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Added automatic computation of dKdiag_dtheta in kernpart.py using dK_dtheta.

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Neil Lawrence 2013-08-17 07:13:51 +01:00
parent bba3dc5458
commit a570198029
3 changed files with 33 additions and 19 deletions
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19
GPy/util/datasets.py
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@ -203,17 +203,22 @@ def swiss_roll(N=3000):
Y = mat_data['X_data'][:, 0:N].transpose()
return {'Y': Y, 'X': mat_data['X_data'], 'info': "The first 3,000 points from the swiss roll data of Tennenbaum, de Silva and Langford (2001)."}
def toy_rbf_1d(seed=default_seed):
def toy_rbf_1d(seed=default_seed, num_samples=500):
"""Samples 500 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
"""
np.random.seed(seed=seed)
numIn = 1
N = 500
X = np.random.uniform(low= -1.0, high=1.0, size=(N, numIn))
num_in = 1
X = np.random.uniform(low= -1.0, high=1.0, size=(num_samples, num_in))
X.sort(axis=0)
rbf = GPy.kern.rbf(numIn, variance=1., lengthscale=np.array((0.25,)))
white = GPy.kern.white(numIn, variance=1e-2)
rbf = GPy.kern.rbf(num_in, variance=1., lengthscale=np.array((0.25,)))
white = GPy.kern.white(num_in, variance=1e-2)
kernel = rbf + white
K = kernel.K(X)
y = np.reshape(np.random.multivariate_normal(np.zeros(N), K), (N, 1))
y = np.reshape(np.random.multivariate_normal(np.zeros(num_samples), K), (num_samples, 1))
return {'X':X, 'Y':y, 'info': "Samples 500 values of a function from an RBF covariance with very small noise for inputs uniformly distributed between -1 and 1."}
def toy_rbf_1d_50(seed=default_seed):