# my_GP = GaussianProcess(SMKernel(X.shape[0], 3), X, LogisticFunction()) y = np.matrix( [-1]*N + [1]*N + [-1]*N ).T my_GP.add_task(y) y2 = [] for i in range(3*N): if X[0,i] > 0: y2.append(1) else: y2.append(-1) y2 = np.matrix(y2).T my_GP.add_task(y2) # print [-1+0.1*i for i in range(30)] # x_star = np.matrix([-1+0.1*i for i in range(50)]) x_star = np.matrix(dnorm(0,sigma,N)) # f_mode, log_ML = my_GP.gpc_find_mode([0, 1], my_GP.cov_function.INITIAL_GUESS) # print my_GP.gpc_optimize([0]) tasks = [0] my_GP.hyperparameters = my_GP.gpc_optimize(tasks)[0] print my_GP.hyperparameters y_star = my_GP.gpc_make_prediction([0], x_star) for i in range(len(y_star)): print x_star[0, i], y_star[i]