Examples of org.apache.commons.math.linear.RealMatrix

org.apache.commons.math.linear.RealMatrix
Interface defining a real-valued matrix with basic algebraic operations.
Matrix element indexing is 0-based -- e.g., getEntry(0, 0) returns the element in the first row, first column of the matrix.
@version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 févr. 2011) $

        // Check Y variance consistency
        TestUtils.assertEquals(StatUtils.variance(model.Y.getData()), model.calculateYVariance(), 0);
        
        // Check residual variance consistency
        double[] residuals = model.calculateResiduals().getData();
        RealMatrix X = model.X;
        TestUtils.assertEquals(
                StatUtils.variance(model.calculateResiduals().getData()) * (residuals.length - 1),
                model.calculateErrorVariance() * (X.getRowDimension() - X.getColumnDimension()), 1E-20);
        
    }

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          {25, 35, 45},
          {27, 37, 47}   
        };
        OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
        regression.newSampleData(y, x);
        RealMatrix combinedX = regression.X.copy();
        RealVector combinedY = regression.Y.copy();
        regression.newXSampleData(x);
        regression.newYSampleData(y);
        assertEquals(combinedX, regression.X);
        assertEquals(combinedY, regression.Y);

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            messageBuffer.append("\nexpected " + expected.getRowDimension() +
                    " x " + expected.getColumnDimension());
            Assert.fail(messageBuffer.toString());
        }


        RealMatrix delta = expected.subtract(observed);
        if (delta.getNorm() >= tolerance) {
            StringBuilder messageBuffer = new StringBuilder(msg);
            messageBuffer.append("\nExpected: " + expected);
            messageBuffer.append("\nObserved: " + observed);
            messageBuffer.append("\nexpected - observed: " + delta);
            Assert.fail(messageBuffer.toString());

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    /**
     * Test Longley dataset against R.
     */
    public void testLongly() throws Exception {
        RealMatrix matrix = createRealMatrix(longleyData, 16, 7);
        PearsonsCorrelation corrInstance = new PearsonsCorrelation(matrix);
        RealMatrix correlationMatrix = corrInstance.getCorrelationMatrix();
        double[] rData = new double[] {
                1.000000000000000, 0.9708985250610560, 0.9835516111796693, 0.5024980838759942,
                0.4573073999764817, 0.960390571594376, 0.9713294591921188,
                0.970898525061056, 1.0000000000000000, 0.9915891780247822, 0.6206333925590966,
                0.4647441876006747, 0.979163432977498, 0.9911491900672053,
                0.983551611179669, 0.9915891780247822, 1.0000000000000000, 0.6042609398895580,
                0.4464367918926265, 0.991090069458478, 0.9952734837647849,
                0.502498083875994, 0.6206333925590966, 0.6042609398895580, 1.0000000000000000,
                -0.1774206295018783, 0.686551516365312, 0.6682566045621746,
                0.457307399976482, 0.4647441876006747, 0.4464367918926265, -0.1774206295018783,
                1.0000000000000000, 0.364416267189032, 0.4172451498349454,
                0.960390571594376, 0.9791634329774981, 0.9910900694584777, 0.6865515163653120,
                0.3644162671890320, 1.000000000000000, 0.9939528462329257,
                0.971329459192119, 0.9911491900672053, 0.9952734837647849, 0.6682566045621746,
                0.4172451498349454, 0.993952846232926, 1.0000000000000000
        };
        TestUtils.assertEquals("correlation matrix", createRealMatrix(rData, 7, 7), correlationMatrix, 10E-15);


        double[] rPvalues = new double[] {
                4.38904690369668e-10,
                8.36353208910623e-12, 7.8159700933611e-14,
                0.0472894097790304, 0.01030636128354301, 0.01316878049026582,
                0.0749178049642416, 0.06971758330341182, 0.0830166169296545, 0.510948586323452,
                3.693245043123738e-09, 4.327782576751815e-11, 1.167954621905665e-13, 0.00331028281967516, 0.1652293725106684,
                3.95834476307755e-10, 1.114663916723657e-13, 1.332267629550188e-15, 0.00466039138541463, 0.1078477071581498, 7.771561172376096e-15
        };
        RealMatrix rPMatrix = createLowerTriangularRealMatrix(rPvalues, 7);
        fillUpper(rPMatrix, 0d);
        TestUtils.assertEquals("correlation p values", rPMatrix, corrInstance.getCorrelationPValues(), 10E-15);
    }

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    /**
     * Test R Swiss fertility dataset against R.
     */
    public void testSwissFertility() throws Exception {
         RealMatrix matrix = createRealMatrix(swissData, 47, 5);
         PearsonsCorrelation corrInstance = new PearsonsCorrelation(matrix);
         RealMatrix correlationMatrix = corrInstance.getCorrelationMatrix();
         double[] rData = new double[] {
               1.0000000000000000, 0.3530791836199747, -0.6458827064572875, -0.6637888570350691,  0.4636847006517939,
                 0.3530791836199747, 1.0000000000000000,-0.6865422086171366, -0.6395225189483201, 0.4010950530487398,
                -0.6458827064572875, -0.6865422086171366, 1.0000000000000000, 0.6984152962884830, -0.5727418060641666,
                -0.6637888570350691, -0.6395225189483201, 0.6984152962884830, 1.0000000000000000, -0.1538589170909148,
                 0.4636847006517939, 0.4010950530487398, -0.5727418060641666, -0.1538589170909148, 1.0000000000000000
         };
         TestUtils.assertEquals("correlation matrix", createRealMatrix(rData, 5, 5), correlationMatrix, 10E-15);


         double[] rPvalues = new double[] {
                 0.01491720061472623,
                 9.45043734069043e-07, 9.95151527133974e-08,
                 3.658616965962355e-07, 1.304590105694471e-06, 4.811397236181847e-08,
                 0.001028523190118147, 0.005204433539191644, 2.588307925380906e-05, 0.301807756132683
         };
         RealMatrix rPMatrix = createLowerTriangularRealMatrix(rPvalues, 5);
         fillUpper(rPMatrix, 0d);
         TestUtils.assertEquals("correlation p values", rPMatrix, corrInstance.getCorrelationPValues(), 10E-15);
    }

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            new PearsonsCorrelation().correlation(one, two);
            fail("Expecting IllegalArgumentException");
        } catch (IllegalArgumentException ex) {
            // Expected
        }
        RealMatrix matrix = new BlockRealMatrix(new double[][] {{0},{1}});
        try {
            new PearsonsCorrelation(matrix);
            fail("Expecting IllegalArgumentException");
        } catch (IllegalArgumentException ex) {
            // Expected

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     * Verify that direct t-tests using standard error estimates are consistent
     * with reported p-values
     */
    public void testStdErrorConsistency() throws Exception {
        TDistribution tDistribution = new TDistributionImpl(45);
        RealMatrix matrix = createRealMatrix(swissData, 47, 5);
        PearsonsCorrelation corrInstance = new PearsonsCorrelation(matrix);
        RealMatrix rValues = corrInstance.getCorrelationMatrix();
        RealMatrix pValues = corrInstance.getCorrelationPValues();
        RealMatrix stdErrors = corrInstance.getCorrelationStandardErrors();
        for (int i = 0; i < 5; i++) {
            for (int j = 0; j < i; j++) {
                double t = FastMath.abs(rValues.getEntry(i, j)) / stdErrors.getEntry(i, j);
                double p = 2 * (1 - tDistribution.cumulativeProbability(t));
                assertEquals(p, pValues.getEntry(i, j), 10E-15);
            }
        }
    }

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    /**
     * Verify that creating correlation from covariance gives same results as
     * direct computation from the original matrix
     */
    public void testCovarianceConsistency() throws Exception {
        RealMatrix matrix = createRealMatrix(longleyData, 16, 7);
        PearsonsCorrelation corrInstance = new PearsonsCorrelation(matrix);
        Covariance covInstance = new Covariance(matrix);
        PearsonsCorrelation corrFromCovInstance = new PearsonsCorrelation(covInstance);
        TestUtils.assertEquals("correlation values", corrInstance.getCorrelationMatrix(),
                corrFromCovInstance.getCorrelationMatrix(), 10E-15);

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                corrFromCovInstance2.getCorrelationStandardErrors(), 10E-15);
    }




    public void testConsistency() {
        RealMatrix matrix = createRealMatrix(longleyData, 16, 7);
        PearsonsCorrelation corrInstance = new PearsonsCorrelation(matrix);
        double[][] data = matrix.getData();
        double[] x = matrix.getColumn(0);
        double[] y = matrix.getColumn(1);
        assertEquals(new PearsonsCorrelation().correlation(x, y),
                corrInstance.getCorrelationMatrix().getEntry(0, 1), Double.MIN_VALUE);
        TestUtils.assertEquals("Correlation matrix", corrInstance.getCorrelationMatrix(),
                new PearsonsCorrelation().computeCorrelationMatrix(data), Double.MIN_VALUE);
    }

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        return new BlockRealMatrix(matrixData);
    }


    protected RealMatrix createLowerTriangularRealMatrix(double[] data, int dimension) {
        int ptr = 0;
        RealMatrix result = new BlockRealMatrix(dimension, dimension);
        for (int i = 1; i < dimension; i++) {
            for (int j = 0; j < i; j++) {
                result.setEntry(i, j, data[ptr]);
                ptr++;
            }
        }
        return result;
    }

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