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To compute the distances between two words, you can do the following: >>> import numpy >>> cosine_similarity = numpy.dot(model['spain'], model['france'])/(numpy.linalg.norm(model['spain'])* numpy.linalg.norm(model['france']))
12 lip 2023 · Given a list of words followed by two words, the task is to find the minimum distance between the given two words in the list of words. Examples : Input: S = { “the”, “quick”, “brown”, “fox”, “quick”}, word1 = “the”, word2 = “fox”
31 lip 2016 · This class provides an efficient way to find the shortest distance between two words in a list, optimizing for cases where multiple queries are made against the same list by preprocessing the list into a more accessible form.
The task: Find an efficient algorithm to find the smallest distance (measured in number of words) between any two given words in a string. For example, given words "hello", and "world" an...
30 lip 2016 · 243. Shortest Word Distance Description. Given an array of strings wordsDict and two different strings that already exist in the array word1 and word2, return the shortest distance between these two words in the list. Example 1: Input: wordsDict = ["practice", "makes", "perfect", "coding", "makes"], word1 = "coding", word2 = "practice" Output ...
16 mar 2021 · If the former, I think the problem as you originally described it could be solved by splitting on spaces, iterating through the two lists together, and comparing the Levenshtein distance between the words.
2 mar 2024 · This method iteratively checks for the two words within the text and calculates their positions to find the minimum distance. It utilizes two pointers to keep track of the most recent appearance of each word and computes the gap as it traverses the text. Here’s an example: def find_min_distance(text, word1, word2): words = text.split()
