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Merge pull request #248 from broadinstitute/rp_chartl_mathutils_checks_for_valid_inputs-GSA-767 

Create a new normalDistributionLog10 function that is unit tested for us...
This commit is contained in:
Mark DePristo 2013-05-30 13:39:33 -07:00
parent 56b14be4bc 61af37d0d2
commit ac90e6765e
5 changed files with 59 additions and 10 deletions
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5
protected/java/src/org/broadinstitute/sting/gatk/walkers/variantrecalibration/GaussianMixtureModel.java
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@ -47,7 +47,6 @@
package org.broadinstitute.sting.gatk.walkers.variantrecalibration;
import Jama.Matrix;
import cern.jet.random.Normal;
import org.apache.log4j.Logger;
import org.broadinstitute.sting.gatk.GenomeAnalysisEngine;
import org.broadinstitute.sting.utils.MathUtils;
@ -243,12 +242,10 @@ public class GaussianMixtureModel {
public Double evaluateDatumInOneDimension( final VariantDatum datum, final int iii ) {
if(datum.isNull[iii]) { return null; }
final Normal normal = new Normal(0.0, 1.0, null);
final double[] pVarInGaussianLog10 = new double[gaussians.size()];
int gaussianIndex = 0;
for( final MultivariateGaussian gaussian : gaussians ) {
normal.setState( gaussian.mu[iii], gaussian.sigma.get(iii, iii) );
pVarInGaussianLog10[gaussianIndex++] = gaussian.pMixtureLog10 + Math.log10( normal.pdf( datum.annotations[iii] ) );
pVarInGaussianLog10[gaussianIndex++] = gaussian.pMixtureLog10 + MathUtils.normalDistributionLog10(gaussian.mu[iii], gaussian.sigma.get(iii, iii), datum.annotations[iii]);
}
return MathUtils.log10sumLog10(pVarInGaussianLog10); // Sum(pi_k * p(v|n,k))
}

42
public/java/src/org/broadinstitute/sting/utils/MathUtils.java
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@ -63,6 +63,8 @@ public class MathUtils {
*/
public final static double LOG10_P_OF_ZERO = -1000000.0;
public final static double FAIR_BINOMIAL_PROB_LOG10_0_5 = Math.log10(0.5);
private final static double NATURAL_LOG_OF_TEN = Math.log(10.0);
private final static double SQUARE_ROOT_OF_TWO_TIMES_PI = Math.sqrt(2.0 * Math.PI);
static {
log10Cache = new double[LOG10_CACHE_SIZE];
@ -301,12 +303,46 @@ public class MathUtils {
return 1;
}
public static double NormalDistribution(final double mean, final double sd, final double x) {
double a = 1.0 / (sd * Math.sqrt(2.0 * Math.PI));
double b = Math.exp(-1.0 * (Math.pow(x - mean, 2.0) / (2.0 * sd * sd)));
/**
* Calculate f(x) = Normal(x | mu = mean, sigma = sd)
* @param mean the desired mean of the Normal distribution
* @param sd the desired standard deviation of the Normal distribution
* @param x the value to evaluate
* @return a well-formed double
*/
public static double normalDistribution(final double mean, final double sd, final double x) {
final double a = 1.0 / (sd * SQUARE_ROOT_OF_TWO_TIMES_PI);
final double b = Math.exp(-1.0 * (square(x - mean) / (2.0 * square(sd))));
return a * b;
}
/**
* Calculate f(x) = log10 ( Normal(x | mu = mean, sigma = sd) )
* @param mean the desired mean of the Normal distribution
* @param sd the desired standard deviation of the Normal distribution
* @param x the value to evaluate
* @return a well-formed double
*/
public static double normalDistributionLog10(final double mean, final double sd, final double x) {
if( sd < 0 )
throw new IllegalArgumentException("sd: Standard deviation of normal must be >0");
if ( ! wellFormedDouble(mean) || ! wellFormedDouble(sd) || ! wellFormedDouble(x) )
throw new IllegalArgumentException("mean, sd, or, x : Normal parameters must be well formatted (non-INF, non-NAN)");
final double a = -1.0 * Math.log10(sd * SQUARE_ROOT_OF_TWO_TIMES_PI);
final double b = -1.0 * (square(x - mean) / (2.0 * square(sd))) / NATURAL_LOG_OF_TEN;
return a + b;
}
/**
* Calculate f(x) = x^2
* @param x the value to square
* @return x * x
*/
public static double square(final double x) {
return x * x;
}
/**
* Calculates the log10 of the binomial coefficient. Designed to prevent
* overflows even with very large numbers.

3
public/java/src/org/broadinstitute/sting/utils/activeregion/BandPassActivityProfile.java
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@ -31,7 +31,6 @@ import org.broadinstitute.sting.utils.GenomeLocParser;
import org.broadinstitute.sting.utils.GenomeLocSortedSet;
import org.broadinstitute.sting.utils.MathUtils;
import java.util.ArrayList;
import java.util.Collection;
import java.util.LinkedList;
@ -108,7 +107,7 @@ public class BandPassActivityProfile extends ActivityProfile {
final int bandSize = 2 * filterSize + 1;
final double[] kernel = new double[bandSize];
for( int iii = 0; iii < bandSize; iii++ ) {
kernel[iii] = MathUtils.NormalDistribution(filterSize, sigma, iii);
kernel[iii] = MathUtils.normalDistribution(filterSize, sigma, iii);
}
return MathUtils.normalizeFromRealSpace(kernel);
}

17
public/java/test/org/broadinstitute/sting/utils/MathUtilsUnitTest.java
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@ -25,6 +25,7 @@
package org.broadinstitute.sting.utils;
import cern.jet.random.Normal;
import org.broadinstitute.sting.BaseTest;
import org.testng.Assert;
import org.testng.annotations.BeforeClass;
@ -398,4 +399,20 @@ public class MathUtilsUnitTest extends BaseTest {
Assert.assertEquals(MathUtils.logDotProduct(new double[]{-5.0,-3.0,2.0}, new double[]{6.0,7.0,8.0}),10.0,1e-3);
Assert.assertEquals(MathUtils.logDotProduct(new double[]{-5.0}, new double[]{6.0}),1.0,1e-3);
}
@Test
public void testNormalDistribution() {
final double requiredPrecision = 1E-10;
final Normal n = new Normal(0.0, 1.0, null);
for( final double mu : new double[]{-5.0, -3.2, -1.5, 0.0, 1.2, 3.0, 5.8977} ) {
for( final double sigma : new double[]{1.2, 3.0, 5.8977} ) {
for( final double x : new double[]{-5.0, -3.2, -1.5, 0.0, 1.2, 3.0, 5.8977} ) {
n.setState(mu, sigma);
Assert.assertEquals(n.pdf(x), MathUtils.normalDistribution(mu, sigma, x), requiredPrecision);
Assert.assertEquals(Math.log10(n.pdf(x)), MathUtils.normalDistributionLog10(mu, sigma, x), requiredPrecision);
}
}
}
}
}

2
public/java/test/org/broadinstitute/sting/utils/activeregion/ActivityProfileUnitTest.java
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@ -450,7 +450,7 @@ public class ActivityProfileUnitTest extends BaseTest {
private double[] makeGaussian(final int mean, final int range, final double sigma) {
final double[] gauss = new double[range];
for( int iii = 0; iii < range; iii++ ) {
gauss[iii] = MathUtils.NormalDistribution(mean, sigma, iii) + ActivityProfile.ACTIVE_PROB_THRESHOLD;
gauss[iii] = MathUtils.normalDistribution(mean, sigma, iii) + ActivityProfile.ACTIVE_PROB_THRESHOLD;
}
return gauss;
}