ml

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v3.0.0 Latest Latest Go to latest Published: Oct 11, 2026 License: MIT Imports: 5 Imported by: 0

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Valid go.mod file
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github.com/hilthontt/luascript

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Documentation ¶

Index ¶

func Logistic(x, a float64) float64
func Normal(stdDev, mean float64) float64
func Uniform(stdDev, mean float64) float64
type ActivationType
- func OutputActivation(c Mode) ActivationType
type BinaryCrossEntropy
- func (l BinaryCrossEntropy) Df(estimate, ideal, activation float64) float64
- func (l BinaryCrossEntropy) F(estimate, ideal [][]float64) float64
type Config
type CrossEntropy
- func (l CrossEntropy) Df(estimate, ideal, activation float64) float64
- func (l CrossEntropy) F(estimate, ideal [][]float64) float64
type Differentiable
- func GetActivation(act ActivationType) Differentiable
type Dump
type Layer
- func NewLayer(n int, activation ActivationType) *Layer
- func (l *Layer) ApplyBias(weight WeightInitializer) []*Synapse
- func (l *Layer) Connect(next *Layer, weight WeightInitializer)
- func (l Layer) String() string
type Linear
- func (a Linear) Df(x float64) float64
- func (a Linear) F(x float64) float64
type Loss
- func GetLoss(loss LossType) Loss
type LossType
- func (l LossType) String() string
type MeanSquared
- func (l MeanSquared) Df(estimate, ideal, activation float64) float64
- func (l MeanSquared) F(estimate, ideal [][]float64) float64
type Mode
type Neural
- func FromDump(dump *Dump) *Neural
- func NewNeural(c *Config) *Neural
- func Unmarshal(bytes []byte) (*Neural, error)
- func (n *Neural) ApplyWeights(weights [][][]float64)
- func (n Neural) Dump() *Dump
- func (n *Neural) Forward(input []float64) error
- func (n Neural) Marshal() ([]byte, error)
- func (n *Neural) NumWeights() (num int)
- func (n *Neural) Predict(input []float64) []float64
- func (n *Neural) String() string
- func (n Neural) Weights() [][][]float64
type Neuron
- func NewNeuron(activation ActivationType) *Neuron
- func (n *Neuron) Activate(x float64) float64
- func (n *Neuron) DActivate(x float64) float64
type ReLU
- func (a ReLU) Df(y float64) float64
- func (a ReLU) F(x float64) float64
type Sigmoid
- func (a Sigmoid) Df(y float64) float64
- func (a Sigmoid) F(x float64) float64
type Synapse
- func NewSynapse(weight float64) *Synapse
type Tanh
- func (a Tanh) Df(y float64) float64
- func (a Tanh) F(x float64) float64
type WeightInitializer
- func NewNormal(stdDev, mean float64) WeightInitializer
- func NewUniform(stdDev, mean float64) WeightInitializer

Constants ¶

This section is empty.

Variables ¶

This section is empty.

Functions ¶

func Logistic ¶

func Logistic(x, a float64) float64

func Normal ¶

func Normal(stdDev, mean float64) float64

func Uniform ¶

func Uniform(stdDev, mean float64) float64

Types ¶

type ActivationType ¶

type ActivationType int

const (
	ActivationNone ActivationType = iota
	ActivationSigmoid
	ActivationTanh
	ActivationReLU
	ActivationLinear
	ActivationSoftmax
)

func OutputActivation ¶

func OutputActivation(c Mode) ActivationType

type BinaryCrossEntropy ¶

type BinaryCrossEntropy struct{}

func (BinaryCrossEntropy) Df ¶

func (l BinaryCrossEntropy) Df(estimate, ideal, activation float64) float64

func (BinaryCrossEntropy) F ¶

func (l BinaryCrossEntropy) F(estimate, ideal [][]float64) float64

type Config ¶

type Config struct {
	Inputs     int
	Layout     []int
	Activation ActivationType
	Mode       Mode
	Weight     WeightInitializer `json:"-"`
	Loss       LossType
	Bias       bool
}

type CrossEntropy ¶

type CrossEntropy struct{}

func (CrossEntropy) Df ¶

func (l CrossEntropy) Df(estimate, ideal, activation float64) float64

func (CrossEntropy) F ¶

func (l CrossEntropy) F(estimate, ideal [][]float64) float64

type Differentiable ¶

type Differentiable interface {
	F(float64) float64
	Df(float64) float64
}

func GetActivation ¶

func GetActivation(act ActivationType) Differentiable

type Dump ¶

type Dump struct {
	Config  *Config
	Weights [][][]float64
}

type Layer ¶

type Layer struct {
	Neurons []*Neuron
	A       ActivationType
}

func NewLayer ¶

func NewLayer(n int, activation ActivationType) *Layer

func (*Layer) ApplyBias ¶

func (l *Layer) ApplyBias(weight WeightInitializer) []*Synapse

func (*Layer) Connect ¶

func (l *Layer) Connect(next *Layer, weight WeightInitializer)

func (Layer) String ¶

func (l Layer) String() string

type Linear ¶

type Linear struct{}

func (Linear) Df ¶

func (a Linear) Df(x float64) float64

func (Linear) F ¶

func (a Linear) F(x float64) float64

type Loss ¶

type Loss interface {
	F(estimate, ideal [][]float64) float64
	Df(estimate, ideal, activation float64) float64
}

func GetLoss ¶

func GetLoss(loss LossType) Loss

type LossType ¶

type LossType int

const (
	LossNone LossType = iota
	LossCrossEntropy
	LossBinaryCrossEntropy
	LossMeanSquared
)

func (LossType) String ¶

func (l LossType) String() string

type MeanSquared ¶

type MeanSquared struct{}

func (MeanSquared) Df ¶

func (l MeanSquared) Df(estimate, ideal, activation float64) float64

func (MeanSquared) F ¶

func (l MeanSquared) F(estimate, ideal [][]float64) float64

type Mode ¶

type Mode int

const (
	ModeDefault Mode = iota
	ModeMultiClass
	ModeRegression
	ModeBinary
	ModeMultiLabel
)

type Neural ¶

type Neural struct {
	Layers []*Layer
	Biases [][]*Synapse
	Config *Config
}

func FromDump ¶

func FromDump(dump *Dump) *Neural

func NewNeural ¶

func NewNeural(c *Config) *Neural

func Unmarshal ¶

func Unmarshal(bytes []byte) (*Neural, error)

func (*Neural) ApplyWeights ¶

func (n *Neural) ApplyWeights(weights [][][]float64)

func (Neural) Dump ¶

func (n Neural) Dump() *Dump

func (*Neural) Forward ¶

func (n *Neural) Forward(input []float64) error

func (Neural) Marshal ¶

func (n Neural) Marshal() ([]byte, error)

func (*Neural) NumWeights ¶

func (n *Neural) NumWeights() (num int)

func (*Neural) Predict ¶

func (n *Neural) Predict(input []float64) []float64

func (*Neural) String ¶

func (n *Neural) String() string

func (Neural) Weights ¶

func (n Neural) Weights() [][][]float64

type Neuron ¶

type Neuron struct {
	A     ActivationType `json:"-"`
	In    []*Synapse
	Out   []*Synapse
	Value float64 `json:"-"`
}

func NewNeuron ¶

func NewNeuron(activation ActivationType) *Neuron

func (*Neuron) Activate ¶

func (n *Neuron) Activate(x float64) float64

func (*Neuron) DActivate ¶

func (n *Neuron) DActivate(x float64) float64

type ReLU ¶

type ReLU struct{}

func (ReLU) Df ¶

func (a ReLU) Df(y float64) float64

func (ReLU) F ¶

func (a ReLU) F(x float64) float64

type Sigmoid ¶

type Sigmoid struct{}

func (Sigmoid) Df ¶

func (a Sigmoid) Df(y float64) float64

func (Sigmoid) F ¶

func (a Sigmoid) F(x float64) float64

type Synapse ¶

type Synapse struct {
	Weight  float64
	In, Out float64 `json:"-"`
	IsBias  bool
}

func NewSynapse ¶

func NewSynapse(weight float64) *Synapse

type Tanh ¶

type Tanh struct{}

func (Tanh) Df ¶

func (a Tanh) Df(y float64) float64

func (Tanh) F ¶

func (a Tanh) F(x float64) float64

type WeightInitializer ¶

type WeightInitializer func() float64

func NewNormal ¶

func NewNormal(stdDev, mean float64) WeightInitializer

func NewUniform ¶

func NewUniform(stdDev, mean float64) WeightInitializer

Source Files ¶

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Directories ¶

Path	Synopsis
luaml
training

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