def __init__(self,stateObjLea,chain):
''' initializes StateAlea instance's attributes
corresponding to the probability distribution given in stateObjLea
and referring to the given chain, instance of Chain
'''
Alea.__init__(self,stateObjLea.getAlea().genVPs())
self._chain = chain
def fromMatrix(stateObjs,*transProbsPerState):
''' returns a new Chain instance from given arguments
stateObjs is a sequence of objects representing states (strings, usually);
transProbsPerState arguments contain the transiiton probability weights;
there is one such argument per state, it is a tuple (stateObj,transProbs)
where transProbs is the sequence of probability weights of transition from
stateObj to each declared state, in the order of their declarations
'''
nextStateLeas = (Alea.fromValFreqs(*zip(stateObjs,transProbs)) for (stateObj,transProbs) in transProbsPerState)
nextStateLeaPerState = tuple(zip(stateObjs,nextStateLeas))
return Chain(nextStateLeaPerState)
def fromSeq(stateObjSeq):
''' returns a new Chain instance from given sequence of state objects
the probabilities of state transitions are set according to transition
frequencies in the given sequence
'''
(fromStateObjIter,toStateObjIter) = tee(stateObjSeq);
for _ in toStateObjIter:
break
nextStateObjsDict = dict()
for (fromStateObj,toStateObj) in zip(fromStateObjIter,toStateObjIter):
nextStateObjs = nextStateObjsDict.get(fromStateObj)
if nextStateObjs is None:
nextStateObjs = []
nextStateObjsDict[fromStateObj] = nextStateObjs
nextStateObjs.append(toStateObj)
nextStateNameAndObjs = list(nextStateObjsDict.items())
nextStateNameAndObjs.sort()
nextStateLeaPerState = tuple((stateObj,Alea.fromVals(*nextStateObjs)) for (stateObj,nextStateObjs) in nextStateNameAndObjs)
return Chain(nextStateLeaPerState)
