locality-sensitive-hash

问题 How can use fuzzy matching in pandas to detect duplicate rows (efficiently) How to find duplicates of one column vs. all the other ones without a gigantic for loop of converting row_i toString() and then comparing it to all the other ones? 回答1: Not pandas specific, but within the python ecosystem the dedupe python library would seem to do what you want. In particular, it allows you to compare each column of a row separately and then combine the information into a single probability score of a

问题 How can use fuzzy matching in pandas to detect duplicate rows (efficiently) How to find duplicates of one column vs. all the other ones without a gigantic for loop of converting row_i toString() and then comparing it to all the other ones? 回答1: Not pandas specific, but within the python ecosystem the dedupe python library would seem to do what you want. In particular, it allows you to compare each column of a row separately and then combine the information into a single probability score of a

问题 How can use fuzzy matching in pandas to detect duplicate rows (efficiently) How to find duplicates of one column vs. all the other ones without a gigantic for loop of converting row_i toString() and then comparing it to all the other ones? 回答1: Not pandas specific, but within the python ecosystem the dedupe python library would seem to do what you want. In particular, it allows you to compare each column of a row separately and then combine the information into a single probability score of a

问题 As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance. Closed 7 years ago . Are there any relatively simple to understand (and simple to implement) locality-sensitive hash examples in C/C++/Java/C#? I'd like to

问题 As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance. Closed 7 years ago . Are there any relatively simple to understand (and simple to implement) locality-sensitive hash examples in C/C++/Java/C#? I'd like to

问题 I am trying to implement LSH spark to find nearest neighbours for each user on very large datasets containing 50000 rows and ~5000 features for each row. Here is the code related to this. MinHashLSH mh = new MinHashLSH().setNumHashTables(3).setInputCol("features") .setOutputCol("hashes"); MinHashLSHModel model = mh.fit(dataset); Dataset<Row> approxSimilarityJoin = model .approxSimilarityJoin(dataset, dataset, config.getJaccardLimit(), "JaccardDistance"); approxSimilarityJoin.show(); The job

问题 This is my first image processing application, so please be kind with this filthy peasant. THE APPLICATION: I want to implement a fast application ( performance are crucial even over accuracy) where given a photo (taken by mobile phone) containing a movie poster finds the most similar photo in a given dataset and return a similarity score. The dataset is composed by similar pictures (taken by mobile phone, containing a movie poster). The images can be of different size, resolutions and can be

问题 This is my first image processing application, so please be kind with this filthy peasant. THE APPLICATION: I want to implement a fast application ( performance are crucial even over accuracy) where given a photo (taken by mobile phone) containing a movie poster finds the most similar photo in a given dataset and return a similarity score. The dataset is composed by similar pictures (taken by mobile phone, containing a movie poster). The images can be of different size, resolutions and can be

问题 This is my first image processing application, so please be kind with this filthy peasant. THE APPLICATION: I want to implement a fast application ( performance are crucial even over accuracy) where given a photo (taken by mobile phone) containing a movie poster finds the most similar photo in a given dataset and return a similarity score. The dataset is composed by similar pictures (taken by mobile phone, containing a movie poster). The images can be of different size, resolutions and can be

问题 I am implementing a near-neighbor search application which will find similar documents. So far I have read a good portion of LSH related materials (theory behind LSH is some kind of confusing and I am not able to comphrened it 100% yet). My code is able to compute the signature matrix using the minhash functions (I am close to the end). I also apply the banding strategy on the signature matrix. However I am not able to understand how to hash signature vectors (of columns) in a band into