def main():
toParse = sys.stdin.read()
HMMstructures = BaulmWelch()
sequence, transitionDict, emissionDict, availableStates = HMMstructures.parseInput(
toParse)
forwardGraph = HMMstructures.makeGraph(sequence, availableStates,
transitionDict)
reversedGraph = HMMstructures.reverseGraph(availableStates, forwardGraph)
dagProb = DirectedAcyclicGraph()
forwardWeights, forwardSum = dagProb.longestPath(1, len(sequence), 1,
sequence, forwardGraph,
emissionDict)
backWeights, backSum = dagProb.longestPath(
len(sequence) - 1, 0, -1, sequence, reversedGraph, emissionDict)
stateProbabilities = HMMstructures.calcEachNode(forwardSum, forwardWeights,
backWeights, sequence,
availableStates)
for prob in stateProbabilities:
print(*prob, sep="\t")
def main():
toParse = sys.stdin.read()
HMMstructures = HMM()
sequence, transitionDict, emissionDict, availableStates = HMMstructures.parseInput(
toParse)
sys.stdin = open('/dev/tty')
forwardGraph = HMMstructures.makeGraph(sequence, availableStates,
transitionDict)
reversedGraph = HMMstructures.reverseGraph(availableStates, forwardGraph)
print(forwardGraph)
print('*************')
print(reversedGraph)
dagProb = DirectedAcyclicGraph()
forwardWeights, forwardSum = dagProb.longestPath(1, len(sequence), 1,
sequence, forwardGraph,
emissionDict)
# print(forwardWeights)
print('forward Done')
print(forwardSum)
backWeights, backSum = dagProb.longestPath(
len(sequence) - 1, 0, -1, sequence, reversedGraph, emissionDict)
def main():
toParse = sys.stdin.read()
HMMstructures = HMM()
sequence, transitionDict, emissionDict, availableStates = HMMstructures.parseInput(
toParse)
dagGraph = HMMstructures.makeGraph(sequence, availableStates,
transitionDict)
dagProb = DirectedAcyclicGraph()
getViterbi = dagProb.longestPath(sequence, dagGraph, transitionDict,
emissionDict)
def main():
toParse = sys.stdin.read()
HMMstructures = ForwardBackward()
sequence, transitionDict, emissionDict, availableStates = HMMstructures.parseInput(
toParse)
dagProb = DirectedAcyclicGraph()
forwardWeights, forwardSum = dagProb.longestPath(1, len(sequence), 1,
sequence, emissionDict)
backWeights, backSum = dagProb.longestPath(
len(sequence) - 1, 0, -1, sequence, reversedGraph, emissionDict)
accurateProbs, printProbs = HMMstructures.calcEachNode(
forwardSum, forwardWeights, backWeights, sequence, availableStates)
for prob in printProbs:
print(*prob, sep="\t")
def runBaulmWelch(iterations, sequence, alphabet, transitionDict, emissionDict, availableStates):
"""Runs Baulm Welch parameter learning algorithm for given iterations and returns the final transition/emission parameters for the emitted sequence"""
dagProb = DirectedAcyclicGraph()
baulmWelch = BaulmWelch()
forwardBackward = ForwardBackward()
for i in range(int(iterations)):
#run forward/backward algorithm on parameters
accurateProbs, forward, forwardSum, backward = forwardBackward.getForwardBackward(sequence, transitionDict, emissionDict, availableStates, dagProb)
#build pi* and pi**
piStar, emisSums = baulmWelch.getPiStar(emissionDict, sequence, alphabet, availableStates)
piStarStar, rowState, transitions = baulmWelch.getPiStarStar(sequence, availableStates, forward, backward, forwardSum, transitionDict, emissionDict)
#rebuild normalized emission and transition dictionaries
emissionDict = baulmWelch.emisMatrixtoDict(availableStates, alphabet, piStar, emisSums)
transitionDict = baulmWelch.transMatrixtoDict(availableStates, piStarStar, rowState, transitions)
return emissionDict, transitionDict