def __init__(self, input_dimensionality, output_dimensionality, scaler='default'):
"""
Creats a Linear SEF object
:param input_dimensionality: dimensionality of the input space
:param output_dimensionality: dimensionality of the target space
:param learning_rate: learning rate to be used for the optimization
:param regularizer_weight: the weight of the regularizer
:param scaler:
"""
# Call base constructor
SEF_Base.__init__(self, input_dimensionality, output_dimensionality, scaler)
# Projection weights variables
W = np.float32(0.1 * np.random.randn(self.input_dimensionality, output_dimensionality))
self.W = Variable(torch.from_numpy(W), requires_grad=True)
self.trainable_params = [self.W]
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