private Matrix walk() throws IOException {
// Step 0. Initialise transition probabilities
this.logger.showTimedMessage("Initialise transition probabilities");
Matrix P = this.initialiseTransitionProbabilities();
logger.showMemoryUsage();
// 2. initialise random walkers.
// keep in mind that this makes sense because of the way the indexes were
// loaded into this.goTermIndex. Otherwise, we would have to retrieve
// from this.goTermIndex
Matrix W = new Matrix(this.getNumGoTerms(), this.getNumGoTerms());
for (int i = 0; i < this.getNumGoTerms(); i++) {
W.set(i, i, 1.0f);
}
// walk!
Matrix W_star = W.copy();
double convergence;
// int i=0;
do {
W = W_star;
W_star = P.times(W);
convergence = W_star.minus(W).normF();
this.logger.showTimedMessage(
"\t Convergence difference: " + (new Double(convergence)).toString());
// this.logger.showTimedMessage("\t Doing iteration i=" + i);
// i++;
// }while (i<15);
} while (convergence > this.epsilon);
return W_star;
}
public Matrix computeISM() throws IOException {
logger.showMemoryUsage();
// Step 1. Walk!
this.logger.showTimedMessage("Walking...");
Matrix W = walk();
logger.showMemoryUsage();
// Step 2. Compute the random wal contribution
if (this.termwise) {
this.setRandomWalkContributionTermwise(W);
} else {
this.setRandomWalkContributionGeneWise(W);
}
W = null;
System.gc();
logger.showMemoryUsage();
// Step 3. get the ISM which we return.
logger.showMemoryUsage();
return this.getISM();
}
