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_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 generate_next_inpcrds(self, msm, clusters):
"""
Writes the input coordinate files for the next generation.
Each file is in its own numbered directory and called just "inpcrd"
"""
# Check if inpcrds are already made
sysdir = os.path.join(self.dir, "systems", str(self.generation + 1))
if len(glob(os.path.join(sysdir, "*.inpcrd"))) == self.nreps:
print(" Already have samplers... skipping inpcrd_generation")
return
# Make directory to contain topologies and inpcrds
if not os.path.isdir(os.path.join(self.dir, "systems")):
os.mkdir(os.path.join(self.dir, "systems"))
gendir = os.path.join(self.dir, "systems", str(self.generation + 1))
if not os.path.isdir(gendir):
os.mkdir(gendir)
scores = hub_scores(msm)
utils.dump(scores, "mmsm_scores.pkl")
gen = InpcrdGenerator(prodfiles=self.prodfiles,
clusters=clusters,
msm=msm,
scores=scores,
config=self.config,
criteria=self.config.get("model",
"criteria",
fallback="hub_scores"))
gen.run()
def test_hubscore():
#Make an actual hub!
tprob = np.array([[0.8, 0.0, 0.2, 0.0, 0.0], [0.0, 0.8, 0.2, 0.0, 0.0],
[0.1, 0.1, 0.6, 0.1, 0.1], [0.0, 0.0, 0.2, 0.8, 0.0],
[0.0, 0.0, 0.2, 0.0, 0.8]])
msm = MarkovStateModel(lag_time=1)
msm.transmat_ = tprob
msm.n_states_ = 5
score = tpt.hub_scores(msm, 2)[0]
assert score == 1.0
def test_hubscore():
# Make an actual hub!
tprob = np.array([[0.8, 0.0, 0.2, 0.0, 0.0],
[0.0, 0.8, 0.2, 0.0, 0.0],
[0.1, 0.1, 0.6, 0.1, 0.1],
[0.0, 0.0, 0.2, 0.8, 0.0],
[0.0, 0.0, 0.2, 0.0, 0.8]])
msm = MarkovStateModel(lag_time=1)
msm.transmat_ = tprob
msm.n_states_ = 5
score = tpt.hub_scores(msm, 2)[0]
assert score == 1.0
