def main():
startStateProbabilities = {"A" : 0.25, "B" : 0.4, "C" : 0.35}
markovChain = MarkovChain()
markovChain.addState("A", {"A" : 0.1, "B" : 0.8, "C" : 0.1})
markovChain.addState("B", {"A" : 0.1, "B" : 0.1, "C" : 0.8})
markovChain.addState("C", {"A" : 0.8, "B" : 0.1, "C" : 0.1})
markovChain.setStartStateProbabilities(startStateProbabilities)
print markovChain
for i in range(0, 10):
print markovChain.generateData(30)
f = open("my_new_pickle.pickle", 'w')
pickle.dump(markovChain, f)
f.close()
def buildMarkovChain(counts):
markovChain = MarkovChain()
print "Counts: " + str(counts)
for origin in counts.keys():
# Get sum of outgoing edges from current origin to determine denominator of probability calculation
totalOutSum = 0
for count in counts[origin].values():
totalOutSum += count
# Calculate transition probabilities from current origin
transitionProbabilities = {}
for destination, count in counts[origin].iteritems():
transitionProbabilities[destination] = count / float(totalOutSum)
markovChain.addState(origin, transitionProbabilities)
print "Built Markov chain:\n" + str(markovChain)
return markovChain
