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