114 lines
2.4 KiB
Go
114 lines
2.4 KiB
Go
|
package stats
|
||
|
|
|
||
|
|
import "math"
|
||
|
|
|
||
|
|
// Series is a container for a series of data
|
||
|
|
type Series []Coordinate
|
||
|
|
|
||
|
|
// Coordinate holds the data in a series
|
||
|
|
type Coordinate struct {
|
||
|
|
X, Y float64
|
||
|
|
}
|
||
|
|
|
||
|
|
// LinearRegression finds the least squares linear regression on data series
|
||
|
|
func LinearRegression(s Series) (regressions Series, err error) {
|
||
|
|
|
||
|
|
if len(s) == 0 {
|
||
|
|
return nil, EmptyInput
|
||
|
|
}
|
||
|
|
|
||
|
|
// Placeholder for the math to be done
|
||
|
|
var sum [5]float64
|
||
|
|
|
||
|
|
// Loop over data keeping index in place
|
||
|
|
i := 0
|
||
|
|
for ; i < len(s); i++ {
|
||
|
|
sum[0] += s[i].X
|
||
|
|
sum[1] += s[i].Y
|
||
|
|
sum[2] += s[i].X * s[i].X
|
||
|
|
sum[3] += s[i].X * s[i].Y
|
||
|
|
sum[4] += s[i].Y * s[i].Y
|
||
|
|
}
|
||
|
|
|
||
|
|
// Find gradient and intercept
|
||
|
|
f := float64(i)
|
||
|
|
gradient := (f*sum[3] - sum[0]*sum[1]) / (f*sum[2] - sum[0]*sum[0])
|
||
|
|
intercept := (sum[1] / f) - (gradient * sum[0] / f)
|
||
|
|
|
||
|
|
// Create the new regression series
|
||
|
|
for j := 0; j < len(s); j++ {
|
||
|
|
regressions = append(regressions, Coordinate{
|
||
|
|
X: s[j].X,
|
||
|
|
Y: s[j].X*gradient + intercept,
|
||
|
|
})
|
||
|
|
}
|
||
|
|
|
||
|
|
return regressions, nil
|
||
|
|
|
||
|
|
}
|
||
|
|
|
||
|
|
// ExponentialRegression returns an exponential regression on data series
|
||
|
|
func ExponentialRegression(s Series) (regressions Series, err error) {
|
||
|
|
|
||
|
|
if len(s) == 0 {
|
||
|
|
return nil, EmptyInput
|
||
|
|
}
|
||
|
|
|
||
|
|
var sum [6]float64
|
||
|
|
|
||
|
|
for i := 0; i < len(s); i++ {
|
||
|
|
sum[0] += s[i].X
|
||
|
|
sum[1] += s[i].Y
|
||
|
|
sum[2] += s[i].X * s[i].X * s[i].Y
|
||
|
|
sum[3] += s[i].Y * math.Log(s[i].Y)
|
||
|
|
sum[4] += s[i].X * s[i].Y * math.Log(s[i].Y)
|
||
|
|
sum[5] += s[i].X * s[i].Y
|
||
|
|
}
|
||
|
|
|
||
|
|
denominator := (sum[1]*sum[2] - sum[5]*sum[5])
|
||
|
|
a := math.Pow(math.E, (sum[2]*sum[3]-sum[5]*sum[4])/denominator)
|
||
|
|
b := (sum[1]*sum[4] - sum[5]*sum[3]) / denominator
|
||
|
|
|
||
|
|
for j := 0; j < len(s); j++ {
|
||
|
|
regressions = append(regressions, Coordinate{
|
||
|
|
X: s[j].X,
|
||
|
|
Y: a * math.Exp(b*s[j].X),
|
||
|
|
})
|
||
|
|
}
|
||
|
|
|
||
|
|
return regressions, nil
|
||
|
|
|
||
|
|
}
|
||
|
|
|
||
|
|
// LogarithmicRegression returns an logarithmic regression on data series
|
||
|
|
func LogarithmicRegression(s Series) (regressions Series, err error) {
|
||
|
|
|
||
|
|
if len(s) == 0 {
|
||
|
|
return nil, EmptyInput
|
||
|
|
}
|
||
|
|
|
||
|
|
var sum [4]float64
|
||
|
|
|
||
|
|
i := 0
|
||
|
|
for ; i < len(s); i++ {
|
||
|
|
sum[0] += math.Log(s[i].X)
|
||
|
|
sum[1] += s[i].Y * math.Log(s[i].X)
|
||
|
|
sum[2] += s[i].Y
|
||
|
|
sum[3] += math.Pow(math.Log(s[i].X), 2)
|
||
|
|
}
|
||
|
|
|
||
|
|
f := float64(i)
|
||
|
|
a := (f*sum[1] - sum[2]*sum[0]) / (f*sum[3] - sum[0]*sum[0])
|
||
|
|
b := (sum[2] - a*sum[0]) / f
|
||
|
|
|
||
|
|
for j := 0; j < len(s); j++ {
|
||
|
|
regressions = append(regressions, Coordinate{
|
||
|
|
X: s[j].X,
|
||
|
|
Y: b + a*math.Log(s[j].X),
|
||
|
|
})
|
||
|
|
}
|
||
|
|
|
||
|
|
return regressions, nil
|
||
|
|
|
||
|
|
}
|