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Alan Saul 2013-05-06 10:15:39 +01:00
parent bd346643db
commit f95666a8f9
2 changed files with 6 additions and 10 deletions
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15
GPy/models/GP.py
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@ -6,15 +6,9 @@ import numpy as np
import pylab as pb
from .. import kern
from ..core import model
<<<<<<< HEAD
from ..util.linalg import pdinv,mdot
from ..util.plot import gpplot,x_frame1D,x_frame2D, Tango
from ..likelihoods import EP, Laplace
=======
from ..util.linalg import pdinv, mdot
from ..util.plot import gpplot, x_frame1D, x_frame2D, Tango
from ..likelihoods import EP
>>>>>>> upstream/devel
from ..likelihoods import EP, Laplace
class GP(model):
"""
@ -34,6 +28,7 @@ class GP(model):
"""
def __init__(self, X, likelihood, kernel, normalize_X=False):
self.has_uncertain_inputs=False
# parse arguments
self.X = X
@ -128,12 +123,12 @@ class GP(model):
Note, we use the chain rule: dL_dtheta = dL_dK * d_K_dtheta
"""
dL_dthetaK = self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X, slices1=self.Xslices, slices2=self.Xslices)
dL_dthetaK = self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X)
if isinstance(self.likelihood, Laplace):
dL_dthetaK_explicit = dL_dthetaK
#Need to pass in a matrix of ones to get access to raw dK_dthetaK values without being chained
fake_dL_dKs = np.ones(self.dL_dK.shape)
dK_dthetaK = self.kern.dK_dtheta(dL_dK=fake_dL_dKs, X=self.X, slices1=self.Xslices, slices2=self.Xslices)
dK_dthetaK = self.kern.dK_dtheta(dL_dK=fake_dL_dKs, X=self.X)
dL_dthetaK_implicit = self.likelihood._Kgradients(self.dL_dK, dK_dthetaK)
dL_dthetaK = dL_dthetaK_explicit + dL_dthetaK_implicit
@ -251,7 +246,7 @@ class GP(model):
else:
raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
def plot(self, samples=0, plot_limits=None, which_data='all', which_functions='all', resolution=None, levels=20):
def plot(self, samples=0, plot_limits=None, which_data='all', which_functions='all', which_parts='all', resolution=None, levels=20):
"""
TODO: Docstrings!
:param levels: for 2D plotting, the number of contour levels to use