def __init__(self, type, key_words, secondary_words, excluding_words):
self.type_name = type
key_words_tree = Tree.AVLTree()
self.key_words = key_words_tree.insert_array(key_words)
secondary_words_tree = Tree.AVLTree()
self.secondary_words = secondary_words_tree.insert_array(
secondary_words)
excluding_Words_tree = Tree.AVLTree()
self.excluding_words = excluding_Words_tree.insert_array(
excluding_words)
def palabras_repetidas_dictionary_with_tree(text_to_classify):
import heapq
import time
start = time.time()
print()
print(" -------------------- Dictionary&Tree ----------------------- ")
mongo_dictionary = DB.GET_dictionary_from_DB() #from mongodb
dictionary = []
#como estan guardados por orden podemos cogerlos tal cual
for i in range(Utils.MIN_TYPES, Utils.MAX_TYPES):
key_words_tree = Tree.AVLTree()
key_words_tree.insert_array(mongo_dictionary[i].key_words)
secondary_words_tree = Tree.AVLTree()
secondary_words_tree.insert_array(mongo_dictionary[i].secondary_words)
excluding_words_tree = Tree.AVLTree()
excluding_words_tree.insert_array(mongo_dictionary[i].excluding_words)
dictionary.append(
Utils.Dictionary(mongo_dictionary[i].type_name, key_words_tree,
secondary_words_tree, excluding_words_tree))
# print('Write a text: ')
# text_to_classify = input().lower()
text_to_classify = Utils.delete_text_punctuation(text_to_classify)
#the algorithm
#while text_to_classify != '1' and text_to_classify != 'exit':
key_words_value = []
secondary_words_value = []
excluding_words_value = []
found_1words, found_2words, found_exwords = [], [], []
empty_words_tree = Tree.AVLTree()
empty_words_tree.insert_array(DB.GET_empty_words_from_DB())
for sport in dictionary:
#print('----------------------------------------------------------------------',sport.type_name)
value, words = sport.key_words.find_words_in_text(
text_to_classify, word_mark=1, empty_words_tree=empty_words_tree)
key_words_value.append(value)
found_1words.append(words)
value, words = sport.secondary_words.find_words_in_text(
text_to_classify,
word_mark=0.25,
empty_words_tree=empty_words_tree)
secondary_words_value.append(value)
found_2words.append(words)
value, words = sport.excluding_words.find_words_in_text(
text_to_classify, word_mark=1.5, empty_words_tree=empty_words_tree)
excluding_words_value.append(value * -1)
found_exwords.append(words)
#print(key_words_value)
#print(secondary_words_value[:])
#print(excluding_words_value[:])
i = 0
for exclude_value in excluding_words_value:
key_words_value[i] -= exclude_value
secondary_words_value[i] -= exclude_value * 2
i += 1
max_values_key = heapq.nlargest(
1, key_words_value) # se escoge las dos mas altas
max_values_secondary = heapq.nlargest(
3, secondary_words_value) # se escoge las dos mas altas
i = 0
# print('MAX VALUES KEY')
if max_values_key[i] != 0:
#words_repetidas_key = set(words_repetidas_key)
print('Segun primary words:',
Utils.get_data_name(key_words_value.index(max_values_key[0])),
found_1words[key_words_value.index(max_values_key[0])])
else:
print('Ninguna key word encontrada')
# for _ in max_values_key:
# print("->", max_values_key[i], "puntos -> ",
# Utils.get_data_name(key_words_value.index(max_values_key[i])))
# i += 1
# i = 0
#print('MAX VALUES SECONDARY')
if max_values_secondary[i] != 0:
#words_repetidas_secondary = set(words_repetidas_secondary)
print(
'Segun secondary words:',
Utils.get_data_name(
secondary_words_value.index(max_values_secondary[0])),
found_2words[secondary_words_value.index(max_values_secondary[0])])
else:
print('Ninguna secondary word encontrada')
# ok = 1
# for _ in max_values_secondary:
# if ok == 1:
# print("|")
# print("v", "%0.2f" % max_values_secondary[i], "puntos -> ",
# Utils.get_data_name(secondary_words_value.index(max_values_secondary[i])))
# if i > 0:
# if Utils.get_data_name(secondary_words_value.index(max_values_secondary[i])) == Utils.get_data_name(secondary_words_value.index(max_values_secondary[i-1])):
# ok = 0
# i += 1
# print('Write a text: ')
# input_value = input().lower()
# input_value = Utils.delete_text_punctuation(input_value)
end = time.time()
print('Ha tardo:', end - start, 'seg')
