def viterbi(seq, filenameEmission, filenameTransmission):
forwardGraph = graph.createGraph(filenameEmission, filenameTransmission)
#print forwardGraph
char = seq[1]
states = forwardGraph["E"]["E", "States"]
statesCompl = states[:]
statesCompl.insert(0, "0")
statesCompl.append("0")
m = matrix.createMatrix(len(states) + 2, len(seq), 0)
trace = matrix.createMatrix(len(states) + 2, len(seq), -1)
# intialisation part
matrix.atualiseValueIntoMatrix(m, 0, 0, 1)
# recurrence part
# recurrence 1
trace[1][1] = trace[2][1] = 0
for i in range(1, len(m) - 1):
m[i][1] = float(forwardGraph["A"]["0", statesCompl[i]]) * float(
forwardGraph["E"][statesCompl[i], char])
# recurrence 2
for j in range(2, len(m[0]) - 1): #jump the first 2 collumns
char = seq[j]
for i in range(1, len(m) - 1): # jump the begin and end states
scores = []
for l in range(1, len(m) - 1):
#print "L:",statesCompl[l],", K:",statesCompl[i]
scores.append(
float(m[l][j - 1]) *
float(forwardGraph["A"][statesCompl[l], statesCompl[i]]) *
float(forwardGraph["E"][statesCompl[i], char]))
'''print "partial value: ",scores[-1]
print "lines value:", float(m[l][j-1])
print "AKL: ",float(forwardGraph["A"][statesCompl[l],statesCompl[i]])
print "Elc: ", float(forwardGraph["E"][statesCompl[i],char])'''
m[i][j] = max(scores)
#print "Max: ", scores.index(max(scores))+1
trace[i][j] = scores.index(max(scores)) + 1
#print (m[i][j])
#print "---------------------------------"
del (scores)
scores = []
for i in range(1, len(m) - 1):
scores.append(
float(m[i][len(m[0]) - 2]) *
float(forwardGraph["A"][statesCompl[i], "0"]))
m[len(m) - 1][len(m[0]) - 1] = max(scores)
trace[len(m) - 1][len(m[0]) - 1] = scores.index(max(scores)) + 1
#print "__________________________"
#matrix.printMatrix(m)
#matrix.printMatrix(trace)
return trace
def aPosterioriDecoding(seq, fileNameEmis, fileNameTrans):
statesGraph = graph.createGraph(fileNameEmis,
fileNameTrans) # it's a dictionary
f = forward(seq, fileNameEmis, fileNameTrans)
b = backward(seq, fileNameEmis, fileNameTrans)
seq = seq[1:-1]
psm = f[len(f) - 1][len(f[0]) - 1]
pi = matrix.createMatrix(len(statesGraph["A"]["A", "States"]), len(seq),
0.)
print "F:"
matrix.printMatrix(f)
print "B:"
matrix.printMatrix(b)
print "Seq:", seq
print "Size of Seq: ", len(seq)
for i in range(len(seq)): #pi(i) -> position in the sequence = collunms
scores = []
for k in range(1, len(statesGraph["A"][
"A", "States"])): #num of states that produce chars = lines
scores.append((f[k][i + 1] * b[k][i + 1]) / psm)
pi[0][i] = scores.index(max(scores)) + 1 #states are from 1 to lines-1
pi[1][i] = scores[0]
pi[2][i] = scores[1]
del (scores)
return pi
def backward(seq, filenameEmission, filenameTransmission):
backwardGraph = graph.createGraph(filenameEmission, filenameTransmission)
#print backwardGraph
char = seq[len(seq) - 2]
states = backwardGraph["E"]["E", "States"]
statesCompl = states[:]
statesCompl.insert(0, "0")
statesCompl.append("0")
seq = seq[:-1]
m = matrix.createMatrix(len(states) + 2, len(seq), 0)
# intialisation part
# the final state
for i in range(1, len(m) - 1):
m[i][len(m[0]) - 1] = float(backwardGraph["A"][statesCompl[i], "0"])
#matrix.printMatrix(m)
# recurrence
for j in range(len(m[0]) - 2, 0, -1): #jump the first 2 collumns
char = seq[j + 1]
#print "char:", char
for i in range(1, len(m) - 1): # jump the begin and end states
for l in range(len(m) - 2, 0, -1):
#print "L:",statesCompl[l],", K:",statesCompl[i]
m[i][j] += float(m[l][j + 1]) * float(backwardGraph["A"][
statesCompl[i], statesCompl[l]]) * float(
backwardGraph["E"][statesCompl[l], char])
''' print "partial value: ",float(m[i][j])
print "lines value:", float(m[l][j+1])
print "Alk: ",float(backwardGraph["A"][statesCompl[i],statesCompl[l]])
print "Elc: ", float(backwardGraph["E"][statesCompl[l],char])'''
for l in range(1, len(m) - 1):
m[0][0] += float(m[l][1]) * float(
backwardGraph["A"]["0", statesCompl[l]]) * float(
backwardGraph["E"][statesCompl[l], seq[1]])
'''print "psm: ",psm
print "lines value:", float(m[l][j+1])
print "Alk: ",float(backwardGraph["A"]["0",statesCompl[l]])
print "Elc: ", float(backwardGraph["E"][statesCompl[l],seq[1]])
print "Char: ", seq[1]'''
#matrix.printMatrix(m)
return m
def forward(seq, filenameEmission, filenameTransmission):
forwardGraph = graph.createGraph(filenameEmission, filenameTransmission)
#print forwardGraph
char = seq[1]
states = forwardGraph["E"]["E", "States"]
statesCompl = states[:]
statesCompl.insert(0, "0")
statesCompl.append("0")
statesCompl = ["0", "Y", "N", "0"]
print statesCompl
m = matrix.createMatrix(len(states) + 2, len(seq), 0)
# intialisation part
matrix.atualiseValueIntoMatrix(m, 0, 0, 1)
# recurrence part
# recurrence 1
for i in range(1, len(m) - 1):
m[i][1] = float(forwardGraph["A"]["0", statesCompl[i]]) * float(
forwardGraph["E"][statesCompl[i], char])
# recurrence 2
for j in range(2, len(m[0]) - 1): #jump the first 2 collumns
char = seq[j]
for i in range(1, len(m) - 1): # jump the begin and end states
for l in range(1, len(m) - 1):
#print "L:",statesCompl[l],", K:",statesCompl[i]
m[i][j] += float(m[l][j - 1]) * float(
forwardGraph["A"][statesCompl[l], statesCompl[i]]) * float(
forwardGraph["E"][statesCompl[i], char])
'''print "partial value: ",float(m[i][j])
print "lines value:", float(m[l][j-1])
print "AKL: ",float(forwardGraph["A"][statesCompl[l],statesCompl[i]])
print "Elc: ", float(forwardGraph["E"][statesCompl[i],char])'''
for i in range(1, len(m) - 1):
m[len(m) - 1][len(m[0]) - 1] += float(m[i][len(m[0]) - 2]) * float(
forwardGraph["A"][statesCompl[i], "0"])
matrix.printMatrix(m)
return m
upperyearcourses = ['COSC 222', 'MATH 200', 'PHYS 216', 'BIOL 200', 'CHEM 201']
allcourses = loweryearcourses + upperyearcourses
#all the majors that we are studying
majors = ['COSC', 'MATH', 'PHYS', 'BIOL', 'CHEM']
#the possible decisions and their utilities
enjoy_and_do_well = 10
enjoy_and_do_bad = 0
dislike_and_do_well = 0
dislike_and_do_bad = -10
do_not_take_course = 5
#The different lotteries
lottery1 = [
enjoy_and_do_well, enjoy_and_do_bad, dislike_and_do_well,
dislike_and_do_bad
]
lottery2 = [do_not_take_course]
#extract the students from the csv file
lower_year_students = extract.lower_year
upper_year_students = extract.upper_year
#create the grade matrices of the students
loweryeargrades = matrix.createMatrix(lower_year_students)
upperyeargrades = matrix.createMatrix(upper_year_students)
#extract the averages of all the students
upperyearaverages = [student.average for student in upper_year_students]
loweryearaverages = [student.average for student in lower_year_students]