本文整理汇总了Java中beast.util.Randomizer.nextGamma方法的典型用法代码示例。如果您正苦于以下问题：Java Randomizer.nextGamma方法的具体用法？Java Randomizer.nextGamma怎么用？Java Randomizer.nextGamma使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类beast.util.Randomizer的用法示例。
在下文中一共展示了Randomizer.nextGamma方法的5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: sample
import beast.util.Randomizer; //导入方法依赖的package包/类
@Override
public Double[][] sample(int size){
double scaleVal = scale.getValue();
Double[][] samples = new Double[size][1];
try{
for(int i =0; i < size;i++){
Double[] dirichletSample = new Double[2];
double sum = 0.0;
for(int j =0; j < 2;j++){
dirichletSample[j] = Randomizer.nextGamma(alpha.getValue(j) * scaleVal, 1.0);
sum += dirichletSample[j];
}
dirichletSample[0] = dirichletSample[0]/sum;
samples[i][0] = dirichletSample[0];
}
}catch(Exception e){
throw new RuntimeException(e);
}
return samples;
}
示例2: nextBetaScale
import beast.util.Randomizer; //导入方法依赖的package包/类
public static double nextBetaScale(Double[] alpha, double scaleVal){
Double[] sample = new Double[alpha.length];
double sum = 0.0;
for(int j = 0; j < sample.length;j++){
sample[j] = Randomizer.nextGamma(alpha[j] * scaleVal, 1.0);
sum += sample[j];
}
return sample[0]/sum;
}
示例3: sample
import beast.util.Randomizer; //导入方法依赖的package包/类
@Override
public Double[][] sample(int size){
double scaleVal = scale.getValue();
int dim = alpha.getDimension();
Double[][] samples = new Double[size][];
try{
for(int i =0; i < size;i++){
Double[] dirichletSample = new Double[dim];
double sum = 0.0;
for(int j =0; j < dim;j++){
dirichletSample[j] = Randomizer.nextGamma(alpha.getValue(j)*scaleVal,1.0);
sum += dirichletSample[j];
}
for(int j = 0; j < dim;j++){
dirichletSample[j] = dirichletSample[j]/sum;
}
samples[i] = dirichletSample;
}
}catch(Exception e){
throw new RuntimeException(e);
}
return samples;
}
示例4: nextDirichletScale
import beast.util.Randomizer; //导入方法依赖的package包/类
public static Double[] nextDirichletScale(Double[] alpha, double scaleVal){
Double[] sample = new Double[alpha.length];
double sum = 0.0;
for(int j =0; j < sample.length;j++){
sample[j] = Randomizer.nextGamma(alpha[j]*scaleVal,1.0);
sum += sample[j];
}
for(int j = 0; j < sample.length;j++){
sample[j] = sample[j]/sum;
}
return sample;
}
示例5: testPdf
import beast.util.Randomizer; //导入方法依赖的package包/类
public void testPdf() throws MathException {
final int numberOfTests = 300;
double totErr = 0;
double ptotErr = 0; int np = 0;
double qtotErr = 0;
Randomizer.setSeed(551);
for(int i = 0; i < numberOfTests; i++){
final double mean = .01 + (3-0.01) * Randomizer.nextDouble();
final double var = .01 + (3-0.01) * Randomizer.nextDouble();
double scale0 = var / mean;
double shape = mean / scale0;
final Gamma gamma = new Gamma();
Gamma.mode mode = Gamma.mode.values()[Randomizer.nextInt(4)];
double other = 0;
switch (mode) {
case ShapeScale: other = scale0; break;
case ShapeRate: other = 1/scale0; break;
case ShapeMean: other = scale0 * shape; break;
case OneParameter: other = 1/shape; scale0 = 1/shape; break;
}
final double scale = scale0;
gamma.initByName("alpha", shape +"", "beta", other +"", "mode", mode);
final double value = Randomizer.nextGamma(shape, 1/scale);
final double mypdf = mypdf(value, shape, scale);
final double pdf = gamma.density(value);
if ( Double.isInfinite(mypdf) && Double.isInfinite(pdf)) {
continue;
}
assertFalse(Double.isNaN(mypdf));
assertFalse(Double.isNaN(pdf));
totErr += mypdf != 0 ? Math.abs((pdf - mypdf)/mypdf) : pdf;
assertFalse("nan", Double.isNaN(totErr));
//assertEquals("" + shape + "," + scale + "," + value, mypdf,gamma.pdf(value),1e-10);
final double cdf = gamma.cumulativeProbability(value);
UnivariateRealFunction f = new UnivariateRealFunction() {
public double value(double v) throws FunctionEvaluationException {
return mypdf(v, shape, scale);
}
};
final UnivariateRealIntegrator integrator = new RombergIntegrator();
integrator.setAbsoluteAccuracy(1e-14);
integrator.setMaximalIterationCount(16); // fail if it takes too much time
double x;
try {
x = integrator.integrate(f, 0, value);
ptotErr += cdf != 0.0 ? Math.abs(x-cdf)/cdf : x;
np += 1;
//assertTrue("" + shape + "," + scale + "," + value + " " + Math.abs(x-cdf)/x + "> 1e-6", Math.abs(1-cdf/x) < 1e-6);
final double q = gamma.inverseCumulativeProbability(cdf);
qtotErr += q != 0 ? Math.abs(q-value)/q : value;
//System.out.println(shape + "," + scale + " " + value);
} catch( ConvergenceException e ) {
// can't integrate , skip test
//System.out.print(" theta(" + shape + "," + scale + ") skipped");
}
// assertEquals("" + shape + "," + scale + "," + value + " " + Math.abs(q-value)/value, q, value, 1e-6);
// System.out.print("\n" + np + ": " + mode + " " + totErr/np + " " + qtotErr/np + " " + ptotErr/np);
}
//System.out.println( !Double.isNaN(totErr) );
// System.out.println(totErr);
// bad test, but I can't find a good threshold that works for all individual cases
assertTrue("failed " + totErr/numberOfTests, totErr/numberOfTests < 1e-7);
assertTrue("failed " + qtotErr/numberOfTests , qtotErr/numberOfTests < 1e-10);
assertTrue("failed " + ptotErr/np, np > 0 ? (ptotErr/np < 2e-7) : true);
}