base.py 文件源码

def __init__(self, name, distribution, n_samples, observed=None):
        self._name = name
        self._distribution = distribution
        self._n_samples = n_samples
        self._dtype = distribution.dtype
        if observed is not None:
            try:
                observed = tf.convert_to_tensor(observed, dtype=self.dtype)
            except ValueError as e:
                raise ValueError(
                    "StochasticTensor('{}') not compatible "
                    "with its observed value. Error message: {}".format(
                        self._name, e))
        self._observed = observed
        try:
            self._net = BayesianNet.get_context()
            self._net._add_stochastic_tensor(self)
        except RuntimeError:
            self._net = None
        super(StochasticTensor, self).__init__()