util

Documentation

Overview

Package util ...

Index

• func AnnealingLearningRate(eta0, decay float64, epoch int) float64
• func GenInputsAndLabelsFromCorpus[T TokenType](corpus [][]T, vocab map[T]int, ctxLen int) ([][]int, []int)
• func GenVocabFromCorpus[T TokenType](corpus [][]T, eos T) map[T]int
• func GetIndexToToken[T TokenType](vocab map[T]int) []T
• func IndexesToTokens[T TokenType](indexes []int, i2t []T) []T
• func InteractiveTrain(cfg *TrainConfig, interactive bool, train func() time.Duration, predict func())
• func MaskedAttention[T any](ks, qs, vs [][]T, mul func([]T, []T) T, softmax func([]T) []T) (out []T)
• func Must1[T any](x T, err error) T
• func NumberSliceConvert[From, To constraints.Integer | constraints.Float](vs []From) []To
• func OneHot(index, vocabSize int) []float64
• func OneHots(indexes []int, vocabSize int) []float64
• func PredictionPrecision(p Predictor, testSet []Sample, isCorrect IsCorrectFunc) float32
• func RandMultinomial(probDist []float64) int
• func RandomUniformSample(min, max float64) float64
• func ReadCSVDataSet(file string) ([][]string, error)
• func ShuffleSamples[T any](samples []T)
• func SliceConvert[From, To any](s []From) []To
• func Softmax(t float64, xs []float64) []float64
• func SplitDataSet[T any](samples []T, trainSplit, valSplit float64) (training, validation, testing []T)
• func TokensToIndexes[T TokenType](tokens []T, vocab map[T]int) []int
• func Transpose[T any](mat [][]T) [][]T
• type IsCorrectFunc
• type LearningRateFunc
  • func AnnealingLearningRateFunc(eta, decay float64, epoch int) LearningRateFunc
• type Optimizer
  • func AdamOptimizer(params []Parameter, eta, beta1, beta2, epsilon float64) Optimizer
  • func DefaultAdamOptimizer(params []Parameter, eta float64) Optimizer
  • func SGDOptimizer(params []Parameter, eta float64) Optimizer
• type Parameter
• type Predictor
• type Sample
  • func GenDatasetFromInputsAndLabels(inputs [][]int, labels []int) (samples []Sample)
  • func GenOneHotDatasetFromInputsAndLabels(inputs [][]int, labels []int, vocabSize int) (samples []Sample)
• type TokenType
• type TrainConfig

Functions

func AnnealingLearningRate

func AnnealingLearningRate(eta0, decay float64, epoch int) float64

AnnealingLearningRate returns an annealed learning rate by the following formula:

η = η_0 / (1 + decay * epoch)

func GenInputsAndLabelsFromCorpus

func GenInputsAndLabelsFromCorpus[T TokenType](corpus [][]T, vocab map[T]int, ctxLen int) ([][]int, []int)

func GenVocabFromCorpus

func GenVocabFromCorpus[T TokenType](corpus [][]T, eos T) map[T]int

func GetIndexToToken

func GetIndexToToken[T TokenType](vocab map[T]int) []T

func IndexesToTokens

func IndexesToTokens[T TokenType](indexes []int, i2t []T) []T

func InteractiveTrain

func InteractiveTrain(cfg *TrainConfig, interactive bool, train func() time.Duration, predict func())

func MaskedAttention

func MaskedAttention[T any](ks, qs, vs [][]T, mul func([]T, []T) T, softmax func([]T) []T) (out []T)

func Must1

func Must1[T any](x T, err error) T

func NumberSliceConvert

func NumberSliceConvert[From, To constraints.Integer | constraints.Float](vs []From) []To

func OneHot

func OneHot(index, vocabSize int) []float64

func OneHots

func OneHots(indexes []int, vocabSize int) []float64

func PredictionPrecision

func PredictionPrecision(p Predictor, testSet []Sample, isCorrect IsCorrectFunc) float32

func RandMultinomial

func RandMultinomial(probDist []float64) int

func RandomUniformSample

func RandomUniformSample(min, max float64) float64

func ReadCSVDataSet

func ReadCSVDataSet(file string) ([][]string, error)

func ShuffleSamples

func ShuffleSamples[T any](samples []T)

func SliceConvert

func SliceConvert[From, To any](s []From) []To

func Softmax

func Softmax(t float64, xs []float64) []float64

func SplitDataSet

func SplitDataSet[T any](samples []T, trainSplit, valSplit float64) (training, validation, testing []T)

func TokensToIndexes

func TokensToIndexes[T TokenType](tokens []T, vocab map[T]int) []int

func Transpose

func Transpose[T any](mat [][]T) [][]T

Types

type IsCorrectFunc

type IsCorrectFunc func(pred, label []float64) bool

type LearningRateFunc

type LearningRateFunc func(grad float64) (rate float64)

func AnnealingLearningRateFunc

func AnnealingLearningRateFunc(eta, decay float64, epoch int) LearningRateFunc

type Optimizer

type Optimizer interface {
	Learn() float64
}

func AdamOptimizer

func AdamOptimizer(params []Parameter, eta, beta1, beta2, epsilon float64) Optimizer

func DefaultAdamOptimizer

func DefaultAdamOptimizer(params []Parameter, eta float64) Optimizer

func SGDOptimizer

func SGDOptimizer(params []Parameter, eta float64) Optimizer

type Parameter

type Parameter interface {
	Learn(delta float64)
	SetV(float64)
	ZeroG()
	G() float64
}

type Predictor

type Predictor interface {
	Predict([]float64) []float64
}

type Sample

type Sample struct {
	X []float64 // input
	Y []float64 // output
}

func GenDatasetFromInputsAndLabels

func GenDatasetFromInputsAndLabels(inputs [][]int, labels []int) (samples []Sample)

func GenOneHotDatasetFromInputsAndLabels

func GenOneHotDatasetFromInputsAndLabels(inputs [][]int, labels []int, vocabSize int) (samples []Sample)

type TokenType

type TokenType interface {
	~string | byte
}

type TrainConfig

type TrainConfig struct {
	BatchSize        int
	Epochs           int
	StopEps          float64
	Optimizer        string
	LearningRate     float64
	LogEpochInterval int
}