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* 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,
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* See the License for the specific language governing permissions and
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package org.apache.commons.math4.stat.correlation;
import org.apache.commons.math4.TestUtils;
import org.apache.commons.math4.exception.MathIllegalArgumentException;
import org.apache.commons.math4.exception.NotStrictlyPositiveException;
import org.apache.commons.math4.linear.Array2DRowRealMatrix;
import org.apache.commons.math4.linear.RealMatrix;
import org.apache.commons.math4.stat.correlation.Covariance;
import org.apache.commons.math4.stat.descriptive.moment.Variance;
import org.junit.Assert;
import org.junit.Test;
public class CovarianceTest {
protected final double[] longleyData = 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
};
protected final double[] swissData = 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
};
/**
* Test Longley dataset against R.
* 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.
*
* Data are from NIST:
* http://www.itl.nist.gov/div898/strd/lls/data/LINKS/DATA/Longley.dat
*/
@Test
public void testLongly() {
RealMatrix matrix = createRealMatrix(longleyData, 16, 7);
RealMatrix covarianceMatrix = new Covariance(matrix).getCovarianceMatrix();
double[] rData = new double[] {
12333921.73333333246, 3.679666000000000e+04, 343330206.333333313,
1649102.666666666744, 1117681.066666666651, 23461965.733333334, 16240.93333333333248,
36796.66000000000, 1.164576250000000e+02, 1063604.115416667,
6258.666250000000, 3490.253750000000, 73503.000000000, 50.92333333333334,
343330206.33333331347, 1.063604115416667e+06, 9879353659.329166412,
56124369.854166664183, 30880428.345833335072, 685240944.600000024, 470977.90000000002328,
1649102.66666666674, 6.258666250000000e+03, 56124369.854166664,
873223.429166666698, -115378.762499999997, 4462741.533333333, 2973.03333333333330,
1117681.06666666665, 3.490253750000000e+03, 30880428.345833335,
-115378.762499999997, 484304.095833333326, 1764098.133333333, 1382.43333333333339,
23461965.73333333433, 7.350300000000000e+04, 685240944.600000024,
4462741.533333333209, 1764098.133333333302, 48387348.933333330, 32917.40000000000146,
16240.93333333333, 5.092333333333334e+01, 470977.900000000,
2973.033333333333, 1382.433333333333, 32917.40000000, 22.66666666666667
};
TestUtils.assertEquals("covariance matrix", createRealMatrix(rData, 7, 7), covarianceMatrix, 10E-9);
}
/**
* Test R Swiss fertility dataset against R.
* Data Source: R datasets package
*/
@Test
public void testSwissFertility() {
RealMatrix matrix = createRealMatrix(swissData, 47, 5);
RealMatrix covarianceMatrix = new Covariance(matrix).getCovarianceMatrix();
double[] rData = new double[] {
156.0424976873265, 100.1691489361702, -64.36692876965772, -79.7295097132285, 241.5632030527289,
100.169148936170251, 515.7994172062905, -124.39283071230344, -139.6574005550416, 379.9043755781684,
-64.3669287696577, -124.3928307123034, 63.64662349676226, 53.5758556891767, -190.5606105457909,
-79.7295097132285, -139.6574005550416, 53.57585568917669, 92.4560592044403, -61.6988297872340,
241.5632030527289, 379.9043755781684, -190.56061054579092, -61.6988297872340, 1739.2945371877890
};
TestUtils.assertEquals("covariance matrix", createRealMatrix(rData, 5, 5), covarianceMatrix, 10E-13);
}
/**
* Constant column
*/
@Test
public void testConstant() {
double[] noVariance = new double[] {1, 1, 1, 1};
double[] values = new double[] {1, 2, 3, 4};
Assert.assertEquals(0d, new Covariance().covariance(noVariance, values, true), Double.MIN_VALUE);
Assert.assertEquals(0d, new Covariance().covariance(noVariance, noVariance, true), Double.MIN_VALUE);
}
/**
* One column
*/
@Test
public void testOneColumn() {
RealMatrix cov = new Covariance(new double[][] {{1}, {2}}, false).getCovarianceMatrix();
Assert.assertEquals(1, cov.getRowDimension());
Assert.assertEquals(1, cov.getColumnDimension());
Assert.assertEquals(0.25, cov.getEntry(0, 0), 1.0e-15);
}
/**
* Insufficient data
*/
@Test
public void testInsufficientData() {
double[] one = new double[] {1};
double[] two = new double[] {2};
try {
new Covariance().covariance(one, two, false);
Assert.fail("Expecting MathIllegalArgumentException");
} catch (MathIllegalArgumentException ex) {
// Expected
}
try {
new Covariance(new double[][] {{},{}});
Assert.fail("Expecting NotStrictlyPositiveException");
} catch (NotStrictlyPositiveException ex) {
// Expected
}
}
/**
* Verify that diagonal entries are consistent with Variance computation and matrix matches
* column-by-column covariances
*/
@Test
public void testConsistency() {
final RealMatrix matrix = createRealMatrix(swissData, 47, 5);
final RealMatrix covarianceMatrix = new Covariance(matrix).getCovarianceMatrix();
// Variances on the diagonal
Variance variance = new Variance();
for (int i = 0; i < 5; i++) {
Assert.assertEquals(variance.evaluate(matrix.getColumn(i)), covarianceMatrix.getEntry(i,i), 10E-14);
}
// Symmetry, column-consistency
Assert.assertEquals(covarianceMatrix.getEntry(2, 3),
new Covariance().covariance(matrix.getColumn(2), matrix.getColumn(3), true), 10E-14);
Assert.assertEquals(covarianceMatrix.getEntry(2, 3), covarianceMatrix.getEntry(3, 2), Double.MIN_VALUE);
// All columns same -> all entries = column variance
RealMatrix repeatedColumns = new Array2DRowRealMatrix(47, 3);
for (int i = 0; i < 3; i++) {
repeatedColumns.setColumnMatrix(i, matrix.getColumnMatrix(0));
}
RealMatrix repeatedCovarianceMatrix = new Covariance(repeatedColumns).getCovarianceMatrix();
double columnVariance = variance.evaluate(matrix.getColumn(0));
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 3; j++) {
Assert.assertEquals(columnVariance, repeatedCovarianceMatrix.getEntry(i, j), 10E-14);
}
}
// Check bias-correction defaults
double[][] data = matrix.getData();
TestUtils.assertEquals("Covariances",
covarianceMatrix, new Covariance().computeCovarianceMatrix(data),Double.MIN_VALUE);
TestUtils.assertEquals("Covariances",
covarianceMatrix, new Covariance().computeCovarianceMatrix(data, true),Double.MIN_VALUE);
double[] x = data[0];
double[] y = data[1];
Assert.assertEquals(new Covariance().covariance(x, y),
new Covariance().covariance(x, y, true), Double.MIN_VALUE);
}
protected RealMatrix createRealMatrix(double[] data, int nRows, int nCols) {
double[][] matrixData = new double[nRows][nCols];
int ptr = 0;
for (int i = 0; i < nRows; i++) {
System.arraycopy(data, ptr, matrixData[i], 0, nCols);
ptr += nCols;
}
return new Array2DRowRealMatrix(matrixData);
}
}