Fit a GP model

GP_fit.py

    def fit(self, X, y):
        """GP model training
        
        Input
        -----
        X: 2D array of shape (n_samples, n_features)
        y: 2D array of shape (n_samples, 1)
        """
        
        self.X, self.y = X, y
        lb, ub = -3, 2
        
        # Generate random starting points (Latin Hypercube)
        lhd = lhs(self.X.shape[1], samples=self.n_restarts)
        
        # Scale random samples to the given bounds 
        initial_points = (ub-lb)*lhd + lb
        
        # Create A Bounds instance for optimization
        bnds = Bounds(lb*np.ones(X.shape[1]),ub*np.ones(X.shape[1]))
        
        # Run local optimizer on all points
        opt_para = np.zeros((self.n_restarts, self.X.shape[1]))
        opt_func = np.zeros((self.n_restarts, 1))
        for i in range(self.n_restarts):
            res = minimize(self.Neglikelihood, initial_points[i,:], method=self.optimizer,
                bounds=bnds)
            opt_para[i,:] = res.x
            opt_func[i,:] = res.fun
        
        # Locate the optimum results
        self.theta = opt_para[np.argmin(opt_func)]
        
        # Update attributes
        self.NegLnlike = self.Neglikelihood(self.theta)