cern.jet.random.Uniform Java Examples

/**
 * Generate the training data of the given size. The original data becomes
 * the testing data.
 * 
 * @param nOfTraining
 * @return
 */
public DataSet splitIntoTrainingAndTesting(int nOfTraining) {
	DataSet training = new DataSet(_variables);
	for (int i = 0; i < nOfTraining; i++) {
		double total = getTotalWeight();
		double random = Uniform.staticNextDoubleFromTo(0.0, total);
		double accumulator = 0.0;
		// Find the datacase to be moved
		for (DataCase data : getData()) {
			double weight = data.getWeight();
			accumulator += weight;
			if (accumulator > random) {
				training.addDataCase(data.getStates(), 1.0);
				if (weight == 1.0) {
					getData().remove(data);
				} else {
					data.setWeight(weight - 1.0);
				}
				_totalWeight = _totalWeight - 1.0;
				break;
			}
		}
	}
	return training;
}
 

/**
 * Generate the training data of the given size from the current data. The
 * method is sample from the current data with replacement. No side effect
 * on the input.
 * 
 * @param nOfTraining
 * @return
 */
public DataSet sampleWithReplacement(int nOfTraining) {
	DataSet training = new DataSet(_variables);
	for (int i = 0; i < nOfTraining; i++) {
		double total = getTotalWeight();
		double random = Uniform.staticNextDoubleFromTo(0.0, total);
		double accumulator = 0.0;
		// Find the datacase to be moved
		for (DataCase data : getData()) {
			double weight = data.getWeight();
			accumulator += weight;
			if (accumulator > random) {
				training.addDataCase(data.getStates(), 1.0);
				break;
			}
		}
	}
	return training;
}
 

/**
 * Returns a deep copy of the receiver.
 */
public Object clone() {
	WeightedRandomSampler copy = (WeightedRandomSampler) super.clone();
	copy.generator = (Uniform) this.generator.clone();
	return copy;
}
 

/**
 * Returns a deep copy of the receiver.
 */
public Object clone() {
	WeightedRandomSampler copy = (WeightedRandomSampler) super.clone();
	copy.generator = (Uniform) this.generator.clone();
	return copy;
}
 

/**
 * Chooses exactly one random element from successive blocks of <tt>weight</tt> input elements each.
 * For example, if weight==2, and the input is 5*2=10 elements long, then chooses 5 random elements from the 10 elements such that
 * one is chosen from the first block, one from the second, ..., one from the last block.
 * weight == 1.0 --> all elements are consumed (sampled). 10.0 --> Consumes one random element from successive blocks of 10 elements each. Etc.
 * @param weight the weight.
 * @param randomGenerator a random number generator. Set this parameter to <tt>null</tt> to use the default random number generator.
 */
public WeightedRandomSampler(int weight, RandomEngine randomGenerator) {
	if (randomGenerator==null) randomGenerator = cern.jet.random.AbstractDistribution.makeDefaultGenerator();
	this.generator = new Uniform(randomGenerator);
	setWeight(weight);
}
 

/**
 * Chooses exactly one random element from successive blocks of <tt>weight</tt> input elements each.
 * For example, if weight==2, and the input is 5*2=10 elements long, then chooses 5 random elements from the 10 elements such that
 * one is chosen from the first block, one from the second, ..., one from the last block.
 * weight == 1.0 --> all elements are consumed (sampled). 10.0 --> Consumes one random element from successive blocks of 10 elements each. Etc.
 * @param weight the weight.
 * @param randomGenerator a random number generator. Set this parameter to <tt>null</tt> to use the default random number generator.
 */
public WeightedRandomSampler(int weight, RandomEngine randomGenerator) {
	if (randomGenerator==null) randomGenerator = cern.jet.random.AbstractDistribution.makeDefaultGenerator();
	this.generator = new Uniform(randomGenerator);
	setWeight(weight);
}