org.dmg.pmml.GaussianDistribution Java Examples

static
public double probability(GaussianDistribution gaussianDistribution, Number x){
	Number mean = gaussianDistribution.getMean();
	if(mean == null){
		throw new MissingAttributeException(gaussianDistribution, PMMLAttributes.GAUSSIANDISTRIBUTION_MEAN);
	}

	Number variance = gaussianDistribution.getVariance();
	if(variance == null){
		throw new MissingAttributeException(gaussianDistribution, PMMLAttributes.GAUSSIANDISTRIBUTION_VARIANCE);
	} // End if

	if(variance.doubleValue() <= 0d){
		throw new InvalidAttributeException(gaussianDistribution, PMMLAttributes.GAUSSIANDISTRIBUTION_VARIANCE, variance);
	}

	NormalDistribution distribution = new NormalDistribution(mean.doubleValue(), Math.sqrt(variance.doubleValue()));

	return distribution.density(x.doubleValue());
}
 

private void calculateContinuousProbabilities(ProbabilityMap<Object, ?> probabilities, TargetValueStats targetValueStats, Number threshold, FieldValue value){
	Number x = value.asNumber();

	for(TargetValueStat targetValueStat : targetValueStats){
		Object targetCategory = targetValueStat.getValue();
		if(targetCategory == null){
			throw new MissingAttributeException(targetValueStat, PMMLAttributes.TARGETVALUESTAT_VALUE);
		}

		ContinuousDistribution distribution = targetValueStat.getContinuousDistribution();
		if(distribution == null){
			throw new MissingElementException(MissingElementException.formatMessage(XPathUtil.formatElement(targetValueStat.getClass()) + "/<ContinuousDistribution>"), targetValueStat);
		} // End if

		Number probability;

		// "For Naive Bayes models, continuous distribution types are restricted to Gaussian and Poisson distributions"
		if((distribution instanceof GaussianDistribution) || (distribution instanceof PoissonDistribution)){
			probability = DistributionUtil.probability(distribution, x);
		} else

		{
			throw new MisplacedElementException(distribution);
		}

		// The calculated probability cannot fall below the default probability
		if(NumberUtil.compare(probability, threshold) < 0){
			probability = threshold;
		}

		probabilities.multiply(targetCategory, probability);
	}
}
 

/**
 * <p>
 * Calculates the value of the specified probability function at the specified point.
 * </p>
 */
static
public double probability(ContinuousDistribution distribution, Number x){

	if(distribution instanceof GaussianDistribution){
		return probability((GaussianDistribution)distribution, x);
	} else

	if(distribution instanceof PoissonDistribution){
		return probability((PoissonDistribution)distribution, x);
	}

	throw new UnsupportedElementException(distribution);
}
 

@Test
public void estimateDensity(){
	ContinuousDistribution distribution = new GaussianDistribution(5d, Math.pow(1.5d, 2d));

	assertEquals(0.00759732d, DistributionUtil.probability(distribution, 1d), 1e-8);
	assertEquals(0.10934005d, DistributionUtil.probability(distribution, 3d), 1e-8);
	assertEquals(0.26596152d, DistributionUtil.probability(distribution, 5d), 1e-8);
}
 

static
private TargetValueStats encodeTargetValueStats(List<?> values, List<? extends Number> means, List<? extends Number> variances){
	TargetValueStats targetValueStats = new TargetValueStats();

	ClassDictUtil.checkSize(values, means, variances);

	for(int i = 0; i < values.size(); i++){
		GaussianDistribution gaussianDistribution = new GaussianDistribution(means.get(i), variances.get(i));

		TargetValueStat targetValueStat = new TargetValueStat(values.get(i), gaussianDistribution);

		targetValueStats.addTargetValueStats(targetValueStat);
	}

	return targetValueStats;
}