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If you are reading this on Wikimedia servers, please note that we have rate limited downloaders and we are capping the number of per-ip connections to 2. This will help to ensure that everyone can access the files with reasonable download times. Clients that try to evade these limits may be blocked. Our mirror sites do not have this cap. Data downloads The Wikimedia Foundation is requesting help t
We run a site that pulls data from wikipedia.org and recently the site stopped working. The site was using the following code to interact with the wikipedia.org API; this code queries the API to see if a page with the given title exists. $url = sprintf('http://en.wikipedia.org/w/api.php?action=query&titles=%s&prop=info&format=json', urlencode($search)); $f = fopen($url, 'r'); $res = ''; while (!fe
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This is an auto-generated MediaWiki API documentation page. Documentation and examples: https://www.mediawiki.org/wiki/Special:MyLanguage/API:Main_page Main module Status: The MediaWiki API is a mature and stable interface that is actively supported and improved. While we try to avoid it, we may occasionally need to make breaking changes; subscribe to the mediawiki-api-announce mailing list for no
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View and edit Wikipedia articles as if they were real files ! Warning! WikipediaFS is currently unmaintained. We are looking for a new maintainer to take over the development of the project. If you are interested, please tell us on the project's mailing-list. WikipediaFS is a mountable Linux virtual file system that allows to read and edit articles from Wikipedia (or any Mediawiki-based site) as i
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Pemeliharaan Terjadwal: Crowd Play pada 2023-11-30 dari 7:00 AM sampai 2025-06-02 6:30 PM (GMT + 7). Selama waktu ini, Crowd Play permainan tidak akan tersedia. Kami memohon maaf atas ketidaknyamanan yang mungkin ditimbulkan. Pemeliharaan Terjadwal: ESports Bull pada 2024-05-20 dari 10:00 AM sampai 2025-06-03 11:00 AM (GMT + 7). Selama waktu ini, ESports Bull permainan tidak akan tersedia. Kami me
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods, and perform updates based on current estimates, like dynamic programming methods.[1] While Monte Carlo methods only adjust their estimates once the outcome
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