def add_type_frequencies(self, sent): a = nltktest.tag_in_twos(sent) # [TAG, TAG2, TAG3] for elem in a: if elem[1] in self.type_frequencies: val_arr = self.type_frequencies[elem[1]] found = False for i in range(0, len(val_arr)): if elem[0] == val_arr[i][0]: found = True val_arr[i] = (val_arr[i][0], val_arr[i][1] + 1) if not found: self.type_frequencies[elem[1]].append((elem[0], 1)) else: self.type_frequencies[elem[1]] = [(elem[0], 1)] print(self.type_frequencies)
def add_type_frequencies(self, sent): a = nltktest.tag_in_twos(sent) # [TAG, TAG2, TAG3] for elem in a: if elem[1] in self.type_frequencies: val_arr = self.type_frequencies[elem[1]] found = False for i in range(0, len(val_arr)): if elem[0] == val_arr[i][0]: found = True val_arr[i] = (val_arr[i][0], val_arr[i][1]+1) if not found: self.type_frequencies[elem[1]].append((elem[0], 1)) else: self.type_frequencies[elem[1]] = [(elem[0], 1)] print(self.type_frequencies)
def train(self, sent): """ Single sentence will be used to fill the state transition matrix. :param sent: the sentence to be trained on """ a = nltktest.tag_in_twos(sent) prev_list = () for i in range(0, len(a)): if prev_list in self.model.keys(): val_arr = self.model[prev_list] found = False for j in range(0, len(val_arr)): if a[i] == val_arr[j][0]: found = True val_arr[j] = (val_arr[j][0], val_arr[j][1] + 1) if not found: self.model[prev_list].append((a[i], 1)) else: self.model[prev_list] = [(a[i], 1)] prev_list = prev_list + tuple([a[i]]) if (len(prev_list) > self.n): prev_list = prev_list[1:] self.model[prev_list] = ()
def train(self, sent): """ Single sentence will be used to fill the state transition matrix. :param sent: the sentence to be trained on """ a = nltktest.tag_in_twos(sent) prev_list = () for i in range(0, len(a)): if prev_list in self.model.keys(): val_arr = self.model[prev_list] found = False for j in range(0, len(val_arr)): if a[i] == val_arr[j][0]: found = True val_arr[j] = (val_arr[j][0], val_arr[j][1]+1) if not found: self.model[prev_list].append((a[i], 1)) else: self.model[prev_list] = [(a[i], 1)] prev_list = prev_list + tuple([a[i]]) if(len(prev_list) > self.n): prev_list = prev_list[1:] self.model[prev_list] = ()