package neuralnetworkbase

import (
	math "math"
	rand "math/rand"
	"time"

	mat "gonum.org/v1/gonum/mat"
)

func generateRandomDense(rows, columns int) *mat.Dense {
	rand.Seed(time.Now().UnixNano())
	data := make([]float64, rows*columns)
	for i := range data {
		data[i] = rand.NormFloat64()
	}
	return mat.NewDense(rows, columns, data)
}

func applySigmoid(_, _ int, x float64) float64 {
	return sigmoid(x)
}

func applySigmoidPrime(_, _ int, x float64) float64 {
	return sigmoidPrime(x)
}

func sigmoid(x float64) float64 {
	return 1.0 / (1.0 + math.Exp(-x))
}

func sigmoidPrime(x float64) float64 {
	return sigmoid(x) * (1 - sigmoid(x))
}

func makeBackGradien(in mat.Matrix, actual mat.Matrix, alpha float64) *mat.Dense {
	scaled := &mat.Dense{}
	result := &mat.Dense{}

	scaled.Scale(alpha, in)
	result.Sub(actual, scaled)
	return result
}