Add a new interp package with interpolation algorithms: constant, piecewise linear and piecewise constant

interp/interp_test.go

@@ -0,0 +1,193 @@
+// Copyright ©2020 The Gonum Authors. All rights reserved.
+// Use of this source code is governed by a BSD-style
+// license that can be found in the LICENSE file.
+
+package interp
+
+import (
+	"fmt"
+	"math"
+	"testing"
+)
+
+func TestConstant(t *testing.T) {
+	t.Parallel()
+	const value = 42.0
+	c := Constant(value)
+	xs := []float64{math.Inf(-1), -11, 0.4, 1e9, math.Inf(1)}
+	for _, x := range xs {
+		y := c.Predict(x)
+		if y != value {
+			t.Errorf("unexpected Predict(%g) value: got: %g want: %g", x, y, value)
+		}
+	}
+}
+
+func TestFunction(t *testing.T) {
+	fn := func(x float64) float64 { return math.Exp(x) }
+	predictor := Function(fn)
+	xs := []float64{-100, -1, 0, 0.5, 15}
+	for _, x := range xs {
+		want := fn(x)
+		got := predictor.Predict(x)
+		if got != want {
+			t.Errorf("unexpected Predict(%g) value: got: %g want: %g", x, got, want)
+		}
+	}
+}
+
+func TestFindSegment(t *testing.T) {
+	t.Parallel()
+	xs := []float64{0, 1, 2}
+	testXs := []float64{-0.6, 0, 0.3, 1, 1.5, 2, 2.8}
+	expectedIs := []int{-1, 0, 0, 1, 1, 2, 2}
+	for k, x := range testXs {
+		i := findSegment(xs, x)
+		if i != expectedIs[k] {
+			t.Errorf("unexpected value of findSegment(xs, %g): got %d want: %d", x, i, expectedIs[k])
+		}
+	}
+}
+
+func BenchmarkFindSegment(b *testing.B) {
+	xs := []float64{0, 1.5, 3, 4.5, 6, 7.5, 9, 12, 13.5, 16.5}
+	for i := 0; i < b.N; i++ {
+		findSegment(xs, 0)
+		findSegment(xs, 16.5)
+		findSegment(xs, -1)
+		findSegment(xs, 8.25)
+		findSegment(xs, 4.125)
+		findSegment(xs, 13.6)
+		findSegment(xs, 23.6)
+		findSegment(xs, 13.5)
+		findSegment(xs, 6)
+		findSegment(xs, 4.5)
+	}
+}
+
+// testPiecewiseInterpolatorCreation tests common functionality in creating piecewise  interpolators.
+func testPiecewiseInterpolatorCreation(t *testing.T, fp FittablePredictor) {
+	type errorParams struct {
+		xs              []float64
+		ys              []float64
+		expectedMessage string
+	}
+	errorParamSets := []errorParams{
+		{[]float64{0, 1, 2}, []float64{-0.5, 1.5}, "xs and ys have different lengths"},
+		{[]float64{0.3}, []float64{0}, "too few points for interpolation"},
+		{[]float64{0.3, 0.3}, []float64{0, 0}, "xs values not strictly increasing"},
+		{[]float64{0.3, -0.3}, []float64{0, 0}, "xs values not strictly increasing"},
+	}
+	for _, params := range errorParamSets {
+		err := fp.Fit(params.xs, params.ys)
+		expectedMessage := fmt.Sprintf("interp: %s", params.expectedMessage)
+		if err == nil || err.Error() != expectedMessage {
+			t.Errorf("expected error for xs: %v and ys: %v with message: %s", params.xs, params.ys, expectedMessage)
+		}
+	}
+}
+
+func TestPiecewiseLinearFit(t *testing.T) {
+	t.Parallel()
+	testPiecewiseInterpolatorCreation(t, &PiecewiseLinear{})
+}
+
+// testInterpolatorPredict tests evaluation of a  interpolator.
+func testInterpolatorPredict(t *testing.T, p Predictor, xs []float64, expectedYs []float64, tol float64) {
+	for i, x := range xs {
+		y := p.Predict(x)
+		yErr := math.Abs(y - expectedYs[i])
+		if yErr > tol {
+			if tol == 0 {
+				t.Errorf("unexpected Predict(%g) value: got: %g want: %g", x, y, expectedYs[i])
+			} else {
+				t.Errorf("unexpected Predict(%g) value: got: %g want: %g with tolerance: %g", x, y, expectedYs[i], tol)
+			}
+		}
+	}
+}
+
+func TestPiecewiseLinearPredict(t *testing.T) {
+	t.Parallel()
+	xs := []float64{0, 1, 2}
+	ys := []float64{-0.5, 1.5, 1}
+	var pl PiecewiseLinear
+	err := pl.Fit(xs, ys)
+	if err != nil {
+		t.Errorf("Fit error: %s", err.Error())
+	}
+	testInterpolatorPredict(t, pl, xs, ys, 0)
+	testInterpolatorPredict(t, pl, []float64{-0.4, 2.6}, []float64{-0.5, 1}, 0)
+	testInterpolatorPredict(t, pl, []float64{0.1, 0.5, 0.8, 1.2}, []float64{-0.3, 0.5, 1.1, 1.4}, 1e-15)
+}
+
+func BenchmarkNewPiecewiseLinear(b *testing.B) {
+	xs := []float64{0, 1.5, 3, 4.5, 6, 7.5, 9, 12, 13.5, 16.5}
+	ys := []float64{0, 1, 2, 2.5, 2, 1.5, 4, 10, -2, 2}
+	var pl PiecewiseLinear
+	for i := 0; i < b.N; i++ {
+		_ = pl.Fit(xs, ys)
+	}
+}
+
+func BenchmarkPiecewiseLinearPredict(b *testing.B) {
+	xs := []float64{0, 1.5, 3, 4.5, 6, 7.5, 9, 12, 13.5, 16.5}
+	ys := []float64{0, 1, 2, 2.5, 2, 1.5, 4, 10, -2, 2}
+	var pl PiecewiseLinear
+	_ = pl.Fit(xs, ys)
+	for i := 0; i < b.N; i++ {
+		pl.Predict(0)
+		pl.Predict(16.5)
+		pl.Predict(-2)
+		pl.Predict(4)
+		pl.Predict(7.32)
+		pl.Predict(9.0001)
+		pl.Predict(1.4)
+		pl.Predict(1.6)
+		pl.Predict(30)
+		pl.Predict(13.5)
+		pl.Predict(4.5)
+	}
+}
+
+func TestNewPiecewiseConstant(t *testing.T) {
+	var pc PiecewiseConstant
+	testPiecewiseInterpolatorCreation(t, &pc)
+}
+
+func benchmarkPiecewiseConstantPredict(b *testing.B) {
+	xs := []float64{0, 1.5, 3, 4.5, 6, 7.5, 9, 12, 13.5, 16.5}
+	ys := []float64{0, 1, 2, 2.5, 2, 1.5, 4, 10, -2, 2}
+	var pc PiecewiseConstant
+	_ = pc.Fit(xs, ys)
+	for i := 0; i < b.N; i++ {
+		pc.Predict(0)
+		pc.Predict(16.5)
+		pc.Predict(4)
+		pc.Predict(7.32)
+		pc.Predict(9.0001)
+		pc.Predict(1.4)
+		pc.Predict(1.6)
+		pc.Predict(13.5)
+		pc.Predict(4.5)
+	}
+}
+
+func BenchmarkPiecewiseConstantPredict(b *testing.B) {
+	benchmarkPiecewiseConstantPredict(b)
+}
+
+func TestPiecewiseConstantPredict(t *testing.T) {
+	t.Parallel()
+	xs := []float64{0, 1, 2}
+	ys := []float64{-0.5, 1.5, 1}
+	var pc PiecewiseConstant
+	err := pc.Fit(xs, ys)
+	if err != nil {
+		t.Errorf("Fit error: %s", err.Error())
+	}
+	testInterpolatorPredict(t, pc, xs, ys, 0)
+	testXs := []float64{-0.9, 0.1, 0.5, 0.8, 1.2, 3.1}
+	leftYs := []float64{-0.5, 1.5, 1.5, 1.5, 1, 1}
+	testInterpolatorPredict(t, pc, testXs, leftYs, 0)
+}