def test_EMclass():
for i in [2,3]:
data = np.asarray(utils.load_gaussian(i))
#em_algo = em.EMComp()
#em_algo.initialize(data)
#print em_algo.models[0].mu
gmm = GMM(n_components=i, covariance_type='full').fit(data)
print(gmm.means_)
print(gmm.covars_)
def q4():
"""
:return: mean, cov_matrix (std_dev), number in class
"""
for data_set in [3]: #[2,3]:
print '\n\nData set {}'.format(data_set)
if data_set == 1:
data = numpy.random.normal(loc=(-1, 1), size=(4000, 2))
else:
data = asarray(utils.load_gaussian(data_set))
em_algo = em.EMComp()
em_algo.emgm(data, data_set, 100)
def test_EM_validation():
data = np.asarray(utils.load_gaussian(2))
em_algo = emv.gaussianEM(data)
def test_assign():
data = np.asarray(utils.load_gaussian(2))
em_algo = em.EMComp()
em_algo.initialize(data)
print em_algo.models[0].mu
em_algo.maximize(data)
