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feat: added set_xy for coregionalized model

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Kevin 2020-08-21 17:17:15 +02:00
parent ec20f9ed3a
commit 8ba67e00ca
1 changed files with 51 additions and 10 deletions
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59
GPy/models/gp_coregionalized_regression.py
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@ -6,6 +6,7 @@ from ..core import GP
from .. import likelihoods from .. import likelihoods
from .. import kern from .. import kern
from .. import util from .. import util
from paramz import ObsAr
class GPCoregionalizedRegression(GP): class GPCoregionalizedRegression(GP):
""" """
@ -28,19 +29,59 @@ class GPCoregionalizedRegression(GP):
:param kernel_name: name of the kernel :param kernel_name: name of the kernel
:type kernel_name: string :type kernel_name: string
""" """
def __init__(self, X_list, Y_list, kernel=None, likelihoods_list=None, name='GPCR',W_rank=1,kernel_name='coreg'):
#Input and Output def __init__(
X,Y,self.output_index = util.multioutput.build_XY(X_list,Y_list) self,
X_list,
Y_list,
kernel=None,
likelihoods_list=None,
name="GPCR",
W_rank=1,
kernel_name="coreg",
):
# Input and Output
X, Y, self.output_index = util.multioutput.build_XY(X_list, Y_list)
Ny = len(Y_list) Ny = len(Y_list)
#Kernel # Kernel
if kernel is None: if kernel is None:
kernel = kern.RBF(X.shape[1]-1) kernel = kern.RBF(X.shape[1] - 1)
kernel = util.multioutput.ICM(input_dim=X.shape[1]-1, num_outputs=Ny, kernel=kernel, W_rank=W_rank,name=kernel_name) kernel = util.multioutput.ICM(
input_dim=X.shape[1] - 1,
num_outputs=Ny,
kernel=kernel,
W_rank=W_rank,
name=kernel_name,
)
#Likelihood # Likelihood
likelihood = util.multioutput.build_likelihood(Y_list,self.output_index,likelihoods_list) likelihood = util.multioutput.build_likelihood(
Y_list, self.output_index, likelihoods_list
)
super(GPCoregionalizedRegression, self).__init__(X,Y,kernel,likelihood, Y_metadata={'output_index':self.output_index}) super(GPCoregionalizedRegression, self).__init__(
X, Y, kernel, likelihood, Y_metadata={"output_index": self.output_index}
)
def set_XY(self, X=None, Y=None):
if isinstance(X, list):
X, _, self.output_index = util.multioutput.build_XY(X, None)
if isinstance(Y, list):
_, Y, self.output_index = util.multioutput.build_XY(Y, Y)
self.update_model(False)
if Y is not None:
self.Y = ObsAr(Y)
self.Y_normalized = self.Y
if X is not None:
self.X = ObsAr(X)
self.Y_metadata = {
"output_index": self.output_index,
"trials": np.ones(self.output_index.shape),
}
self.update_model(True)