def predict_proba(self, X):
        """Probability estimates.

        Parameters
        ----------
        X : array-like, shape = (n_samples, n_timestamps)
            Test samples.

        Returns
        -------
        T : array, shape = (n_samples, n_classes)
            Probability of the samples for each class in the model,
            where classes are ordered as they are in ``self.classes_``.

        """
        X_new = self.decision_function(X)
        n_samples = X_new.shape[0]
        if self._multi_class == 'binary':
            proba = _expit(X_new)
            return np.c_[1 - proba, proba]
        elif self._multi_class == 'multinomial':
            proba = _softmax(X_new, n_samples, len(self.classes_))
        else:
            # OvR normalization, like LibLinear's predict_probability
            proba = _expit(X_new)
            proba /= proba.sum(axis=1).reshape((proba.shape[0], -1))
        return proba
Probability estimates.

        Parameters
        ----------
        X : array-like, shape = (n_samples, n_timestamps)
            Test samples.

        Returns
        -------
        T : array, shape = (n_samples, n_classes)
            Probability of the samples for each class in the model,
            where classes are ordered as they are in ``self.classes_``.

        
pyts/classification/learning_shapelets.py