def kmeans_classification():
print("[+] K-Means Classification")
x_train, x_test, y_train, y_test = load_digits()
print("[+] K-Means Vanilla")
kmeans_classification_builder(k_vanilla, x_train, x_test, y_train, y_test)
print()
print("[+] K-Means Plus Plus")
kmeans_classification_builder(k_plus, x_train, x_test, y_train, y_test)
try:
linear_classifier = LogisticRegression()
linear_classifier.fit(x_train, y_train)
y_hat_test = linear_classifier.predict(x_test)
except:
pass
print('[*] Accuracy of logistic regression classifier is {}'
.format(np.mean(y_hat_test == y_test)))
KNNClassifier = KNeighborsClassifier()
KNNClassifier.fit(x_train, y_train)
y_hat_test = KNNClassifier.predict(x_test)
print('[*] Accuracy of Nearest Neighbour classifier is {}'
.format(np.mean(y_hat_test == y_test)))
def kmeans_classification():
x_train, x_test, y_train, y_test = load_digits()
# plot some train data
N = 25
l = int(np.ceil(np.sqrt(N)))
#print(l)
im = np.zeros((10 * l, 10 * l))
for m in range(l):
for n in range(l):
if (m * l + n < N):
im[10 * m:10 * m + 8,
10 * n:10 * n + 8] = x_train[m * l + n].reshape([8, 8])
plt.imsave('plots/digits.png', im, cmap='Greys')
n_cluster = 30
classifier = KMeansClassifier(n_cluster=n_cluster, max_iter=100, e=1e-6)
classifier.fit(x_train, y_train)
y_hat_test = classifier.predict(x_test)
assert y_hat_test.shape == y_test.shape, \
'y_hat_test and y_test should have same shape'
print('Prediction accuracy of K-means classifier with {} cluster is {}'.
format(n_cluster, np.mean(y_hat_test == y_test)))
linear_classifier = LogisticRegression()
linear_classifier.fit(x_train, y_train)
y_hat_test = linear_classifier.predict(x_test)
print('Accuracy of logistic regression classifier is {}'.format(
np.mean(y_hat_test == y_test)))
KNNClassifier = KNeighborsClassifier()
KNNClassifier.fit(x_train, y_train)
y_hat_test = KNNClassifier.predict(x_test)
print('Accuracy of Nearest Neighbour classifier is {}'.format(
np.mean(y_hat_test == y_test)))
np.savez('results/k_means_classification.npz',
y_hat_test=y_hat_test,
y_test=y_test,
centroids=classifier.centroids,
centroid_labels=classifier.centroid_labels)
# fig.ax.scatter(gmm.means[:, 0], gmm.means[:, 1], c='red')
for component in range(n_cluster):
a, b, angle = compute_elipse_params(gmm.variances[component])
e = Ellipse(xy=gmm.means[component], width=a * 5, height=b * 5,
angle=angle, alpha=gmm.pi_k[component])
fig.ax.add_artist(e)
fig.savefig('plots/gmm_toy_dataset_{}.png'.format(i))
################################################################################
# GMM on digits dataset
# We fit a gaussian distribution on digits dataset and show generate samples from the distribution
# Complete implementation of sample function for GMM class in gmm.py
################################################################################
x_train, x_test, y_train, y_test = load_digits()
print('x_test:',x_test.shape)
for i in init:
n_cluster = 30
gmm = GMM(n_cluster=n_cluster, max_iter=1000, init=i, e=1e-10)
iterations = gmm.fit(x_train)
ll = gmm.compute_log_likelihood(x_train)
print('GMM for digits dataset with {} init converged in {} iterations. Final log-likelihood of data: {}'.format(i, iterations, ll))
# plot cluster means
means = gmm.means
from matplotlib import pyplot as plt
l = int(np.ceil(np.sqrt(n_cluster)))
im = np.zeros((10 * l, 10 * l))
