private void initializeBN(SelectedSet selectedSet) {
int NS = selectedSet.getN();
int SIZE = selectedSet.getIndividual(0).getIndividual().length;
this.bestScoreGain = Double.NEGATIVE_INFINITY;
Individual[] individuals = selectedSet.getIndividuals();
for (int i = 0; i < Problem.n; i++) { // Refresh the Bayesian Network structure.
parentList[i] =
new HashSet<Integer>(
Problem.n); // Initial Capacity = stringSize; Default Load Factor = 0.75
adjacencyList[i] =
new HashSet<Integer>(
Problem.n); // Initial Capacity = stringSize; Default Load Factor = 0.75
splitList[i] =
new HashSet<Integer>(
2 * Problem.n); // Initial Capacity = 2*stringSize; Default Load Factor = 0.75
for (int n = 0; n < Problem.n; n++) {
if (n != i) {
splitList[i].add(n);
}
}
int mOne =
selectedSet.getUniFrequencies(i); // Construct the initial single leaf decision graphs.
int mZero = NS - mOne;
Leaf newLeaf =
new Leaf(
0, -1, mZero, mOne,
SIZE); // There is only a single leaf per variable, at depth 0, with side -1.
if (mZero > 0
&& mOne
> 0) { // If mZero = 0 or mOne = 0 there is no need to try any split with this leaf.
for (int j = 0; j < NS; j++) {
char alleleJI = individuals[j].getAllele(i); // Value of Xi in individual j.
for (int s : splitList[i]) {
char alleleS = individuals[j].getAllele(s); // Value of Xs in individual j.
if (alleleJI == '0') {
if (alleleS == '0') {
newLeaf.addPossibleSplitFrequency(0, s); // m00[s]++;
} else {
newLeaf.addPossibleSplitFrequency(1, s); // m01[s]++;
}
} else if (alleleS == '0') {
newLeaf.addPossibleSplitFrequency(2, s); // m10[s]++;
} else {
newLeaf.addPossibleSplitFrequency(3, s); // m11[s]++;
}
}
}
for (int s : splitList[i]) {
int m00 = newLeaf.getPossibleSplitFrequency(0, s);
int m01 = newLeaf.getPossibleSplitFrequency(1, s);
int m10 = newLeaf.getPossibleSplitFrequency(2, s);
int m11 = newLeaf.getPossibleSplitFrequency(3, s);
double scoreGain = bayesianMetric.computeScoreGain(mZero, mOne, m00, m01, m10, m11);
newLeaf.setScoreGain(s, scoreGain);
newLeaf.updateBestSplit(
s,
scoreGain); // Responsible for updating the value of the best split score gain in this
// leaf.
}
} // END: if(mZero > 0 ...)
decisionGraphs[i] =
new DecisionGraph(
newLeaf); // Initially each graph as a single leaf and there are n-1 possible splits.
decisionGraphs[i].updateBestLeaf();
double scoreGain = decisionGraphs[i].getBestLeafScoreGain();
if (scoreGain
> this
.bestScoreGain) { // Update the value of the global best score gain and position (DG).
this.bestDecisionGraphPos = i;
this.bestScoreGain = scoreGain;
}
} // END: for(int i = 0 ...)
} // END: initializeBN(...)
