cosine-similarity

Calculate cosine similarity given 2 sentence strings

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A simple pure-Python implementation would be: import math import re from collections import Counter WORD = re.compile(r”\w+”) def get_cosine(vec1, vec2): intersection = set(vec1.keys()) & set(vec2.keys()) numerator = sum([vec1[x] * vec2[x] for x in intersection]) sum1 = sum([vec1[x] ** 2 for x in list(vec1.keys())]) sum2 = sum([vec2[x] ** 2 for x in list(vec2.keys())]) denominator = math.sqrt(sum1) … Read more

What’s the fastest way in Python to calculate cosine similarity given sparse matrix data?

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You can compute pairwise cosine similarity on the rows of a sparse matrix directly using sklearn. As of version 0.17 it also supports sparse output: from sklearn.metrics.pairwise import cosine_similarity from scipy import sparse A = np.array([[0, 1, 0, 0, 1], [0, 0, 1, 1, 1],[1, 1, 0, 1, 0]]) A_sparse = sparse.csr_matrix(A) similarities = cosine_similarity(A_sparse) … Read more

Cosine Similarity between 2 Number Lists

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You should try SciPy. It has a bunch of useful scientific routines for example, “routines for computing integrals numerically, solving differential equations, optimization, and sparse matrices.” It uses the superfast optimized NumPy for its number crunching. See here for installing. Note that spatial.distance.cosine computes the distance, and not the similarity. So, you must subtract the … Read more

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