Source code for nltk.test.unit.test_hmm

import pytest

from nltk.tag import hmm


def _wikipedia_example_hmm():
    # Example from wikipedia
    # (https://en.wikipedia.org/wiki/Forward%E2%80%93backward_algorithm)

    states = ["rain", "no rain"]
    symbols = ["umbrella", "no umbrella"]

    A = [[0.7, 0.3], [0.3, 0.7]]  # transition probabilities
    B = [[0.9, 0.1], [0.2, 0.8]]  # emission probabilities
    pi = [0.5, 0.5]  # initial probabilities

    seq = ["umbrella", "umbrella", "no umbrella", "umbrella", "umbrella"]
    seq = list(zip(seq, [None] * len(seq)))

    model = hmm._create_hmm_tagger(states, symbols, A, B, pi)
    return model, states, symbols, seq


[docs]def test_forward_probability(): from numpy.testing import assert_array_almost_equal # example from p. 385, Huang et al model, states, symbols = hmm._market_hmm_example() seq = [("up", None), ("up", None)] expected = [[0.35, 0.02, 0.09], [0.1792, 0.0085, 0.0357]] fp = 2 ** model._forward_probability(seq) assert_array_almost_equal(fp, expected)
[docs]def test_forward_probability2(): from numpy.testing import assert_array_almost_equal model, states, symbols, seq = _wikipedia_example_hmm() fp = 2 ** model._forward_probability(seq) # examples in wikipedia are normalized fp = (fp.T / fp.sum(axis=1)).T wikipedia_results = [ [0.8182, 0.1818], [0.8834, 0.1166], [0.1907, 0.8093], [0.7308, 0.2692], [0.8673, 0.1327], ] assert_array_almost_equal(wikipedia_results, fp, 4)
[docs]def test_backward_probability(): from numpy.testing import assert_array_almost_equal model, states, symbols, seq = _wikipedia_example_hmm() bp = 2 ** model._backward_probability(seq) # examples in wikipedia are normalized bp = (bp.T / bp.sum(axis=1)).T wikipedia_results = [ # Forward-backward algorithm doesn't need b0_5, # so .backward_probability doesn't compute it. # [0.6469, 0.3531], [0.5923, 0.4077], [0.3763, 0.6237], [0.6533, 0.3467], [0.6273, 0.3727], [0.5, 0.5], ] assert_array_almost_equal(wikipedia_results, bp, 4)
[docs]def setup_module(module): pytest.importorskip("numpy")