models

Documentation

Index

• func KNN(training, testing [][]float64, trainingname, testingname []string, ...) [][]string
• type Network
  • func CreateNN(input, hidden, output int, rate float64) (net Network)
  • func (net *Network) Predict(training [][]float64) [][]string
  • func (n *Network) Train(training [][]float64, trainingname []string, epochcount int)

Functions

func KNN

func KNN(training, testing [][]float64, trainingname, testingname []string, k, threads int, train, regression bool) [][]string

Types

type Network

type Network struct {
	Inputs        int
	Hiddens       int
	Outputs       int
	HiddenWeights *mat.Dense
	OutputWeights *mat.Dense
	LearningRate  float64
	Targetmap     map[int]string
}

ANN & helper FUNCTIONS //////////////////////////////////////////////////////////////////////// 3 LAYER NEURAL NETWORK MODEL - initial code courtesy of ---> github.com/sausheong/gonn I have added multiple layers of functionality to make it avaialble to all sorts of custom datasets.

func CreateNN

func CreateNN(input, hidden, output int, rate float64) (net Network)

func (*Network) Predict

func (net *Network) Predict(training [][]float64) [][]string

func (*Network) Train

func (n *Network) Train(training [][]float64, trainingname []string, epochcount int)