以下是我的设置和错误消息：
a=0.5
b=-0.01
s=1
ydata=np.empty((100, 100))
n, m =np.shape(ydata)
for t in range(1, n+1):
phi_prob=s*((t**(-b))/a-1)
process=Beta('process_%s' % str(t-1), value=0.5, alpha=s, beta=phi_prob)
for i in range(m):
ydata[t-1][i]=process.random()
#giving it some missing values, as that is part of the real problem I try to solve
for i, j in zip([choice(range(n)) for i in range(4*n)], [choice(range(m)) for j in range(4*m)]):
ydata[i][j]=None
y_df=pd.DataFrame(ydata)
ydata=y_df.fillna(98) #making sure that it's all numeric, or else I get an error that I do understand
masked_data = np.ma.masked_equal(np.array(ydata), value=98)
alpha=Beta("alpha", 1, 1)
time=np.arange(1, len(masked_data)+1)
@pymc.stochastic(observed=False)
def beta(value=-0.01):
if (value >=0 or value <-0.5):
return -np.inf #the constraint that beta must be between -0.5 and 0: note that log(0)=-inf
return -1.5 * np.log(1 + value ** 2)
@pymc.stochastic(observed=False)
def sigma(value=1):
return -np.log(abs(value))
# Define phi as a deterministic function of alpha, beta and time
@pymc.deterministic
def phi_model(t=np.arange(1, 100), alpha=alpha, beta=beta, sigma=sigma):
return sigma*((t**(-beta))/alpha-1)
y=np.empty(len(time), dtype=object) #numpy.empty(N, dtype=object)
for t in time:
data=masked_data[time-1]
y[t-1] = Beta('y %s' % str(t-1), value=masked_data[t-1], alpha=sigma, beta=phi_model, observed=True)
---------------------------------------------------------------------------
error Traceback (most recent call last)
<ipython-input-412-a64e9ce685cf> in <module>()
1 for t in time:
2 data=masked_data[time-1]
----> 3 y[t-1] = Beta('y %s' % str(t-1), value=masked_data[t-1], alpha=sigma, beta=phi_model, observed=True)
/usr/lib/pymodules/python2.7/pymc/distributions.pyc in __init__(self, *args, **kwds)
268 random = debug_wrapper(random)
269 else:
--> 270 Stochastic.__init__(self, logp=logp, random=random, logp_partial_gradients = logp_partial_gradients, dtype=dtype, **arg_dict_out)
271
272 new_class.__name__ = name
/usr/lib/pymodules/python2.7/pymc/PyMCObjects.pyc in __init__(self, logp, doc, name, parents, random, trace, value, dtype, rseed, observed, cache_depth, plot, verbose, isdata, check_logp, logp_partial_gradients)
703 dtype=dtype,
704 plot=plot,
--> 705 verbose=verbose)
706
707 # self._logp.force_compute()
/usr/lib/pymodules/python2.7/pymc/Node.pyc in __init__(self, doc, name, parents, cache_depth, trace, dtype, plot, verbose)
199 self.extended_children = set()
200
--> 201 Node.__init__(self, doc, name, parents, cache_depth, verbose=verbose)
202
203 if self.dtype is None:
/usr/lib/pymodules/python2.7/pymc/Node.pyc in __init__(self, doc, name, parents, cache_depth, verbose)
117
118 # Initialize
--> 119 self.parents = parents
120
121 def _get_parents(self):
/usr/lib/pymodules/python2.7/pymc/Node.pyc in _set_parents(self, new_parents)
138
139 # Get new lazy function
--> 140 self.gen_lazy_function()
141
142 parents = property(_get_parents, _set_parents, doc="Self's parents: the variables referred to in self's declaration.")
/usr/lib/pymodules/python2.7/pymc/PyMCObjects.pyc in gen_lazy_function(self)
743 ultimate_args = self.extended_parents | set([self]),
744 cache_depth = self._cache_depth)
--> 745 self._logp.force_compute()
746
747
LazyFunction.pyx in pymc.LazyFunction.LazyFunction.force_compute (pymc/LazyFunction.c:2411)()
/usr/lib/pymodules/python2.7/pymc/distributions.pyc in wrapper(**kwds)
2770 def wrapper(**kwds):
2771 value = kwds.pop('value')
-> 2772 return f(value, **kwds)
2773
2774 if arguments is None:
/usr/lib/pymodules/python2.7/pymc/distributions.pyc in beta_like(x, alpha, beta)
708 # except ZeroProbability:
709 # return -np.Inf
--> 710 return flib.beta_like(x, alpha, beta)
711
712 beta_grad_like = {'value' : flib.beta_grad_x,
error: (nb==1 || nb==len(x)) failed for hidden nb: beta_like:nb=99