@Override public int sample() { double randomVal = realDist.sample(); long longVal = Math.round(randomVal); return (int) longVal; } }
/** * {@inheritDoc} * * The default implementation generates the sample by calling * {@link #sample()} in a loop. */ public double[] sample(int sampleSize) { if (sampleSize <= 0) { throw new NotStrictlyPositiveException(LocalizedFormats.NUMBER_OF_SAMPLES, sampleSize); } double[] out = new double[sampleSize]; for (int i = 0; i < sampleSize; i++) { out[i] = sample(); } return out; }
private Object generateSingleRowValue() { Object ret = null; ValueType type = schema.getType(); if (distribution instanceof AbstractIntegerDistribution) { ret = ((AbstractIntegerDistribution) distribution).sample(); } else if (distribution instanceof AbstractRealDistribution) { ret = ((AbstractRealDistribution) distribution).sample(); } else if (distribution instanceof EnumeratedDistribution) { ret = ((EnumeratedDistribution) distribution).sample(); } ret = convertType(ret, type); return ret; }
@Override public double sample () { return impl.sample(); } }
@Override public long next() { return offset(min, delta, delegate.sample()); }
@Override public long next() { return bound(min, max, delegate.sample()); }
public double nextDouble() { return offsetDouble(min, delta, delegate.sample()); }
public double nextDouble() { return boundDouble(min, max, delegate.sample()); }
/** * {@inheritDoc} * * The default implementation generates the sample by calling * {@link #sample()} in a loop. */ public double[] sample(int sampleSize) { if (sampleSize <= 0) { throw new NotStrictlyPositiveException(LocalizedFormats.NUMBER_OF_SAMPLES, sampleSize); } double[] out = new double[sampleSize]; for (int i = 0; i < sampleSize; i++) { out[i] = sample(); } return out; }
/** * {@inheritDoc} * * The default implementation generates the sample by calling * {@link #sample()} in a loop. */ public double[] sample(int sampleSize) { if (sampleSize <= 0) { throw new NotStrictlyPositiveException(LocalizedFormats.NUMBER_OF_SAMPLES, sampleSize); } double[] out = new double[sampleSize]; for (int i = 0; i < sampleSize; i++) { out[i] = sample(); } return out; }
public double[][] getUncorrelatedShocks(int sampleSize) { AbstractRealDistribution distribution; AbstractRealDistribution varDist = null; if (errorsNormal) { distribution = new NormalDistribution(new Well1024a(++seed), 0, 1); varDist = new UniformRealDistribution(varLow, varHigh); } else { distribution = new BetaDistribution(new Well1024a(++seed), getBetaLeftValue(), getBetaRightValue()); } int numVars = variableNodes.size(); setupModel(numVars); double[][] shocks = new double[sampleSize][numVars]; for (int j = 0; j < numVars; j++) { for (int i = 0; i < sampleSize; i++) { double sample = distribution.sample(); if (errorsNormal) { sample *= sqrt(varDist.sample()); } shocks[i][j] = sample; } } return shocks; }