theanets/feedforward.py
def predict_proba(self, x, **kwargs):
        '''Compute class posterior probabilities for the given set of data.

        Parameters
        ----------
        x : ndarray (num-examples, num-variables)
            An array containing examples to predict. Examples are given as the
            rows in this array.

        Returns
        -------
        p : ndarray (num-examples, num-classes)
            An array of class posterior probability values, one per row of input
            data.
        '''
        return self.feed_forward(x, **kwargs)[self.layers[-1].output_name]
def predict_proba(self, x, **kwargs):
        '''Compute class posterior probabilities for the given set of data.

        Parameters
        ----------
        x : ndarray (num-examples, num-variables)
            An array containing examples to predict. Examples are given as the
            rows in this array.

        Returns
        -------
        p : ndarray (num-examples, num-classes)
            An array of class posterior probability values, one per row of input
            data.
        '''
        return self.feed_forward(x, **kwargs)[self.layers[-1].output_name]
Compute class posterior probabilities for the given set of data.

        Parameters
        ----------
        x : ndarray (num-examples, num-variables)
            An array containing examples to predict. Examples are given as the
            rows in this array.

        Returns
        -------
        p : ndarray (num-examples, num-classes)
            An array of class posterior probability values, one per row of input
            data.