Example #1
0
    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)
Example #2
0
    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)
Example #5
0
    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