def test_1NN(digitsdat, selected, all_test_m):
for testm in all_test_m:
classifier = Classifier()
classifier.build_model(digitsdat.X[selected[0:testm], :], digitsdat.y[ selected[0:testm]])
print("m=%d error=%f" % ( testm, classifier.classify(digitsdat.testX, digitsdat.testy)))
def test_1NN(digitsdat, selected, all_test_m):
for testm in all_test_m:
classifier = Classifier()
# model = build(digitsdat.X[selected[0:testm], :], digitsdat.y[selected[0:testm]])
classifier.build_model(digitsdat.X[selected[0:testm], :],
digitsdat.y[selected[0:testm]])
error = classifier.classify(digitsdat.testX, digitsdat.testy)
# accuracy = res(model)
# print("m=%d error=%f" % (testm, 100-accuracy))
print("m=%d error=%f" % (testm, error))
# global M, e
M.append(testm)
e.append(error)
