nn/main.go

package nn

import (
	"fmt"
	"math/rand"
	"strings"

	"github.com/go-gota/gota/dataframe"
	"github.com/gofiber/fiber/v2"
)

type NN struct {
	CSVData        string   `json:"csv_data"`
	Features       []string `json:"features"`
	Target         string   `json:"target"`
	Epochs         int      `json:"epochs"`
	HiddenSize     int      `json:"hidden_size"`
	LearningRate   float64  `json:"learning_rate"`
	ActivationFunc string   `json:"activation"`
	TestSize       float64  `json:"test_size"`

	Df     *dataframe.DataFrame
	XTrain dataframe.DataFrame
	YTrain dataframe.DataFrame
	XTest  dataframe.DataFrame
	YTest  dataframe.DataFrame
	Wh     [][]float64
	Bh     []float64
	Wo     [][]float64
	Bo     []float64
}

func NewNN(c *fiber.Ctx) (*NN, error) {
	newNN := new(NN)
	err := c.BodyParser(newNN)
	if err != nil {
		return nil, fmt.Errorf("invalid JSON data: %v", err)
	}
	df := dataframe.ReadCSV(strings.NewReader(newNN.CSVData))
	newNN.Df = &df
	return newNN, nil
}

func (nn *NN) Train() {
	// train test split the data
	_, _, _, _ = nn.trainTestSplit()

	nn.InitWnB()

	// iterate n times where n = nn.Epochs
	// use backprop algorithm on each iteration
	// to fit the model to the data
	for i := 0; i < nn.Epochs; i++ {
	}

}

func (nn *NN) InitWnB() {
	// randomly initialize weights and biases to start
	inputSize := len(nn.Features)
	hiddenSize := nn.HiddenSize
	outputSize := 1 // only predicting one thing

	// input hidden layer weights
	wh := make([][]float64, inputSize)
	for i := range wh {
		wh[i] = make([]float64, hiddenSize)
		for j := range wh[i] {
			wh[i][j] = rand.Float64() - 0.5
		}
	}

	bh := make([]float64, hiddenSize)
	for i := range bh {
		bh[i] = rand.Float64() - 0.5
	}

	// initialize weights and biases for hidden -> output layer
	wo := make([][]float64, hiddenSize)
	for i := range wo {
		wo[i] = make([]float64, outputSize)
		for j := range wo[i] {
			wo[i][j] = rand.Float64() - 0.5
		}
	}

	bo := make([]float64, outputSize)
	for i := range bo {
		bo[i] = rand.Float64() - 0.5
	}

	nn.Wh = wh
	nn.Bh = bh
	nn.Wo = wo
	nn.Bo = bo
}