gmm.fit(X)
means = gmm.means_
covs = gmm.covars_
n_clusters_ = len(means)
ax = liac.plot.gca()
for i in xrange(n_clusters_):
pi = idx == i
plot.scatter(X[pi, 0], X[pi, 1], color=liac.random.make_color(i + 10))
e = liac.plot.Gaussian(means[i],
covs[i],
5,
color=liac.random.make_color(i),
alpha=0.75)
ax.add_artist(e)
x, y = means[i]
liac.plot.plot(x, y, 'x', markersize=14, markeredgewidth=2, color='k')
liac.plot.plot(x,
y,
'x',
markersize=12,
markeredgewidth=2,
color=liac.random.make_color(i))
# for i, center in enumerate(centers):
# X, idx = liac.random.make_gaussian(n_samples=100, centers=center)
# plot.scatter(X[:,0], X[:,1], color=liac.random.make_color(i))
plot.show()
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
import numpy as np
import liac
from liac import plot
data = liac.dataset.load('iris')
classes = data['class'].unique()
n_classes = len(classes)
fig, axes = plot.subplots(nrows=1, ncols=3)
for i, label in enumerate(classes):
subdata = data.iloc[data['class']==label, 0:4]
ax = axes[i]
subdata.plot(ax=ax, marker='o', linestyle='None')
ax.set_title(label)
plot.show()