apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-24 14:15:14 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

REVERT a53690ab7f, flapack back substitued in

Browse source
This commit is contained in:
Max Zwiessele 2013-05-31 12:09:11 +01:00
parent 43c2f8af87
commit 4eebf99bfb
15 changed files with 96 additions and 101 deletions
Download patch file Download diff file Expand all files Collapse all files

11
GPy/examples/dimensionality_reduction.py
View file

@ -17,11 +17,11 @@ def BGPLVM(seed=default_seed):
D = 4
# generate GPLVM-like data
X = np.random.rand(N, Q)
k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N), K, D).T
k = GPy.kern.rbf(Q, ARD=True) + GPy.kern.linear(Q, ARD=True) + GPy.kern.rbf(Q, ARD=True) + GPy.kern.white(Q)
k = GPy.kern.rbf(Q, ARD=True) + GPy.kern.linear(Q, ARD=True) + GPy.kern.rbf(Q, ARD=True) + GPy.kern.white(Q)
# k = GPy.kern.rbf(Q) + GPy.kern.rbf(Q) + GPy.kern.white(Q)
# k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
# k = GPy.kern.rbf(Q, ARD = False) + GPy.kern.white(Q, 0.00001)
@ -48,10 +48,9 @@ def GPLVM_oil_100(optimize=True):
Y = data['X']
# create simple GP model
kernel = GPy.kern.rbf(6, ARD=True) + GPy.kern.bias(6) + GPy.kern.white(6)
kernel = GPy.kern.rbf(6, ARD=True) + GPy.kern.bias(6)
m = GPy.models.GPLVM(Y, 6, kernel=kernel)
m.data_labels = data['Y'].argmax(axis=1)
m['noise'] = .01
# optimize
m.ensure_default_constraints()
@ -294,7 +293,7 @@ def mrd_simulation(optimize=True, plot_sim=False, **kw):
for i, Y in enumerate(Ylist):
m['{}_noise'.format(i + 1)] = Y.var() / 100.
m.constrain('variance|noise', logexp_clipped(1e-6))
# m.constrain('variance|noise', logexp_clipped(1e-6))
m.ensure_default_constraints()
# DEBUG
@ -324,7 +323,7 @@ def brendan_faces():
m.ensure_default_constraints()
m.optimize('scg', messages=1, max_f_eval=10000)
ax = m.plot_latent(which_indices=(0, 1))
ax = m.plot_latent(which_indices=(0,1))
y = m.likelihood.Y[0, :]
data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)