def fit(self, samples, labels):
    ' Training\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        '
    assert (len(samples.shape) == 3)
    no_channels = samples.shape[1]
    self.no_riem = int(((no_channels * (no_channels + 1)) / 2))
    self.no_features = ((self.no_riem * self.no_bands) * self.no_time_windows)
    self.riemannian.prepare_quantization(samples)
    features = self.riemannian.fit(samples)
    self.classifier.fit(features, labels)
    self.scale_weight = max(self.scale_weight, np.abs(self.classifier.coef_).max())
    weights = quantize(self.classifier.coef_, self.scale_weight, self.num_bits, do_round=True)
    self.classifier.coef_ = weights
Training

Parameters
----------

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

labels: np.array, size=(N)
        training labels
def fit(self, samples, labels):
    ' Training\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        '
    assert (len(samples.shape) == 3)
    no_channels = samples.shape[1]
    self.no_riem = int(((no_channels * (no_channels + 1)) / 2))
    self.no_features = ((self.no_riem * self.no_bands) * self.no_time_windows)
    self.riemannian.prepare_quantization(samples)
    features = self.riemannian.fit(samples)
    self.classifier.fit(features, labels)
    self.scale_weight = max(self.scale_weight, np.abs(self.classifier.coef_).max())
    weights = quantize(self.classifier.coef_, self.scale_weight, self.num_bits, do_round=True)
    self.classifier.coef_ = weights