Я собираюсь разместить смесь пользовательских распределений. Для начала я сначала хочу научиться делать это с помощью обычной гауссовой смеси. Сначала я генерирую некоторые фиктивные данные:
import numpy as np
import pymc3 as pm
import theano.tensor as tt
import theano
from copy import copy
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
from theano.compile.ops import as_op
import scipy.integrate as integrate
# Sample parameters
nsamples = 2000
mu1_true = 0.0
mu2_true = 1.5
sig1_true = 0.5
sig2_true = 0.5
p_true = 0.2
# Samples generation
np.random.seed(3)
s1 = distributions.norm.rvs(mu1_true, sig1_true, size=round(p_true*nsamples))
s2 = distributions.norm.rvs(mu2_true, sig2_true, size=round((1-p_true)*nsamples))
samples = np.hstack([s1, s2])
plt.hist(samples,bins=30,density=True);
Вот что я дошел до того, что касается подгонки:
u1=samples
def gauss(x, mu, sigma):
return 1/np.sqrt(2.*np.pi*np.power(sigma,2)) * np.exp(-np.power(x-mu,2)/(2.*np.power(sigma,2)))
def likelihood(x):
return p*gauss(x, mu1, sigma1) + (1-p)*gauss(x, mu2, sigma2)
with pm.Model() as model:
p = pm.Uniform('p', lower=0, upper=1)
mu1 = pm.Uniform('mu1', lower=-0.5, upper=0.5)
mu2 = pm.Uniform('mu2', lower=1, upper=2)
sigma1 = pm.Uniform('sigma1', lower=0, upper=1)
sigma2 = pm.Uniform('sigma2', lower=0, upper=1)
with model:
like=pm.DensityDist('like', likelihood, observed=u1)
step = pm.Metropolis()
trace = pm.sample(1000, step=step)
Это с треском проваливается:
---------------------------------------------------------------------------
BrokenPipeError Traceback (most recent call last)
~\Anaconda3\lib\site-packages\pymc3\parallel_sampling.py in __init__(self, draws, tune, step_method, chain, seed, start)
241 try:
--> 242 self._process.start()
243 except IOError as e:
~\Anaconda3\lib\multiprocessing\process.py in start(self)
111 _cleanup()
--> 112 self._popen = self._Popen(self)
113 self._sentinel = self._popen.sentinel
~\Anaconda3\lib\multiprocessing\context.py in _Popen(process_obj)
222 def _Popen(process_obj):
--> 223 return _default_context.get_context().Process._Popen(process_obj)
224
~\Anaconda3\lib\multiprocessing\context.py in _Popen(process_obj)
321 from .popen_spawn_win32 import Popen
--> 322 return Popen(process_obj)
323
~\Anaconda3\lib\multiprocessing\popen_spawn_win32.py in __init__(self, process_obj)
64 reduction.dump(prep_data, to_child)
---> 65 reduction.dump(process_obj, to_child)
66 finally:
~\Anaconda3\lib\multiprocessing\reduction.py in dump(obj, file, protocol)
59 '''Replacement for pickle.dump() using ForkingPickler.'''
---> 60 ForkingPickler(file, protocol).dump(obj)
61
BrokenPipeError: [Errno 32] Broken pipe
During handling of the above exception, another exception occurred:
RuntimeError Traceback (most recent call last)
<ipython-input-33-8398f6d24814> in <module>
17 like=pm.DensityDist('like', likelihood, observed=u1)
18 step = pm.Metropolis()
---> 19 trace = pm.sample(1000, step=step)
~\Anaconda3\lib\site-packages\pymc3\sampling.py in sample(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, **kwargs)
435 _print_step_hierarchy(step)
436 try:
--> 437 trace = _mp_sample(**sample_args)
438 except pickle.PickleError:
439 _log.warning("Could not pickle model, sampling singlethreaded.")
~\Anaconda3\lib\site-packages\pymc3\sampling.py in _mp_sample(draws, tune, step, chains, cores, chain, random_seed, start, progressbar, trace, model, **kwargs)
963 sampler = ps.ParallelSampler(
964 draws, tune, chains, cores, random_seed, start, step,
--> 965 chain, progressbar)
966 try:
967 try:
~\Anaconda3\lib\site-packages\pymc3\parallel_sampling.py in __init__(self, draws, tune, chains, cores, seeds, start_points, step_method, start_chain_num, progressbar)
359 draws, tune, step_method, chain + start_chain_num, seed, start
360 )
--> 361 for chain, seed, start in zip(range(chains), seeds, start_points)
362 ]
363
~\Anaconda3\lib\site-packages\pymc3\parallel_sampling.py in <listcomp>(.0)
359 draws, tune, step_method, chain + start_chain_num, seed, start
360 )
--> 361 for chain, seed, start in zip(range(chains), seeds, start_points)
362 ]
363
~\Anaconda3\lib\site-packages\pymc3\parallel_sampling.py in __init__(self, draws, tune, step_method, chain, seed, start)
249 # all its error message
250 time.sleep(0.2)
--> 251 raise exc
252 raise
253
RuntimeError: The communication pipe between the main process and its spawned children is broken.
In Windows OS, this usually means that the child process raised an exception while it was being spawned, before it was setup to communicate to the main process.
The exceptions raised by the child process while spawning cannot be caught or handled from the main process, and when running from an IPython or jupyter notebook interactive kernel, the child's exception and traceback appears to be lost.
A known way to see the child's error, and try to fix or handle it, is to run the problematic code as a batch script from a system's Command Prompt. The child's exception will be printed to the Command Promt's stderr, and it should be visible above this error and traceback.
Note that if running a jupyter notebook that was invoked from a Command Prompt, the child's exception should have been printed to the Command Prompt on which the notebook is running.
Как это сделать? Это правильное направление? (Конечная цель состоит в подборе смеси некоторых обобщенных косо-нормальных распределений.)