algorithms_and_data_structures | 180+ Algorithm & Data Structure Problems using C++ | Learning library

 by   mandliya C++ Version: Current License: GPL-2.0

kandi X-RAY | algorithms_and_data_structures Summary

kandi X-RAY | algorithms_and_data_structures Summary

algorithms_and_data_structures is a C++ library typically used in Tutorial, Learning, Example Codes, LeetCode applications. algorithms_and_data_structures has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has medium support. You can download it from GitHub.

180+ Algorithm & Data Structure Problems using C++
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              algorithms_and_data_structures has a medium active ecosystem.
              It has 5346 star(s) with 1279 fork(s). There are 219 watchers for this library.
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              It had no major release in the last 6 months.
              There are 13 open issues and 7 have been closed. On average issues are closed in 195 days. There are 161 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of algorithms_and_data_structures is current.

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              algorithms_and_data_structures has no bugs reported.

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              algorithms_and_data_structures has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

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              algorithms_and_data_structures is licensed under the GPL-2.0 License. This license is Strong Copyleft.
              Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

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              algorithms_and_data_structures releases are not available. You will need to build from source code and install.

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            Community Discussions

            Trending Discussions on algorithms_and_data_structures

            QUESTION

            Scraping Wikipedia Subcategories (Pages) with Multiple Depths?
            Asked 2018-Oct-11 at 14:00

            If you open the computer science category in wikipedia (https://en.wikipedia.org/wiki/Category:Computer_science), it displays a total of 19 subcategories (https://en.wikipedia.org/wiki/Category:Computer_science). Now, for all these 19 subcategories, if I want to extract only the page names (the titles of pages). For example Pages in category Computer science has 45 pages which is displayed as bullets just below the wikipedia subcategories list. Now for all the other associated subcategories, for example the Areas of computer science is a subcategory with 3 pages (https://en.wikipedia.org/wiki/Category:Areas_of_computer_science). But, again it has 17 sub-categories (i.e. depth 1, considering the traversal, i.e. depth = 1 means, we are 1 deep). Again, algorithm and data structures (https://en.wikipedia.org/wiki/Category:Algorithms_and_data_structures) having 5 pages, and artificial intelligence (https://en.wikipedia.org/wiki/Category:Artificial_intelligence) having 333 pages with some additional categories and subcategories spanned in multiple pages (see Pages in category "Artificial intelligence") with 37 categories and 333 pages, like this the list goes on more deeper. We are now in depth 2. What I need is to extract all the pages (titles) for the traversal with depth 1 and depth 2. Does there exist any algorithm to achieve the same?

            For Example: the subcategory area of computer science is again having some (17) subcategories with a total number of pages 5+333+127+79+216+315+37+47+95+37+246+103+21+2+55+113+94 pages considering all the (17) subcategories. This is depth 2 because, I toggled twice the list. Similarly, same thing needs to be incorporated for the rest 18 subcategories (https://en.wikipedia.org/wiki/Category:Computer_science) with a depth of 2 for the base root Computer science?

            Does there exist any way to achieve this? Displaying and extracting that many number of pages is difficult because it will be huge. Thus, maximum threshold of 10,000 pages would be absolutely okay.

            Does there exist any way to do this? Any small help is deeply appreciated!

            ...

            ANSWER

            Answered 2018-Oct-11 at 14:00

            There is a tool called PetScan hosted by Wikimedia labs. You can easily type the category title, then select the depth you want to reach, and then it's done!. https://petscan.wmflabs.org/

            Also, see how it works https://meta.m.wikimedia.org/wiki/PetScan/en

            Source https://stackoverflow.com/questions/52747218

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