base

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 Go to latest Published: Jul 11, 2020 License: MIT Imports: 12 Imported by: 12

Documentation

Overview

Package base contains miscellaneous utilities common to other packages

Index

Constants

This section is empty.

Variables

View Source 
var Activations map[string]Activation

Activations is the map of implemented activation functions

View Source 
var GOMethodCreators = map[string]GOMethodCreator{
	"sgd":             func() optimize.Method { return NewSGDOptimizer() },
	"adagrad":         func() optimize.Method { return NewAdagradOptimizer() },
	"rmsprop":         func() optimize.Method { return NewRMSPropOptimizer() },
	"adadelta":        func() optimize.Method { return NewAdadeltaOptimizer() },
	"adam":            func() optimize.Method { return NewAdamOptimizer() },
	"bfgs":            func() optimize.Method { return &optimize.BFGS{} },
	"cg":              func() optimize.Method { return &optimize.CG{} },
	"gradientdescent": func() optimize.Method { return &optimize.GradientDescent{} },
	"lbfgs":           func() optimize.Method { return &optimize.LBFGS{} },
	"neldermead":      func() optimize.Method { return &optimize.NelderMead{} },
}

GOMethodCreators is the map of all gonum optimize method creators (gonum native method creators + base.Optimizer creators)

View Source 
var Solvers = map[string]OptimCreator{
	"sgd":      func() Optimizer { return NewSGDOptimizer() },
	"adagrad":  func() Optimizer { return NewAdagradOptimizer() },
	"rmsprop":  func() Optimizer { return NewRMSPropOptimizer() },
	"adadelta": func() Optimizer { return NewAdadeltaOptimizer() },
	"adam":     func() Optimizer { return NewAdamOptimizer() },
}

Solvers is the map for common Optimizer creators agd,adagrad,rmsprop,adadelta,adam

Functions

func FromDense 

func FromDense(dst mat.Mutable, dense *mat.Dense) *mat.Dense

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

func MatDenseColSlice 

func MatDenseColSlice(src mat.RawMatrixer, j, l int) *mat.Dense

MatDenseColSlice returns a *mat.Dense view of partial underlaying data of M

func MatDenseRowSlice 

func MatDenseRowSlice(src mat.RawMatrixer, i, k int) *mat.Dense

MatDenseRowSlice returns a *mat.Dense view of partial underlaying data of M

func MatDenseSlice 

func MatDenseSlice(src mat.RawMatrixer, i, k, j, l int) *mat.Dense

MatDenseSlice returns a *mat.Dense view of partial underlaying data of M

func MatDimsCheck 

func MatDimsCheck(op string, R, X, Y mat.Matrix)

MatDimsCheck checks compat of operator op and its Matrix parameters Dims. R is result of op, X and Y are operands of op. "." is dot product. "+","-","*","/" are elementwize opts

func MatDimsString 

func MatDimsString(mats ...mat.Matrix) string

MatDimsString returns a string representing Dims of its several Matrix parameters

func MatGeneralColSlice 

func MatGeneralColSlice(M blas64.General, j, l int) blas64.General

MatGeneralColSlice returns a blas64.General view of partial underlaying data of M

func MatGeneralRowSlice 

func MatGeneralRowSlice(M blas64.General, i, k int) blas64.General

MatGeneralRowSlice returns a blas64.General view of partial underlaying data of M

func MatGeneralSlice 

func MatGeneralSlice(M blas64.General, i, k, j, l int) blas64.General

MatGeneralSlice returns a blas64.General view of partial underlaying data of M

func MatStr 

func MatStr(Xs ...mat.Matrix) string

MatStr return a string from a mat.Matrix

func NewSource 

func NewSource(seed uint64) *randomkit.RKState

NewSource returns a new pseudo-random Source seeded with the given value.

func Parallelize 

func Parallelize(threads, NSamples int, f func(th, start, end int))

Parallelize split execution over NSamples across threads

func ToDense 

func ToDense(m mat.Matrix) *mat.Dense

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

Types

type Activation 

type Activation interface {
	F(x float64) float64
	Fprime(y float64) float64
}

Activation is the inteface for an activation function

type Fiter 

type Fiter interface {
	Fit(X, Y mat.Matrix) Fiter
}

Fiter have a Fit(Matrix,Matrix) Fitter method. may be a Predicter or a Transformer

type Float64er 

type Float64er interface {
	Float64() float64
}

Float64er is implemented by a random source having a method Float64() float64

type GOMethodCreator 

type GOMethodCreator func() optimize.Method

GOMethodCreator is a func that creates a gonum/optimize.Method

type Identity 

type Identity struct{}

Identity ...

func (Identity) F 

func (Identity) F(x float64) float64

F ...

func (Identity) Fprime 

func (Identity) Fprime(y float64) float64

Fprime ...y=F(X*Theta)

type Intner 

type Intner interface {
	Intn(int) int
}

Intner is implemented by a random source having a method Intn() float64

type LockedSource 

type LockedSource struct {
	// contains filtered or unexported fields
}

LockedSource is an implementation of Source that is concurrency-safe. It is just a standard Source with its operations protected by a sync.Mutex.

func NewLockedSource 

func NewLockedSource(seed uint64) *LockedSource

NewLockedSource returns a rand.Source safe for concurrent access

func (*LockedSource) Seed 

func (s *LockedSource) Seed(seed uint64)

Seed ...

func (*LockedSource) SourceClone 

func (s *LockedSource) SourceClone() Source

SourceClone ...

func (*LockedSource) Uint64 

func (s *LockedSource) Uint64() (n uint64)

Uint64 ...

func (*LockedSource) WithLock 

func (s *LockedSource) WithLock(f func(Source))

WithLock executes f while s is locked

type Logistic 

type Logistic struct{}

Logistic ...

func (Logistic) F 

func (Logistic) F(x float64) float64

F ...

func (Logistic) Fprime 

func (Logistic) Fprime(y float64) float64

Fprime ...

type MatConst 

type MatConst struct {
	Rows, Columns int
	Value         float64
}

MatConst is a matrix where all cless have the same value

func (MatConst) At 

func (m MatConst) At(i, j int) float64

At for MatConst

func (MatConst) Dims 

func (m MatConst) Dims() (int, int)

Dims for MatConst

func (MatConst) T 

func (m MatConst) T() mat.Matrix

T for MatConst

type MatOnesPrepended 

type MatOnesPrepended struct{ mat.Matrix }

MatOnesPrepended is a matrix override representing its initializer with an initial column of ones added

type MatRowSlice 

type MatRowSlice struct {
	mat.Matrix
	Start, End int
}

MatRowSlice is a matrix row chunk

func (MatRowSlice) At 

func (m MatRowSlice) At(i, j int) float64

At for MatRowSlice

func (MatRowSlice) Dims 

func (m MatRowSlice) Dims() (int, int)

Dims for MatRowSlice

func (MatRowSlice) Set 

func (m MatRowSlice) Set(i, j int, v float64)

Set for MatRowSlice

func (MatRowSlice) T 

func (m MatRowSlice) T() mat.Matrix

T for MatRowSlice

type MatTranspose 

type MatTranspose struct{ mat.Matrix }

MatTranspose is a matrix override to transpose a mat.Matrix from its initializer

func (MatTranspose) At 

func (m MatTranspose) At(i, j int) float64

At for MatTranspose

func (MatTranspose) Dims 

func (m MatTranspose) Dims() (int, int)

Dims for MatTranspose

func (MatTranspose) Set 

func (m MatTranspose) Set(i, j int, v float64)

Set for MatTranspose

func (MatTranspose) T 

func (m MatTranspose) T() mat.Matrix

T for MatTranspose

type NormFloat64er 

type NormFloat64er interface {
	NormFloat64() float64
}

NormFloat64er is implemented by a random source having a method NormFloat64() float64

type OptimCreator 

type OptimCreator func() Optimizer

OptimCreator is the type for functions returning an Optimizer

func NewSolver 

func NewSolver(name string) OptimCreator

NewSolver returns an OptimCreator

type Optimizer 

type Optimizer interface {
	GetUpdate(update *mat.Dense, grad mat.Matrix)
	UpdateParams(grad mat.Matrix)
	SetTheta(Theta *mat.Dense)
	GetTheta() *mat.Dense
	GetTimeStep() uint64
	String() string
}

Optimizer has updateParams method to update theta from gradient

func NewOptimizer 

func NewOptimizer(name string) Optimizer

NewOptimizer only accepts SGD|adagrad|adadelta|rmsprop|adam

type Permer 

type Permer interface {
	Perm(int) []int
}

Permer is implemented by a random source having a method Perm(int) []int

type Predicter 

type Predicter interface {
	Fiter
	GetNOutputs() int
	Predict(X mat.Matrix, Y mat.Mutable) *mat.Dense
	Score(X, Y mat.Matrix) float64
	IsClassifier() bool
	PredicterClone() Predicter
}

Predicter have Predict(Matrix,Mutable). if 2nd arg (result receiver) is nil, il will be allocated and returned by Predict

type RandomState 

type RandomState = Source

RandomState represents a bit more than random_state pythonic attribute. it's not only a seed but a source with a state as it's name states

type ReLU 

type ReLU struct{}

ReLU ...

func (ReLU) F 

func (ReLU) F(x float64) float64

F ...

func (ReLU) Fprime 

func (ReLU) Fprime(y float64) float64

Fprime ...

type SGDOptimizer 

type SGDOptimizer struct {
	// StepSize is used for all variants
	// Momentum can be used for all variants
	// GradientClipping is used if >0 to limit gradient L2 norm
	// RMSPropGamma is the momentum for rmsprop and adadelta
	// Epsilon is used to avoid division by zero in adagrad,rmsprop,adadelta,adam
	StepSize, Momentum, GradientClipping, RMSPropGamma, Epsilon, BatchPart float64
	// Adagrad, Adadelta, RMSProp, Adam are variants. At most one should be true
	Adagrad, Adadelta, RMSProp, Adam bool
	// NFeature,NOutputs need only to be initialized wher SGDOptimizer is used as an optimize.Method
	NFeatures, NOutputs int

	// running Parameters (don't set them yourself)
	GtNorm, Theta, PrevUpdate, Update, AdagradG, AdadeltaU *mat.Dense
	TimeStep                                               float64
	// Adam specific
	Beta1, Beta2 float64
	Mt, Vt       *mat.Dense
	// contains filtered or unexported fields
}

SGDOptimizer is struct for SGD solver v https://en.wikipedia.org/wiki/Stochastic_gradient_descent

func NewAdadeltaOptimizer 

func NewAdadeltaOptimizer() *SGDOptimizer

NewAdadeltaOptimizer return a *SGDOptimizer setup for adadelta

func NewAdagradOptimizer 

func NewAdagradOptimizer() *SGDOptimizer

NewAdagradOptimizer return a *SGDOptimizer setup for adagrad

func NewAdamOptimizer 

func NewAdamOptimizer() *SGDOptimizer

NewAdamOptimizer returns an initialized adam solver

func NewRMSPropOptimizer 

func NewRMSPropOptimizer() *SGDOptimizer

NewRMSPropOptimizer return a *SGDOptimizer setup for rmsprop

func NewSGDOptimizer 

func NewSGDOptimizer() *SGDOptimizer

NewSGDOptimizer returns an initialized *SGDOptimizer with stepsize 1e-4 and momentum 0.9

func (*SGDOptimizer) GetTheta 

func (s *SGDOptimizer) GetTheta() *mat.Dense

GetTheta can be called anytime after SetTheta to get read access to theta

func (*SGDOptimizer) GetTimeStep 

func (s *SGDOptimizer) GetTimeStep() uint64

GetTimeStep return the number of theta updates already occurred

func (*SGDOptimizer) GetUpdate 

func (s *SGDOptimizer) GetUpdate(update *mat.Dense, grad mat.Matrix)

GetUpdate compute the update from grad

func (*SGDOptimizer) Init 

func (s *SGDOptimizer) Init(dim, tasks int) int

Init initializes the method based on the initial data in loc, updates it and returns the first operation to be carried out by the caller. The initial location must be valid as specified by Needs.

func (*SGDOptimizer) Run 

func (s *SGDOptimizer) Run(operation chan<- optimize.Task, result <-chan optimize.Task, tasks []optimize.Task)

Run implements optimize.Method.Run for SGDOptimizer

func (*SGDOptimizer) SetTheta 

func (s *SGDOptimizer) SetTheta(Theta *mat.Dense)

SetTheta should be called before first call to UpdateParams to let the solver know the theta pointer

func (*SGDOptimizer) String 

func (s *SGDOptimizer) String() string

func (*SGDOptimizer) UpdateParams 

func (s *SGDOptimizer) UpdateParams(grad mat.Matrix)

UpdateParams updates theta from gradient. first call allocates required temporary storage

func (*SGDOptimizer) Uses 

func (*SGDOptimizer) Uses(has optimize.Available) (optimize.Available, error)

Uses is for when SGDOptimizer is used as an optimize.Method

type Shuffler 

type Shuffler interface {
	Shuffler(int, func(int, int))
}

Shuffler is implemented by a random source having a method Shuffle(int,func(int,int))

type Sigmoid 

type Sigmoid = Logistic

Sigmoid ...

type Source 

type Source = rand.Source

A Source represents a source of uniformly-distributed pseudo-random int64 values in the range [0, 1<<64).

type SourceCloner 

type SourceCloner interface {
	SourceClone() Source
}

SourceCloner is an "golang.org/x/exp/rand".Source with a Clone method

type Tanh 

type Tanh struct{}

Tanh ...

func (Tanh) F 

func (Tanh) F(x float64) float64

F ...

func (Tanh) Fprime 

func (Tanh) Fprime(y float64) float64

Fprime ...

type Transformer 

type Transformer interface {
	Fiter
	Transform(X, Y mat.Matrix) (Xout, Yout *mat.Dense)
	FitTransform(X, Y mat.Matrix) (Xout, Yout *mat.Dense)
	TransformerClone() Transformer
}

Transformer transforms X,Y into Xout,Yout

Source Files

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