Java Code Examples for org.apache.commons.math.util.MathUtils#SAFE_MIN
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org.apache.commons.math.util.MathUtils#SAFE_MIN .
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Example 1
| Source File: EigenDecompositionImplTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/** test a matrix already in tridiagonal form. */
@Test
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();
Assert.assertEquals(ref.length, eigenValues.length);
for (int i = 0; i < ref.length; ++i) {
Assert.assertEquals(ref[ref.length - i - 1], eigenValues[i], 2.0e-14);
}
}
Example 2
| Source File: EigenDecompositionImplTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/** 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);
}
}
Example 3
| Source File: EigenDecompositionImplTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* 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);
}
Example 4
| Source File: EigenDecompositionImplTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/** 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);
}
}
Example 5
| Source File: EigenDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/** 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());
}
Example 6
| Source File: EigenDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/** 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);
}
}
Example 7
| Source File: EigenDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
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);
}
Example 8
| Source File: EigenDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
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);
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);
}
Example 9
| Source File: EigenDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testDimension1() {
RealMatrix matrix =
MatrixUtils.createRealMatrix(new double[][] { { 1.5 } });
EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
Assert.assertEquals(1.5, ed.getRealEigenvalue(0), 1.0e-15);
}
Example 10
| Source File: EigenDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* Matrix with eigenvalues {2, 0, 12}
*/
@Test
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);
}
Example 11
| Source File: EigenDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* 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);
}
Example 12
| Source File: EigenDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/** test dimensions */
@Test
public void testDimensions() {
final int m = matrix.getRowDimension();
EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
Assert.assertEquals(m, ed.getV().getRowDimension());
Assert.assertEquals(m, ed.getV().getColumnDimension());
Assert.assertEquals(m, ed.getD().getColumnDimension());
Assert.assertEquals(m, ed.getD().getColumnDimension());
Assert.assertEquals(m, ed.getVT().getRowDimension());
Assert.assertEquals(m, ed.getVT().getColumnDimension());
}
Example 13
| Source File: EigenDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/** 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());
}
Example 14
| Source File: EigenDecompositionImplTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
public void testMathpbx02() {
double[] mainTridiagonal = {
7484.860960227216, 18405.28129035345, 13855.225609560746,
10016.708722343366, 559.8117399576674, 6750.190788301587,
71.21428769782159
};
double[] secondaryTridiagonal = {
-4175.088570476366,1975.7955858241994,5193.178422374075,
1995.286659169179,75.34535882933804,-234.0808002076056
};
// the reference values have been computed using routine DSTEMR
// from the fortran library LAPACK version 3.2.1
double[] refEigenValues = {
20654.744890306974412,16828.208208485466457,
6893.155912634994820,6757.083016675340332,
5887.799885688558788,64.309089923240379,
57.992628792736340
};
RealVector[] refEigenVectors = {
new ArrayRealVector(new double[] {-0.270356342026904, 0.852811091326997, 0.399639490702077, 0.198794657813990, 0.019739323307666, 0.000106983022327, -0.000001216636321}),
new ArrayRealVector(new double[] {0.179995273578326,-0.402807848153042,0.701870993525734,0.555058211014888,0.068079148898236,0.000509139115227,-0.000007112235617}),
new ArrayRealVector(new double[] {-0.399582721284727,-0.056629954519333,-0.514406488522827,0.711168164518580,0.225548081276367,0.125943999652923,-0.004321507456014}),
new ArrayRealVector(new double[] {0.058515721572821,0.010200130057739,0.063516274916536,-0.090696087449378,-0.017148420432597,0.991318870265707,-0.034707338554096}),
new ArrayRealVector(new double[] {0.855205995537564,0.327134656629775,-0.265382397060548,0.282690729026706,0.105736068025572,-0.009138126622039,0.000367751821196}),
new ArrayRealVector(new double[] {-0.002913069901144,-0.005177515777101,0.041906334478672,-0.109315918416258,0.436192305456741,0.026307315639535,0.891797507436344}),
new ArrayRealVector(new double[] {-0.005738311176435,-0.010207611670378,0.082662420517928,-0.215733886094368,0.861606487840411,-0.025478530652759,-0.451080697503958})
};
// the following line triggers the exception
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-3);
if (refEigenVectors[i].dotProduct(decomposition.getEigenvector(i)) < 0) {
assertEquals(0, refEigenVectors[i].add(decomposition.getEigenvector(i)).getNorm(), 1.0e-5);
} else {
assertEquals(0, refEigenVectors[i].subtract(decomposition.getEigenvector(i)).getNorm(), 1.0e-5);
}
}
}
Example 15
| Source File: Arja_0086_s.java From coming with MIT License | 4 votes |
|
/**
* Perform two iterations with Li's tests for initial splits.
* @param n number of rows of the matrix to process
*/
private void initialSplits(final int n) {
pingPong = 0;
for (int k = 0; k < 2; ++k) {
// apply Li's reverse test
double d = work[4 * (n - 1) + pingPong];
for (int i = 4 * (n - 2) + pingPong; i >= 0; i -= 4) {
if (work[i + 2] <= TOLERANCE_2 * d) {
work[i + 2] = -0.0;
d = work[i];
} else {
d *= work[i] / (d + work[i + 2]);
}
}
// apply dqd plus Li's forward test.
d = work[pingPong];
for (int i = 2 + pingPong; i < 4 * n - 2; i += 4) {
final int j = i - 2 * pingPong - 1;
work[j] = d + work[i];
if (work[i] <= TOLERANCE_2 * d) {
work[i] = -0.0;
work[j] = d;
work[j + 2] = 0.0;
d = work[i + 2];
} else if ((MathUtils.SAFE_MIN * work[i + 2] < work[j]) &&
(MathUtils.SAFE_MIN * work[j] < work[i + 2])) {
final double tmp = work[i + 2] / work[j];
work[j + 2] = work[i] * tmp;
d *= tmp;
} else {
work[j + 2] = work[i + 2] * (work[i] / work[j]);
d *= work[i + 2] / work[j];
}
}
work[4 * n - 3 - pingPong] = d;
// from ping to pong
pingPong = 1 - pingPong;
}
}
Example 16
| Source File: Arja_00128_s.java From coming with MIT License | 4 votes |
|
/**
* Perform two iterations with Li's tests for initial splits.
* @param n number of rows of the matrix to process
*/
private void initialSplits(final int n) {
pingPong = 0;
for (int k = 0; k < 2; ++k) {
// apply Li's reverse test
double d = work[4 * (n - 1) + pingPong];
for (int i = 4 * (n - 2) + pingPong; i >= 0; i -= 4) {
if (work[i + 2] <= TOLERANCE_2 * d) {
work[i + 2] = -0.0;
d = work[i];
} else {
d *= work[i] / (d + work[i + 2]);
}
}
// apply dqd plus Li's forward test.
d = work[pingPong];
for (int i = 2 + pingPong; i < 4 * n - 2; i += 4) {
final int j = i - 2 * pingPong - 1;
work[j] = d + work[i];
if (work[i] <= TOLERANCE_2 * d) {
work[i] = -0.0;
work[j] = d;
work[j + 2] = 0.0;
d = work[i + 2];
} else if ((MathUtils.SAFE_MIN * work[i + 2] < work[j]) &&
(MathUtils.SAFE_MIN * work[j] < work[i + 2])) {
final double tmp = work[i + 2] / work[j];
work[j + 2] = work[i] * tmp;
d *= tmp;
} else {
work[j + 2] = work[i + 2] * (work[i] / work[j]);
d *= work[i + 2] / work[j];
}
}
work[4 * n - 3 - pingPong] = d;
// from ping to pong
pingPong = 1 - pingPong;
}
}
Example 17
| Source File: EigenDecompositionImplTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
public void testMathpbx02() {
double[] mainTridiagonal = {
7484.860960227216, 18405.28129035345, 13855.225609560746,
10016.708722343366, 559.8117399576674, 6750.190788301587,
71.21428769782159
};
double[] secondaryTridiagonal = {
-4175.088570476366,1975.7955858241994,5193.178422374075,
1995.286659169179,75.34535882933804,-234.0808002076056
};
// the reference values have been computed using routine DSTEMR
// from the fortran library LAPACK version 3.2.1
double[] refEigenValues = {
20654.744890306974412,16828.208208485466457,
6893.155912634994820,6757.083016675340332,
5887.799885688558788,64.309089923240379,
57.992628792736340
};
RealVector[] refEigenVectors = {
new ArrayRealVector(new double[] {-0.270356342026904, 0.852811091326997, 0.399639490702077, 0.198794657813990, 0.019739323307666, 0.000106983022327, -0.000001216636321}),
new ArrayRealVector(new double[] {0.179995273578326,-0.402807848153042,0.701870993525734,0.555058211014888,0.068079148898236,0.000509139115227,-0.000007112235617}),
new ArrayRealVector(new double[] {-0.399582721284727,-0.056629954519333,-0.514406488522827,0.711168164518580,0.225548081276367,0.125943999652923,-0.004321507456014}),
new ArrayRealVector(new double[] {0.058515721572821,0.010200130057739,0.063516274916536,-0.090696087449378,-0.017148420432597,0.991318870265707,-0.034707338554096}),
new ArrayRealVector(new double[] {0.855205995537564,0.327134656629775,-0.265382397060548,0.282690729026706,0.105736068025572,-0.009138126622039,0.000367751821196}),
new ArrayRealVector(new double[] {-0.002913069901144,-0.005177515777101,0.041906334478672,-0.109315918416258,0.436192305456741,0.026307315639535,0.891797507436344}),
new ArrayRealVector(new double[] {-0.005738311176435,-0.010207611670378,0.082662420517928,-0.215733886094368,0.861606487840411,-0.025478530652759,-0.451080697503958})
};
// the following line triggers the exception
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-3);
if (refEigenVectors[i].dotProduct(decomposition.getEigenvector(i)) < 0) {
assertEquals(0, refEigenVectors[i].add(decomposition.getEigenvector(i)).getNorm(), 1.0e-5);
} else {
assertEquals(0, refEigenVectors[i].subtract(decomposition.getEigenvector(i)).getNorm(), 1.0e-5);
}
}
}
Example 18
| Source File: Cardumen_0061_s.java From coming with MIT License | 4 votes |
|
/**
* Perform two iterations with Li's tests for initial splits.
* @param n number of rows of the matrix to process
*/
private void initialSplits(final int n) {
pingPong = 0;
for (int k = 0; k < 2; ++k) {
// apply Li's reverse test
double d = work[4 * (n - 1) + pingPong];
for (int i = 4 * (n - 2) + pingPong; i >= 0; i -= 4) {
if (work[i + 2] <= TOLERANCE_2 * d) {
work[i + 2] = -0.0;
d = work[i];
} else {
d *= work[i] / (d + work[i + 2]);
}
}
// apply dqd plus Li's forward test.
d = work[pingPong];
for (int i = 2 + pingPong; i < 4 * n - 2; i += 4) {
final int j = i - 2 * pingPong - 1;
work[j] = d + work[i];
if (work[i] <= TOLERANCE_2 * d) {
work[i] = -0.0;
work[j] = d;
work[j + 2] = 0.0;
d = work[i + 2];
} else if ((MathUtils.SAFE_MIN * work[i + 2] < work[j]) &&
(MathUtils.SAFE_MIN * work[j] < work[i + 2])) {
final double tmp = work[i + 2] / work[j];
work[j + 2] = work[i] * tmp;
d *= tmp;
} else {
work[j + 2] = work[i + 2] * (work[i] / work[j]);
d *= work[i + 2] / work[j];
}
}
work[4 * n - 3 - pingPong] = d;
// from ping to pong
pingPong = 1 - pingPong;
}
}
Example 19
| Source File: Math_80_EigenDecompositionImpl_s.java From coming with MIT License | 4 votes |
|
/**
* Perform two iterations with Li's tests for initial splits.
* @param n number of rows of the matrix to process
*/
private void initialSplits(final int n) {
pingPong = 0;
for (int k = 0; k < 2; ++k) {
// apply Li's reverse test
double d = work[4 * (n - 1) + pingPong];
for (int i = 4 * (n - 2) + pingPong; i >= 0; i -= 4) {
if (work[i + 2] <= TOLERANCE_2 * d) {
work[i + 2] = -0.0;
d = work[i];
} else {
d *= work[i] / (d + work[i + 2]);
}
}
// apply dqd plus Li's forward test.
d = work[pingPong];
for (int i = 2 + pingPong; i < 4 * n - 2; i += 4) {
final int j = i - 2 * pingPong - 1;
work[j] = d + work[i];
if (work[i] <= TOLERANCE_2 * d) {
work[i] = -0.0;
work[j] = d;
work[j + 2] = 0.0;
d = work[i + 2];
} else if ((MathUtils.SAFE_MIN * work[i + 2] < work[j]) &&
(MathUtils.SAFE_MIN * work[j] < work[i + 2])) {
final double tmp = work[i + 2] / work[j];
work[j + 2] = work[i] * tmp;
d *= tmp;
} else {
work[j + 2] = work[i + 2] * (work[i] / work[j]);
d *= work[i + 2] / work[j];
}
}
work[4 * n - 3 - pingPong] = d;
// from ping to pong
pingPong = 1 - pingPong;
}
}
Example 20
| Source File: 1_EigenDecompositionImpl.java From SimFix with GNU General Public License v2.0 | 4 votes |
|
/**
* Perform two iterations with Li's tests for initial splits.
* @param n number of rows of the matrix to process
*/
private void initialSplits(final int n) {
pingPong = 0;
for (int k = 0; k < 2; ++k) {
// apply Li's reverse test
double d = work[4 * (n - 1) + pingPong];
for (int i = 4 * (n - 2) + pingPong; i >= 0; i -= 4) {
if (work[i + 2] <= TOLERANCE_2 * d) {
work[i + 2] = -0.0;
d = work[i];
} else {
d *= work[i] / (d + work[i + 2]);
}
}
// apply dqd plus Li's forward test.
d = work[pingPong];
for (int i = 2 + pingPong; i < 4 * n - 2; i += 4) {
final int j = i - 2 * pingPong - 1;
work[j] = d + work[i];
if (work[i] <= TOLERANCE_2 * d) {
work[i] = -0.0;
work[j] = d;
work[j + 2] = 0.0;
d = work[i + 2];
} else if ((MathUtils.SAFE_MIN * work[i + 2] < work[j]) &&
(MathUtils.SAFE_MIN * work[j] < work[i + 2])) {
final double tmp = work[i + 2] / work[j];
work[j + 2] = work[i] * tmp;
d *= tmp;
} else {
work[j + 2] = work[i + 2] * (work[i] / work[j]);
d *= work[i + 2] / work[j];
}
}
work[4 * n - 3 - pingPong] = d;
// from ping to pong
pingPong = 1 - pingPong;
}
}
