# @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");
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);
from __future__ import division;
import Plot_Sampler_Tools as pst;
import pymc as pm;
import warnings;
def run_Bernoulli_Normal():
#Cluster 1
Uh1 = pm.Uniform('UnifH1', lower=-50, upper=50); # @UndefinedVariable
Nc1 = pm.Normal('NormC1', mu=Uh1, tau=1)#, observed=True, value=10); # @UndefinedVariable
#Cluster 2
Uh2 = pm.Uniform('UnifH2', lower=-50, upper=50); # @UndefinedVariable
Nc2 = pm.Normal('NormC2', mu=Uh2, tau=1)#, observed=True, value=10); # @UndefinedVariable
#Points
normalObs1 = pm.Normal('NormX1', mu=Nc1, tau=1, observed=True, value=-10); # @UndefinedVariable
normalObs2 = pm.Normal('NormX2', mu=Nc2, tau=1, observed=True, value=10); # @UndefinedVariable
return [Nc1,Nc2,Uh1,Uh2,normalObs1,normalObs2];
if __name__ == '__main__':
warnings.filterwarnings("ignore");
aNodes = run_Bernoulli_Normal();
model = pst.get_Model(aNodes);
mcmc = pst.sample_MCMC(model, 10000);
pst.plot_Samples(mcmc, nBins=500);
# graph = pm.graph.graph(model);
# graph.write_pdf("./graph4.pdf");
from __future__ import division
import Plot_Sampler_Tools as pst
import numpy as np
import pymc as pm
import warnings
def getModel():
D = pm.Dirichlet('1-Dirichlet', theta=[2, 1, 2, 4])
#@UndefinedVariable
# p_B = pm.Lambda('p_Bern', lambda b=B: np.where(b==0, 0.9, 0.1), doc='Pr[Bern|Beta]');
C = pm.Categorical('2-Cat', D)
#@UndefinedVariable
# C = pm.Categorical('1-Cat', [0.2, 0.4, 0.1, 0.3], observed=True, value=3); #@UndefinedVariable
p_N = pm.Lambda(
'p_Norm',
lambda n=C: np.select([n == 0, n == 1, n == 2, n == 3],
[[-5, -5], [0, 0], [5, 5], [10, 10]]),
doc='Pr[Norm|Cat]')
N = pm.MvNormal('3-Norm_2D', mu=p_N, tau=np.eye(2, 2))
#@UndefinedVariable
# N = pm.MvNormal('2-Norm_2D', mu=p_N, tau=np.eye(2,2), observed=True, value=[2.5,2.5]); #@UndefinedVariable
return pm.Model([D, C, N])
if __name__ == '__main__':
warnings.filterwarnings("ignore")
mcmc = pst.sample_MCMC(getModel(), 10000)
pst.plot_Samples(mcmc, aBins=[100, 4, 100], aKDE=[])
#@UndefinedVariable
N0_3 = pm.Normal('7-Norm0_3', mu=30, tau=1)
#@UndefinedVariable
N0_4 = pm.Normal('16-Norm0_3', mu=-30, tau=1)
#@UndefinedVariable
aMu = [N0_1.value, N0_2.value, N0_3.value, N0_4.value]
p_N1 = pm.Lambda('p_Norm1', lambda n=C1: aMu[n], doc='Pr[Norm|Cat]')
# p_N2 = pm.Lambda('p_Norm2', lambda n=C2: aMu[n], doc='Pr[Norm|Cat]');
# p_N3 = pm.Lambda('p_Norm3', lambda n=C3: aMu[n], doc='Pr[Norm|Cat]');
# p_N4 = pm.Lambda('p_Norm4', lambda n=C4: aMu[n], doc='Pr[Norm|Cat]');
# p_N5 = pm.Lambda('p_Norm6', lambda n=C5: aMu[n], doc='Pr[Norm|Cat]');
N = pm.Normal('3-Norm', mu=p_N1, tau=1)
#@UndefinedVariable
# obsN1 = pm.Normal('8-Norm', mu=p_N2, tau=1, observed=True, value=40); #@UndefinedVariable @UnusedVariable
# obsN2 = pm.Normal('9-Norm', mu=p_N3, tau=1, observed=True, value=40); #@UndefinedVariable @UnusedVariable
# obsN3 = pm.Normal('12-Norm', mu=p_N4, tau=1, observed=True, value=-40); #@UndefinedVariable @UnusedVariable
# obsN4 = pm.Normal('13-Norm', mu=p_N5, tau=1, observed=True, value=-40); #@UndefinedVariable @UnusedVariable
return pm.Model([D, C1, N, N0_1, N0_2, N0_3, N0_4, N])
# return pm.Model([D,C1,C2,C3,N,G0_0,N0_1,N0_2,N0_3,N0_4,N,obsN1,obsN2,obsN3,obsN4]);
if __name__ == '__main__':
warnings.filterwarnings("ignore")
mcmc = pst.sample_MCMC(getModel(), 10000)
aBins = [100] * len(mcmc.nodes)
for i in [1]:
aBins[i] = 4
aShow = [1, 2, 3]
#[4,5,6,7,10,11 ,12,13,14,15,16];
pst.plot_Samples(mcmc, aBins=aBins, aKDE=[], aRowCols=[3, 1], aShow=aShow)
# @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, Gam], aUh, aNc, aC, aN])
if __name__ == '__main__':
warnings.filterwarnings("ignore")
aNodes = run_DP()
model = pst.get_Model(aNodes)
mcmc = pst.sample_MCMC(model, 5000)
pst.plot_Samples(mcmc, nBins=500, nCols=4, aShow=range(8))
# graph = pm.graph.graph(model);
# graph.write_pdf("./graph9.pdf");
from __future__ import division;
import Plot_Sampler_Tools as pst;
import pymc as pm;
import warnings;
def getModel():
nA, nB, nK = 5, 2, 10;
B = pm.Beta('1-Beta', alpha=nA/nK, beta=nB*(nK-1)/nK); #@UndefinedVariable
# p_B = pm.Lambda('p_Bern', lambda b=B: np.where(b==0, 0.9, 0.1), doc='Pr[Bern|Beta]');
C = pm.Categorical('2-Cat', [1-B, B]); #@UndefinedVariable
# C = pm.Categorical('1-Cat', [0.2, 0.4, 0.1, 0.3], observed=True, value=3); #@UndefinedVariable
return pm.Model([B,C]);
if __name__ == '__main__':
warnings.filterwarnings("ignore");
mcmc = pst.sample_MCMC(getModel(), 1000);
pst.plot_Samples(mcmc, aBins=[100,2,100], aKDE=[0,0,0]);
pm.Categorical('Cat' + str(i) + '_' + sDP, Dir) for i in range(nPts)
]
# @UndefinedVariable
aL = [
pm.Lambda('p_Norm' + str(i) + '_' + sDP,
lambda k=aC[i], aNcl=aNc: aNcl[int(k)]) for i in range(nPts)
]
# @UndefinedVariable
#Points
aN = [
pm.Normal('NormX' + str(i) + '_' + sDP,
mu=aL[i],
tau=1,
observed=True,
value=aD[i]) for i in range(nPts - 1)
]
# @UndefinedVariable
Nz = pm.Normal('NormZ_' + sDP, mu=aL[-1], tau=1)
# @UndefinedVariable
return np.concatenate([[Nz, Dir], aUh, aNc, aC, aN])
if __name__ == '__main__':
warnings.filterwarnings("ignore")
aNodes = run_HDP()
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("./Graphs/HDP_ParamLearning.pdf")