def toy_rbf_1d(seed=default_seed, num_samples=<NUM_LIT>):
import GPy<EOL>np.random.seed(seed=seed)<EOL>num_in = <NUM_LIT:1><EOL>X = np.random.uniform(low= -<NUM_LIT:1.0>, high=<NUM_LIT:1.0>, size=(num_samples, num_in))<EOL>X.sort(axis=<NUM_LIT:0>)<EOL>rbf = GPy.kern.RBF(num_in, variance=<NUM_LIT:1.>, lengthscale=np.array((<NUM_LIT>,)))<EOL>white = GPy.kern.White(num_in, variance=<NUM_LIT>)<EOL>kernel = rbf + white<EOL>K = kernel.K(X)<EOL>y = np.reshape(np.random.multivariate_normal(np.zeros(num_samples), K), (num_samples, <NUM_LIT:1>))<EOL>return {'<STR_LIT:X>':X, '<STR_LIT:Y>':y, '<STR_LIT:info>': "<STR_LIT>" + str(num_samples) + "<STR_LIT>"}<EOL>
Samples values of a function from an RBF covariance with very small noise for inputs uniformly distributed between -1 and 1.

:param seed: seed to use for random sampling.
:type seed: int
:param num_samples: number of samples to sample in the function (default 500).
:type num_samples: int