Exemplo n.º 1
0
    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)
Exemplo n.º 2
0
def main():
    nnodes = 3
    # make matrix of transition rates with three states
    transition_matrix = [ [0., 1., 1.], 
                          [1., 0., 1.], 
                          [1., 1., 0.] ]
    print "Computing rates and committor probabilities for transition matrix"
    print transition_matrix
    # an easy way to set up the graph is to make
    # a dictionary of rates
    rates = dict()
    for i in range(nnodes):
        for j in range(nnodes):
            if i != j:
                rates[(i,j)] = transition_matrix[i][j]


    # we want to compute rates from node 0 to node 1
    A = [0]
    B = [1]
    x = 0
    
    # set up the class which will compute rates for us
    reducer = GraphReduction(rates, A, B)
    
    
    # now to check the values do a kinetic monte carlo run
    # use the utility function to build the rate graph with the correct formatting
    kmc_graph = kmcgraph_from_rates(rates)
    kmc = KineticMonteCarlo(kmc_graph)
    
    niterlist = [10, 100, 1000, 10000]

    print ""
    print "computing the probability for a trajectory to start at", x, "and reach", B, "before reaching", A
    PxB = reducer.compute_committor_probability(x)
    print "the committor probability computed by graph transformation is", PxB
    for niter in niterlist:
        PxB_KMC = kmc.committor_probability(x, A, B, niter=niter)
        print "the KMC committor probability averaged over %8d trajectories is %s. abs(PxB-PxB_KMC) = %s" % (niter, PxB_KMC, abs(PxB-PxB_KMC))


    print ""
    print "computing the transition rate from nodes", A, "to nodes", B
    print "the exact rate for this network is 1.0"
    reducer.compute_rates()
    rAB = reducer.get_rate_AB()
    print "the transition rate computed by graph transformation is", rAB
    for niter in niterlist:
        rAB_KMC = kmc.mean_rate(A, B, niter=niter)
        print "the KMC rate averaged over %8d trajectories is %s. abs(rAB-rAB_KMC) = %s" % (niter, rAB_KMC, abs(rAB-rAB_KMC))
Exemplo n.º 3
0
def main():
    nnodes = 6
    # the graph need not be made from a transition matrix, but it's an 
    # easy way to make a random graph ensuring that everything is connected
    transition_matrix = np.random.uniform(0,1,[nnodes, nnodes])
    print "Computing rates and committor probabilities for transition matrix"
    print transition_matrix
    # an easy way to set up the graph is to make
    # a dictionary of rates
    rates = dict()
    for i in range(nnodes):
        for j in range(nnodes):
            if i != j:
                rates[(i,j)] = transition_matrix[i][j]


    # we want to compute rates from node 0 to node 1
    A = [0,1]
    B = [4,5]
    x = 2
    
    # set up the class which will compute rates for us
    reducer = GraphReduction(rates, A, B)
    
    
    # now to check the values do a kinetic monte carlo run
    # use the utility function to build the rate graph with the correct formatting
    kmc_graph = kmcgraph_from_rates(rates)
    kmc = KineticMonteCarlo(kmc_graph)
    
    niterlist = [10, 100, 1000, 10000]

    print ""
    print "computing the probability for a trajectory to start at", x, "and reach", B, "before reaching", A
    PxB = reducer.compute_committor_probability(x)
    print "the committor probability computed by graph transformation is", PxB
    for niter in niterlist:
        PxB_KMC = kmc.committor_probability(x, A, B, niter=niter)
        print "the KMC committor probability averaged over %8d trajectories is %s. abs(PxB-PxB_KMC) = %s" % (niter, PxB_KMC, abs(PxB-PxB_KMC))


    print ""
    print "computing the transition rate from nodes", A, "to nodes", B
    reducer.compute_rates()
    rAB = reducer.get_rate_AB()
    print "the transition rate computed by graph transformation is", rAB
    for niter in niterlist:
        rAB_KMC = kmc.mean_rate(A, B, niter=niter)
        print "the KMC rate averaged over %8d trajectories is %s. abs(rAB-rAB_KMC) = %s" % (niter, rAB_KMC, abs(rAB-rAB_KMC))