KNN.py

## K NEAREST NEIGHBOUR MACHINE LEARNING ALGORITHM ON PYTHON ##

def train(X_train, y_train):
	# do nothing 
	return
def predict(X_train, y_train, x_test, k):
	# create list for distances and targets
	distances = []
	targets = []

	for i in range(len(X_train)):
		# first we compute the euclidean distance
		distance = np.sqrt(np.sum(np.square(x_test - X_train[i, :])))
		# add it to list of distances
		distances.append([distance, i])

	# sort the list
	distances = sorted(distances)

	# make a list of the k neighbors' targets
	for i in range(k):
		index = distances[i][1]
		targets.append(y_train[index])

	# return most common target
	return Counter(targets).most_common(1)[0][0]
def kNearestNeighbor(X_train, y_train, X_test, predictions, k):
	# check if k larger than n
	if k > len(X_train):
		raise ValueError
		
	# train on the input data
	train(X_train, y_train)

	# predict for each testing observation
	for i in range(len(X_test)):
		predictions.append(predict(X_train, y_train, X_test[i, :], k))

# making our predictions 
predictions = []
try:
	kNearestNeighbor(X_train, y_train, X_test, predictions, 7)
	predictions = np.asarray(predictions)

	# evaluating accuracy
	accuracy = accuracy_score(y_test, predictions) * 100
	print('\nThe accuracy of OUR classifier is %d%%' % accuracy)

except ValueError:
	print('Can\'t have more neighbors than training samples!!')