def MCMCMC(starting_trees,
posterior_function,
summaries,
temperature_scheme,
printing_schemes,
iteration_scheme,
overall_thinnings,
proposal_scheme,
cores=4,
no_chains=None,
numpy_seeds=None,
multiplier=None,
result_file=None,
store_permuts=False,
stop_criteria=None,
make_outfile_stills=[],
save_only_coldest_chain=False,
posterior_function_list=[]):
'''
this function runs a MC3 using the basic_chain_unpacker. Let no_chains=number of chains. The inputs are
starting_trees: a list of one or more trees that the chains should be started with
posterior_function: one 'initialized'(see MCMC.py) unnormalized posterior function, that should be simulated from.
summaries: a list of instances of realization of concrete classes of the superclass Summary. It is closely linked to printing_scheme,
because they are only meaningful if specified in that.
temperature_scheme: one instance of a class that has the functions:
- get_temp(i): returns the the temperature for the i'th chain
- update_temp(permut): updates the temperatures for each chain using the permutation, permut
printing_scheme: a list of either one or no_chains dictionaries, where each dictionary gives the sample_verbose_scheme of basic_chain
iteration_scheme: a list that sums to the total number of iterations, where each entry is the number of MH-steps before the next flipping
overall_thinnings: an integer indicating how much should be skipped before each summary statistics is calculated
proposal_scheme: a list of instances of classes that handles proposals and updates of said proposals. It has the basic functions
- prop(x,pks): proposes the next tree(and returns proposal densities and statistics in pks)
- adapt(mhr): updates the proposals based on the mhr ratio, mhr
- extract_new_values(): after two chains have changed position in the flipping, this function gets the information that should be changed_later
- wear_new_values(information): the new values from extract_new_values replaces the old values.
#ps: - a list of doubles in the interval [0,1] indicating the parameter of the geometrically distributed prior on the number of admixture events for each chain. Or:
# - a double in the interval [0,1] indicating the same parameter for all geometrically distributed prior on the number of admixture events.
'''
#h=hpy()
if no_chains is None:
no_chains = cores
#if isinstance(ps, int):
# ps=[ps]*no_chains
if len(printing_schemes) == 1:
printing_schemes = [printing_schemes[0]] * no_chains
df_result = None
total_permutation = range(no_chains)
permuts = []
xs = starting_trees
freeze_support()
if numpy_seeds is None:
numpy_seeds = [None] * no_chains
if posterior_function_list:
pool = basic_chain_pool(summaries, None, proposal_scheme, numpy_seeds,
posterior_function_list)
rs = [posterior_f.base_r for posterior_f in posterior_function_list]
ps = [posterior_f.p for posterior_f in posterior_function_list]
posteriors = [
posterior_f(x)
for x, posterior_f in zip(xs, posterior_function_list)
]
else:
rs = []
ps = [posterior_function.p]
pool = basic_chain_pool(summaries, posterior_function, proposal_scheme,
numpy_seeds)
posteriors = [posterior_function(x) for x in xs]
proposal_updates = [
proposal.get_exportable_state() for proposal in proposal_scheme
]
cum_iterations = 0
start_time = time.time()
for no_iterations in iteration_scheme:
#letting each chain run for no_iterations:
#print h.heap()
iteration_object = _pack_everything(xs, posteriors, temperature_scheme,
printing_schemes,
overall_thinnings, no_iterations,
cum_iterations, proposal_updates,
multiplier)
if no_chains == 1: #debugging purposes
new_state = [_basic_chain_unpacker(iteration_object.next())]
else:
new_state = pool.order_calculation(iteration_object)
xs, posteriors, df_add, proposal_updates = _unpack_everything(
new_state, summaries, total_permutation)
df_result = _update_results(df_result, df_add)
if result_file is not None:
if cum_iterations == 0:
start_data_frame(
df_result,
result_file,
save_only_coldest_chain=save_only_coldest_chain)
elif df_result.shape[0] > 1000:
add_to_data_frame(
df_result,
result_file,
save_only_coldest_chain=save_only_coldest_chain)
df_result = df_result[0:0]
#making the mc3 flips and updating:
if not posterior_function_list:
xs, posteriors, permut, proposal_updates = flipping(
xs, posteriors, temperature_scheme, proposal_updates, rs, ps,
[posterior_function
]) # trees, posteriors, range(len(trees)),[None]*len(trees)#
else:
xs, posteriors, permut, proposal_updates = flipping(
xs, posteriors, temperature_scheme, proposal_updates, rs, ps,
posterior_function_list
) #trees, posteriors, range(len(trees)),[None]*len(trees)#
if store_permuts:
permuts.append(permut)
temperature_scheme.update_temps(permut)
#proposal_updates=_handle_flipping_of_proposals(proposal_updates, permut)
total_permutation = _update_permutation(total_permutation, permut)
cum_iterations += no_iterations
if stop_criteria is not None:
if stop_criteria(cum_iterations, result_file):
break
if make_outfile_stills:
if make_outfile_stills[0] * 60 * 60 < time.time() - start_time:
cp_command = [
'cp', result_file,
result_file + str(make_outfile_stills[0])
]
subprocess.call(cp_command)
make_outfile_stills.pop(0)
pool.terminate()
if result_file is None and store_permuts:
return df_result, permuts
elif result_file is None:
return df_result
elif store_permuts:
return permuts
# lowdown = open(folder_string + 'lowdown.txt', 'w')
# lowdown.write('Round 2: ')
run_specs['folder'] = folder_string
# csv_stats = folder_string + 'performance_measures' + '.csv'
# statistics = open(csv_stats, 'ab')
# stat_writer = csv.writer(statistics)
# stat_writer.writerow(('seed', 'blue rel', 'red rel'))
seeds = []
for u in xrange(31):
seeds.append(random.randint(0, 10000))
seed_index = int(raw_input("Input the experiment seed (1-30): "))
for i in xrange(number_of_experiments):
round_three_allvbest.run(seeds[seed_index], i, run_specs, test_set)
# stat_writer.writerow(stats)
seed_index += 1
# statistics.close()
if __name__ == "__main__":
freeze_support()
main()
# cProfile.run("main()")
button = tk.Button(master=root, text='Quit', command=_quit)
button.pack(side=tk.BOTTOM)
F1 = tk.Frame(root)
F1.pack()
L0 = tk.Label(F1, text='Input Data File')
L0.pack(side='left')
E0text = tk.StringVar()
E0text.set('4520')
E0 = tk.Entry(F1, textvariable=E0text)
E0.pack(side='left')
B0 = tk.Button(F1, text='Read Data', command=clickReadData)
B0.pack(side='left')
root.mainloop()
if __name__ == '__main__':
mp.freeze_support()
__spec__ = "ModuleSpec(name='builtins', loader=<class '_frozen_importlib.BuiltinImporter'>)"
pool = mp.Pool(1)
temp = pool.apply_async(maindo, (
images,
noise_img,
testImg,
))
# print(temp.get())
pool.close() # 将进程池关闭，不再接受新的进程
pool.join() # 主进程阻
