# initialize the values of k for our k-Nearest Neighbor classifier along with the
# list of accuracies for each value of k
kVals = range(1, 30, 2)
accuracies = []

# loop over various values of `k` for the k-Nearest Neighbor classifier
for k in range(1, 30, 2):
	# train the k-Nearest Neighbor classifier with the current value of `k`
	model = KNeighborsClassifier(n_neighbors=k)
	model.fit(trainData, trainLabels)

	# evaluate the model and update the accuracies list
	score = model.score(valData, valLabels)
	print("k=%d, accuracy=%.2f%%" % (k, score * 100))
	accuracies.append(score)

# find the value of k that has the largest accuracy
i = int(np.argmax(accuracies))
print("k=%d achieved highest accuracy of %.2f%% on validation data" % (kVals[i],
	accuracies[i] * 100))