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')
assert gmm.pi_k.shape == (
n_cluster,), 'pi_k should be numpy vector of size'.format(n_cluster)
assert iterations > 0 and type(
iterations) == int, 'Number of updates should be positive integer'
assert type(ll) == float, 'log-likelihood should be float'
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
################################################################################
import pickle
from PIL import ImageFont, ImageDraw, Image
from utils import Figure
TEXT = "0123456789."
fnt = ImageFont.truetype("FreeMonoBold", 100)
FIGURES = {}
for c in TEXT:
bbox = fnt.getbbox(c, "1")
shape = (bbox[0] + bbox[2], bbox[1] + bbox[3])
im = Image.new("1", shape)
d = ImageDraw.Draw(im)
d.text((0, 0), c, font=fnt, fill=(1,))
FIGURES[c] = Figure(shape, list(im.getdata()))
with open("figures.dat", "wb") as f:
pickle.dump(FIGURES, f)
# im.show()