@Override public org.deeplearning4j.nn.conf.distribution.BinomialDistribution getBackend() { return new org.deeplearning4j.nn.conf.distribution.BinomialDistribution(numberOfTrials, backend.getProbabilityOfSuccess()); } }
public void setProbabilityOfSuccess(double probabilityOfSuccess) { backend.setProbabilityOfSuccess(probabilityOfSuccess); }
public static org.nd4j.linalg.api.rng.distribution.Distribution createDistribution(Distribution dist) { if (dist == null) return null; if (dist instanceof NormalDistribution) { NormalDistribution nd = (NormalDistribution) dist; return Nd4j.getDistributions().createNormal(nd.getMean(), nd.getStd()); } if (dist instanceof GaussianDistribution) { GaussianDistribution nd = (GaussianDistribution) dist; return Nd4j.getDistributions().createNormal(nd.getMean(), nd.getStd()); } if (dist instanceof UniformDistribution) { UniformDistribution ud = (UniformDistribution) dist; return Nd4j.getDistributions().createUniform(ud.getLower(), ud.getUpper()); } if (dist instanceof BinomialDistribution) { BinomialDistribution bd = (BinomialDistribution) dist; return Nd4j.getDistributions().createBinomial(bd.getNumberOfTrials(), bd.getProbabilityOfSuccess()); } throw new RuntimeException("unknown distribution type: " + dist.getClass()); } }
@Override public void initializeBackend() { // Constructs binomial distribution with 1 trial and success probability 0.5 backend = new org.deeplearning4j.nn.conf.distribution.BinomialDistribution(numberOfTrials, 0.5); }
@OptionMetadata( displayName = "probability of success", description = "The probability of success (default = 0.5).", commandLineParamName = "prob", commandLineParamSynopsis = "-prob <double>", displayOrder = 1 ) public double getProbabilityOfSuccess() { return backend.getProbabilityOfSuccess(); }
return new BinomialDistribution(num, p); } else { throw new JsonParseException(