def classify(probabilities, testX, testY): """ Uses the sum rule to combine two predictions and then classify the data. """ n = len(testX) P = sum_rule(probabilities, n) E = fit(testX, P) (e_rate, se, interval) = error.confidenceInterval(testY, E) return (P, E, e_rate, se, interval)
def classify(trainX, trainY, testX, testY): """ Uses the Bayesian Classifier to classify the test data. """ trainC = getClasses(trainY) P = estimatePosterior(trainX, trainC, testX) E = fit(testX, P) (e_rate, se, interval) = error.confidenceInterval(testY, E) return (P, E, e_rate, se, interval)
def classify(trainX, trainY, testX, testY, k): """ Uses the KNN to classify the test data. """ P = estimatePosterior(trainX, trainY, testX, k) E = fit(testX, P) (e_rate, se, interval) = error.confidenceInterval(testY, E) return (P, E, e_rate, se, interval)