def test_KNN_dists_oneloop_shape(sample_train, sample_test):
Xtrain, ytrain = sample_train(count=40)
Xtest, ytest = sample_test(count=10)
Xtrain = np.reshape(Xtrain, (Xtrain.shape[0], -1))
Xtest = np.reshape(Xtest, (Xtest.shape[0], -1))
knn = KNearestNeighbor()
knn.train(Xtrain,ytrain)
assert knn.compute_distances_one_loop(Xtest).shape == (Xtest.shape[0], Xtrain.shape[0])
def test_KNN_dists_one_to_none(sample_train, sample_test):
Xtrain, ytrain = sample_train(count=40)
Xtest, ytest = sample_test(count=10)
Xtrain = np.reshape(Xtrain, (Xtrain.shape[0], -1))
Xtest = np.reshape(Xtest, (Xtest.shape[0], -1))
knn = KNearestNeighbor()
knn.train(Xtrain,ytrain)
dist_one = knn.compute_distances_one_loop(Xtest)
dist_no = knn.compute_distances_no_loops(Xtest)
assert np.linalg.norm(dist_one - dist_no, ord='fro') < 0.001
# 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))
y_test_pred = classifier.predict_labels(dists, k=5)
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))
# Now lets speed up distance matrix computation by using partial vectorization
# with one loop. Implement the function compute_distances_one_loop and run the
# code below:
dists_one = classifier.compute_distances_one_loop(X_test)
# To ensure that our vectorized implementation is correct, we make sure that it
# agrees with the naive implementation. There are many ways to decide whether
# two matrices are similar; one of the simplest is the Frobenius norm. In case
# you haven't seen it before, the Frobenius norm of two matrices is the square
# root of the squared sum of differences of all elements; in other words, reshape
# the matrices into vectors and compute the Euclidean distance between them.
difference = np.linalg.norm(dists - dists_one, ord='fro')
print('Difference was: %f' % (difference, ))
if difference < 0.001:
print('Good! The distance matrices are the same')
else:
print('Uh-oh! The distance matrices are different')
# Now implement the fully vectorized version inside compute_distances_no_loops
