from import cosine_similarity,cosine_distances In the sklearn module, there is an in-built function called cosine_similarity() to calculate the cosine similarity. Use the sklearn Module to Calculate the Cosine Similarity Between Two Lists in Python Similarity_scores = List1.dot(List2)/ (np.linalg.norm(List1, axis=1) * np.linalg.norm(List2)) If there are multiple or a list of vectors and a query vector to calculate cosine similarities, we can use the following code. Result = dot(List1, List2)/(norm(List1)*norm(List2)) We can use these functions with the correct formula to calculate the cosine similarity. The numpy.norm() function returns the vector norm. The numpy.dot() function calculates the dot product of the two vectors passed as parameters. Use the NumPy Module to Calculate the Cosine Similarity Between Two Lists in Python The () function from the scipy module calculates the distance instead of the cosine similarity, but to achieve that, we can subtract the value of the distance from 1. Use the scipy Module to Calculate the Cosine Similarity Between Two Lists in Python In this article, we will calculate the cosine similarity between two lists of equal sizes. This means for two overlapping vectors, the value of cosine will be maximum and minimum for two precisely opposite vectors. If you consider the cosine function, its value at 0 degrees is 1 and -1 at 180 degrees. The cosine similarity measures the similarity between vector lists by calculating the cosine angle between the two vector lists. Use the torch Module to Calculate the Cosine Similarity Between Two Lists in Python.Use the sklearn Module to Calculate the Cosine Similarity Between Two Lists in Python.Use the NumPy Module to Calculate the Cosine Similarity Between Two Lists in Python.Use the scipy Module to Calculate the Cosine Similarity Between Two Lists in Python.
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