calc.go

package main

import (
	"math"
)

type NormalDist struct {
	Mu, Sigma float64
}

func (c *Classifier) avg(param string, ctg float64) float64 {
	var total float64 = 0.0
	var jumlah float64 = 0.0
	for i := 0; i < len(c.datatrain); i++ {
		//cek classnya
		if c.datatrain[i]["cuaca"] != ctg {
			continue
		}
		value, isFound := c.datatrain[i][param]
		if isFound {
			total += value
			jumlah += 1
		}
	}
	return (total / jumlah)
}

// Calculate the standard deviation of a list of numbers
func (c *Classifier) stdev(param string, ctg float64) float64 {
	var sd float64
	var total float64 = 0.0
	var jumlah float64 = 0.0
	for i := 0; i < len(c.datatrain); i++ {
		//cek classnya
		if c.datatrain[i]["cuaca"] != ctg {
			continue
		}
		// The use of Pow math function func Pow(x, y float64) float64
		sd += math.Pow(c.datatrain[i][param]-c.avg(param, ctg), 2)
		value, isFound := c.datatrain[i][param]
		if isFound {
			total += value
			jumlah += 1
		}
	}
	// The use of Sqrt math function func Sqrt(x float64) float64
	sd = math.Sqrt(sd / float64(len(c.datatrain)))
	return sd
}

// 1/sqrt(2 * pi)
const invSqrt2Pi = 0.39894228040143267793994605993438186847585863116493465766592583

func (n NormalDist) PDF(x float64) float64 {
	z := x - n.Mu
	return math.Exp(-z*z/(2*n.Sigma*n.Sigma)) * invSqrt2Pi / n.Sigma
}

// Calculate the mean of a list of numbers
func mean(numbers []float64) float64 {
	return sum(numbers) / float64(len(numbers))
}

// Calculate the standard deviation of a list of numbers
func stdev1(numbers []float64) float64 {
	var sd float64
	for j := 0; j < len(numbers); j++ {
		// The use of Pow math function func Pow(x, y float64) float64
		sd += math.Pow(numbers[j]-mean(numbers), 2)
	}
	// The use of Sqrt math function func Sqrt(x float64) float64
	sd = math.Sqrt(sd / float64(len(numbers)))
	return sd //sqrt(variance)
}

func sum(arr []float64) float64 {
	var res float64
	res = 0
	for i := 0; i < len(arr); i++ {
		res += arr[i]
	}
	return res
}