本文整理汇总了Java中org.apache.commons.math.util.MathUtils.SAFE_MIN属性的典型用法代码示例。如果您正苦于以下问题：Java MathUtils.SAFE_MIN属性的具体用法？Java MathUtils.SAFE_MIN怎么用？Java MathUtils.SAFE_MIN使用的例子？那么, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类org.apache.commons.math.util.MathUtils的用法示例。
在下文中一共展示了MathUtils.SAFE_MIN属性的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: testBigMatrix
/** 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2: testTridiagonal
/** 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: testZeroDivide
/**
* 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4: testDistinctEigenvalues
/**
* Matrix with eigenvalues {2, 0, 12}
*/
public void testDistinctEigenvalues() {
RealMatrix distinct = MatrixUtils.createRealMatrix(new double[][] {
{3, 1, -4},
{1, 3, -4},
{-4, -4, 8}
});
EigenDecomposition ed = new EigenDecompositionImpl(distinct, MathUtils.SAFE_MIN);
checkEigenValues((new double[] {2, 0, 12}), ed, 1E-12);
checkEigenVector((new double[] {1, -1, 0}), ed, 1E-12);
checkEigenVector((new double[] {1, 1, 1}), ed, 1E-12);
checkEigenVector((new double[] {-1, -1, 2}), ed, 1E-12);
}
示例5: testEigenvectors
/** test eigenvectors */
@Test
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);
Assert.assertEquals(0, mV.subtract(v.mapMultiplyToSelf(lambda)).getNorm(), 1.0e-13);
}
}
示例6: testDiagonal
/** test diagonal matrix */
public void testDiagonal() {
double[] diagonal = new double[] { -3.0, -2.0, 2.0, 5.0 };
RealMatrix m = createDiagonalMatrix(diagonal, diagonal.length, diagonal.length);
EigenDecomposition ed = new EigenDecompositionImpl(m, MathUtils.SAFE_MIN);
assertEquals(diagonal[0], ed.getRealEigenvalue(3), 2.0e-15);
assertEquals(diagonal[1], ed.getRealEigenvalue(2), 2.0e-15);
assertEquals(diagonal[2], ed.getRealEigenvalue(1), 2.0e-15);
assertEquals(diagonal[3], ed.getRealEigenvalue(0), 2.0e-15);
}
示例7: testDimension4WithSplit
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8: testDimension3
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: testDimensions
/** 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10: testMath308
public void testMath308() {
double[] mainTridiagonal = {
22.330154644539597, 46.65485522478641, 17.393672330044705, 54.46687435351116, 80.17800767709437
};
double[] secondaryTridiagonal = {
13.04450406501361, -5.977590941539671, 2.9040909856707517, 7.1570352792841225
};
// the reference values have been computed using routine DSTEMR
// from the fortran library LAPACK version 3.2.1
double[] refEigenValues = {
82.044413207204002, 53.456697699894512, 52.536278520113882, 18.847969733754262, 14.138204224043099
};
RealVector[] refEigenVectors = {
new ArrayRealVector(new double[] { -0.000462690386766, -0.002118073109055, 0.011530080757413, 0.252322434584915, 0.967572088232592 }),
new ArrayRealVector(new double[] { 0.314647769490148, 0.750806415553905, -0.167700312025760, -0.537092972407375, 0.143854968127780 }),
new ArrayRealVector(new double[] { 0.222368839324646, 0.514921891363332, -0.021377019336614, 0.801196801016305, -0.207446991247740 }),
new ArrayRealVector(new double[] { -0.713933751051495, 0.190582113553930, -0.671410443368332, 0.056056055955050, -0.006541576993581 }),
new ArrayRealVector(new double[] { -0.584677060845929, 0.367177264979103, 0.721453187784497, -0.052971054621812, 0.005740715188257 })
};
EigenDecomposition decomposition =
new EigenDecompositionImpl(mainTridiagonal, secondaryTridiagonal, MathUtils.SAFE_MIN);
double[] eigenValues = decomposition.getRealEigenvalues();
for (int i = 0; i < refEigenValues.length; ++i) {
assertEquals(refEigenValues[i], eigenValues[i], 1.0e-5);
assertEquals(0, refEigenVectors[i].subtract(decomposition.getEigenvector(i)).getNorm(), 2.0e-7);
}
}
示例11: testDimension4WithSplit
@Test
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);
Assert.assertEquals(1.0, ed.getRealEigenvalue(0), 1.0e-15);
Assert.assertEquals(0.4, ed.getRealEigenvalue(1), 1.0e-15);
Assert.assertEquals(0.2, ed.getRealEigenvalue(2), 1.0e-15);
Assert.assertEquals(0.1, ed.getRealEigenvalue(3), 1.0e-15);
}
示例12: testRepeatedEigenvalue
/**
* Matrix with eigenvalues {8, -1, -1}
*/
public void testRepeatedEigenvalue() {
RealMatrix repeated = MatrixUtils.createRealMatrix(new double[][] {
{3, 2, 4},
{2, 0, 2},
{4, 2, 3}
});
EigenDecomposition ed = new EigenDecompositionImpl(repeated, MathUtils.SAFE_MIN);
checkEigenValues((new double[] {8, -1, -1}), ed, 1E-12);
checkEigenVector((new double[] {2, 1, 2}), ed, 1E-12);
}
示例13: testAEqualVDVt
/** 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14: testRepeatedEigenvalue
/**
* Matrix with eigenvalues {8, -1, -1}
*/
public void testRepeatedEigenvalue() {
RealMatrix repeated = MatrixUtils.createRealMatrix(new double[][] {
{3, 2, 4},
{2, 0, 2},
{4, 2, 3}
});
EigenDecomposition ed = new EigenDecompositionImpl(repeated, MathUtils.SAFE_MIN);
checkEigenValues((new double[] {8, -1, -1}), ed, 1E-12);
checkEigenVector((new double[] {2, 1, 2}), ed, 1E-12);
}
示例15: testDimension1
public void testDimension1() {
RealMatrix matrix =
MatrixUtils.createRealMatrix(new double[][] { { 1.5 } });
EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
assertEquals(1.5, ed.getRealEigenvalue(0), 1.0e-15);
}