diff: imported diff as a subtree

diff/fd/gradient_test.go

@@ -0,0 +1,187 @@
+// Copyright ©2014 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 fd
+
+import (
+	"math"
+	"math/rand"
+	"testing"
+
+	"github.com/gonum/floats"
+)
+
+type Rosenbrock struct {
+	nDim int
+}
+
+func (r Rosenbrock) F(x []float64) (sum float64) {
+	deriv := make([]float64, len(x))
+	return r.FDf(x, deriv)
+}
+
+func (r Rosenbrock) FDf(x []float64, deriv []float64) (sum float64) {
+	for i := range deriv {
+		deriv[i] = 0
+	}
+
+	for i := 0; i < len(x)-1; i++ {
+		sum += math.Pow(1-x[i], 2) + 100*math.Pow(x[i+1]-math.Pow(x[i], 2), 2)
+	}
+	for i := 0; i < len(x)-1; i++ {
+		deriv[i] += -1 * 2 * (1 - x[i])
+		deriv[i] += 2 * 100 * (x[i+1] - math.Pow(x[i], 2)) * (-2 * x[i])
+	}
+	for i := 1; i < len(x); i++ {
+		deriv[i] += 2 * 100 * (x[i] - math.Pow(x[i-1], 2))
+	}
+
+	return sum
+}
+
+func TestGradient(t *testing.T) {
+	rand.Seed(1)
+	for i, test := range []struct {
+		nDim    int
+		tol     float64
+		formula Formula
+	}{
+		{
+			nDim:    2,
+			tol:     2e-4,
+			formula: Forward,
+		},
+		{
+			nDim:    2,
+			tol:     1e-6,
+			formula: Central,
+		},
+		{
+			nDim:    40,
+			tol:     2e-4,
+			formula: Forward,
+		},
+		{
+			nDim:    40,
+			tol:     1e-6,
+			formula: Central,
+		},
+	} {
+		x := make([]float64, test.nDim)
+		for i := range x {
+			x[i] = rand.Float64()
+		}
+		xcopy := make([]float64, len(x))
+		copy(xcopy, x)
+
+		r := Rosenbrock{len(x)}
+		trueGradient := make([]float64, len(x))
+		r.FDf(x, trueGradient)
+
+		// Try with gradient nil.
+		gradient := Gradient(nil, r.F, x, &Settings{
+			Formula: test.formula,
+		})
+		if !floats.EqualApprox(gradient, trueGradient, test.tol) {
+			t.Errorf("Case %v: gradient mismatch in serial with nil. Want: %v, Got: %v.", i, trueGradient, gradient)
+		}
+		if !floats.Equal(x, xcopy) {
+			t.Errorf("Case %v: x modified during call to gradient in serial with nil.", i)
+		}
+
+		// Try with provided gradient.
+		for i := range gradient {
+			gradient[i] = rand.Float64()
+		}
+		Gradient(gradient, r.F, x, &Settings{
+			Formula: test.formula,
+		})
+		if !floats.EqualApprox(gradient, trueGradient, test.tol) {
+			t.Errorf("Case %v: gradient mismatch in serial. Want: %v, Got: %v.", i, trueGradient, gradient)
+		}
+		if !floats.Equal(x, xcopy) {
+			t.Errorf("Case %v: x modified during call to gradient in serial with non-nil.", i)
+		}
+
+		// Try with known value.
+		for i := range gradient {
+			gradient[i] = rand.Float64()
+		}
+		Gradient(gradient, r.F, x, &Settings{
+			Formula:     test.formula,
+			OriginKnown: true,
+			OriginValue: r.F(x),
+		})
+		if !floats.EqualApprox(gradient, trueGradient, test.tol) {
+			t.Errorf("Case %v: gradient mismatch with known origin in serial. Want: %v, Got: %v.", i, trueGradient, gradient)
+		}
+
+		// Try with concurrent evaluation.
+		for i := range gradient {
+			gradient[i] = rand.Float64()
+		}
+		Gradient(gradient, r.F, x, &Settings{
+			Formula:    test.formula,
+			Concurrent: true,
+		})
+		if !floats.EqualApprox(gradient, trueGradient, test.tol) {
+			t.Errorf("Case %v: gradient mismatch with unknown origin in parallel. Want: %v, Got: %v.", i, trueGradient, gradient)
+		}
+		if !floats.Equal(x, xcopy) {
+			t.Errorf("Case %v: x modified during call to gradient in parallel", i)
+		}
+
+		// Try with concurrent evaluation with origin known.
+		for i := range gradient {
+			gradient[i] = rand.Float64()
+		}
+		Gradient(gradient, r.F, x, &Settings{
+			Formula:     test.formula,
+			Concurrent:  true,
+			OriginKnown: true,
+			OriginValue: r.F(x),
+		})
+		if !floats.EqualApprox(gradient, trueGradient, test.tol) {
+			t.Errorf("Case %v: gradient mismatch with known origin in parallel. Want: %v, Got: %v.", i, trueGradient, gradient)
+		}
+
+		// Try with nil settings.
+		for i := range gradient {
+			gradient[i] = rand.Float64()
+		}
+		Gradient(gradient, r.F, x, nil)
+		if !floats.EqualApprox(gradient, trueGradient, test.tol) {
+			t.Errorf("Case %v: gradient mismatch with default settings. Want: %v, Got: %v.", i, trueGradient, gradient)
+		}
+
+		// Try with zero-valued settings.
+		for i := range gradient {
+			gradient[i] = rand.Float64()
+		}
+		Gradient(gradient, r.F, x, &Settings{})
+		if !floats.EqualApprox(gradient, trueGradient, test.tol) {
+			t.Errorf("Case %v: gradient mismatch with zero settings. Want: %v, Got: %v.", i, trueGradient, gradient)
+		}
+	}
+}
+
+func Panics(fun func()) (b bool) {
+	defer func() {
+		err := recover()
+		if err != nil {
+			b = true
+		}
+	}()
+	fun()
+	return
+}
+
+func TestGradientPanics(t *testing.T) {
+	// Test that it panics
+	if !Panics(func() {
+		Gradient([]float64{0.0}, func(x []float64) float64 { return x[0] * x[0] }, []float64{0.0, 0.0}, nil)
+	}) {
+		t.Errorf("Gradient did not panic with length mismatch")
+	}
+}