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#!/usr/bin/env python
# encoding: utf-8
"""
Rand_Params.py
Created by Daniel Mack on 2009-10-27.
Generates Random Parameters for a Skeleton
"""
import sys
import os
import networkx as nx
import random as rand
#Uniform
def flatGen(SANet, tasks, conds):
effLinks = {}
preLinks = {}
priors = {}
#use the tasks as the starting point for assigning probabilities
for each in tasks:
effects = SANet.successors(each)
effLinks[each] = []
effAr = effLinks[each]
for eff in effects:
#uses uniform for the time being
weight = rand.uniform(0,2) - 1
#a tuple for efficient look up
lnk = (eff, weight)
effAr.append(lnk)
effLinks[each] = effAr
preconds = SANet.predecessors(each)
#this is uniform at .5 probability across all tasks
prior = 1.0
preprob = 0.0
#must have preconditions
#TEMP#TEMP#TEMP#TEMPTEMP#TEMP#TEMP#TEMP
#currently all preconditions must be positive
#TEMP#TEMP#TEMP#TEMP#TEMP#TEMP#TEMP#TEMP
if len(preconds) > 0:
#Value is .5 to the power of the number of preconditions
prior = pow(.5, len(preconds))
#Each precondition probability is .5 to the number of preconditions - 1
#Should work for positive and negative, but for now, all preconditions are positive.
preprob = pow(.5, (len(preconds) - 1))
priors[each] = prior
#Book keeping for the graph.
#Assemble information on precondition links
for pre in preconds:
if pre in preLinks:
temp = preLinks[pre]
#A pair of task and precondition probability from the given conditon
temp.append((each,preprob))
preLinks[pre] =temp
else:
lnks = []
lnks.append((each, preprob))
preLinks[pre] = lnks
#returns the network structure, the priors on the tasks and hashmaps of the links and their values
info = (SANet, priors, preLinks, effLinks)
return info
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