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'''
A module for creating and managing node data. Node data in this library can have many types, dependent on whether the conditional probability distributions are discrete, linear Gaussian, or hybrid, and on whether the Bayesian network is static or dynamic. For example input files, see :doc:`unittestdict`, :doc:`unittesthdict`, :doc:`unittestlgdict`, and :doc:`unittestdyndict`.
'''
from dictionary import Dictionary
[docs]class NodeData(Dictionary):
'''
This class represents the node data for each node in a graph. If the Bayesian network is static, it contains the attribute *Vdata*. If the Bayesian network is dynamic, it contains two attributes, *initial_Vdata* and *twotbn_Vdata*. If the Bayesian network has hybrid CPDs, it contains the additional attribute *nodes*.
'''
def __init__(self):
self.Vdata = None
'''A dictionary of node data.'''
self.initial_Vdata = None
'''In dynamic graphs, a dictionary containing node data for the initial time interval.'''
self.twotbn_Vdata = None
'''In dynamic graphs, a dictionary containing node data for every time step interval after the first one.'''
self.nodes = None
'''In hybrid graphs, a dictionary of {key:value} pairs linking the name of each node (the key) to a clas instance (the value) which represents the node, its data, and its sampling function.'''
[docs] def load(self, path):
'''
Load node data from an input file located at *path*. Input file must be a plaintext .txt file with a JSON-style representation of a dict. The dict must have the top-level key ``Vdata`` or two top-level keys, ``initial_Vdata`` and ``twotbn_Vdata``. For example::
{
"Vdata": {
"<vertex 1>": <dict containing vertex 1 data>,
...
"<vertex n>": <dict containing vertex n data>
}
}
or::
{
"initial_Vdata": {
"<vertex 1>": <dict containing vertex 1 data>,
...
"<vertex n>": <dict containing vertex n data>
}
"twotbn_Vdata": {
"<vertex 1>": <dict containing vertex 1 data>,
...
"<vertex n>": <dict containing vertex n data>
}
}
The function takes the following arguments:
1. *path* -- The path to the text file that contains input data (e.g., "mydictionary.txt")
In the static case, it modifies *Vdata* to hold the dictionary found at path. In the dynamic case, it modifies the *initial_Vdata* and *twotbn_Vdata* attributes to hold the dictionaries found at path.
'''
self.dictload(path)
# try to load both for normal and dynamic cases
try:
self.Vdata = self.alldata["Vdata"]
except KeyError:
try:
self.initial_Vdata = self.alldata["initial_Vdata"]
self.twotbn_Vdata = self.alldata["twotbn_Vdata"]
except KeyError:
print "Error: NodeData did not recognize input file format."
# free unused memory
del self.alldata
[docs] def entriestoinstances(self):
'''
For each node, convert dictionary entry to class instance.
This method is used only when dealing with Hybrid Bayesian networks as found in the :doc:`hybayesiannetwork` module.
The type of the node must be located in the 'type' attribute of the node's dictionary entry. To see an example of such a dictionary, see :doc:`unittesthdict`. This type is used to instantiate a corresponding class from libpgm/CPDtypes/, and store the node's dictionary info in that class. Thus we lose none of the dictionary data, yet we gain the ability to use the instantiated class's built-in function to choose its own outcome based on the outcomes of its parents.
In order for this method to be called, the self.Vdata attribute must have dictionary entries of the following form::
<vertex name>: {
'type': <type of node -- must correspond to module in /CPDtypes>,
'parents': <array of parents of node>,
'children': <array of children of node>,
<whatever further entries are required by the type*>
}
For instance, type "discrete" requires a "cprob" entry, while type "lg"
requires "mean_base", "mean_scal", and "variance" entries.
The function draws on the data in the *Vdata* attribute, and instantiates the attribute *nodes*, which is a dictionary of {name: instance} pairs where 'name' is the name of the node and 'instance' is a class instance containing the node data and the proper sampling function.
'''
# declare result dict
rarray = dict()
# transform into class instances
for entry in self.Vdata.keys():
# import module containing class
path = str(self.Vdata[entry]["type"])
exec("import CPDtypes." + path + " as " + path)
# instantiate class
exec("tmpnode = " + path + "." + str.capitalize(path) + "(self.Vdata[entry])")
# append to array
exec("rarray['" + str(entry) + "'] = tmpnode")
self.nodes = rarray