Java Code Examples for org.apache.commons.math.random.RandomGenerator#setSeed()
The following examples show how to use
org.apache.commons.math.random.RandomGenerator#setSeed() .
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Example 1
| Source File: NaturalRankingTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
public void testNaNsFixedTiesRandom() {
RandomGenerator randomGenerator = new JDKRandomGenerator();
randomGenerator.setSeed(1000);
NaturalRanking ranking = new NaturalRanking(NaNStrategy.FIXED,
randomGenerator);
double[] ranks = ranking.rank(exampleData);
double[] correctRanks = { 5, 4, 6, 7, 3, 8, Double.NaN, 1, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesFirst);
correctRanks = new double[] { 1, 1, 4, 3, 5 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesLast);
correctRanks = new double[] { 3, 4, 2, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleNaNs);
correctRanks = new double[] { 1, 2, Double.NaN, Double.NaN };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleTies);
correctRanks = new double[] { 3, 2, 5, 5, 7, 6, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(allSame);
correctRanks = new double[] { 1, 3, 4, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
}
Example 2
| Source File: NaturalRankingTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
public void testNaNsFixedTiesRandom() {
RandomGenerator randomGenerator = new JDKRandomGenerator();
randomGenerator.setSeed(1000);
NaturalRanking ranking = new NaturalRanking(NaNStrategy.FIXED,
randomGenerator);
double[] ranks = ranking.rank(exampleData);
double[] correctRanks = { 5, 4, 6, 7, 3, 8, Double.NaN, 1, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesFirst);
correctRanks = new double[] { 1, 1, 4, 3, 5 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesLast);
correctRanks = new double[] { 3, 4, 2, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleNaNs);
correctRanks = new double[] { 1, 2, Double.NaN, Double.NaN };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleTies);
correctRanks = new double[] { 3, 2, 5, 5, 7, 6, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(allSame);
correctRanks = new double[] { 1, 3, 4, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
}
Example 3
| Source File: NaturalRankingTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
public void testNaNsFixedTiesRandom() {
RandomGenerator randomGenerator = new JDKRandomGenerator();
randomGenerator.setSeed(1000);
NaturalRanking ranking = new NaturalRanking(NaNStrategy.FIXED,
randomGenerator);
double[] ranks = ranking.rank(exampleData);
double[] correctRanks = { 5, 4, 6, 7, 3, 8, Double.NaN, 1, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesFirst);
correctRanks = new double[] { 1, 1, 4, 3, 5 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesLast);
correctRanks = new double[] { 3, 4, 2, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleNaNs);
correctRanks = new double[] { 1, 2, Double.NaN, Double.NaN };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleTies);
correctRanks = new double[] { 3, 2, 5, 5, 7, 6, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(allSame);
correctRanks = new double[] { 1, 3, 4, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
}
Example 4
| Source File: NaturalRankingTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
public void testNaNsFixedTiesRandom() {
RandomGenerator randomGenerator = new JDKRandomGenerator();
randomGenerator.setSeed(1000);
NaturalRanking ranking = new NaturalRanking(NaNStrategy.FIXED,
randomGenerator);
double[] ranks = ranking.rank(exampleData);
double[] correctRanks = { 5, 4, 6, 7, 3, 8, Double.NaN, 1, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesFirst);
correctRanks = new double[] { 1, 1, 4, 3, 5 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesLast);
correctRanks = new double[] { 3, 4, 2, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleNaNs);
correctRanks = new double[] { 1, 2, Double.NaN, Double.NaN };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleTies);
correctRanks = new double[] { 3, 2, 5, 5, 7, 6, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(allSame);
correctRanks = new double[] { 1, 3, 4, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
}
Example 5
| Source File: NaturalRankingTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
public void testNaNsFixedTiesRandom() {
RandomGenerator randomGenerator = new JDKRandomGenerator();
randomGenerator.setSeed(1000);
NaturalRanking ranking = new NaturalRanking(NaNStrategy.FIXED,
randomGenerator);
double[] ranks = ranking.rank(exampleData);
double[] correctRanks = { 5, 4, 6, 7, 3, 8, Double.NaN, 1, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesFirst);
correctRanks = new double[] { 1, 1, 4, 3, 5 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesLast);
correctRanks = new double[] { 3, 4, 2, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleNaNs);
correctRanks = new double[] { 1, 2, Double.NaN, Double.NaN };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleTies);
correctRanks = new double[] { 3, 2, 5, 5, 7, 6, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(allSame);
correctRanks = new double[] { 1, 3, 4, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
}
Example 6
| Source File: NaturalRankingTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testNaNsFixedTiesRandom() {
RandomGenerator randomGenerator = new JDKRandomGenerator();
randomGenerator.setSeed(1000);
NaturalRanking ranking = new NaturalRanking(NaNStrategy.FIXED,
randomGenerator);
double[] ranks = ranking.rank(exampleData);
double[] correctRanks = { 5, 4, 6, 7, 3, 8, Double.NaN, 1, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesFirst);
correctRanks = new double[] { 1, 1, 4, 3, 5 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesLast);
correctRanks = new double[] { 3, 4, 2, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleNaNs);
correctRanks = new double[] { 1, 2, Double.NaN, Double.NaN };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleTies);
correctRanks = new double[] { 3, 2, 5, 5, 7, 6, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(allSame);
correctRanks = new double[] { 1, 3, 4, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
}
Example 7
| Source File: NaturalRankingTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
public void testNaNsFixedTiesRandom() {
RandomGenerator randomGenerator = new JDKRandomGenerator();
randomGenerator.setSeed(1000);
NaturalRanking ranking = new NaturalRanking(NaNStrategy.FIXED,
randomGenerator);
double[] ranks = ranking.rank(exampleData);
double[] correctRanks = { 5, 4, 6, 7, 3, 8, Double.NaN, 1, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesFirst);
correctRanks = new double[] { 1, 1, 4, 3, 5 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesLast);
correctRanks = new double[] { 3, 4, 2, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleNaNs);
correctRanks = new double[] { 1, 2, Double.NaN, Double.NaN };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleTies);
correctRanks = new double[] { 3, 2, 5, 5, 7, 6, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(allSame);
correctRanks = new double[] { 1, 3, 4, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
}
Example 8
| Source File: NaturalRankingTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
public void testNaNsFixedTiesRandom() {
RandomGenerator randomGenerator = new JDKRandomGenerator();
randomGenerator.setSeed(1000);
NaturalRanking ranking = new NaturalRanking(NaNStrategy.FIXED,
randomGenerator);
double[] ranks = ranking.rank(exampleData);
double[] correctRanks = { 5, 4, 6, 7, 3, 8, Double.NaN, 1, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesFirst);
correctRanks = new double[] { 1, 1, 4, 3, 5 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesLast);
correctRanks = new double[] { 3, 4, 2, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleNaNs);
correctRanks = new double[] { 1, 2, Double.NaN, Double.NaN };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleTies);
correctRanks = new double[] { 3, 2, 5, 5, 7, 6, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(allSame);
correctRanks = new double[] { 1, 3, 4, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
}
Example 9
| Source File: NaturalRankingTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
public void testNaNsFixedTiesRandom() {
RandomGenerator randomGenerator = new JDKRandomGenerator();
randomGenerator.setSeed(1000);
NaturalRanking ranking = new NaturalRanking(NaNStrategy.FIXED,
randomGenerator);
double[] ranks = ranking.rank(exampleData);
double[] correctRanks = { 5, 4, 6, 7, 3, 8, Double.NaN, 1, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesFirst);
correctRanks = new double[] { 1, 1, 4, 3, 5 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesLast);
correctRanks = new double[] { 3, 4, 2, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleNaNs);
correctRanks = new double[] { 1, 2, Double.NaN, Double.NaN };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleTies);
correctRanks = new double[] { 3, 2, 5, 5, 7, 6, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(allSame);
correctRanks = new double[] { 1, 3, 4, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
}
Example 10
| Source File: NaturalRankingTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
public void testNaNsFixedTiesRandom() {
RandomGenerator randomGenerator = new JDKRandomGenerator();
randomGenerator.setSeed(1000);
NaturalRanking ranking = new NaturalRanking(NaNStrategy.FIXED,
randomGenerator);
double[] ranks = ranking.rank(exampleData);
double[] correctRanks = { 5, 4, 6, 7, 3, 8, Double.NaN, 1, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesFirst);
correctRanks = new double[] { 1, 1, 4, 3, 5 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesLast);
correctRanks = new double[] { 3, 4, 2, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleNaNs);
correctRanks = new double[] { 1, 2, Double.NaN, Double.NaN };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleTies);
correctRanks = new double[] { 3, 2, 5, 5, 7, 6, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(allSame);
correctRanks = new double[] { 1, 3, 4, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
}
Example 11
| Source File: NaturalRankingTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
public void testNaNsFixedTiesRandom() {
RandomGenerator randomGenerator = new JDKRandomGenerator();
randomGenerator.setSeed(1000);
NaturalRanking ranking = new NaturalRanking(NaNStrategy.FIXED,
randomGenerator);
double[] ranks = ranking.rank(exampleData);
double[] correctRanks = { 5, 4, 6, 7, 3, 8, Double.NaN, 1, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesFirst);
correctRanks = new double[] { 1, 1, 4, 3, 5 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesLast);
correctRanks = new double[] { 3, 4, 2, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleNaNs);
correctRanks = new double[] { 1, 2, Double.NaN, Double.NaN };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleTies);
correctRanks = new double[] { 3, 2, 5, 5, 7, 6, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(allSame);
correctRanks = new double[] { 1, 3, 4, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
}
Example 12
| Source File: NaturalRankingTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testNaNsFixedTiesRandom() {
RandomGenerator randomGenerator = new JDKRandomGenerator();
randomGenerator.setSeed(1000);
NaturalRanking ranking = new NaturalRanking(NaNStrategy.FIXED,
randomGenerator);
double[] ranks = ranking.rank(exampleData);
double[] correctRanks = { 5, 4, 6, 7, 3, 8, Double.NaN, 1, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesFirst);
correctRanks = new double[] { 1, 1, 4, 3, 5 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesLast);
correctRanks = new double[] { 3, 4, 2, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleNaNs);
correctRanks = new double[] { 1, 2, Double.NaN, Double.NaN };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleTies);
correctRanks = new double[] { 3, 2, 5, 5, 7, 6, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(allSame);
correctRanks = new double[] { 1, 3, 4, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
}
Example 13
| Source File: NaturalRankingTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
public void testNaNsFixedTiesRandom() {
RandomGenerator randomGenerator = new JDKRandomGenerator();
randomGenerator.setSeed(1000);
NaturalRanking ranking = new NaturalRanking(NaNStrategy.FIXED,
randomGenerator);
double[] ranks = ranking.rank(exampleData);
double[] correctRanks = { 5, 4, 6, 7, 3, 8, Double.NaN, 1, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesFirst);
correctRanks = new double[] { 1, 1, 4, 3, 5 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesLast);
correctRanks = new double[] { 3, 4, 2, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleNaNs);
correctRanks = new double[] { 1, 2, Double.NaN, Double.NaN };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleTies);
correctRanks = new double[] { 3, 2, 5, 5, 7, 6, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(allSame);
correctRanks = new double[] { 1, 3, 4, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
}
Example 14
| Source File: NaturalRankingTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
public void testNaNsFixedTiesRandom() {
RandomGenerator randomGenerator = new JDKRandomGenerator();
randomGenerator.setSeed(1000);
NaturalRanking ranking = new NaturalRanking(NaNStrategy.FIXED,
randomGenerator);
double[] ranks = ranking.rank(exampleData);
double[] correctRanks = { 5, 4, 6, 7, 3, 8, Double.NaN, 1, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesFirst);
correctRanks = new double[] { 1, 1, 4, 3, 5 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(tiesLast);
correctRanks = new double[] { 3, 4, 2, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleNaNs);
correctRanks = new double[] { 1, 2, Double.NaN, Double.NaN };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(multipleTies);
correctRanks = new double[] { 3, 2, 5, 5, 7, 6, 1 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
ranks = ranking.rank(allSame);
correctRanks = new double[] { 1, 3, 4, 4 };
TestUtils.assertEquals(correctRanks, ranks, 0d);
}
Example 15
| Source File: DirectSearchOptimizer.java From cacheonix-core with GNU Lesser General Public License v2.1 | 5 votes |
|
/** Minimizes a cost function.
* <p>The initial simplex is built from two vertices that are
* considered to represent two opposite vertices of a box parallel
* to the canonical axes of the space. The simplex is the subset of
* vertices encountered while going from vertexA to vertexB
* traveling along the box edges only. This can be seen as a scaled
* regular simplex using the projected separation between the given
* points as the scaling factor along each coordinate axis.</p>
* <p>The optimization is performed in multi-start mode.</p>
* @param f cost function
* @param maxEvaluations maximal number of function calls for each
* start (note that the number will be checked <em>after</em>
* complete simplices have been evaluated, this means that in some
* cases this number will be exceeded by a few units, depending on
* the dimension of the problem)
* @param checker object to use to check for convergence
* @param vertexA first vertex
* @param vertexB last vertex
* @param starts number of starts to perform (including the
* first one), multi-start is disabled if value is less than or
* equal to 1
* @param seed seed for the random vector generator
* @return the point/cost pairs giving the minimal cost
* @exception CostException if the cost function throws one during
* the search
* @exception ConvergenceException if none of the starts did
* converge (it is not thrown if at least one start did converge)
*/
public PointCostPair minimize(CostFunction f, int maxEvaluations,
ConvergenceChecker checker,
double[] vertexA, double[] vertexB,
int starts, long seed)
throws CostException, ConvergenceException {
// set up the simplex traveling around the box
buildSimplex(vertexA, vertexB);
// we consider the simplex could have been produced by a generator
// having its mean value at the center of the box, the standard
// deviation along each axe being the corresponding half size
double[] mean = new double[vertexA.length];
double[] standardDeviation = new double[vertexA.length];
for (int i = 0; i < vertexA.length; ++i) {
mean[i] = 0.5 * (vertexA[i] + vertexB[i]);
standardDeviation[i] = 0.5 * Math.abs(vertexA[i] - vertexB[i]);
}
RandomGenerator rg = new JDKRandomGenerator();
rg.setSeed(seed);
UniformRandomGenerator urg = new UniformRandomGenerator(rg);
RandomVectorGenerator rvg =
new UncorrelatedRandomVectorGenerator(mean, standardDeviation, urg);
setMultiStart(starts, rvg);
// compute minimum
return minimize(f, maxEvaluations, checker);
}
Example 16
| Source File: LSHPigTest.java From datafu with Apache License 2.0 | 5 votes |
|
@Test
public void testL1UDFSparse() throws Exception
{
setMemorySettings();
RandomGenerator rg = new JDKRandomGenerator();
rg.setSeed(0);
RandomData rd = new RandomDataImpl(rg);
int n = 1000;
List<RealVector> vectors = LSHTest.getVectors(rd, 1000, n);
PigTest test = createPigTestFromString(l1SparseTest);
writeLinesToFile("input", getSparseLines(vectors));
List<RealVector> queries = LSHTest.getVectors(rd, 1000, 10);
writeLinesToFile("queries", getSparseLines(queries));
test.runScript();
List<Tuple> neighbors = this.getLinesForAlias(test, "NEIGHBOR_CNT");
Assert.assertEquals( queries.size(), neighbors.size() );
for(long cnt : getCounts(neighbors))
{
Assert.assertTrue(cnt >= 3);
}
Distance d = new Distance()
{
@Override
public double distance(RealVector v1, RealVector v2) {
return L1.distance(v1, v2);
}
};
verifyPoints(neighbors, d, 1000);
}
Example 17
| Source File: DirectSearchOptimizer.java From cacheonix-core with GNU Lesser General Public License v2.1 | 5 votes |
|
/** Minimizes a cost function.
* <p>The simplex is built from all its vertices.</p>
* <p>The optimization is performed in multi-start mode.</p>
* @param f cost function
* @param maxEvaluations maximal number of function calls for each
* start (note that the number will be checked <em>after</em>
* complete simplices have been evaluated, this means that in some
* cases this number will be exceeded by a few units, depending on
* the dimension of the problem)
* @param checker object to use to check for convergence
* @param vertices array containing all vertices of the simplex
* @param starts number of starts to perform (including the
* first one), multi-start is disabled if value is less than or
* equal to 1
* @param seed seed for the random vector generator
* @return the point/cost pairs giving the minimal cost
* @exception NotPositiveDefiniteMatrixException if the vertices
* array is degenerated
* @exception CostException if the cost function throws one during
* the search
* @exception ConvergenceException if none of the starts did
* converge (it is not thrown if at least one start did converge)
*/
public PointCostPair minimize(CostFunction f, int maxEvaluations,
ConvergenceChecker checker,
double[][] vertices,
int starts, long seed)
throws NotPositiveDefiniteMatrixException,
CostException, ConvergenceException {
try {
// store the points into the simplex
buildSimplex(vertices);
// compute the statistical properties of the simplex points
VectorialMean meanStat = new VectorialMean(vertices[0].length);
VectorialCovariance covStat = new VectorialCovariance(vertices[0].length, true);
for (int i = 0; i < vertices.length; ++i) {
meanStat.increment(vertices[i]);
covStat.increment(vertices[i]);
}
double[] mean = meanStat.getResult();
RealMatrix covariance = covStat.getResult();
RandomGenerator rg = new JDKRandomGenerator();
rg.setSeed(seed);
RandomVectorGenerator rvg =
new CorrelatedRandomVectorGenerator(mean,
covariance, 1.0e-12 * covariance.getNorm(),
new UniformRandomGenerator(rg));
setMultiStart(starts, rvg);
// compute minimum
return minimize(f, maxEvaluations, checker);
} catch (DimensionMismatchException dme) {
// this should not happen
throw new RuntimeException("internal error");
}
}
Example 18
| Source File: GLSMultipleLinearRegressionTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* Generate an error covariance matrix and sample data representing models
* with this error structure. Then verify that GLS estimated coefficients,
* on average, perform better than OLS.
*/
@Test
public void testGLSEfficiency() throws Exception {
RandomGenerator rg = new JDKRandomGenerator();
rg.setSeed(200); // Seed has been selected to generate non-trivial covariance
// Assume model has 16 observations (will use Longley data). Start by generating
// non-constant variances for the 16 error terms.
final int nObs = 16;
double[] sigma = new double[nObs];
for (int i = 0; i < nObs; i++) {
sigma[i] = 10 * rg.nextDouble();
}
// Now generate 1000 error vectors to use to estimate the covariance matrix
// Columns are draws on N(0, sigma[col])
final int numSeeds = 1000;
RealMatrix errorSeeds = MatrixUtils.createRealMatrix(numSeeds, nObs);
for (int i = 0; i < numSeeds; i++) {
for (int j = 0; j < nObs; j++) {
errorSeeds.setEntry(i, j, rg.nextGaussian() * sigma[j]);
}
}
// Get covariance matrix for columns
RealMatrix cov = (new Covariance(errorSeeds)).getCovarianceMatrix();
// Create a CorrelatedRandomVectorGenerator to use to generate correlated errors
GaussianRandomGenerator rawGenerator = new GaussianRandomGenerator(rg);
double[] errorMeans = new double[nObs]; // Counting on init to 0 here
CorrelatedRandomVectorGenerator gen = new CorrelatedRandomVectorGenerator(errorMeans, cov,
1.0e-12 * cov.getNorm(), rawGenerator);
// Now start generating models. Use Longley X matrix on LHS
// and Longley OLS beta vector as "true" beta. Generate
// Y values by XB + u where u is a CorrelatedRandomVector generated
// from cov.
OLSMultipleLinearRegression ols = new OLSMultipleLinearRegression();
ols.newSampleData(longley, nObs, 6);
final RealVector b = ols.calculateBeta().copy();
final RealMatrix x = ols.X.copy();
// Create a GLS model to reuse
GLSMultipleLinearRegression gls = new GLSMultipleLinearRegression();
gls.newSampleData(longley, nObs, 6);
gls.newCovarianceData(cov.getData());
// Create aggregators for stats measuring model performance
DescriptiveStatistics olsBetaStats = new DescriptiveStatistics();
DescriptiveStatistics glsBetaStats = new DescriptiveStatistics();
// Generate Y vectors for 10000 models, estimate GLS and OLS and
// Verify that OLS estimates are better
final int nModels = 10000;
for (int i = 0; i < nModels; i++) {
// Generate y = xb + u with u cov
RealVector u = MatrixUtils.createRealVector(gen.nextVector());
double[] y = u.add(x.operate(b)).getData();
// Estimate OLS parameters
ols.newYSampleData(y);
RealVector olsBeta = ols.calculateBeta();
// Estimate GLS parameters
gls.newYSampleData(y);
RealVector glsBeta = gls.calculateBeta();
// Record deviations from "true" beta
double dist = olsBeta.getDistance(b);
olsBetaStats.addValue(dist * dist);
dist = glsBeta.getDistance(b);
glsBetaStats.addValue(dist * dist);
}
// Verify that GLS is on average more efficient, lower variance
assert(olsBetaStats.getMean() > 1.5 * glsBetaStats.getMean());
assert(olsBetaStats.getStandardDeviation() > glsBetaStats.getStandardDeviation());
}
Example 19
| Source File: GLSMultipleLinearRegressionTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* Generate an error covariance matrix and sample data representing models
* with this error structure. Then verify that GLS estimated coefficients,
* on average, perform better than OLS.
*/
@Test
public void testGLSEfficiency() throws Exception {
RandomGenerator rg = new JDKRandomGenerator();
rg.setSeed(200); // Seed has been selected to generate non-trivial covariance
// Assume model has 16 observations (will use Longley data). Start by generating
// non-constant variances for the 16 error terms.
final int nObs = 16;
double[] sigma = new double[nObs];
for (int i = 0; i < nObs; i++) {
sigma[i] = 10 * rg.nextDouble();
}
// Now generate 1000 error vectors to use to estimate the covariance matrix
// Columns are draws on N(0, sigma[col])
final int numSeeds = 1000;
RealMatrix errorSeeds = MatrixUtils.createRealMatrix(numSeeds, nObs);
for (int i = 0; i < numSeeds; i++) {
for (int j = 0; j < nObs; j++) {
errorSeeds.setEntry(i, j, rg.nextGaussian() * sigma[j]);
}
}
// Get covariance matrix for columns
RealMatrix cov = (new Covariance(errorSeeds)).getCovarianceMatrix();
// Create a CorrelatedRandomVectorGenerator to use to generate correlated errors
GaussianRandomGenerator rawGenerator = new GaussianRandomGenerator(rg);
double[] errorMeans = new double[nObs]; // Counting on init to 0 here
CorrelatedRandomVectorGenerator gen = new CorrelatedRandomVectorGenerator(errorMeans, cov,
1.0e-12 * cov.getNorm(), rawGenerator);
// Now start generating models. Use Longley X matrix on LHS
// and Longley OLS beta vector as "true" beta. Generate
// Y values by XB + u where u is a CorrelatedRandomVector generated
// from cov.
OLSMultipleLinearRegression ols = new OLSMultipleLinearRegression();
ols.newSampleData(longley, nObs, 6);
final RealVector b = ols.calculateBeta().copy();
final RealMatrix x = ols.X.copy();
// Create a GLS model to reuse
GLSMultipleLinearRegression gls = new GLSMultipleLinearRegression();
gls.newSampleData(longley, nObs, 6);
gls.newCovarianceData(cov.getData());
// Create aggregators for stats measuring model performance
DescriptiveStatistics olsBetaStats = new DescriptiveStatistics();
DescriptiveStatistics glsBetaStats = new DescriptiveStatistics();
// Generate Y vectors for 10000 models, estimate GLS and OLS and
// Verify that OLS estimates are better
final int nModels = 10000;
for (int i = 0; i < nModels; i++) {
// Generate y = xb + u with u cov
RealVector u = MatrixUtils.createRealVector(gen.nextVector());
double[] y = u.add(x.operate(b)).getData();
// Estimate OLS parameters
ols.newYSampleData(y);
RealVector olsBeta = ols.calculateBeta();
// Estimate GLS parameters
gls.newYSampleData(y);
RealVector glsBeta = gls.calculateBeta();
// Record deviations from "true" beta
double dist = olsBeta.getDistance(b);
olsBetaStats.addValue(dist * dist);
dist = glsBeta.getDistance(b);
glsBetaStats.addValue(dist * dist);
}
// Verify that GLS is on average more efficient, lower variance
assert(olsBetaStats.getMean() > 1.5 * glsBetaStats.getMean());
assert(olsBetaStats.getStandardDeviation() > glsBetaStats.getStandardDeviation());
}
Example 20
| Source File: LSHCreator.java From datafu with Apache License 2.0 | 4 votes |
|
public RandomGenerator createGenerator()
{
RandomGenerator rg = new JDKRandomGenerator();
rg.setSeed(seed);
return rg;
}
