Python 在PyMC 2.3中创建随机装饰器

我试图复制PyMC 2.3中给出的示例：

@pm2.stochastic(dtype=int)
    def switchpoint(value=1900, t_l=1851, t_h=1962):
    """The switchpoint for the rate of disaster occurrence."""
    if value > t_h or value < t_l:
        # Invalid values
        return -np.inf
    else:
        # Uniform log-likelihood
        return -np.log(t_h - t_l + 1)

我收到了错误消息（在本文末尾填写错误消息）：

TypeError:“numpy.ndarray”对象不可调用

我猜这是一个兼容性问题；我使用的是Python2.7.6（64位）发行版。Numpy版本1.8.0和Scipy版本0.13.3

有人能帮我找到问题所在吗

注：我在谷歌上发现了一个相对较小的群体，显然也有同样的问题。但是，线程没有问题状态的更新

以下是完整的错误消息：

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/arash/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pymc/CommonDeterministics.py", line 975, in __call__
    plot=False)
  File "/Users/arash/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pymc/PyMCObjects.py", line 435, in __init__
    verbose=verbose)
  File "/Users/arash/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pymc/Node.py", line 216, in __init__
    Node.__init__(self, doc, name, parents, cache_depth, verbose=verbose)
  File "/Users/arash/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pymc/Node.py", line 127, in __init__
    self.parents = parents
  File "/Users/arash/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pymc/Node.py", line 150, in _set_parents
    self.gen_lazy_function()
  File "/Users/arash/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pymc/PyMCObjects.py", line 446, in gen_lazy_function
    self._value.force_compute()
  File "LazyFunction.pyx", line 257, in pymc.LazyFunction.LazyFunction.force_compute (pymc/LazyFunction.c:2409)
  File "/Users/arash/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pymc/CommonDeterministics.py", line 967, in eval_fun
    return self(*args, **kwargs)
TypeError: 'numpy.ndarray' object is not callable

回溯（最近一次呼叫最后一次）：
文件“”，第1行，在
文件“/Users/arash/Library/enthund/Canopy_64bit/User/lib/python2.7/site packages/pymc/CommonDeterministics.py”，第975行，在调用中__
plot=False）
文件“/Users/arash/Library/enthund/Canopy_64bit/User/lib/python2.7/site packages/pymc/PyMCObjects.py”，第435行，在__
详细的
文件“/Users/arash/Library/enthund/Canopy_64bit/User/lib/python2.7/site packages/pymc/Node.py”，第216行，在__
节点.\uuuuu init\uuuuuuuuuuuu（self，doc，name，parent，cache\u depth，verbose=verbose）
文件“/Users/arash/Library/enthund/Canopy_64bit/User/lib/python2.7/site packages/pymc/Node.py”，第127行，在_init中__
self.parents=父母
文件“/Users/arash/Library/enthund/Canopy_64bit/User/lib/python2.7/site packages/pymc/Node.py”，第150行，在父集合中
self.gen_lazy_函数（）
gen_lazy_函数中的文件“/Users/arash/Library/enthund/Canopy_64bit/User/lib/python2.7/site packages/pymc/PyMCObjects.py”，第446行
self.\u value.force\u compute（）
pymc.LazyFunction.LazyFunction.force_compute（pymc/LazyFunction.c:2409）中的文件“LazyFunction.pyx”，第257行
文件“/Users/arash/Library/enthund/Canopy_64bit/User/lib/python2.7/site packages/pymc/CommonDeterministics.py”，第967行，在eval_-fun中
返回自我（*args，**kwargs）
TypeError:“numpy.ndarray”对象不可调用
PyMC实际上将
开关点
转换为正则变量。所以你只需要做

test = switchpoint

这看起来很奇怪，因为您将它定义为一个修饰函数，但实际上您不应该这样使用它。如果你看一下定义随机变量的其他方法，它会更有意义，如下所示：

switchpoint = DiscreteUniform('switchpoint', lower=0, upper=110, doc='Switchpoint[year]')

这是：

def switchpoint_logp(value, t_l, t_h):
    if value > t_h or value < t_l:
        return -np.inf
    else:
        return -np.log(t_h - t_l + 1)

def switchpoint_rand(t_l, t_h):
    from numpy.random import random
    return np.round( (t_l - t_h) * random() ) + t_l

switchpoint = Stochastic( logp = switchpoint_logp,
                doc = 'The switchpoint for the rate of disaster occurrence.',
                name = 'switchpoint',
                parents = {'t_l': 1851, 't_h': 1962},
                random = switchpoint_rand,
                trace = True,
                value = 1900,
                dtype=int,
                rseed = 1.,
                observed = False,
                cache_depth = 2,
                plot=True,
                verbose = 0)

def开关点日志（值、t\u l、t\u h）：
如果值>t_h或值
谢谢！当时我没有掌握这个概念。你的回答解决了问题，真的很有帮助。
def switchpoint_logp(value, t_l, t_h):
    if value > t_h or value < t_l:
        return -np.inf
    else:
        return -np.log(t_h - t_l + 1)

def switchpoint_rand(t_l, t_h):
    from numpy.random import random
    return np.round( (t_l - t_h) * random() ) + t_l

switchpoint = Stochastic( logp = switchpoint_logp,
                doc = 'The switchpoint for the rate of disaster occurrence.',
                name = 'switchpoint',
                parents = {'t_l': 1851, 't_h': 1962},
                random = switchpoint_rand,
                trace = True,
                value = 1900,
                dtype=int,
                rseed = 1.,
                observed = False,
                cache_depth = 2,
                plot=True,
                verbose = 0)