83 lines
2.2 KiB
Python
83 lines
2.2 KiB
Python
import pytest
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from nltk.tag import hmm
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def _wikipedia_example_hmm():
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# Example from wikipedia
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# (https://en.wikipedia.org/wiki/Forward%E2%80%93backward_algorithm)
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states = ["rain", "no rain"]
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symbols = ["umbrella", "no umbrella"]
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A = [[0.7, 0.3], [0.3, 0.7]] # transition probabilities
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B = [[0.9, 0.1], [0.2, 0.8]] # emission probabilities
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pi = [0.5, 0.5] # initial probabilities
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seq = ["umbrella", "umbrella", "no umbrella", "umbrella", "umbrella"]
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seq = list(zip(seq, [None] * len(seq)))
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model = hmm._create_hmm_tagger(states, symbols, A, B, pi)
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return model, states, symbols, seq
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def test_forward_probability():
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from numpy.testing import assert_array_almost_equal
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# example from p. 385, Huang et al
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model, states, symbols = hmm._market_hmm_example()
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seq = [("up", None), ("up", None)]
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expected = [[0.35, 0.02, 0.09], [0.1792, 0.0085, 0.0357]]
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fp = 2 ** model._forward_probability(seq)
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assert_array_almost_equal(fp, expected)
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def test_forward_probability2():
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from numpy.testing import assert_array_almost_equal
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model, states, symbols, seq = _wikipedia_example_hmm()
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fp = 2 ** model._forward_probability(seq)
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# examples in wikipedia are normalized
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fp = (fp.T / fp.sum(axis=1)).T
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wikipedia_results = [
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[0.8182, 0.1818],
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[0.8834, 0.1166],
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[0.1907, 0.8093],
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[0.7308, 0.2692],
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[0.8673, 0.1327],
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]
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assert_array_almost_equal(wikipedia_results, fp, 4)
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def test_backward_probability():
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from numpy.testing import assert_array_almost_equal
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model, states, symbols, seq = _wikipedia_example_hmm()
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bp = 2 ** model._backward_probability(seq)
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# examples in wikipedia are normalized
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bp = (bp.T / bp.sum(axis=1)).T
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wikipedia_results = [
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# Forward-backward algorithm doesn't need b0_5,
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# so .backward_probability doesn't compute it.
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# [0.6469, 0.3531],
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[0.5923, 0.4077],
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[0.3763, 0.6237],
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[0.6533, 0.3467],
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[0.6273, 0.3727],
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[0.5, 0.5],
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]
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assert_array_almost_equal(wikipedia_results, bp, 4)
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def setup_module(module):
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pytest.importorskip("numpy")
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