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", old_div((rAB - rAB_KMC), rAB))
print("normalized difference to linalg", old_div((rAB - rAB_LA), rAB))
self.assertLess(old_div(abs(rAB - rAB_KMC), rAB), .1)
self.assertLess(old_div(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", old_div((rBA - rBA_KMC), rBA))
self.assertLess(old_div(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 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_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
self.assertLess(abs(rAB - rAB_KMC)/rAB, .1)
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 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)