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The following commit(s) were added to refs/heads/master by this push:
new bab00341a Allow fitting single component data
bab00341a is described below
commit bab00341a9c4072015dde0d94d301bf60cd16f3b
Author: Alex Herbert <aherb...@apache.org>
AuthorDate: Mon Mar 11 21:55:54 2024 +0000
Allow fitting single component data
---
...ariateNormalMixtureExpectationMaximization.java | 6 +-
...teNormalMixtureExpectationMaximizationTest.java | 139 ++++++++++++---------
2 files changed, 84 insertions(+), 61 deletions(-)
diff --git
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/fitting/MultivariateNormalMixtureExpectationMaximization.java
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/fitting/MultivariateNormalMixtureExpectationMaximization.java
index 8b51195ab..a3c7397d2 100644
---
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/fitting/MultivariateNormalMixtureExpectationMaximization.java
+++
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/fitting/MultivariateNormalMixtureExpectationMaximization.java
@@ -294,7 +294,7 @@ public class
MultivariateNormalMixtureExpectationMaximization {
* @return Multivariate normal mixture model estimated from the data
* @throws NumberIsTooLargeException if {@code numComponents} is greater
* than the number of data rows.
- * @throws NumberIsTooSmallException if {@code numComponents < 2}.
+ * @throws NumberIsTooSmallException if {@code numComponents < 1}.
* @throws NotStrictlyPositiveException if data has less than 2 rows
* @throws DimensionMismatchException if rows of data have different
numbers
* of columns
@@ -306,8 +306,8 @@ public class
MultivariateNormalMixtureExpectationMaximization {
if (data.length < 2) {
throw new NotStrictlyPositiveException(data.length);
}
- if (numComponents < 2) {
- throw new NumberIsTooSmallException(numComponents, 2, true);
+ if (numComponents < 1) {
+ throw new NumberIsTooSmallException(numComponents, 1, true);
}
if (numComponents > data.length) {
throw new NumberIsTooLargeException(numComponents, data.length,
true);
diff --git
a/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/distribution/fitting/MultivariateNormalMixtureExpectationMaximizationTest.java
b/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/distribution/fitting/MultivariateNormalMixtureExpectationMaximizationTest.java
index 801cff467..281036456 100644
---
a/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/distribution/fitting/MultivariateNormalMixtureExpectationMaximizationTest.java
+++
b/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/distribution/fitting/MultivariateNormalMixtureExpectationMaximizationTest.java
@@ -191,104 +191,127 @@ public class
MultivariateNormalMixtureExpectationMaximizationTest {
}
@Test
- public void testFit() {
- // Test that the loglikelihood, weights, and models are determined and
- // fitted correctly
+ public void testFit2Dimensions2Components() {
final double[][] data = getTestSamples();
- final double correctLogLikelihood = -4.292431006791994;
- final double[] correctWeights = new double[] { 0.2962324189652912,
0.7037675810347089 };
- final double[][] correctMeans = new double[][]{
- {-1.4213112715121132, 1.6924690505757753},
- {4.213612224374709, 7.975621325853645}
- };
+ // Fit using the test samples using Matlab R2023b (Update 6):
+ // GMModel = fitgmdist(X,2);
- final RealMatrix[] correctCovMats = new Array2DRowRealMatrix[2];
- correctCovMats[0] = new Array2DRowRealMatrix(new double[][] {
- { 1.739356907285747, -0.5867644251487614 },
- { -0.5867644251487614, 1.0232932029324642 } }
- );
- correctCovMats[1] = new Array2DRowRealMatrix(new double[][] {
- { 4.245384898007161, 2.5797798966382155 },
- { 2.5797798966382155, 3.9200272522448367 } });
+ // Expected results use the component order generated by the CM code
for convenience
+ // i.e. ComponentProportion from matlab is reversed: [0.703722,
0.296278]
- final MultivariateNormalDistribution[] correctMVNs = new
MultivariateNormalDistribution[2];
- correctMVNs[0] = new MultivariateNormalDistribution(correctMeans[0],
correctCovMats[0].getData());
- correctMVNs[1] = new MultivariateNormalDistribution(correctMeans[1],
correctCovMats[1].getData());
+ // NegativeLogLikelihood (CM code use the positive log-likehood
divided by the number of observations)
+ final double logLikelihood = -4.292430883324220e+02 / data.length;
+ // ComponentProportion
+ final double[] weights = new double[] {0.2962324189652912,
0.7037675810347089};
+ // mu
+ final double[][] means = new double[][]{
+ {-1.421239458366293, 1.692604555824222},
+ {4.213949861591596, 7.975974466776790}
+ };
+ // Sigma
+ final double[][][] covar = new double[][][] {
+ {{1.739441346307267, -0.586740858187563},
+ {-0.586740858187563, 1.023420964341543}},
+ {{4.243780645051973, 2.578176622652551},
+ {2.578176622652551, 3.918302056479298}}
+ };
- MultivariateNormalMixtureExpectationMaximization fitter
- = new MultivariateNormalMixtureExpectationMaximization(data);
+ assertFit(data, 2, logLikelihood, weights, means, covar, 1e-3);
+ }
- MixtureMultivariateNormalDistribution initialMix
- = MultivariateNormalMixtureExpectationMaximization.estimate(data,
2);
- fitter.fit(initialMix);
- MixtureMultivariateNormalDistribution fittedMix =
fitter.getFittedModel();
- List<Pair<Double, MultivariateNormalDistribution>> components =
fittedMix.getComponents();
+ @Test
+ public void testFit1Dimension2Components() {
+ // Use only the first column of the test data
+ final double[][] data = Arrays.stream(getTestSamples())
+ .map(x -> new double[] {x[0]}).toArray(double[][]::new);
+
+ // Fit the first column of test samples using Matlab R2023b (Update 6):
+ // GMModel = fitgmdist(X,2);
- Assert.assertEquals(correctLogLikelihood,
- fitter.getLogLikelihood(),
- Math.ulp(1d));
+ // NegativeLogLikelihood (CM code use the positive log-likehood
divided by the number of observations)
+ final double logLikelihood = -2.512197016873482e+02 / data.length;
+ // ComponentProportion
+ final double[] weights = new double[] {0.240510201974078,
0.759489798025922};
+ // Since data has 1 dimension the means and covariances are single
values
+ // mu
+ final double[][] means = new double[][]{
+ {-1.736139126623031},
+ {3.899886984922886}
+ };
+ // Sigma
+ final double[][][] covar = new double[][][] {
+ {{1.371327786710623}},
+ {{5.254286022455004}}
+ };
- int i = 0;
- for (Pair<Double, MultivariateNormalDistribution> component :
components) {
- final double weight = component.getFirst();
- final MultivariateNormalDistribution mvn = component.getSecond();
- final double[] mean = mvn.getMeans();
- final RealMatrix covMat = mvn.getCovariances();
- Assert.assertEquals(correctWeights[i], weight, Math.ulp(1d));
- Assert.assertArrayEquals(correctMeans[i], mean, 0.0);
- Assert.assertEquals(correctCovMats[i], covMat);
- i++;
- }
+ assertFit(data, 2, logLikelihood, weights, means, covar, 0.05);
}
@Test
- public void testFit1() {
- // Test that the fit can be performed on data with a single dimension
+ public void testFit1Dimension1Component() {
// Use only the first column of the test data
final double[][] data = Arrays.stream(getTestSamples())
.map(x -> new double[] {x[0]}).toArray(double[][]::new);
// Fit the first column of test samples using Matlab R2023b (Update 6):
- // GMModel = fitgmdist(X,2);
+ // GMModel = fitgmdist(X,1);
// NegativeLogLikelihood (CM code use the positive log-likehood
divided by the number of observations)
- final double correctLogLikelihood = -2.512197016873482e+02 /
data.length;
+ final double logLikelihood = -2.576329329354790e+02 / data.length;
// ComponentProportion
- final double[] correctWeights = new double[] {0.240510201974078,
0.759489798025922};
+ final double[] weights = new double[] {1.0};
// Since data has 1 dimension the means and covariances are single
values
// mu
- final double[] correctMeans = new double[] {-1.736139126623031,
3.899886984922886};
+ final double[][] means = new double[][]{
+ {2.544365206503801},
+ };
// Sigma
- final double[] correctCov = new double[] {1.371327786710623,
5.254286022455004};
+ final double[][][] covar = new double[][][] {
+ {{10.122711799089901}},
+ };
+ assertFit(data, 1, logLikelihood, weights, means, covar, 1e-3);
+ }
+
+ private static void assertFit(double[][] data, int numComponents,
+ double logLikelihood, double[] weights,
+ double[][] means, double[][][] covar, double relError) {
MultivariateNormalMixtureExpectationMaximization fitter
= new MultivariateNormalMixtureExpectationMaximization(data);
MixtureMultivariateNormalDistribution initialMix
- = MultivariateNormalMixtureExpectationMaximization.estimate(data,
2);
+ = MultivariateNormalMixtureExpectationMaximization.estimate(data,
numComponents);
fitter.fit(initialMix);
MixtureMultivariateNormalDistribution fittedMix =
fitter.getFittedModel();
List<Pair<Double, MultivariateNormalDistribution>> components =
fittedMix.getComponents();
- final double relError = 0.05;
- Assert.assertEquals(correctLogLikelihood,
- fitter.getLogLikelihood(),
- Math.abs(correctLogLikelihood) * relError);
+ Assert.assertEquals(logLikelihood,
+ fitter.getLogLikelihood(),
+ Math.abs(logLikelihood) * relError);
int i = 0;
for (Pair<Double, MultivariateNormalDistribution> component :
components) {
final double weight = component.getFirst();
final MultivariateNormalDistribution mvn = component.getSecond();
- final double[] mean = mvn.getMeans();
- final RealMatrix covMat = mvn.getCovariances();
- Assert.assertEquals(correctWeights[i], weight, correctWeights[i] *
relError);
- Assert.assertEquals(correctMeans[i], mean[0],
Math.abs(correctMeans[i]) * relError);
- Assert.assertEquals(correctCov[i], covMat.getEntry(0, 0),
correctCov[i] * relError);
+ Assert.assertEquals(weights[i], weight, weights[i] * relError);
+ assertArrayEquals(means[i], mvn.getMeans(), relError);
+ final double[][] c = mvn.getCovariances().getData();
+ Assert.assertEquals(covar[i].length, c.length);
+ for (int j = 0; j < covar[i].length; j++) {
+ assertArrayEquals(covar[i][j], c[j], relError);
+ }
i++;
}
}
+ private static void assertArrayEquals(double[] e, double[] a, double
relError) {
+ Assert.assertEquals("length", e.length, a.length);
+ for (int i = 0; i < e.length; i++) {
+ Assert.assertEquals(e[i], a[i], Math.abs(e[i]) * relError);
+ }
+ }
+
private double[][] getTestSamples() {
// generated using R Mixtools rmvnorm with mean vectors [-1.5, 2] and
// [4, 8.2]
