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/*
* Copyright (c) 2009-2017, Peter Abeles. All Rights Reserved.
*
* This file is part of Efficient Java Matrix Library (EJML).
*
* Licensed 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.ejml.example;
import org.ejml.UtilEjml;
import org.ejml.dense.row.RandomMatrices_DDRM;
import org.junit.Test;
import java.util.Random;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;
/**
* @author Peter Abeles
*/
public class TestPrincipleComponentAnalysis {
Random rand = new Random(234345);
/**
* Sees if the projection error increases as the DOF decreases in the number of basis vectors.
*/
@Test
public void checkBasisError() {
int M = 30;
int N = 5;
double obs[][] = new double[M][];
PrincipalComponentAnalysis pca = new PrincipalComponentAnalysis();
// add observations
pca.setup(M,N);
for( int i = 0; i < M; i++ ) {
obs[i] = RandomMatrices_DDRM.rectangle(N,1,-1,1,rand).data;
pca.addSample(obs[i]);
}
// as a more crude estimate is made of the input data the error should increase
pca.computeBasis(N);
double errorPrev = computeError(pca,obs);
assertEquals(errorPrev,0, UtilEjml.TEST_F64);
for( int i = N-1; i >= 1; i-- ) {
pca.computeBasis(i);
double error = computeError(pca,obs);
assertTrue(error > errorPrev );
errorPrev = error;
}
}
private double computeError(PrincipalComponentAnalysis pca, double[][] obs ) {
double error = 0;
for (double[] o : obs) {
error += pca.errorMembership(o);
}
return error;
}
/**
* Checks sampleToEigenSpace and sampleToEigenSpace when the basis vectors can
* fully describe the vector.
*/
@Test
public void sampleToEigenSpace() {
int M = 30;
int N = 5;
double obs[][] = new double[M][];
PrincipalComponentAnalysis pca = new PrincipalComponentAnalysis();
// add observations
pca.setup(M,N);
for( int i = 0; i < M; i++ ) {
obs[i] = RandomMatrices_DDRM.rectangle(N,1,-1,1,rand).data;
pca.addSample(obs[i]);
}
// when the basis is N vectors it should perfectly describe the vector
pca.computeBasis(N);
for( int i = 0; i < M; i++ ) {
double s[] = pca.sampleToEigenSpace(obs[i]);
assertTrue(error(s,obs[i]) > 1e-8 );
double o[] = pca.eigenToSampleSpace(s);
assertTrue(error(o,obs[i]) <= 1e-8 );
}
}
private double error( double[] a , double []b ) {
double ret = 0;
for( int i = 0; i < a.length; i++ ) {
ret += Math.abs(a[i]-b[i]);
}
return ret;
}
/**
* Makes sure the response is not zero. Perhaps this is too simple of a test
*/
@Test
public void response() {
int M = 30;
int N = 5;
double obs[][] = new double[M][];
PrincipalComponentAnalysis pca = new PrincipalComponentAnalysis();
// add observations
pca.setup(M,N);
for( int i = 0; i < M; i++ ) {
obs[i] = RandomMatrices_DDRM.rectangle(N,1,-1,1,rand).data;
pca.addSample(obs[i]);
}
pca.computeBasis(N-2);
for( int i = 0; i < M; i++ ) {
double responseObs = pca.response(obs[i]);
assertTrue(responseObs > 0 );
}
}
}
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