tests.

linear_regression_series.go

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+package chart
+
+// LinearRegressionSeries is a series that plots the n-nearest neighbors
+// linear regression for the values.
+type LinearRegressionSeries struct {
+	Name  string
+	Style Style
+	YAxis YAxisType
+
+	Window      int
+	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.
+func (lrs LinearRegressionSeries) GetYAxis() YAxisType {
+	return lrs.YAxis
+}
+
+// Len returns the number of elements in the series.
+func (lrs LinearRegressionSeries) Len() int {
+	return lrs.InnerSeries.Len()
+}
+
+// GetWindow returns the window size.
+func (lrs LinearRegressionSeries) GetWindow() int {
+	if lrs.Window == 0 {
+		return lrs.InnerSeries.Len()
+	}
+	return lrs.Window
+}
+
+// GetEffectiveWindowEnd returns the effective window end.
+func (lrs LinearRegressionSeries) GetEffectiveWindowEnd() int {
+	offset := lrs.GetOffset()
+	windowEnd := offset + lrs.GetWindow()
+	return MinInt(windowEnd, lrs.Len()-1)
+}
+
+// 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 {
+		return
+	}
+	if lrs.m == 0 && lrs.b == 0 {
+		lrs.computeCoefficients()
+	}
+	offset := lrs.GetOffset()
+	x, y = lrs.InnerSeries.GetValue(index + offset)
+	y = (lrs.m * lrs.normalize(x)) + lrs.b
+	return
+}
+
+// GetLastValue computes the last moving average value but walking back window size samples,
+// and recomputing the last moving average chunk.
+func (lrs *LinearRegressionSeries) GetLastValue() (x, y float64) {
+	if lrs.InnerSeries == nil {
+		return
+	}
+	if lrs.m == 0 && lrs.b == 0 {
+		lrs.computeCoefficients()
+	}
+	endIndex := lrs.GetEffectiveWindowEnd()
+	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()
+	endIndex := lrs.GetEffectiveWindowEnd()
+
+	valueCount := endIndex - startIndex
+
+	p := float64(endIndex - startIndex)
+
+	xvalues := NewRingBufferWithCapacity(valueCount)
+	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)
+	DrawLineSeries(r, canvasBox, xrange, yrange, style, lrs)
+}