Author: dimpbx
Date: Sun Feb 21 21:46:12 2010
New Revision: 912413
URL: http://svn.apache.org/viewvc?rev=912413&view=rev
Log:
MATH-432 fixed
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math/linear/EigenDecompositionImpl.java
commons/proper/math/trunk/src/main/java/org/apache/commons/math/linear/SingularValueDecompositionImpl.java
commons/proper/math/trunk/src/site/xdoc/changes.xml
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math/linear/EigenDecompositionImpl.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math/linear/EigenDecompositionImpl.java?rev=912413&r1=912412&r2=912413&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math/linear/EigenDecompositionImpl.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math/linear/EigenDecompositionImpl.java
Sun Feb 21 21:46:12 2010
@@ -479,6 +479,29 @@
}
realEigenvalues[n - 1] = main[n - 1];
e[n - 1] = 0.0;
+
+ // Determine the largest main and secondary value in absolute term.
+ double maxAbsoluteValue=0.0;
+ for (int i = 0; i < n; i++) {
+ if (Math.abs(realEigenvalues[i])>maxAbsoluteValue) {
+ maxAbsoluteValue=Math.abs(realEigenvalues[i]);
+ }
+ if (Math.abs(e[i])>maxAbsoluteValue) {
+ maxAbsoluteValue=Math.abs(e[i]);
+ }
+ }
+ // Make null any main and secondary value too small to be significant
+ if (maxAbsoluteValue!=0.0) {
+ for (int i=0; i < n; i++) {
+ if
(Math.abs(realEigenvalues[i])<=MathUtils.EPSILON*maxAbsoluteValue) {
+ realEigenvalues[i]=0.0;
+ }
+ if (Math.abs(e[i])<=MathUtils.EPSILON*maxAbsoluteValue) {
+ e[i]=0.0;
+ }
+ }
+ }
+
for (int j = 0; j < n; j++) {
int its = 0;
int m;
@@ -568,7 +591,7 @@
}
// Determine the largest eigen value in absolute term.
- double maxAbsoluteValue=0.0;
+ maxAbsoluteValue=0.0;
for (int i = 0; i < n; i++) {
if (Math.abs(realEigenvalues[i])>maxAbsoluteValue) {
maxAbsoluteValue=Math.abs(realEigenvalues[i]);
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=912413&r1=912412&r2=912413&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
Sun Feb 21 21:46:12 2010
@@ -88,11 +88,12 @@
// create A^T*A
//
for (int i = 0; i < n; i++) {
- for (int j = 0; j < n; j++) {
+ for (int j = i; j < n; j++) {
matATA[i][j] = 0.0;
for (int k = 0; k < m; k++) {
matATA[i][j] += localcopy[k][i] * localcopy[k][j];
}
+ matATA[j][i]=matATA[i][j];
}
}
@@ -101,11 +102,12 @@
// create A*A^T
//
for (int i = 0; i < m; i++) {
- for (int j = 0; j < m; j++) {
+ for (int j = i; j < m; j++) {
matAAT[i][j] = 0.0;
for (int k = 0; k < n; k++) {
matAAT[i][j] += localcopy[i][k] * localcopy[j][k];
}
+ matAAT[j][i]=matAAT[i][j];
}
}
int p;
Modified: commons/proper/math/trunk/src/site/xdoc/changes.xml
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/site/xdoc/changes.xml?rev=912413&r1=912412&r2=912413&view=diff
==============================================================================
--- commons/proper/math/trunk/src/site/xdoc/changes.xml (original)
+++ commons/proper/math/trunk/src/site/xdoc/changes.xml Sun Feb 21 21:46:12 2010
@@ -39,6 +39,12 @@
</properties>
<body>
<release version="2.1" date="TBD" description="TBD">
+ <action dev="dimpbx" type="fix" issue="MATH-342">
+ In SVD, the matrices passed to EigenDecomposition are now symmetric
+ by construction (rather than simply by definition). In
EigenDecomposition,
+ once the tridiagonal form is obtained, the non-significant elements are
+ set to 0.
+ </action>
<action dev="dimpbx" type="fix" issue="MATH-333">
A EigenDecompositionImpl simplified makes it possible to compute
the SVD of a singular matrix (with the right number of elements in
