def predict_with_intermediate(self, sample, verbose=True):
    ' Predict some data\n\n        Parameters\n        ----------\n\n        samples: np.array, size=(C, T)\n                 training sample\n\n        Returns\n        -------\n\n        ordered dictionary including every intermediate result and the output\n        '
    if verbose:
        print('Predict sample with intermediate matrices')
    assert (len(sample.shape) == 2)
    result = self.riemannian.onetrial_feature_with_intermediate(sample)
    features = next(reversed(result.values()))
    features = features.reshape(1, (- 1))
    result['svm_result'] = self.classifier.decision_function(features)
    result['prediction'] = self.classifier.predict(features)
    return result
Predict some data

Parameters
----------

samples: np.array, size=(C, T)
         training sample

Returns
-------

ordered dictionary including every intermediate result and the output
def predict_with_intermediate(self, sample, verbose=True):
    ' Predict some data\n\n        Parameters\n        ----------\n\n        samples: np.array, size=(C, T)\n                 training sample\n\n        Returns\n        -------\n\n        ordered dictionary including every intermediate result and the output\n        '
    if verbose:
        print('Predict sample with intermediate matrices')
    assert (len(sample.shape) == 2)
    result = self.riemannian.onetrial_feature_with_intermediate(sample)
    features = next(reversed(result.values()))
    features = features.reshape(1, (- 1))
    result['svm_result'] = self.classifier.decision_function(features)
    result['prediction'] = self.classifier.predict(features)
    return result