import numpy as np
import k_nearest_neighbor as knn
import sys
x = np.array([[1, 2], [3, 4]])
y = np.array([0, 1])
test = np.array([[1, 2], [3, 4]])
if __name__ == '__main__':
if len(sys.argv) == 2:
choice = sys.argv[1]
if choice == '2':
kk = knn.KNearestNeighbor()
kk.train(x, y)
dist = kk.compute_distances_two_loops(test)
print dist
print kk.predict_labels(dist)
elif choice == '1':
kk = knn.KNearestNeighbor()
kk.train(x, y)
dist = kk.compute_distances_one_loop(test)
print dist
print kk.predict_labels(dist)
elif choice == '0':
kk = knn.KNearestNeighbor()
kk.train(x, y)
dist = kk.compute_distances_no_loops(test)
print dist
num_training = 50000
X_train = X_train[:num_training]
y_train = y_train[:num_training]
num_test = 100
X_test = X_test[:num_test]
y_test = y_test[:num_test]
# Reshape the image data into rows: each item in these arrays is a 3072-element
# vector representing 3 colors per image pixel.
X_train = np.reshape(X_train, (X_train.shape[0], -1))
X_test = np.reshape(X_test, (X_test.shape[0], -1))
print 'Reshaped training data shape: ', X_train.shape, X_train.dtype
print 'Reshaped test data shape: ', X_test.shape, X_test.dtype
import k_nearest_neighbor
knn = k_nearest_neighbor.KNearestNeighbor()
knn.train(X_train, y_train)
with timer.Timer('Computing distances'):
dists = knn.compute_distances_no_loops(X_test)
with timer.Timer('Running label prediction'):
y_test_pred = knn.predict_labels(dists, k=5)
# 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_train = y_train[mask] num_test = 500 mask = list(range(num_test)) X_test = X_test[mask] y_test = y_test[mask] # Reshape the image data into rows 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) # 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 = k_nearest_neighbor.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) print(dists.shape) # 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: