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Scale factor added to sparse_GP_regression

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and sparse_GP_demo ammended to be less annoying (m1)
This commit is contained in:
James Hensman 2013-01-18 15:09:48 +00:00
parent 9f05744374
commit 126b45673b
2 changed files with 61 additions and 52 deletions
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21
GPy/examples/sparse_GP_regression_demo.py
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@ -11,8 +11,8 @@ import numpy as np
import GPy
np.random.seed(2)
pb.ion()
N = 500
M = 5
N = 1200
M = 20
######################################
## 1 dimensional example
@ -27,20 +27,21 @@ noise = GPy.kern.white(1)
kernel = rbf + noise
# create simple GP model
m1 = GPy.models.sparse_GP_regression(X, Y, kernel, M=M)
m = GPy.models.sparse_GP_regression(X, Y, kernel, M=M)
# contrain all parameters to be positive
m1.constrain_positive('(variance|lengthscale|precision)')
#m1.constrain_positive('(variance|lengthscale)')
#m1.constrain_fixed('prec',10.)
m.constrain_positive('(variance|lengthscale|precision)')
#m.constrain_positive('(variance|lengthscale)')
#m.constrain_fixed('prec',10.)
#check gradient FIXME unit test please
m1.checkgrad()
m.checkgrad(verbose=1)
stop
# optimize and plot
m1.optimize('tnc', messages = 1)
m1.plot()
# print(m1)
m.optimize('tnc', messages = 1)
m.plot()
# print(m)
######################################
## 2 dimensional example