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问题 Yes, I'm dead serious with this question. How does searching with pip work? The documentation of the keyword search refers to a "pip search reference" at https://pip.pypa.io/en/stable/user_guide/#searching-for-packages which is everything but a reference. I can't conclude from search attempts how searching works. E.g. if I search for "exec" I get a variety of results such as exec-pypeline (0.4.2) - an incredible python package . I even get results with package names that have nothing to do

问题 Yes, I'm dead serious with this question. How does searching with pip work? The documentation of the keyword search refers to a "pip search reference" at https://pip.pypa.io/en/stable/user_guide/#searching-for-packages which is everything but a reference. I can't conclude from search attempts how searching works. E.g. if I search for "exec" I get a variety of results such as exec-pypeline (0.4.2) - an incredible python package . I even get results with package names that have nothing to do

问题 It's showing the wrong answer. Can anybody please tell me which test case I am missing ? Without Adjacent Given an array arr[] of N positive integers. The task is to find a subsequence with maximum sum such that there should be no adjacent elements from the array in the subsequence. Input: First line of input contains number of testcases T. For each testcase, first line of input contains size of array N. Next line contains N elements of the array space seperated. Output: For each testcase,

问题 I can check for the availability of a an individual domain via whois abc123.com . I can't figure out how to check the availability of a whole set of domains that match criteria , like XXX YYY . Z . where X is any 3 of the same letters, Y is any 3 of the same numbers, and Z is any of com, org, or io. Like aaa111.org That's just an example case, but you get the idea - I'd like to specify strings, patterns, and endings, and see what's available. I can do this kind of string matching with Regex,

问题 Given a sorted array A and an element x , I need to find an algorithm that returns the index of x in A or -1 if x is not in A . the time complexity of the algorithm should be Θ(logd) when d is the number of elements that appears before x in A , or if x is not in A , d is the number of elements that were before x if he was in A . Binary search is not good enough because its best case is O(1). I thought of starting from the beginning of the array, and start checking the indexes that are powers

问题 Auto suggest/complete fields are used all over the web. Google has appeared to master it given that as soon as one types in a search query, suggestions are returned almost instantaneously. I'm assuming the framework for achieving this involves a fast, in-memory data store on the web tier. We're building a Grails app based around retail products, so a user may search for Can which should suggest things like Canon , Cancun , etc, and wondering if a Java B-tree cached in memory would suffice for

问题 I want to create a animation on search loop icon when hovering in it.First the loop only is displayed like this picture And when I will hover on the loop icon the text input must appear from the right inceasing its width to left and get 100% width. I tried this code jQuery(".main_search .search-field").focusin(function() { jQuery(".main_search .screen-reader-text").css( "display", "none"); }); jQuery(".main_search .search-field").focusout(function() { jQuery(".main_search .screen-reader-text"

问题 I want to create a animation on search loop icon when hovering in it.First the loop only is displayed like this picture And when I will hover on the loop icon the text input must appear from the right inceasing its width to left and get 100% width. I tried this code jQuery(".main_search .search-field").focusin(function() { jQuery(".main_search .screen-reader-text").css( "display", "none"); }); jQuery(".main_search .search-field").focusout(function() { jQuery(".main_search .screen-reader-text"

问题 for s_index, s in enumerate(sentences): s_tokens = s.split() if (local_q_set.intersection(set(s_tokens)) == local_q_set): q_results.append(s_index) The code snippet above is the core algorithm I used to find relevant sentences in massive text data that include all tokens in the query. For example, for a query "happy apple", it finds all sentences that include exactly one or more of all of the given tokens, (i.e. "happy" and "apple"). My method is very simple: find common intersecting sets and

问题 for s_index, s in enumerate(sentences): s_tokens = s.split() if (local_q_set.intersection(set(s_tokens)) == local_q_set): q_results.append(s_index) The code snippet above is the core algorithm I used to find relevant sentences in massive text data that include all tokens in the query. For example, for a query "happy apple", it finds all sentences that include exactly one or more of all of the given tokens, (i.e. "happy" and "apple"). My method is very simple: find common intersecting sets and