print("Output file {} cannot be created".format(file_name))
sys.exit(1)
# Write header for output file
f_out.write('{}\t{}\t{}\t{}\n'.format('Value of k', 'Accuracy',
'Precision', 'Recall'))
############################## KNN algorithm ####################################
# Create the k-NN object.
knn = KNNClassifier(train_X[:, 1:], train_y[:, 1:], metric='euclidean')
# Iterate through all possible values of k:
for k in range(min_k, max_k + 1):
knn.set_k(k)
# 1. Perform KNN training and classify all the test points. In this step, you will
# obtain a prediction for each test point.
y_pred = []
for i in range(test_X.shape[0]):
result = knn.predict(test_X[i, 1:])
if result:
y_pred.append(result)
else:
knn.set_k(k - 1)
y_pred.append(knn.predict(test_X[i, 1:]))
knn.set_k(k)
