neural/linear/arx.py [21:50]:
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        self.lag_u = lag_u
        self.lag_y = lag_y
        self.maxlag = np.max([self.lag_u, self.lag_y])
        self.scaling = scaling
        self.penal_weight = penal_weight

        # model architecture

        # learned feats
        self.weights = np.array([])
        self.weights_u = np.array([])
        self.weights_y = np.array([])
        self.A = np.array([])

        # data properties
        self.n_channels_y = 0
        self.n_channels_u = 0
        self.n_feats_x = 0
        self.n_feats_v = 0
        self.scaler_target = StandardScaler()
        self.scaler_y = StandardScaler()
        self.scaler_u = StandardScaler()

        self.solver = solver
        self.model = 0

        self.regressors_names = list()
        self.residuals = 0

        self.log = log
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neural/linear/lin_model_template.py [23:52]:
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        self.lag_u = lag_u
        self.lag_y = lag_y
        self.maxlag = np.max([self.lag_u, self.lag_y])
        self.scaling = scaling
        self.penal_weight = penal_weight

        # model architecture

        # learned feats
        self.weights = np.array([])
        self.weights_u = np.array([])
        self.weights_y = np.array([])
        self.A = np.array([])

        # data properties
        self.n_channels_y = 0
        self.n_channels_u = 0
        self.n_feats_x = 0
        self.n_feats_v = 0
        self.scaler_target = StandardScaler()
        self.scaler_y = StandardScaler()
        self.scaler_u = StandardScaler()

        self.solver = solver
        self.model = 0

        self.regressors_names = list()
        self.residuals = 0

        self.log = log
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