Package org.apache.commons.math.linear

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

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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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    /**
     * Test Longley dataset against R.
     */
    @Override
    public void testLongly() throws Exception {
        RealMatrix matrix = createRealMatrix(longleyData, 16, 7);
        SpearmansCorrelation corrInstance = new SpearmansCorrelation(matrix);
        RealMatrix correlationMatrix = corrInstance.getCorrelationMatrix();
        double[] rData = new double[] {
                1, 0.982352941176471, 0.985294117647059, 0.564705882352941, 0.2264705882352941, 0.976470588235294,
                0.976470588235294, 0.982352941176471, 1, 0.997058823529412, 0.664705882352941, 0.2205882352941176,
                0.997058823529412, 0.997058823529412, 0.985294117647059, 0.997058823529412, 1, 0.638235294117647,
                0.2235294117647059, 0.9941176470588236, 0.9941176470588236, 0.564705882352941, 0.664705882352941,
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    /**
     * Test R swiss fertility dataset.
     */
    public void testSwiss() throws Exception {
        RealMatrix matrix = createRealMatrix(swissData, 47, 5);
        SpearmansCorrelation corrInstance = new SpearmansCorrelation(matrix);
        RealMatrix correlationMatrix = corrInstance.getCorrelationMatrix();
        double[] rData = new double[] {
                1, 0.2426642769364176, -0.660902996352354, -0.443257690360988, 0.4136455623012432,
                0.2426642769364176, 1, -0.598859938748963, -0.650463814145816, 0.2886878090882852,
               -0.660902996352354, -0.598859938748963, 1, 0.674603831406147, -0.4750575257171745,
               -0.443257690360988, -0.650463814145816, 0.674603831406147, 1, -0.1444163088302244,
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            new SpearmansCorrelation().correlation(one, two);
            fail("Expecting IllegalArgumentException");
        } catch (IllegalArgumentException ex) {
            // Expected
        }
        RealMatrix matrix = new BlockRealMatrix(new double[][] {{0},{1}});
        try {
            new SpearmansCorrelation(matrix);
            fail("Expecting IllegalArgumentException");
        } catch (IllegalArgumentException ex) {
            // Expected
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        }
    }

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

    private void checkAEqualUSVt(RealMatrix matrix) {
        BiDiagonalTransformer transformer = new BiDiagonalTransformer(matrix);
        RealMatrix u = transformer.getU();
        RealMatrix b = transformer.getB();
        RealMatrix v = transformer.getV();
        double norm = u.multiply(b).multiply(v.transpose()).subtract(matrix).getNorm();
        Assert.assertEquals(0, norm, 1.0e-14);
    }
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        checkOrthogonal(new BiDiagonalTransformer(MatrixUtils.createRealMatrix(testNonSquare)).getV());
        checkOrthogonal(new BiDiagonalTransformer(MatrixUtils.createRealMatrix(testNonSquare).transpose()).getV());
    }

    private void checkOrthogonal(RealMatrix m) {
        RealMatrix mTm = m.transpose().multiply(m);
        RealMatrix id  = MatrixUtils.createRealIdentityMatrix(mTm.getRowDimension());
        Assert.assertEquals(0, mTm.subtract(id).getNorm(), 1.0e-14);
    }
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                { 3.0, 5.0, 7.0 }
            }));
       final double s3  = Math.sqrt(3.0);
       final double s14 = Math.sqrt(14.0);
       final double s1553 = Math.sqrt(1553.0);
       RealMatrix uRef = MatrixUtils.createRealMatrix(new double[][] {
           {  -1.0 / s14,  5.0 / (s3 * s14),  1.0 / s3 },
           {  -2.0 / s14, -4.0 / (s3 * s14),  1.0 / s3 },
           {  -3.0 / s14,  1.0 / (s3 * s14), -1.0 / s3 }
       });
       RealMatrix bRef = MatrixUtils.createRealMatrix(new double[][] {
           { -s14, s1553 / s14,   0.0 },
           {  0.0, -87 * s3 / (s14 * s1553), -s3 * s14 / s1553 },
           {  0.0, 0.0, 0.0 }
       });
       RealMatrix vRef = MatrixUtils.createRealMatrix(new double[][] {
           { 1.0,   0.0,         0.0        },
           { 0.0,  -23 / s1553,  32 / s1553 },
           { 0.0,  -32 / s1553, -23 / s1553 }
       });

       // check values against known references
       RealMatrix u = transformer.getU();
       Assert.assertEquals(0, u.subtract(uRef).getNorm(), 1.0e-14);
       RealMatrix b = transformer.getB();
       Assert.assertEquals(0, b.subtract(bRef).getNorm(), 1.0e-14);
       RealMatrix v = transformer.getV();
       Assert.assertEquals(0, v.subtract(vRef).getNorm(), 1.0e-14);

       // check the same cached instance is returned the second time
       Assert.assertTrue(u == transformer.getU());
       Assert.assertTrue(b == transformer.getB());
       Assert.assertTrue(v == transformer.getV());
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    @Test
    public void testMatricesValues() {
       BiDiagonalTransformer transformer =
            new BiDiagonalTransformer(MatrixUtils.createRealMatrix(testSquare));
       final double s17 = Math.sqrt(17.0);
        RealMatrix uRef = MatrixUtils.createRealMatrix(new double[][] {
                {  -8 / (5 * s17), 19 / (5 * s17) },
                { -19 / (5 * s17), -8 / (5 * s17) }
        });
        RealMatrix bRef = MatrixUtils.createRealMatrix(new double[][] {
                { -3 * s17 / 5, 32 * s17 / 85 },
                {      0.0,     -5 * s17 / 17 }
        });
        RealMatrix vRef = MatrixUtils.createRealMatrix(new double[][] {
                { 1.0,  0.0 },
                { 0.0, -1.0 }
        });

        // check values against known references
        RealMatrix u = transformer.getU();
        Assert.assertEquals(0, u.subtract(uRef).getNorm(), 1.0e-14);
        RealMatrix b = transformer.getB();
        Assert.assertEquals(0, b.subtract(bRef).getNorm(), 1.0e-14);
        RealMatrix v = transformer.getV();
        Assert.assertEquals(0, v.subtract(vRef).getNorm(), 1.0e-14);

        // check the same cached instance is returned the second time
        Assert.assertTrue(u == transformer.getU());
        Assert.assertTrue(b == transformer.getB());
        Assert.assertTrue(v == transformer.getV());
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