def good_graph(X):
a = EM.EM_algorithm(components=2, tol=1e-6, max_iter=60)
a.fit(X)
y = a.res['gamma'].argmax(axis=1)
plt.scatter(X[:, 0], X[:, 1], c=y, s=90)
plt.title('Good clastering', fontsize=30)
plt.show()
def inc_log(X):
a = EM.EM_algorithm(components=10, tol=1e-10, max_iter=60)
a.fit(X)
plt.title('Increase of log-likelihood', fontsize=30)
plt.xlabel('number of iteration', fontsize=15)
plt.ylabel('log-likelihood', fontsize=15)
plt.plot(a.res['likelihood'])
plt.show()
def bad_graph(X):
mu_s = np.array([[-5, 0],
[0, 5]])
a = EM.EM_algorithm(components=2, mu_s=mu_s, tol=1e-6, max_iter=60)
a.fit(X)
y = a.res['gamma'].argmax(axis=1)
plt.scatter(X[:, 0], X[:, 1], c=y, s=90)
plt.title('Bad clastering', fontsize=30)
plt.show()
def EM_s(X, count, max_iter=100, mu_s=None, sigma_s=None,
pi_s=None, components=10, tol=1e-3, cov_type='full'):
like_hood = -np.inf
for i in range(count):
a = EM.EM_algorithm(sigma_s=sigma_s, mu_s=mu_s,
pi_s=pi_s, cov_type=cov_type,
components=components, tol=tol,
max_iter=max_iter)
a.fit(X)
like_hood = a.res['likelihood'][-1]
sigma = a.res['sigma']
mu = a.res['mu']
pi = a.res['pi']
