def get_data(key): s = sim.SimulateClassification(3) x_P, y_P = s.simulateP_(int(key[1])) x_Q, y_Q = s.simulateQ_(20) x_test, bayes = s.bayes_rule_(num_points) return [(key, [x_P, y_P, x_Q, y_Q, x_test[i], bayes[i]]) for i in range(len(x_test))]
def classify(y): key, data = y print(str(key)) s = sim.SimulateClassification(3) cl = ts.AdaClassifier(data[0], data[1], data[2], data[3], kernel="cauchy") predict = cl.predict_one(data[4]) predict_knn = cl.knn_(data[2], data[3], data[4]) prop = np.absolute(s.prop_(data[4]) - 0.5) return key, [ np.absolute(data[5] - predict) * prop, np.absolute(data[5] - predict_knn) * prop ]