/**
* Creates a new dataset of the same size using random sampling with replacement according to the
* given weight vector. The weights of the instances in the new dataset are set to one. The length
* of the weight vector has to be the same as the number of instances in the dataset, and all
* weights have to be positive.
*
* @param data the data to be sampled from
* @param random a random number generator
* @param sampled indicating which instance has been sampled
* @return the new dataset
* @exception IllegalArgumentException if the weights array is of the wrong length or contains
* negative weights.
*/
public final Instances resampleWithWeights(Instances data, Random random, boolean[] sampled) {
double[] weights = new double[data.numInstances()];
for (int i = 0; i < weights.length; i++) {
weights[i] = data.instance(i).weight();
}
Instances newData = new Instances(data, data.numInstances());
if (data.numInstances() == 0) {
return newData;
}
double[] probabilities = new double[data.numInstances()];
double sumProbs = 0, sumOfWeights = Utils.sum(weights);
for (int i = 0; i < data.numInstances(); i++) {
sumProbs += random.nextDouble();
probabilities[i] = sumProbs;
}
Utils.normalize(probabilities, sumProbs / sumOfWeights);
// Make sure that rounding errors don't mess things up
probabilities[data.numInstances() - 1] = sumOfWeights;
int k = 0;
int l = 0;
sumProbs = 0;
while ((k < data.numInstances() && (l < data.numInstances()))) {
if (weights[l] < 0) {
throw new IllegalArgumentException("Weights have to be positive.");
}
sumProbs += weights[l];
while ((k < data.numInstances()) && (probabilities[k] <= sumProbs)) {
newData.add(data.instance(l));
sampled[l] = true;
newData.instance(k).setWeight(1);
k++;
}
l++;
}
return newData;
}
/**
* Calculates the class membership probabilities for the given test instance.
*
* @param instance the instance to be classified
* @return preedicted class probability distribution
* @exception Exception if distribution can't be computed successfully
*/
@Override
public double[] distributionForInstance(Instance instance) throws Exception {
double[] sums = new double[instance.numClasses()], newProbs;
for (int i = 0; i < m_NumIterations; i++) {
if (instance.classAttribute().isNumeric() == true) {
sums[0] += m_Classifiers[i].classifyInstance(instance);
} else {
newProbs = m_Classifiers[i].distributionForInstance(instance);
for (int j = 0; j < newProbs.length; j++) sums[j] += newProbs[j];
}
}
if (instance.classAttribute().isNumeric() == true) {
sums[0] /= m_NumIterations;
return sums;
} else if (Utils.eq(Utils.sum(sums), 0)) {
return sums;
} else {
Utils.normalize(sums);
return sums;
}
}
