def test_KNN_dists_twoloop_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_two_loops(Xtest).shape == (Xtest.shape[0], Xtrain.shape[0])
def test_KNN_dists_two_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_two = knn.compute_distances_two_loops(Xtest)
dist_no = knn.compute_distances_no_loops(Xtest)
assert np.linalg.norm(dist_two - dist_no, ord='fro') < 0.001
X_train = np.reshape(X_train, (X_train.shape[0], -1))
X_test = np.reshape(X_test, (X_test.shape[0], -1))
print(X_train.shape, X_test.shape)
from cs231n.classifiers.k_nearest_neighbor import KNearestNeighbor
# Create a kNN classifier instance.
# Remember that training a kNN classifier is a noop:
# the Classifier simply remembers the data and does no further processing
classifier = KNearestNeighbor()
classifier.train(X_train, y_train)
# Open cs231n/classifiers/k_nearest_neighbor.py and implement
# compute_distances_two_loops.
# Test your implementation:
dists = classifier.compute_distances_two_loops(X_test)
# We can visualize the distance matrix: each row is a single test example and
# its distances to training examples
plt.imshow(dists, interpolation='none')
plt.show()
# 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))
