173 lines
6.6 KiB
Python
173 lines
6.6 KiB
Python
import copy
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import re
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import spacy
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nlp_en = spacy.load('en_core_web_lg')
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reply = "at(coffee,Table)"
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# 正则表达式
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replay_words = re.sub(r'[^A-Za-z0-9]', ' ', reply)
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replay_words = replay_words.split() #['at','coffee','Table']
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noun_words_ls = [['At','On','Is'],[]]# 完整文档n*2(动作，单词)
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together_words_ls = []
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# 示例代码：如何使用Python逐行读取txt文件
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# 打开一个示例的txt文件（这里假设文件名为example.txt）
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file_path = './goal_states_unique.txt'
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try:
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with open(file_path, 'r', encoding='utf-8') as file:
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# 逐行读取文件
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for line in file:
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cleaned_line = re.sub(r'[^A-Za-z0-9]', ' ', line)
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words = cleaned_line.split()
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# print(words)
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noun_words_ls[-1].extend(words)
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# print(line.strip()) # 打印每一行内容，去除行尾的换行符
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cleaned_line = line.replace("{", "").replace("}", "").replace("\n", "")
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together_words_ls.append(cleaned_line)
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except FileNotFoundError:
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print(f"File not found: {file_path}")
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except Exception as e:
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print(f"An error occurred: {e}")
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# 建立语料库
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file_path = './goal_states_unique.txt'
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try:
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with open(file_path, 'r', encoding='utf-8') as file:
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# 逐行读取文件
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for line in file:
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cleaned_line = re.sub(r'[^A-Za-z0-9]', ' ', line)
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words = cleaned_line.split()
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# print(words)
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noun_words_ls[-1].extend(words)
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# print(line.strip()) # 打印每一行内容，去除行尾的换行符
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cleaned_line = line.replace("{", "").replace("}", "").replace("\n", "")
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together_words_ls.append(cleaned_line)
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except FileNotFoundError:
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print(f"File not found: {file_path}")
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except Exception as e:
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print(f"An error occurred: {e}")
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# import datetime
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# from gensim.models import word2vec
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# import numpy as np
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# from scipy import spatial
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# pre_time=datetime.datetime.now()
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# model = word2vec.Word2Vec(together_words_ls,
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# vector_size=10,#特征向量的维度
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# alpha=0.04,#学习率
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# window=5,#一个句子内，当前词和预测词之间的最大距离 文本（window）大小：skip-gram通常在10附近，CBOW通常在5附近
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# min_count=0,#最低词频 没有大的变化
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# max_vocab_size=None,
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# sample=0.0001, #随机下采样的阈值
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# seed=1,#随机数种子
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# workers=10,#进程数
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# min_alpha=0.00001,#学习率下降的最小值
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# sg=1, #训练算法的选择，sg=1，采用skip-gram，sg=0，采用CBOW---skip-gram（慢、对罕见字有利）vs CBOW（快）
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# hs=1,# hs=1,采用hierarchica·softmax，hs=0,采用negative sampling
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# #分层softmax（对罕见字有利）vs 负采样（对常见词和低纬向量有利）
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# negative=0,#这个值大于0，使用negative sampling去掉'noise words'的个数（通常设置5-20）；为0，不使用negative sampling
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# #cbow_mean=1,#为0，使用词向量的和，为1，使用均值；只适用于cbow的情况
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# null_word = 0,
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# trim_rule = None, #裁剪词汇规则，使用None（会使用最小min_count）
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# sorted_vocab =1,#对词汇降序排序
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# batch_words = 8192,#训练时，每一批次的单词数量
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# compute_loss = False,
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# callbacks = ())
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# model.train(together_words_ls, total_examples=len(together_words_ls), epochs=10)
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# model.save("./W2V_CI.model") # 保存模型
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# post_time=datetime.datetime.now()
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# print("word2vec模型训练保存结束，时间为: ",(post_time-pre_time).seconds*1.0)#1106.0s
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#
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# w2v_model = word2vec.Word2Vec.load("./W2V_CI.model")
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# # w2v_model[word]
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#
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# def replay_together_w2v(reply):
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# return model.wv.most_similar(reply)
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# # max_similarity = -1
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# # similar_word = None
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# # query_token = w2v_model[reply]
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# # for state in together_words_ls:
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# # word_token = w2v_model[state]
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# # # 计算余弦相似度. spatial.distance.cosine 函数计算的是余弦距离
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# # # 余弦相似度（Cosine similarity），如在 Word2Vec 模型中用来比较两个向量的相似性，其值的范围是 -1 到 1。
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# # similarity = 1 - spatial.distance.cosine(query_token, word_token)
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# # # print("similarity:",similarity,real_obj_name)
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# # if similarity > max_similarity:
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# # max_similarity = similarity
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# # similar_word = state
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# # if similar_word==None:
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# # print("Error: Not Match!")
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# # else:
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# # return similar_word
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def replay_one_by_one(replay_words):
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replace_ind = []
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replace_word = []
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for i,word in enumerate(replay_words):
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query_token = nlp_en(word)
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k=1
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if i==0: k=0
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if not word in noun_words_ls[k]:
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max_similarity = 0
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similar_word = None
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for act in noun_words_ls[k]:
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word_token = nlp_en(act)
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# print(act)
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# print(word_token)
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similarity = query_token.similarity(word_token)
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# print("similarity:",similarity,real_obj_name)
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if similarity > max_similarity:
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max_similarity = similarity
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similar_word = act
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if similar_word==None:
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print("Error: Not Match!")
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else:
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replay_words[i]=similar_word
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# replace_word.append(similar_word)
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# replace_ind.append(i)
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new_replay = f'{replay_words[0]}({replay_words[1]},{replay_words[2]})'
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return new_replay
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# print(replace_word)
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# print(replace_ind)
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# replace_word = ['on','Table1']
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# replace_ind = [0,2]
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# 替换reply中单词
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# for new_word,ind in zip(replace_word,replace_ind):
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# 把第 ind 个单词替换成 new_word
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def replay_together(reply):
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max_similarity = 0
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similar_word = None
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query_token = nlp_en(reply)
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for state in together_words_ls:
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word_token = nlp_en(state)
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similarity = query_token.similarity(word_token)
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# print("similarity:",similarity,real_obj_name)
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if similarity > max_similarity:
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max_similarity = similarity
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similar_word = state
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if similar_word==None:
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print("Error: Not Match!")
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else:
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return similar_word
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print("原来的：",reply)
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new_replay = replay_one_by_one(copy.deepcopy(replay_words))
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print("逐个比较后的现在的：",new_replay)
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new_replay2 = replay_together(copy.deepcopy(reply))
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print("集体比较后的现在的：",new_replay2)
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# new_replay3 = replay_together_w2v(copy.deepcopy(reply))
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# print("W2V比较后的现在的：",new_replay3)
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