approximate

Recursive program to compute Taylor approx of cosine not functioning in Prolog

由 这一生的挚爱 提交于 2020-02-15 10:25:45
问题 I'm still pretty new to Prolog, and i'm not sure why this code isn't working. I believe it is most likely a problem with the base case or in the last 3 lines of the recursive case. Everything else works just fine. This program determines cosine calculated with series approximation, to do so it needs to calculate the factorial of 2K, also -1 ^ K, and then uses these 2 calculations in the final equation (this is done in % Recursive Case). % Factorial from class fact(0, 1). fact(N, F) :- N > 0,

Approximate string matching algorithms state-of-the-art [closed]

由 ⅰ亾dé卋堺 提交于 2020-01-26 04:42:51
问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 4 years ago . I seek a state of the art algorithms to approximate string matching. Do you offer me references(article, thesis,...)? thank you 回答1: You might want to read about Levenshtein distance. http://en.wikipedia.org/wiki/Levenshtein_distance 回答2: You might have got your answer already but I want to convey my points on

Excel Approximate Text Match [closed]

由 久未见 提交于 2019-12-19 20:48:57
问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 5 years ago . I'm trying to check how many strings in column A approximately match a string in column B. Example: If I have the string "angry_birds_iph_app" in column B, and "angry_birds_iph_app" and "angry_birds_adrd_app" appear somewhere in column A, I would like the function to return 2. 回答1: Take a look at the Excel Fuzzy

Approximate string matching

由 佐手、 提交于 2019-12-12 07:11:03
问题 I know this question have been asked a lot of time. I want a suggestion on which algorithm is suitable for approximate string matching. The application is specifically for company name matching only and nothing else. The biggest challenge is probably the company end name part and short named part Example: 1. companyA pty ltd vs companyA pty. ltd. vs companyA 2. WES Engineering vs W.E.S. Engineering (extremely rare occurance) Do you think Levenshtein Edit Distance is adequate? I'm using C#

Approximate regular expression library for Java?

由 陌路散爱 提交于 2019-12-10 11:40:47
问题 I have just written some code for approximate string matching. I would like to benchmark my naive algorithm against a more mature implementation running on the JVM. Any suggestions? 回答1: I found these answers elsewhere on this site for similar problems. Commons Lang has an implementation of Levenshtein distance. http://commons.apache.org/lang/api-release/org/apache/commons/lang/StringUtils.html Commons Codec has an implementation of soundex and metaphone. http://commons.apache.org/codec/api

Approximate regular expression library for Java?

由 依然范特西╮ 提交于 2019-12-08 19:58:27
I have just written some code for approximate string matching. I would like to benchmark my naive algorithm against a more mature implementation running on the JVM. Any suggestions? Gunslinger47 I found these answers elsewhere on this site for similar problems. Commons Lang has an implementation of Levenshtein distance. http://commons.apache.org/lang/api-release/org/apache/commons/lang/StringUtils.html Commons Codec has an implementation of soundex and metaphone. http://commons.apache.org/codec/api-release/org/apache/commons/codec/language/Soundex.html http://commons.apache.org/codec/api

How to use n-grams approximate matching with Solr?

由 空扰寡人 提交于 2019-12-03 12:37:01
问题 We have a database of movies and series, and as the data comes from many sources of varying reliability, we'd like to be able to do fuzzy string matching on the titles of episodes. We are using Solr for search in our application, but the default matching mechanisms operate on word levels, which is not good enough for short strings, like titles I had used n-grams approximate matching in the past, and I was very happy to find that Lucene (and Solr) supports something this out of the box.

Excel Approximate Text Match [closed]

由 余生颓废 提交于 2019-12-01 19:17:05
I'm trying to check how many strings in column A approximately match a string in column B. Example: If I have the string "angry_birds_iph_app" in column B, and "angry_birds_iph_app" and "angry_birds_adrd_app" appear somewhere in column A, I would like the function to return 2. Take a look at the Excel Fuzzy Lookup add-in. It is free to download from Microsoft. The tool implements the Levenshtein edit distance algorithm to return a similarity score between rows. Among other customizable features, the tool also allows you to set a threshold for how well the values must match in order to return

How do I find the closest array element to an arbitrary (non-member) number?

由 南楼画角 提交于 2019-12-01 18:13:53
Seemingly similar questions: " Finding closest number in an array " (in Java) and " find nearest match to array of doubles " (actually a geography problem). I have a (sorted) array of doubles. Given an arbitrary number (which may or may not be an exact match for one of the array elements), how can I return the index of the number which is the closest match? For example, using the following array: 1.8 2.4 2.7 3.1 4.5 Querying 2.5 would return with an index of 1, corresponding to the value of 2.4. Bonus points for detecting values that lie completely outside of the range of the array elements.

How do I find the closest array element to an arbitrary (non-member) number?

由 ￣綄美尐妖づ 提交于 2019-12-01 16:32:23
问题 Seemingly similar questions: "Finding closest number in an array" (in Java) and "find nearest match to array of doubles" (actually a geography problem). I have a (sorted) array of doubles. Given an arbitrary number (which may or may not be an exact match for one of the array elements), how can I return the index of the number which is the closest match? For example, using the following array: 1.8 2.4 2.7 3.1 4.5 Querying 2.5 would return with an index of 1, corresponding to the value of 2.4.
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