def kmeans_toy():
x, y = toy_dataset(4)
fig = Figure()
fig.ax.scatter(x[:, 0], x[:, 1], c=y)
fig.savefig('plots/toy_dataset_real_labels.png')
fig.ax.scatter(x[:, 0], x[:, 1])
fig.savefig('plots/toy_dataset.png')
n_cluster = 4
k_means = KMeans(n_cluster=n_cluster, max_iter=100, e=1e-8)
centroids, membership, i = k_means.fit(x)
assert centroids.shape == (n_cluster, 2), \
('centroids for toy dataset should be numpy array of size {} X 2'
.format(n_cluster))
assert membership.shape == (50 * n_cluster,), \
'membership for toy dataset should be a vector of size 200'
assert type(i) == int and i > 0, \
'Number of updates for toy datasets should be integer and positive'
print('[success] : kmeans clustering done on toy dataset')
print('Toy dataset K means clustering converged in {} steps'.format(i))
fig = Figure()
fig.ax.scatter(x[:, 0], x[:, 1], c=membership)
fig.ax.scatter(centroids[:, 0], centroids[:, 1], c='red')
fig.savefig('plots/toy_dataset_predicted_labels.png')
np.savez('results/k_means_toy.npz',
centroids=centroids,
step=i,
membership=membership,
y=y)
def kmeans_builder(centroid_func):
samples_per_cluster = 50
n_cluster = 9
x, y = toy_dataset(n_cluster, samples_per_cluster)
fig = Figure()
fig.ax.scatter(x[:, 0], x[:, 1], c=y)
fig.savefig('plots/toy_dataset_real_labels.png')
fig.ax.scatter(x[:, 0], x[:, 1])
fig.savefig('plots/toy_dataset.png')
k_means = KMeans(n_cluster=n_cluster, max_iter=100, e=1e-8)
centroids, membership, i = k_means.fit(x, centroid_func)
assert centroids.shape == (n_cluster, 2), \
('centroids for toy dataset should be numpy array of size {} X 2'
.format(n_cluster))
assert membership.shape == (samples_per_cluster * n_cluster,), \
'membership for toy dataset should be a vector of size {}'.format(len(membership))
assert type(i) == int and i > 0, \
'Number of updates for toy datasets should be integer and positive'
print('[success] : kmeans clustering done on toy dataset')
print('Toy dataset K means clustering converged in {} steps'.format(i))
fig = Figure()
fig.ax.scatter(x[:, 0], x[:, 1], c=membership)
fig.ax.scatter(centroids[:, 0], centroids[:, 1], c='red')
fig.savefig('plots/toy_dataset_predicted_labels.png')
def kmeans_builder(centroid_func):
samples_per_cluster = 50
n_cluster = 9
x, y = toy_dataset(n_cluster, samples_per_cluster)
# print("x: ", x)
# print("y: ", y)
# plot the scatter plot with color coded by cluster index
fig = Figure()
fig.ax.scatter(x[:, 0], x[:, 1], c=y)
fig.savefig('plots/toy_dataset_real_labels.png')
fig.ax.scatter(x[:, 0], x[:, 1])
fig.savefig('plots/toy_dataset.png')
# create a class kmeans
k_means = KMeans(n_cluster=n_cluster, max_iter=100, e=1e-8)
# fit the kmeans to data x using centroid_func to initialize
centroids, membership, i = k_means.fit(x, centroid_func)
assert centroids.shape == (n_cluster, 2), \
('centroids for toy dataset should be numpy array of size {} X 2'
.format(n_cluster))
assert membership.shape == (samples_per_cluster * n_cluster,), \
'membership for toy dataset should be a vector of size {}'.format(len(membership))
assert type(i) == int and i > 0, \
'Number of updates for toy datasets should be integer and positive'
print('[success] : kmeans clustering done on toy dataset')
print('Toy dataset K means clustering converged in {} steps'.format(i))
# plot toy dataset labelling using OUR method
fig = Figure()
fig.ax.scatter(x[:, 0], x[:, 1], c=membership)
fig.ax.scatter(centroids[:, 0], centroids[:, 1], c='red')
# plt.show()
fig.savefig('plots/toy_dataset_predicted_labels.png')
print('GMM for toy dataset with {} init converged in {} iteration. Final log-likelihood of data: {}'.format(
i, iterations, ll))
np.savez('results/gmm_toy_{}.npz'.format(i), iterations=iterations,
variances=gmm.variances, pi_k=gmm.pi_k, means=gmm.means, log_likelihood=ll, x=x, y=y)
# plot
fig = Figure()
fig.ax.scatter(x[:, 0], x[:, 1], c=y)
# 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)
