From b4928f2beb9d89cfc2a4fdf1ee2ca1b11b03f162 Mon Sep 17 00:00:00 2001
From: Hendrik Buschmeier <hbuschme@uni-bielefeld.de>
Date: Sat, 5 Apr 2014 00:41:18 +0200
Subject: [PATCH] Refactoring: Adapted imports.
---
primo/densities.py | 7 +++---
primo/networks.py | 36 +++++++++++++++----------------
primo/nodes.py | 13 ++++++-----
primo/reasoning/particlefilter.py | 7 +++---
4 files changed, 31 insertions(+), 32 deletions(-)
diff --git a/primo/densities.py b/primo/densities.py
index 06c9268..f2e608f 100644
--- a/primo/densities.py
+++ b/primo/densities.py
@@ -7,8 +7,7 @@ import random
import numpy
from scipy.stats
-from primo.nodes import ContinuousNode
-
+from primo.nodes
class Density(object):
'''TODO: write doc'''
@@ -46,6 +45,7 @@ class ExponentialParameters(object):
#a dict (node,coefficient) that holds the weights that define the depency on each node
self.b=b
+
class GaussParameters(object):
'''
This represents the parameters for the Gauss-density class.
@@ -58,6 +58,7 @@ class GaussParameters(object):
#the variance
self.var=var
+
class NDGaussParameters(object):
def __init__(self, mu, cov):
self.mu=mu
@@ -591,7 +592,7 @@ class Gauss(Density):
def add_variable(self, variable):
- if not isinstance(variable, ContinuousNode):
+ if not isinstance(variable, primo.nodes.ContinuousNode):
raise Exception("Tried to add Variable into Gaussian densitiy, but variable is not continuous")
self.b[variable]=0.0
diff --git a/primo/networks.py b/primo/networks.py
index 74ee197..2d7d828 100644
--- a/primo/networks.py
+++ b/primo/networks.py
@@ -2,7 +2,7 @@ import networkx as nx
import primo.nodes
-class BayesNet(object):
+class BayesianNetwork(object):
def __init__(self):
self.graph = nx.DiGraph()
@@ -15,7 +15,7 @@ class BayesNet(object):
self.node_lookup[node.name]=node
self.graph.add_node(node)
else:
- raise Exception("Can only add 'Node' and its subclasses as nodes into the BayesNet")
+ raise Exception("Can only add 'Node' and its subclasses as nodes into the BayesianNetwork")
def add_edge(self, node_from, node_to):
if node_from in self.graph.nodes() and node_to in self.graph.nodes():
@@ -38,14 +38,14 @@ class BayesNet(object):
try:
self.graph.remove_edge(node_from, node_to)
except nx.exception.NetworkXError:
- raise Exception("Tried to remove an edge which does not exist in the BayesNet")
+ raise Exception("Tried to remove an edge which does not exist in the BayesianNetwork")
#raise Exception("Fixme: Adapt CPD of child-node")
def get_node(self, node_name):
try:
return self.node_lookup[node_name]
except KeyError:
- raise Exception("There is no node with name "+node_name+" in the BayesNet")
+ raise Exception("There is no node with name "+node_name+" in the BayesianNetwork")
def get_all_nodes(self):
return self.graph.nodes()
@@ -137,7 +137,7 @@ class BayesNet(object):
return len(self.graph)
-class BayesianDecisionNetwork(BayesNet):
+class BayesianDecisionNetwork(BayesianNetwork):
def __init__(self):
super(BayesianDecisionNetwork, self).__init__()
@@ -180,7 +180,7 @@ class BayesianDecisionNetwork(BayesNet):
self.node_lookup[node.name]=node
self.graph.add_node(node)
else:
- raise Exception("Can only add 'Node' and its subclasses as nodes into the BayesNet")
+ raise Exception("Can only add 'Node' and its subclasses as nodes into the BayesianNetwork")
def get_all_nodes(self):
'''Returns all RandomNodes'''
@@ -234,7 +234,7 @@ class BayesianDecisionNetwork(BayesNet):
self.partialOrdering = partialOrder
-class DynamicBayesNet(BayesNet):
+class DynamicBayesianNetwork(BayesianNetwork):
''' This is the implementation of a dynamic Bayesian network (also called
temporal Bayesian network).
@@ -248,8 +248,8 @@ class DynamicBayesNet(BayesNet):
'''
def __init__(self):
- super(DynamicBayesNet, self).__init__()
- self._B0 = BayesNet()
+ super(DynamicBayesianNetwork, self).__init__()
+ self._B0 = BayesianNetwork()
self._twoTBN = TwoTBN()
@property
@@ -260,12 +260,12 @@ class DynamicBayesNet(BayesNet):
@B0.setter
def B0(self, value):
''' Set the Bayesian network representing the initial distribution.'''
- if isinstance(value, BayesNet):
+ if isinstance(value, BayesianNetwork):
if not value.is_valid():
- raise Exception("BayesNet is not valid.")
+ raise Exception("BayesianNetwork is not valid.")
self._B0 = value
else:
- raise Exception("Can only set 'BayesNet' and its subclasses as " +
+ raise Exception("Can only set 'BayesianNetwork' and its subclasses as " +
"B0 of a DBN.")
@property
@@ -278,7 +278,7 @@ class DynamicBayesNet(BayesNet):
''' Set the 2-time-slice Bayesian network.'''
if isinstance(value, TwoTBN):
if not value.is_valid():
- raise Exception("BayesNet is not valid.")
+ raise Exception("BayesianNetwork is not valid.")
self._twoTBN = value
else:
raise Exception("Can only set 'TwoTBN' and its subclasses as " +
@@ -294,18 +294,18 @@ class DynamicBayesNet(BayesNet):
" not found in TwoTBN!")
return False;
- return super(DynamicBayesNet, self).is_valid()
+ return super(DynamicBayesianNetwork, self).is_valid()
-class TwoTBN(BayesNet):
+class TwoTBN(BayesianNetwork):
''' This is the implementation of a 2-time-slice Bayesian network (2-TBN).
'''
def __init__(self, bayesnet=None):
- BayesNet.__init__(self)
+ BayesianNetwork.__init__(self)
if bayesnet:
- if not isinstance(bayesnet, BayesNet):
- raise Exception("Parameter 'bayesnet' is not a instance of class BayesNet.")
+ if not isinstance(bayesnet, BayesianNetwork):
+ raise Exception("Parameter 'bayesnet' is not a instance of class BayesianNetwork.")
self.graph = bayesnet.graph
self.node_lookup = bayesnet.node_lookup
self.__initial_nodes = []
diff --git a/primo/nodes.py b/primo/nodes.py
index 8fdd77c..be6e079 100644
--- a/primo/nodes.py
+++ b/primo/nodes.py
@@ -2,10 +2,9 @@ import abc
import random
import re
-import scipy
+import scipy.stats
-from primo.decision.UtilityTable import UtilityTable
-import primo.reasoning.density
+import primo.reasoning.densities
class Node(object):
@@ -99,7 +98,7 @@ class DiscreteNode(RandomNode):
super(DiscreteNode, self).__init__(name)
self.value_range = value_range
- self.cpd = primo.reasoning.density.ProbabilityTable()
+ self.cpd = primo.reasoning.densities.ProbabilityTable()
self.cpd.add_variable(self)
def __str__(self):
@@ -280,7 +279,7 @@ class ContinuousNodeFactory(object):
name,
(-float("Inf"),
float("Inf")),
- primo.reasoning.density.Gauss)
+ primo.reasoning.densities.Gauss)
def createExponentialNode(self, name):
'''
@@ -291,7 +290,7 @@ class ContinuousNodeFactory(object):
return self.createContinuousNode(
name,
(0,float("Inf")),
- primo.reasoning.density.Exponential)
+ primo.reasoning.densities.Exponential)
def createBetaNode(self, name):
'''
@@ -302,7 +301,7 @@ class ContinuousNodeFactory(object):
return self.createContinuousNode(
name,
(0, 1),
- primo.reasoning.density.Beta)
+ primo.reasoning.densities.Beta)
def createContinuousNode(self,name,value_range,density_class):
'''
diff --git a/primo/reasoning/particlefilter.py b/primo/reasoning/particlefilter.py
index e34e6b7..c31d9a7 100644
--- a/primo/reasoning/particlefilter.py
+++ b/primo/reasoning/particlefilter.py
@@ -4,8 +4,7 @@ import copy
import random
import time
-from primo.networks import BayesNet
-from primo.networks import DynamicBayesNet
+import primo.networks
class Particle(object):
'''
@@ -91,7 +90,7 @@ def weighted_sample(network, evidence = {}):
'''
w = 1.0
state = {}
- if not isinstance(network, BayesNet):
+ if not isinstance(network, primo.network.BayesianNetwork):
raise Exception("The given network is not an instance of BayesNet.")
nodes = network.get_nodes_in_topological_sort()
@@ -132,7 +131,7 @@ def particle_filtering_DBN(network, N, T, get_evidence_function, particle_class
Returns a list of N samples
'''
- if not isinstance(network, DynamicBayesNet):
+ if not isinstance(network, primo.networks.DynamicBayesianNetwork):
raise Exception("The given network is not an instance of DynamicBayesNet.")
if not network.is_valid():
--
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