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) }