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)
