def score(self, samples, labels):
    ' Measure the performance, returns success rate\n\n        Parameters\n        ----------\n\n        samples: np.array, size=(N, C, T)\n                 training samples\n\n        labels: np.array, size=(N)\n                training labels\n\n        Returns\n        -------\n\n        float: score of the model\n        '
    features = self.riemannian.features(samples)
    return self.classifier.score(features, labels)
Measure the performance, returns success rate

Parameters
----------

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

labels: np.array, size=(N)
        training labels

Returns
-------

float: score of the model
def score(self, samples, labels):
    ' Measure the performance, returns success rate\n\n        Parameters\n        ----------\n\n        samples: np.array, size=(N, C, T)\n                 training samples\n\n        labels: np.array, size=(N)\n                training labels\n\n        Returns\n        -------\n\n        float: score of the model\n        '
    features = self.riemannian.features(samples)
    return self.classifier.score(features, labels)