def do_check(self, A, B, nnodes=20, nedges=20):
np.random.seed(0)
maker = _MakeRandomGraph(nnodes=20, nedges=20, node_set=A+B)
maker.run()
reducer = NGT(maker.rates, A, B, debug=False)
reducer.compute_rates()
rAB = reducer.get_rate_AB()
rBA = reducer.get_rate_BA()
def do_check(self, A, B, nnodes=20, nedges=20):
np.random.seed(0)
maker = _MakeRandomGraph(nnodes=20, nedges=20, node_set=A+B)
maker.run()
reducer = NGT(maker.rates, A, B, debug=False)
reducer.compute_rates()
rAB = reducer.get_rate_AB()
rBA = reducer.get_rate_BA()
def compare(self, A, B, nnodes=10, nedges=20, weights=None, x=1):
print ""
maker = _MakeRandomGraph(nnodes=nnodes,
nedges=nedges,
node_set=A + B + [x])
graph = maker.run()
graph_backup = graph.copy()
reducer = GraphReduction(maker.rates, A, B, weights=weights)
kmc = KineticMonteCarlo(graph_backup, debug=False)
# test compute_committor_probability()
PxB = reducer.compute_committor_probability(x)
PxB_kmc = kmc.committor_probability(x, A, B, niter=1000)
print "committor probability ", x, "->", B, "=", PxB
print "committor probability kmc", x, "->", B, "=", PxB_kmc
self.assertAlmostEqual(PxB, PxB_kmc, delta=.1)
reducer.compute_rates()
rAB = reducer.get_rate_AB()
rBA = reducer.get_rate_BA()
rAB_SS = reducer.get_rate_AB_SS()
# compute rate via linalg
lin = TwoStateRates(maker.rates, A, B, weights=weights)
lin.compute_rates()
rAB_LA = lin.get_rate_AB()
lin.compute_committors()
rAB_SS_LA = lin.get_rate_AB_SS()
self.assertAlmostEqual(rAB_SS, rAB_SS_LA, 5)
PxB_LA = lin.get_committor(x)
if x not in A and x not in B:
self.assertAlmostEqual(PxB, PxB_LA, 5)
rAB_KMC = kmc.mean_rate(A, B, niter=1000, weights=weights)
print "NGT rate A->B", rAB
print "KMC rate A->B", rAB_KMC
print "normalized difference", (rAB - rAB_KMC) / rAB
print "normalized difference to linalg", (rAB - rAB_LA) / rAB
self.assertLess(abs(rAB - rAB_KMC) / rAB, .1)
self.assertLess(abs(rAB - rAB_LA) / rAB, .00001)
rBA_KMC = kmc.mean_rate(B, A, niter=1000, weights=weights)
print "NGT rate B->A", rBA
print "KMC rate B->A", rBA_KMC
print "normalized difference", (rBA - rBA_KMC) / rBA
self.assertLess(abs(rBA - rBA_KMC) / rBA, .1)
paB = kmc.committor_probability(A[0], [A[0]], B, niter=1000)
print "the committor probability a->B", paB
print "graph reduction committor prob", reducer.get_committor_probabilityAB(
A[0])
self.assertAlmostEqual(paB,
reducer.get_committor_probabilityAB(A[0]),
delta=.1)
def compare(self, A, B, nnodes=10, nedges=20, weights=None, x=1):
print ""
maker = _MakeRandomGraph(nnodes=nnodes, nedges=nedges, node_set=A+B+[x])
graph = maker.run()
graph_backup = graph.copy()
reducer = GraphReduction(maker.rates, A, B, weights=weights)
kmc = KineticMonteCarlo(graph_backup, debug=False)
# test compute_committor_probability()
PxB = reducer.compute_committor_probability(x)
PxB_kmc = kmc.committor_probability(x, A, B, niter=1000)
print "committor probability ", x, "->", B, "=", PxB
print "committor probability kmc", x, "->", B, "=", PxB_kmc
self.assertAlmostEqual(PxB, PxB_kmc, delta=.1)
reducer.compute_rates()
rAB = reducer.get_rate_AB()
rBA = reducer.get_rate_BA()
rAB_SS = reducer.get_rate_AB_SS()
# compute rate via linalg
lin = TwoStateRates(maker.rates, A, B, weights=weights)
lin.compute_rates()
rAB_LA = lin.get_rate_AB()
lin.compute_committors()
rAB_SS_LA = lin.get_rate_AB_SS()
self.assertAlmostEqual(rAB_SS, rAB_SS_LA, 5)
PxB_LA = lin.get_committor(x)
if x not in A and x not in B:
self.assertAlmostEqual(PxB, PxB_LA, 5)
rAB_KMC = kmc.mean_rate(A, B, niter=1000, weights=weights)
print "NGT rate A->B", rAB
print "KMC rate A->B", rAB_KMC
print "normalized difference", (rAB - rAB_KMC)/rAB
print "normalized difference to linalg", (rAB - rAB_LA)/rAB
self.assertLess(abs(rAB - rAB_KMC)/rAB, .1)
self.assertLess(abs(rAB - rAB_LA)/rAB, .00001)
rBA_KMC = kmc.mean_rate(B, A, niter=1000, weights=weights)
print "NGT rate B->A", rBA
print "KMC rate B->A", rBA_KMC
print "normalized difference", (rBA - rBA_KMC)/rBA
self.assertLess(abs(rBA - rBA_KMC)/rBA, .1)
paB = kmc.committor_probability(A[0], [A[0]], B, niter=1000)
print "the committor probability a->B", paB
print "graph reduction committor prob", reducer.get_committor_probabilityAB(A[0])
self.assertAlmostEqual(paB, reducer.get_committor_probabilityAB(A[0]), delta=.1)
def do_check(self, A, B, nnodes=20, nedges=20):
maker = _MakeRandomGraph(nnodes=20, nedges=20, node_set=A+B)
rates = maker.make_rates()
reducer = TwoStateRates(rates, A, B)
reducer.compute_rates()
reducer.compute_committors()
from pele.rates._ngt_cpp import NGT
ngt = NGT(rates, A, B)
ngt.compute_rates()
self.assertAlmostEqual(reducer.get_rate_AB(), ngt.get_rate_AB(), 7)
self.assertAlmostEqual(reducer.get_rate_AB_SS(), ngt.get_rate_AB_SS(), 7)
def do_check(self, A, B, nnodes=20, nedges=20):
maker = _MakeRandomGraph(nnodes=20, nedges=20, node_set=A + B)
rates = maker.make_rates()
reducer = TwoStateRates(rates, A, B)
reducer.compute_rates()
reducer.compute_committors()
from pele.rates._ngt_cpp import NGT
ngt = NGT(rates, A, B)
ngt.compute_rates()
self.assertAlmostEqual(reducer.get_rate_AB(), ngt.get_rate_AB(), 7)
self.assertAlmostEqual(reducer.get_rate_AB_SS(), ngt.get_rate_AB_SS(),
7)
def compare_linalg(self, A, B, nnodes=20, nedges=20):
from kmc_rates import TwoStateRates
maker = _MakeRandomGraph(nnodes=20, nedges=20, node_set=A+B)
maker.run()
reducer = NGT(maker.rates, A, B)
reducer.compute_rates_and_committors()
committors = reducer.get_committors()
la = TwoStateRates(maker.rates, A, B)
# la.compute_rates()
la.compute_committors()
qla = la.committor_dict
for n, qla in la.committor_dict.iteritems():
self.assertAlmostEqual(qla, committors[n], 7)
def compare_linalg(self, A, B, nnodes=20, nedges=20):
from pele.rates._rates_linalg import TwoStateRates
maker = _MakeRandomGraph(nnodes=20, nedges=20, node_set=A+B)
maker.run()
reducer = NGT(maker.rates, A, B)
reducer.compute_rates_and_committors()
committors = reducer.get_committors()
la = TwoStateRates(maker.rates, A, B)
# la.compute_rates()
la.compute_committors()
qla = la.committor_dict
for n, qla in la.committor_dict.iteritems():
self.assertAlmostEqual(qla, committors[n], 7)
def test_committor_probabilities(self, nnodes=10, nedges=20):
A = [0,1,2,3]
B = [8,9]
xx = 5
maker = _MakeRandomGraph(nnodes=nnodes, nedges=nedges, node_set=A+B)
graph = maker.run()
kmc = KineticMonteCarlo(graph, debug=False)
reducer = GraphReduction(maker.rates, A, B)
nodes = set(A + B + [xx])
PxB = reducer.compute_committor_probabilities(nodes)
for x in nodes:
self.assertIn(x, PxB)
for x in nodes:
PxB_kmc = kmc.committor_probability(x, A, B, niter=1000)
self.assertAlmostEqual(PxB[x], PxB_kmc, delta=.1)
def test_committor_probabilities(self, nnodes=10, nedges=20):
A = [0, 1, 2, 3]
B = [8, 9]
xx = 5
maker = _MakeRandomGraph(nnodes=nnodes, nedges=nedges, node_set=A + B)
graph = maker.run()
graph_backup = graph.copy()
kmc = KineticMonteCarlo(graph_backup, debug=False)
reducer = GraphReduction(maker.rates, A, B)
nodes = set(A + B + [xx])
PxB = reducer.compute_committor_probabilities(nodes)
for x in nodes:
self.assertIn(x, PxB)
for x in nodes:
PxB_kmc = kmc.committor_probability(x, A, B, niter=1000)
self.assertAlmostEqual(PxB[x], PxB_kmc, delta=.1)
def do_check(self, A, B, nnodes=20, nedges=20):
maker = _MakeRandomGraph(nnodes=20, nedges=20, node_set=A+B)
rates = maker.make_rates()
reducer = MfptLinalgSparse(rates, B)
reducer.compute_mfpt()
