当观察到的数据是离散的时，我在运行PYMC3模型时遇到困难。奇怪的是，如果观测数据包含零值（0），则模型将运行。
我已在其他帖子中阅读过建议使用的帖子
start = pm.find_MAP(fmin=scipy.optimize.fmin_powell)但这并不能解决问题。
pymc3.__version__ = '3.0'
theano.__version__ = '0.7.0.dev-RELEASE'
numpy.__version__ = '1.8.0rc1'
Python 2.7.10
代码和错误如下。
import pymc3 as pm
data = [6.0,12.0,12.0,46.0,5.0,11.0,11.0,39.0,4.0,10.0,25.0,11.0,8.0,5.0,10.0,2.0,30.0,21.0]
with pm.Model() as model:
alpha = pm.Uniform('alpha', lower=0, upper=100)
mu = pm.Uniform('mu', lower=0, upper=100)
y_pred = pm.NegativeBinomial('y_pred', mu=mu, alpha=alpha)
y_est = pm.NegativeBinomial('y_est',
mu=mu,
alpha=alpha,
observed=data)
start = pm.find_MAP()
step = pm.Metropolis()
trace = pm.sample(20000, step, start, progressbar=True)
我得到的错误是：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-b9f2264fccfc> in <module>()
14 observed=data)
15
---> 16 start = pm.find_MAP()
17
18 step = pm.Metropolis()
/Library/Python/2.7/site-packages/pymc3/tuning/starting.pyc in find_MAP(start, vars, fmin, return_raw, disp, model, *args, **kwargs)
79 if 'fprime' in getargspec(fmin).args:
80 r = fmin(logp_o, bij.map(
---> 81 start), fprime=grad_logp_o, disp=disp, *args, **kwargs)
82 else:
83 r = fmin(logp_o, bij.map(start), disp=disp, *args, **kwargs)
/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/scipy/optimize/optimize.pyc in fmin_bfgs(f, x0, fprime, args, gtol, norm, epsilon, maxiter, full_output, disp, retall, callback)
775 'return_all': retall}
776
--> 777 res = _minimize_bfgs(f, x0, args, fprime, callback=callback, **opts)
778
779 if full_output:
/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/scipy/optimize/optimize.pyc in _minimize_bfgs(fun, x0, args, jac, callback, gtol, norm, eps, maxiter, disp, return_all, **unknown_options)
830 else:
831 grad_calls, myfprime = wrap_function(fprime, args)
--> 832 gfk = myfprime(x0)
833 k = 0
834 N = len(x0)
/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/scipy/optimize/optimize.pyc in function_wrapper(*wrapper_args)
279 def function_wrapper(*wrapper_args):
280 ncalls[0] += 1
--> 281 return function(*(wrapper_args + args))
282
283 return ncalls, function_wrapper
/Library/Python/2.7/site-packages/pymc3/tuning/starting.pyc in grad_logp_o(point)
74
75 def grad_logp_o(point):
---> 76 return nan_to_num(-dlogp(point))
77
78 # Check to see if minimization function actually uses the gradient
/Library/Python/2.7/site-packages/pymc3/blocking.pyc in __call__(self, x)
117
118 def __call__(self, x):
--> 119 return self.fa(self.fb(x))
/Library/Python/2.7/site-packages/pymc3/model.pyc in __call__(self, state)
397
398 def __call__(self, state):
--> 399 return self.f(**state)
400
401 class LoosePointFunc(object):
/Library/Python/2.7/site-packages/theano/compile/function_module.pyc in __call__(self, *args, **kwargs)
862 node=self.fn.nodes[self.fn.position_of_error],
863 thunk=thunk,
--> 864 storage_map=getattr(self.fn, 'storage_map', None))
865 else:
866 # old-style linkers raise their own exceptions
/Library/Python/2.7/site-packages/theano/gof/link.pyc in raise_with_op(node, thunk, exc_info, storage_map)
312 # extra long error message in that case.
313 pass
--> 314 reraise(exc_type, exc_value, exc_trace)
315
316
/Library/Python/2.7/site-packages/theano/compile/function_module.pyc in __call__(self, *args, **kwargs)
850 t0_fn = time.time()
851 try:
--> 852 outputs = self.fn()
853 except Exception:
854 if hasattr(self.fn, 'position_of_error'):
ValueError: Input dimension mis-match. (input[0].shape[0] = 1, input[4].shape[0] = 18)
Apply node that caused the error: Elemwise{Composite{Switch(i0, i1, Switch(i2, Switch(i3, i1, i4), i1))}}(TensorConstant{(1,) of 0}, TensorConstant{(1,) of 0}, Elemwise{mul,no_inplace}.0, InplaceDimShuffle{x}.0, TensorConstant{[ 6. 12... 30. 21.]})
Toposort index: 33
Inputs types: [TensorType(int8, vector), TensorType(int8, (True,)), TensorType(int8, (True,)), TensorType(int8, (True,)), TensorType(float64, vector)]
Inputs shapes: [(1,), (1,), (1,), (1,), (18,)]
Inputs strides: [(1,), (1,), (1,), (1,), (8,)]
Inputs values: [array([0], dtype=int8), array([0], dtype=int8), array([1], dtype=int8), array([0], dtype=int8), 'not shown']
Outputs clients: [[Sum{acc_dtype=float64}(Elemwise{Composite{Switch(i0, i1, Switch(i2, Switch(i3, i1, i4), i1))}}.0)]]
HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizations can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.