1

double[] x1

The first dimension of tabulated control points.

x2

double[] x2

The second dimension of tabulated control points.

Class smile.interpolation.ShepardInterpolation1D

class ShepardInterpolation1D extends Object implements Serializable

Serialized Fields

p

double p

The parameter in the radial basis function.

x

double[] x

The control points.

y

double[] y

The function values.

Class smile.interpolation.ShepardInterpolation2D

class ShepardInterpolation2D extends Object implements Serializable

Serialized Fields

p

double p

The parameter in the radial basis function.

x1

double[] x1

The first dimension of tabulated control points.

x2

double[] x2

The second dimension of tabulated control points.

y

double[] y

The function values.

Package smile.interpolation.variogram

Class smile.interpolation.variogram.ExponentialVariogram

class ExponentialVariogram extends Object implements Serializable

Serialized Fields

a

double a

The range parameter.

b

double b

The sill parameter.

c

double c

The nugget effect parameter.

Class smile.interpolation.variogram.GaussianVariogram

class GaussianVariogram extends Object implements Serializable

Serialized Fields

a

double a

The range parameter.

b

double b

The sill parameter.

c

double c

The nugget effect parameter.

Class smile.interpolation.variogram.PowerVariogram

class PowerVariogram extends Object implements Serializable

Serialized Fields

alpha

double alpha

The parameter fitted by unweighted least squares on data points.

beta

double beta

The fixed parameter 1 <= beta < 2.

nugget

double nugget

The nugget effect parameter.

Class smile.interpolation.variogram.SphericalVariogram

class SphericalVariogram extends Object implements Serializable

Serialized Fields

a

double a

The range parameter.

b

double b

The sill parameter.

c

double c

The nugget effect parameter.

Package smile.manifold

Class smile.manifold.IsoMap

class IsoMap extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

coordinates

double[][] coordinates

The coordinate matrix in embedding space.

graph

AdjacencyList graph

The nearest neighbor graph.

index

int[] index

The original sample index.

Class smile.manifold.KPCA

class KPCA extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

coordinates

double[][] coordinates

The coordinates of projected training data.

data

T[] data

Training data.

kernel

MercerKernel<T> kernel

Mercer kernel.

latent

double[] latent

The eigenvalues of kernel principal components.

mean

double[] mean

The row mean of kernel matrix.

mu

double mu

The mean of kernel matrix.

projection

Matrix projection

The projection matrix.

Class smile.manifold.LaplacianEigenmap

class LaplacianEigenmap extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

coordinates

double[][] coordinates

The coordinate matrix in embedding space.

graph

AdjacencyList graph

Nearest neighbor graph.

index

int[] index

The original sample index.

width

double width

The width of heat kernel.

Class smile.manifold.LLE

class LLE extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

coordinates

double[][] coordinates

The coordinate matrix in embedding space.

graph

AdjacencyList graph

Nearest neighbor graph.

index

int[] index

The original sample index.

Class smile.manifold.TSNE

class TSNE extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

coordinates

double[][] coordinates

The coordinate matrix in embedding space.

cost

double cost

The cost function value.

eta

double eta

The learning rate.

finalMomentum

double finalMomentum

The momentum in later stage.

gains

double[][] gains

The gain matrix.

minGain

double minGain

The floor of gain.

momentum

double momentum

The momentum factor.

momentumSwitchIter

int momentumSwitchIter

The number of iterations at which switch the momentum to finalMomentum.

P

double[][] P

The probability matrix of the distances in the input space.

Q

double[][] Q

The probability matrix of the distances in the feature space.

Qsum

double Qsum

The sum of Q matrix.

totalIter

int totalIter

The number of iterations so far.

Class smile.manifold.UMAP

class UMAP extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

coordinates

double[][] coordinates

The coordinate matrix in embedding space.

graph

AdjacencyList graph

The nearest neighbor graph.

index

int[] index

The original sample index.

Package smile.math

Class smile.math.Complex

class Complex extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

im

double im

The imaginary part.

re

double re

The real part.

Class smile.math.Scaler

class Scaler extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

clip

boolean clip

If true, clip the value in [0, 1].

offset

double offset

The scaling factor.

scale

double scale

The offset.

Package smile.math.distance

Class smile.math.distance.ChebyshevDistance

class ChebyshevDistance extends Object implements Serializable

serialVersionUID:

1L

Class smile.math.distance.CorrelationDistance

class CorrelationDistance extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

cor

ToDoubleBiFunction<double[],double[]> cor

Correlation lambda.

method

String method

The type of correlation is employed.

Class smile.math.distance.DynamicTimeWarping

class DynamicTimeWarping extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

distance

Distance<T> distance

The distance function.

width

double width

The window width of Sakoe-Chiba band.

Class smile.math.distance.EditDistance

class EditDistance extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

brf

smile.math.distance.EditDistance.BRF brf

The lambda to calculate FKP array.

damerau

boolean damerau

Calculate Damerau or basic Levenshitein distance.

FKP

IntArray2D FKP

Cost matrix. Because Java automatically initialize arrays, it takes O(mn) to declare this cost matrix every time before calculate edit distance. But the whole point of Berghel and Roach algorithm is to calculate fewer cells than O(mn). Therefore, we create this cost matrix here. Therefore, the methods using this cost matrix is not multi-thread safe.

r

double r

Radius of Sakoe-Chiba band

weight

IntArray2D weight

Weight matrix for weighted Levenshtein distance.

Class smile.math.distance.EuclideanDistance

class EuclideanDistance extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

weight

double[] weight

The weights used in weighted distance.

Class smile.math.distance.HammingDistance

class HammingDistance extends Object implements Serializable

Class smile.math.distance.JaccardDistance

class JaccardDistance extends Object implements Serializable

serialVersionUID:

1L

Class smile.math.distance.JensenShannonDistance

class JensenShannonDistance extends Object implements Serializable

serialVersionUID:

1L

Class smile.math.distance.LeeDistance

class LeeDistance extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

q

int q

The size of q-ary alphabet.

Class smile.math.distance.MahalanobisDistance

class MahalanobisDistance extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

sigma

Matrix sigma

The covariance matrix.

sigmaInv

Matrix sigmaInv

The inverse of covariance matrix.

Class smile.math.distance.ManhattanDistance

class ManhattanDistance extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

weight

double[] weight

The weights used in weighted distance.

Class smile.math.distance.MinkowskiDistance

class MinkowskiDistance extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

p

int p

The order of Minkowski distance.

weight

double[] weight

The weights used in weighted distance.

Class smile.math.distance.SparseChebyshevDistance

class SparseChebyshevDistance extends Object implements Serializable

serialVersionUID:

1L

Class smile.math.distance.SparseEuclideanDistance

class SparseEuclideanDistance extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

weight

double[] weight

The weights used in weighted distance.

Class smile.math.distance.SparseManhattanDistance

class SparseManhattanDistance extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

weight

double[] weight

The weights used in weighted distance.

Class smile.math.distance.SparseMinkowskiDistance

class SparseMinkowskiDistance extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

p

int p

The order of Minkowski distance.

weight

double[] weight

The weights used in weighted distance.

Package smile.math.kernel

Class smile.math.kernel.BinarySparseGaussianKernel

class BinarySparseGaussianKernel extends Gaussian implements Serializable

Class smile.math.kernel.BinarySparseHyperbolicTangentKernel

class BinarySparseHyperbolicTangentKernel extends HyperbolicTangent implements Serializable

Class smile.math.kernel.BinarySparseLaplacianKernel

class BinarySparseLaplacianKernel extends Laplacian implements Serializable

serialVersionUID:

2L

Class smile.math.kernel.BinarySparseLinearKernel

class BinarySparseLinearKernel extends Object implements Serializable

serialVersionUID:

2L

Class smile.math.kernel.BinarySparseMaternKernel

class BinarySparseMaternKernel extends Matern implements Serializable

Class smile.math.kernel.BinarySparsePolynomialKernel

class BinarySparsePolynomialKernel extends Polynomial implements Serializable

Class smile.math.kernel.BinarySparseThinPlateSplineKernel

class BinarySparseThinPlateSplineKernel extends ThinPlateSpline implements Serializable

Class smile.math.kernel.Gaussian

class Gaussian extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

hi

double hi

The upper bound of length scale for hyperparameter tuning.

lo

double lo

The lower bound of length scale for hyperparameter tuning.

sigma

double sigma

The length scale of the kernel.

Class smile.math.kernel.GaussianKernel

class GaussianKernel extends Gaussian implements Serializable

Class smile.math.kernel.HellingerKernel

class HellingerKernel extends Object implements Serializable

serialVersionUID:

2L

Class smile.math.kernel.HyperbolicTangent

class HyperbolicTangent extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

hi

double[] hi

The upper bound of scale and offset for hyperparameter tuning.

lo

double[] lo

The lower bound of scale and offset for hyperparameter tuning.

offset

double offset

The offset parameter.

scale

double scale

The scale parameter.

Class smile.math.kernel.HyperbolicTangentKernel

class HyperbolicTangentKernel extends HyperbolicTangent implements Serializable

Class smile.math.kernel.Laplacian

class Laplacian extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

hi

double hi

The upper bound of length scale for hyperparameter tuning.

lo

double lo

The lower bound of length scale for hyperparameter tuning.

sigma

double sigma

The length scale of the kernel.

Class smile.math.kernel.LaplacianKernel

class LaplacianKernel extends Laplacian implements Serializable

Class smile.math.kernel.LinearKernel

class LinearKernel extends Object implements Serializable

serialVersionUID:

2L

Class smile.math.kernel.Matern

class Matern extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

hi

double hi

The upper bound of length scale for hyperparameter tuning.

lo

double lo

The lower bound of length scale for hyperparameter tuning.

nu

double nu

The smoothness of the kernel.

sigma

double sigma

The length scale of the kernel.

Class smile.math.kernel.MaternKernel

class MaternKernel extends Matern implements Serializable

Class smile.math.kernel.PearsonKernel

class PearsonKernel extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

C

double C

The coefficient 4 * (2 ^ (1/omega) - 1) / (sigma^2).

hi

double hi

The upper bound of sigma.

lo

double lo

The lower bound of sigma.

omega

double omega

The tailing factor of the peak.

sigma

double sigma

Pearson width.

Class smile.math.kernel.Polynomial

class Polynomial extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

degree

int degree

The degree of polynomial.

hi

double[] hi

The upper bound of scale and offset for hyperparameter tuning.

lo

double[] lo

The lower bound of scale and offset for hyperparameter tuning.

offset

double offset

The offset parameter.

scale

double scale

The scale parameter.

Class smile.math.kernel.PolynomialKernel

class PolynomialKernel extends Polynomial implements Serializable

Class smile.math.kernel.ProductKernel

class ProductKernel extends Object implements Serializable

Serialized Fields

k1

MercerKernel<T> k1

The kernel to combine.

k2

MercerKernel<T> k2

The kernel to combine.

Class smile.math.kernel.SparseGaussianKernel

class SparseGaussianKernel extends Gaussian implements Serializable

Class smile.math.kernel.SparseHyperbolicTangentKernel

class SparseHyperbolicTangentKernel extends HyperbolicTangent implements Serializable

Class smile.math.kernel.SparseLaplacianKernel

class SparseLaplacianKernel extends Laplacian implements Serializable

Class smile.math.kernel.SparseLinearKernel

class SparseLinearKernel extends Object implements Serializable

serialVersionUID:

2L

Class smile.math.kernel.SparseMaternKernel

class SparseMaternKernel extends Matern implements Serializable

Class smile.math.kernel.SparsePolynomialKernel

class SparsePolynomialKernel extends Polynomial implements Serializable

Class smile.math.kernel.SparseThinPlateSplineKernel

class SparseThinPlateSplineKernel extends ThinPlateSpline implements Serializable

Class smile.math.kernel.SumKernel

class SumKernel extends Object implements Serializable

Serialized Fields

k1

MercerKernel<T> k1

The kernel to combine.

k2

MercerKernel<T> k2

The kernel to combine.

Class smile.math.kernel.ThinPlateSpline

class ThinPlateSpline extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

hi

double hi

The upper bound of length scale for hyperparameter tuning.

lo

double lo

The lower bound of length scale for hyperparameter tuning.

sigma

double sigma

The length scale of the kernel.

Class smile.math.kernel.ThinPlateSplineKernel

class ThinPlateSplineKernel extends ThinPlateSpline implements Serializable

Package smile.math.matrix

Class smile.math.matrix.BandMatrix

class BandMatrix extends IMatrix implements Serializable

serialVersionUID:

2L

Serialized Fields

AB

double[] AB

The band matrix storage.

kl

int kl

The number of subdiagonal rows.

ku

int ku

The number of superdiagonal rows.

ld

int ld

The leading dimension.

m

int m

The number of rows.

n

int n

The number of columns.

uplo

UPLO uplo

The upper or lower triangle of the symmetric band matrix.

Class smile.math.matrix.BandMatrix.Cholesky

class Cholesky extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

lu

BandMatrix lu

The Cholesky decomposition.

Class smile.math.matrix.BandMatrix.LU

class LU extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

info

int info

If info = 0, the LU decomposition was successful. If info = i > 0, U(i,i) is exactly zero. The factorization has been completed, but the factor U is exactly singular, and division by zero will occur if it is used to solve a system of equations.

ipiv

int[] ipiv

The pivot vector.

lu

BandMatrix lu

The LU decomposition.

Class smile.math.matrix.BigMatrix

class BigMatrix extends IMatrix implements Serializable

serialVersionUID:

3L

Serialization Methods

readObject

private void readObject(ObjectInputStream in)
                 throws IOException,
ClassNotFoundException

Customized object serialization.

Parameters:

in - the input stream.

Throws:

IOException - when fails to read the stream.

ClassNotFoundException - when fails to load the class.

writeObject

private void writeObject(ObjectOutputStream out)
                  throws IOException

Customized object serialization.

Parameters:

out - the output stream.

Throws:

IOException - when fails to write to the stream.

Serialized Fields

diag

Diag diag

If not null, the matrix is triangular. The flag specifies if a triangular matrix has unit diagonal elements.

m

int m

The number of rows.

n

int n

The number of columns.

uplo

UPLO uplo

If not null, the matrix is symmetric or triangular.

Class smile.math.matrix.BigMatrix.Cholesky

class Cholesky extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

lu

BigMatrix lu

The Cholesky decomposition.

Class smile.math.matrix.BigMatrix.EVD

class EVD extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

Vl

BigMatrix Vl

The left eigenvectors.

Vr

BigMatrix Vr

The right eigenvectors.

wi

org.bytedeco.javacpp.DoublePointer wi

The imaginary part of eigenvalues.

wr

org.bytedeco.javacpp.DoublePointer wr

The real part of eigenvalues. By default, the eigenvalues and eigenvectors are not always in sorted order. The sort function puts the eigenvalues in descending order and reorder the corresponding eigenvectors.

Class smile.math.matrix.BigMatrix.LU

class LU extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

info

int info

If info = 0, the LU decomposition was successful. If info = i > 0, U(i,i) is exactly zero. The factorization has been completed, but the factor U is exactly singular, and division by zero will occur if it is used to solve a system of equations.

ipiv

org.bytedeco.javacpp.IntPointer ipiv

The pivot vector.

lu

BigMatrix lu

The LU decomposition.

Class smile.math.matrix.BigMatrix.QR

class QR extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

qr

BigMatrix qr

The QR decomposition.

tau

org.bytedeco.javacpp.DoublePointer tau

The scalar factors of the elementary reflectors

Class smile.math.matrix.BigMatrix.SVD

class SVD extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

m

int m

The number of rows of matrix.

n

int n

The number of columns of matrix.

s

org.bytedeco.javacpp.DoublePointer s

The singular values in descending order.

U

BigMatrix U

The left singular vectors U.

V

BigMatrix V

The right singular vectors V.

Class smile.math.matrix.IMatrix

class IMatrix extends Object implements Serializable

Serialized Fields

colNames

String[] colNames

The column names.

rowNames

String[] rowNames

The row names.

Class smile.math.matrix.Matrix

class Matrix extends IMatrix implements Serializable

serialVersionUID:

3L

Serialized Fields

A

double[] A

The matrix storage.

diag

Diag diag

If not null, the matrix is triangular. The flag specifies if a triangular matrix has unit diagonal elements.

ld

int ld

The leading dimension.

m

int m

The number of rows.

n

int n

The number of columns.

uplo

UPLO uplo

If not null, the matrix is symmetric or triangular.

Class smile.math.matrix.Matrix.Cholesky

class Cholesky extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

lu

Matrix lu

The Cholesky decomposition.

Class smile.math.matrix.Matrix.EVD

class EVD extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

Vl

Matrix Vl

The left eigenvectors.

Vr

Matrix Vr

The right eigenvectors.

wi

double[] wi

The imaginary part of eigenvalues.

wr

double[] wr

The real part of eigenvalues. By default, the eigenvalues and eigenvectors are not always in sorted order. The sort function puts the eigenvalues in descending order and reorder the corresponding eigenvectors.

Class smile.math.matrix.Matrix.LU

class LU extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

info

int info

If info = 0, the LU decomposition was successful. If info = i > 0, U(i,i) is exactly zero. The factorization has been completed, but the factor U is exactly singular, and division by zero will occur if it is used to solve a system of equations.

ipiv

int[] ipiv

The pivot vector.

lu

Matrix lu

The LU decomposition.

Class smile.math.matrix.Matrix.QR

class QR extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

qr

Matrix qr

The QR decomposition.

tau

double[] tau

The scalar factors of the elementary reflectors

Class smile.math.matrix.Matrix.SVD

class SVD extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

m

int m

The number of rows of matrix.

n

int n

The number of columns of matrix.

s

double[] s

The singular values in descending order.

U

Matrix U

The left singular vectors U.

V

Matrix V

The right singular vectors V.

Class smile.math.matrix.SparseMatrix

class SparseMatrix extends IMatrix implements Serializable

serialVersionUID:

2L

Serialized Fields

colIndex

int[] colIndex

The index of the start of columns.

m

int m

The number of rows.

n

int n

The number of columns.

nonzeros

double[] nonzeros

The array of nonzero values stored column by column.

rowIndex

int[] rowIndex

The row indices of nonzero values.

Class smile.math.matrix.SymmMatrix

class SymmMatrix extends IMatrix implements Serializable

serialVersionUID:

2L

Serialized Fields

AP

double[] AP

The packed matrix storage.

n

int n

The number of rows/columns.

uplo

UPLO uplo

The upper or lower triangle of the symmetric matrix.

Class smile.math.matrix.SymmMatrix.BunchKaufman

class BunchKaufman extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

info

int info

If info = 0, the LU decomposition was successful. If info = i > 0, U(i,i) is exactly zero. The factorization has been completed, but the factor U is exactly singular, and division by zero will occur if it is used to solve a system of equations.

ipiv

int[] ipiv

The pivot vector.

lu

SymmMatrix lu

The Bunch–Kaufman decomposition.

Class smile.math.matrix.SymmMatrix.Cholesky

class Cholesky extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

lu

SymmMatrix lu

The Cholesky decomposition.

Package smile.math.matrix.fp32

Class smile.math.matrix.fp32.BandMatrix

class BandMatrix extends IMatrix implements Serializable

serialVersionUID:

2L

Serialized Fields

AB

float[] AB

The band matrix storage.

kl

int kl

The number of subdiagonal rows.

ku

int ku

The number of superdiagonal rows.

ld

int ld

The leading dimension.

m

int m

The number of rows.

n

int n

The number of columns.

uplo

UPLO uplo

The upper or lower triangle of the symmetric band matrix.

Class smile.math.matrix.fp32.BandMatrix.Cholesky

class Cholesky extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

lu

BandMatrix lu

The Cholesky decomposition.

Class smile.math.matrix.fp32.BandMatrix.LU

class LU extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

info

int info

If info = 0, the LU decomposition was successful. If info = i > 0, U(i,i) is exactly zero. The factorization has been completed, but the factor U is exactly singular, and division by zero will occur if it is used to solve a system of equations.

ipiv

int[] ipiv

The pivot vector.

lu

BandMatrix lu

The LU decomposition.

Class smile.math.matrix.fp32.IMatrix

class IMatrix extends Object implements Serializable

Serialized Fields

colNames

String[] colNames

The column names.

rowNames

String[] rowNames

The row names.

Class smile.math.matrix.fp32.Matrix

class Matrix extends IMatrix implements Serializable

serialVersionUID:

3L

Serialized Fields

A

float[] A

The matrix storage.

diag

Diag diag

If not null, the matrix is triangular. The flag specifies if a triangular matrix has unit diagonal elements.

ld

int ld

The leading dimension.

m

int m

The number of rows.

n

int n

The number of columns.

uplo

UPLO uplo

If not null, the matrix is symmetric or triangular.

Class smile.math.matrix.fp32.Matrix.Cholesky

class Cholesky extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

lu

Matrix lu

The Cholesky decomposition.

Class smile.math.matrix.fp32.Matrix.EVD

class EVD extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

Vl

Matrix Vl

The left eigenvectors.

Vr

Matrix Vr

The right eigenvectors.

wi

float[] wi

The imaginary part of eigenvalues.

wr

float[] wr

The real part of eigenvalues. By default, the eigenvalues and eigenvectors are not always in sorted order. The sort function puts the eigenvalues in descending order and reorder the corresponding eigenvectors.

Class smile.math.matrix.fp32.Matrix.LU

class LU extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

info

int info

If info = 0, the LU decomposition was successful. If info = i > 0, U(i,i) is exactly zero. The factorization has been completed, but the factor U is exactly singular, and division by zero will occur if it is used to solve a system of equations.

ipiv

int[] ipiv

The pivot vector.

lu

Matrix lu

The LU decomposition.

Class smile.math.matrix.fp32.Matrix.QR

class QR extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

qr

Matrix qr

The QR decomposition.

tau

float[] tau

The scalar factors of the elementary reflectors

Class smile.math.matrix.fp32.Matrix.SVD

class SVD extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

m

int m

The number of rows of matrix.

n

int n

The number of columns of matrix.

s

float[] s

The singular values in descending order.

U

Matrix U

The left singular vectors U.

V

Matrix V

The right singular vectors V.

Class smile.math.matrix.fp32.SparseMatrix

class SparseMatrix extends IMatrix implements Serializable

serialVersionUID:

2L

Serialized Fields

colIndex

int[] colIndex

The index of the start of columns.

m

int m

The number of rows.

n

int n

The number of columns.

nonzeros

float[] nonzeros

The array of nonzero values stored column by column.

rowIndex

int[] rowIndex

The row indices of nonzero values.

Class smile.math.matrix.fp32.SymmMatrix

class SymmMatrix extends IMatrix implements Serializable

serialVersionUID:

2L

Serialized Fields

AP

float[] AP

The packed matrix storage.

n

int n

The number of rows/columns.

uplo

UPLO uplo

The upper or lower triangle of the symmetric matrix.

Class smile.math.matrix.fp32.SymmMatrix.BunchKaufman

class BunchKaufman extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

info

int info

If info = 0, the LU decomposition was successful. If info = i > 0, U(i,i) is exactly zero. The factorization has been completed, but the factor U is exactly singular, and division by zero will occur if it is used to solve a system of equations.

ipiv

int[] ipiv

The pivot vector.

lu

SymmMatrix lu

The Bunch–Kaufman decomposition.

Class smile.math.matrix.fp32.SymmMatrix.Cholesky

class Cholesky extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

lu

SymmMatrix lu

The Cholesky decomposition.

Package smile.math.rbf

Class smile.math.rbf.GaussianRadialBasis

class GaussianRadialBasis extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

r0

double r0

The scale factor.

Class smile.math.rbf.InverseMultiquadricRadialBasis

class InverseMultiquadricRadialBasis extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

r02

double r02

The scale factor.

Class smile.math.rbf.MultiquadricRadialBasis

class MultiquadricRadialBasis extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

r02

double r02

The scale factor.

Class smile.math.rbf.ThinPlateRadialBasis

class ThinPlateRadialBasis extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

r0

double r0

The scale factor.

Package smile.neighbor

Class smile.neighbor.BKTree

class BKTree extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

count

int count

The number of nodes in the tree.

distance

Metric<K> distance

The distance metric. Note that the metric must be a discrete distance, e.g. edit distance, Hamming distance, Lee distance, Jaccard distance, and taxonomic distance, etc.

root

BKTree<K,V>.smile.neighbor.BKTree.Node root

The root of BK-tree.

Class smile.neighbor.CoverTree

class CoverTree extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

base

double base

The base of our expansion constant. In other words the 2 in 2^i used in covering tree and separation invariants of a cover tree. In paper, it's suggested the separation invariant is relaxed in batch construction.

data

List<V> data

The data objects.

distance

Metric<K> distance

The distance/metric function for nearest neighbor search.

invLogBase

double invLogBase

If we have base 2 then this can be viewed as 1/ln(2), which can be used later on to do invLogBase*ln(d) instead of ln(d)/ln(2), to get log2(d), in getScale method.

keys

List<K> keys

The object keys.

root

CoverTree<K,V>.smile.neighbor.CoverTree.Node root

The root node.

Class smile.neighbor.KDTree

class KDTree extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

data

E[] data

The data objects.

index

int[] index

The index of objects in each node.

keys

double[][] keys

The object keys.

root

smile.neighbor.KDTree.Node root

The root node of KD-Tree.

Class smile.neighbor.LinearSearch

class LinearSearch extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

data

List<V> data

The data objects.

distance

Distance<K> distance

The distance function used to determine nearest neighbors.

keys

List<K> keys

The object keys.

Class smile.neighbor.LSH

class LSH extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

data

ArrayList<E> data

The data objects.

H

int H

The size of hash table.

hash

List<Hash> hash

Hash functions.

k

int k

The number of random projections per hash value.

keys

ArrayList<double[]> keys

The object keys.

w

double w

The width of projection. The hash function is defined as floor((a * x + b) / w). The value of w determines the bucket interval.

Class smile.neighbor.MPLSH

class MPLSH extends LSH<E> implements Serializable

serialVersionUID:

2L

Serialized Fields

model

List<PosterioriModel> model

Pre-computed posteriori lookup table to generate multiple probes.

Class smile.neighbor.MutableLSH

class MutableLSH extends LSH<E> implements Serializable

Class smile.neighbor.SNLSH

class SNLSH extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

bands

LinkedHashMap<Long,IntArrayList>[] bands

Signature fractions

data

List<V> data

The data objects.

keys

List<K> keys

The keys of data object.

mask

long mask

Hash function mask.

signatures

List<Long> signatures

The signatures generated by simhash

simhash

SimHash<K> simhash

SimHash function.

Package smile.neighbor.lsh

Class smile.neighbor.lsh.Bucket

class Bucket extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

bucket

int bucket

The bucket id is given by the universal bucket hashing. The id is used instead of the full k-vector (value of the hash function g) describing the bucket. With a high probability all buckets will have different pairs of id's.

entry

IntArrayList entry

The indices of points that all have the same value for hash function g.

Class smile.neighbor.lsh.Hash

class Hash extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

a

Matrix a

The random vectors with entries chosen independently from a Gaussian distribution.

b

double[] b

Real numbers chosen uniformly from the range [0, w].

c

int[] c

The random integer used for universal bucket hashing.

d

int d

The dimensionality of data.

H

int H

The size of hash table.

k

int k

The number of random projections per hash value.

MAX_HASH_RND

int MAX_HASH_RND

The range of universal hashing random integers [0, 2²⁹).

P

int P

The prime number in universal bucket hashing.

table

Bucket[] table

Hash table.

w

double w

The width of projection. The hash function is defined as floor((a * x + b) / w). The value of w determines the bucket interval.

Class smile.neighbor.lsh.MultiProbeHash

class MultiProbeHash extends Hash implements Serializable

serialVersionUID:

2L

Serialized Fields

umax

double[] umax

The maximum values of hashing functions for given dataset.

umin

double[] umin

The minimum values of hashing functions for given dataset.

Class smile.neighbor.lsh.PosterioriModel

class PosterioriModel extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

hash

MultiProbeHash hash

The hash function to model.

lookup

PrH[][][] lookup

The posteriori probabilities lookup table.

Record Class smile.neighbor.lsh.PrH

class PrH extends Record implements Serializable

serialVersionUID:

3L

Serialized Fields

pr

double pr

The field for the pr record component.

u

int u

The field for the u record component.

Package smile.nlp.pos

Class smile.nlp.pos.HMMPOSTagger

class HMMPOSTagger extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

a

double[][] a

First order state transition probabilities.

b

double[][] b

Symbol emission probabilities.

c

double[][] c

Suffix emission probabilities.

pi

double[] pi

Initial state probabilities.

suffix

Map<String,Integer> suffix

The emission symbol suffix of HMM and corresponding indices.

symbol

Map<String,Integer> symbol

The emission symbols of HMM and corresponding indices.

Package smile.plot.swing

Class smile.plot.swing.Headless

class Headless extends JInternalFrame implements Serializable

Class smile.plot.swing.PlotGrid

class PlotGrid extends JPanel implements Serializable

Serialized Fields

contentPane

JPanel contentPane

The content panel to hold plots.

toolbar

JToolBar toolbar

Optional toolbar to control plots.

Class smile.plot.swing.PlotPanel

class PlotPanel extends JPanel implements Serializable

Serialized Fields

canvas

Canvas canvas

The canvas of plots.

contentPane

smile.plot.swing.PlotPanel.JCanvas contentPane

The Swing component to draw the canvas.

popup

JPopupMenu popup

Right-click popup menu.

propertyTable

JTable propertyTable

Property table.

scrollPane

JScrollPane scrollPane

toolbar

JToolBar toolbar

Optional toolbar to control plots.

Package smile.regression

Class smile.regression.GaussianProcessRegression

class GaussianProcessRegression extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

cholesky

Matrix.Cholesky cholesky

The Cholesky decomposition of kernel matrix.

kernel

MercerKernel<T> kernel

The covariance/kernel function.

L

double L

The log marginal likelihood, which may be not available (NaN) when the model is fit with approximate methods.

mean

double mean

The mean of responsible variable.

noise

double noise

The variance of noise.

regressors

T[] regressors

The regressors.

sd

double sd

The standard deviation of responsible variable.

w

double[] w

The linear weights.

Class smile.regression.GradientTreeBoost

class GradientTreeBoost extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

b

double b

The intercept.

formula

Formula formula

The model formula.

importance

double[] importance

Variable importance. Every time a split of a node is made on variable the impurity criterion for the two descendent nodes is less than the parent node. Adding up the decreases for each individual variable over all trees in the forest gives a simple variable importance.

shrinkage

double shrinkage

The shrinkage parameter in (0, 1] controls the learning rate of procedure.

trees

RegressionTree[] trees

Forest of regression trees.

Class smile.regression.KernelMachine

class KernelMachine extends KernelMachine<T> implements Serializable

serialVersionUID:

2L

Class smile.regression.LinearModel

class LinearModel extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

adjustedRSquared

double adjustedRSquared

Adjusted R². The adjusted R² has almost same explanation as R², but it penalizes the statistic as extra variables are included in the model.

b

double b

The intercept.

bias

boolean bias

True if the linear weights w includes the intercept.

df

int df

The degree-of-freedom of residual standard error.

error

double error

Residual standard error.

F

double F

The F-statistic of the goodness-of-fit of the model.

fittedValues

double[] fittedValues

The fitted values.

formula

Formula formula

Design matrix formula

p

int p

The dimensionality.

predictors

String[] predictors

The predictors of design matrix.

pvalue

double pvalue

The p-value of the goodness-of-fit test of the model.

residuals

double[] residuals

The residuals, that is response minus fitted values.

RSquared

double RSquared

R². R² is a statistic that will give some information about the goodness of fit of a model. In regression, the R² coefficient of determination is a statistical measure of how well the regression line approximates the real data points. An R² of 1.0 indicates that the regression line perfectly fits the data.

In the case of ordinary least-squares regression, R² increases as we increase the number of variables in the model (R² will not decrease). This illustrates a drawback to one possible use of R², where one might try to include more variables in the model until "there is no more improvement". This leads to the alternative approach of looking at the adjusted R².

RSS

double RSS

Residual sum of squares.

schema

StructType schema

The schema of design matrix.

ttest

double[][] ttest

The coefficients, their standard errors, t-scores, and p-values.

V

Matrix V

First initialized to the matrix (X^TX)^-1, it is updated with each new learning instance.

w

double[] w

The linear weights.

Class smile.regression.MLP

class MLP extends MultilayerPerceptron implements Serializable

serialVersionUID:

2L

Serialized Fields

scaler

Scaler scaler

The scaling function of output values.

Class smile.regression.RandomForest

class RandomForest extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

formula

Formula formula

The model formula.

importance

double[] importance

Variable importance. Every time a split of a node is made on variable the impurity criterion for the two descendent nodes is less than the parent node. Adding up the decreases for each individual variable over all trees in the forest gives a fast variable importance that is often very consistent with the permutation importance measure.

metrics

RegressionMetrics metrics

The overall out-of-bag metrics, which are quite accurate given that enough trees have been grown (otherwise the OOB error estimate can bias upward).

models

RandomForest.Model[] models

Forest of regression trees.

Class smile.regression.RandomForest.Model

class Model extends Object implements Serializable

Serialized Fields

metrics

RegressionMetrics metrics

The performance metrics on out-of-bag samples.

tree

RegressionTree tree

The decision tree.

Class smile.regression.RBFNetwork

class RBFNetwork extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

normalized

boolean normalized

True to fit a normalized RBF network.

rbf

RBF<T>[] rbf

The radial basis functions.

w

double[] w

The linear weights.

Class smile.regression.RegressionTree

class RegressionTree extends CART implements Serializable

serialVersionUID:

2L

Package smile.sequence

Class smile.sequence.CRF

class CRF extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

potentials

RegressionTree[][] potentials

The potential functions for each class.

schema

StructType schema

The schema of (x, s_j).

shrinkage

double shrinkage

The learning rate.

Class smile.sequence.CRFLabeler

class CRFLabeler extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

features

Function<T,Tuple> features

The feature function.

model

CRF model

The CRF model.

Class smile.sequence.HMM

class HMM extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

a

Matrix a

State transition probabilities.

b

Matrix b

Symbol emission probabilities.

pi

double[] pi

Initial state probabilities.

Class smile.sequence.HMMLabeler

class HMMLabeler extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

model

HMM model

The HMM model.

ordinal

ToIntFunction<T> ordinal

The lambda returns the ordinal numbers of symbols.

Package smile.stat.distribution

Class smile.stat.distribution.BernoulliDistribution

class BernoulliDistribution extends DiscreteDistribution implements Serializable

serialVersionUID:

2L

Serialized Fields

entropy

double entropy

Shannon entropy.

p

double p

Probability of success.

q

double q

Probability of failure.

Class smile.stat.distribution.BetaDistribution

class BetaDistribution extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

alpha

double alpha

The shape parameter.

beta

double beta

The shape parameter.

entropy

double entropy

The entropy.

mean

double mean

The mean.

rng

smile.stat.distribution.BetaDistribution.RejectionLogLogistic rng

The random number generator.

variance

double variance

The variance.

Class smile.stat.distribution.BinomialDistribution

class BinomialDistribution extends DiscreteDistribution implements Serializable

serialVersionUID:

2L

Serialized Fields

entropy

double entropy

The entropy.

n

int n

The number of experiments.

p

double p

The probability of success.

Class smile.stat.distribution.ChiSquareDistribution

class ChiSquareDistribution extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

entropy

double entropy

The entropy.

fac

double fac

The constant part of log-probability function.

nu

int nu

The degrees of freedom.

Class smile.stat.distribution.DiscreteDistribution

class DiscreteDistribution extends Object implements Serializable

Class smile.stat.distribution.DiscreteExponentialFamilyMixture

class DiscreteExponentialFamilyMixture extends DiscreteMixture implements Serializable

serialVersionUID:

2L

Serialized Fields

bic

double bic

The BIC score when the distribution is fit on a sample data.

L

double L

The log-likelihood when the distribution is fit on a sample data.

Class smile.stat.distribution.DiscreteMixture

class DiscreteMixture extends DiscreteDistribution implements Serializable

serialVersionUID:

2L

Serialized Fields

components

DiscreteMixture.Component[] components

The components of finite mixture model.

Class smile.stat.distribution.EmpiricalDistribution

class EmpiricalDistribution extends DiscreteDistribution implements Serializable

serialVersionUID:

2L

Serialized Fields

a

int[] a

The workspace of Walker's alias method to generate random samples.

cdf

double[] cdf

CDF at each x.

entropy

double entropy

Shannon entropy.

mean

double mean

The mean value.

p

double[] p

The probabilities for each x.

q

double[] q

The workspace of Walker's alias method to generate random samples.

sd

double sd

The standard deviation.

variance

double variance

The variance.

x

IntSet x

The possible values of random variable.

Class smile.stat.distribution.ExponentialDistribution

class ExponentialDistribution extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

lambda

double lambda

The rate parameter.

Class smile.stat.distribution.ExponentialFamilyMixture

class ExponentialFamilyMixture extends Mixture implements Serializable

serialVersionUID:

2L

Serialized Fields

bic

double bic

The BIC score when the distribution is fit on a sample data.

L

double L

The log-likelihood when the distribution is fit on a sample data.

Class smile.stat.distribution.FDistribution

class FDistribution extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

fac

double fac

The constant part in the pdf function.

nu1

int nu1

The degrees of freedom of chi-square distribution in numerator.

nu2

int nu2

The degrees of freedom chi-square distribution in denominator.

Class smile.stat.distribution.GammaDistribution

class GammaDistribution extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

entropy

double entropy

Shannon entropy.

k

double k

The shape parameter.

logGammaK

double logGammaK

log(theta * gamma(k))

logTheta

double logTheta

log(theta)

theta

double theta

The scale parameter.

thetaGammaK

double thetaGammaK

theta * gamma(k)

Class smile.stat.distribution.GaussianDistribution

class GaussianDistribution extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

entropy

double entropy

Shannon entropy.

mu

double mu

The mean.

pdfConstant

double pdfConstant

The constant factor in PDF.

sigma

double sigma

The standard deviation.

variance

double variance

The variance.

z1

double z1

The Box-Muller algorithm generate a pair of random numbers. z1 is to cache the second one.

Class smile.stat.distribution.GaussianMixture

class GaussianMixture extends ExponentialFamilyMixture implements Serializable

serialVersionUID:

2L

Class smile.stat.distribution.GeometricDistribution

class GeometricDistribution extends DiscreteDistribution implements Serializable

serialVersionUID:

2L

Serialized Fields

expDist

ExponentialDistribution expDist

The exponential distribution to generate Geometric distributed random number.

p

double p

Probability of success on each trial.

Class smile.stat.distribution.HyperGeometricDistribution

class HyperGeometricDistribution extends DiscreteDistribution implements Serializable

serialVersionUID:

2L

Serialized Fields

m

int m

The number of defects.

n

int n

The number of draws.

N

int N

The number of total samples.

rng

smile.stat.distribution.HyperGeometricDistribution.RandomNumberGenerator rng

The random number generator.

Class smile.stat.distribution.KernelDensity

class KernelDensity extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

gaussian

GaussianDistribution gaussian

The kernel -- a symmetric but not necessarily positive function that integrates to one. Here we just use Gaussian density function.

h

double h

h > 0 is a smoothing parameter called the bandwidth.

mean

double mean

The mean value.

sd

double sd

The standard deviation.

variance

double variance

The variance.

x

double[] x

The samples to estimate the density function.

Class smile.stat.distribution.LogisticDistribution

class LogisticDistribution extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

mu

double mu

The location parameter.

scale

double scale

The scale parameter.

Class smile.stat.distribution.LogNormalDistribution

class LogNormalDistribution extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

entropy

double entropy

Shannon entropy.

gaussian

GaussianDistribution gaussian

The corresponding Gaussian distribution.

mean

double mean

The mean.

mu

double mu

The mean of normal distribution.

sigma

double sigma

The standard deviation of normal distribution.

variance

double variance

The variance.

Class smile.stat.distribution.Mixture

class Mixture extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

components

Mixture.Component[] components

The components of finite mixture model.

Record Class smile.stat.distribution.Mixture.Component

class Component extends Record implements Serializable

Serialized Fields

distribution

Distribution distribution

The field for the distribution record component.

priori

double priori

The field for the priori record component.

Class smile.stat.distribution.MultivariateExponentialFamilyMixture

class MultivariateExponentialFamilyMixture extends MultivariateMixture implements Serializable

serialVersionUID:

2L

Serialized Fields

bic

double bic

The BIC score when the distribution is fit on a sample data.

L

double L

The log-likelihood when the distribution is fit on a sample data.

Class smile.stat.distribution.MultivariateGaussianDistribution

class MultivariateGaussianDistribution extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

diagonal

boolean diagonal

True if the covariance matrix is diagonal.

dim

int dim

The dimension.

length

int length

The number of parameters.

mu

double[] mu

The mean vector.

pdfConstant

double pdfConstant

The constant factor in PDF.

sigma

Matrix sigma

The covariance matrix.

sigmaDet

double sigmaDet

The determinant of covariance matrix.

sigmaInv

Matrix sigmaInv

The inverse of covariance matrix.

sigmaL

Matrix sigmaL

The Cholesky decomposition of covariance matrix.

Class smile.stat.distribution.MultivariateGaussianMixture

class MultivariateGaussianMixture extends MultivariateExponentialFamilyMixture implements Serializable

serialVersionUID:

2L

Class smile.stat.distribution.MultivariateMixture

class MultivariateMixture extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

components

MultivariateMixture.Component[] components

The components of finite mixture model.

Record Class smile.stat.distribution.MultivariateMixture.Component

class Component extends Record implements Serializable

Serialized Fields

distribution

MultivariateDistribution distribution

The field for the distribution record component.

priori

double priori

The field for the priori record component.

Class smile.stat.distribution.NegativeBinomialDistribution

class NegativeBinomialDistribution extends DiscreteDistribution implements Serializable

serialVersionUID:

2L

Serialized Fields

p

double p

The success probability in each experiment.

r

double r

The number of failures until the experiment is stopped.

Class smile.stat.distribution.PoissonDistribution

class PoissonDistribution extends DiscreteDistribution implements Serializable

serialVersionUID:

2L

Serialized Fields

entropy

double entropy

Shannon entropy.

lambda

double lambda

The average number of events per interval.

rng

smile.stat.distribution.PoissonDistribution.RandomNumberGenerator rng

The random number generator.

Class smile.stat.distribution.ShiftedGeometricDistribution

class ShiftedGeometricDistribution extends DiscreteDistribution implements Serializable

serialVersionUID:

2L

Serialized Fields

entropy

double entropy

Shannon entropy.

expDist

ExponentialDistribution expDist

The exponential distribution to generate Geometric distributed random number.

p

double p

The probability of success.

Class smile.stat.distribution.TDistribution

class TDistribution extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

entropy

double entropy

Shannon entropy.

fac

double fac

The constant factor in PDF.

np

double np

The constant factor in PDF.

nu

int nu

The degree of freedom.

Class smile.stat.distribution.WeibullDistribution

class WeibullDistribution extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

entropy

double entropy

Shannon entropy.

k

double k

The shape parameter.

lambda

double lambda

The scale parameter.

mean

double mean

The mean value.

variance

double variance

The variance.

Package smile.swing

Class smile.swing.Button

class Button extends JButton implements Serializable

Class smile.swing.FileChooser

class FileChooser extends JFileChooser implements Serializable

Class smile.swing.FontChooser

class FontChooser extends JComponent implements Serializable

Serialized Fields

dialogResultValue

int dialogResultValue

fontFamilyNames

String[] fontFamilyNames

fontFamilyTextField

JTextField fontFamilyTextField

fontNameList

JList<String> fontNameList

fontNamePanel

JPanel fontNamePanel

fontSizeList

JList<String> fontSizeList

fontSizePanel

JPanel fontSizePanel

fontSizeStrings

String[] fontSizeStrings

fontSizeTextField

JTextField fontSizeTextField

fontStyleList

JList<String> fontStyleList

fontStyleNames

String[] fontStyleNames

fontStylePanel

JPanel fontStylePanel

fontStyleTextField

JTextField fontStyleTextField

messageCatalog

ResourceBundle messageCatalog

samplePanel

JPanel samplePanel

sampleText

JTextField sampleText

Class smile.swing.Table

class Table extends org.jdesktop.swingx.JXTable implements Serializable

Serialized Fields

rowHeader

Table.RowHeader rowHeader

Row header.

Class smile.swing.Table.RowHeader

class RowHeader extends org.jdesktop.swingx.JXTable implements Serializable

Package smile.swing.table

Class smile.swing.table.ButtonCellRenderer

class ButtonCellRenderer extends AbstractCellEditor implements Serializable

Serialized Fields

editButton

JButton editButton

editorValue

Object editorValue

focusBorder

Border focusBorder

isButtonColumnEditor

boolean isButtonColumnEditor

mnemonic

int mnemonic

originalBorder

Border originalBorder

renderButton

JButton renderButton

table

JTable table

Class smile.swing.table.ByteArrayCellRenderer

class ByteArrayCellRenderer extends DefaultTableCellRenderer implements Serializable

Class smile.swing.table.ColorCellEditor

class ColorCellEditor extends AbstractCellEditor implements Serializable

Serialized Fields

button

JButton button

Editor component.

colorChooser

JColorChooser colorChooser

Color chooser.

currentColor

Color currentColor

Current color.

dialog

JDialog dialog

Color chooser dialog.

Class smile.swing.table.ColorCellRenderer

class ColorCellRenderer extends DefaultTableCellRenderer implements Serializable

Serialized Fields

isBordered

boolean isBordered

True if it has border.

selectedBorder

Border selectedBorder

Border when selected.

unselectedBorder

Border unselectedBorder

Border when not selected.

Class smile.swing.table.DateCellEditor

class DateCellEditor extends DefaultCellEditor implements Serializable

Serialized Fields

dateFormat

DateFormat dateFormat

textField

JFormattedTextField textField

Class smile.swing.table.DateCellRenderer

class DateCellRenderer extends DefaultTableCellRenderer implements Serializable

Serialized Fields

dateFormat

DateFormat dateFormat

Class smile.swing.table.DefaultTableHeaderCellRenderer

class DefaultTableHeaderCellRenderer extends DefaultTableCellRenderer implements Serializable

Class smile.swing.table.DoubleArrayCellEditor

class DoubleArrayCellEditor extends DefaultCellEditor implements Serializable

Serialized Fields

textField

JFormattedTextField textField

Class smile.swing.table.DoubleArrayCellRenderer

class DoubleArrayCellRenderer extends DefaultTableCellRenderer implements Serializable

Class smile.swing.table.DoubleCellEditor

class DoubleCellEditor extends DefaultCellEditor implements Serializable

Serialized Fields

doubleFormat

NumberFormat doubleFormat

textField

JFormattedTextField textField

Class smile.swing.table.FloatArrayCellRenderer

class FloatArrayCellRenderer extends DefaultTableCellRenderer implements Serializable

Class smile.swing.table.FontCellEditor

class FontCellEditor extends AbstractCellEditor implements Serializable

Serialized Fields

button

JButton button

Editor component.

currentFont

Font currentFont

Current font.

fontChooser

FontChooser fontChooser

Font chooser.

Class smile.swing.table.FontCellRenderer

class FontCellRenderer extends DefaultTableCellRenderer implements Serializable

Class smile.swing.table.IntegerArrayCellEditor

class IntegerArrayCellEditor extends DefaultCellEditor implements Serializable

Serialized Fields

textField

JFormattedTextField textField

Class smile.swing.table.IntegerArrayCellRenderer

class IntegerArrayCellRenderer extends DefaultTableCellRenderer implements Serializable

Class smile.swing.table.IntegerCellEditor

class IntegerCellEditor extends DefaultCellEditor implements Serializable

Serialized Fields

integerFormat

NumberFormat integerFormat

textField

JFormattedTextField textField

Class smile.swing.table.LongArrayCellRenderer

class LongArrayCellRenderer extends DefaultTableCellRenderer implements Serializable

Class smile.swing.table.MultiColumnSortTableHeaderCellRenderer

class MultiColumnSortTableHeaderCellRenderer extends DefaultTableHeaderCellRenderer implements Serializable

Serialized Fields

alpha

float alpha

Class smile.swing.table.NumberCellRenderer

class NumberCellRenderer extends DefaultTableCellRenderer implements Serializable

Serialized Fields

numberFormat

NumberFormat numberFormat

Format string.

Class smile.swing.table.PageTableModel

class PageTableModel extends AbstractTableModel implements Serializable

Serialized Fields

firstPageAction

Action firstPageAction

gotoPageAction

Action gotoPageAction

lastPageAction

Action lastPageAction

page

int page

The current page.

pageCountLabel

JLabel pageCountLabel

pageCountLabelFormat

String pageCountLabelFormat

pageDownAction

Action pageDownAction

Paging event action.

pageField

JTextField pageField

pageSize

int pageSize

Number of rows per page.

pageSizeAction

Action pageSizeAction

pageSizeField

JTextField pageSizeField

Controls on toolbar.

pageSizeLabel

JLabel pageSizeLabel

pageUpAction

Action pageUpAction

toolbar

JToolBar toolbar

Associate toolbar for page control.

totalRowCountLabel

JLabel totalRowCountLabel

totalRowCountLabelFormat

String totalRowCountLabelFormat

Class smile.swing.table.ShortArrayCellRenderer

class ShortArrayCellRenderer extends DefaultTableCellRenderer implements Serializable

Package smile.swing.text

Class smile.swing.text.FloatArrayFormatter

class FloatArrayFormatter extends DefaultFormatter implements Serializable

Class smile.swing.text.IntegerArrayFormatter

class IntegerArrayFormatter extends DefaultFormatter implements Serializable

Package smile.taxonomy

Class smile.taxonomy.TaxonomicDistance

class TaxonomicDistance extends Object implements Serializable

Serialized Fields

taxonomy

Taxonomy taxonomy

The taxonomy basis to calculate the distance.

Package smile.timeseries

Class smile.timeseries.AR

class AR extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

adjustedR2

double adjustedR2

Adjusted R². The adjusted R² has almost same explanation as R², but it penalizes the statistic as extra variables are included in the model.

ar

double[] ar

The linear weights of AR.

b

double b

The intercept.

df

int df

The degree-of-freedom of residual variance.

fittedValues

double[] fittedValues

The fitted values.

mean

double mean

The mean of time series.

method

AR.Method method

The fitting method.

p

int p

The order.

R2

double R2

R². R² is a statistic that will give some information about the goodness of fit of a model. In regression, the R² coefficient of determination is a statistical measure of how well the regression line approximates the real data points. An R² of 1.0 indicates that the regression line perfectly fits the data.

In the case of ordinary least-squares regression, R² increases as we increase the number of variables in the model (R² will not decrease). This illustrates a drawback to one possible use of R², where one might try to include more variables in the model until "there is no more improvement". This leads to the alternative approach of looking at the adjusted R².

residuals

double[] residuals

The residuals, that is response minus fitted values.

RSS

double RSS

Residual sum of squares.

ttest

double[][] ttest

The coefficients, their standard errors, t-scores, and p-values.

variance

double variance

Estimated variance.

x

double[] x

The time series.

Class smile.timeseries.ARMA

class ARMA extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

adjustedR2

double adjustedR2

Adjusted R². The adjusted R² has almost same explanation as R², but it penalizes the statistic as extra variables are included in the model.

ar

double[] ar

The linear weights of AR.

b

double b

The intercept.

df

int df

The degree-of-freedom of residual variance.

fittedValues

double[] fittedValues

The fitted values.

ma

double[] ma

The linear weights of MA.

mean

double mean

The mean of time series.

p

int p

The order of AR.

q

int q

The order of MA.

R2

double R2

R². R² is a statistic that will give some information about the goodness of fit of a model. In regression, the R² coefficient of determination is a statistical measure of how well the regression line approximates the real data points. An R² of 1.0 indicates that the regression line perfectly fits the data.

In the case of ordinary least-squares regression, R² increases as we increase the number of variables in the model (R² will not decrease). This illustrates a drawback to one possible use of R², where one might try to include more variables in the model until "there is no more improvement". This leads to the alternative approach of looking at the adjusted R².

residuals

double[] residuals

The residuals, that is response minus fitted values.

RSS

double RSS

Residual sum of squares.

ttest

double[][] ttest

The coefficients, their standard errors, t-scores, and p-values.

variance

double variance

Estimated variance.

x

double[] x

The time series.

Package smile.util

Class smile.util.DoubleArrayList

class DoubleArrayList extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

data

double[] data

The data of the list.

size

int size

The index after the last entry in the list.

Class smile.util.IntArrayList

class IntArrayList extends Object implements Serializable

serialVersionUID:

1L

Serialized Fields

data

int[] data

The data of the list.

size

int size

The index after the last entry in the list.

Class smile.util.IntSet

class IntSet extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

index

Map<Integer,Integer> index

Map of values to index.

max

int max

The maximum of values.

min

int min

The minimum of values.

values

int[] values

Map of index to original values.

Class smile.util.SparseArray

class SparseArray extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

index

IntArrayList index

The index of nonzero entries.

value

DoubleArrayList value

The value of nonzero entries.

Package smile.validation

Record Class smile.validation.Bag

class Bag extends Record implements Serializable

serialVersionUID:

3L

Serialized Fields

oob

int[] oob

The field for the oob record component.

samples

int[] samples

The field for the samples record component.

Record Class smile.validation.ClassificationMetrics

class ClassificationMetrics extends Record implements Serializable

serialVersionUID:

3L

Serialized Fields

accuracy

double accuracy

The field for the accuracy record component.

auc

double auc

The field for the auc record component.

crossentropy

double crossentropy

The field for the crossentropy record component.

error

int error

The field for the error record component.

f1

double f1

The field for the f1 record component.

fitTime

double fitTime

The field for the fitTime record component.

logloss

double logloss

The field for the logloss record component.

mcc

double mcc

The field for the mcc record component.

precision

double precision

The field for the precision record component.

scoreTime

double scoreTime

The field for the scoreTime record component.

sensitivity

double sensitivity

The field for the sensitivity record component.

size

int size

The field for the size record component.

specificity

double specificity

The field for the specificity record component.

Class smile.validation.ClassificationValidation

class ClassificationValidation extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

confusion

ConfusionMatrix confusion

The confusion matrix.

metrics

ClassificationMetrics metrics

The classification metrics.

model

M model

The model.

posteriori

double[][] posteriori

The posteriori probability of prediction if the model is a soft classifier.

prediction

int[] prediction

The model prediction.

truth

int[] truth

The true class labels of validation data.

Class smile.validation.ClassificationValidations

class ClassificationValidations extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

avg

ClassificationMetrics avg

The average of metrics.

rounds

List<ClassificationValidation<M>> rounds

The multiple round validations.

sd

ClassificationMetrics sd

The standard deviation of metrics.

Record Class smile.validation.RegressionMetrics

class RegressionMetrics extends Record implements Serializable

serialVersionUID:

3L

Serialized Fields

fitTime

double fitTime

The field for the fitTime record component.

mad

double mad

The field for the mad record component.

mse

double mse

The field for the mse record component.

r2

double r2

The field for the r2 record component.

rmse

double rmse

The field for the rmse record component.

rss

double rss

The field for the rss record component.

scoreTime

double scoreTime

The field for the scoreTime record component.

size

int size

The field for the size record component.

Class smile.validation.RegressionValidation

class RegressionValidation extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

metrics

RegressionMetrics metrics

The regression metrics.

model

M model

The model.

prediction

double[] prediction

The model prediction.

truth

double[] truth

The true response variable of validation data.

Class smile.validation.RegressionValidations

class RegressionValidations extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

avg

RegressionMetrics avg

The average of metrics.

rounds

List<RegressionValidation<M>> rounds

The multiple round validations.

sd

RegressionMetrics sd

The standard deviation of metrics.

Package smile.validation.metric

Class smile.validation.metric.Accuracy

class Accuracy extends Object implements Serializable

serialVersionUID:

2L

Class smile.validation.metric.AdjustedMutualInformation

class AdjustedMutualInformation extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

method

AdjustedMutualInformation.Method method

The normalization method.

Class smile.validation.metric.AdjustedRandIndex

class AdjustedRandIndex extends Object implements Serializable

serialVersionUID:

2L

Class smile.validation.metric.AUC

class AUC extends Object implements Serializable

serialVersionUID:

2L

Record Class smile.validation.metric.ConfusionMatrix

class ConfusionMatrix extends Record implements Serializable

serialVersionUID:

3L

Serialized Fields

matrix

int[][] matrix

The field for the matrix record component.

Class smile.validation.metric.Error

class Error extends Object implements Serializable

serialVersionUID:

2L

Class smile.validation.metric.Fallout

class Fallout extends Object implements Serializable

serialVersionUID:

2L

Class smile.validation.metric.FDR

class FDR extends Object implements Serializable

serialVersionUID:

2L

Class smile.validation.metric.FScore

class FScore extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

beta

double beta

A positive value such that F-score measures the effectiveness of retrieval with respect to a user who attaches β times as much importance to recall as precision. The default value 1.0 corresponds to F1-score.

strategy

Averaging strategy

The aggregating strategy for multi-classes.

Class smile.validation.metric.LogLoss

class LogLoss extends Object implements Serializable

serialVersionUID:

2L

Class smile.validation.metric.MAD

class MAD extends Object implements Serializable

serialVersionUID:

2L

Class smile.validation.metric.MatthewsCorrelation

class MatthewsCorrelation extends Object implements Serializable

serialVersionUID:

2L

Class smile.validation.metric.MSE

class MSE extends Object implements Serializable

serialVersionUID:

2L

Class smile.validation.metric.MutualInformation

class MutualInformation extends Object implements Serializable

serialVersionUID:

2L

Class smile.validation.metric.NormalizedMutualInformation

class NormalizedMutualInformation extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

method

NormalizedMutualInformation.Method method

The normalization method.

Class smile.validation.metric.Precision

class Precision extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

strategy

Averaging strategy

The aggregating strategy for multi-classes.

Class smile.validation.metric.R2

class R2 extends Object implements Serializable

serialVersionUID:

2L

Class smile.validation.metric.RandIndex

class RandIndex extends Object implements Serializable

serialVersionUID:

2L

Class smile.validation.metric.Recall

class Recall extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

strategy

Averaging strategy

The aggregating strategy for multi-classes.

Class smile.validation.metric.RMSE

class RMSE extends Object implements Serializable

serialVersionUID:

2L

Class smile.validation.metric.RSS

class RSS extends Object implements Serializable

serialVersionUID:

2L

Class smile.validation.metric.Sensitivity

class Sensitivity extends Object implements Serializable

serialVersionUID:

2L

Class smile.validation.metric.Specificity

class Specificity extends Object implements Serializable

serialVersionUID:

2L

Package smile.vq

Class smile.vq.BIRCH

class BIRCH extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

B

int B

The branching factor of non-leaf nodes.

d

int d

The dimensionality of data.

L

int L

The number of CF entries in the leaf nodes.

root

smile.vq.BIRCH.Node root

The root of CF tree.

T

double T

THe maximum radius of a sub-cluster.

Class smile.vq.GrowingNeuralGas

class GrowingNeuralGas extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

alpha

double alpha

Decrease error variables by multiplying them with alpha during inserting a new neuron.

beta

double beta

Decrease all error variables by multiply them with beta.

d

int d

The dimensionality of signals.

edgeLifetime

int edgeLifetime

The maximum age of edges.

epsBest

double epsBest

The learning rate to update best matching neuron.

epsNeighbor

double epsNeighbor

The learning rate to update neighbors of best matching neuron.

lambda

int lambda

If the number of input signals so far is an integer multiple of lambda, insert a new neuron.

neurons

ArrayList<Neuron> neurons

Neurons in the neural network.

t

int t

The number of signals processed so far.

top2

Neuron[] top2

The workspace to find nearest neighbors.

Class smile.vq.NeuralGas

class NeuralGas extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

alpha

TimeFunction alpha

The learning rate function.

dist

double[] dist

The distance between a new observation to neurons.

eps

double eps

The threshold to update neuron if alpha * theta > eps.

graph

AdjacencyMatrix graph

The network of neurons.

lifetime

TimeFunction lifetime

The lifetime of connections.

neurons

smile.vq.NeuralGas.Neuron[] neurons

The neurons.

t

int t

The current iteration.

theta

TimeFunction theta

The neighborhood function.

Class smile.vq.NeuralMap

class NeuralMap extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

beta

double beta

Decrease the freshness of all neurons by multiply them with beta.

edgeLifetime

int edgeLifetime

The maximum age of edges.

epsBest

double epsBest

The learning rate to update nearest neuron.

epsNeighbor

double epsNeighbor

The learning to update neighbors of nearest neuron.

neurons

ArrayList<Neuron> neurons

Neurons in the neural network.

r

double r

The distance radius to activate a neuron for a given signal.

t

int t

The number of signals processed so far.

top2

Neuron[] top2

The workspace to find nearest neighbors.

Class smile.vq.SOM

class SOM extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

alpha

TimeFunction alpha

The learning rate function.

dist

double[] dist

The distance between a new observation to neurons.

map

smile.vq.SOM.Neuron[][] map

The lattice of neurons.

ncol

int ncol

The number of columns in the lattice.

neurons

smile.vq.SOM.Neuron[] neurons

The neurons in linear array.

nrow

int nrow

The number of rows in the lattice.

t

int t

The current iteration.

theta

Neighborhood theta

The neighborhood function.

tol

double tol

The threshold to update neuron if alpha * theta > eps.

Package smile.vq.hebb

Class smile.vq.hebb.Edge

class Edge extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

age

int age

The age of the edges.

neighbor

Neuron neighbor

The neighbor neuron.

Class smile.vq.hebb.Neuron

class Neuron extends Object implements Serializable

serialVersionUID:

2L

Serialized Fields

counter

double counter

The local counter variable (e.g. the accumulated error, freshness, etc.)

edges

List<Edge> edges

The direct connected neighbors.

w

double[] w

The reference vector.