def _test_permutation(self): data1 = self.data data2 = _MultivariateMixture(data1) data2.state_permutation([0, 2, 1]) data2.state_permutation([0, 2, 1]) assert str(data1)==str(data2)
def build_data(self): d11 = Binomial(0, 12, 0.1) d12 = Binomial(0, 12, 0.5) d13 = Binomial(0, 12, 0.8) d21 = Poisson(0, 18.0) d22 = Poisson(0, 5.0) d23 = Poisson(0, .20) data = _MultivariateMixture([0.1, 0.2, 0.7], [[d11, d21], [d12, d22], [d13, d23]]) assert data.nb_component == 3 assert data.nb_variable == 2 return data
def test1(): #ORIGINAL AML stops HERE d11 = Binomial(0, 12, 0.1) d12 = Binomial(2, 13, 0.6) d13 = Binomial(3, 15, 0.9) d21 = Poisson(0, 25.0) d22 = Poisson(0, 5.0) d23 = Poisson(0, 0.2) m = _MultivariateMixture([0.1, 0.2, 0.7], [[d11, d21], [d12, d22], [d13, d23]]) print m #m2 = _MultivariateMixture("mixture_mv1.mixt") #print m2 #print "Egalite des melanges construits par liste ",\ # "de distributions et par fichiers : ", str(str(m)==str(m2)) #m = _MultivariateMixture("mixture_mv_nonparam.mixt") # print m print "Simulation de melanges multivaries : " v = m.simulate(5000) print v Plot(m, variable=1, Title="Simulated mixture") print "Estimation de melanges multivaries ", \ # "d'apres un modele initial : " m_estim_model = v.mixture_estimation(m, 100, [True, True]) extracted_mixture = m_estim_model.extract_mixture(1) extracted_mixture.old_plot(variable=1, Title="Marginal distribution") Plot(m_estim_model, variable=1, Title="Estimated mixture") print "Estimation de melanges multivaries ", \ "d'apres un nombre de composantes : " m_estim_nbcomp = v.mixture_estimation(2, 100, [True, True]) m_estim_nbcomp.plot(variable=1, Title="Estimated mixture") clust_entropy = m_estim_nbcomp.cluster_data(v, True) clust_plain = m_estim_nbcomp.cluster_data(v, False)
def test1(): #ORIGINAL AML stops HERE d11 = Binomial(0, 12, 0.1) d12 = Binomial(2, 13, 0.6) d13 = Binomial(3, 15, 0.9) d21 = Poisson(0, 25.0) d22 = Poisson(0, 5.0) d23 = Poisson(0, 0.2) m = _MultivariateMixture([0.1, 0.2, 0.7], [[d11, d21], [d12, d22], [d13, d23]]) print m #m2 = _MultivariateMixture("mixture_mv1.mixt") #print m2 #print "Egalite des melanges construits par liste ",\ # "de distributions et par fichiers : ", str(str(m)==str(m2)) #m = _MultivariateMixture("mixture_mv_nonparam.mixt") # print m print "Simulation de melanges multivaries : " v = m.simulate(5000) print v Plot(m, variable=1, Title="Simulated mixture") print "Estimation de melanges multivaries ", \ # "d'apres un modele initial : " m_estim_model = v.mixture_estimation(m, 100, [True, True]) extracted_mixture = m_estim_model.extract_mixture(1) extracted_mixture.old_plot(variable=1, Title="Marginal distribution") Plot(m_estim_model, variable = 1, Title="Estimated mixture") print "Estimation de melanges multivaries ", \ "d'apres un nombre de composantes : " m_estim_nbcomp = v.mixture_estimation(2, 100, [True, True]) m_estim_nbcomp.plot(variable = 1, Title="Estimated mixture") clust_entropy = m_estim_nbcomp.cluster_data(v , True) clust_plain = m_estim_nbcomp.cluster_data(v , False)
def test_simulate2(self): d11 = Binomial(0, 12, 0.1) d12 = Binomial(0, 12, 0.5) d13 = Binomial(0, 12, 0.8) d21 = Poisson(0, 18.0) d22 = Poisson(0, 5.0) d23 = Poisson(0, .20) m = _MultivariateMixture([0.1, 0.2, 0.7], [[d11, d21], [d12, d22], [d13, d23]]) v = m.simulate(5000) assert v m_estim_model = v.mixture_estimation(m, 100, [True, True]) assert m_estim_model m_estim_nbcomp = v.mixture_estimation(2) assert m_estim_nbcomp return m, v