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Max Zwiessele 2013-04-26 16:38:19 +01:00
parent 16b64f41d6
commit 0da81bc311
3 changed files with 20 additions and 20 deletions
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15
GPy/examples/dimensionality_reduction.py
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@ -130,9 +130,9 @@ def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
Y2 = S2.dot(np.random.randn(S2.shape[1], D2))
Y3 = S3.dot(np.random.randn(S3.shape[1], D3))
Y1 += .2 * np.random.randn(*Y1.shape)
Y2 += .2 * np.random.randn(*Y2.shape)
Y3 += .2 * np.random.randn(*Y3.shape)
Y1 += .1 * np.random.randn(*Y1.shape)
Y2 += .1 * np.random.randn(*Y2.shape)
Y3 += .1 * np.random.randn(*Y3.shape)
Y1 -= Y1.mean(0)
Y2 -= Y2.mean(0)
@ -173,14 +173,15 @@ def bgplvm_simulation_matlab_compare():
from GPy.models import mrd
from GPy import kern
reload(mrd); reload(kern)
k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
k = kern.rbf(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k,
# X=mu,
# X_variance=S,
# X=mu,
# X_variance=S,
_debug=True)
m.ensure_default_constraints()
m.auto_scale_factor = True
m['noise'] = .01 # Y.var() / 100.
m['linear_variance'] = .01
m['{}_variance'.format(k.parts[0].name)] = .01
return m
def bgplvm_simulation(burnin='scg', plot_sim=False,