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

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

        Returns
        -------
        T : array-like of 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 len(self.classes_) == 2:
            proba = _expit(X_new)
            X_proba = np.c_[1 - proba, proba]
        else:
            X_proba = _softmax(X_new, n_samples, len(self.classes_))
        return X_proba
Probability estimates.

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

        Returns
        -------
        T : array-like of 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