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41 lines
1.1 KiB
Python
41 lines
1.1 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import numpy
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from numpy.testing import assert_allclose
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from ...vocab import Vocab
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from ..._ml import cosine
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def test_vocab_add_vector():
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vocab = Vocab()
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data = numpy.ndarray((5,3), dtype='f')
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data[0] = 1.
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data[1] = 2.
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vocab.set_vector(u'cat', data[0])
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vocab.set_vector(u'dog', data[1])
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cat = vocab[u'cat']
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assert list(cat.vector) == [1., 1., 1.]
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dog = vocab[u'dog']
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assert list(dog.vector) == [2., 2., 2.]
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def test_vocab_prune_vectors():
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vocab = Vocab()
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_ = vocab[u'cat']
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_ = vocab[u'dog']
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_ = vocab[u'kitten']
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data = numpy.ndarray((5,3), dtype='f')
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data[0] = 1.
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data[1] = 2.
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data[2] = 1.1
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vocab.set_vector(u'cat', data[0])
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vocab.set_vector(u'dog', data[1])
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vocab.set_vector(u'kitten', data[2])
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remap = vocab.prune_vectors(2)
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assert list(remap.keys()) == [u'kitten']
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neighbour, similarity = list(remap.values())[0]
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assert neighbour == u'cat', remap
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assert_allclose(similarity, cosine(data[0], data[2]), atol=1e-6)
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