Versions in this module Expand all Collapse all v0 v0.5.0 Nov 6, 2022 v0.4.0 Nov 1, 2022 v0.3.0 Nov 1, 2022 v0.2.0 Oct 30, 2022 v0.0.1 Sep 23, 2022 Changes in this version + func AddDummyFeature(X *mat.Dense) + func DenseMean(Xmean *mat.Dense, X mat.Matrix) *mat.Dense + func IncrementalMeanAndVar(X, lastMean, lastVariance *mat.Dense, lastSampleCount int) (updatedMean, updatedVariance *mat.Dense, updatedSampleCount int) + func Mean(X mat.Matrix) (mean *mat.Dense) + func MeanStdDev(X mat.Matrix) (mean, std *mat.Dense) + func Scale(X *mat.Dense) *mat.Dense + type Binarizer struct + Threshold float64 + func NewBinarizer() *Binarizer + func (m *Binarizer) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (m *Binarizer) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *Binarizer) Transform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *Binarizer) TransformerClone() base.Transformer + type BrentMinimizer struct + Brack []float64 + FnMaxFev func(int) bool + Func func(float64) float64 + Funcalls int + Fval float64 + Iter int + Maxiter int + Tol float64 + Xmin float64 + func NewBrentMinimizer(fun func(float64) float64, tol float64, maxiter int, fnMaxFev func(int) bool) *BrentMinimizer + func (bm *BrentMinimizer) Optimize() (x, fx float64, iter, funcalls int) + func (bm *BrentMinimizer) SetBracket(brack []float64) + type FunctionTransformer struct + Func func(X, Y *mat.Dense) (X1, Y1 *mat.Dense) + InverseFunc func(X, Y *mat.Dense) (X1, Y1 *mat.Dense) + func NewFunctionTransformer(f, invf func(X, Y *mat.Dense) (X1, Y1 *mat.Dense)) *FunctionTransformer + func (m *FunctionTransformer) Fit(X, Y mat.Matrix) base.Fiter + func (m *FunctionTransformer) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *FunctionTransformer) InverseTransform(X, Y *mat.Dense) (X1, Y1 *mat.Dense) + func (m *FunctionTransformer) Transform(X, Y mat.Matrix) (X1, Y1 *mat.Dense) + func (m *FunctionTransformer) TransformerClone() base.Transformer + type Imputer struct + MissingValues []float64 + Strategy string + func NewImputer() *Imputer + func (m *Imputer) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (m *Imputer) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *Imputer) InverseTransform(X, Y *mat.Dense) (Xout, Yout *mat.Dense) + func (m *Imputer) Transform(Xmatrix, Ymatrix mat.Matrix) (Xout, Yout *mat.Dense) + func (m *Imputer) TransformerClone() base.Transformer + type InverseTransformer interface + InverseTransform func(X, Y *mat.Dense) (Xout, Yout *mat.Dense) + type KBinsDiscretizer struct + BinEdges [][]float64 + Encode string + NBins int + Strategy string + func NewKBinsDiscretizer(NBins int) *KBinsDiscretizer + func (m *KBinsDiscretizer) Fit(X, Y mat.Matrix) base.Fiter + func (m *KBinsDiscretizer) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *KBinsDiscretizer) InverseTransform(X mat.Matrix, Y mat.Mutable) (Xout, Yout *mat.Dense) + func (m *KBinsDiscretizer) Transform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *KBinsDiscretizer) TransformerClone() Transformer + type KernelCenterer struct + KFitAll float64 + KFitRows []float64 + func NewKernelCenterer() *KernelCenterer + func (m *KernelCenterer) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (m *KernelCenterer) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *KernelCenterer) Transform(Xmatrix, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *KernelCenterer) TransformerClone() base.Transformer + type LabelBinarizer struct + Classes [][]float64 + NegLabel float64 + PosLabel float64 + func NewLabelBinarizer(NegLabel, PosLabel float64) *LabelBinarizer + func (m *LabelBinarizer) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (m *LabelBinarizer) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *LabelBinarizer) InverseTransform(X, Y *mat.Dense) (Xout, Yout *mat.Dense) + func (m *LabelBinarizer) Transform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *LabelBinarizer) TransformerClone() base.Transformer + type LabelEncoder struct + Classes [][]float64 + Support [][]float64 + func NewLabelEncoder() *LabelEncoder + func (m *LabelEncoder) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (m *LabelEncoder) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *LabelEncoder) InverseTransform(X, Y *mat.Dense) (Xout, Yout *mat.Dense) + func (m *LabelEncoder) PartialFit(X, Y *mat.Dense) base.Transformer + func (m *LabelEncoder) Transform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *LabelEncoder) TransformerClone() base.Transformer + type MaxAbsScaler struct + MaxAbs []float64 + NSamplesSeen int + Scale []float64 + func NewMaxAbsScaler() *MaxAbsScaler + func (m *MaxAbsScaler) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (m *MaxAbsScaler) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *MaxAbsScaler) InverseTransform(X, Y *mat.Dense) (Xout, Yout *mat.Dense) + func (m *MaxAbsScaler) PartialFit(X, Y *mat.Dense) base.Transformer + func (m *MaxAbsScaler) Transform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *MaxAbsScaler) TransformerClone() base.Transformer + type MinMaxScaler struct + DataMax *mat.Dense + DataMin *mat.Dense + DataRange *mat.Dense + FeatureRange []float + Min *mat.Dense + NSamplesSeen int + Scale *mat.Dense + func NewMinMaxScaler(featureRange []float) *MinMaxScaler + func (scaler *MinMaxScaler) Fit(X, Y mat.Matrix) base.Fiter + func (scaler *MinMaxScaler) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (scaler *MinMaxScaler) InverseTransform(X, Y *mat.Dense) (Xout, Yout *mat.Dense) + func (scaler *MinMaxScaler) PartialFit(Xmatrix, Ymatrix mat.Matrix) Transformer + func (scaler *MinMaxScaler) Reset() *MinMaxScaler + func (scaler *MinMaxScaler) Transform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (scaler *MinMaxScaler) TransformerClone() base.Transformer + type MultiLabelBinarizer struct + Classes []interface{} + Less func(i, j int) bool + func NewMultiLabelBinarizer() *MultiLabelBinarizer + func (m *MultiLabelBinarizer) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (m *MultiLabelBinarizer) Fit2(X mat.Matrix, Y interface{}) *MultiLabelBinarizer + func (m *MultiLabelBinarizer) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *MultiLabelBinarizer) FitTransform2(X mat.Matrix, Y interface{}) (Xout, Yout *mat.Dense) + func (m *MultiLabelBinarizer) InverseTransform(X, Y *mat.Dense) (Xout *mat.Dense, Yout interface{}) + func (m *MultiLabelBinarizer) Transform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *MultiLabelBinarizer) Transform2(X mat.Matrix, Y interface{}) (Xout, Yout *mat.Dense) + func (m *MultiLabelBinarizer) TransformerClone() base.Transformer + type Normalizer struct + Axis int + Norm string + func NewNormalizer() *Normalizer + func (m *Normalizer) Fit(X, Y mat.Matrix) base.Fiter + func (m *Normalizer) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *Normalizer) InverseTransform(X, Y *mat.Dense) (Xout, Yout *mat.Dense) + func (m *Normalizer) Transform(Xmatrix, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *Normalizer) TransformerClone() base.Transformer + type NumpyLike struct + func (NumpyLike) Var(X mat.Matrix) *mat.Dense + func (m NumpyLike) Std(X mat.Matrix) *mat.Dense + type OneHotEncoder struct + FeatureIndices []int + NValues []int + Values [][]float64 + func NewOneHotEncoder() *OneHotEncoder + func (m *OneHotEncoder) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (m *OneHotEncoder) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *OneHotEncoder) InverseTransform(X, Y *mat.Dense) (Xout, Yout *mat.Dense) + func (m *OneHotEncoder) Transform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *OneHotEncoder) TransformerClone() base.Transformer + type PCA struct + ExplainedVarianceRatio []float64 + MinVarianceRatio float64 + NComponents int + SingularValues []float64 + func NewPCA() *PCA + func (m *PCA) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (m *PCA) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *PCA) InverseTransform(X, Y *mat.Dense) (Xout, Yout *mat.Dense) + func (m *PCA) Transform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *PCA) TransformerClone() base.Transformer + type PolynomialFeatures struct + Degree int + IncludeBias bool + InteractionOnly bool + Powers [][]int + func NewPolynomialFeatures(degree int) *PolynomialFeatures + func (poly *PolynomialFeatures) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (poly *PolynomialFeatures) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (poly *PolynomialFeatures) InverseTransform(X, Y *mat.Dense) (Xout, Yout *mat.Dense) + func (poly *PolynomialFeatures) Transform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (poly *PolynomialFeatures) TransformerClone() base.Transformer + type PowerTransformer struct + Lambdas []float64 + Method string + Scaler *StandardScaler + Standardize bool + func NewPowerTransformer() *PowerTransformer + func (m *PowerTransformer) Fit(X, Y mat.Matrix) base.Fiter + func (m *PowerTransformer) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *PowerTransformer) InverseTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *PowerTransformer) Transform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *PowerTransformer) TransformerClone() base.Transformer + type QuantilePair struct + Left float64 + Right float64 + type QuantileTransformer struct + NQuantiles int + OutputDistribution string + Quantiles mat.Matrix + RandomState rand.Source + Subsample int + func NewQuantileTransformer(NQuantiles int, outputDistribution string, RandomState rand.Source) *QuantileTransformer + func (m *QuantileTransformer) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (m *QuantileTransformer) FitTransform(Xmatrix, Ymatrix mat.Matrix) (Xout, Yout *mat.Dense) + func (m *QuantileTransformer) Transform(Xmatrix, Ymatrix mat.Matrix) (Xout, Yout *mat.Dense) + func (m *QuantileTransformer) TransformerClone() Transformer + type RobustScaler struct + Center bool + Median *mat.Dense + QuantileDivider *mat.Dense + Quantiles *QuantilePair + Scale bool + Tmp *mat.Dense + func NewDefaultRobustScaler() *RobustScaler + func NewRobustScaler(center bool, scale bool, quantiles *QuantilePair) *RobustScaler + func (scaler *RobustScaler) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (scaler *RobustScaler) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (scaler *RobustScaler) InverseTransform(X, Y *mat.Dense) (Xout, Yout *mat.Dense) + func (scaler *RobustScaler) PartialFit(Xmatrix, Ymatrix mat.Matrix) Transformer + func (scaler *RobustScaler) Reset() *RobustScaler + func (scaler *RobustScaler) Transform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (scaler *RobustScaler) TransformerClone() base.Transformer + type Shuffler struct + Perm []int + RandomState base.Source + func NewShuffler() *Shuffler + func (m *Shuffler) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (m *Shuffler) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *Shuffler) InverseTransform(X, Y *mat.Dense) (Xout, Yout *mat.Dense) + func (m *Shuffler) Transform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (m *Shuffler) TransformerClone() base.Transformer + type StandardScaler struct + Mean *mat.Dense + NSamplesSeen int + Scale *mat.Dense + Var *mat.Dense + WithMean bool + WithStd bool + func NewStandardScaler() *StandardScaler + func (scaler *StandardScaler) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (scaler *StandardScaler) FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (scaler *StandardScaler) InverseTransform(X, Y *mat.Dense) (Xout, Yout *mat.Dense) + func (scaler *StandardScaler) PartialFit(X, Y *mat.Dense) Transformer + func (scaler *StandardScaler) Reset() *StandardScaler + func (scaler *StandardScaler) Transform(X, Y mat.Matrix) (Xout, Yout *mat.Dense) + func (scaler *StandardScaler) TransformerClone() base.Transformer + type Transformer = base.Transformer
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