GP model Predict

GP_predict.py

    def predict(self, X_test):
        """GP model predicting
        
        Input
        -----
        X_test: test set, array of shape (n_samples, n_features)
        
        Output
        ------
        f: GP predictions
        SSqr: Prediction variances"""
        
        n = self.X.shape[0]
        one = np.ones((n,1))
        
        # Construct correlation matrix between test and train data
        k = self.Corr(self.X, X_test, 10**self.theta)
        
        # Mean prediction
        f = self.mu + k.T @ self.inv_K @ (self.y-self.mu*one)
        
        # Variance prediction
        SSqr = self.SigmaSqr*(1 - np.diag(k.T @ self.inv_K @ k))
        
        return f.flatten(), SSqr.flatten()