/**
* Setter for initialProbability
*
* @param initialProbability Initial probability Vector over the states. Each entry must be
* nonnegative and the Vector must sum to 1.
*/
public void setInitialProbability(Vector initialProbability) {
final int k = initialProbability.getDimensionality();
double sum = 0.0;
for (int i = 0; i < k; i++) {
double value = initialProbability.getElement(i);
if (value < 0.0) {
throw new IllegalArgumentException("Initial Probabilities must be >= 0.0");
}
sum += value;
}
if (sum != 1.0) {
initialProbability.scaleEquals(1.0 / sum);
}
this.initialProbability = initialProbability;
}
/**
* Returns the steady-state distribution of the state distribution. This is also the largest
* eigenvector of the transition-probability matrix, which has an eigenvalue of 1.
*
* @return Steady-state probability distribution of state distribution.
*/
public Vector getSteadyStateDistribution() {
final double tolerance = 1e-5;
final int maxIterations = 100;
Vector p =
EigenvectorPowerIteration.estimateEigenvector(
this.initialProbability, this.transitionProbability, tolerance, maxIterations);
// We do the manual sum (instead of norm1) in case the EVD found
// the negative of the eigenvector.
double sum = 0.0;
for (int i = 0; i < p.getDimensionality(); i++) {
sum += p.getElement(i);
}
p.scaleEquals(1.0 / sum);
return p;
}
