Я пытаюсь подобрать модель lotka-volterra stan, определяемую как:
functions{
real[] dz_dt(real t, real[] z, real[] theta,real[] x_r, int[] x_i){
real u = z[1];
real v = z[2];
real alpha = theta[1];
real beta = theta[2];
real gamma = theta[3];
real delta = theta[4];
real du_dt = (alpha - beta * v) * u;
real dv_dt = (-gamma + delta * u) * v;
return { du_dt, dv_dt };
}
}
data {
int<lower = 0> N; // num measurements
real ts[N]; // measurement times > 0
real y_init[2]; // initial measured population
real<lower = 0> y[N, 2]; // measured population at measurement times
}
parameters {
real<lower = 0> theta[4]; // theta = { alpha, beta, gamma, delta }
real<lower = 0> z_init[2]; // initial population
real<lower = 0> sigma[2]; // error scale
}
transformed parameters {
real z[N, 2] = integrate_ode_rk45(dz_dt, z_init, 0, ts, theta,rep_array(0.0, 0), rep_array(0, 0),1e-6, 1e-5, 1e3);
}
model {
theta[{1, 3}] ~ normal(1, 0.5);
theta[{2, 4}] ~ normal(0.05, 0.05);
sigma ~ lognormal(-1, 1);
z_init ~ lognormal(log(10), 1);
for (k in 1:2) {
y_init[k] ~ lognormal(log(z_init[k]), sigma[k]);
y[ , k] ~ lognormal(log(z[, k]), sigma[k]);
}
}
Данные, которые я использовал для подгонки модели, - это массивы с нулевыми значениями, если они представляют собой векторы или матрицу:
datos={"N":N,"ts":ts,"y_init":y[0,:],"y":y}
# where N=100
# ts.shape=(100,)
# y[0,:].shape=(2,)
# y.shape=(100,2)
Код компилируется правильно:
sm = pystan.StanModel(file='lotka-volterra.stan')
, но когда я пытаюсь соответствовать модели
fit=sm.sampling(data=datos)
возвращает большую ошибку:
---------------------------------------------------------------------------
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "/usr/lib/python3.6/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.6/multiprocessing/pool.py", line 44, in mapstar
return list(map(*args))
File "stanfit4anon_model_97aa305000da3f2b4cb00e52fda1eeba_134896954346517730.pyx", line 371, in stanfit4anon_model_97aa305000da3f2b4cb00e52fda1eeba_134896954346517730._call_sampler_star
File "stanfit4anon_model_97aa305000da3f2b4cb00e52fda1eeba_134896954346517730.pyx", line 404, in stanfit4anon_model_97aa305000da3f2b4cb00e52fda1eeba_134896954346517730._call_sampler
RuntimeError: Initialization failed.
"""
The above exception was the direct cause of the following exception:
RuntimeError Traceback (most recent call last)
<ipython-input-14-93e36dfa9d3e> in <module>
----> 1 fit=sm.sampling(data=datos)
/usr/local/lib/python3.6/dist-packages/pystan/model.py in sampling(self, data, pars, chains, iter, warmup, thin, seed, init, sample_file, diagnostic_file, verbose, algorithm, control, n_jobs, **kwargs)
776 call_sampler_args = izip(itertools.repeat(data), args_list, itertools.repeat(pars))
777 call_sampler_star = self.module._call_sampler_star
--> 778 ret_and_samples = _map_parallel(call_sampler_star, call_sampler_args, n_jobs)
779 samples = [smpl for _, smpl in ret_and_samples]
780
/usr/local/lib/python3.6/dist-packages/pystan/model.py in _map_parallel(function, args, n_jobs)
83 try:
84 pool = multiprocessing.Pool(processes=n_jobs)
---> 85 map_result = pool.map(function, args)
86 finally:
87 pool.close()
/usr/lib/python3.6/multiprocessing/pool.py in map(self, func, iterable, chunksize)
264 in a list that is returned.
265 '''
--> 266 return self._map_async(func, iterable, mapstar, chunksize).get()
267
268 def starmap(self, func, iterable, chunksize=None):
/usr/lib/python3.6/multiprocessing/pool.py in get(self, timeout)
642 return self._value
643 else:
--> 644 raise self._value
645
646 def _set(self, i, obj):
RuntimeError: Initialization failed.
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Python 3 | Idle
infiriendo_con_stan.ipynb
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