go-chart/linear_regression_series.go

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package chart
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import "fmt"
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// LinearRegressionSeries is a series that plots the n-nearest neighbors
// linear regression for the values.
type LinearRegressionSeries struct {
Name string
Style Style
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YAxis YAxisType
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Limit int
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Offset int
InnerSeries ValueProvider
m float64
b float64
avgx float64
stddevx float64
}
// 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.
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func (lrs LinearRegressionSeries) GetYAxis() YAxisType {
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return lrs.YAxis
}
// Len returns the number of elements in the series.
func (lrs LinearRegressionSeries) Len() int {
return Math.MinInt(lrs.GetLimit(), lrs.InnerSeries.Len()-lrs.GetOffset())
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}
// GetLimit returns the window size.
func (lrs LinearRegressionSeries) GetLimit() int {
if lrs.Limit == 0 {
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return lrs.InnerSeries.Len()
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}
return lrs.Limit
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}
// GetEndIndex returns the effective limit end.
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func (lrs LinearRegressionSeries) GetEndIndex() int {
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windowEnd := lrs.GetOffset() + lrs.GetLimit()
innerSeriesLastIndex := lrs.InnerSeries.Len() - 1
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return Math.MinInt(windowEnd, innerSeriesLastIndex)
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}
// GetOffset returns the data offset.
func (lrs LinearRegressionSeries) GetOffset() int {
if lrs.Offset == 0 {
return 0
}
return lrs.Offset
}
// GetValue gets a value at a given index.
func (lrs *LinearRegressionSeries) GetValue(index int) (x, y float64) {
if lrs.InnerSeries == nil || lrs.InnerSeries.Len() == 0 {
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return
}
if lrs.m == 0 && lrs.b == 0 {
lrs.computeCoefficients()
}
offset := lrs.GetOffset()
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effectiveIndex := Math.MinInt(index+offset, lrs.InnerSeries.Len())
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x, y = lrs.InnerSeries.GetValue(effectiveIndex)
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y = (lrs.m * lrs.normalize(x)) + lrs.b
return
}
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// GetLastValue computes the last linear regression value.
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func (lrs *LinearRegressionSeries) GetLastValue() (x, y float64) {
if lrs.InnerSeries == nil || lrs.InnerSeries.Len() == 0 {
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return
}
if lrs.m == 0 && lrs.b == 0 {
lrs.computeCoefficients()
}
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endIndex := lrs.GetEndIndex()
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x, y = lrs.InnerSeries.GetValue(endIndex)
y = (lrs.m * lrs.normalize(x)) + lrs.b
return
}
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()
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endIndex := lrs.GetEndIndex()
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p := float64(endIndex - startIndex)
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xvalues := NewRingBufferWithCapacity(lrs.Len())
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for index := startIndex; index < endIndex; index++ {
x, _ := lrs.InnerSeries.GetValue(index)
xvalues.Enqueue(x)
}
lrs.avgx = xvalues.Average()
lrs.stddevx = xvalues.StdDev()
var sumx, sumy, sumxx, sumxy float64
for index := startIndex; index < endIndex; index++ {
x, y := lrs.InnerSeries.GetValue(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)
}
// Render renders the series.
func (lrs *LinearRegressionSeries) Render(r Renderer, canvasBox Box, xrange, yrange Range, defaults Style) {
style := lrs.Style.InheritFrom(defaults)
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Draw.LineSeries(r, canvasBox, xrange, yrange, style, lrs)
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}
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// 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
}