def test_dna():
# HMM example taken from Borodovsky and Ekisheva (2006), Problems and
# Solutions in Biological Sequence Analysis, p. 80.
# Four (one-hot) features T, C, A and G, two states H and L
# (high and low C+G content).
start = np.log([.5, .5])
final = start
trans = np.log([[.5, .5],
[.4, .6]])
# XXX in a binary problem, w of shape (n_features,) should be enough
w = np.log([[.2, .3, .2, .3],
[.3, .2, .3, .2]])
X = np.array([[0, 0, 0, 1], # G
[0, 0, 0, 1], # G
[0, 1, 0, 0], # C
[0, 0, 1, 0], # A
[0, 1, 0, 0], # C
[1, 0, 0, 0], # T
[0, 0, 0, 1], # G
[0, 0, 1, 0], # A
[0, 0, 1, 0]]) # A
Phi = np.dot(X, w.T)
# HHHLLLLLL
y_true = np.array([0, 0, 0, 1, 1, 1, 1, 1, 1])
assert_array_equal(viterbi(Phi, trans, start, final), y_true)
# For this problem, Viterbi actually is better than best-first.
bf = bestfirst(Phi, trans, start, final)
assert_greater(accuracy_score(y_true, bf), .75)
def test_dna():
# HMM example taken from Borodovsky and Ekisheva (2006), Problems and
# Solutions in Biological Sequence Analysis, p. 80.
# Four (one-hot) features T, C, A and G, two states H and L
# (high and low C+G content).
start = np.log([.5, .5])
final = start
trans = np.log([[.5, .5],
[.4, .6]])
# XXX in a binary problem, w of shape (n_features,) should be enough
w = np.log([[.2, .3, .2, .3],
[.3, .2, .3, .2]])
X = np.array([[0, 0, 0, 1], # G
[0, 0, 0, 1], # G
[0, 1, 0, 0], # C
[0, 0, 1, 0], # A
[0, 1, 0, 0], # C
[1, 0, 0, 0], # T
[0, 0, 0, 1], # G
[0, 0, 1, 0], # A
[0, 0, 1, 0]]) # A
score = np.dot(X, w.T)
# HHHLLLLLL
y_true = np.array([0, 0, 0, 1, 1, 1, 1, 1, 1])
assert_array_equal(viterbi(score, None, trans, start, final), y_true)
# For this problem, Viterbi actually is better than best-first.
bf = bestfirst(score, None, trans, start, final)
assert_greater(accuracy_score(y_true, bf), .75)
def test_wikipedia_example():
# HMM example taken from Wikipedia. Samples can be "normal", "cold" or
# "dizzy" (represented as one-hot feature vectors). States are "Healthy"
# and "Fever". ['normal', 'cold', 'dizzy'] has optimal state sequence
# ['Healthy', 'Healthy', 'Fever'].
start = np.log([.6, .4])
final = np.log([.5, .5]) # not given, so assume uniform probabilities
trans = np.log([[.7, .3],
[.4, .6]])
w = np.log([[.5, .4, .1],
[.1, .3, .6]])
X = np.array([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
Phi = np.dot(X, w.T)
assert_array_equal(bestfirst(Phi, trans, start, final), [0, 0, 1])
assert_array_equal(viterbi(Phi, trans, start, final), [0, 0, 1])
def test_wikipedia_example():
# HMM example taken from Wikipedia. Samples can be "normal", "cold" or
# "dizzy" (represented as one-hot feature vectors). States are "Healthy"
# and "Fever". ['normal', 'cold', 'dizzy'] has optimal state sequence
# ['Healthy', 'Healthy', 'Fever'].
start = np.log([.6, .4])
final = np.log([.5, .5]) # not given, so assume uniform probabilities
trans = np.log([[.7, .3],
[.4, .6]])
w = np.log([[.5, .4, .1],
[.1, .3, .6]])
X = np.array([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
score = np.dot(X, w.T)
assert_array_equal(bestfirst(score, None, trans, start, final), [0, 0, 1])
assert_array_equal(viterbi(score, None, trans, start, final), [0, 0, 1])