dists1 = classifier.compute_distances_one_loop(X_test)
dists0 = classifier.compute_distances_no_loops(X_test)
dists = classifier.compute_distances_no_loops(X_test)
print dists.shape
# Now implement the function predict_labels and run the code below:
# We use k = 1 (which is Nearest Neighbor).
y_test_pred = classifier.predict_labels(dists, k=1)
# Compute and print the fraction of correctly predicted examples
num_correct = np.sum(y_test_pred == y_test)
accuracy = float(num_correct) / num_test
print 'Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy)
classifier.predict(X_test, k=1, num_loops=0)
classifier.pridict_currency(X_test, y_test, k=1, num_loops=0)
# cross validation
num_folds = 5
k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]
X_train_folds = []
y_train_folds = []
X_train_folds = np.array_split(X_train, num_folds)
y_train_folds = np.array_split(y_train, num_folds)
k_to_accuracies = {}
for k in k_choices:
validation_accuracies = []
for i in range(num_folds):
current_x_test = X_train_folds[i]
current_y_test = y_train_folds[i]
