carpet/lista_base.py
def fit(self, x, lbda):
        """ Compute the output of the network, given x and regularization lbda

        Parameters
        ----------
        x : ndarray, shape (n_samples, n_dim)
            input of the network.
        lbda: float
            Regularization level for the optimization problem.
        """
        x = check_tensor(x, device=self.device)
        lbda = check_tensor(lbda, device=self.device)
        self._fit_all_network_batch_gradient_descent(x, lbda)
        return self
 Compute the output of the network, given x and regularization lbda

        Parameters
        ----------
        x : ndarray, shape (n_samples, n_dim)
            input of the network.
        lbda: float
            Regularization level for the optimization problem.
        
Compute the output of the network, given x and regularization lbda
Parameters

x : ndarray, shape (n_samples, n_dim)
input of the network.
lbda: float
Regularization level for the optimization problem.
Compute the output of the network given x and regularization lbda Parameters x : ndarray shape ( n_samples n_dim ) input of the network . lbda : float Regularization level for the optimization problem .
def fit(self, x, lbda):
        x = check_tensor(x, device=self.device)
        lbda = check_tensor(lbda, device=self.device)
        self._fit_all_network_batch_gradient_descent(x, lbda)
        return self
def fit ( self , x , lbda ) : x = check_tensor ( x , device = self . device ) lbda = check_tensor ( lbda , device = self . device ) self . _fit_all_network_batch_gradient_descent ( x , lbda ) return self
Compute the output of the network, given x and regularization lbda
Parameters
Compute the output of the network given x and regularization lbda Parameters
""" Compute the output of the network, given x and regularization lbda

        Parameters
        ----------
        x : ndarray, shape (n_samples, n_dim)
            input of the network.
        lbda: float
            Regularization level for the optimization problem.
        """
self
x
lbda