Examples with Identity org.apache.commons.math3.analysis.function.Identity used on opensource projects

Example 1 with Identity

use of org.apache.commons.math3.analysis.function.Identity in project deeplearning4j by deeplearning4j.

the class TestReconstructionDistributions method testGaussianLogProb.

@Test
public void testGaussianLogProb() {
    Nd4j.getRandom().setSeed(12345);
    int inputSize = 4;
    int[] mbs = new int[] { 1, 2, 5 };
    for (boolean average : new boolean[] { true, false }) {
        for (int minibatch : mbs) {
            INDArray x = Nd4j.rand(minibatch, inputSize);
            INDArray mean = Nd4j.randn(minibatch, inputSize);
            INDArray logStdevSquared = Nd4j.rand(minibatch, inputSize).subi(0.5);
            INDArray distributionParams = Nd4j.createUninitialized(new int[] { minibatch, 2 * inputSize });
            distributionParams.get(NDArrayIndex.all(), NDArrayIndex.interval(0, inputSize)).assign(mean);
            distributionParams.get(NDArrayIndex.all(), NDArrayIndex.interval(inputSize, 2 * inputSize)).assign(logStdevSquared);
            ReconstructionDistribution dist = new GaussianReconstructionDistribution("identity");
            double negLogProb = dist.negLogProbability(x, distributionParams, average);
            INDArray exampleNegLogProb = dist.exampleNegLogProbability(x, distributionParams);
            assertArrayEquals(new int[] { minibatch, 1 }, exampleNegLogProb.shape());
            //Calculate the same thing, but using Apache Commons math
            double logProbSum = 0.0;
            for (int i = 0; i < minibatch; i++) {
                double exampleSum = 0.0;
                for (int j = 0; j < inputSize; j++) {
                    double mu = mean.getDouble(i, j);
                    double logSigma2 = logStdevSquared.getDouble(i, j);
                    double sigma = Math.sqrt(Math.exp(logSigma2));
                    NormalDistribution nd = new NormalDistribution(mu, sigma);
                    double xVal = x.getDouble(i, j);
                    double thisLogProb = nd.logDensity(xVal);
                    logProbSum += thisLogProb;
                    exampleSum += thisLogProb;
                }
                assertEquals(-exampleNegLogProb.getDouble(i), exampleSum, 1e-6);
            }
            double expNegLogProb;
            if (average) {
                expNegLogProb = -logProbSum / minibatch;
            } else {
                expNegLogProb = -logProbSum;
            }
            //                System.out.println(expLogProb + "\t" + logProb + "\t" + (logProb / expLogProb));
            assertEquals(expNegLogProb, negLogProb, 1e-6);
            //Also: check random sampling...
            int count = minibatch * inputSize;
            INDArray arr = Nd4j.linspace(-3, 3, count).reshape(minibatch, inputSize);
            INDArray sampleMean = dist.generateAtMean(arr);
            INDArray sampleRandom = dist.generateRandom(arr);
        }
    }
}

Example 2 with Identity

use of org.apache.commons.math3.analysis.function.Identity in project FSensor by KalebKE.

the class FitPoints method generateRIV.

/**
 * Generates a vector from the identity matrix of r.
 *
 * @param subr centered algebraic form of the ellipsoid
 */
private RealVector generateRIV(RealMatrix subr) {
    riv = new ArrayRealVector(3);
    riv.setEntry(0, Math.abs(subr.getEntry(0, 0)));
    riv.setEntry(1, Math.abs(subr.getEntry(1, 1)));
    riv.setEntry(2, Math.abs(subr.getEntry(2, 2)));
    return riv;
}

Example 3 with Identity

use of org.apache.commons.math3.analysis.function.Identity in project gatk-protected by broadinstitute.

the class IntegerCopyNumberTransitionMatrix method getPaddedTransitionMatrix.

/**
     * Pad the transition matrix with extra hidden states. The old transition matrix will be embedded on the upper left
     * corner of an identity matrix. As a result, there will be no mixing between the existing states and the hidden
     * states.
     *
     * Note: padding is performed for convenience and the extra hidden states are meant to be inaccessible. It is the
     * user's responsibility to make sure that the prior probabilities prohibits the occupancy of these states.
     *
     * @param originalTransitionMatrix the original non-padded transition matrix
     * @param padding non-negative number of extra hidden states to pad
     * @return an instance of {@link IntegerCopyNumberTransitionMatrix}
     */
private static RealMatrix getPaddedTransitionMatrix(final RealMatrix originalTransitionMatrix, final int padding) {
    if (padding > 0) {
        final int maxCopyNumber = originalTransitionMatrix.getColumnDimension() - 1;
        final int newMaxCopyNumber = maxCopyNumber + padding;
        final RealMatrix paddedTransitionMatrix = MatrixUtils.createRealIdentityMatrix(newMaxCopyNumber + 1);
        for (int i = 0; i <= maxCopyNumber; i++) {
            for (int j = 0; j <= maxCopyNumber; j++) {
                paddedTransitionMatrix.setEntry(i, j, originalTransitionMatrix.getEntry(i, j));
            }
        }
        return paddedTransitionMatrix;
    } else {
        return originalTransitionMatrix;
    }
}

Example 4 with Identity

use of org.apache.commons.math3.analysis.function.Identity in project gatk by broadinstitute.

the class IntegerCopyNumberTransitionMatrix method getPaddedTransitionMatrix.

/**
     * Pad the transition matrix with extra hidden states. The old transition matrix will be embedded on the upper left
     * corner of an identity matrix. As a result, there will be no mixing between the existing states and the hidden
     * states.
     *
     * Note: padding is performed for convenience and the extra hidden states are meant to be inaccessible. It is the
     * user's responsibility to make sure that the prior probabilities prohibits the occupancy of these states.
     *
     * @param originalTransitionMatrix the original non-padded transition matrix
     * @param padding non-negative number of extra hidden states to pad
     * @return an instance of {@link IntegerCopyNumberTransitionMatrix}
     */
private static RealMatrix getPaddedTransitionMatrix(final RealMatrix originalTransitionMatrix, final int padding) {
    if (padding > 0) {
        final int maxCopyNumber = originalTransitionMatrix.getColumnDimension() - 1;
        final int newMaxCopyNumber = maxCopyNumber + padding;
        final RealMatrix paddedTransitionMatrix = MatrixUtils.createRealIdentityMatrix(newMaxCopyNumber + 1);
        for (int i = 0; i <= maxCopyNumber; i++) {
            for (int j = 0; j <= maxCopyNumber; j++) {
                paddedTransitionMatrix.setEntry(i, j, originalTransitionMatrix.getEntry(i, j));
            }
        }
        return paddedTransitionMatrix;
    } else {
        return originalTransitionMatrix;
    }
}

Example 5 with Identity

use of org.apache.commons.math3.analysis.function.Identity in project FSensor by KalebKE.

the class RotationKalmanFilter method correct.

/**
 * Correct the current state estimate with an actual measurement.
 *
 * @param z
 *            the measurement vector
 * @throws NullArgumentException
 *             if the measurement vector is {@code null}
 * @throws DimensionMismatchException
 *             if the dimension of the measurement vector does not fit
 * @throws SingularMatrixException
 *             if the covariance matrix could not be inverted
 */
public void correct(final RealVector z) throws NullArgumentException, DimensionMismatchException, SingularMatrixException {
    // sanity checks
    MathUtils.checkNotNull(z);
    if (z.getDimension() != measurementMatrix.getRowDimension()) {
        throw new DimensionMismatchException(z.getDimension(), measurementMatrix.getRowDimension());
    }
    // S = H * P(k) * H' + R
    RealMatrix s = measurementMatrix.multiply(errorCovariance).multiply(measurementMatrixT).add(measurementModel.getMeasurementNoise());
    // Inn = z(k) - H * xHat(k)-
    RealVector innovation = z.subtract(measurementMatrix.operate(stateEstimation));
    // calculate gain matrix
    // K(k) = P(k)- * H' * (H * P(k)- * H' + R)^-1
    // K(k) = P(k)- * H' * S^-1
    // instead of calculating the inverse of S we can rearrange the formula,
    // and then solve the linear equation A x X = B with A = S', X = K' and
    // B = (H * P)'
    // K(k) * S = P(k)- * H'
    // S' * K(k)' = H * P(k)-'
    RealMatrix kalmanGain = new CholeskyDecomposition(s).getSolver().solve(measurementMatrix.multiply(errorCovariance.transpose())).transpose();
    // update estimate with measurement z(k)
    // xHat(k) = xHat(k)- + K * Inn
    stateEstimation = stateEstimation.add(kalmanGain.operate(innovation));
    // update covariance of prediction error
    // P(k) = (I - K * H) * P(k)-
    RealMatrix identity = MatrixUtils.createRealIdentityMatrix(kalmanGain.getRowDimension());
    errorCovariance = identity.subtract(kalmanGain.multiply(measurementMatrix)).multiply(errorCovariance);
}