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()