(self, prob)
return log_loss
Compute expected log-loss.

        Parameters
        ----------
        prob: 2d array, shape [n_samples, n_classes]
            The probabilistic prediction matrix for the unlabeled set.

        Returns
        -------
        log_loss: float
            The sum of log_loss for the prob.
Compute expected log-loss.
Compute expected log - loss .
def log_loss(self, prob):
        """Compute expected log-loss.

        Parameters
        ----------
        prob: 2d array, shape [n_samples, n_classes]
            The probabilistic prediction matrix for the unlabeled set.

        Returns
        -------
        log_loss: float
            The sum of log_loss for the prob.
        """
        log_loss = 0.0
        for i in range(len(prob)):
            for p in list(prob[i]):
                log_loss -= p * np.log(p)
        return log_loss
def log_loss ( self , prob ) : log_loss = 0.0 for i in range ( len ( prob ) ) : for p in list ( prob [ i ] ) : log_loss -= p * np . log ( p ) return log_loss