def classifiy(class_num, subsample_size, window_size, cluster_num, max_iter, rnd_number, neighbor_num, train_X, train_y, test_X, test_y):
print("=== Classify Start ===\n")
features = FeatureExtraction.read_features(FeatureExtraction.gen_feature_fname(class_num, subsample_size, window_size, cluster_num))
knn = KNeighborsClassifier(n_neighbors=neighbor_num)
feature_vectors_train = FeatureExtraction.trainset_project(features, train_X, True)
feature_vectors_test = FeatureExtraction.trainset_project(features, test_X, True)
start = time.time()
knn.fit(feature_vectors_train, train_y)
print("KNN Fit Time: ",time.time()-start)
start = time.time()
acc = 0
test_len = len(feature_vectors_test)
for i in range(0, test_len):
if knn.predict(feature_vectors_test[i]) == test_y[i]:
acc += 1
# end if
# end for
print("KNN Predict Time: ",time.time()-start)
print("Accuracy: ",(acc/test_len),", Neighbor Number: ",neighbor_num)
print("=== Classify Finish ===")
