Examples with DefaultRealMatrixChangingVisitor org.apache.commons.math3.linear.DefaultRealMatrixChangingVisitor used on opensource projects

Example 6 with DefaultRealMatrixChangingVisitor

use of org.apache.commons.math3.linear.DefaultRealMatrixChangingVisitor in project gatk-protected by broadinstitute.

the class SomaticGenotypingEngine method getAsRealMatrix.

//convert a likelihood matrix of alleles x reads into a RealMatrix
public static RealMatrix getAsRealMatrix(final LikelihoodMatrix<Allele> matrix) {
    final RealMatrix result = new Array2DRowRealMatrix(matrix.numberOfAlleles(), matrix.numberOfReads());
    result.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {

        @Override
        public double visit(int row, int column, double value) {
            return matrix.get(row, column);
        }
    });
    return result;
}

Example 7 with DefaultRealMatrixChangingVisitor

use of org.apache.commons.math3.linear.DefaultRealMatrixChangingVisitor in project gatk by broadinstitute.

the class NormalizeSomaticReadCountsIntegrationTest method assertBetaHatsRobustToOutliers.

/**
     * Asserts that the calculation of beta hats is not significantly affected by zero-coverage outlier counts
     * We perform this check by randomly setting some coverages to zero in copy ratio space (-infinity in log space).
     * betaHats imputes 0 in log space (1 in copy ratio space) whenever coverage is below a certain low threshold
     * and should thus be robust to this type of noise.
     */
private void assertBetaHatsRobustToOutliers(final ReadCountCollection preTangentNormalized, final File ponFile) {
    try (final HDF5File ponReader = new HDF5File(ponFile)) {
        final PCACoveragePoN pon = new HDF5PCACoveragePoN(ponReader);
        final List<String> ponTargets = pon.getPanelTargetNames();
        final RealMatrix input = reorderTargetsToPoNOrder(preTangentNormalized, ponTargets);
        // randomly set some entries to zero in copy-ratio space (-infinity in log space)
        final Random random = new Random(13);
        final double noiseProportion = 0.01;
        final RealMatrix noisyInput = input.copy();
        noisyInput.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {

            @Override
            public double visit(final int row, final int column, final double value) {
                return random.nextDouble() < noiseProportion ? Double.NEGATIVE_INFINITY : value;
            }
        });
        final RealMatrix betaHats = PCATangentNormalizationUtils.calculateBetaHats(pon.getReducedPanelPInverseCounts(), input, PCATangentNormalizationUtils.EPSILON);
        final RealMatrix noisyBetaHats = PCATangentNormalizationUtils.calculateBetaHats(pon.getReducedPanelPInverseCounts(), noisyInput, PCATangentNormalizationUtils.EPSILON);
        final RealMatrix difference = betaHats.subtract(noisyBetaHats);
        difference.walkInOptimizedOrder(new DefaultRealMatrixPreservingVisitor() {

            @Override
            public void visit(final int row, int column, double value) {
                Assert.assertEquals(value, 0, 0.01);
            }
        });
    }
}

Example 8 with DefaultRealMatrixChangingVisitor

use of org.apache.commons.math3.linear.DefaultRealMatrixChangingVisitor in project gatk by broadinstitute.

the class HDF5LibraryUnitTest method createMatrixOfGaussianValues.

private RealMatrix createMatrixOfGaussianValues(int numRows, int numCols, final double mean, final double sigma) {
    final RealMatrix bigCounts = new Array2DRowRealMatrix(numRows, numCols);
    final RandomDataGenerator randomDataGenerator = new RandomDataGenerator();
    randomDataGenerator.reSeed(337337337);
    bigCounts.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {

        @Override
        public double visit(int row, int column, double value) {
            return randomDataGenerator.nextGaussian(mean, sigma);
        }
    });
    return bigCounts;
}

Example 9 with DefaultRealMatrixChangingVisitor

use of org.apache.commons.math3.linear.DefaultRealMatrixChangingVisitor in project gatk by broadinstitute.

the class GCBiasSimulatedData method simulatedData.

// visible for the integration test
public static Pair<ReadCountCollection, double[]> simulatedData(final int numTargets, final int numSamples) {
    final List<Target> phonyTargets = SimulatedTargets.phonyTargets(numTargets);
    final List<String> phonySamples = SimulatedSamples.phonySamples(numSamples);
    final Random random = new Random(13);
    final double[] gcContentByTarget = IntStream.range(0, numTargets).mapToDouble(n -> 0.5 + 0.2 * random.nextGaussian()).map(x -> Math.min(x, 0.95)).map(x -> Math.max(x, 0.05)).toArray();
    final double[] gcBiasByTarget = Arrays.stream(gcContentByTarget).map(QUADRATIC_GC_BIAS_CURVE::apply).toArray();
    // model mainly GC bias with a small random amount of non-GC bias
    // thus noise after GC correction should be nearly zero
    final RealMatrix counts = new Array2DRowRealMatrix(numTargets, numSamples);
    counts.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {

        @Override
        public double visit(final int target, final int column, final double value) {
            return gcBiasByTarget[target] * (1.0 + 0.01 * random.nextDouble());
        }
    });
    final ReadCountCollection rcc = new ReadCountCollection(phonyTargets, phonySamples, counts);
    return new ImmutablePair<>(rcc, gcContentByTarget);
}

Example 10 with DefaultRealMatrixChangingVisitor

use of org.apache.commons.math3.linear.DefaultRealMatrixChangingVisitor in project gatk-protected by broadinstitute.

the class XHMMSegmentCallerBase method standardizeByTarget.

/**
     * Standardize read counts (per-target).
     * Note: modification is done in-place.
     *
     * @param counts original read counts
     */
private void standardizeByTarget(final RealMatrix counts) {
    final double[] rowMeans = GATKProtectedMathUtils.rowMeans(counts);
    final double[] rowStdDev = GATKProtectedMathUtils.rowStdDevs(counts);
    counts.walkInColumnOrder(new DefaultRealMatrixChangingVisitor() {

        @Override
        public double visit(final int row, final int column, final double value) {
            return (value - rowMeans[row]) / rowStdDev[row];
        }
    });
}