From 3ca3d5905aa179fcf8e17fb68f9360b26f0f9f53 Mon Sep 17 00:00:00 2001
From: Manuel Baum <mbaum@techfak.uni-bielefeld.de>
Date: Tue, 9 Jul 2013 17:09:07 +0200
Subject: [PATCH] Worked on MCMC examples
---
examples/MCMC_Continuous.py | 50 +++++++++----
examples/MCMC_Discrete.py | 41 ++++++-----
examples/MCMC_Gauss.py | 74 --------------------
primo/reasoning/ContinuousNode.py | 14 +++-
primo/reasoning/DiscreteNode.py | 6 ++
primo/reasoning/density/Gauss.py | 6 +-
primo/reasoning/density/LinearBeta.py | 29 +++++---
primo/reasoning/density/LinearExponential.py | 14 ++--
8 files changed, 106 insertions(+), 128 deletions(-)
delete mode 100644 examples/MCMC_Gauss.py
diff --git a/examples/MCMC_Continuous.py b/examples/MCMC_Continuous.py
index 4ca269b..b419255 100644
--- a/examples/MCMC_Continuous.py
+++ b/examples/MCMC_Continuous.py
@@ -5,15 +5,14 @@ from primo.reasoning.density import LinearBetaParameters
from primo.reasoning.density import LinearGaussParameters
from primo.reasoning.density import Gauss
from primo.reasoning import MCMC
+from primo.reasoning.convergence_test import ConvergenceTestSimpleCounting
from primo.reasoning import EvidenceEqual as EvEqual
-from primo.reasoning import EvidenceIntervall as EvInterval
+from primo.reasoning import EvidenceLower as EvLower
import numpy
-#Construct some simple BayesianNetwork. In this example it models
-#the linear relationship between the age of a plant and the height that
-#it has grown up to (+noise)
+#Construct some simple BayesianNetwork.
#topology
bn = BayesNet()
@@ -28,17 +27,34 @@ bn.add_edge(age,height)
bn.add_edge(age,diameter)
#parameterization
-age_parameters=LinearExponentialParameters(1.0,{},2.0)
+#Semantics: Many young plants and the higher the age the lower the probabilty
+#->lambda=2.0
+age_parameters=LinearExponentialParameters(0.0,{})
age.set_density_parameters(age_parameters)
-height_parameters=LinearGaussParameters(0.1,{age:1},0.3)
+#Semantics: plants start at 0.1 meters underground and grow each year by 1 meter.
+# variance is 0.3
+height_parameters=LinearGaussParameters(-0.1,{age:1},0.3)
height.set_density_parameters(height_parameters)
-diameter_parameters=LinearBetaParameters(0.01,{age:0.2},2,5)
+#Semantics: At small age: low alpha, high beta -> skew to the left: thin plants
+# At higher age: high alpha, low beta -> skew to the right: thick plants
+diameter_parameters=LinearBetaParameters(-10.0,{age:4.0},10.0,{age:-4.0})
diameter.set_density_parameters(diameter_parameters)
-mcmc_ask=MCMC(bn,1000)
+mcmc_ask=MCMC(bn,100000,convergence_test=ConvergenceTestSimpleCounting(50000))
+
+
+print "------PriorMarginal:------"
+pm=mcmc_ask.calculate_PriorMarginal([age],Gauss)
+print pm
+print "Ground truth: mu=0.5 C=[0.25]"
+#pm=mcmc_ask.calculate_PriorMarginal([height,diameter],Gauss)
+pm=mcmc_ask.calculate_PriorMarginal([height],Gauss)
+print pm
+print ""
+
evidence={age:EvEqual(2)}
@@ -47,14 +63,18 @@ evidence={age:EvEqual(2)}
#poe=mcmc_ask.calculate_PoE(evidence)
#print poe
-print "PosteriorMarginal:"
+print "------PosteriorMarginal:------"
pm=mcmc_ask.calculate_PosteriorMarginal([age,height],evidence,Gauss)
#pm=mcmc_ask.calculate_PosteriorMarginal([height],evidence,Gauss)
+print "P(age,height|age=2):"
print pm
+print "Ground truth: age=2, height=mu:1.9,C=0.3"
+
+print "------PropabilityOfEvidence------"
+poe=mcmc_ask.calculate_PoE({age:EvLower(0.347)})
+print "Probabilty that age is lower than it's median:"
+print "p(age<0.347)="+str(poe)
+print "Ground truth=0.5"
+
+
-print "PriorMarginal:"
-pm=mcmc_ask.calculate_PriorMarginal([age],Gauss)
-print pm
-#pm=mcmc_ask.calculate_PriorMarginal([height,diameter],Gauss)
-pm=mcmc_ask.calculate_PriorMarginal([height],Gauss)
-print pm
diff --git a/examples/MCMC_Discrete.py b/examples/MCMC_Discrete.py
index 0a98ec2..010710a 100644
--- a/examples/MCMC_Discrete.py
+++ b/examples/MCMC_Discrete.py
@@ -11,13 +11,15 @@ import pdb
#Construct some simple BayesianNetwork
bn = BayesNet()
burglary = DiscreteNode("Burglary", ["Intruder","Safe"])
-alarm = DiscreteNode("Alarm", ["Ringing", "Silent","Kaputt"])
+alarm = DiscreteNode("Alarm", ["Ringing", "Silent","Destroyed"])
bn.add_node(burglary)
bn.add_node(alarm)
bn.add_edge(burglary,alarm)
+
+#Parametrize the network
burglary_cpt=numpy.array([0.2,0.8])
burglary.set_probability_table(burglary_cpt, [burglary])
@@ -25,29 +27,36 @@ alarm_cpt=numpy.array([[0.8,0.15,0.05],[0.05,0.9,0.05]])
alarm.set_probability_table(alarm_cpt, [burglary,alarm])
+#Get some inference object
+mcmc_ask=MCMC(bn,5000)
-
-mcmc_ask=MCMC(bn,1000)
-
+#Do some Inferences
evidence={burglary:EvEq("Intruder")}
-
-print "---ProbabilityOfEvidence:---"
+print "-------ProbabilityOfEvidence:-------"
poe=mcmc_ask.calculate_PoE(evidence)
-print poe
+print "p(evidence=Intruder)="+str(poe)
+print "Ground truth=0.2\n"
-print "---PosteriorMarginal:---"
+print "-------PosteriorMarginal:-------"
pm=mcmc_ask.calculate_PosteriorMarginal([alarm],evidence,ProbabilityTable)
-print pm
+print "P(alarm|burglary=Intruder)="+str(pm)
+print "Ground truth=[0.8, 0.15, 0.05]\n"
-print "---PriorMarginal:---"
+print "-------PriorMarginal:-------"
pm=mcmc_ask.calculate_PriorMarginal([alarm],ProbabilityTable)
-print "Alarm: " + str(pm)
+print "P(Alarm)= " + str(pm)
+print "Ground truth=[0.2, 0.75, 0.05]\n"
+
pm=mcmc_ask.calculate_PriorMarginal([burglary],ProbabilityTable)
-print "Burglary: " + str(pm)
+print "P(Burglary)= " + str(pm)
+print "Ground truth=[0.2, 0.8]\n"
-print "---MAP:---"
+print "-------MAP:-------"
hyp=mcmc_ask.calculate_MAP([alarm],evidence,ProbabilityTable)
-print str(evidence) + ": " + str(hyp)
-hyp=mcmc_ask.calculate_MAP([alarm],{},ProbabilityTable)
-print str({}) + ": "+str(hyp)
+print "MAP(alarm|burglary=intruder)=" + str(hyp)
+print "Ground truth=\"Ringing\"\n"
+
+hyp=mcmc_ask.calculate_MAP([burglary,alarm],{},ProbabilityTable)
+print "MAP(burglary,alarm)="+str(hyp)
+print "Ground truth=\"Safe\",\"Silent\"\n"
diff --git a/examples/MCMC_Gauss.py b/examples/MCMC_Gauss.py
deleted file mode 100644
index 21e59ff..0000000
--- a/examples/MCMC_Gauss.py
+++ /dev/null
@@ -1,74 +0,0 @@
-from primo.core import BayesNet
-from primo.reasoning import ContinuousNodeFactory
-from primo.reasoning.density import LinearGaussParameters
-from primo.reasoning.density import Gauss
-from primo.reasoning import MCMC
-from primo.reasoning import EvidenceEqual as EvEq
-from primo.reasoning import EvidenceLower as EvLower
-from primo.reasoning import EvidenceIntervall as EvInterval
-
-import numpy
-
-#Construct some simple BayesianNetwork. In this example it models
-#the linear relationship between the age of a plant and the height that
-#it has grown up to (+noise)
-
-#topology
-bn = BayesNet()
-cnf=ContinuousNodeFactory()
-age = cnf.createLinearGaussNode("Plant_age")
-height = cnf.createLinearGaussNode("Plant_height")
-diameter = cnf.createLinearGaussNode("Plant_diameter")
-bn.add_node(age)
-bn.add_node(height)
-bn.add_node(diameter)
-bn.add_edge(age,height)
-bn.add_edge(age,diameter)
-
-
-
-
-#parameterization
-age_b0=4.0
-age_var=2.0
-age_b={}
-age_parameters=LinearGaussParameters(age_b0,age_b,age_var)
-age.set_density_parameters(age_parameters)
-
-height_b0=2.0
-height_var=1.5
-height_b={age:0.5}
-height_parameters=LinearGaussParameters(height_b0,height_b,height_var)
-height.set_density_parameters(height_parameters)
-
-diameter_parameters=LinearGaussParameters(0.1,{age:-0.2},0.1)
-diameter.set_density_parameters(diameter_parameters)
-
-
-mcmc_ask=MCMC(bn,1000)
-
-evidence={age:EvEq(2)}
-
-
-#print "ProbabilityOfEvidence: "
-#poe=mcmc_ask.calculate_PoE(evidence)
-#print poe
-
-print "PosteriorMarginal:"
-pm=mcmc_ask.calculate_PosteriorMarginal([height,diameter],evidence,Gauss)
-print pm
-
-print "PriorMarginal:"
-pm=mcmc_ask.calculate_PriorMarginal([height,diameter],Gauss)
-print pm
-
-print "ProbabilityOfEvidence:"
-poe=mcmc_ask.calculate_PoE({age:EvLower(4.0)})
-print "p(age<4.0):"+str(poe)+ " GroundTruth:0.5"
-poe=mcmc_ask.calculate_PoE({age:EvInterval(4.0-2.0**0.5,4.0)})
-print "p(4.0-2.0^0.5<age<4.0):"+str(poe)+ " GroundTruth:1/3"
-#print "PriorMarginal:"
-#pm=mcmc_ask.calculate_PriorMarginal([alarm])
-#print "Alarm: " + str(pm)
-#pm=mcmc_ask.calculate_PriorMarginal([burglary])
-#print "Burglary: " + str(pm)
diff --git a/primo/reasoning/ContinuousNode.py b/primo/reasoning/ContinuousNode.py
index 77149f3..10c6ce1 100644
--- a/primo/reasoning/ContinuousNode.py
+++ b/primo/reasoning/ContinuousNode.py
@@ -24,6 +24,9 @@ class ContinuousNode(RandomNode):
def __str__(self):
return self.name
+ def __repr__(self):
+ return "str(ContinuousNode)"+self.name+")"
+
def set_density_parameters(self, density_parameters):
self.cpd.set_parameters(density_parameters)
@@ -50,10 +53,15 @@ class ContinuousNode(RandomNode):
reimplement it by constructing the integral over the normalvariate in the
intervalls allowed by the evidence and then generate a sample directly.
Currently this method has O(inf).'''
- v=random.normalvariate(x,1.0)
+ std_walk=0.1
+ v=random.normalvariate(x,std_walk)
if self in evidence.keys():
- while not evidence[self].is_compatible(v):
- v=random.normalvariate(x,1.0)
+ while not (evidence[self].is_compatible(v) and self.value_range[0]<v and v<self.value_range[1]):
+ v=random.normalvariate(x,std_walk)
+ else:
+ while not (self.value_range[0]<v and v<self.value_range[1]):
+ v=random.normalvariate(x,std_walk)
+ #print "REEEEEEEEEEEEEEEEEEEEEEEEEEEEEE"+self.name+" " +str(v)
return v
def sample_global(self, state):
diff --git a/primo/reasoning/DiscreteNode.py b/primo/reasoning/DiscreteNode.py
index 90bf85f..551132e 100644
--- a/primo/reasoning/DiscreteNode.py
+++ b/primo/reasoning/DiscreteNode.py
@@ -14,6 +14,12 @@ class DiscreteNode(RandomNode):
self.value_range = value_range
self.cpd = ProbabilityTable()
self.cpd.add_variable(self)
+
+ def __str__(self):
+ return self.name
+
+ def __repr__(self):
+ return "DiscreteNode("+self.name+")"
def set_probability(self, value, node_value_pairs):
self.cpd.set_probability(value, node_value_pairs)
diff --git a/primo/reasoning/density/Gauss.py b/primo/reasoning/density/Gauss.py
index 6e9c90c..3bb5587 100644
--- a/primo/reasoning/density/Gauss.py
+++ b/primo/reasoning/density/Gauss.py
@@ -61,9 +61,5 @@ class Gauss(Density):
return self
def __str__(self):
- ret= "Gauss:\n"
- ret=ret+ "--mu--\n"
- ret=ret+ str(self.mu)+"\n"
- ret=ret+ "--C--\n"
- ret=ret+ str(self.C)+"\n"
+ ret= "Gauss(\nmu="+str(self.mu)+"\nC="+str(self.C)+")"
return ret
diff --git a/primo/reasoning/density/LinearBeta.py b/primo/reasoning/density/LinearBeta.py
index f141dea..d1bc190 100644
--- a/primo/reasoning/density/LinearBeta.py
+++ b/primo/reasoning/density/LinearBeta.py
@@ -10,6 +10,7 @@ class LinearBetaParameters():
self.p0=p0
self.q0=q0
+
class LinearBeta(Density):
def __init__(self, node):
self.p0=1
@@ -18,6 +19,9 @@ class LinearBeta(Density):
self.q={}
self.node=node
+ self.input_scale=0.1
+ self.output_scale=5.0
+
def set_parameters(self,parameters):
self.p0=parameters.p0
self.q0=parameters.q0
@@ -38,28 +42,35 @@ class LinearBeta(Density):
def get_probability(self,value, node_value_pairs):
#print "Probability to compute for value:"+str(value)
#Compute the offset for the density and displace the value accordingly
- p = self.p0
- q = self.q0
-
- for node,node_value in node_value_pairs:
- p = p + self.p[node]*node_value
- q = q + self.q[node]*node_value
+ #p = self.p0
+ #q = self.q0
- return beta(p, q).pdf(value)
+ #for node,node_value in node_value_pairs:
+ # p = p + self.p[node]*node_value
+ # q = q + self.q[node]*node_value
+ #p=1.0/(1.0+math.exp(-p))
+ #q=1.0/(1.0+math.exp(-q))
+ p=self._compute_p_given_parents(dict(node_value_pairs))
+ q=self._compute_q_given_parents(dict(node_value_pairs))
+ #print node_value_pairs
+ #print "beta "+str(p)+" "+str(q)
+ probability = beta(p, q).pdf(value)
+ #print "/beta"
+ return probability
def _compute_p_given_parents(self, state):
x = self.p0
for node in self.p.keys():
if node in state.keys():
x = x + self.p[node]*state[node]
- return 1.0/(1.0+math.exp(-x))
+ return self.output_scale*1.0/(1.0+math.exp(-x*self.input_scale))
def _compute_q_given_parents(self, state):
x = self.q0
for node in self.q.keys():
if node in state.keys():
x = x + self.q[node]*state[node]
- return 1.0/(1.0+math.exp(-x))
+ return self.output_scale*1.0/(1.0+math.exp(-x*self.input_scale))
def sample_global(self, state):
diff --git a/primo/reasoning/density/LinearExponential.py b/primo/reasoning/density/LinearExponential.py
index 2e6e464..54c2e18 100644
--- a/primo/reasoning/density/LinearExponential.py
+++ b/primo/reasoning/density/LinearExponential.py
@@ -17,6 +17,9 @@ class LinearExponential(Density):
self.b0=0
self.node=node
+ self.input_scale=1.0
+ self.output_scale=4.0
+
def set_parameters(self,parameters):
self.b=parameters.b
self.b0=parameters.b0
@@ -33,10 +36,7 @@ class LinearExponential(Density):
def get_probability(self,value, node_value_pairs):
#Compute the offset for the density and displace the value accordingly
- x = self.b0
- for node,value in node_value_pairs:
- x = x + self.b[node]*value
- _lambda=1.0/(1.0+math.exp(-x))
+ _lambda = self._compute_lambda_given_parents(dict(node_value_pairs))
#print "lambda:"+str(_lambda)
#Evaluate the displaced density at value
return _lambda*math.exp(-_lambda*value)
@@ -46,9 +46,11 @@ class LinearExponential(Density):
for node in self.b.keys():
if node in state.keys():
x = x + self.b[node]*state[node]
- _lambda=1.0/(1.0+math.exp(-x))
+ _lambda=self.output_scale*1.0/(1.0+math.exp(-x*self.input_scale))
return _lambda
def sample_global(self,state):
_lambda=self._compute_lambda_given_parents(state)
- return random.expovariate(_lambda)
+ sample=random.expovariate(_lambda)
+ #print "EXPO-SAMPLE: "+str(sample)+" at lambda: "+str(_lambda)
+ return sample
--
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