apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-06-02 14:45:15 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

coregionalization examples fixed

Browse source
This commit is contained in:
Ricardo 2014-03-18 16:00:23 +00:00
parent 9e64f116d8
commit 59ae2f0e34
1 changed files with 17 additions and 46 deletions
Download patch file Download diff file Expand all files Collapse all files

63
GPy/examples/regression.py
View file

@ -25,80 +25,51 @@ def olympic_marathon_men(optimize=True, plot=True):
return m
def coregionalization_toy2(optimize=True, plot=True):
def coregionalization_toy(optimize=True, plot=True):
"""
A simple demonstration of coregionalization on two sinusoidal functions.
"""
#build a design matrix with a column of integers indicating the output
X1 = np.random.rand(50, 1) * 8
X2 = np.random.rand(30, 1) * 5
index = np.vstack((np.zeros_like(X1), np.ones_like(X2)))
X = np.hstack((np.vstack((X1, X2)), index))
#build a suitable set of observed variables
Y1 = np.sin(X1) + np.random.randn(*X1.shape) * 0.05
Y2 = np.sin(X2) + np.random.randn(*X2.shape) * 0.05 + 2.
Y = np.vstack((Y1, Y2))
#build the kernel
k1 = GPy.kern.RBF(1) + GPy.kern.Bias(1)
k2 = GPy.kern.Coregionalize(2,1)
k = k1**k2
m = GPy.models.GPRegression(X, Y, kernel=k)
m = GPy.models.GPCoregionalizedRegression(X_list=[X1,X2], Y_list=[Y1,Y2])
if optimize:
m.optimize('bfgs', max_iters=100)
if plot:
m.plot(fixed_inputs=[(1,0)])
m.plot(fixed_inputs=[(1,1)], ax=pb.gca())
slices = GPy.util.multioutput.get_slices([X1,X2])
m.plot(fixed_inputs=[(1,0)],which_data_rows=slices[0],Y_metadata={'output_index':0})
m.plot(fixed_inputs=[(1,1)],which_data_rows=slices[1],Y_metadata={'output_index':1},ax=pb.gca())
return m
#FIXME: Needs recovering once likelihoods are consolidated
#def coregionalization_toy(optimize=True, plot=True):
# """
# A simple demonstration of coregionalization on two sinusoidal functions.
# """
# X1 = np.random.rand(50, 1) * 8
# X2 = np.random.rand(30, 1) * 5
# X = np.vstack((X1, X2))
# Y1 = np.sin(X1) + np.random.randn(*X1.shape) * 0.05
# Y2 = -np.sin(X2) + np.random.randn(*X2.shape) * 0.05
# Y = np.vstack((Y1, Y2))
#
# k1 = GPy.kern.RBF(1)
# m = GPy.models.GPMultioutputRegression(X_list=[X1,X2],Y_list=[Y1,Y2],kernel_list=[k1])
# m.constrain_fixed('.*rbf_var', 1.)
# m.optimize(max_iters=100)
#
# fig, axes = pb.subplots(2,1)
# m.plot(fixed_inputs=[(1,0)],ax=axes[0])
# m.plot(fixed_inputs=[(1,1)],ax=axes[1])
# axes[0].set_title('Output 0')
# axes[1].set_title('Output 1')
# return m
def coregionalization_sparse(optimize=True, plot=True):
"""
A simple demonstration of coregionalization on two sinusoidal functions using sparse approximations.
"""
#fetch the data from the non sparse examples
m = coregionalization_toy2(optimize=False, plot=False)
X, Y = m.X, m.Y
#build a design matrix with a column of integers indicating the output
X1 = np.random.rand(50, 1) * 8
X2 = np.random.rand(30, 1) * 5
k = GPy.kern.RBF(1)**GPy.kern.Coregionalize(2)
#build a suitable set of observed variables
Y1 = np.sin(X1) + np.random.randn(*X1.shape) * 0.05
Y2 = np.sin(X2) + np.random.randn(*X2.shape) * 0.05 + 2.
#construct a model
m = GPy.models.SparseGPRegression(X,Y, num_inducing=25, kernel=k)
m.Z[:,1].fix() # don't optimize the inducing input indexes
m = GPy.models.SparseGPCoregionalizedRegression(X_list=[X1,X2], Y_list=[Y1,Y2])
if optimize:
m.optimize('bfgs', max_iters=100, messages=1)
m.optimize('bfgs', max_iters=100)
if plot:
m.plot(fixed_inputs=[(1,0)])
m.plot(fixed_inputs=[(1,1)], ax=pb.gca())
slices = GPy.util.multioutput.get_slices([X1,X2])
m.plot(fixed_inputs=[(1,0)],which_data_rows=slices[0],Y_metadata={'output_index':0})
m.plot(fixed_inputs=[(1,1)],which_data_rows=slices[1],Y_metadata={'output_index':1},ax=pb.gca())
pb.ylim(-3,)
return m