def saveTransitionProb(self, transFile):
transProb = {}
''' your code here '''
for oldTile, newDict in self.posDict.iteritems():
weightSum = 0.0
for newTile, weight in newDict.iteritems():
weightSum += weight
for newTile, weight in newDict.iteritems():
self.posDict[oldTile][newTile] = weight / weightSum
transProb = self.posDict
util.saveTransProb(transProb, transFile)
def saveTransitionProb(self, transFile):
transProb = {}
# transProb is a dict {(oldTile, newTile) : prob}
# First normalize the counters
for oldTile in self.transitions:
counter = self.transitions[oldTile]
s = float(sum(counter.values()))
for key in counter:
counter[key] /= s
for oldTile in self.transitions:
for newTile in self.transitions:
transProb[(oldTile, newTile)] = self.transitions[oldTile][newTile]
util.saveTransProb(transProb, transFile) ### COMMENTED SO THAT WE DO NOT OVERRIDE ANY LEARNED PROBABILITIES
def saveTransitionProb(self, transFile):
transProb = {}
transProb = self.transTable
print transProb
for oldRow in self.transTable:
for oldCol in self.transTable[oldRow]:
total = float(self.visitedTable[oldRow][oldCol])
for newRow in self.transTable[oldRow][oldCol]:
for newCol, freq in self.transTable[oldRow][oldCol][newRow].iteritems():
transProb[oldRow][oldCol][newRow][newCol] = freq/total
#print "transProb: ", transProb
#data = [transProb, self.visitedTable]
#util.saveTransProb(data, transFile)
util.saveTransProb(transProb, transFile)
def saveTransitionProb(self, transFile):
transProb = {}
''' your code here '''
for oldTile in self.info.keys():
sum = (float)(0)
for newTile in self.info[oldTile].keys():
sum += self.info[oldTile][newTile]
for newTile in self.info[oldTile].keys():
val = self.info[oldTile][newTile] / sum
self.info[oldTile][newTile] = val
for oldTile in self.info.keys():
for newTile in self.info[oldTile].keys():
transProb[(oldTile, newTile)] = self.info[oldTile][newTile]
util.saveTransProb(transProb, transFile)
def saveTransitionProb(self, transFile):
transProb = {}
''' your code here '''
util.saveTransProb(transProb, transFile)
