From d2d1d58db39a5d78907b21777a93d19b4d0c9cff Mon Sep 17 00:00:00 2001 From: Max Zwiessele Date: Wed, 6 Nov 2013 15:26:09 +0000 Subject: [PATCH] BGPLVM test for crossterms --- GPy/examples/dimensionality_reduction.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py index bde249c8..666209f9 100644 --- a/GPy/examples/dimensionality_reduction.py +++ b/GPy/examples/dimensionality_reduction.py @@ -12,10 +12,10 @@ from GPy.likelihoods.gaussian import Gaussian default_seed = np.random.seed(123344) def BGPLVM(seed=default_seed): - N = 5 - num_inducing = 4 - Q = 3 - D = 2 + N = 13 + num_inducing = 5 + Q = 6 + D = 25 # generate GPLVM-like data X = np.random.rand(N, Q) lengthscales = np.random.rand(Q) @@ -25,9 +25,12 @@ def BGPLVM(seed=default_seed): Y = np.random.multivariate_normal(np.zeros(N), K, D).T lik = Gaussian(Y, normalize=True) - k = GPy.kern.rbf_inv(Q, .5, np.ones(Q) * 2., ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q) + # k = GPy.kern.rbf_inv(Q, .5, np.ones(Q) * 2., ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q) # k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001) # k = GPy.kern.rbf(Q, ARD = False) + GPy.kern.white(Q, 0.00001) + # k = GPy.kern.rbf(Q, .5, np.ones(Q) * 2., ARD=True) + GPy.kern.rbf(Q, .3, np.ones(Q) * .2, ARD=True) + k = GPy.kern.rbf(Q, .5, np.ones(Q) * 2., ARD=True) + GPy.kern.linear(Q, np.ones(Q) * .2, ARD=True) + # k = GPy.kern.rbf(Q, .5, 2., ARD=0) + GPy.kern.rbf(Q, .3, .2, ARD=0) m = GPy.models.BayesianGPLVM(lik, Q, kernel=k, num_inducing=num_inducing) m.lengthscales = lengthscales