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
]
