PYMC MAP适合问题

Question

我使用PyMC来实现多项 - 迪里克莱对。我想为所有的实例映射模型。 我面临的问题是，一旦MAP.fit（）先前的分布发生了变化。因此，对于每一个新实例，我都需要有一个新的事先分配，这应该没问题。但是，我总是看到这样的错误：

Traceback (most recent call last): 
    File "/Users/xingweiy/Project/StarRating/TimePlot/BayesianPrediction/DiricheletMultinomialStarRating.py", line 41, in <module> 
    prediction = predict.predict(input,prior) 
    File "/Users/xingweiy/Project/StarRating/TimePlot/BayesianPrediction/predict.py", line 12, in predict 
    likelihood = pm.Categorical('rating',prior,value = exp_data,observed = True) 
    File "/Library/Python/2.7/site-packages/pymc-2.3.4-py2.7-macosx-10.9-intel.egg/pymc/distributions.py", line 3170, in __init__ 
    verbose=verbose, **kwds) 
    File "/Library/Python/2.7/site-packages/pymc-2.3.4-py2.7-macosx-10.9-intel.egg/pymc/PyMCObjects.py", line 772, in __init__ 
    if not isinstance(self.logp, float): 
    File "/Library/Python/2.7/site-packages/pymc-2.3.4-py2.7-macosx-10.9-intel.egg/pymc/PyMCObjects.py", line 929, in get_logp 
    raise ZeroProbability(self.errmsg) 
pymc.Node.ZeroProbability: Stochastic rating's value is outside its support, 
or it forbids its parents' current values. 

下面是代码：

alpha= np.array([0.1,0.1,0.1,0.1,0.1]) 
prior = pm.Dirichlet('prior',alpha) 
exp_data = np.array(input) 
likelihood = pm.Categorical('rating',prior,value = exp_data,observed = True) 
MaximumPosterior = inf.inference(prior, likelihood, exp_data) 

def inference(prior,likelihood,observation): 
    model = Model({'likelihood':likelihood,'prior':prior}) 
    M = MAP(model) 
    M.fit() 
    result = M.prior.value 
    result = np.append(result,1- np.sum(M.prior.value)) 
    return result 

我认为这是pymc包的错误。有没有办法做到MAP而不改变先前的分布？

感谢

下面的链接答案解决我的问题：

https://groups.google.com/forum/#!topic/pymc/uYQSGW4acf8

Answer 1

基本上，狄氏分布生成的概率接近于0

下面的链接来解决我的问题： https://groups.google.com/forum/#!topic/pymc/uYQSGW4acf8