def __compute_probabilityCheck2(self, matrix, items, base, hyper,blanketNew):
""" Calculating posterior.
Arguments:
matrix -- transition or emission
items -- (hypothesis, evidence)
base -- base probability
hyper -- hyperparameter
"""
x = get_value(matrix, items[0], items[1], items[2])
y = get_value(matrix, items[1], items[2])
return (x + self.__check21(blanketNew)+self.__check42(blanketNew)+ hyper) / (y +self.__check22(blanketNew)+self.__check32(blanketNew)+ base *hyper)
def __compute_probability(self, matrix, items, base, hyper):
""" Calculating posterior.
Arguments:
matrix -- transition or emission
items -- (hypothesis, evidence)
base -- base probability
hyper -- hyperparameter
"""
x = get_value(matrix, items[0], items[1], items[2])
y = get_value(matrix, items[1], items[2])
return (x +hyper) / (y + base * hyper)
def __compute_probability(self, matrix, items, base, hyper):
""" Calculating posterior.
Arguments:
matrix -- transition or emission
items -- (hypothesis, evidence)
base -- base probability
hyper -- hyperparameter
"""
x = get_value(matrix, items[0], items[1])
y = get_value(matrix, items[1])
return (x + base * hyper) / (y + hyper)
def __compute_label_probabilities(self, blanket):
""" Computes the probability of each label.
Arguments:
blanket -- Markov blanket
"""
_, previous_previous_label,previous_label, following_label,following_following_label, current_observation = blanket
# Probabilities of each possible label
probabilities = []
for label in range(self.labels):
blanketNew=label,previous_previous_label,previous_label, following_label,following_following_label
# Chain rule
probability = (self.__compute_probability(self.transition,
(label,previous_previous_label, previous_label),
self.labels,
self.alpha) *
self.__compute_probabilityCheck1(self.transition,
(following_label,previous_label, label),
self.labels,
self.alpha,blanketNew) *
self.__compute_probabilityCheck2(self.transition,
(following_following_label, label,following_label),
self.labels,
self.alpha,blanketNew) *
self.__compute_probability1(self.emission,
(current_observation, label),
get_value(self.stemdict, current_observation),
self.beta))
probabilities.append(probability)
return probabilities
def Start(self, params):
self.params = params
self.tamount.SetValue("")
self.tamount.SetFocus()
self.params['fvalue'] = get_value()
self.ftcvalue.SetLabel(str(self.params['fvalue']))
self.Show()
def __compute_probabilityUnigram(self, matrix, item, hyper):
""" Calculating posterior.
Arguments:
matrix -- transition or emission
items -- (hypothesis, evidence)
base -- base probability
hyper -- hyperparameter
"""
y = self.wordcount
x = get_value(matrix, item)
z = len(set(self.stemlist))
# print(x,y,base)
return (x + hyper) / (y + z * hyper)
def set_variable_info(self, scope, variable, read_memory):
"""Set the result analyzed by the debugger in the instance variable
Args:
scope (str): Scope in the script to debug
variable (SBValue): Variable infomation by LLDB
read_memory (function): Look into the memory of the current process
"""
memory_raw = read_memory(int(str(variable.GetLocation()), 16),
variable.GetByteSize())
self._address = str(variable.GetLocation())
self._scope = scope
self._name = variable.GetName()
self._data = get_value(str(variable))
self._raw = format_raw(memory_raw)
self._type = variable.GetTypeName()
