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dim reduction examples

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mzwiessele 2014-01-29 11:06:25 +00:00
parent 6f3b1f06a2
commit 0fb894a2c3
1 changed files with 4 additions and 2 deletions
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6
GPy/examples/dimensionality_reduction.py
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@ -264,8 +264,9 @@ def bgplvm_simulation(optimize=True, verbose=1,
D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 30, 3, 10
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
Y = Ylist[0]
k = kern.linear(Q, ARD=True) + kern.bias(Q, _np.exp(-2)) + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
k = kern.linear(Q, ARD=True)
m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k)
m.X_variance = m.X_variance * .05
m['noise'] = Y.var() / 100.
if optimize:
@ -286,8 +287,9 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
likelihood_list = [Gaussian(x, normalize=True) for x in Ylist]
k = kern.linear(Q, ARD=True) + kern.bias(Q, _np.exp(-2)) + kern.white(Q, _np.exp(-2))
k = kern.linear(Q, ARD=True)# + kern.bias(Q, _np.exp(-2)) + kern.white(Q, _np.exp(-2))
m = MRD(likelihood_list, input_dim=Q, num_inducing=num_inducing, kernels=k, initx="", initz='permute', **kw)
m.X_variance = m.X_variance * .05
m.ensure_default_constraints()
for i, bgplvm in enumerate(m.bgplvms):