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c4c4cbff9c
Also adds example of fit output for piecewise constant, piecewise linear, Akima and Fritsch-Butland methods.
105 lines
3.5 KiB
Go
105 lines
3.5 KiB
Go
// Copyright ©2020 The Gonum Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style
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// license that can be found in the LICENSE file.
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package interp_test
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import (
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"fmt"
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"math"
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"os"
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"text/tabwriter"
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"gonum.org/v1/gonum/interp"
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)
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func ExamplePredictor() {
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// An example of fitting different interpolation
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// algorithms to (X, Y) data with widely varying slope.
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//
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// Cubic interpolators have to balance the smoothness
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// of the generated curve with suppressing ugly wiggles
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// (compare the output of AkimaSpline with that of
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// FritschButland).
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xs := []float64{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}
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ys := []float64{0, 0.001, 0.002, 0.1, 1, 2, 2.5, -10, -10.01, 2.49, 2.53, 2.55}
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var pc interp.PiecewiseConstant
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var pl interp.PiecewiseLinear
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var as interp.AkimaSpline
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var fb interp.FritschButland
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predictors := []interp.FittablePredictor{&pc, &pl, &as, &fb}
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for i, p := range predictors {
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err := p.Fit(xs, ys)
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if err != nil {
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panic(fmt.Sprintf("Error fitting %d-th predictor: %v", i, err))
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}
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}
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n := len(xs)
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dx := 0.25
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nPts := int(math.Round(float64(n-1)/dx)) + 1
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w := tabwriter.NewWriter(os.Stdout, 8, 0, 1, ' ', tabwriter.AlignRight)
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fmt.Fprintln(w, "x\tPC\tPL\tAS\tFB\t")
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for i := 0; i < nPts; i++ {
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x := xs[0] + float64(i)*dx
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fmt.Fprintf(w, "%.2f", x)
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for _, predictor := range predictors {
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y := predictor.Predict(x)
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fmt.Fprintf(w, "\t%.2f", y)
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}
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fmt.Fprintln(w, "\t")
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}
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fmt.Fprintln(w)
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w.Flush()
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// Output:
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// x PC PL AS FB
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// 0.00 0.00 0.00 0.00 0.00
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// 0.25 0.00 0.00 0.00 0.00
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// 0.50 0.00 0.00 0.00 0.00
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// 0.75 0.00 0.00 0.00 0.00
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// 1.00 0.00 0.00 0.00 0.00
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// 1.25 0.00 0.00 0.00 0.00
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// 1.50 0.00 0.00 0.00 0.00
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// 1.75 0.00 0.00 0.00 0.00
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// 2.00 0.00 0.00 0.00 0.00
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// 2.25 0.10 0.03 -0.01 0.01
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// 2.50 0.10 0.05 -0.01 0.03
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// 2.75 0.10 0.08 0.02 0.06
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// 3.00 0.10 0.10 0.10 0.10
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// 3.25 1.00 0.33 0.26 0.22
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// 3.50 1.00 0.55 0.49 0.45
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// 3.75 1.00 0.78 0.75 0.73
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// 4.00 1.00 1.00 1.00 1.00
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// 4.25 2.00 1.25 1.24 1.26
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// 4.50 2.00 1.50 1.50 1.54
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// 4.75 2.00 1.75 1.75 1.79
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// 5.00 2.00 2.00 2.00 2.00
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// 5.25 2.50 2.12 2.22 2.17
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// 5.50 2.50 2.25 2.37 2.33
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// 5.75 2.50 2.38 2.47 2.45
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// 6.00 2.50 2.50 2.50 2.50
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// 6.25 -10.00 -0.62 0.83 0.55
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// 6.50 -10.00 -3.75 -2.98 -3.75
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// 6.75 -10.00 -6.88 -7.18 -8.04
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// 7.00 -10.00 -10.00 -10.00 -10.00
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// 7.25 -10.01 -10.00 -11.16 -10.00
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// 7.50 -10.01 -10.00 -11.55 -10.01
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// 7.75 -10.01 -10.01 -11.18 -10.01
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// 8.00 -10.01 -10.01 -10.01 -10.01
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// 8.25 2.49 -6.88 -7.18 -8.06
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// 8.50 2.49 -3.76 -2.99 -3.77
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// 8.75 2.49 -0.63 0.82 0.53
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// 9.00 2.49 2.49 2.49 2.49
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// 9.25 2.53 2.50 2.50 2.51
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// 9.50 2.53 2.51 2.51 2.52
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// 9.75 2.53 2.52 2.52 2.52
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// 10.00 2.53 2.53 2.53 2.53
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// 10.25 2.55 2.53 2.54 2.54
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// 10.50 2.55 2.54 2.54 2.54
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// 10.75 2.55 2.54 2.55 2.55
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// 11.00 2.55 2.55 2.55 2.55
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}
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