Author: erans
Date: Fri Aug 12 13:00:58 2011
New Revision: 1157083
URL: http://svn.apache.org/viewvc?rev=1157083&view=rev
Log:
Replaced 2 calls to "max(m,n)" by the already known value ("m"), as requested
by Greg Sterijevski on the "dev" ML.
Added "final" keyword.
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math/linear/SingularValueDecompositionImpl.java
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math/linear/SingularValueDecompositionImpl.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math/linear/SingularValueDecompositionImpl.java?rev=1157083&r1=1157082&r2=1157083&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math/linear/SingularValueDecompositionImpl.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math/linear/SingularValueDecompositionImpl.java
Fri Aug 12 13:00:58 2011
@@ -44,17 +44,17 @@ public class SingularValueDecompositionI
/** Computed singular values. */
private double[] singularValues;
- /** Row dimension. */
- private int m;
+ /** max(row dimension, column dimension). */
+ private final int m;
- /** Column dimension. */
- private int n;
+ /** min(row dimension, column dimension). */
+ private final int n;
/** Indicator for transposed matrix. */
- private boolean transposed;
+ private final boolean transposed;
/** Cached value of U matrix. */
- private RealMatrix cachedU;
+ private final RealMatrix cachedU;
/** Cached value of transposed U matrix. */
private RealMatrix cachedUt;
@@ -63,7 +63,7 @@ public class SingularValueDecompositionI
private RealMatrix cachedS;
/** Cached value of V matrix. */
- private RealMatrix cachedV;
+ private final RealMatrix cachedV;
/** Cached value of transposed V matrix. */
private RealMatrix cachedVt;
@@ -74,12 +74,9 @@ public class SingularValueDecompositionI
* @param matrix Matrix to decompose.
*/
public SingularValueDecompositionImpl(final RealMatrix matrix) {
+ final double[][] A;
- // Derived from LINPACK code.
- // Initialize.
- double[][] A;
- m = matrix.getRowDimension();
- n = matrix.getColumnDimension();
+ // "m" is always the largest dimension.
if (matrix.getRowDimension() < matrix.getColumnDimension()) {
transposed = true;
A = matrix.transpose().getData();
@@ -91,18 +88,19 @@ public class SingularValueDecompositionI
m = matrix.getRowDimension();
n = matrix.getColumnDimension();
}
- int nu = FastMath.min(m, n);
+
+ final int nu = FastMath.min(m, n);
singularValues = new double[FastMath.min(m + 1, n)];
- double[][] U = new double[m][nu];
- double[][] V = new double[n][n];
- double[] e = new double[n];
- double[] work = new double[m];
+ final double[][] U = new double[m][nu];
+ final double[][] V = new double[n][n];
+ final double[] e = new double[n];
+ final double[] work = new double[m];
boolean wantu = true;
boolean wantv = true;
// Reduce A to bidiagonal form, storing the diagonal elements
// in s and the super-diagonal elements in e.
- int nct = FastMath.min(m - 1, n);
- int nrt = FastMath.max(0, FastMath.min(n - 2, m));
+ final int nct = FastMath.min(m - 1, n);
+ final int nrt = FastMath.max(0, FastMath.min(n - 2, m));
for (int k = 0; k < FastMath.max(nct, nrt); k++) {
if (k < nct) {
// Compute the transformation for the k-th column and
@@ -175,7 +173,7 @@ public class SingularValueDecompositionI
}
}
for (int j = k + 1; j < n; j++) {
- double t = -e[j] / e[k + 1];
+ final double t = -e[j] / e[k + 1];
for (int i = k + 1; i < m; i++) {
A[i][j] += t * work[i];
}
@@ -259,7 +257,7 @@ public class SingularValueDecompositionI
}
}
// Main iteration loop for the singular values.
- int pp = p - 1;
+ final int pp = p - 1;
int iter = 0;
while (p > 0) {
int k;
@@ -277,8 +275,9 @@ public class SingularValueDecompositionI
if (k == -1) {
break;
}
- final double threshold =
- TINY + EPS * (FastMath.abs(singularValues[k]) +
FastMath.abs(singularValues[k + 1]));
+ final double threshold
+ = TINY + EPS * (FastMath.abs(singularValues[k]) +
+ FastMath.abs(singularValues[k + 1]));
if (FastMath.abs(e[k]) <= threshold) {
e[k] = 0.0;
break;
@@ -292,8 +291,8 @@ public class SingularValueDecompositionI
if (ks == k) {
break;
}
- double t = (ks != p ? FastMath.abs(e[ks]) : 0.0) +
- (ks != k + 1 ? FastMath.abs(e[ks - 1]) : 0.0);
+ final double t = (ks != p ? FastMath.abs(e[ks]) : 0.0) +
+ (ks != k + 1 ? FastMath.abs(e[ks - 1]) : 0.0);
if (FastMath.abs(singularValues[ks]) <= TINY + EPS * t) {
singularValues[ks] = 0.0;
break;
@@ -317,8 +316,8 @@ public class SingularValueDecompositionI
e[p - 2] = 0.0;
for (int j = p - 2; j >= k; j--) {
double t = FastMath.hypot(singularValues[j], f);
- double cs = singularValues[j] / t;
- double sn = f / t;
+ final double cs = singularValues[j] / t;
+ final double sn = f / t;
singularValues[j] = t;
if (j != k) {
f = -sn * e[j - 1];
@@ -340,8 +339,8 @@ public class SingularValueDecompositionI
e[k - 1] = 0.0;
for (int j = k; j < p; j++) {
double t = FastMath.hypot(singularValues[j], f);
- double cs = singularValues[j] / t;
- double sn = f / t;
+ final double cs = singularValues[j] / t;
+ final double sn = f / t;
singularValues[j] = t;
f = -sn * e[j];
e[j] = cs * e[j];
@@ -358,16 +357,16 @@ public class SingularValueDecompositionI
// Perform one qr step.
case 3: {
// Calculate the shift.
- double scale =
FastMath.max(FastMath.max(FastMath.max(FastMath.max(
+ final double scale =
FastMath.max(FastMath.max(FastMath.max(FastMath.max(
FastMath.abs(singularValues[p - 1]),
FastMath.abs(singularValues[p - 2])), FastMath.abs(e[p - 2])),
FastMath.abs(singularValues[k])),
FastMath.abs(e[k]));
- double sp = singularValues[p - 1] / scale;
- double spm1 = singularValues[p - 2] / scale;
- double epm1 = e[p - 2] / scale;
- double sk = singularValues[k] / scale;
- double ek = e[k] / scale;
- double b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1) / 2.0;
- double c = (sp * epm1) * (sp * epm1);
+ final double sp = singularValues[p - 1] / scale;
+ final double spm1 = singularValues[p - 2] / scale;
+ final double epm1 = e[p - 2] / scale;
+ final double sk = singularValues[k] / scale;
+ final double ek = e[k] / scale;
+ final double b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1)
/ 2.0;
+ final double c = (sp * epm1) * (sp * epm1);
double shift = 0.0;
if ((b != 0.0) | (c != 0.0)) {
shift = FastMath.sqrt(b * b + c);
@@ -525,7 +524,7 @@ public class SingularValueDecompositionI
if (dimension == 0) {
throw new
NumberIsTooLargeException(LocalizedFormats.TOO_LARGE_CUTOFF_SINGULAR_VALUE,
- minSingularValue, singularValues[0], true);
+ minSingularValue,
singularValues[0], true);
}
final double[][] data = new double[dimension][p];
@@ -554,7 +553,7 @@ public class SingularValueDecompositionI
/** {@inheritDoc} */
public int getRank() {
- double tol = FastMath.max(m, n) * singularValues[0] * EPS;
+ final double tol = m * singularValues[0] * EPS;
int r = 0;
for (int i = 0; i < singularValues.length; i++) {
if (singularValues[i] > tol) {
@@ -566,7 +565,7 @@ public class SingularValueDecompositionI
/** {@inheritDoc} */
public DecompositionSolver getSolver() {
- return new Solver(singularValues, getUT(), getV(), getRank() ==
Math.max(m, n));
+ return new Solver(singularValues, getUT(), getV(), getRank() == m);
}
/** Specialized solver. */
@@ -585,8 +584,8 @@ public class SingularValueDecompositionI
* @param nonSingular Singularity indicator.
*/
private Solver(final double[] singularValues, final RealMatrix uT,
- final RealMatrix v, final boolean nonSingular) {
- double[][] suT = uT.getData();
+ final RealMatrix v, final boolean nonSingular) {
+ final double[][] suT = uT.getData();
for (int i = 0; i < singularValues.length; ++i) {
final double a;
if (singularValues[i] > 0) {
