gonum.org/v1/gonum@v0.14.0/mat/matrix_test.go

// Copyright ©2013 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 mat

import (
	"fmt"
	"math"
	"reflect"
	"testing"

	"golang.org/x/exp/rand"
	"gonum.org/v1/gonum/blas"
	"gonum.org/v1/gonum/blas/blas64"
	"gonum.org/v1/gonum/floats/scalar"
)

func panics(fn func()) (panicked bool, message string) {
	defer func() {
		r := recover()
		panicked = r != nil
		message = fmt.Sprint(r)
	}()
	fn()
	return
}

func flatten(f [][]float64) (r, c int, d []float64) {
	r = len(f)
	if r == 0 {
		panic("bad test: no row")
	}
	c = len(f[0])
	d = make([]float64, 0, r*c)
	for _, row := range f {
		if len(row) != c {
			panic("bad test: ragged input")
		}
		d = append(d, row...)
	}
	return r, c, d
}

func unflatten(r, c int, d []float64) [][]float64 {
	m := make([][]float64, r)
	for i := 0; i < r; i++ {
		m[i] = d[i*c : (i+1)*c]
	}
	return m
}

// eye returns a new identity matrix of size n×n.
func eye(n int) *Dense {
	d := make([]float64, n*n)
	for i := 0; i < n*n; i += n + 1 {
		d[i] = 1
	}
	return NewDense(n, n, d)
}

func TestCol(t *testing.T) {
	t.Parallel()
	for id, af := range [][][]float64{
		{
			{1, 2, 3},
			{4, 5, 6},
			{7, 8, 9},
		},
		{
			{1, 2, 3},
			{4, 5, 6},
			{7, 8, 9},
			{10, 11, 12},
		},
		{
			{1, 2, 3, 4},
			{5, 6, 7, 8},
			{9, 10, 11, 12},
		},
	} {
		a := NewDense(flatten(af))
		col := make([]float64, a.mat.Rows)
		for j := range af[0] {
			for i := range col {
				col[i] = float64(i*a.mat.Cols + j + 1)
			}

			if got := Col(nil, j, a); !reflect.DeepEqual(got, col) {
				t.Errorf("test %d: unexpected values returned for dense col %d: got: %v want: %v",
					id, j, got, col)
			}

			got := make([]float64, a.mat.Rows)
			if Col(got, j, a); !reflect.DeepEqual(got, col) {
				t.Errorf("test %d: unexpected values filled for dense col %d: got: %v want: %v",
					id, j, got, col)
			}
		}
	}

	denseComparison := func(a *Dense) interface{} {
		r, c := a.Dims()
		ans := make([][]float64, c)
		for j := range ans {
			ans[j] = make([]float64, r)
			for i := range ans[j] {
				ans[j][i] = a.At(i, j)
			}
		}
		return ans
	}

	f := func(a Matrix) interface{} {
		_, c := a.Dims()
		ans := make([][]float64, c)
		for j := range ans {
			ans[j] = Col(nil, j, a)
		}
		return ans
	}
	testOneInputFunc(t, "Col", f, denseComparison, sameAnswerF64SliceOfSlice, isAnyType, isAnySize)

	f = func(a Matrix) interface{} {
		r, c := a.Dims()
		ans := make([][]float64, c)
		for j := range ans {
			ans[j] = make([]float64, r)
			Col(ans[j], j, a)
		}
		return ans
	}
	testOneInputFunc(t, "Col", f, denseComparison, sameAnswerF64SliceOfSlice, isAnyType, isAnySize)
}

func TestRow(t *testing.T) {
	t.Parallel()
	for id, af := range [][][]float64{
		{
			{1, 2, 3},
			{4, 5, 6},
			{7, 8, 9},
		},
		{
			{1, 2, 3},
			{4, 5, 6},
			{7, 8, 9},
			{10, 11, 12},
		},
		{
			{1, 2, 3, 4},
			{5, 6, 7, 8},
			{9, 10, 11, 12},
		},
	} {
		a := NewDense(flatten(af))
		for i, row := range af {
			if got := Row(nil, i, a); !reflect.DeepEqual(got, row) {
				t.Errorf("test %d: unexpected values returned for dense row %d: got: %v want: %v",
					id, i, got, row)
			}

			got := make([]float64, len(row))
			if Row(got, i, a); !reflect.DeepEqual(got, row) {
				t.Errorf("test %d: unexpected values filled for dense row %d: got: %v want: %v",
					id, i, got, row)
			}
		}
	}

	denseComparison := func(a *Dense) interface{} {
		r, c := a.Dims()
		ans := make([][]float64, r)
		for i := range ans {
			ans[i] = make([]float64, c)
			for j := range ans[i] {
				ans[i][j] = a.At(i, j)
			}
		}
		return ans
	}

	f := func(a Matrix) interface{} {
		r, _ := a.Dims()
		ans := make([][]float64, r)
		for i := range ans {
			ans[i] = Row(nil, i, a)
		}
		return ans
	}
	testOneInputFunc(t, "Row", f, denseComparison, sameAnswerF64SliceOfSlice, isAnyType, isAnySize)

	f = func(a Matrix) interface{} {
		r, c := a.Dims()
		ans := make([][]float64, r)
		for i := range ans {
			ans[i] = make([]float64, c)
			Row(ans[i], i, a)
		}
		return ans
	}
	testOneInputFunc(t, "Row", f, denseComparison, sameAnswerF64SliceOfSlice, isAnyType, isAnySize)
}

func TestCond(t *testing.T) {
	t.Parallel()
	for i, test := range []struct {
		a       *Dense
		condOne float64
		condTwo float64
		condInf float64
	}{
		{
			a: NewDense(3, 3, []float64{
				8, 1, 6,
				3, 5, 7,
				4, 9, 2,
			}),
			condOne: 16.0 / 3.0,
			condTwo: 4.330127018922192,
			condInf: 16.0 / 3.0,
		},
		{
			a: NewDense(4, 4, []float64{
				2, 9, 3, 2,
				10, 9, 9, 3,
				1, 1, 5, 2,
				8, 4, 10, 2,
			}),
			condOne: 1 / 0.024740155174938,
			condTwo: 34.521576567075087,
			condInf: 1 / 0.012034465570035,
		},
		{
			a: NewDense(3, 3, []float64{
				5, 6, 7,
				8, -2, 1,
				7, 7, 7}),
			condOne: 30.769230769230749,
			condTwo: 21.662689498448440,
			condInf: 31.153846153846136,
		},
	} {
		orig := DenseCopyOf(test.a)
		condOne := Cond(test.a, 1)
		if !scalar.EqualWithinAbsOrRel(test.condOne, condOne, 1e-13, 1e-13) {
			t.Errorf("Case %d: one norm mismatch. Want %v, got %v", i, test.condOne, condOne)
		}
		if !Equal(test.a, orig) {
			t.Errorf("Case %d: unexpected mutation of input matrix for one norm. Want %v, got %v", i, orig, test.a)
		}
		condTwo := Cond(test.a, 2)
		if !scalar.EqualWithinAbsOrRel(test.condTwo, condTwo, 1e-13, 1e-13) {
			t.Errorf("Case %d: two norm mismatch. Want %v, got %v", i, test.condTwo, condTwo)
		}
		if !Equal(test.a, orig) {
			t.Errorf("Case %d: unexpected mutation of input matrix for two norm. Want %v, got %v", i, orig, test.a)
		}
		condInf := Cond(test.a, math.Inf(1))
		if !scalar.EqualWithinAbsOrRel(test.condInf, condInf, 1e-13, 1e-13) {
			t.Errorf("Case %d: inf norm mismatch. Want %v, got %v", i, test.condInf, condInf)
		}
		if !Equal(test.a, orig) {
			t.Errorf("Case %d: unexpected mutation of input matrix for inf norm. Want %v, got %v", i, orig, test.a)
		}
	}

	for _, test := range []struct {
		name string
		norm float64
	}{
		{
			name: "CondOne",
			norm: 1,
		},
		{
			name: "CondTwo",
			norm: 2,
		},
		{
			name: "CondInf",
			norm: math.Inf(1),
		},
	} {
		f := func(a Matrix) interface{} {
			return Cond(a, test.norm)
		}
		denseComparison := func(a *Dense) interface{} {
			return Cond(a, test.norm)
		}
		testOneInputFunc(t, test.name, f, denseComparison, sameAnswerFloatApproxTol(1e-12), isAnyType, isAnySize)
	}
}

func TestDet(t *testing.T) {
	t.Parallel()
	for c, test := range []struct {
		a   *Dense
		ans float64
	}{
		{
			a:   NewDense(2, 2, []float64{1, 0, 0, 1}),
			ans: 1,
		},
		{
			a:   NewDense(2, 2, []float64{1, 0, 0, -1}),
			ans: -1,
		},
		{
			a: NewDense(3, 3, []float64{
				1, 2, 0,
				0, 1, 2,
				0, 2, 1,
			}),
			ans: -3,
		},
		{
			a: NewDense(3, 3, []float64{
				1, 2, 3,
				5, 7, 9,
				6, 9, 12,
			}),
			ans: 0,
		},
	} {
		a := DenseCopyOf(test.a)
		det := Det(a)
		if !Equal(a, test.a) {
			t.Errorf("Input matrix changed during Det. Case %d.", c)
		}
		if !scalar.EqualWithinAbsOrRel(det, test.ans, 1e-14, 1e-14) {
			t.Errorf("Det mismatch case %d. Got %v, want %v", c, det, test.ans)
		}
	}
	// Perform the normal list test to ensure it works for all types.
	f := func(a Matrix) interface{} {
		return Det(a)
	}
	denseComparison := func(a *Dense) interface{} {
		return Det(a)
	}
	testOneInputFunc(t, "Det", f, denseComparison, sameAnswerFloatApproxTol(1e-12), isAnyType, isSquare)

	// Check that it gives approximately the same answer as Cholesky
	// Ensure the input matrices are wider than tall so they are full rank
	isWide := func(ar, ac int) bool {
		return ar <= ac
	}
	f = func(a Matrix) interface{} {
		ar, ac := a.Dims()
		if !isWide(ar, ac) {
			panic(ErrShape)
		}
		var tmp Dense
		tmp.Mul(a, a.T())
		return Det(&tmp)
	}
	denseComparison = func(a *Dense) interface{} {
		ar, ac := a.Dims()
		if !isWide(ar, ac) {
			panic(ErrShape)
		}
		var tmp SymDense
		tmp.SymOuterK(1, a)
		var chol Cholesky
		ok := chol.Factorize(&tmp)
		if !ok {
			panic("bad chol test")
		}
		return chol.Det()
	}
	testOneInputFunc(t, "DetVsChol", f, denseComparison, sameAnswerFloatApproxTol(1e-10), isAnyType, isWide)
}

func TestDot(t *testing.T) {
	t.Parallel()
	f := func(a, b Matrix) interface{} {
		return Dot(a.(Vector), b.(Vector))
	}
	denseComparison := func(a, b *Dense) interface{} {
		ra, ca := a.Dims()
		rb, cb := b.Dims()
		if ra != rb || ca != cb {
			panic(ErrShape)
		}
		var sum float64
		for i := 0; i < ra; i++ {
			for j := 0; j < ca; j++ {
				sum += a.At(i, j) * b.At(i, j)
			}
		}
		return sum
	}
	testTwoInputFunc(t, "Dot", f, denseComparison, sameAnswerFloatApproxTol(1e-12), legalTypesVectorVector, legalSizeSameVec)
}

func TestEqual(t *testing.T) {
	t.Parallel()
	f := func(a, b Matrix) interface{} {
		return Equal(a, b)
	}
	denseComparison := func(a, b *Dense) interface{} {
		return Equal(a, b)
	}
	testTwoInputFunc(t, "Equal", f, denseComparison, sameAnswerBool, legalTypesAll, isAnySize2)
}

func TestMax(t *testing.T) {
	t.Parallel()
	// A direct test of Max with *Dense arguments is in TestNewDense.
	f := func(a Matrix) interface{} {
		return Max(a)
	}
	denseComparison := func(a *Dense) interface{} {
		return Max(a)
	}
	testOneInputFunc(t, "Max", f, denseComparison, sameAnswerFloat, isAnyType, isAnySize)
}

func TestMin(t *testing.T) {
	t.Parallel()
	// A direct test of Min with *Dense arguments is in TestNewDense.
	f := func(a Matrix) interface{} {
		return Min(a)
	}
	denseComparison := func(a *Dense) interface{} {
		return Min(a)
	}
	testOneInputFunc(t, "Min", f, denseComparison, sameAnswerFloat, isAnyType, isAnySize)
}

func TestNorm(t *testing.T) {
	t.Parallel()
	for i, test := range []struct {
		a    [][]float64
		ord  float64
		norm float64
	}{
		{
			a:    [][]float64{{1, 2, 3}, {4, 5, 6}, {7, 8, 9}, {10, 11, 12}},
			ord:  1,
			norm: 30,
		},
		{
			a:    [][]float64{{1, 2, 3}, {4, 5, 6}, {7, 8, 9}, {10, 11, 12}},
			ord:  2,
			norm: 25.495097567963924,
		},
		{
			a:    [][]float64{{1, 2, 3}, {4, 5, 6}, {7, 8, 9}, {10, 11, 12}},
			ord:  math.Inf(1),
			norm: 33,
		},
		{
			a:    [][]float64{{1, -2, -2}, {-4, 5, 6}},
			ord:  1,
			norm: 8,
		},
		{
			a:    [][]float64{{1, -2, -2}, {-4, 5, 6}},
			ord:  math.Inf(1),
			norm: 15,
		},
	} {
		a := NewDense(flatten(test.a))
		if math.Abs(Norm(a, test.ord)-test.norm) > 1e-14 {
			t.Errorf("Mismatch test %d: %v norm = %f", i, test.a, test.norm)
		}
	}

	for _, test := range []struct {
		name string
		norm float64
	}{
		{"NormOne", 1},
		{"NormTwo", 2},
		{"NormInf", math.Inf(1)},
	} {
		f := func(a Matrix) interface{} {
			return Norm(a, test.norm)
		}
		denseComparison := func(a *Dense) interface{} {
			return Norm(a, test.norm)
		}
		testOneInputFunc(t, test.name, f, denseComparison, sameAnswerFloatApproxTol(1e-12), isAnyType, isAnySize)
	}
}

func TestNormZero(t *testing.T) {
	t.Parallel()
	for _, a := range []Matrix{
		&Dense{},
		&SymDense{},
		&SymDense{mat: blas64.Symmetric{Uplo: blas.Upper}},
		&TriDense{},
		&TriDense{mat: blas64.Triangular{Uplo: blas.Upper, Diag: blas.NonUnit}},
		&VecDense{},
	} {
		for _, norm := range []float64{1, 2, math.Inf(1)} {
			panicked, message := panics(func() { Norm(a, norm) })
			if !panicked {
				t.Errorf("expected panic for Norm(&%T{}, %v)", a, norm)
			}
			if message != ErrZeroLength.Error() {
				t.Errorf("unexpected panic string for Norm(&%T{}, %v): got:%s want:%s",
					a, norm, message, ErrShape.Error())
			}
		}
	}
}

func TestSum(t *testing.T) {
	t.Parallel()
	f := func(a Matrix) interface{} {
		return Sum(a)
	}
	denseComparison := func(a *Dense) interface{} {
		return Sum(a)
	}
	testOneInputFunc(t, "Sum", f, denseComparison, sameAnswerFloatApproxTol(1e-12), isAnyType, isAnySize)
}

func TestTrace(t *testing.T) {
	t.Parallel()
	for _, test := range []struct {
		a     *Dense
		trace float64
	}{
		{
			a:     NewDense(3, 3, []float64{1, 2, 3, 4, 5, 6, 7, 8, 9}),
			trace: 15,
		},
	} {
		trace := Trace(test.a)
		if trace != test.trace {
			t.Errorf("Trace mismatch. Want %v, got %v", test.trace, trace)
		}
	}
	f := func(a Matrix) interface{} {
		return Trace(a)
	}
	denseComparison := func(a *Dense) interface{} {
		return Trace(a)
	}
	testOneInputFunc(t, "Trace", f, denseComparison, sameAnswerFloatApproxTol(1e-15), isAnyType, isSquare)
}

func TestTracer(t *testing.T) {
	t.Parallel()
	for _, test := range []struct {
		a    Tracer
		want float64
	}{
		{
			a:    NewDense(3, 3, []float64{1, 2, 3, 4, 5, 6, 7, 8, 9}),
			want: 15,
		},
		{
			a:    NewSymDense(4, []float64{1, 2, 3, 4, 0, 5, 6, 7, 0, 0, 8, 9, 0, 0, 0, 10}),
			want: 24,
		},
		{
			a:    NewBandDense(6, 6, 1, 2, []float64{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 0, 19, 20, 0, 0}),
			want: 65,
		},
		{
			a:    NewDiagDense(6, []float64{1, 2, 3, 4, 5, 6}),
			want: 21,
		},
		{
			a:    NewSymBandDense(6, 2, []float64{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 0, 15, 0, 0}),
			want: 50,
		},
		{
			a:    NewTriBandDense(6, 2, Upper, []float64{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 0, 15, 0, 0}),
			want: 50,
		},
		{
			a:    NewTriBandDense(6, 2, Lower, []float64{0, 0, 1, 0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}),
			want: 46,
		},
	} {
		got := test.a.Trace()
		if got != test.want {
			t.Errorf("Trace mismatch. Want %v, got %v", test.want, got)
		}
	}
}

func TestDoer(t *testing.T) {
	t.Parallel()
	type MatrixDoer interface {
		Matrix
		NonZeroDoer
		RowNonZeroDoer
		ColNonZeroDoer
	}
	ones := func(n int) []float64 {
		data := make([]float64, n)
		for i := range data {
			data[i] = 1
		}
		return data
	}
	for i, m := range []MatrixDoer{
		NewTriDense(3, Lower, ones(3*3)),
		NewTriDense(3, Upper, ones(3*3)),
		NewBandDense(6, 6, 1, 1, ones(3*6)),
		NewBandDense(6, 10, 1, 1, ones(3*6)),
		NewBandDense(10, 6, 1, 1, ones(7*3)),
		NewSymBandDense(3, 0, ones(3)),
		NewSymBandDense(3, 1, ones(3*(1+1))),
		NewSymBandDense(6, 1, ones(6*(1+1))),
		NewSymBandDense(6, 2, ones(6*(2+1))),
		NewTriBandDense(3, 0, Upper, ones(3)),
		NewTriBandDense(3, 1, Upper, ones(3*(1+1))),
		NewTriBandDense(6, 1, Upper, ones(6*(1+1))),
		NewTriBandDense(6, 2, Upper, ones(6*(2+1))),
		NewTriBandDense(3, 0, Lower, ones(3)),
		NewTriBandDense(3, 1, Lower, ones(3*(1+1))),
		NewTriBandDense(6, 1, Lower, ones(6*(1+1))),
		NewTriBandDense(6, 2, Lower, ones(6*(2+1))),
		NewTridiag(1, nil, ones(1), nil),
		NewTridiag(2, ones(1), ones(2), ones(1)),
		NewTridiag(3, ones(2), ones(3), ones(2)),
		NewTridiag(4, ones(3), ones(4), ones(3)),
		NewTridiag(7, ones(6), ones(7), ones(6)),
		NewTridiag(10, ones(9), ones(10), ones(9)),
	} {
		r, c := m.Dims()

		want := Sum(m)

		// got and fn sum the accessed elements in
		// the Doer that is being operated on.
		// fn also tests that the accessed elements
		// are within the writable areas of the
		// matrix to check that only valid elements
		// are operated on.
		var got float64
		fn := func(i, j int, v float64) {
			got += v
			switch m := m.(type) {
			case MutableTriangular:
				m.SetTri(i, j, v)
			case MutableBanded:
				m.SetBand(i, j, v)
			case MutableSymBanded:
				m.SetSymBand(i, j, v)
			case MutableTriBanded:
				m.SetTriBand(i, j, v)
			default:
				panic("bad test: need mutable type")
			}
		}

		panicked, message := panics(func() { m.DoNonZero(fn) })
		if panicked {
			t.Errorf("unexpected panic for Doer test %d: %q", i, message)
			continue
		}
		if got != want {
			t.Errorf("unexpected Doer sum: got:%f want:%f", got, want)
		}

		// Reset got for testing with DoRowNonZero.
		got = 0
		panicked, message = panics(func() {
			for i := 0; i < r; i++ {
				m.DoRowNonZero(i, fn)
			}
		})
		if panicked {
			t.Errorf("unexpected panic for RowDoer test %d: %q", i, message)
			continue
		}
		if got != want {
			t.Errorf("unexpected RowDoer sum: got:%f want:%f", got, want)
		}

		// Reset got for testing with DoColNonZero.
		got = 0
		panicked, message = panics(func() {
			for j := 0; j < c; j++ {
				m.DoColNonZero(j, fn)
			}
		})
		if panicked {
			t.Errorf("unexpected panic for ColDoer test %d: %q", i, message)
			continue
		}
		if got != want {
			t.Errorf("unexpected ColDoer sum: got:%f want:%f", got, want)
		}
	}
}

func TestMulVecToer(t *testing.T) {
	t.Parallel()
	const tol = 1e-14

	rnd := rand.New(rand.NewSource(1))
	random := func(n int) []float64 {
		d := make([]float64, n)
		for i := range d {
			d[i] = rnd.NormFloat64()
		}
		return d
	}

	type mulVecToer interface {
		Matrix
		MulVecTo(*VecDense, bool, Vector)
	}
	for _, a := range []mulVecToer{
		NewBandDense(1, 1, 0, 0, random(1)),
		NewBandDense(3, 1, 0, 0, random(1)),
		NewBandDense(3, 1, 1, 0, random(4)),
		NewBandDense(1, 3, 0, 0, random(1)),
		NewBandDense(1, 3, 0, 1, random(2)),
		NewBandDense(7, 10, 0, 0, random(7)),
		NewBandDense(7, 10, 2, 3, random(42)),
		NewBandDense(10, 7, 0, 0, random(7)),
		NewBandDense(10, 7, 2, 3, random(54)),
		NewBandDense(10, 10, 0, 0, random(10)),
		NewBandDense(10, 10, 2, 3, random(60)),
		NewSymBandDense(1, 0, random(1)),
		NewSymBandDense(3, 0, random(3)),
		NewSymBandDense(3, 1, random(6)),
		NewSymBandDense(10, 0, random(10)),
		NewSymBandDense(10, 1, random(20)),
		NewSymBandDense(10, 4, random(50)),
		NewTridiag(1, nil, random(1), nil),
		NewTridiag(2, random(1), random(2), random(1)),
		NewTridiag(3, random(2), random(3), random(2)),
		NewTridiag(4, random(3), random(4), random(3)),
		NewTridiag(7, random(6), random(7), random(6)),
		NewTridiag(10, random(9), random(10), random(9)),
	} {
		// Dense copy of A used for computing the expected result.
		var aDense Dense
		aDense.CloneFrom(a)

		r, c := a.Dims()
		for _, trans := range []bool{false, true} {
			m, n := r, c
			if trans {
				m, n = c, r
			}
			for _, dst := range []*VecDense{
				new(VecDense),
				NewVecDense(m, random(m)),
			} {
				for xType := 0; xType <= 3; xType++ {
					var x Vector
					switch xType {
					case 0:
						x = NewVecDense(n, random(n))
					case 1:
						if m != n {
							continue
						}
						x = dst
					case 2:
						x = &rawVector{asBasicVector(NewVecDense(n, random(n)))}
					case 3:
						x = asBasicVector(NewVecDense(n, random(n)))
					default:
						panic("bad xType")
					}

					var want VecDense
					if !trans {
						want.MulVec(&aDense, x)
					} else {
						want.MulVec(aDense.T(), x)
					}

					a.MulVecTo(dst, trans, x)

					var diff VecDense
					diff.SubVec(dst, &want)
					if resid := Norm(&diff, 1); resid > tol*float64(m) {
						t.Errorf("r=%d,c=%d,trans=%t,xType=%d: unexpected result; resid=%v, want<=%v",
							r, c, trans, xType, resid, tol*float64(m))
					}
				}
			}
		}
	}
}