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removed some X_normalising things

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James Hensman 2014-01-24 10:24:17 +00:00
parent b9a51a2ab8
commit 63ec89567f
1 changed files with 9 additions and 10 deletions
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19
GPy/core/gp.py
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@ -9,7 +9,8 @@ from ..util.plot import gpplot, Tango, x_frame1D, x_frame2D
from model import Model
from parameterization import ObservableArray
from .. import likelihoods
from GPy.likelihoods.gaussian import Gaussian
from ..likelihoods.gaussian import Gaussian
from ..inference.latent_function_inference import exact_gaussian_inference
class GP(Model):
"""
@ -26,7 +27,7 @@ class GP(Model):
"""
def __init__(self, X, Y, kernel, likelihood, inference_method=None, name='gp'):
super(GPBase, self).__init__(name)
super(GP, self).__init__(name)
assert X.ndim == 2
self.X = ObservableArray(X)
@ -50,13 +51,13 @@ class GP(Model):
else:
inference_method = expectation_propagation
print "defaulting to ", inference_method, "for latent function inference"
super(GP, self).__init__(X, Y, kernel, likelihood, inference_method, name)
self.parameters_changed()
self.inference_method = inference_method
self.add_parameter(self.kern, gradient=self.dL_dtheta_K)
self.add_parameter(self.likelihood, gradient=lambda:self.posterior.dL_dtheta_lik)
self.parameters_changed()
def parameters_changed(self):
super(GP, self).parameters_changed()
self.K = self.kern.K(self.X)
@ -241,8 +242,7 @@ class GP(Model):
#define the frame on which to plot
resolution = resolution or 200
Xu = self.X * self._Xscale + self._Xoffset #NOTE self.X are the normalized values now
Xnew, xmin, xmax = x_frame1D(Xu[:,free_dims], plot_limits=plot_limits)
Xnew, xmin, xmax = x_frame1D(self.X[:,free_dims], plot_limits=plot_limits)
Xgrid = np.empty((Xnew.shape[0],self.input_dim))
Xgrid[:,free_dims] = Xnew
for i,v in fixed_inputs:
@ -259,7 +259,7 @@ class GP(Model):
Y = self.Y
for d in which_data_ycols:
gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax, edgecol=linecol, fillcol=fillcol)
ax.plot(Xu[which_data_rows,free_dims], Y[which_data_rows, d], 'kx', mew=1.5)
ax.plot(self.X[which_data_rows,free_dims], Y[which_data_rows, d], 'kx', mew=1.5)
#optionally plot some samples
if samples: #NOTE not tested with fixed_inputs
@ -279,8 +279,7 @@ class GP(Model):
#define the frame for plotting on
resolution = resolution or 50
Xu = self.X * self._Xscale + self._Xoffset #NOTE self.X are the normalized values now
Xnew, _, _, xmin, xmax = x_frame2D(Xu[:,free_dims], plot_limits, resolution)
Xnew, _, _, xmin, xmax = x_frame2D(self.X[:,free_dims], plot_limits, resolution)
Xgrid = np.empty((Xnew.shape[0],self.input_dim))
Xgrid[:,free_dims] = Xnew
for i,v in fixed_inputs: