/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.commons.math3.stat.regression;


import org.apache.commons.math3.TestUtils;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.DefaultRealMatrixChangingVisitor;
import org.apache.commons.math3.linear.MatrixUtils;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.RealVector;
import org.apache.commons.math3.stat.StatUtils;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;

public class OLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbstractTest {

    private double[] y;
    private double[][] x;

    @Before
    @Override
    public void setUp(){
        y = new double[]{11.0, 12.0, 13.0, 14.0, 15.0, 16.0};
        x = new double[6][];
        x[0] = new double[]{0, 0, 0, 0, 0};
        x[1] = new double[]{2.0, 0, 0, 0, 0};
        x[2] = new double[]{0, 3.0, 0, 0, 0};
        x[3] = new double[]{0, 0, 4.0, 0, 0};
        x[4] = new double[]{0, 0, 0, 5.0, 0};
        x[5] = new double[]{0, 0, 0, 0, 6.0};
        super.setUp();
    }

    @Override
    protected OLSMultipleLinearRegression createRegression() {
        OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
        regression.newSampleData(y, x);
        return regression;
    }

    @Override
    protected int getNumberOfRegressors() {
        return x[0].length + 1;
    }

    @Override
    protected int getSampleSize() {
        return y.length;
    }

    @Test(expected=IllegalArgumentException.class)
    public void cannotAddSampleDataWithSizeMismatch() {
        double[] y = new double[]{1.0, 2.0};
        double[][] x = new double[1][];
        x[0] = new double[]{1.0, 0};
        createRegression().newSampleData(y, x);
    }

    @Test
    public void testPerfectFit() {
        double[] betaHat = regression.estimateRegressionParameters();
        TestUtils.assertEquals(betaHat,
                               new double[]{ 11.0, 1.0 / 2.0, 2.0 / 3.0, 3.0 / 4.0, 4.0 / 5.0, 5.0 / 6.0 },
                               1e-14);
        double[] residuals = regression.estimateResiduals();
        TestUtils.assertEquals(residuals, new double[]{0d,0d,0d,0d,0d,0d},
                               1e-14);
        RealMatrix errors =
            new Array2DRowRealMatrix(regression.estimateRegressionParametersVariance(), false);
        final double[] s = { 1.0, -1.0 /  2.0, -1.0 /  3.0, -1.0 /  4.0, -1.0 /  5.0, -1.0 /  6.0 };
        RealMatrix referenceVariance = new Array2DRowRealMatrix(s.length, s.length);
        referenceVariance.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
            @Override
            public double visit(int row, int column, double value) {
                if (row == 0) {
                    return s[column];
                }
                double x = s[row] * s[column];
                return (row == column) ? 2 * x : x;
            }
        });
       Assert.assertEquals(0.0,
                     errors.subtract(referenceVariance).getNorm(),
                     5.0e-16 * referenceVariance.getNorm());
       Assert.assertEquals(1, ((OLSMultipleLinearRegression) regression).calculateRSquared(), 1E-12);
    }


    /**
     * Test Longley dataset against certified values provided by NIST.
     * Data Source: J. Longley (1967) "An Appraisal of Least Squares
     * Programs for the Electronic Computer from the Point of View of the User"
     * Journal of the American Statistical Association, vol. 62. September,
     * pp. 819-841.
     *
     * Certified values (and data) are from NIST:
     * http://www.itl.nist.gov/div898/strd/lls/data/LINKS/DATA/Longley.dat
     */
    @Test
    public void testLongly() {
        // Y values are first, then independent vars
        // Each row is one observation
        double[] design = new double[] {
            60323,83.0,234289,2356,1590,107608,1947,
            61122,88.5,259426,2325,1456,108632,1948,
            60171,88.2,258054,3682,1616,109773,1949,
            61187,89.5,284599,3351,1650,110929,1950,
            63221,96.2,328975,2099,3099,112075,1951,
            63639,98.1,346999,1932,3594,113270,1952,
            64989,99.0,365385,1870,3547,115094,1953,
            63761,100.0,363112,3578,3350,116219,1954,
            66019,101.2,397469,2904,3048,117388,1955,
            67857,104.6,419180,2822,2857,118734,1956,
            68169,108.4,442769,2936,2798,120445,1957,
            66513,110.8,444546,4681,2637,121950,1958,
            68655,112.6,482704,3813,2552,123366,1959,
            69564,114.2,502601,3931,2514,125368,1960,
            69331,115.7,518173,4806,2572,127852,1961,
            70551,116.9,554894,4007,2827,130081,1962
        };

        final int nobs = 16;
        final int nvars = 6;

        // Estimate the model
        OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();
        model.newSampleData(design, nobs, nvars);

        // Check expected beta values from NIST
        double[] betaHat = model.estimateRegressionParameters();
        TestUtils.assertEquals(betaHat,
          new double[]{-3482258.63459582, 15.0618722713733,
                -0.358191792925910E-01,-2.02022980381683,
                -1.03322686717359,-0.511041056535807E-01,
                 1829.15146461355}, 2E-8); //

        // Check expected residuals from R
        double[] residuals = model.estimateResiduals();
        TestUtils.assertEquals(residuals, new double[]{
                267.340029759711,-94.0139423988359,46.28716775752924,
                -410.114621930906,309.7145907602313,-249.3112153297231,
                -164.0489563956039,-13.18035686637081,14.30477260005235,
                 455.394094551857,-17.26892711483297,-39.0550425226967,
                -155.5499735953195,-85.6713080421283,341.9315139607727,
                -206.7578251937366},
                      1E-8);

        // Check standard errors from NIST
        double[] errors = model.estimateRegressionParametersStandardErrors();
        TestUtils.assertEquals(new double[] {890420.383607373,
                       84.9149257747669,
                       0.334910077722432E-01,
                       0.488399681651699,
                       0.214274163161675,
                       0.226073200069370,
                       455.478499142212}, errors, 1E-6);

        // Check regression standard error against R
        Assert.assertEquals(304.8540735619638, model.estimateRegressionStandardError(), 1E-10);

        // Check R-Square statistics against R
        Assert.assertEquals(0.995479004577296, model.calculateRSquared(), 1E-12);
        Assert.assertEquals(0.992465007628826, model.calculateAdjustedRSquared(), 1E-12);

        checkVarianceConsistency(model);

        // Estimate model without intercept
        model.setNoIntercept(true);
        model.newSampleData(design, nobs, nvars);

        // Check expected beta values from R
        betaHat = model.estimateRegressionParameters();
        TestUtils.assertEquals(betaHat,
          new double[]{-52.99357013868291, 0.07107319907358,
                -0.42346585566399,-0.57256866841929,
                -0.41420358884978, 48.41786562001326}, 1E-11);

        // Check standard errors from R
        errors = model.estimateRegressionParametersStandardErrors();
        TestUtils.assertEquals(new double[] {129.54486693117232, 0.03016640003786,
                0.41773654056612, 0.27899087467676, 0.32128496193363,
                17.68948737819961}, errors, 1E-11);

        // Check expected residuals from R
        residuals = model.estimateResiduals();
        TestUtils.assertEquals(residuals, new double[]{
                279.90274927293092, -130.32465380836874, 90.73228661967445, -401.31252201634948,
                -440.46768772620027, -543.54512853774793, 201.32111639536299, 215.90889365977932,
                73.09368242049943, 913.21694494481869, 424.82484953610174, -8.56475876776709,
                -361.32974610842876, 27.34560497213464, 151.28955976355002, -492.49937355336846},
                      1E-10);

        // Check regression standard error against R
        Assert.assertEquals(475.1655079819517, model.estimateRegressionStandardError(), 1E-10);

        // Check R-Square statistics against R
        Assert.assertEquals(0.9999670130706, model.calculateRSquared(), 1E-12);
        Assert.assertEquals(0.999947220913, model.calculateAdjustedRSquared(), 1E-12);

    }

    /**
     * Test R Swiss fertility dataset against R.
     * Data Source: R datasets package
     */
    @Test
    public void testSwissFertility() {
        double[] design = new double[] {
            80.2,17.0,15,12,9.96,
            83.1,45.1,6,9,84.84,
            92.5,39.7,5,5,93.40,
            85.8,36.5,12,7,33.77,
            76.9,43.5,17,15,5.16,
            76.1,35.3,9,7,90.57,
            83.8,70.2,16,7,92.85,
            92.4,67.8,14,8,97.16,
            82.4,53.3,12,7,97.67,
            82.9,45.2,16,13,91.38,
            87.1,64.5,14,6,98.61,
            64.1,62.0,21,12,8.52,
            66.9,67.5,14,7,2.27,
            68.9,60.7,19,12,4.43,
            61.7,69.3,22,5,2.82,
            68.3,72.6,18,2,24.20,
            71.7,34.0,17,8,3.30,
            55.7,19.4,26,28,12.11,
            54.3,15.2,31,20,2.15,
            65.1,73.0,19,9,2.84,
            65.5,59.8,22,10,5.23,
            65.0,55.1,14,3,4.52,
            56.6,50.9,22,12,15.14,
            57.4,54.1,20,6,4.20,
            72.5,71.2,12,1,2.40,
            74.2,58.1,14,8,5.23,
            72.0,63.5,6,3,2.56,
            60.5,60.8,16,10,7.72,
            58.3,26.8,25,19,18.46,
            65.4,49.5,15,8,6.10,
            75.5,85.9,3,2,99.71,
            69.3,84.9,7,6,99.68,
            77.3,89.7,5,2,100.00,
            70.5,78.2,12,6,98.96,
            79.4,64.9,7,3,98.22,
            65.0,75.9,9,9,99.06,
            92.2,84.6,3,3,99.46,
            79.3,63.1,13,13,96.83,
            70.4,38.4,26,12,5.62,
            65.7,7.7,29,11,13.79,
            72.7,16.7,22,13,11.22,
            64.4,17.6,35,32,16.92,
            77.6,37.6,15,7,4.97,
            67.6,18.7,25,7,8.65,
            35.0,1.2,37,53,42.34,
            44.7,46.6,16,29,50.43,
            42.8,27.7,22,29,58.33
        };

        final int nobs = 47;
        final int nvars = 4;

        // Estimate the model
        OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();
        model.newSampleData(design, nobs, nvars);

        // Check expected beta values from R
        double[] betaHat = model.estimateRegressionParameters();
        TestUtils.assertEquals(betaHat,
                new double[]{91.05542390271397,
                -0.22064551045715,
                -0.26058239824328,
                -0.96161238456030,
                 0.12441843147162}, 1E-12);

        // Check expected residuals from R
        double[] residuals = model.estimateResiduals();
        TestUtils.assertEquals(residuals, new double[]{
                7.1044267859730512,1.6580347433531366,
                4.6944952770029644,8.4548022690166160,13.6547432343186212,
               -9.3586864458500774,7.5822446330520386,15.5568995563859289,
                0.8113090736598980,7.1186762732484308,7.4251378771228724,
                2.6761316873234109,0.8351584810309354,7.1769991119615177,
               -3.8746753206299553,-3.1337779476387251,-0.1412575244091504,
                1.1186809170469780,-6.3588097346816594,3.4039270429434074,
                2.3374058329820175,-7.9272368576900503,-7.8361010968497959,
               -11.2597369269357070,0.9445333697827101,6.6544245101380328,
               -0.9146136301118665,-4.3152449403848570,-4.3536932047009183,
               -3.8907885169304661,-6.3027643926302188,-7.8308982189289091,
               -3.1792280015332750,-6.7167298771158226,-4.8469946718041754,
               -10.6335664353633685,11.1031134362036958,6.0084032641811733,
                5.4326230830188482,-7.2375578629692230,2.1671550814448222,
                15.0147574652763112,4.8625103516321015,-7.1597256413907706,
                -0.4515205619767598,-10.2916870903837587,-15.7812984571900063},
                1E-12);

        // Check standard errors from R
        double[] errors = model.estimateRegressionParametersStandardErrors();
        TestUtils.assertEquals(new double[] {6.94881329475087,
                0.07360008972340,
                0.27410957467466,
                0.19454551679325,
                0.03726654773803}, errors, 1E-10);

        // Check regression standard error against R
        Assert.assertEquals(7.73642194433223, model.estimateRegressionStandardError(), 1E-12);

        // Check R-Square statistics against R
        Assert.assertEquals(0.649789742860228, model.calculateRSquared(), 1E-12);
        Assert.assertEquals(0.6164363850373927, model.calculateAdjustedRSquared(), 1E-12);

        checkVarianceConsistency(model);

        // Estimate the model with no intercept
        model = new OLSMultipleLinearRegression();
        model.setNoIntercept(true);
        model.newSampleData(design, nobs, nvars);

        // Check expected beta values from R
        betaHat = model.estimateRegressionParameters();
        TestUtils.assertEquals(betaHat,
                new double[]{0.52191832900513,
                  2.36588087917963,
                  -0.94770353802795,
                  0.30851985863609}, 1E-12);

        // Check expected residuals from R
        residuals = model.estimateResiduals();
        TestUtils.assertEquals(residuals, new double[]{
                44.138759883538249, 27.720705122356215, 35.873200836126799,
                34.574619581211977, 26.600168342080213, 15.074636243026923, -12.704904871199814,
                1.497443824078134, 2.691972687079431, 5.582798774291231, -4.422986561283165,
                -9.198581600334345, 4.481765170730647, 2.273520207553216, -22.649827853221336,
                -17.747900013943308, 20.298314638496436, 6.861405135329779, -8.684712790954924,
                -10.298639278062371, -9.896618896845819, 4.568568616351242, -15.313570491727944,
                -13.762961360873966, 7.156100301980509, 16.722282219843990, 26.716200609071898,
                -1.991466398777079, -2.523342564719335, 9.776486693095093, -5.297535127628603,
                -16.639070567471094, -10.302057295211819, -23.549487860816846, 1.506624392156384,
                -17.939174438345930, 13.105792202765040, -1.943329906928462, -1.516005841666695,
                -0.759066561832886, 20.793137744128977, -2.485236153005426, 27.588238710486976,
                2.658333257106881, -15.998337823623046, -5.550742066720694, -14.219077806826615},
                1E-12);

        // Check standard errors from R
        errors = model.estimateRegressionParametersStandardErrors();
        TestUtils.assertEquals(new double[] {0.10470063765677, 0.41684100584290,
                0.43370143099691, 0.07694953606522}, errors, 1E-10);

        // Check regression standard error against R
        Assert.assertEquals(17.24710630547, model.estimateRegressionStandardError(), 1E-10);

        // Check R-Square statistics against R
        Assert.assertEquals(0.946350722085, model.calculateRSquared(), 1E-12);
        Assert.assertEquals(0.9413600915813, model.calculateAdjustedRSquared(), 1E-12);
    }

    /**
     * Test hat matrix computation
     *
     */
    @Test
    public void testHat() {

        /*
         * This example is from "The Hat Matrix in Regression and ANOVA",
         * David C. Hoaglin and Roy E. Welsch,
         * The American Statistician, Vol. 32, No. 1 (Feb., 1978), pp. 17-22.
         *
         */
        double[] design = new double[] {
                11.14, .499, 11.1,
                12.74, .558, 8.9,
                13.13, .604, 8.8,
                11.51, .441, 8.9,
                12.38, .550, 8.8,
                12.60, .528, 9.9,
                11.13, .418, 10.7,
                11.7, .480, 10.5,
                11.02, .406, 10.5,
                11.41, .467, 10.7
        };

        int nobs = 10;
        int nvars = 2;

        // Estimate the model
        OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();
        model.newSampleData(design, nobs, nvars);

        RealMatrix hat = model.calculateHat();

        // Reference data is upper half of symmetric hat matrix
        double[] referenceData = new double[] {
                .418, -.002,  .079, -.274, -.046,  .181,  .128,  .222,  .050,  .242,
                       .242,  .292,  .136,  .243,  .128, -.041,  .033, -.035,  .004,
                              .417, -.019,  .273,  .187, -.126,  .044, -.153,  .004,
                                     .604,  .197, -.038,  .168, -.022,  .275, -.028,
                                            .252,  .111, -.030,  .019, -.010, -.010,
                                                   .148,  .042,  .117,  .012,  .111,
                                                          .262,  .145,  .277,  .174,
                                                                 .154,  .120,  .168,
                                                                        .315,  .148,
                                                                               .187
        };

        // Check against reference data and verify symmetry
        int k = 0;
        for (int i = 0; i < 10; i++) {
            for (int j = i; j < 10; j++) {
                Assert.assertEquals(referenceData[k], hat.getEntry(i, j), 10e-3);
                Assert.assertEquals(hat.getEntry(i, j), hat.getEntry(j, i), 10e-12);
                k++;
            }
        }

        /*
         * Verify that residuals computed using the hat matrix are close to
         * what we get from direct computation, i.e. r = (I - H) y
         */
        double[] residuals = model.estimateResiduals();
        RealMatrix I = MatrixUtils.createRealIdentityMatrix(10);
        double[] hatResiduals = I.subtract(hat).operate(model.getY()).toArray();
        TestUtils.assertEquals(residuals, hatResiduals, 10e-12);
    }

    /**
     * test calculateYVariance
     */
    @Test
    public void testYVariance() {

        // assumes: y = new double[]{11.0, 12.0, 13.0, 14.0, 15.0, 16.0};

        OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();
        model.newSampleData(y, x);
        TestUtils.assertEquals(model.calculateYVariance(), 3.5, 0);
    }

    /**
     * Verifies that calculateYVariance and calculateResidualVariance return consistent
     * values with direct variance computation from Y, residuals, respectively.
     */
    protected void checkVarianceConsistency(OLSMultipleLinearRegression model) {
        // Check Y variance consistency
        TestUtils.assertEquals(StatUtils.variance(model.getY().toArray()), model.calculateYVariance(), 0);

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

    }

    /**
     * Verifies that setting X and Y separately has the same effect as newSample(X,Y).
     */
    @Test
    public void testNewSample2() {
        double[] y = new double[] {1, 2, 3, 4};
        double[][] x = new double[][] {
          {19, 22, 33},
          {20, 30, 40},
          {25, 35, 45},
          {27, 37, 47}
        };
        OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
        regression.newSampleData(y, x);
        RealMatrix combinedX = regression.getX().copy();
        RealVector combinedY = regression.getY().copy();
        regression.newXSampleData(x);
        regression.newYSampleData(y);
        Assert.assertEquals(combinedX, regression.getX());
        Assert.assertEquals(combinedY, regression.getY());

        // No intercept
        regression.setNoIntercept(true);
        regression.newSampleData(y, x);
        combinedX = regression.getX().copy();
        combinedY = regression.getY().copy();
        regression.newXSampleData(x);
        regression.newYSampleData(y);
        Assert.assertEquals(combinedX, regression.getX());
        Assert.assertEquals(combinedY, regression.getY());
    }

    @Test(expected=IllegalArgumentException.class)
    public void testNewSampleDataYNull() {
        createRegression().newSampleData(null, new double[][] {});
    }

    @Test(expected=IllegalArgumentException.class)
    public void testNewSampleDataXNull() {
        createRegression().newSampleData(new double[] {}, null);
    }

     /*
     * This is a test based on the Wampler1 data set
     * http://www.itl.nist.gov/div898/strd/lls/data/Wampler1.shtml
     */
    @Test
    public void testWampler1() {
        double[] data = new double[]{
            1, 0,
            6, 1,
            63, 2,
            364, 3,
            1365, 4,
            3906, 5,
            9331, 6,
            19608, 7,
            37449, 8,
            66430, 9,
            111111, 10,
            177156, 11,
            271453, 12,
            402234, 13,
            579195, 14,
            813616, 15,
            1118481, 16,
            1508598, 17,
            2000719, 18,
            2613660, 19,
            3368421, 20};
        OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();


        final int nvars = 5;
        final int nobs = 21;
        double[] tmp = new double[(nvars + 1) * nobs];
        int off = 0;
        int off2 = 0;
        for (int i = 0; i < nobs; i++) {
            tmp[off2] = data[off];
            tmp[off2 + 1] = data[off + 1];
            tmp[off2 + 2] = tmp[off2 + 1] * tmp[off2 + 1];
            tmp[off2 + 3] = tmp[off2 + 1] * tmp[off2 + 2];
            tmp[off2 + 4] = tmp[off2 + 1] * tmp[off2 + 3];
            tmp[off2 + 5] = tmp[off2 + 1] * tmp[off2 + 4];
            off2 += (nvars + 1);
            off += 2;
        }
        model.newSampleData(tmp, nobs, nvars);
        double[] betaHat = model.estimateRegressionParameters();
        TestUtils.assertEquals(betaHat,
                new double[]{1.0,
                    1.0, 1.0,
                    1.0, 1.0,
                    1.0}, 1E-8);

        double[] se = model.estimateRegressionParametersStandardErrors();
        TestUtils.assertEquals(se,
                new double[]{0.0,
                    0.0, 0.0,
                    0.0, 0.0,
                    0.0}, 1E-8);

        TestUtils.assertEquals(1.0, model.calculateRSquared(), 1.0e-10);
        TestUtils.assertEquals(0, model.estimateErrorVariance(), 1.0e-7);
        TestUtils.assertEquals(0.00, model.calculateResidualSumOfSquares(), 1.0e-6);

        return;
    }

    /*
     * This is a test based on the Wampler2 data set
     * http://www.itl.nist.gov/div898/strd/lls/data/Wampler2.shtml
     */
    @Test
    public void testWampler2() {
        double[] data = new double[]{
            1.00000, 0,
            1.11111, 1,
            1.24992, 2,
            1.42753, 3,
            1.65984, 4,
            1.96875, 5,
            2.38336, 6,
            2.94117, 7,
            3.68928, 8,
            4.68559, 9,
            6.00000, 10,
            7.71561, 11,
            9.92992, 12,
            12.75603, 13,
            16.32384, 14,
            20.78125, 15,
            26.29536, 16,
            33.05367, 17,
            41.26528, 18,
            51.16209, 19,
            63.00000, 20};
        OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();


        final int nvars = 5;
        final int nobs = 21;
        double[] tmp = new double[(nvars + 1) * nobs];
        int off = 0;
        int off2 = 0;
        for (int i = 0; i < nobs; i++) {
            tmp[off2] = data[off];
            tmp[off2 + 1] = data[off + 1];
            tmp[off2 + 2] = tmp[off2 + 1] * tmp[off2 + 1];
            tmp[off2 + 3] = tmp[off2 + 1] * tmp[off2 + 2];
            tmp[off2 + 4] = tmp[off2 + 1] * tmp[off2 + 3];
            tmp[off2 + 5] = tmp[off2 + 1] * tmp[off2 + 4];
            off2 += (nvars + 1);
            off += 2;
        }
        model.newSampleData(tmp, nobs, nvars);
        double[] betaHat = model.estimateRegressionParameters();
        TestUtils.assertEquals(betaHat,
                new double[]{
                    1.0,
                    1.0e-1,
                    1.0e-2,
                    1.0e-3, 1.0e-4,
                    1.0e-5}, 1E-8);

        double[] se = model.estimateRegressionParametersStandardErrors();
        TestUtils.assertEquals(se,
                new double[]{0.0,
                    0.0, 0.0,
                    0.0, 0.0,
                    0.0}, 1E-8);
        TestUtils.assertEquals(1.0, model.calculateRSquared(), 1.0e-10);
        TestUtils.assertEquals(0, model.estimateErrorVariance(), 1.0e-7);
        TestUtils.assertEquals(0.00, model.calculateResidualSumOfSquares(), 1.0e-6);
        return;
    }

    /*
     * This is a test based on the Wampler3 data set
     * http://www.itl.nist.gov/div898/strd/lls/data/Wampler3.shtml
     */
    @Test
    public void testWampler3() {
        double[] data = new double[]{
            760, 0,
            -2042, 1,
            2111, 2,
            -1684, 3,
            3888, 4,
            1858, 5,
            11379, 6,
            17560, 7,
            39287, 8,
            64382, 9,
            113159, 10,
            175108, 11,
            273291, 12,
            400186, 13,
            581243, 14,
            811568, 15,
            1121004, 16,
            1506550, 17,
            2002767, 18,
            2611612, 19,
            3369180, 20};

        OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();
        final int nvars = 5;
        final int nobs = 21;
        double[] tmp = new double[(nvars + 1) * nobs];
        int off = 0;
        int off2 = 0;
        for (int i = 0; i < nobs; i++) {
            tmp[off2] = data[off];
            tmp[off2 + 1] = data[off + 1];
            tmp[off2 + 2] = tmp[off2 + 1] * tmp[off2 + 1];
            tmp[off2 + 3] = tmp[off2 + 1] * tmp[off2 + 2];
            tmp[off2 + 4] = tmp[off2 + 1] * tmp[off2 + 3];
            tmp[off2 + 5] = tmp[off2 + 1] * tmp[off2 + 4];
            off2 += (nvars + 1);
            off += 2;
        }
        model.newSampleData(tmp, nobs, nvars);
        double[] betaHat = model.estimateRegressionParameters();
        TestUtils.assertEquals(betaHat,
                new double[]{
                    1.0,
                    1.0,
                    1.0,
                    1.0,
                    1.0,
                    1.0}, 1E-8);

        double[] se = model.estimateRegressionParametersStandardErrors();
        TestUtils.assertEquals(se,
                new double[]{2152.32624678170,
                    2363.55173469681, 779.343524331583,
                    101.475507550350, 5.64566512170752,
                    0.112324854679312}, 1E-8); //

        TestUtils.assertEquals(.999995559025820, model.calculateRSquared(), 1.0e-10);
        TestUtils.assertEquals(5570284.53333333, model.estimateErrorVariance(), 1.0e-6);
        TestUtils.assertEquals(83554268.0000000, model.calculateResidualSumOfSquares(), 1.0e-5);
        return;
    }

    /*
     * This is a test based on the Wampler4 data set
     * http://www.itl.nist.gov/div898/strd/lls/data/Wampler4.shtml
     */
    @Test
    public void testWampler4() {
        double[] data = new double[]{
            75901, 0,
            -204794, 1,
            204863, 2,
            -204436, 3,
            253665, 4,
            -200894, 5,
            214131, 6,
            -185192, 7,
            221249, 8,
            -138370, 9,
            315911, 10,
            -27644, 11,
            455253, 12,
            197434, 13,
            783995, 14,
            608816, 15,
            1370781, 16,
            1303798, 17,
            2205519, 18,
            2408860, 19,
            3444321, 20};

        OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();
        final int nvars = 5;
        final int nobs = 21;
        double[] tmp = new double[(nvars + 1) * nobs];
        int off = 0;
        int off2 = 0;
        for (int i = 0; i < nobs; i++) {
            tmp[off2] = data[off];
            tmp[off2 + 1] = data[off + 1];
            tmp[off2 + 2] = tmp[off2 + 1] * tmp[off2 + 1];
            tmp[off2 + 3] = tmp[off2 + 1] * tmp[off2 + 2];
            tmp[off2 + 4] = tmp[off2 + 1] * tmp[off2 + 3];
            tmp[off2 + 5] = tmp[off2 + 1] * tmp[off2 + 4];
            off2 += (nvars + 1);
            off += 2;
        }
        model.newSampleData(tmp, nobs, nvars);
        double[] betaHat = model.estimateRegressionParameters();
        TestUtils.assertEquals(betaHat,
                new double[]{
                    1.0,
                    1.0,
                    1.0,
                    1.0,
                    1.0,
                    1.0}, 1E-6);

        double[] se = model.estimateRegressionParametersStandardErrors();
        TestUtils.assertEquals(se,
                new double[]{215232.624678170,
                    236355.173469681, 77934.3524331583,
                    10147.5507550350, 564.566512170752,
                    11.2324854679312}, 1E-8);

        TestUtils.assertEquals(.957478440825662, model.calculateRSquared(), 1.0e-10);
        TestUtils.assertEquals(55702845333.3333, model.estimateErrorVariance(), 1.0e-4);
        TestUtils.assertEquals(835542680000.000, model.calculateResidualSumOfSquares(), 1.0e-3);
        return;
    }

    /**
     * Anything requiring beta calculation should advertise SME.
     */
    @Test(expected=org.apache.commons.math3.linear.SingularMatrixException.class)
    public void testSingularCalculateBeta() {
        OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();
        model.newSampleData(new double[] {1,  2,  3, 1, 2, 3, 1, 2, 3}, 3, 2);
        model.calculateBeta();
    }

    @Test
    public void testNoSSTOCalculateRsquare() {
        OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();
        model.newSampleData(new double[] {1,  2,  3, 1, 7, 8, 1, 10, 12}, 3, 2);
        Assert.assertTrue(Double.isNaN(model.calculateRSquared()));
    }

    @Test(expected=NullPointerException.class)
    public void testNoDataNPECalculateBeta() {
        OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();
        model.calculateBeta();
    }

    @Test(expected=NullPointerException.class)
    public void testNoDataNPECalculateHat() {
        OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();
        model.calculateHat();
    }

    @Test(expected=NullPointerException.class)
    public void testNoDataNPESSTO() {
        OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();
        model.calculateTotalSumOfSquares();
    }
}