galaktaglare

package module
v2.0.5 Latest Latest
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 Go to latest Published: Apr 12, 2024 License: BSD-3-Clause Imports: 21 Imported by: 0

Documentation

Index

Constants

This section is empty.

Variables

This section is empty.

Functions

func Backward 

func Backward(result *Variable)

func ReLU 

func ReLU(x float64) float64

func Sigmoid 

func Sigmoid(x float64) float64

Types

type ActivationFunc 

type ActivationFunc func(float64) float64

type BatchNormalization 

type BatchNormalization struct {
	Gamma    float64
	Beta     float64
	Mean     []float64
	Variance []float64
	Epsilon  float64
	Input    []float64
	Output   []float64
}

func NewBatchNormalization 

func NewBatchNormalization(gamma, beta, epsilon float64) *BatchNormalization

func (*BatchNormalization) Forward 

func (bn *BatchNormalization) Forward(input []float64) []float64

type DataAnalysis 

type DataAnalysis struct{}

func (*DataAnalysis) Mean 

func (da *DataAnalysis) Mean(data []float64) float64

func (*DataAnalysis) Median 

func (da *DataAnalysis) Median(data []float64) float64

func (*DataAnalysis) Mode 

func (da *DataAnalysis) Mode(data []float64) []float64

type DecisionTree 

type DecisionTree struct {
	Root *TreeNode
}

func (*DecisionTree) Predict 

func (dt *DecisionTree) Predict(features []float64) string

func (*DecisionTree) Train 

func (dt *DecisionTree) Train(data []Example, maxDepth, minSamplesSplit int)

type DenseLayer 

type DenseLayer struct {
	InputSize            int
	OutputSize           int
	Weights              [][]float64
	Biases               []float64
	Activation           ActivationFunc
	Inputs               []float64
	WeightDecayL1        float64
	WeightDecayL2        float64
	DropoutRate          float64
	DropoutMask          []float64
	LearningRateSchedule LearningRateSchedule
	BatchNorm            *BatchNormalization
}

func NewDenseLayer 

func NewDenseLayer(inputSize, outputSize int, activation ActivationFunc, weightDecayL1, weightDecayL2, dropoutRate float64, lrSchedule LearningRateSchedule) *DenseLayer

func (*DenseLayer) Backpropagate 

func (l *DenseLayer) Backpropagate(output, target []float64, learningRate float64) []float64

func (*DenseLayer) Forward 

func (l *DenseLayer) Forward(input []float64) []float64

func (*DenseLayer) GetAdjustedLearningRate 

func (l *DenseLayer) GetAdjustedLearningRate(epoch int) float64

type Example 

type Example struct {
	Features []float64
	Label    string
}

type GalaktaGlare 

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

func New 

func New() *GalaktaGlare

func (*GalaktaGlare) AnomalyDetector 

func (gg *GalaktaGlare) AnomalyDetector(data []float64, threshold float64) []int

func (*GalaktaGlare) ExtractEntities 

func (gg *GalaktaGlare) ExtractEntities(text string, customStopwords map[string]bool) []string

func (*GalaktaGlare) ImageDB 

func (gg *GalaktaGlare) ImageDB(folderPath string) error

func (*GalaktaGlare) ImageScan 

func (gg *GalaktaGlare) ImageScan(imagePath string) (float64, error)

func (*GalaktaGlare) Numseq added in v2.0.4 

func (gg *GalaktaGlare) Numseq(s string) float64

func (*GalaktaGlare) StrNQ added in v2.0.4 

func (gg *GalaktaGlare) StrNQ(n float64) string

func (*GalaktaGlare) TextClassifier 

func (gg *GalaktaGlare) TextClassifier(text, config string) (string, error)

func (*GalaktaGlare) TextToSpeech added in v2.0.2 

func (gg *GalaktaGlare) TextToSpeech(text, lang string) error

type LearningRateSchedule 

type LearningRateSchedule struct {
	InitialRate float64
	Decay       float64
}

type NeuralNetwork 

type NeuralNetwork struct {
	Layers []*DenseLayer
}

func LoadModel 

func LoadModel(filename string) (*NeuralNetwork, error)

func NewNeuralNetwork 

func NewNeuralNetwork(layers ...*DenseLayer) *NeuralNetwork

func (*NeuralNetwork) Backpropagate 

func (nn *NeuralNetwork) Backpropagate(output, target []float64, learningRate float64)

func (*NeuralNetwork) MultiplyMatrixVector 

func (nn *NeuralNetwork) MultiplyMatrixVector(matrix [][]float64, vector []float64) []float64

func (*NeuralNetwork) MultiplyVectors 

func (nn *NeuralNetwork) MultiplyVectors(v1 []float64, v2 []float64) []float64

func (*NeuralNetwork) Predict 

func (nn *NeuralNetwork) Predict(input []float64) []float64

func (*NeuralNetwork) SaveModel 

func (nn *NeuralNetwork) SaveModel(filename string) error

func (*NeuralNetwork) Train 

func (nn *NeuralNetwork) Train(inputs, targets [][]float64, initialLearningRate float64, epochs int)

type Tensor 

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

func NewTensor 

func NewTensor(shape []int) *Tensor

func (*Tensor) Get 

func (t *Tensor) Get(indices ...int) float64

func (*Tensor) Set 

func (t *Tensor) Set(value float64, indices ...int)

type TrainingMonitor 

type TrainingMonitor struct {
	Epochs        int
	DisplayPeriod int
}

func NewTrainingMonitor 

func NewTrainingMonitor(epochs, displayPeriod int) *TrainingMonitor

func (*TrainingMonitor) MonitorTraining 

func (tm *TrainingMonitor) MonitorTraining(nn *NeuralNetwork, inputs, targets [][]float64, initialLearningRate float64)

type TreeNode 

type TreeNode struct {
	SplitFeature   int
	SplitValue     float64
	PredictedClass string
	Left           *TreeNode
	Right          *TreeNode
}

type Variable 

type Variable struct {
	Value     float64
	Gradient  float64
	Children  []*Variable
	Operation string
}

func Add 

func Add(a, b *Variable) *Variable

func Cos 

func Cos(a *Variable) *Variable

func Mul 

func Mul(a, b *Variable) *Variable

func NewVariable 

func NewVariable(value float64, operation string) *Variable

func Sin 

func Sin(a *Variable) *Variable

Source Files

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