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)