From 1061bf52482aa3bf6769db810c955d5fbf51ceae Mon Sep 17 00:00:00 2001 From: Zhenwen Dai Date: Tue, 12 Aug 2014 10:38:58 +0100 Subject: [PATCH] change for ssgplvm example --- GPy/examples/dimensionality_reduction.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py index 3d73a62c..842d0bf8 100644 --- a/GPy/examples/dimensionality_reduction.py +++ b/GPy/examples/dimensionality_reduction.py @@ -317,7 +317,7 @@ def bgplvm_simulation(optimize=True, verbose=1, def ssgplvm_simulation(optimize=True, verbose=1, plot=True, plot_sim=False, - max_iters=2e4, + max_iters=2e4, useGPU=False ): from GPy import kern from GPy.models import SSGPLVM @@ -325,7 +325,7 @@ def ssgplvm_simulation(optimize=True, verbose=1, D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 3, 9 _, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim) Y = Ylist[0] - k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q) + k = kern.Linear(Q, ARD=True, useGPU=useGPU)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q) #k = kern.RBF(Q, ARD=True, lengthscale=10.) m = SSGPLVM(Y, Q, init="pca", num_inducing=num_inducing, kernel=k) m.X.variance[:] = _np.random.uniform(0,.01,m.X.shape)