Example usage for org.apache.commons.math3.linear RealMatrix setEntry

Introduction

In this page you can find the example usage for org.apache.commons.math3.linear RealMatrix setEntry.

Prototype

void setEntry(int row, int column, double value) throws OutOfRangeException;

Source Link

Document

Set the entry in the specified row and column.

Usage

From source file:edu.ucdenver.bios.powersvc.resource.ContrastHelper.java

/**
 * Create a trend contrast of the specified type
 * @param spacing list of integer positions representing spacing of measurements
 * @param trendType type of trend contrast
 * @param transpose if true, return the transpose of the contrast
 * @return trend contrast//from  ww w  .  jav  a 2 s .c  om
 */
private static RealMatrix getTrendContrast(double[] spacing, HypothesisTrendTypeEnum trendType,
        boolean transpose) {
    int levels = spacing.length;
    // get all possible polynomial trends
    RealMatrix allTrendContrast = OrthogonalPolynomials.orthogonalPolynomialCoefficients(spacing, levels - 1);
    // select a subset of the polynomial trends based on the hypothesis of interest
    RealMatrix trendContrast = null;
    switch (trendType) {
    case NONE:
        RealMatrix negIdentity = org.apache.commons.math3.linear.MatrixUtils
                .createRealIdentityMatrix(levels - 1).scalarMultiply(-1);
        RealMatrix column1s = MatrixUtils.getRealMatrixWithFilledValue(levels - 1, 1, 1);
        trendContrast = MatrixUtils.getHorizontalAppend(column1s, negIdentity).transpose();
        break;
    case CHANGE_FROM_BASELINE:
        trendContrast = MatrixUtils.getRealMatrixWithFilledValue(levels, 1, 0);
        trendContrast.setEntry(0, 0, 1);
        trendContrast.setEntry(levels - 1, 0, -1);
        break;
    case ALL_POLYNOMIAL:
        trendContrast = allTrendContrast;
        break;
    case LINEAR:
        if (allTrendContrast.getRowDimension() > 1) {
            trendContrast = allTrendContrast.getColumnMatrix(1);
        }
        break;
    case QUADRATIC:
        if (allTrendContrast.getRowDimension() > 2) {
            trendContrast = allTrendContrast.getColumnMatrix(2);
        }
        break;
    case CUBIC:
        if (allTrendContrast.getRowDimension() > 3) {
            trendContrast = allTrendContrast.getColumnMatrix(3);
        }
        break;
    }
    if (transpose) {
        return trendContrast.transpose();
    } else {
        return trendContrast;
    }
}

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From source file:edu.ucdenver.bios.powersvc.resource.PowerResourceHelper.java

/**
 * Create a sigma covariate matrix from the study design.
 * @param studyDesign study design object
 * @return sigma covariate matrix//from  ww  w .  ja v a 2s  . c  o m
 */
public static RealMatrix sigmaCovariateMatrixFromStudyDesign(StudyDesign studyDesign) {
    if (studyDesign.getViewTypeEnum() == StudyDesignViewTypeEnum.MATRIX_MODE) {
        return toRealMatrix(studyDesign.getNamedMatrix(PowerConstants.MATRIX_SIGMA_GAUSSIAN));
    } else {
        // in Guided Mode, the user specifies this as a standard deviation, so we square it
        RealMatrix sigmaG = toRealMatrix(studyDesign.getNamedMatrix(PowerConstants.MATRIX_SIGMA_GAUSSIAN));
        if (sigmaG != null && sigmaG.getRowDimension() > 0 && sigmaG.getColumnDimension() > 0) {
            double stddev = sigmaG.getEntry(0, 0);
            sigmaG.setEntry(0, 0, stddev * stddev);
        }
        return sigmaG;
    }
}

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From source file:hivemall.utils.math.MatrixUtils.java

@Nonnull
public static RealMatrix[] unflatten(@Nonnull final double[] data, final int rows, final int cols,
        final int len) {
    final RealMatrix[] grid = new RealMatrix[len];
    int offset = 0;
    for (int k = 0; k < len; k++) {
        RealMatrix cell = new BlockRealMatrix(rows, cols);
        grid[k] = cell;// w w w. j av  a  2 s .c o  m
        for (int i = 0; i < rows; i++) {
            for (int j = 0; j < cols; j++) {
                if (offset >= data.length) {
                    throw new IndexOutOfBoundsException(
                            "Offset " + offset + " exceeded data.length " + data.length);
                }
                double value = data[offset];
                cell.setEntry(i, j, value);
                offset++;
            }
        }
    }
    if (offset != data.length) {
        throw new IllegalArgumentException("Invalid data for unflatten");
    }
    return grid;
}

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From source file:edu.cmu.tetrad.util.MatrixUtils1.java

public static double[][] pseudoInverse(double[][] x) {
    SingularValueDecomposition svd = new SingularValueDecomposition(new BlockRealMatrix(x));

    RealMatrix U = svd.getU();/*from   ww w.j  a  v a  2 s.c om*/
    RealMatrix V = svd.getV();
    RealMatrix S = svd.getS();

    for (int i = 0; i < S.getRowDimension(); i++) {
        for (int j = 0; j < S.getColumnDimension(); j++) {
            double v = S.getEntry(i, j);
            S.setEntry(i, j, v == 0 ? 0.0 : 1.0 / v);
        }
    }

    return V.multiply(S.multiply(U.transpose())).getData();
}

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From source file:edu.ucdenver.bios.powersvc.resource.CovarianceHelper.java

/**
 * Convert a correlation into a covariance matrix
 * @param covariance/*from  w w  w  .j  ava2s.  c o m*/
 * @return
 */
private static RealMatrix buildCovarianceFromCorrelation(Covariance covariance) {
    RealMatrix covarianceData = null;
    Blob2DArray blob = covariance.getBlob();

    if (blob != null && blob.getData() != null) {
        List<StandardDeviation> stddevList = covariance.getStandardDeviationList();
        if (stddevList.size() == covariance.getRows()) {
            covarianceData = new Array2DRowRealMatrix(covariance.getBlob().getData());
            /* For each diagonal cell, square the standard deviation
             * For each off-diagonal cell, use formula
             * covariance = correlation * sqrt (var1 * var2) 
             */
            for (int row = 0; row < covariance.getRows(); row++) {
                for (int col = 0; col < covariance.getColumns(); col++) {
                    double value = covarianceData.getEntry(row, col);
                    if (row == col) {
                        double stddev = stddevList.get(row).getValue();
                        covarianceData.setEntry(row, col, stddev * stddev);
                    } else {
                        double stddevRow = stddevList.get(row).getValue();
                        double stddevCol = stddevList.get(col).getValue();
                        covarianceData.setEntry(row, col,
                                value * Math.sqrt(stddevRow * stddevRow * stddevCol * stddevCol));
                    }
                }
            }
        }
    }
    return covarianceData;
}

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From source file:edu.stanford.cfuller.imageanalysistools.filter.GradientFilter.java

/**
 * Applies the GradientFilter to the specified Image.
 * @param im    The Image that will be replaced by its gradient.
 *///from w ww. j  a  va  2  s .  co m
@Override
public void apply(WritableImage im) {

    final int kernelSize = 3;
    int halfKernelSize = (kernelSize - 1) / 2;

    RealMatrix kernel1 = new Array2DRowRealMatrix(kernelSize, kernelSize);

    kernel1.setEntry(0, 0, 1);
    kernel1.setEntry(1, 0, 0);
    kernel1.setEntry(2, 0, -1);
    kernel1.setEntry(0, 1, 1);
    kernel1.setEntry(1, 1, 0);
    kernel1.setEntry(2, 1, -1);
    kernel1.setEntry(0, 2, 1);
    kernel1.setEntry(1, 2, 0);
    kernel1.setEntry(2, 2, -1);

    RealMatrix kernel2 = new Array2DRowRealMatrix(kernelSize, kernelSize);

    kernel2.setEntry(0, 0, -1);
    kernel2.setEntry(1, 0, -1);
    kernel2.setEntry(2, 0, -1);
    kernel2.setEntry(0, 1, 0);
    kernel2.setEntry(1, 1, 0);
    kernel2.setEntry(2, 1, 0);
    kernel2.setEntry(0, 2, 1);
    kernel2.setEntry(1, 2, 1);
    kernel2.setEntry(2, 2, 1);

    Image copy = ImageFactory.create(im);

    ImageCoordinate ic = ImageCoordinate.createCoordXYZCT(0, 0, 0, 0, 0);

    for (ImageCoordinate i : im) {

        double outputVal = 0;
        double output1 = 0;
        double output2 = 0;

        if (i.get(ImageCoordinate.X) == 0 || i.get(ImageCoordinate.Y) == 0
                || i.get(ImageCoordinate.X) == copy.getDimensionSizes().get(ImageCoordinate.X) - 1
                || i.get(ImageCoordinate.Y) == copy.getDimensionSizes().get(ImageCoordinate.Y) - 1) {
            outputVal = 0;
        } else {
            for (int p = -1 * halfKernelSize; p < halfKernelSize + 1; p++) {
                for (int q = -1 * halfKernelSize; q < halfKernelSize + 1; q++) {
                    ic.set(ImageCoordinate.X, i.get(ImageCoordinate.X) + p);
                    ic.set(ImageCoordinate.Y, i.get(ImageCoordinate.Y) + q);
                    output1 += kernel1.getEntry(p + halfKernelSize, q + halfKernelSize) * copy.getValue(ic);
                    output2 += kernel2.getEntry(p + halfKernelSize, q + halfKernelSize) * copy.getValue(ic);
                }
            }

            outputVal = FastMath.hypot(output1, output2);
        }

        im.setValue(i, (float) Math.floor(outputVal));

    }

    ic.recycle();

}

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From source file:edu.ucdenver.bios.power.ConditionalMultivariateWithConfidenceLimitsTest.java

/**
 * Test valid inputs for a univariate linear model with only fixed predictors
 *//* www  . ja  v  a  2 s  .c o  m*/
@Test
public void testMultivariateWithConfidenceLimits() {
    GLMMTestFactory.Test[] testList = { GLMMTestFactory.Test.UNIREP_GEISSER_GREENHOUSE };
    for (GLMMTestFactory.Test test : testList) {
        GLMMPowerParameters params = buildInputs(test);

        for (double delta = 0.0008; delta < 0.2001; delta += 0.0008) {
            // increase the gender difference by 2 * delta
            RealMatrix betaMatrix = params.getBeta().getFixedMatrix();
            for (int row = 0; row < 5; row++)
                betaMatrix.setEntry(row, 2, beta[row][2] + delta);
            for (int row = 5; row < 10; row++)
                betaMatrix.setEntry(row, 2, beta[row][2] - delta);

            checker.checkPower(params);
        }
    }

    // we add all of the tests back into the params to ensure the report lists them all
    // we do this to ensure the order of results in the SAS output matches the order
    // in the JavaStatistics calls.
    GLMMPowerParameters params = buildInputs(GLMMTestFactory.Test.UNIREP);
    params.clearTestList();
    for (GLMMTestFactory.Test test : testList) {
        params.addTest(test);
    }

    // output the results
    ValidationReportBuilder reportBuilder = new ValidationReportBuilder();
    reportBuilder.createValidationReportAsStdout(checker, TITLE, false);
    assertTrue("SAS deviation " + checker.getMaxSasDeviation() + " is not below tolerance " + TOLERANCE,
            checker.isSASDeviationBelowTolerance(TOLERANCE));
}

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From source file:ellipsoidFit.FitPoints.java

/**
 * Create a matrix in the algebraic form of the polynomial Ax^2 + By^2 +
 * Cz^2 + 2Dxy + 2Exz + 2Fyz + 2Gx + 2Hy + 2Iz = 1.
 * //from w w w.  j av a  2s . co  m
 * @param v
 *            the vector polynomial.
 * @return the matrix of the algebraic form of the polynomial.
 */
private RealMatrix formAlgebraicMatrix(RealVector v) {
    // a =
    // [ Ax^2 2Dxy 2Exz 2Gx ]
    // [ 2Dxy By^2 2Fyz 2Hy ]
    // [ 2Exz 2Fyz Cz^2 2Iz ]
    // [ 2Gx 2Hy 2Iz -1 ] ]
    RealMatrix a = new Array2DRowRealMatrix(4, 4);

    a.setEntry(0, 0, v.getEntry(0));
    a.setEntry(0, 1, v.getEntry(3));
    a.setEntry(0, 2, v.getEntry(4));
    a.setEntry(0, 3, v.getEntry(6));
    a.setEntry(1, 0, v.getEntry(3));
    a.setEntry(1, 1, v.getEntry(1));
    a.setEntry(1, 2, v.getEntry(5));
    a.setEntry(1, 3, v.getEntry(7));
    a.setEntry(2, 0, v.getEntry(4));
    a.setEntry(2, 1, v.getEntry(5));
    a.setEntry(2, 2, v.getEntry(2));
    a.setEntry(2, 3, v.getEntry(8));
    a.setEntry(3, 0, v.getEntry(6));
    a.setEntry(3, 1, v.getEntry(7));
    a.setEntry(3, 2, v.getEntry(8));
    a.setEntry(3, 3, -1);

    return a;
}

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From source file:com.itemanalysis.psychometrics.factoranalysis.VarimaxCriteria.java

/**
 * Computes the function value for varimax rotation.
 *
 * @param L matrix of factor loadings./*from www.  j av a2s.  com*/
 */
public void computeValues(RealMatrix L) {
    //initialize dimensions and column mean array
    int nrow = L.getRowDimension();
    int ncol = L.getColumnDimension();
    Mean[] colMean = new Mean[ncol];
    for (int i = 0; i < ncol; i++) {
        colMean[i] = new Mean();
    }

    //square each element in matrix
    RealMatrix L2 = L.copy();
    double value = 0.0;
    for (int i = 0; i < nrow; i++) {
        for (int j = 0; j < ncol; j++) {
            value = L.getEntry(i, j);
            value *= value;
            L2.setEntry(i, j, value);
            colMean[j].increment(value);
        }
    }

    double dif = 0.0;
    RealMatrix QL = new Array2DRowRealMatrix(nrow, ncol);
    for (int i = 0; i < nrow; i++) {
        for (int j = 0; j < ncol; j++) {
            dif = L2.getEntry(i, j) - colMean[j].getResult();
            QL.setEntry(i, j, dif);
        }
    }

    //compute gradientAt
    gradient = new Array2DRowRealMatrix(nrow, ncol);
    for (int i = 0; i < nrow; i++) {
        for (int j = 0; j < ncol; j++) {
            value = -L.getEntry(i, j) * QL.getEntry(i, j);
            gradient.setEntry(i, j, value);
        }
    }

    //compute function value
    RealMatrix B = QL.transpose().multiply(QL);
    double sum = B.getTrace();
    functionValue = -sum / 4.0;

}

---

From source file:edu.cudenver.bios.power.test.paper.TestConditionalMultivariateWithConfidenceLimits.java

/**
 * Test valid inputs for a univariate linear model with only fixed predictors
 *//*from   ww  w  . ja  v  a  2s.  c  om*/
public void testMultivariateWithConfidenceLimits() {
    Test[] testList = { Test.UNIREP_GEISSER_GREENHOUSE };
    for (Test test : testList) {
        GLMMPowerParameters params = buildInputs(test);

        for (double delta = 0.0008; delta < 0.2001; delta += 0.0008) {
            // increase the gender difference by 2 * delta
            RealMatrix betaMatrix = params.getBeta().getFixedMatrix();
            for (int row = 0; row < 5; row++)
                betaMatrix.setEntry(row, 2, beta[row][2] + delta);
            for (int row = 5; row < 10; row++)
                betaMatrix.setEntry(row, 2, beta[row][2] - delta);

            checker.checkPower(params);
        }
    }

    // we add all of the tests back into the params to ensure the report lists them all
    // we do this to ensure the order of results in the SAS output matches the order
    // in the JavaStatistics calls.
    GLMMPowerParameters params = buildInputs(Test.UNIREP);
    params.clearTestList();
    for (Test test : testList) {
        params.addTest(test);
    }
    // output the results
    try {
        ValidationReportBuilder reportBuilder = new ValidationReportBuilder();
        reportBuilder.createValidationReportAsStdout(checker, TITLE, false);
        reportBuilder.createValidationReportAsLaTex(OUTPUT_FILE, TITLE, AUTHOR, STUDY_DESIGN_DESCRIPTION,
                params, checker);
    } catch (Exception e) {
        System.err.println(e.getMessage());
    }
    assertTrue(checker.isSASDeviationBelowTolerance());
    checker.reset();
}

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