/*
* Copyright (c) 2009-2013, 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 mikera.matrixx.solve.chol;
import mikera.matrixx.Matrix;
import mikera.matrixx.solve.impl.CholeskyLDUSolver;
import org.junit.Test;
import java.util.Random;
import static org.junit.Assert.assertArrayEquals;
import static org.junit.Assert.assertTrue;
/**
* @author Peter Abeles
*/
public class TestCholLDUSolver {
Random rand = new Random(3466);
@Test
public void testInverseAndSolve() {
double[][] dataA = {{1,2,4},
{2,13,23},
{4,23,90}};
Matrix A = Matrix.create(dataA);
double[][] dataB = {{17},{97},{320}};
Matrix b = Matrix.create(dataB);
// Matrix x = Matrix.createRandom(3, 1);
Matrix x;
CholeskyLDUSolver solver = new CholeskyLDUSolver();
assertTrue(solver.setA(A));
Matrix A_inv_result = solver.invert().toMatrix();
x = solver.solve(b).toMatrix();
double[][] data_A_inv_expected = {{1.453515, -0.199546, -0.013605},
{-0.199546, 0.167800, -0.034014},
{-0.013605, -0.034014, 0.020408}};
Matrix A_inv_expected = Matrix.create(data_A_inv_expected);
double[][] data_x_expected = {{1},{2},{3}};
Matrix x_expected = Matrix.create(data_x_expected);
assertArrayEquals(A_inv_expected.getElements(),A_inv_result.getElements(),1e-5);
assertArrayEquals(x_expected.getElements(),x.getElements(),1e-5);
}
}