我正在尝试使用开关点的确定性函数而不是使用 theano 的 switch 函数来重现煤矿开采示例。代码：
%matplotlib inline
import matplotlib.pyplot as plt
import pymc3
import numpy as np
import theano.tensor as t
import theano
data = np.hstack((np.random.poisson(15,1000),np.random.poisson(2,100)))
plt.plot(data)
@theano.compile.ops.as_op(itypes=[t.lscalar, t.dscalar,t.dscalar],otypes=[t.dvector])
def rate1(sw,mu1,mu2):
n = len(data)
out = np.empty(n)
out[:sw] = mu1
out[sw:] = mu2
return out
with pymc3.Model() as dis:
switchpoint = pymc3.DiscreteUniform('switchpoint',lower=0, upper=len(data)-1)
mu1 = pymc3.Exponential('mu1', lam=1.)
mu2 = pymc3.Exponential('mu2',lam=1.)
disasters=pymc3.Poisson('disasters', mu=rate1, observed = data)
但是这段代码会出现错误：
-------------------------------------------------- ------------------------- KeyError Traceback（最近一次调用最后一次） c:\program files\git\theano\theano\tensor\type.py在 dtype_specs(self) 266 'complex64': (complex, 'theano_complex64', 'NPY_COMPLEX64') --> 267 }[self.dtype] 268 除了 KeyError：
键错误：'对象'
在处理上述异常的过程中，又出现了一个异常：
TypeError Traceback (最近一次调用最后) c:\program files\git\theano\theano\tensor\basic.py in constant_or_value(x, rtype, name, ndim, dtype) 407 rval = rtype( --> 408 TensorType(dtype =x_.dtype，可广播=bcastable），409 x_.copy（），
c:\program files\git\theano\theano\tensor\type.py in init (self, dtype, broadcastable, name, sparse_grad) 49 self.broadcastable = tuple(bool(b) for b in broadcastable) ---> 50 self.dtype_specs() # 错误检查在那里完成 51 self.name = name
c:\program files\git\theano\theano\tensor\type.py in dtype_specs(self) 269 raise TypeError("Unsupported dtype for %s: %s" --> 270 % ( self.class.name , self .第271章
TypeError：TensorType 不支持的 dtype：对象
在处理上述异常的过程中，又出现了一个异常：
TypeError Traceback (最近一次调用最后一次) c:\program files\git\theano\theano\tensor\basic.py in as_tensor_variable(x, name, ndim) 201 try: --> 202 return constant(x, name=name, ndim=ndim) 203 除了类型错误：
c:\program files\git\theano\theano\tensor\basic.py in constant(x, name, ndim, dtype) 421 ret = constant_or_value(x, rtype=TensorConstant, name=name, ndim=ndim, -->第422章 423
c:\program files\git\theano\theano\tensor\basic.py in constant_or_value(x, rtype, name, ndim, dtype) 416 except Exception: --> 417 raise TypeError("Could not convert %s to TensorType" ％x，类型（x））418
TypeError: ('Could not convert FromFunctionOp{rate1} to TensorType', )
在处理上述异常的过程中，又出现了一个异常：
AsTensorError Traceback (最近一次调用最后一次) in () 14 mu2 = pymc3.Exponential('mu2',lam=1.) 15 #rate1 = pymc3.switch(switchpoint >= np.arange(len(data)), mu1, mu2) ---> 16 个灾难=pymc3.Poisson('disasters'，mu=rate1，observed = data)
C:\Users\User\Anaconda3\lib\site-packages\pymc3\distributions\distribution.py in new (cls, name, *args, **kwargs) 19 if isinstance(name, str): 20 data = kwargs. pop('observed', None) ---> 21 dist = cls.dist(*args, **kwargs) 22 return model.Var(name, dist, data) 23 elif name is None:
C:\Users\User\Anaconda3\lib\site-packages\pymc3\distributions\distribution.py in dist(cls, *args, **kwargs) 32 def dist(cls, *args, **kwargs): 33 dist =对象。新（cls）---> 34 dist。init (*args, **kwargs) 35 返回 dist 36
C:\Users\User\Anaconda3\lib\site-packages\pymc3\distributions\discrete.py init (self, mu, *args, **kwargs) 185 super(Poisson, self)。init (*args, **kwargs) 186 self.mu = mu --> 187 self.mode = floor(mu).astype('int32') 188 189 def random(self, point=None, size=None, 重复=无）：
c:\program files\git\theano\theano\gof\op.py in call (self, *inputs, **kwargs) 598 """ 599 return_list = kwargs.pop('return_list', False) --> 600 node = self.make_node(*inputs, **kwargs) 601 602 if config.compute_test_value != 'off':
c:\program files\git\theano\theano\tensor\elemwise.py 在 make_node(self, *inputs) 540 中使用 DimShuffle。541 """ --> 542 输入 = 列表（映射（as_tensor_variable，输入）） 543 阴影 = self.scalar_op.make_node（544 *[get_scalar_type（dtype=i.type.dtype）.make_variable（）
c:\program files\git\theano\theano\tensor\basic.py in as_tensor_variable(x, name, ndim) 206 例外：207 str_x = repr(x) --> 208 raise AsTensorError("Cannot convert %s to TensorType" % str_x, type(x)) 209 210 # 这个有不同的名字，因为 _as_tensor_variable 是
AsTensorError: ('无法将 FromFunctionOp{rate1} 转换为 TensorType', )
我如何处理这个？
第二件事 - 当我像这样使用 pymc3.switch 函数时：
with pymc3.Model() as dis:
switchpoint = pymc3.DiscreteUniform('switchpoint',lower=0, upper=len(data)-1)
mu1 = pymc3.Exponential('mu1', lam=1.)
mu2 = pymc3.Exponential('mu2',lam=1.)
rate1 = pymc3.switch(switchpoint >= np.arange(len(data)), mu1,mu2)
disasters=pymc3.Poisson('disasters', mu=rate1, observed = data)
接下来尝试采样：
with dis:
step1 = pymc3.NUTS([mu1, mu2])
step2 = pymc3.Metropolis([switchpoint])
trace = pymc3.sample(10000, step = [step1,step2])
我收到一个错误：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
c:\program files\git\theano\theano\compile\function_module.py in __call__(self, *args, **kwargs)
858 try:
--> 859 outputs = self.fn()
860 except Exception:
TypeError: expected type_num 9 (NPY_INT64) got 7
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-4-3247d908f897> in <module>()
2 step1 = pymc3.NUTS([mu1, mu2])
3 step2 = pymc3.Metropolis([switchpoint])
----> 4 trace = pymc3.sample(10000, step = [step1,step2])
C:\Users\User\Anaconda3\lib\site-packages\pymc3\sampling.py in sample(draws, step, start, trace, chain, njobs, tune, progressbar, model, random_seed)
153 sample_args = [draws, step, start, trace, chain,
154 tune, progressbar, model, random_seed]
--> 155 return sample_func(*sample_args)
156
157
C:\Users\User\Anaconda3\lib\site-packages\pymc3\sampling.py in _sample(draws, step, start, trace, chain, tune, progressbar, model, random_seed)
162 progress = progress_bar(draws)
163 try:
--> 164 for i, strace in enumerate(sampling):
165 if progressbar:
166 progress.update(i)
C:\Users\User\Anaconda3\lib\site-packages\pymc3\sampling.py in _iter_sample(draws, step, start, trace, chain, tune, model, random_seed)
244 if i == tune:
245 step = stop_tuning(step)
--> 246 point = step.step(point)
247 strace.record(point)
248 yield strace
C:\Users\User\Anaconda3\lib\site-packages\pymc3\step_methods\compound.py in step(self, point)
11 def step(self, point):
12 for method in self.methods:
---> 13 point = method.step(point)
14 return point
C:\Users\User\Anaconda3\lib\site-packages\pymc3\step_methods\arraystep.py in step(self, point)
116 bij = DictToArrayBijection(self.ordering, point)
117
--> 118 apoint = self.astep(bij.map(point))
119 return bij.rmap(apoint)
120
C:\Users\User\Anaconda3\lib\site-packages\pymc3\step_methods\metropolis.py in astep(self, q0)
123
124
--> 125 q_new = metrop_select(self.delta_logp(q,q0), q, q0)
126
127 if q_new is q:
c:\program files\git\theano\theano\compile\function_module.py in __call__(self, *args, **kwargs)
869 node=self.fn.nodes[self.fn.position_of_error],
870 thunk=thunk,
--> 871 storage_map=getattr(self.fn, 'storage_map', None))
872 else:
873 # old-style linkers raise their own exceptions
c:\program files\git\theano\theano\gof\link.py 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
C:\Users\User\Anaconda3\lib\site-packages\six.py in reraise(tp, value, tb)
656 value = tp()
657 if value.__traceback__ is not tb:
--> 658 raise value.with_traceback(tb)
659 raise value
660
c:\program files\git\theano\theano\compile\function_module.py in __call__(self, *args, **kwargs)
857 t0_fn = time.time()
858 try:
--> 859 outputs = self.fn()
860 except Exception:
861 if hasattr(self.fn, 'position_of_error'):
TypeError: expected type_num 9 (NPY_INT64) got 7
Apply node that caused the error: Elemwise{Composite{Switch(GE(i0, i1), i2, i3)}}(InplaceDimShuffle{x}.0, TensorConstant{[ 0 1..1098 1099]}, InplaceDimShuffle{x}.0, InplaceDimShuffle{x}.0)
Toposort index: 11
Inputs types: [TensorType(int64, (True,)), TensorType(int32, vector), TensorType(float64, (True,)), TensorType(float64, (True,))]
Inputs shapes: [(1,), (1100,), (1,), (1,)]
Inputs strides: [(4,), (4,), (8,), (8,)]
Inputs values: [array([549]), 'not shown', array([ 1.07762995]), array([ 1.01502801])]
Outputs clients: [[Elemwise{eq,no_inplace}(Elemwise{Composite{Switch(GE(i0, i1), i2, i3)}}.0, TensorConstant{(1,) of 0}), Elemwise{Composite{Switch(GE(i0, i1), ((Switch(i2, i3, (i4 * log(i0))) - i5) - i0), i3)}}[(0, 0)](Elemwise{Composite{Switch(GE(i0, i1), i2, i3)}}.0, TensorConstant{(1,) of 0}, InplaceDimShuffle{x}.0, TensorConstant{(1,) of -inf}, TensorConstant{[ 13. 13... 0. 1.]}, TensorConstant{[ 22.55216... ]})]]
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.
作为一个简单的分析师，我是否应该学习所有关于 theano 的知识才能解决我的统计问题？具有渐变功能的新 mcmc 采样器是否只是促使我从 pymc2 切换到 pymc3 的一件事？