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// 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 (
"math/rand"
"testing"
)
func TestLUD(t *testing.T) {
for _, n := range []int{1, 5, 10, 11, 50} {
a := NewDense(n, n, nil)
for i := 0; i < n; i++ {
for j := 0; j < n; j++ {
a.Set(i, j, rand.NormFloat64())
}
}
var want Dense
want.Clone(a)
var lu LU
lu.Factorize(a)
l := lu.LTo(nil)
u := lu.UTo(nil)
var p Dense
pivot := lu.Pivot(nil)
p.Permutation(n, pivot)
var got Dense
got.Product(&p, l, u)
if !EqualApprox(&got, &want, 1e-12) {
t.Errorf("PLU does not equal original matrix.\nWant: %v\n Got: %v", want, got)
}
}
}
func TestLURankOne(t *testing.T) {
for _, pivoting := range []bool{true} {
for _, n := range []int{3, 10, 50} {
// Construct a random LU factorization
lu := &LU{}
lu.lu = NewDense(n, n, nil)
for i := 0; i < n; i++ {
for j := 0; j < n; j++ {
lu.lu.Set(i, j, rand.Float64())
}
}
lu.pivot = make([]int, n)
for i := range lu.pivot {
lu.pivot[i] = i
}
if pivoting {
// For each row, randomly swap with itself or a row after (like is done)
// in the actual LU factorization.
for i := range lu.pivot {
idx := i + rand.Intn(n-i)
lu.pivot[i], lu.pivot[idx] = lu.pivot[idx], lu.pivot[i]
}
}
// Apply a rank one update. Ensure the update magnitude is larger than
// the equal tolerance.
alpha := rand.Float64() + 1
x := NewVector(n, nil)
y := NewVector(n, nil)
for i := 0; i < n; i++ {
x.setVec(i, rand.Float64()+1)
y.setVec(i, rand.Float64()+1)
}
a := luReconstruct(lu)
a.RankOne(a, alpha, x, y)
var luNew LU
luNew.RankOne(lu, alpha, x, y)
lu.RankOne(lu, alpha, x, y)
aR1New := luReconstruct(&luNew)
aR1 := luReconstruct(lu)
if !Equal(aR1, aR1New) {
t.Error("Different answer when new receiver")
}
if !EqualApprox(aR1, a, 1e-10) {
t.Errorf("Rank one mismatch, pivot %v.\nWant: %v\nGot:%v\n", pivoting, a, aR1)
}
}
}
}
// luReconstruct reconstructs the original A matrix from an LU decomposition.
func luReconstruct(lu *LU) *Dense {
var L, U TriDense
lu.LTo(&L)
lu.UTo(&U)
var P Dense
pivot := lu.Pivot(nil)
P.Permutation(len(pivot), pivot)
var a Dense
a.Mul(&L, &U)
a.Mul(&P, &a)
return &a
}
func TestSolveLU(t *testing.T) {
for _, test := range []struct {
n, bc int
}{
{5, 5},
{5, 10},
{10, 5},
} {
n := test.n
bc := test.bc
a := NewDense(n, n, nil)
for i := 0; i < n; i++ {
for j := 0; j < n; j++ {
a.Set(i, j, rand.NormFloat64())
}
}
b := NewDense(n, bc, nil)
for i := 0; i < n; i++ {
for j := 0; j < bc; j++ {
b.Set(i, j, rand.NormFloat64())
}
}
var lu LU
lu.Factorize(a)
var x Dense
if err := lu.Solve(&x, false, b); err != nil {
continue
}
var got Dense
got.Mul(a, &x)
if !EqualApprox(&got, b, 1e-12) {
t.Errorf("Solve mismatch for non-singular matrix. n = %v, bc = %v.\nWant: %v\nGot: %v", n, bc, b, got)
}
}
// TODO(btracey): Add testOneInput test when such a function exists.
}
func TestSolveLUCond(t *testing.T) {
for _, test := range []*Dense{
NewDense(2, 2, []float64{1, 0, 0, 1e-20}),
} {
m, _ := test.Dims()
var lu LU
lu.Factorize(test)
b := NewDense(m, 2, nil)
var x Dense
if err := lu.Solve(&x, false, b); err == nil {
t.Error("No error for near-singular matrix in matrix solve.")
}
bvec := NewVector(m, nil)
var xvec Vector
if err := lu.SolveVec(&xvec, false, bvec); err == nil {
t.Error("No error for near-singular matrix in matrix solve.")
}
}
}
func TestSolveLUVec(t *testing.T) {
for _, n := range []int{5, 10} {
a := NewDense(n, n, nil)
for i := 0; i < n; i++ {
for j := 0; j < n; j++ {
a.Set(i, j, rand.NormFloat64())
}
}
b := NewVector(n, nil)
for i := 0; i < n; i++ {
b.SetVec(i, rand.NormFloat64())
}
var lu LU
lu.Factorize(a)
var x Vector
if err := lu.SolveVec(&x, false, b); err != nil {
continue
}
var got Vector
got.MulVec(a, &x)
if !EqualApprox(&got, b, 1e-12) {
t.Errorf("Solve mismatch n = %v.\nWant: %v\nGot: %v", n, b, got)
}
}
// TODO(btracey): Add testOneInput test when such a function exists.
}