def test_harder_hubscore():
# depends on tpt.committors and tpt.conditional_committors
assignments = np.random.randint(10, size=(10, 1000))
msm = MarkovStateModel(lag_time=1)
msm.fit(assignments)
hub_scores = tpt.hub_scores(msm)
ref_hub_scores = np.zeros(10)
for A in range(10):
for B in range(10):
committors = tpt.committors(A, B, msm)
denom = msm.transmat_[A, :].dot(committors)
for C in range(10):
if A == B or A == C or B == C:
continue
cond_committors = tpt.conditional_committors(A, B, C, msm)
temp = 0.0
for i in range(10):
if i in [A, B]:
continue
temp += cond_committors[i] * msm.transmat_[A, i]
temp /= denom
ref_hub_scores[C] += temp
ref_hub_scores /= (9 * 8)
npt.assert_array_almost_equal(ref_hub_scores, hub_scores)
def test_cond_committors():
# depends on tpt.committors
msm = MarkovStateModel(lag_time=1)
assignments = np.random.randint(4, size=(10, 1000))
msm.fit(assignments)
tprob = msm.transmat_
for_committors = tpt.committors(0, 3, msm)
cond_committors = tpt.conditional_committors(0, 3, 2, msm)
# The committor for state one can be decomposed into paths that
# do and do not visit state 2 along the way. The paths that do not
# visit state 1 must look like 1, 1, 1, ..., 1, 1, 3. So we can
# compute them with a similar approximation as the forward committor
# Since we want the other component of the forward committor, we
# subtract that probability from the forward committor
ref = for_committors[1] - np.power(tprob[1, 1],
np.arange(5000)).sum() * tprob[1, 3]
#print (ref / for_committors[1])
ref = [0, ref, for_committors[2], 0]
#print(cond_committors, ref)
npt.assert_array_almost_equal(ref, cond_committors)
def test_cond_committors():
# depends on tpt.committors
msm = MarkovStateModel(lag_time=1)
assignments = np.random.randint(4, size=(10, 1000))
msm.fit(assignments)
tprob = msm.transmat_
for_committors = tpt.committors(0, 3, msm)
cond_committors = tpt.conditional_committors(0, 3, 2, msm)
# The committor for state one can be decomposed into paths that
# do and do not visit state 2 along the way. The paths that do not
# visit state 1 must look like 1, 1, 1, ..., 1, 1, 3. So we can
# compute them with a similar approximation as the forward committor
# Since we want the other component of the forward committor, we
# subtract that probability from the forward committor
ref = for_committors[1] - np.power(tprob[1, 1], np.arange(5000)).sum() * tprob[1, 3]
#print (ref / for_committors[1])
ref = [0, ref, for_committors[2], 0]
#print(cond_committors, ref)
npt.assert_array_almost_equal(ref, cond_committors)
def test_harder_hubscore():
# depends on tpt.committors and tpt.conditional_committors
assignments = np.random.randint(10, size=(10, 1000))
msm = MarkovStateModel(lag_time=1)
msm.fit(assignments)
hub_scores = tpt.hub_scores(msm)
ref_hub_scores = np.zeros(10)
for A in xrange(10):
for B in xrange(10):
committors = tpt.committors(A, B, msm)
denom = msm.transmat_[A, :].dot(committors) #+ msm.transmat_[A, B]
for C in xrange(10):
if A == B or A == C or B == C:
continue
cond_committors = tpt.conditional_committors(A, B, C, msm)
temp = 0.0
for i in xrange(10):
if i in [A, B]:
continue
temp += cond_committors[i] * msm.transmat_[A, i]
temp /= denom
ref_hub_scores[C] += temp
ref_hub_scores /= (9 * 8)
#print(ref_hub_scores, hub_scores)
npt.assert_array_almost_equal(ref_hub_scores, hub_scores)
def test_cond_committors_2():
# depends on tpt.committors
bmsm = BayesianMarkovStateModel(lag_time=1)
assignments = np.random.randint(4, size=(10, 1000))
bmsm.fit(assignments)
for_committors = tpt.committors(0, 3, bmsm)
cond_committors = tpt.conditional_committors(0, 3, 2, bmsm)
ref = 0
for tprob in bmsm.all_transmats_:
ref += (for_committors[1] -
np.power(tprob[1, 1], np.arange(5000)).sum() *
tprob[1, 3])
ref = [0, ref / 100., for_committors[2], 0]
npt.assert_array_almost_equal(ref, cond_committors, decimal=2)
