def determinerClassesParMethodeNaive():
''' Utilisation d'une méthode naive pour estimer les classes '''
S = app.get_Pi_T_E()
I = S[0]
T = S[1]
E = S[2]
test_table = encodeTestAsMatrix3n()
for index in range(len(test_table)):
if test_table[index][0] != "":
test_table[index][2] = getMaxEmission(E, test_table[index][0])
return test_table
def determinerClassesParMethodeNaive():
S = app.get_Pi_T_E()
I = S[0]
T = S[1]
E = S[2]
print T
test_table = encodeTestAsMatrix3n()
for index in range(len(test_table)):
if test_table[index][0] != "":
test_table[index][2] = getMaxEmission(E, test_table[index][0])
return test_table
def determinerClassesParMethodeNaive():
'''Déterminer la classe en utilisant une methode naive : prendre l'état dont la probabilité est maximale pour l'observable (on ne prends pas en compte les probabilités de transition'''
S = app.get_Pi_T_E()
I = S[0]
T = S[1]
E = S[2]
print T
test_table = encodeTestAsMatrix3n()
for index in range(len(test_table)):
if test_table[index][0] != "":
test_table[index][2] = getMaxEmission(E, test_table[index][0])
return test_table
def determinerClassesParViterbi():
''' Utilisation de Viterbi '''
S = app.get_Pi_T_E()
I = S[0]
T = S[1]
E = S[2]
test_table = encodeTestAsMatrix3n()
reading_observable_initiale = True
for index in range(len(test_table)):
if test_table[index][0] != "":
if reading_observable_initiale:
reading_observable_initiale = False
test_table[index][2] = get_classes_max_proba_initiales(I, E, test_table[index][0])
else:
test_table[index][2] = get_class_max_proba_transition(I, T, E, test_table[index-1][1], test_table[index][0])
else:
reading_observable_initiale = True
return test_table
return T
listState = encod.get_categories(data+"/voc_etats")
listState.append("")
test_table = encodeTestAsMatrix3n()
listObservables = range(len(test_table))
for k in range(len(test_table)):
listObservables[k] = test_table[k][0]
### On construit un hmm avec le fichier d'apprentissage ###
S = app.get_Pi_T_E()
I = S[0]
T = S[1]
E = S[2]
if perturbation:
for obs in listObservables:
for state in listState:
if obs in E and not state=="":
E[obs][state]+=(random.random()-0.5)*coef
###########################################################
hmm = HMM_BW(listObservables, listState, 1)
if hmm_determine:
hmm.Pi = I
hmm.Pi[""] = 0.0
T[""] = hmm.Pi
