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y33-j3T
GitHub Repository: y33-j3T/Coursera-Deep-Learning
Path: blob/master/Natural Language Processing with Classification and Vector Spaces/Week 3 - Vector Space Models/utils.py
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import numpy as np
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def get_vectors(embeddings, words):
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"""
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Input:
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embeddings: a word
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fr_embeddings:
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words: a list of words
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Output:
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X: a matrix where the rows are the embeddings corresponding to the rows on the list
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"""
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m = len(words)
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X = np.zeros((1, 300))
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for word in words:
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english = word
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eng_emb = embeddings[english]
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X = np.row_stack((X, eng_emb))
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X = X[1:,:]
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return X
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