minor update

    # `x` is a :class:`tfsnippet.stochastic.StochasticTensor`, from which

    # you may derive many useful outputs, for example:

    x.tensor  # the `x` samples

    x.log_prob(group_ndims=0)  # element-wise log p(x|z)

    x.log_prob(group_ndims=0)  # element-wise log p(x|z) of sampled x

    x.distribution.log_prob(input_x)  # the reconstruction probability

    x.distribution.mean, x.distribution.std  # mean and std of p(x|z)

import numpy as np

from tfsnippet.utils import docstring_inherit

from tfsnippet.utils import DocInherit

__all__ = ['DataAugmentation', 'MissingDataInjection']

@DocInherit

class DataAugmentation(object):

    """

    Base class for data augmentation in training.

        """Get the ratio of missing points to inject."""

        return self._missing_rate

    @docstring_inherit(DataAugmentation.augment)

    def _augment(self, values, labels, missing):

        inject_y = np.random.binomial(1, self.missing_rate, size=values.shape)

        inject_idx = np.where(inject_y.astype(np.bool))[0]