/
svd.go
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/
svd.go
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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.
// Based on the SingularValueDecomposition class from Jama 1.0.3.
package mat64
import (
"math"
)
type SVDFactors struct {
U *Dense
Sigma []float64
V *Dense
m, n int
}
// SVD performs singular value decomposition for an m-by-n matrix a. The
// singular value decomposition is an m-by-n orthogonal matrix u, an n-by-n
// diagonal matrix s, and an n-by-n orthogonal matrix v so that a = u*s*v'. If
// a is a wide matrix a copy of its transpose is allocated, otherwise a is
// overwritten during the decomposition. Matrices u and v are only created when
// wantu and wantv are true respectively.
//
// The singular values, sigma[k] = s[k][k], are ordered so that
//
// sigma[0] >= sigma[1] >= ... >= sigma[n-1].
//
// The matrix condition number and the effective numerical rank can be computed from
// this decomposition.
func SVD(a *Dense, epsilon, small float64, wantu, wantv bool) SVDFactors {
m, n := a.Dims()
trans := false
if m < n {
a.TCopy(a)
m, n = n, m
wantu, wantv = wantv, wantu
trans = true
}
sigma := make([]float64, min(m+1, n))
nu := min(m, n)
var u, v *Dense
if wantu {
u = NewDense(m, nu, nil)
}
if wantv {
v = NewDense(n, n, nil)
}
var (
e = make([]float64, n)
work = make([]float64, m)
)
// Reduce a to bidiagonal form, storing the diagonal elements
// in sigma and the super-diagonal elements in e.
nct := min(m-1, n)
nrt := max(0, min(n-2, m))
for k := 0; k < max(nct, nrt); k++ {
if k < nct {
// Compute the transformation for the k-th column and
// place the k-th diagonal in sigma[k].
// Compute 2-norm of k-th column without under/overflow.
sigma[k] = 0
for i := k; i < m; i++ {
sigma[k] = math.Hypot(sigma[k], a.at(i, k))
}
if sigma[k] != 0 {
if a.at(k, k) < 0 {
sigma[k] = -sigma[k]
}
for i := k; i < m; i++ {
a.set(i, k, a.at(i, k)/sigma[k])
}
a.set(k, k, a.at(k, k)+1)
}
sigma[k] = -sigma[k]
}
for j := k + 1; j < n; j++ {
if k < nct && sigma[k] != 0 {
// Apply the transformation.
var t float64
for i := k; i < m; i++ {
t += a.at(i, k) * a.at(i, j)
}
t = -t / a.at(k, k)
for i := k; i < m; i++ {
a.set(i, j, a.at(i, j)+t*a.at(i, k))
}
}
// Place the k-th row of a into e for the
// subsequent calculation of the row transformation.
e[j] = a.at(k, j)
}
if wantu && k < nct {
// Place the transformation in u for subsequent back
// multiplication.
for i := k; i < m; i++ {
u.set(i, k, a.at(i, k))
}
}
if k < nrt {
// Compute the k-th row transformation and place the
// k-th super-diagonal in e[k].
// Compute 2-norm without under/overflow.
e[k] = 0
for i := k + 1; i < n; i++ {
e[k] = math.Hypot(e[k], e[i])
}
if e[k] != 0 {
if e[k+1] < 0 {
e[k] = -e[k]
}
for i := k + 1; i < n; i++ {
e[i] /= e[k]
}
e[k+1]++
}
e[k] = -e[k]
if k+1 < m && e[k] != 0 {
// Apply the transformation.
for i := k + 1; i < m; i++ {
work[i] = 0
}
for j := k + 1; j < n; j++ {
for i := k + 1; i < m; i++ {
work[i] += e[j] * a.at(i, j)
}
}
for j := k + 1; j < n; j++ {
t := -e[j] / e[k+1]
for i := k + 1; i < m; i++ {
a.set(i, j, a.at(i, j)+t*work[i])
}
}
}
if wantv {
// Place the transformation in v for subsequent
// back multiplication.
for i := k + 1; i < n; i++ {
v.set(i, k, e[i])
}
}
}
}
// set up the final bidiagonal matrix or order p.
p := min(n, m+1)
if nct < n {
sigma[nct] = a.at(nct, nct)
}
if m < p {
sigma[p-1] = 0
}
if nrt+1 < p {
e[nrt] = a.at(nrt, p-1)
}
e[p-1] = 0
// If requested, generate u.
if wantu {
for j := nct; j < nu; j++ {
for i := 0; i < m; i++ {
u.set(i, j, 0)
}
u.set(j, j, 1)
}
for k := nct - 1; k >= 0; k-- {
if sigma[k] != 0 {
for j := k + 1; j < nu; j++ {
var t float64
for i := k; i < m; i++ {
t += u.at(i, k) * u.at(i, j)
}
t /= -u.at(k, k)
for i := k; i < m; i++ {
u.set(i, j, u.at(i, j)+t*u.at(i, k))
}
}
for i := k; i < m; i++ {
u.set(i, k, -u.at(i, k))
}
u.set(k, k, 1+u.at(k, k))
for i := 0; i < k-1; i++ {
u.set(i, k, 0)
}
} else {
for i := 0; i < m; i++ {
u.set(i, k, 0)
}
u.set(k, k, 1)
}
}
}
// If requested, generate v.
if wantv {
for k := n - 1; k >= 0; k-- {
if k < nrt && e[k] != 0 {
for j := k + 1; j < nu; j++ {
var t float64
for i := k + 1; i < n; i++ {
t += v.at(i, k) * v.at(i, j)
}
t /= -v.at(k+1, k)
for i := k + 1; i < n; i++ {
v.set(i, j, v.at(i, j)+t*v.at(i, k))
}
}
}
for i := 0; i < n; i++ {
v.set(i, k, 0)
}
v.set(k, k, 1)
}
}
// Main iteration loop for the singular values.
pp := p - 1
for iter := 0; p > 0; {
var k, kase int
// Here is where a test for too many iterations would go.
// This section of the program inspects for
// negligible elements in the sigma and e arrays. On
// completion the variables kase and k are set as follows.
//
// kase = 1 if sigma(p) and e[k-1] are negligible and k<p
// kase = 2 if sigma(k) is negligible and k<p
// kase = 3 if e[k-1] is negligible, k<p, and
// sigma(k), ..., sigma(p) are not negligible (qr step).
// kase = 4 if e(p-1) is negligible (convergence).
//
for k = p - 2; k >= -1; k-- {
if k == -1 {
break
}
if math.Abs(e[k]) <= small+epsilon*(math.Abs(sigma[k])+math.Abs(sigma[k+1])) {
e[k] = 0
break
}
}
if k == p-2 {
kase = 4
} else {
var ks int
for ks = p - 1; ks >= k; ks-- {
if ks == k {
break
}
var t float64
if ks != p {
t = math.Abs(e[ks])
}
if ks != k+1 {
t += math.Abs(e[ks-1])
}
if math.Abs(sigma[ks]) <= small+epsilon*t {
sigma[ks] = 0
break
}
}
if ks == k {
kase = 3
} else if ks == p-1 {
kase = 1
} else {
kase = 2
k = ks
}
}
k++
switch kase {
// Deflate negligible sigma(p).
case 1:
f := e[p-2]
e[p-2] = 0
for j := p - 2; j >= k; j-- {
t := math.Hypot(sigma[j], f)
cs := sigma[j] / t
sn := f / t
sigma[j] = t
if j != k {
f = -sn * e[j-1]
e[j-1] *= cs
}
if wantv {
for i := 0; i < n; i++ {
t = cs*v.at(i, j) + sn*v.at(i, p-1)
v.set(i, p-1, -sn*v.at(i, j)+cs*v.at(i, p-1))
v.set(i, j, t)
}
}
}
// Split at negligible sigma(k).
case 2:
f := e[k-1]
e[k-1] = 0
for j := k; j < p; j++ {
t := math.Hypot(sigma[j], f)
cs := sigma[j] / t
sn := f / t
sigma[j] = t
f = -sn * e[j]
e[j] *= cs
if wantu {
for i := 0; i < m; i++ {
t = cs*u.at(i, j) + sn*u.at(i, k-1)
u.set(i, k-1, -sn*u.at(i, j)+cs*u.at(i, k-1))
u.set(i, j, t)
}
}
}
// Perform one qr step.
case 3:
// Calculate the shift.
scale := math.Max(math.Max(math.Max(math.Max(
math.Abs(sigma[p-1]), math.Abs(sigma[p-2])), math.Abs(e[p-2])), math.Abs(sigma[k])), math.Abs(e[k]),
)
sp := sigma[p-1] / scale
spm1 := sigma[p-2] / scale
epm1 := e[p-2] / scale
sk := sigma[k] / scale
ek := e[k] / scale
b := ((spm1+sp)*(spm1-sp) + epm1*epm1) / 2
c := (sp * epm1) * (sp * epm1)
var shift float64
if b != 0 || c != 0 {
shift = math.Sqrt(b*b + c)
if b < 0 {
shift = -shift
}
shift = c / (b + shift)
}
f := (sk+sp)*(sk-sp) + shift
g := sk * ek
// Chase zeros.
for j := k; j < p-1; j++ {
t := math.Hypot(f, g)
cs := f / t
sn := g / t
if j != k {
e[j-1] = t
}
f = cs*sigma[j] + sn*e[j]
e[j] = cs*e[j] - sn*sigma[j]
g = sn * sigma[j+1]
sigma[j+1] *= cs
if wantv {
for i := 0; i < n; i++ {
t = cs*v.at(i, j) + sn*v.at(i, j+1)
v.set(i, j+1, -sn*v.at(i, j)+cs*v.at(i, j+1))
v.set(i, j, t)
}
}
t = math.Hypot(f, g)
cs = f / t
sn = g / t
sigma[j] = t
f = cs*e[j] + sn*sigma[j+1]
sigma[j+1] = -sn*e[j] + cs*sigma[j+1]
g = sn * e[j+1]
e[j+1] *= cs
if wantu && j < m-1 {
for i := 0; i < m; i++ {
t = cs*u.at(i, j) + sn*u.at(i, j+1)
u.set(i, j+1, -sn*u.at(i, j)+cs*u.at(i, j+1))
u.set(i, j, t)
}
}
}
e[p-2] = f
iter++
// Convergence.
case 4:
// Make the singular values positive.
if sigma[k] <= 0 {
if sigma[k] < 0 {
sigma[k] = -sigma[k]
} else {
sigma[k] = 0
}
if wantv {
for i := 0; i <= pp; i++ {
v.set(i, k, -v.at(i, k))
}
}
}
// Order the singular values.
for k < pp {
if sigma[k] >= sigma[k+1] {
break
}
sigma[k], sigma[k+1] = sigma[k+1], sigma[k]
if wantv && k < n-1 {
for i := 0; i < n; i++ {
t := v.at(i, k+1)
v.set(i, k+1, v.at(i, k))
v.set(i, k, t)
}
}
if wantu && k < m-1 {
for i := 0; i < m; i++ {
t := u.at(i, k+1)
u.set(i, k+1, u.at(i, k))
u.set(i, k, t)
}
}
k++
}
iter = 0
p--
}
}
if trans {
return SVDFactors{
U: v,
Sigma: sigma,
V: u,
m: m, n: n,
}
}
return SVDFactors{
U: u,
Sigma: sigma,
V: v,
m: m, n: n,
}
}
// S returns a newly allocated S matrix from the sigma values held by the
// factorisation.
func (f SVDFactors) S() *Dense {
s := NewDense(len(f.Sigma), len(f.Sigma), nil)
for i, v := range f.Sigma {
s.set(i, i, v)
}
return s
}
// Rank returns the number of non-negligible singular values in the sigma held by
// the factorisation with the given epsilon.
func (f SVDFactors) Rank(epsilon float64) int {
if len(f.Sigma) == 0 {
return 0
}
tol := float64(max(f.m, len(f.Sigma))) * f.Sigma[0] * epsilon
var r int
for _, v := range f.Sigma {
if v > tol {
r++
}
}
return r
}
// Cond returns the 2-norm condition number for the S matrix.
func (f SVDFactors) Cond() float64 {
return f.Sigma[0] / f.Sigma[min(f.m, f.n)-1]
}