mirror of
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52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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from ...serialize.packer import _BinaryCodec
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from ...serialize.huffman import HuffmanCodec
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from ...serialize.bits import BitArray
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import numpy
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import pytest
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def test_serialize_codecs_binary():
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codec = _BinaryCodec()
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bits = BitArray()
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array = numpy.array([0, 1, 0, 1, 1], numpy.int32)
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codec.encode(array, bits)
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result = numpy.array([0, 0, 0, 0, 0], numpy.int32)
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bits.seek(0)
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codec.decode(bits, result)
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assert list(array) == list(result)
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def test_serialize_codecs_attribute():
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freqs = {'the': 10, 'quick': 3, 'brown': 4, 'fox': 1, 'jumped': 5,
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'over': 8, 'lazy': 1, 'dog': 2, '.': 9}
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int_map = {'the': 0, 'quick': 1, 'brown': 2, 'fox': 3, 'jumped': 4,
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'over': 5, 'lazy': 6, 'dog': 7, '.': 8}
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codec = HuffmanCodec([(int_map[string], freq) for string, freq in freqs.items()])
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bits = BitArray()
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array = numpy.array([1, 7], dtype=numpy.int32)
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codec.encode(array, bits)
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result = numpy.array([0, 0], dtype=numpy.int32)
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bits.seek(0)
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codec.decode(bits, result)
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assert list(array) == list(result)
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def test_serialize_codecs_vocab(en_vocab):
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words = ["the", "dog", "jumped"]
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for word in words:
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_ = en_vocab[word]
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codec = HuffmanCodec([(lex.orth, lex.prob) for lex in en_vocab])
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bits = BitArray()
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ids = [en_vocab[s].orth for s in words]
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array = numpy.array(ids, dtype=numpy.int32)
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codec.encode(array, bits)
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result = numpy.array(range(len(array)), dtype=numpy.int32)
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bits.seek(0)
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codec.decode(bits, result)
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assert list(array) == list(result)
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