go-chart/linear_regression_series.go
2018-12-06 16:38:58 +01:00

191 lines
4.7 KiB
Go

package chart
import (
"fmt"
"git.gutmet.org/go-chart.git/seq"
util "git.gutmet.org/go-chart.git/util"
)
// Interface Assertions.
var (
_ Series = (*LinearRegressionSeries)(nil)
_ FirstValuesProvider = (*LinearRegressionSeries)(nil)
_ LastValuesProvider = (*LinearRegressionSeries)(nil)
_ LinearCoefficientProvider = (*LinearRegressionSeries)(nil)
)
// LinearRegressionSeries is a series that plots the n-nearest neighbors
// linear regression for the values.
type LinearRegressionSeries struct {
Name string
Style Style
YAxis YAxisType
Limit int
Offset int
InnerSeries ValuesProvider
m float64
b float64
avgx float64
stddevx float64
}
// Coefficients returns the linear coefficients for the series.
func (lrs LinearRegressionSeries) Coefficients() (m, b, stdev, avg float64) {
if lrs.IsZero() {
lrs.computeCoefficients()
}
m = lrs.m
b = lrs.b
stdev = lrs.stddevx
avg = lrs.avgx
return
}
// GetName returns the name of the time series.
func (lrs LinearRegressionSeries) GetName() string {
return lrs.Name
}
// GetStyle returns the line style.
func (lrs LinearRegressionSeries) GetStyle() Style {
return lrs.Style
}
// GetYAxis returns which YAxis the series draws on.
func (lrs LinearRegressionSeries) GetYAxis() YAxisType {
return lrs.YAxis
}
// Len returns the number of elements in the series.
func (lrs LinearRegressionSeries) Len() int {
return util.Math.MinInt(lrs.GetLimit(), lrs.InnerSeries.Len()-lrs.GetOffset())
}
// GetLimit returns the window size.
func (lrs LinearRegressionSeries) GetLimit() int {
if lrs.Limit == 0 {
return lrs.InnerSeries.Len()
}
return lrs.Limit
}
// GetEndIndex returns the effective limit end.
func (lrs LinearRegressionSeries) GetEndIndex() int {
windowEnd := lrs.GetOffset() + lrs.GetLimit()
innerSeriesLastIndex := lrs.InnerSeries.Len() - 1
return util.Math.MinInt(windowEnd, innerSeriesLastIndex)
}
// GetOffset returns the data offset.
func (lrs LinearRegressionSeries) GetOffset() int {
if lrs.Offset == 0 {
return 0
}
return lrs.Offset
}
// GetValues gets a value at a given index.
func (lrs *LinearRegressionSeries) GetValues(index int) (x, y float64) {
if lrs.InnerSeries == nil || lrs.InnerSeries.Len() == 0 {
return
}
if lrs.IsZero() {
lrs.computeCoefficients()
}
offset := lrs.GetOffset()
effectiveIndex := util.Math.MinInt(index+offset, lrs.InnerSeries.Len())
x, y = lrs.InnerSeries.GetValues(effectiveIndex)
y = (lrs.m * lrs.normalize(x)) + lrs.b
return
}
// GetFirstValues computes the first linear regression value.
func (lrs *LinearRegressionSeries) GetFirstValues() (x, y float64) {
if lrs.InnerSeries == nil || lrs.InnerSeries.Len() == 0 {
return
}
if lrs.IsZero() {
lrs.computeCoefficients()
}
x, y = lrs.InnerSeries.GetValues(0)
y = (lrs.m * lrs.normalize(x)) + lrs.b
return
}
// GetLastValues computes the last linear regression value.
func (lrs *LinearRegressionSeries) GetLastValues() (x, y float64) {
if lrs.InnerSeries == nil || lrs.InnerSeries.Len() == 0 {
return
}
if lrs.IsZero() {
lrs.computeCoefficients()
}
endIndex := lrs.GetEndIndex()
x, y = lrs.InnerSeries.GetValues(endIndex)
y = (lrs.m * lrs.normalize(x)) + lrs.b
return
}
// Render renders the series.
func (lrs *LinearRegressionSeries) Render(r Renderer, canvasBox Box, xrange, yrange Range, defaults Style) {
style := lrs.Style.InheritFrom(defaults)
Draw.LineSeries(r, canvasBox, xrange, yrange, style, lrs)
}
// Validate validates the series.
func (lrs *LinearRegressionSeries) Validate() error {
if lrs.InnerSeries == nil {
return fmt.Errorf("linear regression series requires InnerSeries to be set")
}
return nil
}
// IsZero returns if we've computed the coefficients or not.
func (lrs *LinearRegressionSeries) IsZero() bool {
return lrs.m == 0 && lrs.b == 0
}
//
// internal helpers
//
func (lrs *LinearRegressionSeries) normalize(xvalue float64) float64 {
return (xvalue - lrs.avgx) / lrs.stddevx
}
// computeCoefficients computes the `m` and `b` terms in the linear formula given by `y = mx+b`.
func (lrs *LinearRegressionSeries) computeCoefficients() {
startIndex := lrs.GetOffset()
endIndex := lrs.GetEndIndex()
p := float64(endIndex - startIndex)
xvalues := seq.NewBufferWithCapacity(lrs.Len())
for index := startIndex; index < endIndex; index++ {
x, _ := lrs.InnerSeries.GetValues(index)
xvalues.Enqueue(x)
}
lrs.avgx = seq.Seq{Provider: xvalues}.Average()
lrs.stddevx = seq.Seq{Provider: xvalues}.StdDev()
var sumx, sumy, sumxx, sumxy float64
for index := startIndex; index < endIndex; index++ {
x, y := lrs.InnerSeries.GetValues(index)
x = lrs.normalize(x)
sumx += x
sumy += y
sumxx += x * x
sumxy += x * y
}
lrs.m = (p*sumxy - sumx*sumy) / (p*sumxx - sumx*sumx)
lrs.b = (sumy / p) - (lrs.m * sumx / p)
}