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

Fixed merge conflicts, M now num_inducing

Browse source
This commit is contained in:
Alan Saul 2013-06-05 15:41:48 +01:00
commit 39c242a4d5
20 changed files with 537 additions and 232 deletions
Download patch file Download diff file Expand all files Collapse all files

9
GPy/examples/dimensionality_reduction.py
View file

@ -5,7 +5,6 @@ import numpy as np
from matplotlib import pyplot as plt
import GPy
from GPy.util.datasets import swiss_roll_generated
from GPy.core.transformations import logexp
from GPy.models.bayesian_gplvm import BayesianGPLVM
@ -64,7 +63,7 @@ def GPLVM_oil_100(optimize=True):
return m
def swiss_roll(optimize=True, N=1000, num_inducing=15, Q=4, sigma=.2, plot=False):
from GPy.util.datasets import swiss_roll
from GPy.util.datasets import swiss_roll_generated
from GPy.core.transformations import logexp_clipped
data = swiss_roll_generated(N=N, sigma=sigma)
@ -109,10 +108,10 @@ def swiss_roll(optimize=True, N=1000, num_inducing=15, Q=4, sigma=.2, plot=False
m.data_colors = c
m.data_t = t
m.constrain('variance|length', logexp_clipped())
m['lengthscale'] = 1. # X.var(0).max() / X.var(0)
m['noise'] = Y.var() / 100.
m.ensure_default_constraints()
m['rbf_lengthscale'] = 1. # X.var(0).max() / X.var(0)
m['noise_variance'] = Y.var() / 100.
m['bias_variance'] = 0.05
if optimize:
m.optimize('scg', messages=1)

5
GPy/examples/regression.py
View file

@ -159,13 +159,13 @@ def coregionalisation_sparse(optim_iters=100):
k = k1.prod(k2,tensor=True) + GPy.kern.white(2,0.001)
m = GPy.models.SparseGPRegression(X,Y,kernel=k,Z=Z)
m.scale_factor = 10000.
m.constrain_fixed('.*rbf_var',1.)
#m.constrain_positive('kappa')
m.constrain_fixed('iip')
m.constrain_bounded('noise_variance',1e-3,1e-1)
m.ensure_default_constraints()
m.optimize_restarts(5, robust=True, messages=1, max_f_eval=optim_iters)
#plotting:
pb.figure()
Xtest1 = np.hstack((np.linspace(0,9,100)[:,None],np.zeros((100,1))))
Xtest2 = np.hstack((np.linspace(0,9,100)[:,None],np.ones((100,1))))
@ -300,7 +300,6 @@ def sparse_GP_regression_2D(N = 400, num_inducing = 50, optim_iters=100):
m.checkgrad()
# optimize and plot
pb.figure()
m.optimize('tnc', messages = 1, max_f_eval=optim_iters)
m.plot()
print(m)