/**
* @return the estimated conditional probability for the given assertion conditional on the given
* condition.
*/
public double getConditionalProb(Proposition assertion, Proposition condition) {
if (assertion.getVariableSource() != condition.getVariableSource()) {
throw new IllegalArgumentException(
"Assertion and condition must be " + "for the same Bayes IM.");
}
List<Node> assertionVars = assertion.getVariableSource().getVariables();
List<Node> dataVars = dataSet.getVariables();
assertionVars = GraphUtils.replaceNodes(assertionVars, dataVars);
if (!new HashSet<Node>(assertionVars).equals(new HashSet<Node>(dataVars))) {
throw new IllegalArgumentException(
"Assertion variable and data variables"
+ " are either different or in a different order: "
+ "\n\tAssertion vars: "
+ assertionVars
+ "\n\tData vars: "
+ dataVars);
}
int[] point = new int[dims.length];
int count1 = 0;
int count2 = 0;
this.missingValueCaseFound = false;
point:
for (int i = 0; i < numRows; i++) {
for (int j = 0; j < dims.length; j++) {
point[j] = dataSet.getInt(i, j);
if (point[j] == DiscreteVariable.MISSING_VALUE) {
continue point;
}
}
if (condition.isPermissibleCombination(point)) {
count1++;
if (assertion.isPermissibleCombination(point)) {
count2++;
}
}
}
return count2 / (double) count1;
}
/** @return the estimated probability of the given proposition. */
public double getProb(Proposition assertion) {
int[] point = new int[dims.length];
int count = 0;
this.missingValueCaseFound = false;
point:
for (int i = 0; i < numRows; i++) {
for (int j = 0; j < dims.length; j++) {
point[j] = dataSet.getInt(i, j);
if (point[j] == DiscreteVariable.MISSING_VALUE) {
this.missingValueCaseFound = true;
continue point;
}
}
if (assertion.isPermissibleCombination(point)) {
count++;
}
}
return count / (double) this.numRows;
}
