/** * Computes the distribution for a given instance * * @param instance the instance for which distribution is computed * @return the distribution * @throws Exception if the distribution can't be computed successfully */ @Override public double[] distributionForInstance(Instance instance) throws Exception { m_ReplaceMissingValues.input(instance); instance = m_ReplaceMissingValues.output(); m_AttFilter.input(instance); instance = m_AttFilter.output(); m_NominalToBinary.input(instance); instance = m_NominalToBinary.output(); // Extract the predictor columns into an array double[] instDat = new double[m_NumPredictors + 1]; int j = 1; instDat[0] = 1; for (int k = 0; k <= m_NumPredictors; k++) { if (k != m_ClassIndex) { instDat[j++] = instance.value(k); } } double[] distribution = evaluateProbability(instDat); return distribution; }
/** * Computes the distribution for a given instance * * @param instance the instance for which distribution is computed * @return the distribution * @throws Exception if the distribution can't be computed successfully */ @Override public double[] distributionForInstance(Instance instance) throws Exception { m_ReplaceMissingValues.input(instance); instance = m_ReplaceMissingValues.output(); m_AttFilter.input(instance); instance = m_AttFilter.output(); m_NominalToBinary.input(instance); instance = m_NominalToBinary.output(); // Extract the predictor columns into an array double[] instDat = new double[m_NumPredictors + 1]; int j = 1; instDat[0] = 1; for (int k = 0; k <= m_NumPredictors; k++) { if (k != m_ClassIndex) { instDat[j++] = instance.value(k); } } double[] distribution = evaluateProbability(instDat); return distribution; }