Versions in this module Expand all Collapse all v1 v1.0.3 Feb 13, 2024 v1.0.1 Feb 7, 2024 Changes in this version + var Activations32 = map[string]func(z blas32General) + var Activations64 = map[string]func(z blas64General) + var Derivatives32 = map[string]func(Z, deltas blas32General) + var Derivatives64 = map[string]func(Z, deltas blas64General) + var LossFunctions32 = map[string]func(y, h blas32General) float32 + var LossFunctions64 = map[string]func(y, h blas64General) float64 + var M32 = struct{ ... } + var M64 = struct{ ... } + var MXX = M32 + var MaxIdxXX = MaxIdx32 + var Regressors = []base.Predicter + func MaxIdx32(a []float32) int + func MaxIdx64(a []float64) int + type AdamOptimizer32 struct + Beta1 float32 + Beta2 float32 + Epsilon float32 + LearningRate float32 + LearningRateInit float32 + Params []float32 + type AdamOptimizer64 struct + Beta1 float64 + Beta2 float64 + Epsilon float64 + LearningRate float64 + LearningRateInit float64 + Params []float64 + type BaseMultilayerPerceptron32 struct + Activation string + Alpha float32 + BatchNormalize bool + BatchSize int + BestLoss float32 + BestValidationScore float32 + Beta1 float32 + Beta2 float32 + Coefs []blas32General + EarlyStopping bool + Epsilon float32 + HiddenLayerSizes []int + Intercepts [][]float32 + LearningRate string + LearningRateInit float32 + Loss float32 + LossCurve []float32 + LossFuncName string + MaxIter int + Momentum float32 + NIter int + NIterNoChange int + NLayers int + NOutputs int + NesterovsMomentum bool + NoImprovementCount int + OutActivation string + PowerT float32 + RandomState base.RandomState + Shuffle bool + Solver string + Tol float32 + ValidationFraction float32 + ValidationScores []float32 + Verbose bool + WarmStart bool + WeightDecay float32 + func NewBaseMultilayerPerceptron32() *BaseMultilayerPerceptron32 + func (mlp *BaseMultilayerPerceptron32) Fit(X, Y Matrix) + func (mlp *BaseMultilayerPerceptron32) GetNOutputs() int + func (mlp *BaseMultilayerPerceptron32) IsClassifier() bool + func (mlp *BaseMultilayerPerceptron32) Predict(X mat.Matrix, Y Mutable) + func (mlp *BaseMultilayerPerceptron32) Score(Xmatrix, Ymatrix mat.Matrix) float64 + func (mlp *BaseMultilayerPerceptron32) SetParams(params map[string]interface{}) + func (mlp *BaseMultilayerPerceptron32) Unmarshal(buf []byte) error + type BaseMultilayerPerceptron64 struct + Activation string + Alpha float64 + BatchNormalize bool + BatchSize int + BestLoss float64 + BestValidationScore float64 + Beta1 float64 + Beta2 float64 + Coefs []blas64General + EarlyStopping bool + Epsilon float64 + HiddenLayerSizes []int + Intercepts [][]float64 + LearningRate string + LearningRateInit float64 + Loss float64 + LossCurve []float64 + LossFuncName string + MaxIter int + Momentum float64 + NIter int + NIterNoChange int + NLayers int + NOutputs int + NesterovsMomentum bool + NoImprovementCount int + OutActivation string + PowerT float64 + RandomState base.RandomState + Shuffle bool + Solver string + Tol float64 + ValidationFraction float64 + ValidationScores []float64 + Verbose bool + WarmStart bool + WeightDecay float64 + func NewBaseMultilayerPerceptron64() *BaseMultilayerPerceptron64 + func (mlp *BaseMultilayerPerceptron64) Fit(X, Y Matrix) + func (mlp *BaseMultilayerPerceptron64) GetNOutputs() int + func (mlp *BaseMultilayerPerceptron64) IsClassifier() bool + func (mlp *BaseMultilayerPerceptron64) Predict(X mat.Matrix, Y Mutable) + func (mlp *BaseMultilayerPerceptron64) Score(Xmatrix, Ymatrix mat.Matrix) float64 + func (mlp *BaseMultilayerPerceptron64) SetParams(params map[string]interface{}) + func (mlp *BaseMultilayerPerceptron64) Unmarshal(buf []byte) error + type Float32Slice []float32 + func (p Float32Slice) Len() int + func (p Float32Slice) Less(i, j int) bool + func (p Float32Slice) Swap(i, j int) + type Float64Slice []float64 + func (p Float64Slice) Len() int + func (p Float64Slice) Less(i, j int) bool + func (p Float64Slice) Swap(i, j int) + type General32 blas32.General + func FromDense32(dst Mutable, dense General32) General32 + func ToDense32(m Matrix) General32 + func (mat *General32) Copy(a Matrix) + func (mat *General32) SumRows(a General32) + func (mat General32) At(r, c int) float64 + func (mat General32) Dims() (r, c int) + func (mat General32) Len() int + func (mat General32) Less(i, j int) bool + func (mat General32) RawMatrix() blas32General + func (mat General32) RawRowView(i int) []float32 + func (mat General32) RowSlice(i, j int) General32 + func (mat General32) Set(r, c int, v float64) + func (mat General32) Slice(i, j, k, l int) Matrix + func (mat General32) Swap(i, j int) + func (mat General32) T() Matrix + type General64 blas64.General + func FromDense64(dst Mutable, dense General64) General64 + func ToDense64(m Matrix) General64 + func (mat *General64) Copy(a Matrix) + func (mat *General64) SumRows(a General64) + func (mat General64) At(r, c int) float64 + func (mat General64) Dims() (r, c int) + func (mat General64) Len() int + func (mat General64) Less(i, j int) bool + func (mat General64) RawMatrix() blas64General + func (mat General64) RawRowView(i int) []float64 + func (mat General64) RowSlice(i, j int) General64 + func (mat General64) Set(r, c int, v float64) + func (mat General64) Slice(i, j, k, l int) Matrix + func (mat General64) Swap(i, j int) + func (mat General64) T() Matrix + type GeneralXX = General32 + type LabelBinarizer32 struct + Classes [][]float32 + NegLabel float32 + PosLabel float32 + func NewLabelBinarizer32(NegLabel, PosLabel float32) *LabelBinarizer32 + func (m *LabelBinarizer32) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (m *LabelBinarizer32) FitTransform(X, Y mat.Matrix) (Xout, Yout General32) + func (m *LabelBinarizer32) InverseTransform(X, Y General32) (Xout, Yout General32) + func (m *LabelBinarizer32) Transform(X, Y mat.Matrix) (Xout, Yout General32) + func (m *LabelBinarizer32) TransformerClone() *LabelBinarizer32 + type LabelBinarizer64 struct + Classes [][]float64 + NegLabel float64 + PosLabel float64 + func NewLabelBinarizer64(NegLabel, PosLabel float64) *LabelBinarizer64 + func (m *LabelBinarizer64) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (m *LabelBinarizer64) FitTransform(X, Y mat.Matrix) (Xout, Yout General64) + func (m *LabelBinarizer64) InverseTransform(X, Y General64) (Xout, Yout General64) + func (m *LabelBinarizer64) Transform(X, Y mat.Matrix) (Xout, Yout General64) + func (m *LabelBinarizer64) TransformerClone() *LabelBinarizer64 + type MLPClassifier struct + func NewMLPClassifier(hiddenLayerSizes []int, activation string, solver string, Alpha float64) *MLPClassifier + func (*MLPClassifier) IsClassifier() bool + func (mlp *MLPClassifier) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (mlp *MLPClassifier) Predict(X mat.Matrix, Ymutable mat.Mutable) *mat.Dense + func (mlp *MLPClassifier) PredicterClone() base.Predicter + func (mlp *MLPClassifier) Score(Xmatrix, Ymatrix mat.Matrix) float64 + type MLPRegressor struct + func NewMLPRegressor(hiddenLayerSizes []int, activation string, solver string, Alpha float64) *MLPRegressor + func (*MLPRegressor) IsClassifier() bool + func (mlp *MLPRegressor) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter + func (mlp *MLPRegressor) Predict(X mat.Matrix, Ymutable mat.Mutable) *mat.Dense + func (mlp *MLPRegressor) PredicterClone() base.Predicter + func (mlp *MLPRegressor) Score(X, Y mat.Matrix) float64 + type Matrix = mat.Matrix + type Mutable interface + Set func(i, j int, v float64) + type Optimizer32 interface + type Optimizer64 interface + type RawMatrixer32 interface + RawMatrix func() blas32General + type RawMatrixer64 interface + RawMatrix func() blas64General + type RawRowViewer = RawRowViewer64 + type RawRowViewer32 interface + RawRowView func(i int) []float32 + type RawRowViewer64 interface + RawRowView func(i int) []float64 + type RawRowViewerXX = RawRowViewer32 + type SGDOptimizer32 struct + LRSchedule string + LearningRate float32 + LearningRateInit float32 + Momentum float32 + Nesterov bool + Params []float32 + PowerT float32 + type SGDOptimizer64 struct + LRSchedule string + LearningRate float64 + LearningRateInit float64 + Momentum float64 + Nesterov bool + Params []float64 + PowerT float64 + type Slicer interface + Slice func(i, j, k, l int) Matrix