def getModel():
D = pm.Dirichlet('1-Dirichlet', theta=[3,2,4]); #@UndefinedVariable
C1 = pm.Categorical('2-Cat', D); #@UndefinedVariable
C2 = pm.Categorical('10-Cat', D); #@UndefinedVariable
C3 = pm.Categorical('11-Cat', D); #@UndefinedVariable
W0_0 = pm.WishartCov('4-Wishart0_1', n=5, C=np.eye(2)); #@UndefinedVariable
N0_1 = pm.MvNormalCov('5-Norm0_1', mu=[-20,-20], C=np.eye(2)); #@UndefinedVariable
N0_2 = pm.MvNormalCov('6-Norm0_2', mu=[0,0], C=np.eye(2)); #@UndefinedVariable
N0_3 = pm.MvNormalCov('7-Norm0_3', mu=[20,20], C=np.eye(2)); #@UndefinedVariable
aMu = [N0_1.value, N0_2.value, N0_3.value];
fL1 = lambda n=C1: np.select([n==0, n==1, n==2], aMu);
fL2 = lambda n=C2: np.select([n==0, n==1, n==2], aMu);
fL3 = lambda n=C3: np.select([n==0, n==1, n==2], aMu);
p_N1 = pm.Lambda('p_Norm1', fL1, doc='Pr[Norm|Cat]');
p_N2 = pm.Lambda('p_Norm2', fL2, doc='Pr[Norm|Cat]');
p_N3 = pm.Lambda('p_Norm3', fL3, doc='Pr[Norm|Cat]');
N = pm.MvNormalCov('3-Norm', mu=p_N1, C=W0_0); #@UndefinedVariable
obsN1 = pm.MvNormalCov('8-Norm', mu=p_N2, C=W0_0, observed=True, value=[-20,-20]); #@UndefinedVariable @UnusedVariable
obsN2 = pm.MvNormalCov('9-Norm', mu=p_N3, C=W0_0, observed=True, value=[20,20]); #@UndefinedVariable @UnusedVariable
return pm.Model([D,C1,C2,C3,N,W0_0,N0_1,N0_2,N0_3,N,obsN1,obsN2]);
if __name__ == '__main__':
warnings.filterwarnings("ignore");
mcmc = pst.sample_MCMC(getModel(), 5000);
aBins = [100]*11;
aBins[1] = 3;
aBins[9] = 3;
aBins[10] = 3;
aHidden = [4,5,6,7,10,11];
pst.plot_Samples(mcmc, aBins=aBins, aKDE=[], aRowCols=[3,1], aHidden=aHidden);
# @UndefinedVariable
aC = [pm.Categorical('Cat' + str(i), Dir) for i in range(nPts)]
# @UndefinedVariable
aL = [
pm.Lambda('p_Norm' + str(i), lambda k=aC[i], aNcl=aNc: aNcl[int(k)])
for i in range(nPts)
]
# @UndefinedVariable
#Points
aN = [
pm.Normal('NormX' + str(i),
mu=aL[i],
tau=1,
observed=True,
value=aD[i]) for i in range(nPts - 1)
]
# @UndefinedVariable
Nz = pm.Normal('NormZ', mu=aL[-1], tau=1)
# @UndefinedVariable
return np.concatenate([[Nz, Dir], aUh, aNc, aC, aN])
if __name__ == '__main__':
warnings.filterwarnings("ignore")
aNodes = run_Categorical_Normal()
model = pst.get_Model(aNodes)
mcmc = pst.sample_MCMC(model, 1000)
pst.plot_Samples(mcmc, nBins=500, nCols=3)
# graph = pm.graph.graph(model);
# graph.write_pdf("./graph7.pdf");
# Bet = pm.Beta('Beta', alpha=1, beta=1); # @UndefinedVariable
# B = pm.Bernoulli('Bern', Bet); # @UndefinedVariable
# B1 = pm.Bernoulli('Bern1', Bet); # @UndefinedVariable
# B2 = pm.Bernoulli('Bern2', Bet); # @UndefinedVariable
# N = pm.Normal('Norm', mu=U, tau=1)#, observed=True, value=10); # @UndefinedVariable
# N1 = pm.Normal('Norm1', mu=U, tau=1)#, observed=True, value=-10); # @UndefinedVariable
# p_N = pm.Lambda('p_Norm', lambda k=int(B): [N, N1][k]);
# p_N1 = pm.Lambda('p_Norm1', lambda k=int(B1): [N, N1][k]);
# p_N2 = pm.Lambda('p_Norm2', lambda k=int(B2): [N, N1][k]);
# N2 = pm.Normal('Norm2', mu=p_N, tau=1, observed=True, value=2); # @UndefinedVariable
# N3 = pm.Normal('Norm3', mu=p_N1, tau=1, observed=True, value=-2); # @UndefinedVariable
# N4 = pm.Normal('Norm4', mu=p_N2, tau=1); # @UndefinedVariable
# return [N4,U,Bet,B,B1,B2,N2,N3,N,N1]#,B1,B2];
def run_Categorical_Normal():
C = pm.Categorical('Cat', [0.2, 0.4, 0.1, 0.3]); # @UndefinedVariable
p_N = pm.Lambda('p_Norm', lambda node=C: [-5, 0, 5, 10][node]);
N = pm.Normal('Norm', mu=p_N, tau=1); # @UndefinedVariable
return [C,N];
if __name__ == '__main__':
warnings.filterwarnings("ignore");
# aNodes = run_Categorical_Normal();
aNodes = run_Bernoulli_Normal();
model = pst.get_Model(aNodes);
mcmc = pst.sample_MCMC(model, 50000);
pst.plot_Samples(mcmc, nBins=500);
# graph = pm.graph.graph(model);
# graph.write_pdf("./graph4.pdf");