本文整理汇总了Java中org.apache.commons.math.linear.EigenDecompositionImpl类的典型用法代码示例。如果您正苦于以下问题：Java EigenDecompositionImpl类的具体用法？Java EigenDecompositionImpl怎么用？Java EigenDecompositionImpl使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
EigenDecompositionImpl类属于org.apache.commons.math.linear包，在下文中一共展示了EigenDecompositionImpl类的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: getPower
import org.apache.commons.math.linear.EigenDecompositionImpl; //导入依赖的package包/类
/**
* Returns a real matrix raised to some real power
* Currently this method is limited to symmetric matrices only as Commons Math does not support the diagonalization of asymmetric matrices
* @param m The <strong>symmetric</strong> matrix to take the power of.
* @param p The power to raise to matrix to
* @return The result
*/
@Override
public DoubleMatrix2D getPower(final Matrix<?> m, final double p) {
if (m instanceof DoubleMatrix2D) {
final RealMatrix temp = CommonsMathWrapper.wrap((DoubleMatrix2D) m);
final EigenDecomposition eigen = new EigenDecompositionImpl(temp, 0.0);
final double[] rEigenValues = eigen.getRealEigenvalues();
final double[] iEigenValues = eigen.getImagEigenvalues();
final int n = rEigenValues.length;
final double[][] d = new double[n][n];
for (int i = n - 1; i >= 0; --i) {
d[i][i] = Math.pow(rEigenValues[i], p);
if (iEigenValues[i] != 0.0) {
throw new NotImplementedException("Cannot handle complex eigenvalues in getPower");
}
}
final RealMatrix res = eigen.getV().multiply((new Array2DRowRealMatrix(d)).multiply(eigen.getVT()));
return CommonsMathWrapper.unwrap(res);
}
throw new IllegalArgumentException("Can only find pow of DoubleMatrix2D; have " + m.getClass());
}
示例2: testTridiagonal
import org.apache.commons.math.linear.EigenDecompositionImpl; //导入依赖的package包/类
/** test a matrix already in tridiagonal form. */
public void testTridiagonal() {
Random r = new Random(4366663527842l);
double[] ref = new double[30];
for (int i = 0; i < ref.length; ++i) {
if (i < 5) {
ref[i] = 2 * r.nextDouble() - 1;
} else {
ref[i] = 0.0001 * r.nextDouble() + 6;
}
}
Arrays.sort(ref);
TriDiagonalTransformer t =
new TriDiagonalTransformer(createTestMatrix(r, ref));
EigenDecomposition ed =
new EigenDecompositionImpl(t.getMainDiagonalRef(),
t.getSecondaryDiagonalRef(),
MathUtils.SAFE_MIN);
double[] eigenValues = ed.getRealEigenvalues();
assertEquals(ref.length, eigenValues.length);
for (int i = 0; i < ref.length; ++i) {
assertEquals(ref[ref.length - i - 1], eigenValues[i], 2.0e-14);
}
}
示例3: testBigMatrix
import org.apache.commons.math.linear.EigenDecompositionImpl; //导入依赖的package包/类
/** test eigenvalues for a big matrix. */
public void testBigMatrix() {
Random r = new Random(17748333525117l);
double[] bigValues = new double[200];
for (int i = 0; i < bigValues.length; ++i) {
bigValues[i] = 2 * r.nextDouble() - 1;
}
Arrays.sort(bigValues);
EigenDecomposition ed =
new EigenDecompositionImpl(createTestMatrix(r, bigValues), MathUtils.SAFE_MIN);
double[] eigenValues = ed.getRealEigenvalues();
assertEquals(bigValues.length, eigenValues.length);
for (int i = 0; i < bigValues.length; ++i) {
assertEquals(bigValues[bigValues.length - i - 1], eigenValues[i], 2.0e-14);
}
}
示例4: testZeroDivide
import org.apache.commons.math.linear.EigenDecompositionImpl; //导入依赖的package包/类
/**
* Verifies operation on indefinite matrix
*/
public void testZeroDivide() {
RealMatrix indefinite = MatrixUtils.createRealMatrix(new double [][] {
{ 0.0, 1.0, -1.0 },
{ 1.0, 1.0, 0.0 },
{ -1.0,0.0, 1.0 }
});
EigenDecomposition ed = new EigenDecompositionImpl(indefinite, MathUtils.SAFE_MIN);
checkEigenValues((new double[] {2, 1, -1}), ed, 1E-12);
double isqrt3 = 1/Math.sqrt(3.0);
checkEigenVector((new double[] {isqrt3,isqrt3,-isqrt3}), ed, 1E-12);
double isqrt2 = 1/Math.sqrt(2.0);
checkEigenVector((new double[] {0.0,-isqrt2,-isqrt2}), ed, 1E-12);
double isqrt6 = 1/Math.sqrt(6.0);
checkEigenVector((new double[] {2*isqrt6,-isqrt6,isqrt6}), ed, 1E-12);
}
示例5: testNonInvertible
import org.apache.commons.math.linear.EigenDecompositionImpl; //导入依赖的package包/类
/** test non invertible matrix */
public void testNonInvertible() {
Random r = new Random(9994100315209l);
RealMatrix m =
EigenDecompositionImplTest.createTestMatrix(r, new double[] { 1.0, 0.0, -1.0, -2.0, -3.0 });
DecompositionSolver es = new EigenDecompositionImpl(m, MathUtils.SAFE_MIN).getSolver();
assertFalse(es.isNonSingular());
try {
es.getInverse();
fail("an exception should have been thrown");
} catch (InvalidMatrixException ime) {
// expected behavior
} catch (Exception e) {
fail("wrong exception caught");
}
}
示例6: testTridiagonal
import org.apache.commons.math.linear.EigenDecompositionImpl; //导入依赖的package包/类
/** test a matrix already in tridiagonal form. */
public void testTridiagonal() {
Random r = new Random(4366663527842l);
double[] ref = new double[30];
for (int i = 0; i < ref.length; ++i) {
if (i < 5) {
ref[i] = 2 * r.nextDouble() - 1;
} else {
ref[i] = 0.0001 * r.nextDouble() + 6;
}
}
Arrays.sort(ref);
TriDiagonalTransformer t =
new TriDiagonalTransformer(createTestMatrix(r, ref));
EigenDecomposition ed =
new EigenDecompositionImpl(t.getMainDiagonalRef(),
t.getSecondaryDiagonalRef(),
MathUtils.SAFE_MIN);
double[] eigenValues = ed.getRealEigenvalues();
assertEquals(ref.length, eigenValues.length);
for (int i = 0; i < ref.length; ++i) {
assertEquals(ref[ref.length - i - 1], eigenValues[i], 2.0e-14);
}
}
示例7: testDimension2
import org.apache.commons.math.linear.EigenDecompositionImpl; //导入依赖的package包/类
public void testDimension2() {
RealMatrix matrix =
MatrixUtils.createRealMatrix(new double[][] {
{ 59.0, 12.0 },
{ 12.0, 66.0 }
});
EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
assertEquals(75.0, ed.getRealEigenvalue(0), 1.0e-15);
assertEquals(50.0, ed.getRealEigenvalue(1), 1.0e-15);
}
示例8: testDimension3
import org.apache.commons.math.linear.EigenDecompositionImpl; //导入依赖的package包/类
public void testDimension3() {
RealMatrix matrix =
MatrixUtils.createRealMatrix(new double[][] {
{ 39632.0, -4824.0, -16560.0 },
{ -4824.0, 8693.0, 7920.0 },
{ -16560.0, 7920.0, 17300.0 }
});
EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
assertEquals(50000.0, ed.getRealEigenvalue(0), 3.0e-11);
assertEquals(12500.0, ed.getRealEigenvalue(1), 3.0e-11);
assertEquals( 3125.0, ed.getRealEigenvalue(2), 3.0e-11);
}
示例9: testDimension4WithSplit
import org.apache.commons.math.linear.EigenDecompositionImpl; //导入依赖的package包/类
public void testDimension4WithSplit() {
RealMatrix matrix =
MatrixUtils.createRealMatrix(new double[][] {
{ 0.784, -0.288, 0.000, 0.000 },
{ -0.288, 0.616, 0.000, 0.000 },
{ 0.000, 0.000, 0.164, -0.048 },
{ 0.000, 0.000, -0.048, 0.136 }
});
EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
assertEquals(1.0, ed.getRealEigenvalue(0), 1.0e-15);
assertEquals(0.4, ed.getRealEigenvalue(1), 1.0e-15);
assertEquals(0.2, ed.getRealEigenvalue(2), 1.0e-15);
assertEquals(0.1, ed.getRealEigenvalue(3), 1.0e-15);
}
示例10: testDimension4WithoutSplit
import org.apache.commons.math.linear.EigenDecompositionImpl; //导入依赖的package包/类
public void testDimension4WithoutSplit() {
RealMatrix matrix =
MatrixUtils.createRealMatrix(new double[][] {
{ 0.5608, -0.2016, 0.1152, -0.2976 },
{ -0.2016, 0.4432, -0.2304, 0.1152 },
{ 0.1152, -0.2304, 0.3088, -0.1344 },
{ -0.2976, 0.1152, -0.1344, 0.3872 }
});
EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
assertEquals(1.0, ed.getRealEigenvalue(0), 1.0e-15);
assertEquals(0.4, ed.getRealEigenvalue(1), 1.0e-15);
assertEquals(0.2, ed.getRealEigenvalue(2), 1.0e-15);
assertEquals(0.1, ed.getRealEigenvalue(3), 1.0e-15);
}
示例11: testDimensions
import org.apache.commons.math.linear.EigenDecompositionImpl; //导入依赖的package包/类
/** test dimensions */
public void testDimensions() {
final int m = matrix.getRowDimension();
EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
assertEquals(m, ed.getV().getRowDimension());
assertEquals(m, ed.getV().getColumnDimension());
assertEquals(m, ed.getD().getColumnDimension());
assertEquals(m, ed.getD().getColumnDimension());
assertEquals(m, ed.getVT().getRowDimension());
assertEquals(m, ed.getVT().getColumnDimension());
}
示例12: testEigenvalues
import org.apache.commons.math.linear.EigenDecompositionImpl; //导入依赖的package包/类
/** test eigenvalues */
public void testEigenvalues() {
EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
double[] eigenValues = ed.getRealEigenvalues();
assertEquals(refValues.length, eigenValues.length);
for (int i = 0; i < refValues.length; ++i) {
assertEquals(refValues[i], eigenValues[i], 3.0e-15);
}
}
示例13: testEigenvectors
import org.apache.commons.math.linear.EigenDecompositionImpl; //导入依赖的package包/类
/** test eigenvectors */
public void testEigenvectors() {
EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
for (int i = 0; i < matrix.getRowDimension(); ++i) {
double lambda = ed.getRealEigenvalue(i);
RealVector v = ed.getEigenvector(i);
RealVector mV = matrix.operate(v);
assertEquals(0, mV.subtract(v.mapMultiplyToSelf(lambda)).getNorm(), 1.0e-13);
}
}
示例14: testAEqualVDVt
import org.apache.commons.math.linear.EigenDecompositionImpl; //导入依赖的package包/类
/** test A = VDVt */
public void testAEqualVDVt() {
EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
RealMatrix v = ed.getV();
RealMatrix d = ed.getD();
RealMatrix vT = ed.getVT();
double norm = v.multiply(d).multiply(vT).subtract(matrix).getNorm();
assertEquals(0, norm, 6.0e-13);
}
示例15: testVOrthogonal
import org.apache.commons.math.linear.EigenDecompositionImpl; //导入依赖的package包/类
/** test that V is orthogonal */
public void testVOrthogonal() {
RealMatrix v = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN).getV();
RealMatrix vTv = v.transpose().multiply(v);
RealMatrix id = MatrixUtils.createRealIdentityMatrix(vTv.getRowDimension());
assertEquals(0, vTv.subtract(id).getNorm(), 2.0e-13);
}