def test_dishonest_casino_larger_transition_p(self):
'''Dishonest Casino Example.'''
# Create transition probability matrix
A = np.array([[0.9, 0.1],
[0.1, 0.9]])
# Create observable probability distribution matrix. Casino biased toward "6" in state "1"
B = statutil.scale_row_sums(np.array([[ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ],
[ 1.0, 1.0, 1.0, 1.0, 1.0, 5.0 ]]))
# Create set of all observable symbols
V = [1, 2, 3, 4, 5, 6]
# Instantiate an HMM, note Pi is uniform probability distribution by default
m = hmm.HMM(2, A=A, B=B, V=V)
Obs = [ 1, 2, 3, 4, 5, 2, 1, 6, 6, 6, 5, 6 ]
log_prob_Obs, Alpha, c = hmm.forward(m, Obs, scaling=1)
assert_almost_equal(log_prob_Obs, -20.124, decimal=3, err_msg='Wrong observation probability')
Q_star, _, _ = hmm.viterbi(m, Obs, scaling=1)
assert_equal(Q_star, [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1], err_msg='Wrong Viterbi path')
Beta = hmm.backward(m, Obs, c)
Gamma, Q_star = hmm.individually_optimal_states(Alpha, Beta)
assert_almost_equal(Gamma,
[[0.8189770516168013, 0.8482906260695058, 0.8525027084764197, 0.8329611652077556, 0.7834127024175411, 0.6880018120129073, 0.5161970090643716, 0.2130207566284025, 0.12024202874950358, 0.10797060639721641, 0.15902649827833876, 0.14930464162738483], [0.18102294838319855, 0.15170937393049422, 0.14749729152358024, 0.16703883479224435, 0.21658729758245884, 0.31199818798709256, 0.4838029909356284, 0.7869792433715975, 0.8797579712504964, 0.8920293936027837, 0.8409735017216613, 0.8506953583726152]],
decimal=5, err_msg='Wrong state probabilities')
assert_equal(Q_star, [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1], 'Wrong individually-optimal states')
def test_dishonest_casino(self):
'''Dishonest Casino Example.'''
# Create transition probability matrix
A = np.array([[0.99, 0.01],
[0.01, 0.99]])
# Create observable probability distribution matrix. Casino biased toward "6" in state "1".
B = statutil.scale_row_sums(np.array([[ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ],
[ 1.0, 1.0, 1.0, 1.0, 1.0, 5.0 ]]))
# Create set of all observable symbols
V = [1, 2, 3, 4, 5, 6]
# Instantiate an HMM, note Pi is uniform probability distribution by default
m = hmm.HMM(2, A=A, B=B, V=V)
Obs = [ 1, 2, 3, 4, 5, 2, 1, 6, 6, 6, 5, 6 ]
log_prob_Obs, Alpha, c = hmm.forward(m, Obs, scaling=1)
assert_almost_equal(log_prob_Obs, -20.9468006, decimal=5, err_msg='Wrong observation probability')
Q_star, _, _ = hmm.viterbi(m, Obs, scaling=1)
assert_equal(Q_star, [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'Wrong Viterbi path')
Beta = hmm.backward(m, Obs, c)
Gamma, Q_star = hmm.individually_optimal_states(Alpha, Beta)
assert_almost_equal(Gamma,
[[0.63711364302936, 0.6348934929050587, 0.6271179131667495, 0.6117100305977996, 0.5845543683193845, 0.5383975935172204, 0.46091113744414974, 0.3313982095474306, 0.28864618346708165, 0.27562909135388625, 0.27498372625848855, 0.26932891011973825], [0.36288635697064003, 0.3651065070949412, 0.3728820868332506, 0.38828996940220045, 0.4154456316806155, 0.4616024064827796, 0.5390888625558502, 0.6686017904525694, 0.7113538165329184, 0.7243709086461138, 0.7250162737415115, 0.7306710898802617]],
decimal=5, err_msg='Wrong state probabilities')
assert_equal(Q_star, [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1], 'Wrong individually-optimal states')
