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small tweak to the gradients in sparse GP

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This commit is contained in:
James Hensman 2012-12-07 23:03:53 -08:00
parent e6d08348c7
commit ab56f3f9b7
3 changed files with 5 additions and 10 deletions
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8
GPy/examples/uncertain_input_GP_regression_demo.py
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@ -1,7 +1,6 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import pylab as pb
import numpy as np
import GPy
@ -9,18 +8,15 @@ pb.ion()
pb.close('all')
######################################
## 1 dimensional example
# sample inputs and outputs
S = np.ones((20,1))
X = np.random.uniform(-3.,3.,(20,1))
Y = np.sin(X)+np.random.randn(20,1)*0.05
k = GPy.kern.bias(1) + GPy.kern.white(1)
k = GPy.kern.rbf(1) + GPy.kern.white(1)
# create simple GP model
m = GPy.models.uncertain_input_GP_regression(X,Y,S,kernel=k)
m = GPy.models.sparse_GP_regression(X,Y,X_uncertainty=S,kernel=k)
# contrain all parameters to be positive
m.constrain_positive('(variance|prec)')

1
GPy/models/sparse_GPLVM.py
View file

@ -54,5 +54,6 @@ class sparse_GPLVM(sparse_GP_regression, GPLVM):
def plot(self):
GPLVM.plot(self)
#passing Z without a small amout of jitter will induce the white kernel where we don;t want it!
mu, var = sparse_GP_regression.predict(self, self.Z+np.random.randn(*self.Z.shape)*0.0001)
pb.plot(mu[:, 0] , mu[:, 1], 'ko')

6
GPy/models/sparse_GP_regression.py
View file

@ -1,7 +1,6 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
import pylab as pb
from ..util.linalg import mdot, jitchol, chol_inv, pdinv
@ -50,7 +49,7 @@ class sparse_GP_regression(GP_regression):
self.has_uncertain_inputs=False
else:
assert X_uncertainty.shape==X.shape
self.has_uncertain_inputs=False
self.has_uncertain_inputs=True
self.X_uncertainty = X_uncertainty
GP_regression.__init__(self, X, Y, kernel=kernel, normalize_X=normalize_X, normalize_Y=normalize_Y)
@ -99,8 +98,7 @@ class sparse_GP_regression(GP_regression):
# Compute dL_dpsi
self.dL_dpsi0 = - 0.5 * self.D * self.beta * np.ones(self.N)
dC_dpsi1 = (self.LLambdai.T[:,:, None, None] * self.V)
self.dL_dpsi1 = (dC_dpsi1*self.C[None,:,None,:]).sum(1).sum(-1)
self.dL_dpsi1 = (self.LLambdai.T[:,:,None,None]*self.V*self.C[None,:,None,:]).sum(1).sum(-1)
self.dL_dpsi2 = - 0.5 * self.beta * (self.D*(self.LBL_inv - self.Kmmi) + self.G)
# Compute dL_dKmm