青海网站建设哪家好,湖南奉天建设集团网站,网页界面设计与制作书籍,菠萝视频app下载多人运动python-模糊字符串比较我正在努力完成的是一个程序#xff0c;该程序读取文件并根据原始句子比较每个句子。 与原始句子完全匹配的句子将得到1分#xff0c;而与之相反的句子将得到0分。所有其他模糊句子将得到1到0分之间的分数。我不确定要使用哪种操作在Python 3中完成此操…python-模糊字符串比较我正在努力完成的是一个程序该程序读取文件并根据原始句子比较每个句子。 与原始句子完全匹配的句子将得到1分而与之相反的句子将得到0分。所有其他模糊句子将得到1到0分之间的分数。我不确定要使用哪种操作在Python 3中完成此操作。我包括了示例文本其中文本1是原始文本其他前面的字符串是比较文本。文字样本文字1那是一个黑暗而暴风雨的夜晚。 我一个人坐在红色的椅子上。 我并不孤单因为我只有三只猫。文字20那是一个阴暗而暴风雨的夜晚。 我独自一人坐在深红色的椅子上。 我并不孤单因为我有三只猫//应该得分最高但不能得分1文字21那是一个阴暗而狂暴的夜晚。 我一个人坐在一个深红色的大教堂上。 我并不孤单因为我有三只猫//分数应低于文字20文字22我一个人坐在一个深红色的大教堂上。 我并不孤单因为我有三只猫科动物。 那是一个阴暗而狂暴的夜晚。//分数应低于文字21但不能低于0文字24那是一个黑暗而暴风雨的夜晚。 我并不孤单。 我没有坐在红色的椅子上。 我有三只猫。//应该得分为04个解决方案96 votes有一个名为difflib的软件包。通过pip安装pip install fuzzywuzzy简单用法 from fuzzywuzzy import fuzz fuzz.ratio(this is a test, this is a test!)96该软件包建立在difflib的基础上。您问为什么不仅仅使用它 除了更简单之外它还具有许多不同的匹配方法(例如令牌顺序不敏感部分字符串匹配)这使其在实践中更加强大。 process.extract函数特别有用从集合中找到最佳匹配的字符串和比率。 从他们的自述文件偏比 fuzz.partial_ratio(this is a test, this is a test!)100代币分类率 fuzz.ratio(fuzzy wuzzy was a bear, wuzzy fuzzy was a bear)90 fuzz.token_sort_ratio(fuzzy wuzzy was a bear, wuzzy fuzzy was a bear)100代币设定比率 fuzz.token_sort_ratio(fuzzy was a bear, fuzzy fuzzy was a bear)84 fuzz.token_set_ratio(fuzzy was a bear, fuzzy fuzzy was a bear)100处理 choices [Atlanta Falcons, New York Jets, New York Giants, Dallas Cowboys] process.extract(new york jets, choices, limit2)[(New York Jets, 100), (New York Giants, 78)] process.extractOne(cowboys, choices)(Dallas Cowboys, 90)congusbongus answered 2019-10-25T04:21:53Z79 votes标准库中有一个模块(称为SequenceMatcher)可以比较字符串并根据它们的相似性返回分数。 SequenceMatcher类应该做您想要做的。编辑来自python提示符的小例子 from difflib import SequenceMatcher as SM s1 It was a dark and stormy night. I was all alone sitting on a red chair. I was not completely alone as I had three cats. s2 It was a murky and stormy night. I was all alone sitting on a crimson chair. I was not completely alone as I had three felines. SM(None, s1, s2).ratio()0.9112903225806451HTHmac answered 2019-10-25T04:22:25Z15 votesunicode的索引和搜索速度比unicode(bytes)快得多。from fuzzyset import FuzzySetcorpus It was a murky and stormy night. I was all alone sitting on a crimson chair. I was not completely alone as I had three felinesIt was a murky and tempestuous night. I was all alone sitting on a crimson cathedra. I was not completely alone as I had three felinesI was all alone sitting on a crimson cathedra. I was not completely alone as I had three felines. It was a murky and tempestuous night.It was a dark and stormy night. I was not alone. I was not sitting on a red chair. I had three cats.corpus [line.lstrip() for line in corpus.split(\n)]fs FuzzySet(corpus)query It was a dark and stormy night. I was all alone sitting on a red chair. I was not completely alone as I had three cats.fs.get(query)# [(0.873015873015873, It was a murky and stormy night. I was all alone sitting on a crimson chair. I was not completely alone as I had three felines)]警告注意不要在模糊集中混用unicode和bytes。hobs answered 2019-10-25T04:22:59Z1 votes该任务称为复述识别这是自然语言处理研究的活跃领域。 我已经链接了几篇最新的论文您可以在GitHub上找到其中的许多开源代码。请注意所有回答的问题均假设两个句子之间存在某些字符串/表面相似性而实际上两个字符串相似性很少的句子在语义上可以相似。如果您对这种相似性感兴趣可以使用Skip-Thoughts。根据GitHub指南安装软件然后转到自述文件中的释义检测部分import skipthoughtsmodel skipthoughts.load_model()vectors skipthoughts.encode(model, X_sentences)这会将您的句子(X_sentences)转换为向量。 稍后您可以通过以下方式找到两个向量的相似性similarity 1 - scipy.spatial.distance.cosine(vectors[0], vectors[1])我们假设vector [0]和vector1是要查找其分数的X_sentences [0]和X_sentences1的对应向量。还有其他将句子转换为向量的模型您可以在此处找到。将句子转换为向量后相似度只是找到这些向量之间的余弦相似度的问题。Ash answered 2019-10-25T04:24:05Z
