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Some changes regarding what to do when a cycle is completely busted. 

git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@945 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
kiran 2009-06-09 01:00:33 +00:00
parent af0b03a257
commit 0bd78d72d7
1 changed files with 24 additions and 8 deletions
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32
java/src/org/broadinstitute/sting/secondarybase/BasecallingBaseModel.java
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@ -6,6 +6,7 @@ import cern.colt.matrix.DoubleMatrix1D;
import cern.colt.matrix.DoubleMatrix2D;
import cern.colt.matrix.linalg.Algebra;
import org.broadinstitute.sting.utils.BaseUtils;
import org.broadinstitute.sting.utils.MathUtils;
import java.io.File;
import java.io.IOException;
@ -37,6 +38,7 @@ public class BasecallingBaseModel {
private int numTheories = 1;
private boolean readyToCall = false;
private boolean bustedCycle = false;
/**
* Constructor for BasecallingBaseModel.
@ -131,7 +133,16 @@ public class BasecallingBaseModel {
inverseCovariances[basePrevIndex][baseCurIndex] = unscaledCovarianceSums[basePrevIndex][baseCurIndex].copy();
inverseCovariances[basePrevIndex][baseCurIndex].assign(F.div(counts[basePrevIndex][baseCurIndex]));
if (MathUtils.compareDoubles(alg.det(inverseCovariances[basePrevIndex][baseCurIndex]), 0.0) == 0) {
bustedCycle = true;
readyToCall = true;
return;
}
DoubleMatrix2D invcov = alg.inverse(inverseCovariances[basePrevIndex][baseCurIndex]);
inverseCovariances[basePrevIndex][baseCurIndex] = invcov;
norms[basePrevIndex][baseCurIndex] = Math.pow(alg.det(invcov), 0.5)/Math.pow(2.0*Math.PI, 2.0);
@ -155,17 +166,22 @@ public class BasecallingBaseModel {
}
double[][] likedist = new double[numTheories][4];
for (int basePrevIndex = 0; basePrevIndex < numTheories; basePrevIndex++) {
for (int baseCurIndex = 0; baseCurIndex < 4; baseCurIndex++) {
double norm = norms[basePrevIndex][baseCurIndex];
DoubleMatrix1D sub = (DoubleFactory1D.dense).make(fourintensity);
sub.assign(means[basePrevIndex][baseCurIndex], F.minus);
if (bustedCycle) {
likedist[0][0] = 1.0;
} else {
for (int basePrevIndex = 0; basePrevIndex < numTheories; basePrevIndex++) {
for (int baseCurIndex = 0; baseCurIndex < 4; baseCurIndex++) {
double norm = norms[basePrevIndex][baseCurIndex];
DoubleMatrix1D Ax = alg.mult(inverseCovariances[basePrevIndex][baseCurIndex], sub);
double exparg = -0.5*alg.mult(sub, Ax);
DoubleMatrix1D sub = (DoubleFactory1D.dense).make(fourintensity);
sub.assign(means[basePrevIndex][baseCurIndex], F.minus);
likedist[basePrevIndex][baseCurIndex] = norm*Math.exp(exparg);
DoubleMatrix1D Ax = alg.mult(inverseCovariances[basePrevIndex][baseCurIndex], sub);
double exparg = -0.5*alg.mult(sub, Ax);
likedist[basePrevIndex][baseCurIndex] = norm*Math.exp(exparg);
}
}
}