/** * Computes value of splitting criterion before split. * * @param dist the distributions * @return the splitting criterion */ protected double priorVal(double[][] dist) { return ContingencyTables.entropyOverColumns(dist); }
/** * Computes value of splitting criterion before split. * * @param dist the distributions * @return the splitting criterion */ protected double priorVal(double[][] dist) { return ContingencyTables.entropyOverColumns(dist); }
/** * Computes value of splitting criterion before split. * * @param dist * @return the splitting criterion */ protected double priorVal(double[][] dist) { return ContingencyTables.entropyOverColumns(dist); }
/** * Computes value of splitting criterion before split. * * @param dist * @return the splitting criterion */ protected double priorVal(double[][] dist) { return ContingencyTables.entropyOverColumns(dist); }
public double getInfoGain() { return ContingencyTables.entropyOverColumns(getContingencyTable()) - ContingencyTables .entropyConditionedOnRows(getContingencyTable()); }
ContingencyTables.entropyOverRows(matrix)); System.out.println("Entropy of columns: " + ContingencyTables.entropyOverColumns(matrix)); System.out.println("Gain ratio: " + ContingencyTables.gainRatio(matrix));
ContingencyTables.entropyOverRows(matrix)); System.out.println("Entropy of columns: " + ContingencyTables.entropyOverColumns(matrix)); System.out.println("Gain ratio: " + ContingencyTables.gainRatio(matrix));
for (int i = 0; i < data.numAttributes(); i++) { if (i != classIndex) { m_InfoGains[i] = (ContingencyTables.entropyOverColumns(counts[i]) - ContingencyTables .entropyConditionedOnRows(counts[i]));
for (int i = 0; i < data.numAttributes(); i++) { if (i != classIndex) { m_InfoGains[i] = (ContingencyTables.entropyOverColumns(counts[i]) - ContingencyTables .entropyConditionedOnRows(counts[i]));