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constant jitter to Kmm, deleted some white kernels in models and examples

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Max Zwiessele 2013-08-02 16:36:51 +01:00
parent 1cc8f95717
commit 5570e82943
5 changed files with 165 additions and 161 deletions
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24
GPy/examples/dimensionality_reduction.py
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@ -140,30 +140,32 @@ def swiss_roll(optimize=True, N=1000, num_inducing=15, Q=4, sigma=.2, plot=False
m.optimize('scg', messages=1)
return m
def BGPLVM_oil(optimize=True, N=200, Q=10, num_inducing=15, max_iters=150, plot=False, **k):
def BGPLVM_oil(optimize=True, N=200, Q=7, num_inducing=40, max_iters=1000, plot=False, **k):
np.random.seed(0)
data = GPy.util.datasets.oil()
# create simple GP model
kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
kernel = GPy.kern.rbf_inv(Q, 1., [.1] * Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2))
Y = data['X'][:N]
Yn = Y - Y.mean(0)
Yn /= Yn.std(0)
Yn = Gaussian(Y, normalize=True)
# Yn = Y - Y.mean(0)
# Yn /= Yn.std(0)
m = GPy.models.BayesianGPLVM(Yn, Q, kernel=kernel, num_inducing=num_inducing, **k)
m.data_labels = data['Y'][:N].argmax(axis=1)
# m.constrain('variance|leng', logexp_clipped())
# m['.*lengt'] = m.X.var(0).max() / m.X.var(0)
m['noise'] = Yn.var() / 100.
m['noise'] = Yn.Y.var() / 100.
# optimize
if optimize:
# m.constrain_fixed('noise')
# m.optimize('scg', messages=1, max_iters=200, gtol=.05)
# m.constrain_positive('noise')
m.constrain_fixed('noise')
m.optimize('scg', messages=1, max_iters=200, gtol=.05)
m.constrain_positive('noise')
m.constrain_bounded('white', 1e-7, 1)
m.optimize('scg', messages=1, max_iters=max_iters, gtol=.05)
if plot:
@ -271,7 +273,7 @@ def bgplvm_simulation(optimize='scg',
max_iters=2e4,
plot_sim=False):
# from GPy.core.transformations import logexp_clipped
D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 300, 30, 6
D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 30, 3, 10
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
from GPy.models import mrd
@ -296,7 +298,7 @@ def bgplvm_simulation(optimize='scg',
return m
def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
D1, D2, D3, N, num_inducing, Q = 150, 200, 400, 500, 3, 7
D1, D2, D3, N, num_inducing, Q = 30, 10, 15, 60, 3, 10
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
likelihood_list = [Gaussian(x, normalize=True) for x in Ylist]
@ -383,7 +385,7 @@ def stick_bgplvm(model=None):
m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=20, kernel=kernel)
# optimize
m.ensure_default_constraints()
m.optimize(messages=1, max_iters=3000, xtol=1e-300, ftol=1e-300)
m.optimize('scg', messages=1, max_iters=200, xtol=1e-300, ftol=1e-300)
m._set_params(m._get_params())
plt.clf, (latent_axes, sense_axes) = plt.subplots(1, 2)
plt.sca(latent_axes)