{
  "id": 343913,
  "title": " Levenshtein Mean Distance the metric in phase1 .",
  "url": "/competitions/dlsprint/discussion/343913",
  "author_name": "",
  "post_date": "2022-08-12T23:50:37.085665100Z",
  "votes": 4,
  "comment_count": 2,
  "views": 0,
  "content": "<h1>The Levenshtein Algorithm</h1>\n<p>1965 By Vladimir Levenshtein</p>\n<p>\"The Levenshtein distance is a string metric for measuring difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (i.e. insertions, deletions or substitutions) required to change one word into the other. It is named after Vladimir Levenshtein, who considered this distance in 1965.\"</p>\n<p>\"Levenshtein distance may also be referred to as edit distance, although it may also denote a larger family of distance metrics. It is closely related to pairwise string alignments.\"</p>\n<p><a href=\"https://www.cuelogic.com/blog/the-levenshtein-algorithm\" target=\"_blank\">https://www.cuelogic.com/blog/the-levenshtein-algorithm</a></p>\n<h1>Levenshtein Distance Equation</h1>\n<p>Understanding the Levenshtein Distance Equation for Beginners <br>\nAuthor:  Ethan Nam</p>\n<p>\"The Levenshtein distance is a number that tells you how different two strings are. The higher the number, the more different the two strings are.  The Levenshtein distance for strings A and B can be calculated by using a matrix.\"</p>\n<p>\"Thank Vladimir Levenshtein, who came up with his algorithm in 1965. The algorithm hasn’t been improved in over 50 years and for good reason. According to MIT, it may very well be that Levenshtein’s algorithm is the best that we’ll ever get in terms of efficiency.\"</p>\n<p><a href=\"https://medium.com/@ethannam/understanding-the-levenshtein-distance-equation-for-beginners-c4285a5604f0\" target=\"_blank\">https://medium.com/@ethannam/understanding-the-levenshtein-distance-equation-for-beginners-c4285a5604f0</a></p>\n<h1>Levenshtein Distance, in Three Flavors</h1>\n<p>Author: by Michael Gilleland, Merriam Park Software</p>\n<p>(It's in 3 flavors because  the authors presented source code which implements the Levenshtein distance algorithm in the following programming languages: Java, C++ and Visual Basic)</p>\n<p>\"Levenshtein distance (LD) is a measure of the similarity between two strings, which we will refer to as the source string (s) and the target string (t). The distance is the number of deletions, insertions, or substitutions required to transform s into t. For example,\"</p>\n<p>\"If s is \"test\" and t is \"test\", then LD(s,t) = 0, because no transformations are needed. The strings are already identical.<br>\nIf s is \"test\" and t is \"tent\", then LD(s,t) = 1, because one substitution (change \"s\" to \"n\") is sufficient to transform s into t.<br>\nThe greater the Levenshtein distance, the more different the strings are.\"</p>\n<p>\"Levenshtein distance is named after the Russian scientist Vladimir Levenshtein, who devised the algorithm in 1965. If you can't spell or pronounce Levenshtein, the metric is also sometimes called edit distance.\"</p>\n<h1>The Levenshtein distance algorithm has been used in:</h1>\n<p>SPELL CHECKING</p>\n<p>SPEECH RECOGNITION</p>\n<p>DNA ANALYSIS</p>\n<p>PLAGIARISM DETECTION</p>\n<p><a href=\"https://people.cs.pitt.edu/~kirk/cs1501/Pruhs/Spring2006/assignments/editdistance/Levenshtein%20Distance.htm\" target=\"_blank\">https://people.cs.pitt.edu/~kirk/cs1501/Pruhs/Spring2006/assignments/editdistance/Levenshtein%20Distance.htm</a></p>\n<h1>Levenshtein Distance in Python</h1>\n<p>Implementing The Levenshtein Distance for Word Autocompletion and Autocorrection<br>\nAuthor:  Ahmed Fawzy Gad - 2020</p>\n<p>\"Sections covered in this tutorial are as follows:\"</p>\n<p>Creating the distances matrix<br>\nInitializing the distances matrix<br>\nPrinting the distances matrix<br>\nCalculating distances between all prefixes<br>\nDictionary search for autocompletion/autocorrection</p>\n<p><a href=\"https://blog.paperspace.com/implementing-levenshtein-distance-word-autocomplete-autocorrect/#:~:text=The%20Levenshtein%20distance%20is%20a,transform%20one%20word%20into%20another\" target=\"_blank\">https://blog.paperspace.com/implementing-levenshtein-distance-word-autocomplete-autocorrect/#:~:text=The%20Levenshtein%20distance%20is%20a,transform%20one%20word%20into%20another</a>.</p>\n<h1>On Kaggle</h1>\n<p>\"Levenshtein Distance Spelling Correction NLP\" by Bouwe Ceunen.<br>\n<a href=\"https://www.kaggle.com/code/bouweceunen/levenshtein-distance-spelling-correction-nlp\" target=\"_blank\">https://www.kaggle.com/code/bouweceunen/levenshtein-distance-spelling-correction-nlp</a></p>\n<p>\"Levenshtein Distance to predict correct aswer\"  by Meghal Jambhale - Comp. ( chaii - Hindi and Tamil Question Answering)<br>\n<a href=\"https://www.kaggle.com/code/meghaljambhale/levenshtein-distance-to-predict-correct-aswer\" target=\"_blank\">https://www.kaggle.com/code/meghaljambhale/levenshtein-distance-to-predict-correct-aswer</a></p>\n<p>\"Levenshtein Edit Distance\" by Dheeraj Gupta<br>\n<a href=\"https://www.kaggle.com/code/dhgupta/levenshtein-edit-distance\" target=\"_blank\">https://www.kaggle.com/code/dhgupta/levenshtein-edit-distance</a></p>\n<p>\"Levenshtein distance implementation\"  by Rakesh Jarupula - Competition (Bristol-Myers Squibb – Molecular Translation)<br>\n<a href=\"https://www.kaggle.com/code/jarupula/levenshtein-distance-implementation\" target=\"_blank\">https://www.kaggle.com/code/jarupula/levenshtein-distance-implementation</a></p>\n<p>\"Levenshtein distance\"  by Victor Khovanskiy - Competition (Quora Question Pairs) <br>\n<a href=\"https://www.kaggle.com/code/khovanskiy/levenshtein-distance\" target=\"_blank\">https://www.kaggle.com/code/khovanskiy/levenshtein-distance</a></p>\n<p>\"Traditional approach: Levenshtein\" by Daniel Legorreta - Competition (U.S. Patent Phrase to Phrase Matching) <br>\n<a href=\"https://www.kaggle.com/code/legorreta/traditional-approach-levenshtein\" target=\"_blank\">https://www.kaggle.com/code/legorreta/traditional-approach-levenshtein</a></p>\n<p>\"Levenshtein Distance - dynamic programming\"  by Nitin Singh - Comp. (Bristol-Myers Squibb – Molecular Translation)<br>\n<a href=\"https://www.kaggle.com/code/nitinsss/levenshtein-distance-dynamic-programming\" target=\"_blank\">https://www.kaggle.com/code/nitinsss/levenshtein-distance-dynamic-programming</a></p>\n<p>\"Basic String Matchers- Hamming, Levenshtein, Jaro\"  by Shilpa G - Competition (U.S. Patent Phrase to Phrase Matching)<br>\n<a href=\"https://www.kaggle.com/code/shilpagopal/basic-string-matchers-hamming-levenshtein-jaro\" target=\"_blank\">https://www.kaggle.com/code/shilpagopal/basic-string-matchers-hamming-levenshtein-jaro</a></p>\n<h1>If you can't spell or pronounce Levenshtein, the metric is also sometimes called edit distance.</h1>",
  "messages": [
    {
      "id": "1896578",
      "postDate": "08/12/2022 23:50:37",
      "content": "<h1>The Levenshtein Algorithm</h1>\n<p>1965 By Vladimir Levenshtein</p>\n<p>\"The Levenshtein distance is a string metric for measuring difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (i.e. insertions, deletions or substitutions) required to change one word into the other. It is named after Vladimir Levenshtein, who considered this distance in 1965.\"</p>\n<p>\"Levenshtein distance may also be referred to as edit distance, although it may also denote a larger family of distance metrics. It is closely related to pairwise string alignments.\"</p>\n<p><a href=\"https://www.cuelogic.com/blog/the-levenshtein-algorithm\" target=\"_blank\">https://www.cuelogic.com/blog/the-levenshtein-algorithm</a></p>\n<h1>Levenshtein Distance Equation</h1>\n<p>Understanding the Levenshtein Distance Equation for Beginners <br>\nAuthor:  Ethan Nam</p>\n<p>\"The Levenshtein distance is a number that tells you how different two strings are. The higher the number, the more different the two strings are.  The Levenshtein distance for strings A and B can be calculated by using a matrix.\"</p>\n<p>\"Thank Vladimir Levenshtein, who came up with his algorithm in 1965. The algorithm hasn’t been improved in over 50 years and for good reason. According to MIT, it may very well be that Levenshtein’s algorithm is the best that we’ll ever get in terms of efficiency.\"</p>\n<p><a href=\"https://medium.com/@ethannam/understanding-the-levenshtein-distance-equation-for-beginners-c4285a5604f0\" target=\"_blank\">https://medium.com/@ethannam/understanding-the-levenshtein-distance-equation-for-beginners-c4285a5604f0</a></p>\n<h1>Levenshtein Distance, in Three Flavors</h1>\n<p>Author: by Michael Gilleland, Merriam Park Software</p>\n<p>(It's in 3 flavors because  the authors presented source code which implements the Levenshtein distance algorithm in the following programming languages: Java, C++ and Visual Basic)</p>\n<p>\"Levenshtein distance (LD) is a measure of the similarity between two strings, which we will refer to as the source string (s) and the target string (t). The distance is the number of deletions, insertions, or substitutions required to transform s into t. For example,\"</p>\n<p>\"If s is \"test\" and t is \"test\", then LD(s,t) = 0, because no transformations are needed. The strings are already identical.<br>\nIf s is \"test\" and t is \"tent\", then LD(s,t) = 1, because one substitution (change \"s\" to \"n\") is sufficient to transform s into t.<br>\nThe greater the Levenshtein distance, the more different the strings are.\"</p>\n<p>\"Levenshtein distance is named after the Russian scientist Vladimir Levenshtein, who devised the algorithm in 1965. If you can't spell or pronounce Levenshtein, the metric is also sometimes called edit distance.\"</p>\n<h1>The Levenshtein distance algorithm has been used in:</h1>\n<p>SPELL CHECKING</p>\n<p>SPEECH RECOGNITION</p>\n<p>DNA ANALYSIS</p>\n<p>PLAGIARISM DETECTION</p>\n<p><a href=\"https://people.cs.pitt.edu/~kirk/cs1501/Pruhs/Spring2006/assignments/editdistance/Levenshtein%20Distance.htm\" target=\"_blank\">https://people.cs.pitt.edu/~kirk/cs1501/Pruhs/Spring2006/assignments/editdistance/Levenshtein%20Distance.htm</a></p>\n<h1>Levenshtein Distance in Python</h1>\n<p>Implementing The Levenshtein Distance for Word Autocompletion and Autocorrection<br>\nAuthor:  Ahmed Fawzy Gad - 2020</p>\n<p>\"Sections covered in this tutorial are as follows:\"</p>\n<p>Creating the distances matrix<br>\nInitializing the distances matrix<br>\nPrinting the distances matrix<br>\nCalculating distances between all prefixes<br>\nDictionary search for autocompletion/autocorrection</p>\n<p><a href=\"https://blog.paperspace.com/implementing-levenshtein-distance-word-autocomplete-autocorrect/#:~:text=The%20Levenshtein%20distance%20is%20a,transform%20one%20word%20into%20another\" target=\"_blank\">https://blog.paperspace.com/implementing-levenshtein-distance-word-autocomplete-autocorrect/#:~:text=The%20Levenshtein%20distance%20is%20a,transform%20one%20word%20into%20another</a>.</p>\n<h1>On Kaggle</h1>\n<p>\"Levenshtein Distance Spelling Correction NLP\" by Bouwe Ceunen.<br>\n<a href=\"https://www.kaggle.com/code/bouweceunen/levenshtein-distance-spelling-correction-nlp\" target=\"_blank\">https://www.kaggle.com/code/bouweceunen/levenshtein-distance-spelling-correction-nlp</a></p>\n<p>\"Levenshtein Distance to predict correct aswer\"  by Meghal Jambhale - Comp. ( chaii - Hindi and Tamil Question Answering)<br>\n<a href=\"https://www.kaggle.com/code/meghaljambhale/levenshtein-distance-to-predict-correct-aswer\" target=\"_blank\">https://www.kaggle.com/code/meghaljambhale/levenshtein-distance-to-predict-correct-aswer</a></p>\n<p>\"Levenshtein Edit Distance\" by Dheeraj Gupta<br>\n<a href=\"https://www.kaggle.com/code/dhgupta/levenshtein-edit-distance\" target=\"_blank\">https://www.kaggle.com/code/dhgupta/levenshtein-edit-distance</a></p>\n<p>\"Levenshtein distance implementation\"  by Rakesh Jarupula - Competition (Bristol-Myers Squibb – Molecular Translation)<br>\n<a href=\"https://www.kaggle.com/code/jarupula/levenshtein-distance-implementation\" target=\"_blank\">https://www.kaggle.com/code/jarupula/levenshtein-distance-implementation</a></p>\n<p>\"Levenshtein distance\"  by Victor Khovanskiy - Competition (Quora Question Pairs) <br>\n<a href=\"https://www.kaggle.com/code/khovanskiy/levenshtein-distance\" target=\"_blank\">https://www.kaggle.com/code/khovanskiy/levenshtein-distance</a></p>\n<p>\"Traditional approach: Levenshtein\" by Daniel Legorreta - Competition (U.S. Patent Phrase to Phrase Matching) <br>\n<a href=\"https://www.kaggle.com/code/legorreta/traditional-approach-levenshtein\" target=\"_blank\">https://www.kaggle.com/code/legorreta/traditional-approach-levenshtein</a></p>\n<p>\"Levenshtein Distance - dynamic programming\"  by Nitin Singh - Comp. (Bristol-Myers Squibb – Molecular Translation)<br>\n<a href=\"https://www.kaggle.com/code/nitinsss/levenshtein-distance-dynamic-programming\" target=\"_blank\">https://www.kaggle.com/code/nitinsss/levenshtein-distance-dynamic-programming</a></p>\n<p>\"Basic String Matchers- Hamming, Levenshtein, Jaro\"  by Shilpa G - Competition (U.S. Patent Phrase to Phrase Matching)<br>\n<a href=\"https://www.kaggle.com/code/shilpagopal/basic-string-matchers-hamming-levenshtein-jaro\" target=\"_blank\">https://www.kaggle.com/code/shilpagopal/basic-string-matchers-hamming-levenshtein-jaro</a></p>\n<h1>If you can't spell or pronounce Levenshtein, the metric is also sometimes called edit distance.</h1>",
      "rawMarkdown": "#The Levenshtein Algorithm\n\n 1965 By Vladimir Levenshtein\n\n\"The Levenshtein distance is a string metric for measuring difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (i.e. insertions, deletions or substitutions) required to change one word into the other. It is named after Vladimir Levenshtein, who considered this distance in 1965.\"\n\n\"Levenshtein distance may also be referred to as edit distance, although it may also denote a larger family of distance metrics. It is closely related to pairwise string alignments.\"\n\nhttps://www.cuelogic.com/blog/the-levenshtein-algorithm\n\n#Levenshtein Distance Equation \n\nUnderstanding the Levenshtein Distance Equation for Beginners \nAuthor:  Ethan Nam\n\n\"The Levenshtein distance is a number that tells you how different two strings are. The higher the number, the more different the two strings are.  The Levenshtein distance for strings A and B can be calculated by using a matrix.\"\n\n\"Thank Vladimir Levenshtein, who came up with his algorithm in 1965. The algorithm hasn’t been improved in over 50 years and for good reason. According to MIT, it may very well be that Levenshtein’s algorithm is the best that we’ll ever get in terms of efficiency.\"\n\nhttps://medium.com/@ethannam/understanding-the-levenshtein-distance-equation-for-beginners-c4285a5604f0\n\n#Levenshtein Distance, in Three Flavors\n\nAuthor: by Michael Gilleland, Merriam Park Software\n\n(It's in 3 flavors because  the authors presented source code which implements the Levenshtein distance algorithm in the following programming languages: Java, C++ and Visual Basic)\n\n\"Levenshtein distance (LD) is a measure of the similarity between two strings, which we will refer to as the source string (s) and the target string (t). The distance is the number of deletions, insertions, or substitutions required to transform s into t. For example,\"\n\n\"If s is \"test\" and t is \"test\", then LD(s,t) = 0, because no transformations are needed. The strings are already identical.\nIf s is \"test\" and t is \"tent\", then LD(s,t) = 1, because one substitution (change \"s\" to \"n\") is sufficient to transform s into t.\nThe greater the Levenshtein distance, the more different the strings are.\"\n\n\"Levenshtein distance is named after the Russian scientist Vladimir Levenshtein, who devised the algorithm in 1965. If you can't spell or pronounce Levenshtein, the metric is also sometimes called edit distance.\"\n\n#The Levenshtein distance algorithm has been used in:\n\nSPELL CHECKING\n\nSPEECH RECOGNITION\n\nDNA ANALYSIS\n\nPLAGIARISM DETECTION\n\nhttps://people.cs.pitt.edu/~kirk/cs1501/Pruhs/Spring2006/assignments/editdistance/Levenshtein%20Distance.htm\n\n#Levenshtein Distance in Python\n\nImplementing The Levenshtein Distance for Word Autocompletion and Autocorrection\nAuthor:  Ahmed Fawzy Gad - 2020\n\n\"Sections covered in this tutorial are as follows:\"\n\nCreating the distances matrix\nInitializing the distances matrix\nPrinting the distances matrix\nCalculating distances between all prefixes\nDictionary search for autocompletion/autocorrection\n\nhttps://blog.paperspace.com/implementing-levenshtein-distance-word-autocomplete-autocorrect/#:~:text=The%20Levenshtein%20distance%20is%20a,transform%20one%20word%20into%20another.\n\n#On Kaggle\n\n\"Levenshtein Distance Spelling Correction NLP\" by Bouwe Ceunen.\nhttps://www.kaggle.com/code/bouweceunen/levenshtein-distance-spelling-correction-nlp\n\n\"Levenshtein Distance to predict correct aswer\"  by Meghal Jambhale - Comp. ( chaii - Hindi and Tamil Question Answering)\nhttps://www.kaggle.com/code/meghaljambhale/levenshtein-distance-to-predict-correct-aswer\n\n\"Levenshtein Edit Distance\" by Dheeraj Gupta\nhttps://www.kaggle.com/code/dhgupta/levenshtein-edit-distance\n\n\"Levenshtein distance implementation\"  by Rakesh Jarupula - Competition (Bristol-Myers Squibb – Molecular Translation)\nhttps://www.kaggle.com/code/jarupula/levenshtein-distance-implementation\n\n\"Levenshtein distance\"  by Victor Khovanskiy - Competition (Quora Question Pairs) \nhttps://www.kaggle.com/code/khovanskiy/levenshtein-distance\n\n\"Traditional approach: Levenshtein\" by Daniel Legorreta - Competition (U.S. Patent Phrase to Phrase Matching) \nhttps://www.kaggle.com/code/legorreta/traditional-approach-levenshtein\n\n\"Levenshtein Distance - dynamic programming\"  by Nitin Singh - Comp. (Bristol-Myers Squibb – Molecular Translation)\nhttps://www.kaggle.com/code/nitinsss/levenshtein-distance-dynamic-programming\n\n\"Basic String Matchers- Hamming, Levenshtein, Jaro\"  by Shilpa G - Competition (U.S. Patent Phrase to Phrase Matching)\nhttps://www.kaggle.com/code/shilpagopal/basic-string-matchers-hamming-levenshtein-jaro\n\n\n#If you can't spell or pronounce Levenshtein, the metric is also sometimes called edit distance.",
      "votes": null
    },
    {
      "id": "1906448",
      "postDate": "08/19/2022 22:24:21",
      "content": "<p>Great explanation!</p>\n<p>I really like the part about why is it called \"edit distance\". <br>\nYou explained it simply and on most sources this explanation simply don't exist.</p>\n<p>The Devastator.</p>",
      "rawMarkdown": "Great explanation!\n\nI really like the part about why is it called \"edit distance\". \nYou explained it simply and on most sources this explanation simply don't exist.\n\n\nThe Devastator.",
      "votes": null
    },
    {
      "id": "1906475",
      "postDate": "08/19/2022 23:06:54",
      "content": "<p>Thank you Devastator </p>\n<p>The funny Edit quote approaching the difficult to pronounce Levenshtein I copied from Michael Gilleland (Levenshtein Distance, in Three Flavors: Java, C++ and Visual Basic).</p>\n<p>The best part is: \"The algorithm hasn’t been improved in over 50 years and for good reason. According to MIT, it may very well be that Levenshtein’s algorithm is the best that we’ll ever get in terms of efficiency.\"</p>\n<p>Another explanation: Doctor Levenshtein dared someone: Now, edit it! </p>\n<p>Giving what the MIT said (above) about Levenshtein’s algorithm (the best over 50 years in terms of efficiency).  Nobody edited it.  Or maybe it's just a drunk history :)</p>",
      "rawMarkdown": "Thank you Devastator \n\nThe funny Edit quote approaching the difficult to pronounce Levenshtein I copied from Michael Gilleland (Levenshtein Distance, in Three Flavors: Java, C++ and Visual Basic).\n\nThe best part is: \"The algorithm hasn’t been improved in over 50 years and for good reason. According to MIT, it may very well be that Levenshtein’s algorithm is the best that we’ll ever get in terms of efficiency.\"\n\nAnother explanation: Doctor Levenshtein dared someone: Now, edit it! \n\nGiving what the MIT said (above) about Levenshtein’s algorithm (the best over 50 years in terms of efficiency).  Nobody edited it.  Or maybe it's just a drunk history :)",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1906448,
      "author_name": "thedevastator",
      "author_url": "",
      "post_date": "08/19/2022 22:24:21",
      "content": "<p>Great explanation!</p>\n<p>I really like the part about why is it called \"edit distance\". <br>\nYou explained it simply and on most sources this explanation simply don't exist.</p>\n<p>The Devastator.</p>",
      "votes": null,
      "replies": [
        {
          "id": 1906475,
          "author_name": "mpwolke",
          "author_url": "",
          "post_date": "08/19/2022 23:06:54",
          "content": "<p>Thank you Devastator </p>\n<p>The funny Edit quote approaching the difficult to pronounce Levenshtein I copied from Michael Gilleland (Levenshtein Distance, in Three Flavors: Java, C++ and Visual Basic).</p>\n<p>The best part is: \"The algorithm hasn’t been improved in over 50 years and for good reason. According to MIT, it may very well be that Levenshtein’s algorithm is the best that we’ll ever get in terms of efficiency.\"</p>\n<p>Another explanation: Doctor Levenshtein dared someone: Now, edit it! </p>\n<p>Giving what the MIT said (above) about Levenshtein’s algorithm (the best over 50 years in terms of efficiency).  Nobody edited it.  Or maybe it's just a drunk history :)</p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "1896578": "#The Levenshtein Algorithm\n\n 1965 By Vladimir Levenshtein\n\n\"The Levenshtein distance is a string metric for measuring difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (i.e. insertions, deletions or substitutions) required to change one word into the other. It is named after Vladimir Levenshtein, who considered this distance in 1965.\"\n\n\"Levenshtein distance may also be referred to as edit distance, although it may also denote a larger family of distance metrics. It is closely related to pairwise string alignments.\"\n\nhttps://www.cuelogic.com/blog/the-levenshtein-algorithm\n\n#Levenshtein Distance Equation \n\nUnderstanding the Levenshtein Distance Equation for Beginners \nAuthor:  Ethan Nam\n\n\"The Levenshtein distance is a number that tells you how different two strings are. The higher the number, the more different the two strings are.  The Levenshtein distance for strings A and B can be calculated by using a matrix.\"\n\n\"Thank Vladimir Levenshtein, who came up with his algorithm in 1965. The algorithm hasn’t been improved in over 50 years and for good reason. According to MIT, it may very well be that Levenshtein’s algorithm is the best that we’ll ever get in terms of efficiency.\"\n\nhttps://medium.com/@ethannam/understanding-the-levenshtein-distance-equation-for-beginners-c4285a5604f0\n\n#Levenshtein Distance, in Three Flavors\n\nAuthor: by Michael Gilleland, Merriam Park Software\n\n(It's in 3 flavors because  the authors presented source code which implements the Levenshtein distance algorithm in the following programming languages: Java, C++ and Visual Basic)\n\n\"Levenshtein distance (LD) is a measure of the similarity between two strings, which we will refer to as the source string (s) and the target string (t). The distance is the number of deletions, insertions, or substitutions required to transform s into t. For example,\"\n\n\"If s is \"test\" and t is \"test\", then LD(s,t) = 0, because no transformations are needed. The strings are already identical.\nIf s is \"test\" and t is \"tent\", then LD(s,t) = 1, because one substitution (change \"s\" to \"n\") is sufficient to transform s into t.\nThe greater the Levenshtein distance, the more different the strings are.\"\n\n\"Levenshtein distance is named after the Russian scientist Vladimir Levenshtein, who devised the algorithm in 1965. If you can't spell or pronounce Levenshtein, the metric is also sometimes called edit distance.\"\n\n#The Levenshtein distance algorithm has been used in:\n\nSPELL CHECKING\n\nSPEECH RECOGNITION\n\nDNA ANALYSIS\n\nPLAGIARISM DETECTION\n\nhttps://people.cs.pitt.edu/~kirk/cs1501/Pruhs/Spring2006/assignments/editdistance/Levenshtein%20Distance.htm\n\n#Levenshtein Distance in Python\n\nImplementing The Levenshtein Distance for Word Autocompletion and Autocorrection\nAuthor:  Ahmed Fawzy Gad - 2020\n\n\"Sections covered in this tutorial are as follows:\"\n\nCreating the distances matrix\nInitializing the distances matrix\nPrinting the distances matrix\nCalculating distances between all prefixes\nDictionary search for autocompletion/autocorrection\n\nhttps://blog.paperspace.com/implementing-levenshtein-distance-word-autocomplete-autocorrect/#:~:text=The%20Levenshtein%20distance%20is%20a,transform%20one%20word%20into%20another.\n\n#On Kaggle\n\n\"Levenshtein Distance Spelling Correction NLP\" by Bouwe Ceunen.\nhttps://www.kaggle.com/code/bouweceunen/levenshtein-distance-spelling-correction-nlp\n\n\"Levenshtein Distance to predict correct aswer\"  by Meghal Jambhale - Comp. ( chaii - Hindi and Tamil Question Answering)\nhttps://www.kaggle.com/code/meghaljambhale/levenshtein-distance-to-predict-correct-aswer\n\n\"Levenshtein Edit Distance\" by Dheeraj Gupta\nhttps://www.kaggle.com/code/dhgupta/levenshtein-edit-distance\n\n\"Levenshtein distance implementation\"  by Rakesh Jarupula - Competition (Bristol-Myers Squibb – Molecular Translation)\nhttps://www.kaggle.com/code/jarupula/levenshtein-distance-implementation\n\n\"Levenshtein distance\"  by Victor Khovanskiy - Competition (Quora Question Pairs) \nhttps://www.kaggle.com/code/khovanskiy/levenshtein-distance\n\n\"Traditional approach: Levenshtein\" by Daniel Legorreta - Competition (U.S. Patent Phrase to Phrase Matching) \nhttps://www.kaggle.com/code/legorreta/traditional-approach-levenshtein\n\n\"Levenshtein Distance - dynamic programming\"  by Nitin Singh - Comp. (Bristol-Myers Squibb – Molecular Translation)\nhttps://www.kaggle.com/code/nitinsss/levenshtein-distance-dynamic-programming\n\n\"Basic String Matchers- Hamming, Levenshtein, Jaro\"  by Shilpa G - Competition (U.S. Patent Phrase to Phrase Matching)\nhttps://www.kaggle.com/code/shilpagopal/basic-string-matchers-hamming-levenshtein-jaro\n\n\n#If you can't spell or pronounce Levenshtein, the metric is also sometimes called edit distance.",
    "1906448": "Great explanation!\n\nI really like the part about why is it called \"edit distance\". \nYou explained it simply and on most sources this explanation simply don't exist.\n\n\nThe Devastator.",
    "1906475": "Thank you Devastator \n\nThe funny Edit quote approaching the difficult to pronounce Levenshtein I copied from Michael Gilleland (Levenshtein Distance, in Three Flavors: Java, C++ and Visual Basic).\n\nThe best part is: \"The algorithm hasn’t been improved in over 50 years and for good reason. According to MIT, it may very well be that Levenshtein’s algorithm is the best that we’ll ever get in terms of efficiency.\"\n\nAnother explanation: Doctor Levenshtein dared someone: Now, edit it! \n\nGiving what the MIT said (above) about Levenshtein’s algorithm (the best over 50 years in terms of efficiency).  Nobody edited it.  Or maybe it's just a drunk history :)"
  },
  "source": "meta"
}