FromDense32 fills dst (mat.Mutable) with src (mat.Dense).

FromDense64 fills dst (mat.Mutable) with src (mat.Dense).

MaxIdx32 ...

MaxIdx64 ...

NewBaseMultilayerPerceptron32 returns a BaseMultilayerPerceptron32 with defaults.

NewBaseMultilayerPerceptron64 returns a BaseMultilayerPerceptron64 with defaults.

NewLabelBinarizer32 ...

NewLabelBinarizer64 ...

NewMLPClassifier returns a *MLPClassifier with defaults activation is one of logistic,tanh,relu solver is on of agd,adagrad,rmsprop,adadelta,adam (one of the keys of base.Solvers) defaults to "adam" Alpha is the regularization parameter lossName is one of square,log,cross-entropy (one of the keys of lm.LossFunctions) defaults to "log".

NewMLPRegressor returns a *MLPRegressor with defaults activation is one of identity,logistic,tanh,relu solver is on of sgd,adam defaults to "adam" Alpha is the regularization parameter.

ToDense32 returns w view of m if m is a RawMatrixer, et returns a dense copy of m.

ToDense64 returns w view of m if m is a RawMatrixer, et returns a dense copy of m.

Activations32 is a map containing the inplace_activation functions.

Activations64 is a map containing the inplace_activation functions.

Derivatives32 is a map of functions which multiply deltas with derivative of activation function.

Derivatives64 is a map of functions which multiply deltas with derivative of activation function.

LossFunctions32 is a map for loss functions.

LossFunctions64 is a map for loss functions.

M32 has funcs for float32 math.

M64 has funcs for float64 math.

MaxIdxXX ...

MXX has funcs for floatXX math.

Regressors is the list of regressors in this package.

AdamOptimizer32 is the stochastic adam optimizer.

AdamOptimizer64 is the stochastic adam optimizer.

BaseMultilayerPerceptron32 closely matches sklearn/neural_network/multilayer_perceptron.py.

BaseMultilayerPerceptron64 closely matches sklearn/neural_network/multilayer_perceptron.py.

LabelBinarizer32 Binarize labels in a one-vs-all fashion.

LabelBinarizer64 Binarize labels in a one-vs-all fashion.

MLPClassifier ...

MLPRegressor ...

SGDOptimizer32 is the stochastic gradient descent optimizer.

SGDOptimizer64 is the stochastic gradient descent optimizer.

Mutable provide Set to set value at row,col.

Optimizer32 is an interface for stochastic optimizers.

Optimizer64 is an interface for stochastic optimizers.

RawMatrixer32 provide access to blas matrix.

RawMatrixer64 provide access to blas matrix.

RawRowViewer32 returns row as a float slice.

RawRowViewer64 returns row as a float slice.

Slicer provides Slice(startRow,endRow,startCol,endCol).

Float32Slice implements sort.Interface.

Float64Slice implements sort.Interface.

General32 is like blas32.General.

General64 is like blas64.General.

GeneralXX is for easy tranposition to float32 or float64.

Matrix interface (identical to gonum/mat one's).

RawRowViewer returns row as a float slice.

RawRowViewerXX returns row as a float slice.