/** {@inheritDoc} */
@Override
public double getLogProbability(double value) {
double prob = 0;
int index = 0;
for (Normal normal : this.terms) {
prob = prob + this.coefficients[index] * normal.getProbability(value);
index++;
}
return Math.log(prob);
}
/**
* Creates a new GaussianMixture distribution for a given variable.
*
* @param var1 a {@link Variable} object.
*/
public GaussianMixture(Variable var1) {
this.var = var1;
terms = new ArrayList<Normal>();
Normal aux = new Normal(var1);
aux.setMean(0);
aux.setVariance(1);
terms.add(aux);
coefficients = new double[1];
coefficients[0] = 1;
}
/** {@inheritDoc} */
@Override
public double[] getParameters() {
int numParameters = 3 * coefficients.length;
double[] parameters = new double[numParameters];
int index = 0;
for (Normal normal : this.terms) {
parameters[3 * index] = this.coefficients[index];
parameters[3 * index + 1] = normal.getMean();
parameters[3 * index + 2] = normal.getVariance();
index++;
}
return parameters;
}
/**
* Sets the parameters of this GaussianMixture.
*
* @param params an Array of doubles containing the GaussianMixture parameters.
*/
public void setParameters(double[] params) {
if (params.length % 3 != 0) {
throw new UnsupportedOperationException(
"The number of parameters for the Gaussian mixture is not valid");
} else {
int numTerms = params.length / 3;
this.coefficients = new double[numTerms];
this.terms = new ArrayList<>(numTerms);
for (int index = 0; index < numTerms; index++) {
this.coefficients[index] = params[3 * index];
Normal aux = new Normal(this.var);
aux.setMean(params[3 * index + 1]);
aux.setVariance(params[3 * index + 2]);
this.terms.add(aux);
}
}
};
/**
* Randomly initializes this GaussianMixture for a given number of terms.
*
* @param random a {@link java.util.Random} object.
* @param numTerms a number of terms.
*/
public void randomInitialization(Random random, int numTerms) {
this.coefficients = new double[numTerms];
this.terms = new ArrayList<>(numTerms);
for (int k = 0; k < numTerms; k++) {
this.coefficients[k] = random.nextDouble();
Normal aux = new Normal(this.var);
aux.setMean(5 * random.nextGaussian());
aux.setVariance(random.nextDouble());
this.terms.add(aux);
}
;
DoubleStream aux = Arrays.stream(this.coefficients);
double suma = aux.sum();
aux = Arrays.stream(this.coefficients);
this.coefficients = aux.map(x -> x / suma).toArray();
// System.out.println(coefficients);
// this.coefficients = this.coefficients / .map().sum();
}
