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Posted to commits@ignite.apache.org by ch...@apache.org on 2017/12/28 16:13:18 UTC
[4/6] ignite git commit: IGNITE-5217: Gradient descent for OLS lin reg
http://git-wip-us.apache.org/repos/asf/ignite/blob/b2060855/modules/ml/src/test/java/org/apache/ignite/ml/regressions/DistributedBlockOLSMultipleLinearRegressionTest.java
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diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/regressions/DistributedBlockOLSMultipleLinearRegressionTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/DistributedBlockOLSMultipleLinearRegressionTest.java
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@@ -1,901 +0,0 @@
-/*
- * 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.ignite.ml.regressions;
-
-import org.apache.ignite.Ignite;
-import org.apache.ignite.internal.util.IgniteUtils;
-import org.apache.ignite.ml.TestUtils;
-import org.apache.ignite.ml.math.Matrix;
-import org.apache.ignite.ml.math.Vector;
-import org.apache.ignite.ml.math.exceptions.MathIllegalArgumentException;
-import org.apache.ignite.ml.math.exceptions.NullArgumentException;
-import org.apache.ignite.ml.math.exceptions.SingularMatrixException;
-import org.apache.ignite.ml.math.impls.matrix.SparseBlockDistributedMatrix;
-import org.apache.ignite.ml.math.impls.vector.SparseBlockDistributedVector;
-import org.apache.ignite.ml.math.util.MatrixUtil;
-import org.apache.ignite.testframework.junits.common.GridCommonAbstractTest;
-import org.apache.ignite.testframework.junits.common.GridCommonTest;
-import org.junit.Assert;
-
-/**
- * Tests for {@link OLSMultipleLinearRegression}.
- */
-@GridCommonTest(group = "Distributed Models")
-public class DistributedBlockOLSMultipleLinearRegressionTest extends GridCommonAbstractTest {
- /** */
- private double[] y;
-
- /** */
- private double[][] x;
-
- /** */
- private AbstractMultipleLinearRegression regression;
-
- /** Number of nodes in grid */
- private static final int NODE_COUNT = 3;
-
- /** */
- private static final double PRECISION = 1E-12;
-
- /** Grid instance. */
- private Ignite ignite;
-
- /** */
- public DistributedBlockOLSMultipleLinearRegressionTest() {
- super(false);
- }
-
- /** {@inheritDoc} */
- @Override protected void beforeTestsStarted() throws Exception {
- for (int i = 1; i <= NODE_COUNT; i++)
- startGrid(i);
- }
-
- /** {@inheritDoc} */
- @Override protected void afterTestsStopped() throws Exception {
- stopAllGrids();
- }
-
- /**
- * {@inheritDoc}
- */
- @Override protected void beforeTest() throws Exception {
- ignite = grid(NODE_COUNT);
-
- ignite.configuration().setPeerClassLoadingEnabled(true);
-
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
- 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};
-
- regression = createRegression();
- }
-
- /** */
- private OLSMultipleLinearRegression createRegression() {
- OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
- regression.newSampleData(new SparseBlockDistributedVector(y), new SparseBlockDistributedMatrix(x));
- return regression;
- }
-
- /** */
- public void testPerfectFit() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
- double[] betaHat = regression.estimateRegressionParameters();
- TestUtils.assertEquals(new double[] {11.0, 1.0 / 2.0, 2.0 / 3.0, 3.0 / 4.0, 4.0 / 5.0, 5.0 / 6.0},
- betaHat,
- 1e-13);
- double[] residuals = regression.estimateResiduals();
- TestUtils.assertEquals(new double[] {0d, 0d, 0d, 0d, 0d, 0d}, residuals,
- 1e-13);
- Matrix errors = regression.estimateRegressionParametersVariance();
- 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};
- Matrix refVar = new SparseBlockDistributedMatrix(s.length, s.length);
- for (int i = 0; i < refVar.rowSize(); i++)
- for (int j = 0; j < refVar.columnSize(); j++) {
- if (i == 0) {
- refVar.setX(i, j, s[j]);
- continue;
- }
- double x = s[i] * s[j];
- refVar.setX(i, j, (i == j) ? 2 * x : x);
- }
- Assert.assertEquals(0.0,
- TestUtils.maximumAbsoluteRowSum(errors.minus(refVar)),
- 5.0e-16 * TestUtils.maximumAbsoluteRowSum(refVar));
- 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
- */
- public void testLongly() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
- // 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 mdl = new OLSMultipleLinearRegression();
- mdl.newSampleData(design, nobs, nvars, new SparseBlockDistributedMatrix());
-
- // Check expected beta values from NIST
- double[] betaHat = mdl.estimateRegressionParameters();
- TestUtils.assertEquals(betaHat,
- new double[] {
- -3482258.63459582, 15.0618722713733,
- -0.358191792925910E-01, -2.02022980381683,
- -1.03322686717359, -0.511041056535807E-01,
- 1829.15146461355}, 2E-6); //
-
- // Check expected residuals from R
- double[] residuals = mdl.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-7);
-
- // Check standard errors from NIST
- double[] errors = mdl.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, mdl.estimateRegressionStandardError(), 1E-8);
-
- // Check R-Square statistics against R
- Assert.assertEquals(0.995479004577296, mdl.calculateRSquared(), 1E-12);
- Assert.assertEquals(0.992465007628826, mdl.calculateAdjustedRSquared(), 1E-12);
-
- // TODO: IGNITE-5826, uncomment.
- // checkVarianceConsistency(model);
-
- // Estimate model without intercept
- mdl.setNoIntercept(true);
- mdl.newSampleData(design, nobs, nvars, new SparseBlockDistributedMatrix());
-
- // Check expected beta values from R
- betaHat = mdl.estimateRegressionParameters();
- TestUtils.assertEquals(betaHat,
- new double[] {
- -52.99357013868291, 0.07107319907358,
- -0.42346585566399, -0.57256866841929,
- -0.41420358884978, 48.41786562001326}, 1E-8);
-
- // Check standard errors from R
- errors = mdl.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 = mdl.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-8);
-
- // Check regression standard error against R
- Assert.assertEquals(475.1655079819517, mdl.estimateRegressionStandardError(), 1E-10);
-
- // Check R-Square statistics against R
- Assert.assertEquals(0.9999670130706, mdl.calculateRSquared(), 1E-12);
- Assert.assertEquals(0.999947220913, mdl.calculateAdjustedRSquared(), 1E-12);
- }
-
- /**
- * Test R Swiss fertility dataset against R. Data Source: R datasets package
- */
- public void testSwissFertility() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
- 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 mdl = new OLSMultipleLinearRegression();
- mdl.newSampleData(design, nobs, nvars, new SparseBlockDistributedMatrix());
-
- // Check expected beta values from R
- double[] betaHat = mdl.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 = mdl.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 = mdl.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, mdl.estimateRegressionStandardError(), 1E-12);
-
- // Check R-Square statistics against R
- Assert.assertEquals(0.649789742860228, mdl.calculateRSquared(), 1E-12);
- Assert.assertEquals(0.6164363850373927, mdl.calculateAdjustedRSquared(), 1E-12);
-
- // TODO: IGNITE-5826, uncomment.
- // checkVarianceConsistency(model);
-
- // Estimate the model with no intercept
- mdl = new OLSMultipleLinearRegression();
- mdl.setNoIntercept(true);
- mdl.newSampleData(design, nobs, nvars, new SparseBlockDistributedMatrix());
-
- // Check expected beta values from R
- betaHat = mdl.estimateRegressionParameters();
- TestUtils.assertEquals(betaHat,
- new double[] {
- 0.52191832900513,
- 2.36588087917963,
- -0.94770353802795,
- 0.30851985863609}, 1E-12);
-
- // Check expected residuals from R
- residuals = mdl.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 = mdl.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, mdl.estimateRegressionStandardError(), 1E-10);
-
- // Check R-Square statistics against R
- Assert.assertEquals(0.946350722085, mdl.calculateRSquared(), 1E-12);
- Assert.assertEquals(0.9413600915813, mdl.calculateAdjustedRSquared(), 1E-12);
- }
-
- /**
- * Test hat matrix computation
- */
- public void testHat() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
- /*
- * 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 mdl = new OLSMultipleLinearRegression();
- mdl.newSampleData(design, nobs, nvars, new SparseBlockDistributedMatrix());
-
- Matrix hat = mdl.calculateHat();
-
- // Reference data is upper half of symmetric hat matrix
- double[] refData = 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(refData[k], hat.getX(i, j), 10e-3);
- Assert.assertEquals(hat.getX(i, j), hat.getX(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 = mdl.estimateResiduals();
- Matrix id = MatrixUtil.identityLike(hat, 10);
- double[] hatResiduals = id.minus(hat).times(mdl.getY()).getStorage().data();
- TestUtils.assertEquals(residuals, hatResiduals, 10e-12);
- }
-
- /**
- * test calculateYVariance
- */
- public void testYVariance() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- // assumes: y = new double[]{11.0, 12.0, 13.0, 14.0, 15.0, 16.0};
- OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
- mdl.newSampleData(new SparseBlockDistributedVector(y), new SparseBlockDistributedMatrix(x));
- TestUtils.assertEquals(mdl.calculateYVariance(), 3.5, 0);
- }
-
- /**
- * Verifies that setting X and Y separately has the same effect as newSample(X,Y).
- */
- public void testNewSample2() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- 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(new SparseBlockDistributedVector(y), new SparseBlockDistributedMatrix(x));
- Matrix combinedX = regression.getX().copy();
- Vector combinedY = regression.getY().copy();
- regression.newXSampleData(new SparseBlockDistributedMatrix(x));
- regression.newYSampleData(new SparseBlockDistributedVector(y));
- for (int i = 0; i < combinedX.rowSize(); i++) {
- for (int j = 0; j < combinedX.columnSize(); j++)
- Assert.assertEquals(combinedX.get(i, j), regression.getX().get(i, j), PRECISION);
-
- }
- for (int i = 0; i < combinedY.size(); i++)
- Assert.assertEquals(combinedY.get(i), regression.getY().get(i), PRECISION);
-
- // No intercept
- regression.setNoIntercept(true);
- regression.newSampleData(new SparseBlockDistributedVector(y), new SparseBlockDistributedMatrix(x));
- combinedX = regression.getX().copy();
- combinedY = regression.getY().copy();
- regression.newXSampleData(new SparseBlockDistributedMatrix(x));
- regression.newYSampleData(new SparseBlockDistributedVector(y));
-
- for (int i = 0; i < combinedX.rowSize(); i++) {
- for (int j = 0; j < combinedX.columnSize(); j++)
- Assert.assertEquals(combinedX.get(i, j), regression.getX().get(i, j), PRECISION);
-
- }
- for (int i = 0; i < combinedY.size(); i++)
- Assert.assertEquals(combinedY.get(i), regression.getY().get(i), PRECISION);
-
- }
-
- /** */
- public void testNewSampleDataYNull() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
- try {
- createRegression().newSampleData(null, new SparseBlockDistributedMatrix(new double[][] {{1}}));
- fail("Expected NullArgumentException was not caught.");
- }
- catch (NullArgumentException e) {
- return;
- }
- fail("Expected NullArgumentException was not caught.");
- }
-
- /** */
- public void testNewSampleDataXNull() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
- try {
- createRegression().newSampleData(new SparseBlockDistributedVector(new double[] {1}), null);
- fail("Expected NullArgumentException was not caught.");
- }
- catch (NullArgumentException e) {
- return;
- }
- fail("Expected NullArgumentException was not caught.");
- }
-
- /**
- * This is a test based on the Wampler1 data set http://www.itl.nist.gov/div898/strd/lls/data/Wampler1.shtml
- */
- public void testWampler1() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- 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 mdl = 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;
- }
- mdl.newSampleData(tmp, nobs, nvars, new SparseBlockDistributedMatrix());
- double[] betaHat = mdl.estimateRegressionParameters();
- TestUtils.assertEquals(betaHat,
- new double[] {
- 1.0,
- 1.0, 1.0,
- 1.0, 1.0,
- 1.0}, 1E-8);
-
- double[] se = mdl.estimateRegressionParametersStandardErrors();
- TestUtils.assertEquals(se,
- new double[] {
- 0.0,
- 0.0, 0.0,
- 0.0, 0.0,
- 0.0}, 1E-8);
-
- TestUtils.assertEquals(1.0, mdl.calculateRSquared(), 1.0e-10);
- TestUtils.assertEquals(0, mdl.estimateErrorVariance(), 1.0e-7);
- TestUtils.assertEquals(0.00, mdl.calculateResidualSumOfSquares(), 1.0e-6);
- }
-
- /**
- * This is a test based on the Wampler2 data set http://www.itl.nist.gov/div898/strd/lls/data/Wampler2.shtml
- */
- public void testWampler2() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- 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 mdl = 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;
- }
- mdl.newSampleData(tmp, nobs, nvars, new SparseBlockDistributedMatrix());
- double[] betaHat = mdl.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 = mdl.estimateRegressionParametersStandardErrors();
- TestUtils.assertEquals(se,
- new double[] {
- 0.0,
- 0.0, 0.0,
- 0.0, 0.0,
- 0.0}, 1E-8);
- TestUtils.assertEquals(1.0, mdl.calculateRSquared(), 1.0e-10);
- TestUtils.assertEquals(0, mdl.estimateErrorVariance(), 1.0e-7);
- TestUtils.assertEquals(0.00, mdl.calculateResidualSumOfSquares(), 1.0e-6);
- }
-
- /**
- * This is a test based on the Wampler3 data set http://www.itl.nist.gov/div898/strd/lls/data/Wampler3.shtml
- */
- public void testWampler3() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- 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 mdl = 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;
- }
- mdl.newSampleData(tmp, nobs, nvars, new SparseBlockDistributedMatrix());
- double[] betaHat = mdl.estimateRegressionParameters();
- TestUtils.assertEquals(betaHat,
- new double[] {
- 1.0,
- 1.0,
- 1.0,
- 1.0,
- 1.0,
- 1.0}, 1E-8);
-
- double[] se = mdl.estimateRegressionParametersStandardErrors();
- TestUtils.assertEquals(se,
- new double[] {
- 2152.32624678170,
- 2363.55173469681, 779.343524331583,
- 101.475507550350, 5.64566512170752,
- 0.112324854679312}, 1E-8); //
-
- TestUtils.assertEquals(.999995559025820, mdl.calculateRSquared(), 1.0e-10);
- TestUtils.assertEquals(5570284.53333333, mdl.estimateErrorVariance(), 1.0e-6);
- TestUtils.assertEquals(83554268.0000000, mdl.calculateResidualSumOfSquares(), 1.0e-5);
- }
-
- /**
- * This is a test based on the Wampler4 data set http://www.itl.nist.gov/div898/strd/lls/data/Wampler4.shtml
- */
- public void testWampler4() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- 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 mdl = 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;
- }
- mdl.newSampleData(tmp, nobs, nvars, new SparseBlockDistributedMatrix());
- double[] betaHat = mdl.estimateRegressionParameters();
- TestUtils.assertEquals(betaHat,
- new double[] {
- 1.0,
- 1.0,
- 1.0,
- 1.0,
- 1.0,
- 1.0}, 1E-6);
-
- double[] se = mdl.estimateRegressionParametersStandardErrors();
- TestUtils.assertEquals(se,
- new double[] {
- 215232.624678170,
- 236355.173469681, 77934.3524331583,
- 10147.5507550350, 564.566512170752,
- 11.2324854679312}, 1E-8);
-
- TestUtils.assertEquals(.957478440825662, mdl.calculateRSquared(), 1.0e-10);
- TestUtils.assertEquals(55702845333.3333, mdl.estimateErrorVariance(), 1.0e-4);
- TestUtils.assertEquals(835542680000.000, mdl.calculateResidualSumOfSquares(), 1.0e-3);
- }
-
- /**
- * Anything requiring beta calculation should advertise SME.
- */
- public void testSingularCalculateBeta() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression(1e-15);
-
- try {
- mdl.newSampleData(new double[] {1, 2, 3, 1, 2, 3, 1, 2, 3}, 3, 2, new SparseBlockDistributedMatrix());
- mdl.calculateBeta();
- fail("Expected SingularMatrixException was not caught.");
- }
- catch (SingularMatrixException e) {
- return;
- }
- fail("Expected SingularMatrixException was not caught.");
- }
-
- /** */
- public void testNoDataNPECalculateBeta() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
-
- try {
- mdl.calculateBeta();
- fail("Expected NullPointerException was not caught.");
- }
- catch (NullPointerException e) {
- return;
- }
- fail("Expected NullPointerException was not caught.");
- }
-
- /** */
- public void testNoDataNPECalculateHat() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
-
- try {
- mdl.calculateHat();
- fail("Expected NullPointerException was not caught.");
- }
- catch (NullPointerException e) {
- return;
- }
- fail("Expected NullPointerException was not caught.");
- }
-
- /** */
- public void testNoDataNPESSTO() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
-
- try {
- mdl.calculateTotalSumOfSquares();
- fail("Expected NullPointerException was not caught.");
- }
- catch (NullPointerException e) {
- return;
- }
- fail("Expected NullPointerException was not caught.");
- }
-
- /** */
- public void testMathIllegalArgumentException() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
-
- try {
- mdl.validateSampleData(new SparseBlockDistributedMatrix(1, 2), new SparseBlockDistributedVector(1));
- fail("Expected MathIllegalArgumentException was not caught.");
- }
- catch (MathIllegalArgumentException e) {
- return;
- }
- fail("Expected MathIllegalArgumentException was not caught.");
- }
-}
http://git-wip-us.apache.org/repos/asf/ignite/blob/b2060855/modules/ml/src/test/java/org/apache/ignite/ml/regressions/DistributedOLSMultipleLinearRegressionTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/regressions/DistributedOLSMultipleLinearRegressionTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/DistributedOLSMultipleLinearRegressionTest.java
deleted file mode 100644
index f720406..0000000
--- a/modules/ml/src/test/java/org/apache/ignite/ml/regressions/DistributedOLSMultipleLinearRegressionTest.java
+++ /dev/null
@@ -1,903 +0,0 @@
-/*
- * 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.ignite.ml.regressions;
-
-import org.apache.ignite.Ignite;
-import org.apache.ignite.internal.util.IgniteUtils;
-import org.apache.ignite.ml.TestUtils;
-import org.apache.ignite.ml.math.Matrix;
-import org.apache.ignite.ml.math.Vector;
-import org.apache.ignite.ml.math.exceptions.MathIllegalArgumentException;
-import org.apache.ignite.ml.math.exceptions.NullArgumentException;
-import org.apache.ignite.ml.math.exceptions.SingularMatrixException;
-import org.apache.ignite.ml.math.impls.matrix.SparseDistributedMatrix;
-import org.apache.ignite.ml.math.impls.vector.SparseDistributedVector;
-import org.apache.ignite.ml.math.util.MatrixUtil;
-import org.apache.ignite.testframework.junits.common.GridCommonAbstractTest;
-import org.apache.ignite.testframework.junits.common.GridCommonTest;
-import org.junit.Assert;
-
-/**
- * Tests for {@link OLSMultipleLinearRegression}.
- */
-@GridCommonTest(group = "Distributed Models")
-public class DistributedOLSMultipleLinearRegressionTest extends GridCommonAbstractTest {
- /** */
- private double[] y;
-
- /** */
- private double[][] x;
-
- /** */
- private AbstractMultipleLinearRegression regression;
-
- /** Number of nodes in grid */
- private static final int NODE_COUNT = 3;
-
- /** */
- private static final double PRECISION = 1E-12;
-
- /** Grid instance. */
- private Ignite ignite;
-
- /** */
- public DistributedOLSMultipleLinearRegressionTest() {
- super(false);
- }
-
- /** {@inheritDoc} */
- @Override protected void beforeTestsStarted() throws Exception {
- for (int i = 1; i <= NODE_COUNT; i++)
- startGrid(i);
- }
-
- /** {@inheritDoc} */
- @Override protected void afterTestsStopped() throws Exception {
- stopAllGrids();
- }
-
- /**
- * {@inheritDoc}
- */
- @Override protected void beforeTest() throws Exception {
- ignite = grid(NODE_COUNT);
-
- ignite.configuration().setPeerClassLoadingEnabled(true);
-
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
- 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};
-
- regression = createRegression();
- }
-
- /** */
- private OLSMultipleLinearRegression createRegression() {
- OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
- regression.newSampleData(new SparseDistributedVector(y), new SparseDistributedMatrix(x));
- return regression;
- }
-
- /** */
- public void testPerfectFit() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
- double[] betaHat = regression.estimateRegressionParameters();
- TestUtils.assertEquals(new double[] {11.0, 1.0 / 2.0, 2.0 / 3.0, 3.0 / 4.0, 4.0 / 5.0, 5.0 / 6.0},
- betaHat,
- 1e-13);
- double[] residuals = regression.estimateResiduals();
- TestUtils.assertEquals(new double[] {0d, 0d, 0d, 0d, 0d, 0d}, residuals,
- 1e-13);
- Matrix errors = regression.estimateRegressionParametersVariance();
- 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};
- Matrix refVar = new SparseDistributedMatrix(s.length, s.length);
- for (int i = 0; i < refVar.rowSize(); i++)
- for (int j = 0; j < refVar.columnSize(); j++) {
- if (i == 0) {
- refVar.setX(i, j, s[j]);
- continue;
- }
- double x = s[i] * s[j];
- refVar.setX(i, j, (i == j) ? 2 * x : x);
- }
- Assert.assertEquals(0.0,
- TestUtils.maximumAbsoluteRowSum(errors.minus(refVar)),
- 5.0e-16 * TestUtils.maximumAbsoluteRowSum(refVar));
- 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
- */
- public void testLongly() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
- // 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 mdl = new OLSMultipleLinearRegression();
- mdl.newSampleData(design, nobs, nvars, new SparseDistributedMatrix());
-
- // Check expected beta values from NIST
- double[] betaHat = mdl.estimateRegressionParameters();
- TestUtils.assertEquals(betaHat,
- new double[] {
- -3482258.63459582, 15.0618722713733,
- -0.358191792925910E-01, -2.02022980381683,
- -1.03322686717359, -0.511041056535807E-01,
- 1829.15146461355}, 2E-6); //
-
- // Check expected residuals from R
- double[] residuals = mdl.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-7);
-
- // Check standard errors from NIST
- double[] errors = mdl.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, mdl.estimateRegressionStandardError(), 1E-8);
-
- // Check R-Square statistics against R
- Assert.assertEquals(0.995479004577296, mdl.calculateRSquared(), 1E-12);
- Assert.assertEquals(0.992465007628826, mdl.calculateAdjustedRSquared(), 1E-12);
-
- // TODO: IGNITE-5826, uncomment.
- // checkVarianceConsistency(model);
-
- // Estimate model without intercept
- mdl.setNoIntercept(true);
- mdl.newSampleData(design, nobs, nvars, new SparseDistributedMatrix());
-
- // Check expected beta values from R
- betaHat = mdl.estimateRegressionParameters();
- TestUtils.assertEquals(betaHat,
- new double[] {
- -52.99357013868291, 0.07107319907358,
- -0.42346585566399, -0.57256866841929,
- -0.41420358884978, 48.41786562001326}, 1E-8);
-
- // Check standard errors from R
- errors = mdl.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 = mdl.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-8);
-
- // Check regression standard error against R
- Assert.assertEquals(475.1655079819517, mdl.estimateRegressionStandardError(), 1E-10);
-
- // Check R-Square statistics against R
- Assert.assertEquals(0.9999670130706, mdl.calculateRSquared(), 1E-12);
- Assert.assertEquals(0.999947220913, mdl.calculateAdjustedRSquared(), 1E-12);
- }
-
- /**
- * Test R Swiss fertility dataset against R. Data Source: R datasets package
- */
- public void testSwissFertility() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
- 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 mdl = new OLSMultipleLinearRegression();
- mdl.newSampleData(design, nobs, nvars, new SparseDistributedMatrix());
-
- // Check expected beta values from R
- double[] betaHat = mdl.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 = mdl.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 = mdl.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, mdl.estimateRegressionStandardError(), 1E-12);
-
- // Check R-Square statistics against R
- Assert.assertEquals(0.649789742860228, mdl.calculateRSquared(), 1E-12);
- Assert.assertEquals(0.6164363850373927, mdl.calculateAdjustedRSquared(), 1E-12);
-
- // TODO: IGNITE-5826, uncomment.
- // checkVarianceConsistency(model);
-
- // Estimate the model with no intercept
- mdl = new OLSMultipleLinearRegression();
- mdl.setNoIntercept(true);
- mdl.newSampleData(design, nobs, nvars, new SparseDistributedMatrix());
-
- // Check expected beta values from R
- betaHat = mdl.estimateRegressionParameters();
- TestUtils.assertEquals(betaHat,
- new double[] {
- 0.52191832900513,
- 2.36588087917963,
- -0.94770353802795,
- 0.30851985863609}, 1E-12);
-
- // Check expected residuals from R
- residuals = mdl.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 = mdl.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, mdl.estimateRegressionStandardError(), 1E-10);
-
- // Check R-Square statistics against R
- Assert.assertEquals(0.946350722085, mdl.calculateRSquared(), 1E-12);
- Assert.assertEquals(0.9413600915813, mdl.calculateAdjustedRSquared(), 1E-12);
- }
-
- /**
- * Test hat matrix computation
- */
- public void testHat() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
- /*
- * 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 mdl = new OLSMultipleLinearRegression();
- mdl.newSampleData(design, nobs, nvars, new SparseDistributedMatrix());
-
- Matrix hat = mdl.calculateHat();
-
- // Reference data is upper half of symmetric hat matrix
- double[] refData = 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(refData[k], hat.getX(i, j), 10e-3);
- Assert.assertEquals(hat.getX(i, j), hat.getX(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 = mdl.estimateResiduals();
- Matrix id = MatrixUtil.identityLike(hat, 10);
- double[] hatResiduals = id.minus(hat).times(mdl.getY()).getStorage().data();
- TestUtils.assertEquals(residuals, hatResiduals, 10e-12);
- }
-
- /**
- * test calculateYVariance
- */
- public void testYVariance() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- // assumes: y = new double[]{11.0, 12.0, 13.0, 14.0, 15.0, 16.0};
- OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
- mdl.newSampleData(new SparseDistributedVector(y), new SparseDistributedMatrix(x));
- TestUtils.assertEquals(mdl.calculateYVariance(), 3.5, 0);
- }
-
- /**
- * Verifies that setting X and Y separately has the same effect as newSample(X,Y).
- */
- public void testNewSample2() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- 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(new SparseDistributedVector(y), new SparseDistributedMatrix(x));
- Matrix combinedX = regression.getX().copy();
- Vector combinedY = regression.getY().copy();
- regression.newXSampleData(new SparseDistributedMatrix(x));
- regression.newYSampleData(new SparseDistributedVector(y));
- for (int i = 0; i < combinedX.rowSize(); i++) {
- for (int j = 0; j < combinedX.columnSize(); j++)
- Assert.assertEquals(combinedX.get(i, j), regression.getX().get(i, j), PRECISION);
-
- }
- for (int i = 0; i < combinedY.size(); i++)
- Assert.assertEquals(combinedY.get(i), regression.getY().get(i), PRECISION);
-
- // No intercept
- regression.setNoIntercept(true);
- regression.newSampleData(new SparseDistributedVector(y), new SparseDistributedMatrix(x));
- combinedX = regression.getX().copy();
- combinedY = regression.getY().copy();
- regression.newXSampleData(new SparseDistributedMatrix(x));
- regression.newYSampleData(new SparseDistributedVector(y));
-
- for (int i = 0; i < combinedX.rowSize(); i++) {
- for (int j = 0; j < combinedX.columnSize(); j++)
- Assert.assertEquals(combinedX.get(i, j), regression.getX().get(i, j), PRECISION);
-
- }
- for (int i = 0; i < combinedY.size(); i++)
- Assert.assertEquals(combinedY.get(i), regression.getY().get(i), PRECISION);
- }
-
- /** */
- public void testNewSampleDataYNull() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
- try {
- createRegression().newSampleData(null, new SparseDistributedMatrix(new double[][] {{1}}));
- fail("Expected NullArgumentException was not caught.");
- }
- catch (NullArgumentException e) {
- return;
- }
- fail("Expected NullArgumentException was not caught.");
- }
-
- /** */
- public void testNewSampleDataXNull() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
-
- try {
- createRegression().newSampleData(new SparseDistributedVector(new double[] {1}), null);
- fail("Expected NullArgumentException was not caught.");
- }
- catch (NullArgumentException e) {
- return;
- }
- fail("Expected NullArgumentException was not caught.");
-
- }
-
- /**
- * This is a test based on the Wampler1 data set http://www.itl.nist.gov/div898/strd/lls/data/Wampler1.shtml
- */
- public void testWampler1() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- 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 mdl = 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;
- }
- mdl.newSampleData(tmp, nobs, nvars, new SparseDistributedMatrix());
- double[] betaHat = mdl.estimateRegressionParameters();
- TestUtils.assertEquals(betaHat,
- new double[] {
- 1.0,
- 1.0, 1.0,
- 1.0, 1.0,
- 1.0}, 1E-8);
-
- double[] se = mdl.estimateRegressionParametersStandardErrors();
- TestUtils.assertEquals(se,
- new double[] {
- 0.0,
- 0.0, 0.0,
- 0.0, 0.0,
- 0.0}, 1E-8);
-
- TestUtils.assertEquals(1.0, mdl.calculateRSquared(), 1.0e-10);
- TestUtils.assertEquals(0, mdl.estimateErrorVariance(), 1.0e-7);
- TestUtils.assertEquals(0.00, mdl.calculateResidualSumOfSquares(), 1.0e-6);
- }
-
- /**
- * This is a test based on the Wampler2 data set http://www.itl.nist.gov/div898/strd/lls/data/Wampler2.shtml
- */
- public void testWampler2() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- 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 mdl = 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;
- }
- mdl.newSampleData(tmp, nobs, nvars, new SparseDistributedMatrix());
- double[] betaHat = mdl.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 = mdl.estimateRegressionParametersStandardErrors();
- TestUtils.assertEquals(se,
- new double[] {
- 0.0,
- 0.0, 0.0,
- 0.0, 0.0,
- 0.0}, 1E-8);
- TestUtils.assertEquals(1.0, mdl.calculateRSquared(), 1.0e-10);
- TestUtils.assertEquals(0, mdl.estimateErrorVariance(), 1.0e-7);
- TestUtils.assertEquals(0.00, mdl.calculateResidualSumOfSquares(), 1.0e-6);
- }
-
- /**
- * This is a test based on the Wampler3 data set http://www.itl.nist.gov/div898/strd/lls/data/Wampler3.shtml
- */
- public void testWampler3() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- 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 mdl = 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;
- }
- mdl.newSampleData(tmp, nobs, nvars, new SparseDistributedMatrix());
- double[] betaHat = mdl.estimateRegressionParameters();
- TestUtils.assertEquals(betaHat,
- new double[] {
- 1.0,
- 1.0,
- 1.0,
- 1.0,
- 1.0,
- 1.0}, 1E-8);
-
- double[] se = mdl.estimateRegressionParametersStandardErrors();
- TestUtils.assertEquals(se,
- new double[] {
- 2152.32624678170,
- 2363.55173469681, 779.343524331583,
- 101.475507550350, 5.64566512170752,
- 0.112324854679312}, 1E-8); //
-
- TestUtils.assertEquals(.999995559025820, mdl.calculateRSquared(), 1.0e-10);
- TestUtils.assertEquals(5570284.53333333, mdl.estimateErrorVariance(), 1.0e-6);
- TestUtils.assertEquals(83554268.0000000, mdl.calculateResidualSumOfSquares(), 1.0e-5);
- }
-
- /**
- * This is a test based on the Wampler4 data set http://www.itl.nist.gov/div898/strd/lls/data/Wampler4.shtml
- */
- public void testWampler4() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- 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 mdl = 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;
- }
- mdl.newSampleData(tmp, nobs, nvars, new SparseDistributedMatrix());
- double[] betaHat = mdl.estimateRegressionParameters();
- TestUtils.assertEquals(betaHat,
- new double[] {
- 1.0,
- 1.0,
- 1.0,
- 1.0,
- 1.0,
- 1.0}, 1E-6);
-
- double[] se = mdl.estimateRegressionParametersStandardErrors();
- TestUtils.assertEquals(se,
- new double[] {
- 215232.624678170,
- 236355.173469681, 77934.3524331583,
- 10147.5507550350, 564.566512170752,
- 11.2324854679312}, 1E-8);
-
- TestUtils.assertEquals(.957478440825662, mdl.calculateRSquared(), 1.0e-10);
- TestUtils.assertEquals(55702845333.3333, mdl.estimateErrorVariance(), 1.0e-4);
- TestUtils.assertEquals(835542680000.000, mdl.calculateResidualSumOfSquares(), 1.0e-3);
- }
-
- /**
- * Anything requiring beta calculation should advertise SME.
- */
- public void testSingularCalculateBeta() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression(1e-15);
-
- try {
- mdl.newSampleData(new double[] {1, 2, 3, 1, 2, 3, 1, 2, 3}, 3, 2, new SparseDistributedMatrix());
- mdl.calculateBeta();
- fail("Expected SingularMatrixException was not caught.");
- }
- catch (SingularMatrixException e) {
- return;
- }
- fail("Expected SingularMatrixException was not caught.");
-
- }
-
- /** */
- public void testNoDataNPECalculateBeta() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
-
- try {
- mdl.calculateBeta();
- fail("Expected NullPointerException was not caught.");
- }
- catch (NullPointerException e) {
- return;
- }
- fail("Expected NullPointerException was not caught.");
-
- }
-
- /** */
- public void testNoDataNPECalculateHat() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
-
- try {
- mdl.calculateHat();
- fail("Expected NullPointerException was not caught.");
- }
- catch (NullPointerException e) {
- return;
- }
- fail("Expected NullPointerException was not caught.");
- }
-
- /** */
- public void testNoDataNPESSTO() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
-
- try {
- mdl.calculateTotalSumOfSquares();
- fail("Expected NullPointerException was not caught.");
- }
- catch (NullPointerException e) {
- return;
- }
- fail("Expected NullPointerException was not caught.");
- }
-
- /** */
- public void testMathIllegalArgumentException() {
- IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
- OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
-
- try {
- mdl.validateSampleData(new SparseDistributedMatrix(1, 2), new SparseDistributedVector(1));
- fail("Expected MathIllegalArgumentException was not caught.");
- }
- catch (MathIllegalArgumentException e) {
- return;
- }
- fail("Expected MathIllegalArgumentException was not caught.");
- }
-}
http://git-wip-us.apache.org/repos/asf/ignite/blob/b2060855/modules/ml/src/test/java/org/apache/ignite/ml/regressions/OLSMultipleLinearRegressionModelTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/regressions/OLSMultipleLinearRegressionModelTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/OLSMultipleLinearRegressionModelTest.java
deleted file mode 100644
index 74d5524..0000000
--- a/modules/ml/src/test/java/org/apache/ignite/ml/regressions/OLSMultipleLinearRegressionModelTest.java
+++ /dev/null
@@ -1,53 +0,0 @@
-/*
- * 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.ignite.ml.regressions;
-
-import org.apache.ignite.ml.TestUtils;
-import org.apache.ignite.ml.math.Vector;
-import org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix;
-import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
-import org.junit.Test;
-
-/**
- * Tests for {@link OLSMultipleLinearRegressionModel}.
- */
-public class OLSMultipleLinearRegressionModelTest {
- /** */
- @Test
- public void testPerfectFit() {
- Vector val = new DenseLocalOnHeapVector(new double[] {11.0, 12.0, 13.0, 14.0, 15.0, 16.0});
-
- double[] data = new double[] {
- 0, 0, 0, 0, 0, 0, // IMPL NOTE values in this row are later replaced (with 1.0)
- 0, 2.0, 0, 0, 0, 0,
- 0, 0, 3.0, 0, 0, 0,
- 0, 0, 0, 4.0, 0, 0,
- 0, 0, 0, 0, 5.0, 0,
- 0, 0, 0, 0, 0, 6.0};
-
- final int nobs = 6, nvars = 5;
-
- OLSMultipleLinearRegressionTrainer trainer
- = new OLSMultipleLinearRegressionTrainer(0, nobs, nvars, new DenseLocalOnHeapMatrix(1, 1));
-
- OLSMultipleLinearRegressionModel mdl = trainer.train(data);
-
- TestUtils.assertEquals(new double[] {0d, 0d, 0d, 0d, 0d, 0d},
- val.minus(mdl.apply(val)).getStorage().data(), 1e-13);
- }
-}