neural_network.py 文件源码

def fit(self, X, y):
        """Fit the Bayesian linear regression model leveraging the available
        data.

        Parameters:
            X (numpy array): A two-dimensional numpy array representing the
                matrix of covariates. Note that if a bias term is expressly
                desired, it must be included in the design matrix.
            y (numpy array): A matrix of target variables to predict.
        """
        P = X.T.dot(X) + self.prior_prec
        L = spla.cholesky(P, lower=True)
        self.mu = spla.cho_solve((L, True), X.T.dot(y))
        self.sigma_sq = np.mean((X.dot(self.mu) - y) ** 2)
        L_inv = spla.solve_triangular(L.T, np.eye(L.shape[0]))
        self.cov = self.sigma_sq * L_inv.dot(L_inv.T)