from LOTlib.Examples.FormalLanguageTheory.Model.Hypothesis import make_hypothesis from LOTlib.Examples.FormalLanguageTheory.Language.An import An import time from mpi4py import MPI register_primitive(flatten2str) if __name__ == '__main__': comm = MPI.COMM_WORLD rank = comm.Get_rank() # ======================================================================================================== # Process command line arguments # ======================================================================================================== (options, args) = parser.parse_args() suffix = time.strftime('_' + options.NAME + '_%m%d_%H%M%S', time.localtime()) prefix = '../out/simulations/transfer/' # ======================================================================================================== # Running # ======================================================================================================== language = An() show_info('running normal input case..') sampler = probe_MHsampler(make_hypothesis('An', terminals=['b']), language, options, prefix + 'without_prior_out_' + str(rank) + suffix, ret_sampler=True) show_info('running with input using different letter case..') CASE += 1 language = An(atom='b') probe_MHsampler(make_hypothesis('An', terminals=['b']), language, options, prefix + 'with_prior_out_' + str(rank) + suffix, sampler=sampler)
# Process command line arguments # ======================================================================================================== (options, args) = parser.parse_args() suffix = time.strftime('_' + options.NAME + '_%m%d_%H%M%S', time.localtime()) prefix = '../out/simulations/nonadjacent/' # ======================================================================================================== # Running # ======================================================================================================== # case 1 show_info('running predictable input case..') language = LongDependency(C=['c']) probe_MHsampler( make_hypothesis('LongDependency', terminals=['c', 'd', 'e', 'f']), language, options, prefix + 'c_long_' + str(rank) + suffix) show_info('running predictable input case..') CASE += 1 language = LongDependency(C=['c', 'd', 'e', 'f']) probe_MHsampler( make_hypothesis('LongDependency', terminals=['c', 'd', 'e', 'f']), language, options, prefix + 'cdef_' + str(rank) + suffix) # -------------------------------------------------------------------------------------------------------- # case 2 show_info('running predictable input case..') options.FINITE = 4 CASE += 1 language = LongDependency(C=['c', 'd', 'e', 'f'])
# ======================================================================================================== # Process command line arguments # ======================================================================================================== (options, args) = parser.parse_args() suffix = time.strftime('_' + options.NAME + '_%m%d_%H%M%S', time.localtime()) prefix = '../out/simulations/transfer/' # ======================================================================================================== # Running # ======================================================================================================== language = An() show_info('running normal input case..') sampler = probe_MHsampler(make_hypothesis('An', terminals=['b']), language, options, prefix + 'without_prior_out_' + str(rank) + suffix, ret_sampler=True) show_info('running with input using different letter case..') CASE += 1 language = An(atom='b') probe_MHsampler(make_hypothesis('An', terminals=['b']), language, options, prefix + 'with_prior_out_' + str(rank) + suffix, sampler=sampler)
# ======================================================================================================== # Process command line arguments # ======================================================================================================== (options, args) = parser.parse_args() suffix = time.strftime('_' + options.NAME + '_%m%d_%H%M%S', time.localtime()) prefix = '../out/simulations/skewed/' # ======================================================================================================== # Running # ======================================================================================================== language = AnBn() show_info('running skewed input case..') rec = probe_MHsampler(make_hypothesis('AnBn'), language, options, prefix + 'skewed_out_' + str(rank) + suffix) show_info('running normal input case..') CASE += 1 cnt = Counter() num = 64.0 * 2 / options.FINITE for i in xrange(1, options.FINITE / 2 + 1): cnt['a' * i + 'b' * i] = num rec1 = probe_MHsampler(make_hypothesis('AnBn'), language, options, prefix + 'normal_out' + str(rank) + suffix, data=[FunctionData(input=[], output=cnt)])
if staged: length += 4 * (size % 48 == 0) for e in sampler.chains: e.data = get_data(n=size, max_length=length) iter += 1 if __name__ == '__main__': # ======================================================================================================== # Process command line arguments # ======================================================================================================== (options, args) = parser.parse_args() suffix = time.strftime('_' + options.NAME + '_%m%d_%H%M%S', time.localtime()) prefix = '../out/simulations/staged/' # ======================================================================================================== # Running # ======================================================================================================== language = AnBn() show_info('running staged input case..') probe_sampler(lambda: make_hypothesis('AnBn', N=options.N), language, options, prefix + 'staged_out_' + str(rank) + suffix, length=4) show_info('running normal input case..') probe_sampler(lambda: make_hypothesis('AnBn', N=options.N), language, options, prefix + 'normal_out_' + str(rank) + suffix)
In this case, we investigate the effect of different observed data distributions on training convergence. """ if __name__ == '__main__': comm = MPI.COMM_WORLD rank = comm.Get_rank() # ======================================================================================================== # Process command line arguments # ======================================================================================================== (options, args) = parser.parse_args() suffix = time.strftime('_' + options.NAME + '_%m%d_%H%M%S', time.localtime()) prefix = '../out/simulations/skewed/' # ======================================================================================================== # Running # ======================================================================================================== language = AnBn() show_info('running skewed input case..') rec = probe_MHsampler(make_hypothesis('AnBn'), language, options, prefix + 'skewed_out_' + str(rank) + suffix) show_info('running normal input case..') CASE += 1 cnt = Counter() num = 64.0 * 2 / options.FINITE for i in xrange(1, options.FINITE/2+1): cnt['a'*i+'b'*i] = num rec1 = probe_MHsampler(make_hypothesis('AnBn'), language, options, prefix + 'normal_out' + str(rank) + suffix, data=[FunctionData(input=[], output=cnt)])
if iter % 200 == 0 and iter != 0: print rank, '---->', iter fff() Z, weighted_score, score, s, rec = probe(best_hypotheses, evaluation_data, pr_data, language.estimate_precision_and_recall) to_file([[iter, Z, weighted_score, score, s, rec]], name) if iter % options.STEPS == 0 and iter != 0: size += 12 if staged: length += 4 * (size % 48 == 0) for e in sampler.chains: e.data = get_data(n=size, max_length=length) iter += 1 if __name__ == '__main__': # ======================================================================================================== # Process command line arguments # ======================================================================================================== (options, args) = parser.parse_args() suffix = time.strftime('_' + options.NAME + '_%m%d_%H%M%S', time.localtime()) prefix = '../out/simulations/staged/' # ======================================================================================================== # Running # ======================================================================================================== language = AnBn() show_info('running staged input case..') probe_sampler(lambda: make_hypothesis('AnBn', N=options.N), language, options, prefix + 'staged_out_' + str(rank) + suffix, length=4) show_info('running normal input case..') probe_sampler(lambda: make_hypothesis('AnBn', N=options.N), language, options, prefix + 'normal_out_' + str(rank) + suffix)
# ======================================================================================================== # Process command line arguments # ======================================================================================================== (options, args) = parser.parse_args() suffix = time.strftime('_' + options.NAME + '_%m%d_%H%M%S', time.localtime()) prefix = '../out/simulations/nonadjacent/' # ======================================================================================================== # Running # ======================================================================================================== # case 1 show_info('running predictable input case..') language = LongDependency(C=['c']) probe_MHsampler(make_hypothesis('LongDependency', terminals=['c', 'd', 'e', 'f']), language, options, prefix + 'c_long_' + str(rank) + suffix) show_info('running predictable input case..') CASE += 1 language = LongDependency(C=['c', 'd', 'e', 'f']) probe_MHsampler(make_hypothesis('LongDependency', terminals=['c', 'd', 'e', 'f']), language, options, prefix + 'cdef_' + str(rank) + suffix) # -------------------------------------------------------------------------------------------------------- # case 2 show_info('running predictable input case..') options.FINITE = 4 CASE += 1 language = LongDependency(C=['c', 'd', 'e', 'f']) probe_MHsampler(make_hypothesis('LongDependency', terminals=['c', 'd', 'e', 'f']), language, options, prefix + 'c_short_' + str(rank) + suffix)