apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-15 06:52:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Merge kern conflicts in examples

Browse source
This commit is contained in:
Max Zwiessele 2013-06-05 16:16:46 +01:00
commit bfd99c3607
21 changed files with 156 additions and 157 deletions
Download patch file Download diff file Expand all files Collapse all files

12
GPy/examples/regression.py
View file

@ -151,8 +151,8 @@ def coregionalisation_sparse(optim_iters=100):
Y2 = -np.sin(X2) + np.random.randn(*X2.shape)*0.05
Y = np.vstack((Y1,Y2))
M = 40
Z = np.hstack((np.random.rand(M,1)*8,np.random.randint(0,2,M)[:,None]))
num_inducing = 40
Z = np.hstack((np.random.rand(num_inducing,1)*8,np.random.randint(0,2,num_inducing)[:,None]))
k1 = GPy.kern.rbf(1)
k2 = GPy.kern.Coregionalise(2,2)
@ -261,7 +261,7 @@ def _contour_data(data, length_scales, log_SNRs, kernel_call=GPy.kern.rbf):
return np.array(lls)
def sparse_GP_regression_1D(N = 400, M = 5, optim_iters=100):
def sparse_GP_regression_1D(N = 400, num_inducing = 5, optim_iters=100):
"""Run a 1D example of a sparse GP regression."""
# sample inputs and outputs
X = np.random.uniform(-3.,3.,(N,1))
@ -271,7 +271,7 @@ def sparse_GP_regression_1D(N = 400, M = 5, optim_iters=100):
noise = GPy.kern.white(1)
kernel = rbf + noise
# create simple GP Model
m = GPy.models.SparseGPRegression(X, Y, kernel, M=M)
m = GPy.models.SparseGPRegression(X, Y, kernel, num_inducing=num_inducing)
m.ensure_default_constraints()
@ -280,7 +280,7 @@ def sparse_GP_regression_1D(N = 400, M = 5, optim_iters=100):
m.plot()
return m
def sparse_GP_regression_2D(N = 400, M = 50, optim_iters=100):
def sparse_GP_regression_2D(N = 400, num_inducing = 50, optim_iters=100):
"""Run a 2D example of a sparse GP regression."""
X = np.random.uniform(-3.,3.,(N,2))
Y = np.sin(X[:,0:1]) * np.sin(X[:,1:2])+np.random.randn(N,1)*0.05
@ -291,7 +291,7 @@ def sparse_GP_regression_2D(N = 400, M = 50, optim_iters=100):
kernel = rbf + noise
# create simple GP Model
m = GPy.models.SparseGPRegression(X,Y,kernel, M = M)
m = GPy.models.SparseGPRegression(X,Y,kernel, num_inducing = num_inducing)
# contrain all parameters to be positive (but not inducing inputs)
m.ensure_default_constraints()