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

Introduction

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

Prototype

int getColumnDimension();

Source Link

Document

Returns the number of columns in the matrix.

Usage

From source file:edu.dfci.cccb.mev.domain.Heatmap.java

private RealMatrix transpose(final RealMatrix original) {
    return new AbstractRealMatrix() {

        @Override//  w w  w. java2 s .c o  m
        public void setEntry(int row, int column, double value) throws OutOfRangeException {
            original.setEntry(column, row, value);
        }

        @Override
        public int getRowDimension() {
            return original.getColumnDimension();
        }

        @Override
        public double getEntry(int row, int column) throws OutOfRangeException {
            return original.getEntry(column, row);
        }

        @Override
        public int getColumnDimension() {
            return original.getRowDimension();
        }

        @Override
        public RealMatrix createMatrix(int rowDimension, int columnDimension)
                throws NotStrictlyPositiveException {
            return original.createMatrix(rowDimension, columnDimension);
        }

        @Override
        public RealMatrix copy() {
            final RealMatrix result = createMatrix(getRowDimension(), getColumnDimension());
            walkInOptimizedOrder(new RealMatrixPreservingVisitor() {

                @Override
                public void visit(int row, int column, double value) {
                    result.setEntry(row, column, value);
                }

                @Override
                public void start(int rows, int columns, int startRow, int endRow, int startColumn,
                        int endColumn) {
                }

                @Override
                public double end() {
                    return NaN;
                }
            });
            return result;
        }
    };
}

---

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

/**
 * This test shows the least amount of agreement with R. Only one decimal place of precision.
 * I am not sure why because the MINRES method worked fine for the other test.
 *//* w w  w.j av a 2s .co  m*/
@Test
public void m255TestMINRES() {
    System.out.println("MINRES Factor Analysis Test (no rotation): m255 data");
    RealMatrix R = new Array2DRowRealMatrix(readM255());
    ExploratoryFactorAnalysis fa = new ExploratoryFactorAnalysis(R, 3);
    fa.estimateParameters(EstimationMethod.MINRES);
    //        System.out.println();
    System.out.println(fa.printOutput(3));

    double[][] true_loadings = { { 0.6836, -0.3908, 0.1130 }, { 0.7002, -0.3712, 0.1542 },
            { 0.7200, -0.2217, 0.1227 }, { 0.6285, -0.1843, 0.1545 }, { 0.7622, -0.0188, 0.1045 },
            { 0.7216, 0.3458, 0.1759 }, { 0.5962, 0.4292, 0.2326 }, { 0.5214, 0.1782, 0.1026 },
            { 0.7102, 0.1378, -0.0139 }, { 0.5910, 0.2662, -0.0076 }, { 0.8389, 0.0129, -0.3197 },
            { 0.6957, 0.0292, -0.3029 } };

    double[] true_uniqueness = { 0.3675, 0.3482, 0.4172, 0.5472, 0.4075, 0.3284, 0.4082, 0.6869, 0.4762, 0.5798,
            0.1942, 0.4219 };

    FactorMethod factorMethod = fa.getFactorMethod();
    for (int i = 0; i < R.getColumnDimension(); i++) {
        for (int j = 0; j < 3; j++) {
            assertEquals("  Factor loadings: ", true_loadings[i][j],
                    Precision.round(factorMethod.getFactorLoadingAt(i, j), 4), 1e-2);
        }
    }

    for (int i = 0; i < R.getColumnDimension(); i++) {
        assertEquals("  Uniqueness: ", true_uniqueness[i], Precision.round(factorMethod.getUniquenessAt(i), 4),
                1e-2);
    }

}

---

From source file:edu.cmu.tetrad.search.IndTestMultiFisherZ2.java

private boolean indepCollection2(List<Node> aa, List<Node> bb, List<Node> cc, int n, double alpha) {
    //        List<Double> ret = getCutoff1(aa, bb, cc);
    //        List<Double> ret = getCutoff2(aa, bb, cc);
    //        double mean = ret.get(0);
    //        double sd = ret.get(1);

    int numPerm = 10;

    TetradMatrix submatrix = subMatrix(cov, aa, bb, cc);

    TetradMatrix inverse;//from  ww w . j  a  va2  s .c o  m
    int rank;

    try {
        inverse = submatrix.inverse();
        rank = inverse.columns();
    } catch (Exception e) {
        SingularValueDecomposition svd = new SingularValueDecomposition(submatrix.getRealMatrix());
        RealMatrix _inverse = svd.getSolver().getInverse();
        inverse = new TetradMatrix(_inverse, _inverse.getRowDimension(), _inverse.getColumnDimension());
        rank = svd.getRank();
    }

    List<Double> pValues = new ArrayList<Double>();

    for (int i = 0; i < aa.size(); i++) {
        for (int m = 0; m < bb.size(); m++) {
            int j = aa.size() + m;
            double a = -1.0 * inverse.get(i, j);
            double v0 = inverse.get(i, i);
            double v1 = inverse.get(j, j);
            double b = Math.sqrt(v0 * v1);

            double r = a / b;

            int dof = n - 1 - rank;

            if (dof < 0) {
                System.out.println("Negative dof: " + dof + " n = " + n + " cols = " + inverse.columns());
                dof = 0;
            }

            double z = Math.sqrt(dof) * 0.5 * (Math.log(1.0 + r) - Math.log(1.0 - r));
            double p = 2.0 * (1.0 - RandomUtil.getInstance().normalCdf(0, 1, abs(z)));

            pValues.add(p);
        }
    }

    List<Double> zeroes = new ArrayList<Double>();

    for (double d : pValues) {
        if (d == 0)
            zeroes.add(d);
    }

    int k = 0;

    for (double p : pValues) {
        if (p < alpha) {
            k++;
        }
    }

    numTests++;

    int count = countGreater(aa, bb, cc, numPerm, k);
    //        int count = countGreater2(aa, bb, cc, numPerm, k);

    double p = count / (double) numPerm;

    boolean indep = p > alpha;

    //        double p = (1.0 - RandomUtil.getInstance().normalCdf(0, 1, (k - mean) / sd));

    //        boolean indep = p > alpha;

    if (verbose) {
        //            System.out.println("\n#accepted " + k + " cutoff = " + kCutoff + " indep = " + indep);
        //            System.out.println("\n#accepted " + k + " meam = " + mean + " sd = " + sd + " indep = " + indep);
        //            System.out.println("standard deviations " + (k - mean) / sd + " p = " + p + " alpha = " + alpha);
        System.out.println("\n#accepted " + k + " indep = " + indep);
        System.out.println("p = " + p + " alpha = " + alpha);
    }

    numTests++;
    if (!indep)
        numDependent++;

    return indep;

}

---

From source file:edu.cmu.tetrad.search.IndTestMultiFisherZ2.java

private boolean indepCollection(List<Node> aa, List<Node> bb, List<Node> cc, int n, double alpha) {
    //        List<Double> ret = getCutoff1(aa, bb, cc);
    //        List<Double> ret = getCutoff2(aa, bb, cc);
    //        double mean = ret.get(0);
    //        double sd = ret.get(1);

    int numPerm = 10;

    TetradMatrix submatrix = subMatrix(cov, aa, bb, cc);

    TetradMatrix inverse;//from   w  w  w . j ava2 s  .c  om
    int rank;

    try {
        inverse = submatrix.inverse();
        rank = inverse.columns();
    } catch (Exception e) {
        SingularValueDecomposition svd = new SingularValueDecomposition(submatrix.getRealMatrix());
        RealMatrix _inverse = svd.getSolver().getInverse();
        inverse = new TetradMatrix(_inverse, _inverse.getRowDimension(), _inverse.getColumnDimension());
        rank = svd.getRank();
    }

    List<Double> pValues = new ArrayList<Double>();

    for (int i = 0; i < aa.size(); i++) {
        for (int m = 0; m < bb.size(); m++) {
            int j = aa.size() + m;
            double a = -1.0 * inverse.get(i, j);
            double v0 = inverse.get(i, i);
            double v1 = inverse.get(j, j);
            double b = Math.sqrt(v0 * v1);

            double r = a / b;

            int dof = n - 1 - rank;

            if (dof < 0) {
                System.out.println("Negative dof: " + dof + " n = " + n + " cols = " + inverse.columns());
                dof = 0;
            }

            double z = Math.sqrt(dof) * 0.5 * (Math.log(1.0 + r) - Math.log(1.0 - r));
            double p = 2.0 * (1.0 - RandomUtil.getInstance().normalCdf(0, 1, abs(z)));

            pValues.add(p);
        }
    }

    List<Double> zeroes = new ArrayList<Double>();

    for (double d : pValues) {
        if (d == 0)
            zeroes.add(d);
    }

    int k = 0;

    for (double p : pValues) {
        if (p < alpha) {
            k++;
        }
    }

    numTests++;

    int count = countGreater(aa, bb, cc, numPerm, k);
    //        int count = countGreater2(aa, bb, cc, numPerm, k);
    //        int count = countGreater3(aa, bb, cc, numPerm, k, submatrix, inverse);
    //        int count = countGreater4(aa, bb, cc, numPerm, k);

    double p = count / (double) numPerm;

    boolean indep = p > alpha;

    //        double p = (1.0 - RandomUtil.getInstance().normalCdf(0, 1, (k - mean) / sd));

    //        boolean indep = p > alpha;

    if (verbose) {
        //            System.out.println("\n#accepted " + k + " cutoff = " + kCutoff + " indep = " + indep);
        //            System.out.println("\n#accepted " + k + " meam = " + mean + " sd = " + sd + " indep = " + indep);
        //            System.out.println("standard deviations " + (k - mean) / sd + " p = " + p + " alpha = " + alpha);
        System.out.println("\n#accepted " + k + " indep = " + indep);
        System.out.println("p = " + p + " alpha = " + alpha);
    }

    numTests++;
    if (!indep)
        numDependent++;

    return indep;

}

---

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

@Test
public void m255TestMINRESVarimax() {
    System.out.println("MINRES Factor Analysis Test (Varimax rotation): m255 data");
    RealMatrix R = new Array2DRowRealMatrix(readM255());
    ExploratoryFactorAnalysis fa = new ExploratoryFactorAnalysis(R, 3);
    fa.estimateParameters(EstimationMethod.MINRES, RotationMethod.VARIMAX);
    System.out.println();// w ww .ja v a 2s. c om
    System.out.println(fa.printOutput());

    double[][] true_loadings = { { 0.6836, -0.3908, 0.1130 }, { 0.7002, -0.3712, 0.1542 },
            { 0.7200, -0.2217, 0.1227 }, { 0.6285, -0.1843, 0.1545 }, { 0.7622, -0.0188, 0.1045 },
            { 0.7216, 0.3458, 0.1759 }, { 0.5962, 0.4292, 0.2326 }, { 0.5214, 0.1782, 0.1026 },
            { 0.7102, 0.1378, -0.0139 }, { 0.5910, 0.2662, -0.0076 }, { 0.8389, 0.0129, -0.3197 },
            { 0.6957, 0.0292, -0.3029 } };

    double[] true_uniqueness = { 0.3675, 0.3482, 0.4172, 0.5472, 0.4075, 0.3284, 0.4082, 0.6869, 0.4762, 0.5798,
            0.1942, 0.4219 };

    FactorMethod factorMethod = fa.getFactorMethod();
    for (int i = 0; i < R.getColumnDimension(); i++) {
        for (int j = 0; j < 3; j++) {
            //                assertEquals("  Factor loadings: ", true_loadings[i][j], Precision.round(factorMethod.getFactorLoadingAt(i,j), 4), 1e-1);
        }
    }

    for (int i = 0; i < R.getColumnDimension(); i++) {
        //            assertEquals("  Uniqueness: ", true_uniqueness[i], Precision.round(factorMethod.getUniquenessAt(i), 4), 1e-1);
    }

}

---

From source file:edu.cmu.tetrad.search.IndTestMultiFisherZ2.java

public boolean isIndependent(Node x, Node y, List<Node> z) {
    if (verbose) {
        System.out.println("\n" + x + " _||_ " + y + " | " + z);
    }//  ww w . java  2 s . c o  m

    List<Node> aa = nodeMap.get(x);
    List<Node> bb = nodeMap.get(y);

    int[][] twod = new int[aa.size()][bb.size()];

    List<Node> cc = new ArrayList<Node>();

    for (Node _z : z) {
        cc.addAll(nodeMap.get(_z));
    }

    TetradMatrix submatrix = subMatrix(cov, aa, bb, cc);

    TetradMatrix inverse;
    //        int rank;

    try {
        inverse = submatrix.inverse();
        //            rank = inverse.columns();
    } catch (Exception e) {
        SingularValueDecomposition svd = new SingularValueDecomposition(submatrix.getRealMatrix());
        RealMatrix _inverse = svd.getSolver().getInverse();
        inverse = new TetradMatrix(_inverse, _inverse.getRowDimension(), _inverse.getColumnDimension());
        //            rank = svd.getRank();
    }

    final List<Double> pValues = new ArrayList<Double>();
    List<Integer> _i = new ArrayList<Integer>();
    List<Integer> _m = new ArrayList<Integer>();

    for (int i = 0; i < aa.size(); i++) {
        for (int m = 0; m < bb.size(); m++) {
            int j = aa.size() + m;
            double a = -1.0 * inverse.get(i, j);
            double v0 = inverse.get(i, i);
            double v1 = inverse.get(j, j);
            double b = Math.sqrt(v0 * v1);

            double r = a / b;

            int dof = cov.getSampleSize() - 1 - inverse.columns();

            if (dof < 0) {
                dof = 0;
            }

            double z_ = Math.sqrt(dof) * 0.5 * (Math.log(1.0 + r) - Math.log(1.0 - r));
            double p = 2.0 * (1.0 - RandomUtil.getInstance().normalCdf(0, 1, abs(z_)));

            pValues.add(p);
            _i.add(i);
            _m.add(m);
        }
    }

    List<Integer> indices = new ArrayList<Integer>();
    for (int i = 0; i < pValues.size(); i++) {
        indices.add(i);
    }

    Collections.sort(indices, new Comparator<Integer>() {
        @Override
        public int compare(Integer o1, Integer o2) {
            return pValues.get(o1).compareTo(pValues.get(o2));
        }
    });

    // Sort pvalues, _i, and _m together.
    List<Double> pValues2 = new ArrayList<Double>();
    List<Integer> _i2 = new ArrayList<Integer>();
    List<Integer> _m2 = new ArrayList<Integer>();

    for (int _y = 0; _y < indices.size(); _y++) {
        pValues2.add(pValues.get(indices.get(_y)));
        _i2.add(_i.get(indices.get(_y)));
        _m2.add(_m.get(indices.get(_y)));
    }

    Collections.sort(pValues2);

    int k = StatUtils.fdr(alpha, pValues, false);

    int nonzero = -1;

    for (int i = 0; i < pValues2.size(); i++) {
        if (pValues.get(i) != 0) {
            nonzero = i;
            break;
        }
    }

    if (nonzero < k) {
        for (int g = 0; g < k; g++) {
            int x3 = _i2.get(g);
            int y3 = _m2.get(g);
            twod[x3][y3] = 1;
        }

        //            if (verbose) {
        //                System.out.println("Dependent");
        //            }

        return false;
    } else {
        if (verbose) {
            System.out.println("Independent");
        }

        return true;
    }
}

---

From source file:com.clust4j.algo.HDBSCAN.java

@Override
public int[] predict(RealMatrix newData) {
    @SuppressWarnings("unused")
    final int[] fit_labels = getLabels(); // throws the exception if not fit
    final int n = newData.getColumnDimension();

    if (n != this.data.getColumnDimension())
        throw new DimensionMismatchException(n, newData.getColumnDimension());

    // TODO: how to predict these???
    throw new UnsupportedOperationException("HDBSCAN does not currently support predictions");
}

---

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

@Test
public void m255TestMINRESOblimin() {
    System.out.println("MINRES Factor Analysis Test (Oblimin rotation): m255 data");
    RealMatrix R = new Array2DRowRealMatrix(readM255());
    ExploratoryFactorAnalysis fa = new ExploratoryFactorAnalysis(R, 3);
    fa.estimateParameters(EstimationMethod.MINRES, RotationMethod.OBLIMIN);
    System.out.println();/*www.j av a 2 s . co m*/
    System.out.println(fa.printOutput());

    //        double[][] true_loadings = {
    //                {0.6836,-0.3908,0.1130},
    //                {0.7002,-0.3712,0.1542},
    //                {0.7200,-0.2217,0.1227},
    //                {0.6285,-0.1843,0.1545},
    //                {0.7622,-0.0188,0.1045},
    //                {0.7216,0.3458,0.1759},
    //                {0.5962,0.4292,0.2326},
    //                {0.5214,0.1782,0.1026},
    //                {0.7102,0.1378,-0.0139},
    //                {0.5910,0.2662,-0.0076},
    //                {0.8389,0.0129,-0.3197},
    //                {0.6957,0.0292,-0.3029}
    //        };
    //
    //        double[] true_uniqueness = {0.3675, 0.3482, 0.4172, 0.5472, 0.4075, 0.3284, 0.4082, 0.6869, 0.4762, 0.5798,
    //                0.1942, 0.4219};

    FactorMethod factorMethod = fa.getFactorMethod();
    for (int i = 0; i < R.getColumnDimension(); i++) {
        for (int j = 0; j < 3; j++) {
            //                assertEquals("  Factor loadings: ", true_loadings[i][j], Precision.round(factorMethod.getFactorLoadingAt(i,j), 4), 1e-1);
        }
    }

    for (int i = 0; i < R.getColumnDimension(); i++) {
        //            assertEquals("  Uniqueness: ", true_uniqueness[i], Precision.round(factorMethod.getUniquenessAt(i), 4), 1e-1);
    }

}

---

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

@Test
public void m255TestPrincipalComponents() {
    System.out.println("Principal Components Analysis Test: m255 data");
    RealMatrix R = new Array2DRowRealMatrix(readM255());
    ExploratoryFactorAnalysis fa = new ExploratoryFactorAnalysis(R, 3);
    fa.estimateParameters(EstimationMethod.PRINCOMP);
    //        System.out.println();
    //        System.out.println(fa.printOutput());

    //True values obtained from R using code extracted from the psych package in R.
    //Needed to extract code from principal() function to get four decimal places.
    //principal(r=r, nfactors=3, rotate="none")
    double[][] true_loadings = { { 0.7020, -0.465531, 0.06718 }, { 0.7146, -0.437463, 0.11367 },
            { 0.7455, -0.299328, 0.12827 }, { 0.6779, -0.293400, 0.19308 }, { 0.7957, -0.073120, 0.05381 },
            { 0.7476, 0.354893, 0.13867 }, { 0.6342, 0.493008, 0.25213 }, { 0.5783, 0.298165, 0.36896 },
            { 0.7453, 0.159165, -0.14060 }, { 0.6373, 0.387494, -0.19624 }, { 0.8163, -0.009932, -0.34579 },
            { 0.7055, 0.016659, -0.51390 } };

    double[] true_uniqueness = { 0.2859, 0.2851, 0.3382, 0.4171, 0.3586, 0.2959, 0.2912, 0.4405, 0.3994, 0.4052,
            0.2140, 0.2379 };/*w ww.  j a v  a  2  s .c  o m*/

    FactorMethod factorMethod = fa.getFactorMethod();
    for (int i = 0; i < R.getColumnDimension(); i++) {
        for (int j = 0; j < 3; j++) {
            assertEquals("  Factor loadings: ", true_loadings[i][j],
                    Precision.round(factorMethod.getFactorLoadingAt(i, j), 4), 1e-4);
        }
    }

    for (int i = 0; i < R.getColumnDimension(); i++) {
        assertEquals("  Uniqueness: ", true_uniqueness[i], Precision.round(factorMethod.getUniquenessAt(i), 4),
                1e-4);
    }

}

---

From source file:edu.cudenver.bios.power.GLMMPowerCalculator.java

/**
 * Returns true if all values in the beta matrix are identical
 * @param beta beta matrix// w  w w. j a v a2s.  c  o m
 * @return true if no mean difference, false otherwise
 */
private boolean noMeanDifference(GLMMTest test) {
    // get the difference between theta null and the alternative
    RealMatrix sumSqHypothesis = test.getHypothesisSumOfSquares();
    // check if there is at least one non-zero value
    if (sumSqHypothesis != null) {
        for (int r = 0; r < sumSqHypothesis.getRowDimension(); r++) {
            for (int c = 0; c < sumSqHypothesis.getColumnDimension(); c++) {
                if (Math.abs(sumSqHypothesis.getEntry(r, c)) > EPSILON) {
                    return false;
                }
            }
        }
    }
    return true;
}

---

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