(Also note that my knowledge of C++ is very superficial, I'm sure there are ways make it run much faster. Note that although Python is the slowest initially, it beats C++ and Perl when we use NumPy arrays instead of built-in lists. The following test were done on a 8GB/i5 machine. We calculate cosine similarity, repeat this 50 times in total and calculate average runtime. Computational performanceįor testing, 2 vectors of size 50,000 are generated which point at opposite directions (so the calculated cosine similarity should be -1). Output is a number between -1 and 1, where 1 means the two vectors are completely similar (or identical), 0 means they have no similarity at all and -1 Mathematically, the Cosine similarity metric measures the cosine of the angle between two n-dimensional vectors projected in a multi-dimensional space, and value ranges from 0 to 1, where, 1 means more similarity 0 means less similarity. In the test files, we just randomly generate two vectors, therefore the "similarity" between them is also random. Cosine similarity measures the text-similarity between two documents irrespective of their size. Usage InputĮxpected input is two vectors of equal length. This similarity is calculated by measuring the distance between two vectors and normalizing that by the length of the vectors (so the length of the documents don't play a role: a short document can be very similar to a long one and vice versa). With cosine similarity we are then able to measure the similarity between each pair of vectors. We can get vectors by calculating a score for each word in the dataset (frequency or TF-IDF). For example we would like toįind the document that has the highest similarity to the search query. This measure is widely used in document classification and information retrieval where documents are treated as vectors. The average runtime difference between the two Python scripts is about 1:250. The comparison is mainly between the two modules: cos_sim.py (poor performance, but better readability) and cos_sim_np.py which achieves close to C++ performance using NumPy arrays. This package, with functions performing same task in Python, C++ and Perl, is only meant foreducational purposes and I mainly focus here on optimizing Python. Cosine Similarity: Python, Perl and C++ library AboutĬosine Similarity is a measure of similarity between two vectors.
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