Removing untrue comments in the GaussianMixtureModel
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5369 348d0f76-0448-11de-a6fe-93d51630548a
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rpoplin
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@ -445,41 +445,6 @@ public final class VariantGaussianMixtureModel extends VariantOptimizationModel
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return value;
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return value;
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}
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}
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public final double evaluateVariant( GenomeLocParser genomeLocParser, final VariantContext vc ) {
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final double[] log10pVarInCluster = new double[maxGaussians];
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final double[] annotations = new double[dataManager.numAnnotations];
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for( int jjj = 0; jjj < dataManager.numAnnotations; jjj++ ) {
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final double value = decodeAnnotation( genomeLocParser, dataManager.annotationKeys.get(jjj), vc, false );
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double z = (value - dataManager.meanVector[jjj]) / dataManager.varianceVector[jjj];
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// switch ( dataManager.getAnnotationType(jjj) ) {
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// case TWO_TAILED: // z is already fine
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// break;
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// case SMALL_IS_GOOD:
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// if ( z < 0.0 ) {
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//// logger.info(String.format("One-tailed test correction: %s %.2f mu=%.2f sigma=%.2f z=%.2f, newZ = 0",
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//// dataManager.annotationKeys.get(jjj), value, dataManager.meanVector[jjj], dataManager.varianceVector[jjj], z));
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// z = 0.0;
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// }
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// break;
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// case BIG_IS_GOOD:
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// if ( z > 0.0 ) {
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//// logger.info(String.format("One-tailed test correction: %s %.2f mu=%.2f sigma=%.2f z=%.2f, newZ = 0",
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//// dataManager.annotationKeys.get(jjj), value, dataManager.meanVector[jjj], dataManager.varianceVector[jjj], z));
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// z = 0.0;
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// }
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// break;
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// default:
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// throw new ReviewedStingException("Unexpected annotation tail type: " + dataManager.getAnnotationType(jjj));
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// }
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annotations[jjj] = z;
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}
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return evaluateGaussiansForSingleVariant( annotations, log10pVarInCluster );
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}
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// ---------------------------------------------------------------------------------------------------------
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// ---------------------------------------------------------------------------------------------------------
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//
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//
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// Code to determine FDR tranches for VariantDatum[]
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// Code to determine FDR tranches for VariantDatum[]
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@ -713,6 +678,85 @@ public final class VariantGaussianMixtureModel extends VariantOptimizationModel
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// Code to evaluate vectors given an already trained model
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// Code to evaluate vectors given an already trained model
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//
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//
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// ---------------------------------------------------------------------------------------------------------
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// ---------------------------------------------------------------------------------------------------------
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public final double evaluateVariant( GenomeLocParser genomeLocParser, final VariantContext vc ) {
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final double[] log10pVarInCluster = new double[maxGaussians];
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final double[] annotations = new double[dataManager.numAnnotations];
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for( int jjj = 0; jjj < dataManager.numAnnotations; jjj++ ) {
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final double value = decodeAnnotation( genomeLocParser, dataManager.annotationKeys.get(jjj), vc, false );
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double z = (value - dataManager.meanVector[jjj]) / dataManager.varianceVector[jjj];
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// switch ( dataManager.getAnnotationType(jjj) ) {
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// case TWO_TAILED: // z is already fine
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// break;
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// case SMALL_IS_GOOD:
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// if ( z < 0.0 ) {
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//// logger.info(String.format("One-tailed test correction: %s %.2f mu=%.2f sigma=%.2f z=%.2f, newZ = 0",
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//// dataManager.annotationKeys.get(jjj), value, dataManager.meanVector[jjj], dataManager.varianceVector[jjj], z));
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// z = 0.0;
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// }
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// break;
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// case BIG_IS_GOOD:
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// if ( z > 0.0 ) {
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//// logger.info(String.format("One-tailed test correction: %s %.2f mu=%.2f sigma=%.2f z=%.2f, newZ = 0",
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//// dataManager.annotationKeys.get(jjj), value, dataManager.meanVector[jjj], dataManager.varianceVector[jjj], z));
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// z = 0.0;
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// }
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// break;
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// default:
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// throw new ReviewedStingException("Unexpected annotation tail type: " + dataManager.getAnnotationType(jjj));
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// }
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annotations[jjj] = z;
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}
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return evaluateGaussiansForSingleVariant( annotations, log10pVarInCluster );
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}
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private double evaluateGaussiansForSingleVariant( final double[] annotations, final double[] log10pVarInCluster ) {
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final int numAnnotations = annotations.length;
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for( int kkk = 0; kkk < maxGaussians; kkk++ ) {
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final double sigmaVals[][] = sigmaInverse[kkk];
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double sum = 0.0;
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for( int jjj = 0; jjj < numAnnotations; jjj++ ) {
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double value = 0.0;
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for( int ppp = 0; ppp < numAnnotations; ppp++ ) {
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final double myMu = mu[kkk][ppp];
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double myAnn = annotations[ppp];
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final double mySigma = sigmaVals[ppp][jjj];
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double z = (myAnn - myMu) * mySigma;
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switch ( dataManager.getAnnotationType(jjj) ) {
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case TWO_TAILED: break;
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case SMALL_IS_GOOD: if( myAnn < myMu && Math.abs(z) < MAX_SIGMA_TO_BE_IN_GAUSSIAN ) { z = 0.0; } break;
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case BIG_IS_GOOD: if( myAnn > myMu && Math.abs(z) < MAX_SIGMA_TO_BE_IN_GAUSSIAN ) { z = 0.0; } break;
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default: throw new ReviewedStingException("Unexpected annotation tail type: " + dataManager.getAnnotationType(jjj));
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}
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value += z;
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}
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final double jNorm = annotations[jjj] - mu[kkk][jjj];
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final double prod = value * jNorm;
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sum += prod;
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}
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final double denomLog10 = CONSTANT_GAUSSIAN_DENOM_LOG10 + sqrtDeterminantLog10[kkk];
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final double evalGaussianPDFLog10 = (( -0.5 * sum ) / Math.log(10.0)) - denomLog10;
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//final double pVar1 = Math.pow(10.0, pClusterLog10[kkk] + evalGaussianPDFLog10);
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final double pVar1 = pClusterLog10[kkk] + evalGaussianPDFLog10;
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log10pVarInCluster[kkk] = pVar1;
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}
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return MathUtils.log10sumLog10(log10pVarInCluster);
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}
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// ---------------------------------------------------------------------------------------------------------
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//
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// Code to implement the steps of the VBEM algorithm
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//
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// ---------------------------------------------------------------------------------------------------------
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private double evaluateGaussians( final VariantDatum[] data, final double[][] pVarInCluster, final int startCluster, final int stopCluster ) {
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private double evaluateGaussians( final VariantDatum[] data, final double[][] pVarInCluster, final int startCluster, final int stopCluster ) {
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final int numAnnotations = data[0].annotations.length;
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final int numAnnotations = data[0].annotations.length;
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@ -797,46 +841,6 @@ public final class VariantGaussianMixtureModel extends VariantOptimizationModel
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return likelihood / sumWeight;
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return likelihood / sumWeight;
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}
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}
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private double evaluateGaussiansForSingleVariant( final double[] annotations, final double[] log10pVarInCluster ) {
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final int numAnnotations = annotations.length;
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for( int kkk = 0; kkk < maxGaussians; kkk++ ) {
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final double sigmaVals[][] = sigmaInverse[kkk];
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double sum = 0.0;
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for( int jjj = 0; jjj < numAnnotations; jjj++ ) {
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double value = 0.0;
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for( int ppp = 0; ppp < numAnnotations; ppp++ ) {
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final double myMu = mu[kkk][ppp];
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double myAnn = annotations[ppp];
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final double mySigma = sigmaVals[ppp][jjj];
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double z = (myAnn - myMu) * mySigma;
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switch ( dataManager.getAnnotationType(jjj) ) {
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case TWO_TAILED: break;
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case SMALL_IS_GOOD: if( myAnn < myMu && Math.abs(z) < MAX_SIGMA_TO_BE_IN_GAUSSIAN ) { z = 0.0; } break;
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case BIG_IS_GOOD: if( myAnn > myMu && Math.abs(z) < MAX_SIGMA_TO_BE_IN_GAUSSIAN ) { z = 0.0; } break;
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default: throw new ReviewedStingException("Unexpected annotation tail type: " + dataManager.getAnnotationType(jjj));
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}
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value += z;
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}
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final double jNorm = annotations[jjj] - mu[kkk][jjj];
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final double prod = value * jNorm;
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sum += prod;
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}
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final double denomLog10 = CONSTANT_GAUSSIAN_DENOM_LOG10 + sqrtDeterminantLog10[kkk];
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final double evalGaussianPDFLog10 = (( -0.5 * sum ) / Math.log(10.0)) - denomLog10;
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//final double pVar1 = Math.pow(10.0, pClusterLog10[kkk] + evalGaussianPDFLog10);
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final double pVar1 = pClusterLog10[kkk] + evalGaussianPDFLog10;
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log10pVarInCluster[kkk] = pVar1;
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}
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return MathUtils.log10sumLog10(log10pVarInCluster);
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}
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private void maximizeGaussians( final VariantDatum[] data, final double[][] pVarInCluster, final int startCluster, final int stopCluster ) {
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private void maximizeGaussians( final VariantDatum[] data, final double[][] pVarInCluster, final int startCluster, final int stopCluster ) {
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final int numVariants = data.length;
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final int numVariants = data.length;
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