From 70a59a2507f062449c82eca45dbf523eb194c76d Mon Sep 17 00:00:00 2001
From: Hendrik Buschmeier <hbuschme@uni-bielefeld.de>
Date: Fri, 4 Apr 2014 23:48:52 +0200
Subject: [PATCH] Refactoring: Moved individual node modules to nodes.py
---
primo/core/Node.py | 30 ---
primo/nodes.py | 310 +++++++++++++++++++++++
primo/reasoning/ContinuousNode.py | 127 ----------
primo/reasoning/ContinuousNodeFactory.py | 47 ----
primo/reasoning/DiscreteNode.py | 56 ----
primo/reasoning/RandomNode.py | 72 ------
primo/reasoning/Untitled Document 1 | 13 -
7 files changed, 310 insertions(+), 345 deletions(-)
delete mode 100644 primo/core/Node.py
create mode 100644 primo/nodes.py
delete mode 100644 primo/reasoning/ContinuousNode.py
delete mode 100644 primo/reasoning/ContinuousNodeFactory.py
delete mode 100644 primo/reasoning/DiscreteNode.py
delete mode 100644 primo/reasoning/RandomNode.py
delete mode 100644 primo/reasoning/Untitled Document 1
diff --git a/primo/core/Node.py b/primo/core/Node.py
deleted file mode 100644
index 01f7f4c..0000000
--- a/primo/core/Node.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import abc
-import re
-
-
-
-class Node(object):
- __metaclass__ = abc.ABCMeta
-
- name = "UninitializedName"
- position = (0, 0)
-
- def __init__(self, node_name):
- # Remove all special characters and replace " " with "_"
- name = re.sub(r"[^a-zA-Z_0-9 ]*", "", node_name)
- self.name = name.replace(" ", "_")
- # for visual illustration
- self.pos = (0, 0)
-
- # @abc.abstractmethod
- # def announce_parent(self, node):
- # """This method will be called by the graph-management to inform nodes
- # which just became children of other nodes, so they can adapt themselves
- # (e.g. their cpt)"""
- # return
-
- def __str__(self):
- print self.name
- return self.name
-
-
diff --git a/primo/nodes.py b/primo/nodes.py
new file mode 100644
index 0000000..ee26ce1
--- /dev/null
+++ b/primo/nodes.py
@@ -0,0 +1,310 @@
+import abc
+import random
+import re
+
+import scipy
+
+from primo.reasoning.density import Beta
+from primo.reasoning.density import Exponential
+from primo.reasoning.density import Gauss
+from primo.reasoning.density import ProbabilityTable
+
+class Node(object):
+
+ __metaclass__ = abc.ABCMeta
+ name = "UninitializedName"
+ position = (0, 0)
+
+ def __init__(self, node_name):
+ # Remove all special characters and replace " " with "_"
+ name = re.sub(r"[^a-zA-Z_0-9 ]*", "", node_name)
+ self.name = name.replace(" ", "_")
+ # for visual illustration
+ self.pos = (0, 0)
+
+ def __str__(self):
+ return self.name
+
+
+class RandomNode(Node):
+ '''Represents a random variable. There should be subclasses of this for
+ different kinds of data. There are currently DiscreteNode for
+ discrete-valued random variables and ContinuousNode for random Variables
+ with R or an Intervall in R as domain.
+
+ At a later point in time there may be structural nodes too.
+ '''
+
+ #The Continditional Propability Distribution of this random variable
+ cpd = None
+
+ def __init__(self, name):
+ super(RandomNode, self).__init__(name)
+
+ #value_range defines the domain of this random variable
+ self.value_range=None
+
+ def set_cpd(self, cpd):
+ self.cpd = cpd
+
+ def get_cpd(self):
+ return self.cpd
+
+ def announce_parent(self, node):
+ '''
+ Adjust the cpd so a new node is incorporated as dependency.
+ '''
+ self.cpd.add_variable(node)
+
+ def get_cpd_reduced(self, evidence):
+ '''
+ Return a reduced version of the cpd of this node. This reduced version
+ is constructed according to some evidence.
+ @param evidence: A List of (Node,Value) pairs.
+ '''
+ return self.cpd.reduction(evidence)
+
+ def get_value_range(self):
+ return self.value_range
+
+ def sample_gobal(self, x, evidence=None):
+ '''
+ This method can be used to sample from this local distribution.
+
+ @param state: A Dict from Node-objects to values. You can specify the
+ values of this nodes parents in this dict and the conditional
+ probability density will be adjusted accordingly.
+ '''
+ raise Exception("Called unimplemented Method")
+
+ def sample_local(self, x, evidence=None):
+ '''
+ This method can be used to do a random walk in the domain of this node.
+
+ @param x: The spot around which the next sample shall be generated.
+ @param evidence: Evidence which is to be concerned when new samples are
+ being generated. I am not entirely sure that this belongs here or is
+ correct in theory...
+ '''
+ raise Exception("Called unimplemented Method")
+
+
+ def is_valid(self):
+ raise Exception("Called an unimplemented function")
+
+
+class DiscreteNode(RandomNode):
+ '''#TODO: write doc'''
+
+ def __init__(self, name, value_range):
+ super(DiscreteNode, self).__init__(name)
+
+ 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)
+
+ def get_probability(self, value, node_value_pairs):
+ return self.cpd.get_probability([(self,value)] + node_value_pairs)
+
+ def set_probability_table(self, table, nodes):
+ self.cpd.set_probability_table(table, nodes)
+
+ def is_valid(self):
+ return self.cpd.is_normalized_as_cpt(self)
+
+ def sample_global(self, state, evidence):
+ if evidence==None or not self in evidence.keys():
+ compatibles=self.value_range
+ else:
+ compatibles=[]
+ for v in self.value_range:
+ if evidence[self].is_compatible(v):
+ compatibles.append(v)
+
+ return self.cpd.sample_global(state,self,compatibles)
+
+ def sample_local(self, x, evidence=None):
+ if evidence==None or not self in evidence.keys():
+ compatibles=self.value_range
+ else:
+ compatibles=[]
+ for v in self.value_range:
+ if evidence[self].is_compatible(v):
+ compatibles.append(v)
+
+ return random.choice(compatibles), 1.0
+
+
+class ContinuousNode(RandomNode):
+ '''
+ Represents a random-variable with a real-valued domain. Can only be defined
+ on a subset or on whole R. The probability density can have different forms.
+ Objects of this class can be created by a ContinuousNodeFactory.
+ '''
+ def __init__(self, name, value_range, DensityClass):
+ super(ContinuousNode, self).__init__(name)
+
+ #value_range is a 2-tuple that defines this variable's domain.
+ self.value_range = value_range
+ #the class density_class defines the class of function that is used
+ #for this ContinuousNode's pdf.
+ self.density_class = DensityClass
+ #cpd - ConditionalProbabilityDensity is the concrete density function
+ #of this ContinuousNode, conditioned on this Node's parents.
+ self.cpd = DensityClass(self)
+
+ 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)
+
+ def sample_local(self, x, evidence):
+ '''
+ This method can be used to do a random walk in the domain of this node.
+
+ @param x: The spot around which the next sample shall be generated.
+ @param evidence: Evidence which is to be concerned when new samples are
+ being generated. I am not entirely sure that this belongs here or is
+ correct in theory...
+
+ ATTENTION:
+ This is the most simple and stupid implementation of the method. It
+ uses bogo-search to find a sample that fits the evidence. You could
+ 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).'''
+ std_walk=1.0
+
+ #intersect possible evidence-interval with value_range:
+ if self in evidence.keys():
+ evidence_range=evidence[self].get_interval()
+ lower_limit=max(self.value_range[0],evidence_range[0])
+ upper_limit=min(self.value_range[1],evidence_range[1])
+ else:
+ lower_limit=self.value_range[0]
+ upper_limit=self.value_range[1]
+
+ if lower_limit==upper_limit:
+ v=lower_limit
+ if lower_limit>upper_limit:
+ raise Exception("Intersection of random variable's value_range and"
+ "allowed Interval for Evidence is empty - no sampling possible")
+
+ #generate the actual sample
+ distribution=scipy.stats.norm(x, std_walk)
+ lower_cdf=distribution.cdf(lower_limit)
+ upper_cdf=distribution.cdf(upper_limit)
+
+ sample_in_integral=random.uniform(lower_cdf, upper_cdf)
+
+ sample=distribution.ppf(sample_in_integral)
+
+
+
+ a=scipy.stats.norm(self.value_range[0], std_walk).cdf(x)
+ b=scipy.stats.norm(self.value_range[0], std_walk).cdf(sample)
+ cdf_ratio = a/b
+ return sample,cdf_ratio
+
+ def sample_global(self, state, evidence):
+ '''
+ This method can be used to sample from this local distribution.
+
+ @param state: A Dict from Node-objects to values. You can specify the
+ values of this nodes parents in this dict and the conditional
+ probability density will be adjusted accordingly.
+ '''
+ #is there some evidence for this node?
+ if self in evidence.keys():
+ #if only one value is allowed we can return it immediatly
+ unique=evidence[self].get_unique_value()
+ if unique!=None:
+ return unique
+ #if a whole interval is allowed intersect it with this variable's
+ #value_range to get limits for sampling
+ else:
+ evidence_range=evidence[self].get_interval()
+ lower_limit=max(self.value_range[0],evidence_range[0])
+ upper_limit=min(self.value_range[1],evidence_range[1])
+ #without evidence this variable's value_range represents limits for sampling
+ else:
+ lower_limit=self.value_range[0]
+ upper_limit=self.value_range[1]
+ #check if only one value is allowed and in case return immediatly
+ if lower_limit==upper_limit:
+ return lower_limit
+ #check for empty interval
+ if lower_limit>upper_limit:
+ raise Exception("Intersection of random variable's value_range and"
+ "allowed Interval for Evidence is empty - no sampling possible")
+
+ proposal=self.cpd.sample_global(state,lower_limit,upper_limit)
+ return proposal
+
+ def get_probability(self, value, state):
+ '''
+ This method can be used to query the cpd for how probable a value is,
+ given this nodes markov-blanket.
+
+ @param value: The value for this random-variable.
+ @param state: A Dict from Node-objects to values. Should at least contain
+ all variables from this nodes markov-blanket.
+ '''
+ return self.cpd.get_probability(value, state)
+
+
+class ContinuousNodeFactory(object):
+ '''This class offers methods for generating ContinuousNodes'''
+ def __init__(self):
+ pass
+
+ def createGaussNode(self, name):
+ '''
+ Create a LinearGaussNode with linear dependencies on parents.
+
+ @param name: The name of the node.
+ '''
+ return self.createContinuousNode(name,(-float("Inf"),float("Inf")),Gauss)
+
+ def createExponentialNode(self, name):
+ '''
+ Create a LinearExponentialNode with linear dependencies on parents.
+
+ @param name: The name of the node.
+ '''
+ return self.createContinuousNode(name,(0,float("Inf")),Exponential)
+
+ def createBetaNode(self, name):
+ '''
+ Create a LinearBetaNode with linear dependencies on parents.
+
+ @param name: The name of the node.
+ '''
+ return self.createContinuousNode(name,(0,1),Beta)
+
+ def createContinuousNode(self,name,value_range,DensityClass):
+ '''
+ Create a ContinuousNode. This method should only be invoked from
+ outside this class if no specialized method is available.
+
+ @param name: The name of the node.
+ @param value_range: A 2-tuple which represents the interval that is the
+ domain of the variable.
+ @param DensityClass: A class from primo.reasoning.density that shall be
+ the node's pdf
+ '''
+ return ContinuousNode(name,value_range,DensityClass)
+
diff --git a/primo/reasoning/ContinuousNode.py b/primo/reasoning/ContinuousNode.py
deleted file mode 100644
index 3e963e7..0000000
--- a/primo/reasoning/ContinuousNode.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from primo.reasoning import RandomNode
-import random
-import scipy
-
-class ContinuousNode(RandomNode):
- '''
- Represents a random-variable with a real-valued domain. Can only be defined
- on a subset or on whole R. The probability density can have different forms.
- Objects of this class can be created by a ContinuousNodeFactory.
- '''
- def __init__(self, name, value_range, DensityClass):
- super(ContinuousNode, self).__init__(name)
-
- #value_range is a 2-tuple that defines this variable's domain.
- self.value_range = value_range
- #the class density_class defines the class of function that is used
- #for this ContinuousNode's pdf.
- self.density_class = DensityClass
- #cpd - ConditionalProbabilityDensity is the concrete density function
- #of this ContinuousNode, conditioned on this Node's parents.
- self.cpd = DensityClass(self)
-
- 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)
-
- def sample_local(self, x, evidence):
- '''
- This method can be used to do a random walk in the domain of this node.
-
- @param x: The spot around which the next sample shall be generated.
- @param evidence: Evidence which is to be concerned when new samples are
- being generated. I am not entirely sure that this belongs here or is
- correct in theory...
-
- ATTENTION:
- This is the most simple and stupid implementation of the method. It
- uses bogo-search to find a sample that fits the evidence. You could
- 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).'''
- std_walk=1.0
-
- #intersect possible evidence-interval with value_range:
- if self in evidence.keys():
- evidence_range=evidence[self].get_interval()
- lower_limit=max(self.value_range[0],evidence_range[0])
- upper_limit=min(self.value_range[1],evidence_range[1])
- else:
- lower_limit=self.value_range[0]
- upper_limit=self.value_range[1]
-
- if lower_limit==upper_limit:
- v=lower_limit
- if lower_limit>upper_limit:
- raise Exception("Intersection of random variable's value_range and"
- "allowed Interval for Evidence is empty - no sampling possible")
-
- #generate the actual sample
- distribution=scipy.stats.norm(x, std_walk)
- lower_cdf=distribution.cdf(lower_limit)
- upper_cdf=distribution.cdf(upper_limit)
-
- sample_in_integral=random.uniform(lower_cdf, upper_cdf)
-
- sample=distribution.ppf(sample_in_integral)
-
-
-
- a=scipy.stats.norm(self.value_range[0], std_walk).cdf(x)
- b=scipy.stats.norm(self.value_range[0], std_walk).cdf(sample)
- cdf_ratio = a/b
- return sample,cdf_ratio
-
- def sample_global(self, state, evidence):
- '''
- This method can be used to sample from this local distribution.
-
- @param state: A Dict from Node-objects to values. You can specify the
- values of this nodes parents in this dict and the conditional
- probability density will be adjusted accordingly.
- '''
- #is there some evidence for this node?
- if self in evidence.keys():
- #if only one value is allowed we can return it immediatly
- unique=evidence[self].get_unique_value()
- if unique!=None:
- return unique
- #if a whole interval is allowed intersect it with this variable's
- #value_range to get limits for sampling
- else:
- evidence_range=evidence[self].get_interval()
- lower_limit=max(self.value_range[0],evidence_range[0])
- upper_limit=min(self.value_range[1],evidence_range[1])
- #without evidence this variable's value_range represents limits for sampling
- else:
- lower_limit=self.value_range[0]
- upper_limit=self.value_range[1]
- #check if only one value is allowed and in case return immediatly
- if lower_limit==upper_limit:
- return lower_limit
- #check for empty interval
- if lower_limit>upper_limit:
- raise Exception("Intersection of random variable's value_range and"
- "allowed Interval for Evidence is empty - no sampling possible")
-
- proposal=self.cpd.sample_global(state,lower_limit,upper_limit)
- return proposal
-
- def get_probability(self, value, state):
- '''
- This method can be used to query the cpd for how probable a value is,
- given this nodes markov-blanket.
-
- @param value: The value for this random-variable.
- @param state: A Dict from Node-objects to values. Should at least contain
- all variables from this nodes markov-blanket.
- '''
- return self.cpd.get_probability(value, state)
-
diff --git a/primo/reasoning/ContinuousNodeFactory.py b/primo/reasoning/ContinuousNodeFactory.py
deleted file mode 100644
index a400f66..0000000
--- a/primo/reasoning/ContinuousNodeFactory.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from primo.reasoning import ContinuousNode
-from primo.reasoning.density import Gauss
-from primo.reasoning.density import Exponential
-from primo.reasoning.density import Beta
-
-class ContinuousNodeFactory(object):
- '''This class offers methods for generating ContinuousNodes'''
- def __init__(self):
- pass
-
- def createGaussNode(self, name):
- '''
- Create a LinearGaussNode with linear dependencies on parents.
-
- @param name: The name of the node.
- '''
- return self.createContinuousNode(name,(-float("Inf"),float("Inf")),Gauss)
-
- def createExponentialNode(self, name):
- '''
- Create a LinearExponentialNode with linear dependencies on parents.
-
- @param name: The name of the node.
- '''
- return self.createContinuousNode(name,(0,float("Inf")),Exponential)
-
- def createBetaNode(self, name):
- '''
- Create a LinearBetaNode with linear dependencies on parents.
-
- @param name: The name of the node.
- '''
- return self.createContinuousNode(name,(0,1),Beta)
-
- def createContinuousNode(self,name,value_range,DensityClass):
- '''
- Create a ContinuousNode. This method should only be invoked from
- outside this class if no specialized method is available.
-
- @param name: The name of the node.
- @param value_range: A 2-tuple which represents the interval that is the
- domain of the variable.
- @param DensityClass: A class from primo.reasoning.density that shall be
- the node's pdf
- '''
- return ContinuousNode(name,value_range,DensityClass)
-
diff --git a/primo/reasoning/DiscreteNode.py b/primo/reasoning/DiscreteNode.py
deleted file mode 100644
index 9b28eb2..0000000
--- a/primo/reasoning/DiscreteNode.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from primo.reasoning import RandomNode
-from primo.reasoning.density import ProbabilityTable
-import random
-
-
-class DiscreteNode(RandomNode):
- '''#TODO: write doc'''
-
- def __init__(self, name, value_range):
- super(DiscreteNode, self).__init__(name)
-
- 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)
-
- def get_probability(self, value, node_value_pairs):
- return self.cpd.get_probability([(self,value)] + node_value_pairs)
-
- def set_probability_table(self, table, nodes):
- self.cpd.set_probability_table(table, nodes)
-
- def is_valid(self):
- return self.cpd.is_normalized_as_cpt(self)
-
- def sample_global(self, state, evidence):
- if evidence==None or not self in evidence.keys():
- compatibles=self.value_range
- else:
- compatibles=[]
- for v in self.value_range:
- if evidence[self].is_compatible(v):
- compatibles.append(v)
-
- return self.cpd.sample_global(state,self,compatibles)
-
- def sample_local(self, x, evidence=None):
- if evidence==None or not self in evidence.keys():
- compatibles=self.value_range
- else:
- compatibles=[]
- for v in self.value_range:
- if evidence[self].is_compatible(v):
- compatibles.append(v)
-
- return random.choice(compatibles),1.0
diff --git a/primo/reasoning/RandomNode.py b/primo/reasoning/RandomNode.py
deleted file mode 100644
index 54302da..0000000
--- a/primo/reasoning/RandomNode.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from primo.core import Node
-#from primo.reasoning.density import Density
-
-
-class RandomNode(Node):
- '''Represents a random variable. There should be subclasses of this for
- different kinds of data. There are currently DiscreteNode for discrete-valued
- random variables and ContinuousNode for random Variables with R or an Intervall
- in R as domain.
-
- At a later point in time there may be structural nodes too.
- '''
-
- #The Continditional Propability Distribution of this random variable
- cpd = None
-
- def __init__(self, name):
- super(RandomNode, self).__init__(name)
-
- #value_range defines the domain of this random variable
- self.value_range=None
-
- def set_cpd(self, cpd):
- self.cpd = cpd
-
- def get_cpd(self):
- return self.cpd
-
- def announce_parent(self, node):
- '''
- Adjust the cpd so a new node is incorporated as dependency.
- '''
- self.cpd.add_variable(node)
-
- def get_cpd_reduced(self, evidence):
- '''
- Return a reduced version of the cpd of this node. This reduced version
- is constructed according to some evidence.
- @param evidence: A List of (Node,Value) pairs.
- '''
- return self.cpd.reduction(evidence)
-
- def get_value_range(self):
- return self.value_range
-
- def sample_gobal(self, x, evidence=None):
- '''
- This method can be used to sample from this local distribution.
-
- @param state: A Dict from Node-objects to values. You can specify the
- values of this nodes parents in this dict and the conditional
- probability density will be adjusted accordingly.
- '''
- raise Exception("Called unimplemented Method")
-
- def sample_local(self, x, evidence=None):
- '''
- This method can be used to do a random walk in the domain of this node.
-
- @param x: The spot around which the next sample shall be generated.
- @param evidence: Evidence which is to be concerned when new samples are
- being generated. I am not entirely sure that this belongs here or is
- correct in theory...
- '''
- raise Exception("Called unimplemented Method")
-
-
- def is_valid(self):
- raise Exception("Called an unimplemented function")
-
diff --git a/primo/reasoning/Untitled Document 1 b/primo/reasoning/Untitled Document 1
deleted file mode 100644
index a2c36d4..0000000
--- a/primo/reasoning/Untitled Document 1
+++ /dev/null
@@ -1,13 +0,0 @@
-from primo.reasoning import ContinuousNode
-from primo.reasoning import LinearGaussNode
-
-class ContinuousNodeFactory(object):
- def __init__(self):
- pass
-
- def createLinearGaussNode(self, name):
- return self.createContinuousNode(name,(-float("Inf"),float("Inf")),LinearGauss)
-
- def createContinuousNode(self,name,value_range,DensityClass):
- return ContinuousNode(name,value_range,DensityClass)
-
--
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