Beispiel #1
0
def global_minimum():
    if len(sys.argv)==1 or 'first' in sys.argv:
        sequence = convert_sequence('HHPHHPPHPPPHPPHPHHHHPPPPHHPPHPHHHHPP')
    else:
        sequence = convert_sequence('HHHPPHHPHHHHPHHPPHPHHPHPPHPPPHHPPPPP')
    sequence_len = len(sequence)

    if len(sys.argv)>1 and not sys.argv[1].isdigit(): sys.argv.pop(1) # remove first/second

    lenargv = len(sys.argv)
    # GA data:
    # number of individuals per generation
    ind_per_gen  = int(sys.argv[1]) if lenargv>1 else 100
    # number of generations
    num_generations = int(sys.argv[2]) if lenargv>2 else 100

    # number of GA/SA swaps
    num_tries = int(sys.argv[3]) if lenargv>3 else 10

    # SA data:
    alpha = 0.995
    ntemp = int(sys.argv[4]) if lenargv>4 else 1000
    ncycles = int(sys.argv[5]) if lenargv>5 else 100
    orig_T_star = float(sys.argv[6]) if lenargv>6 else 7.0
    # T_star should be <0.2 after ntemp, so
    #    T_star*alpha**ntemp<0.2
    #    (0.2/T_star)**(1.0/ntemp)=alpha
    alpha = (0.2/orig_T_star)**(1.0/(ntemp+1))
    print alpha


    # mutation rates
    rate_cross = 0.8
    rate_mutate = 0.8

    population = birth(sequence_len,ind_per_gen)

    lowest_V = lowest_V_sofar = 0
    highest_V = 1
    equal_generations = 0
    lower_generations = []
    for numtry in xrange(num_tries):
        choose_fit(population,ind_per_gen,sequence) # just so it sorts
        for i in xrange(num_generations):
            print_at('generation %d' % i,0)
            # mate best half?
            with TimingContext(1):
                #mating_population = choose_fit(population,ind_per_gen/2,sequence,resort=False)
                mating_population = population[:ind_per_gen/2]
                population = population[ind_per_gen/2:] # population is pre-sorted by choose_fit

            with TimingContext(2):
                if lowest_V < -1.0 and lowest_V == highest_V: # rebirth soon necessary
                    equal_generations += 1
                else:
                    equal_generations = 0

                if equal_generations > 4: # we lost genetic diversity...
                    mating_population = birth(sequence_len,ind_per_gen)
                    population = sample(population,2)


            with TimingContext(3):
                do_cross_over(mating_population,rate_cross,sequence_len)

            with TimingContext(4):
                do_mutations(mating_population,rate_mutate,sequence_len)




            # keep best ones only
            with TimingContext(5):
                population.extend(mating_population)
            with TimingContext(6):
                population = choose_fit(population,ind_per_gen,sequence)

            # select lowest energy
            with TimingContext(7):
            #if True:
                lowest_conf = population[0]
                lowest_V = calc_genetic_energy(lowest_conf,sequence)
                highest_V = calc_genetic_energy(population[-1],sequence)

            if lowest_V<lowest_V_sofar:
                lowest_V_sofar = lowest_V
                print_at("lowest V: %f for config %s" % (lowest_V,str(lowest_conf).replace(' ','')),8)
                lower_generations.append((i,lowest_V,lowest_conf))
            print_at("current_lowest %f" % lowest_V,9)
            print_at("current_highest %f" % highest_V,10)
            print_at("Generations worth their CPU: %s" % str(lower_generations).replace(' ',''), 11)

        # have lowest conf, try annealing now...
        lowest_conf = decode_lattice(lowest_conf)
        T_star = orig_T_star
        for k in xrange(ntemp):
            print_at("SA temperature %f (%d)" % (T_star,k),25)
            current_config = lowest_conf
            current_V = lowest_V
            print_at("lowest_V: %f" % lowest_V,26)
            for j in xrange(ncycles):
                print_at("Cycle %d" % j,27)
                next_conf,next_V = MC_step(current_config, sequence, T_star)
                if next_V<lowest_V:
                    current_V = next_V
                    current_config = next_conf
                print_at("current V: %f" % next_V,28)
            # take best config from cycles and use that
            if current_V < lowest_V:
                lowest_V = current_V
                lowest_conf = current_config
                print_at("lowest V: %f for config %s" % (lowest_V,str(encode_lattice(lowest_conf)).replace(' ','')),29)
            T_star *= alpha

        # aaaaand start over
        population = [encode_lattice(lowest_conf)]+birth(sequence_len,ind_per_gen)
        mating_population = []

    print_at('',30)
Beispiel #2
0
    while r_line != '':
        r = tuple(int(p) for p in r_line.strip().replace(',', ' ').split())
        #r = Point(coord[0],coord[1],coord[2])
        configuration.append(r)
        #~ print configuration
        r_line = r_file.readline()
    r_file.close()
    start_configuration = configuration
    
    # set up temperature, target folding energy
    temperature = 0.65
    Vmin = -34.5

    # set up sequence and number_of_cycles
    #sequence = convert_sequence('HHPHHPPHPPPHPPHPHHHHPPPPHHPPHPHHHHPP')
    sequence = convert_sequence('HHHPPHHPHHHHPHHPPHPHHPHPPHPPPHHPPPPP')
    Max_number_of_cycles = 100 # CAN INCREASE THIS LATER, not currently used
    
    # counters for MPFT
    sequence_traversals = nbr_moves = 0
    trajectories = 3
    running_total = 0
    
    MFPT = []
    all_sequence_traversals = []
    
    V = energy_function(configuration, sequence)
    print 'Starting V = ' + str(V)
    for i in xrange(trajectories):
        count = 0
        while V > Vmin: