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import numpy as np |
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2
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3
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4
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def normalize(v): |
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5
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if v.ndim != 1: |
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raise ValueError('v should be 1-D, {}-D was given'.format( |
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7
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v.ndim)) |
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8
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norm = np.linalg.norm(v) |
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9
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if norm == 0: |
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10
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return v |
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11
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return v / norm |
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12
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13
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14
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def cosine_similarity(v, u): |
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15
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v_norm = np.linalg.norm(v) |
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16
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u_norm = np.linalg.norm(u) |
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similarity = v @ u / (v_norm * u_norm) |
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return similarity |
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20
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21
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def project_vector(v, u): |
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"""Projecting the vector v onto direction u.""" |
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normalize_u = normalize(u) |
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return (v @ normalize_u) * normalize_u |
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27
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def reject_vector(v, u): |
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"""Rejecting the vector v onto direction u.""" |
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return v - project_vector(v, u) |
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31
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32
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def project_reject_vector(v, u): |
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"""Projecting and rejecting the vector v onto direction u.""" |
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projected_vector = project_vector(v, u) |
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35
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rejected_vector = v - project_vector(v, u) |
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return projected_vector, rejected_vector |
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38
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39
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def update_word_vector(model, word, new_vector): |
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40
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model.syn0[model.vocab[word].index] = new_vector |
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if model.syn0norm is not None: |
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model.syn0norm[model.vocab[word].index] = normalize(new_vector) |
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43
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44
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45
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def generate_one_word_forms(word): |
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46
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return [word.lower(), word.upper(), word.title()] |
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47
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48
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49
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def generate_words_forms(words): |
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50
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return sum([generate_one_word_forms(word) for word in words], []) |
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51
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