Can I call a pymc-function directly?

[Robert]

Hi,

I am new to pymc package. I come across the following tutorial:


The example is from: www.map.ox.ac.uk/media/PDF/Patil_et_al_2010.pdf

.........

import pymc 
import numpy as np 

n = 5*np.ones(4,dtype=int) 
x = np.array([-.86,-.3,-.05,.73]) 

alpha = pymc.Normal('alpha',mu=0,tau=.01) 
beta = pymc.Normal('beta',mu=0,tau=.01) 

@pymc.deterministic 
def theta(a=alpha, b=beta): 
    """theta = logit^{-1}(a+b)""" 
    return pymc.invlogit(a+b*x) 

d = pymc.Binomial('d', n=n, p=theta, value=np.array([0.,1.,3.,5.]),\ 
                    observed=True) 
....... 

import pymc 
import pymc.Matplot 
import mymodel 

S = pymc.MCMC(mymodel, db='pickle') 
S.sample(iter=10000, burn=5000, thin=2) 
pymc.Matplot.plot(S) 
import matplotlib.pyplot as plt 
plt.show() 
///////

Through the above code runs and plots, I would like to know more detail
about function theta. I have tried the following modified code:

import pymc 
import numpy as np 

n = 5*np.ones(4,dtype=int) 
x = np.array([-.86,-.3,-.05,.73]) 

alpha = pymc.Normal('alpha',mu=0,tau=.01) 
beta = pymc.Normal('beta',mu=0,tau=.01) 

@pymc.deterministic 
def theta(a=alpha, b=beta): 
    """theta = logit^{-1}(a+b)""" 
    return pymc.invlogit(a+b*x)
    
p=theta(alpha, beta)
print 'p=', p
//////////////

It has this error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
C:\Users\rj\pyprj\pymc_mymodel0.py in <module>()
     13     return pymc.invlogit(a+b*x)
     14 
---> 15 p=theta(alpha, beta)
     16 print 'p=', p

C:\Users\rj\AppData\Local\Enthought\Canopy\User\lib\site-packages\pymc\CommonDeterministics.pyc in __call__(self, *args, **kwargs)
    989                             {'self': self, 'args': args, 'kwargs': kwargs},
    990                             trace=False,
--> 991                             plot=False)
    992 Variable.__call__ = UnboundMethodType(__call__, None, Variable)
    993 

C:\Users\rj\AppData\Local\Enthought\Canopy\User\lib\site-packages\pymc\PyMCObjects.pyc in __init__(self, eval, doc, name, parents, dtype, trace, cache_depth, plot, verbose, jacobians, jacobian_formats)
    441                           trace=trace,
    442                           plot=plot,
--> 443                           verbose=verbose)
    444 
    445         # self._value.force_compute()

C:\Users\rj\AppData\Local\Enthought\Canopy\User\lib\site-packages\pymc\Node.pyc in __init__(self, doc, name, parents, cache_depth, trace, dtype, plot, verbose)
    212         self.extended_children = set()
    213 
--> 214         Node.__init__(self, doc, name, parents, cache_depth, verbose=verbose)
    215 
    216         if self.dtype is None:

C:\Users\rj\AppData\Local\Enthought\Canopy\User\lib\site-packages\pymc\Node.pyc in __init__(self, doc, name, parents, cache_depth, verbose)
    127 
    128         # Initialize
--> 129         self.parents = parents
    130 
    131     def _get_parents(self):

C:\Users\rj\AppData\Local\Enthought\Canopy\User\lib\site-packages\pymc\Node.pyc in _set_parents(self, new_parents)
    145 
    146         # Get new lazy function
--> 147         self.gen_lazy_function()
    148 
    149     parents = property(

C:\Users\rj\AppData\Local\Enthought\Canopy\User\lib\site-packages\pymc\PyMCObjects.pyc in gen_lazy_function(self)
    452                                    cache_depth=self._cache_depth)
    453 
--> 454         self._value.force_compute()
    455 
    456         self._jacobians = {}

C:\Users\rj\AppData\Local\Enthought\Canopy\User\lib\site-packages\pymc\LazyFunction.pyd in pymc.LazyFunction.LazyFunction.force_compute (pymc\LazyFunction.c:2409)()

C:\Users\rj\AppData\Local\Enthought\Canopy\User\lib\site-packages\pymc\CommonDeterministics.pyc in eval_fun(self, args, kwargs)
    981 
    982     def eval_fun(self, args=args, kwargs=kwargs):
--> 983         return self(*args, **kwargs)
    984     return pm.Deterministic(eval_fun,
    985                             'A Deterministic returning the value of %s(*%s, **%s)' % (

TypeError: 'numpy.ndarray' object is not callable 
///////////////
>From function syntactically, it seems OK for this function calling(random
variables alpha and beta as parameters of theta. 


p=theta(alpha, beta)


Do you know what is wrong 
with my code and idea (call theta by my code, not a random model like:


d = pymc.Binomial('d', n=n, p=theta, value=np.array([0.,1.,3.,5.]),\
                    observed=True)


Thanks,