def create_matrix_org_np(sen_count, word2vec_model, qa_path,
matrix_ques_path_word):
"""
创建问题句向量,设置sen_count=10000, 防止内存不够奔溃
:param sen_count: int, write sentence_encode num per twice
:param word2vec_model: model
:param qa_path: str
:param matrix_ques_path: str
:return:
"""
if os.path.exists(matrix_ques_path_word):
file_matrix_ques = open(matrix_ques_path_word, 'rb')
matrix_ques = pickle.load(file_matrix_ques)
return matrix_ques
print('create_matrix_org_pkl start!')
qa_dail = txtRead(qa_path, encodeType='utf-8')
# questions = []
matrix_ques = []
count = 0
for qa_dail_one in qa_dail:
ques = getChinese(qa_dail_one.split('\t')[0])
# questions.append(ques)
word_list, flag_list = word_flag_cut(ques)
sentence_vec = encoding_basic_question(word2vec_model, word_list,
flag_list)
matrix_ques.append(sentence_vec)
if len(matrix_ques) % sen_count == 0 and len(matrix_ques) != 0:
print("count: " + str(count))
count += 1
np.savetxt(
projectdir + "/Data/sentence_vec_encode_word/" + str(count) +
".txt", matrix_ques)
matrix_ques = []
# break
count += 1
np.savetxt(
projectdir + "/Data/sentence_vec_encode_word/" + str(count) + ".txt",
matrix_ques)
# matrix_ques = []
# file_matrix_ques = open(matrix_ques_path, 'wb')
# pickle.dump(matrix_ques, file_matrix_ques)
print('create_matrix_org_np ok!')
def create_matrix_org_np(sen_count, word2vec_model, qa_path, matrix_ques_path):
"""
创建问题句向量
:param sen_count: int
:param word2vec_model: gensim model
:param qa_path: str
:param matrix_ques_path:str
:return: None
"""
if os.path.exists(matrix_ques_path):
file_matrix_ques = open(matrix_ques_path, 'rb')
matrix_ques = pickle.load(file_matrix_ques)
return matrix_ques
print('create_matrix_org_pkl start!')
qa_dail = txtRead(qa_path, encodeType='utf-8')
# questions = []
matrix_ques = []
count = 0
for qa_dail_one in qa_dail:
ques = getChinese(qa_dail_one.split('\t')[0])
char_list = [ques_char for ques_char in ques]
sentence_vec = question_encoding(word2vec_model, char_list)
matrix_ques.append(sentence_vec)
if len(matrix_ques) % sen_count == 0 and len(matrix_ques) != 0:
print("count: " + str(count))
count += 1
np.savetxt(
projectdir + "/Data/sentence_vec_encode_char/" + str(count) +
".txt", matrix_ques)
matrix_ques = []
break
count += 1
np.savetxt(
projectdir + "/Data/sentence_vec_encode_char/" + str(count) + ".txt",
matrix_ques)
print('create_matrix_org_pkl ok!')
word2vec_model=word2vec_model,
qa_path=chicken_and_gossip_path,
matrix_ques_path_word=matrix_ques_part_path)
# 读取标准问句矩阵
print("np.loadtxt(matrix_ques_part_path) start!")
matrix_ques = np.loadtxt(matrix_ques_part_path)
print("np.loadtxt(matrix_ques_part_path) end!")
# 标准问句矩阵初始化和预处理
matrix_org_norm, matrix_org_index, top_vec = basic_questions_matrix_init(
matrix_ques, top_vec=20)
while True:
print("你: ")
ques_ask = input()
ques_clean = getChinese(ques_ask)
word_list, flag_list = word_flag_cut(ques_clean)
sentence_vec = basic_questions_encoding(word2vec_model, word_list,
flag_list)
top_20_qid = calculate_text_similar(sentence_vec,
matrix_org_norm,
matrix_org_index,
top_vec=top_vec)
try:
print("小姜机器人: " +
syn_qa_dails[top_20_qid[0][0]].strip().split("\t")[1])
print([(syn_qa_dails[top_20_qid[i][0]].strip().split("\t")[0],
syn_qa_dails[top_20_qid[i][0]].strip().split("\t")[1])
for i in range(len(top_20_qid))])
except Exception as e:
# 有的字符可能打不出来