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debugging BGPLVM

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This commit is contained in:
Nicolo Fusi 2013-02-15 16:31:51 +00:00
parent 151c426a3a
commit bf2d641112
3 changed files with 21 additions and 19 deletions
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18
GPy/examples/BGPLVM_demo.py
View file

@ -6,23 +6,21 @@ import pylab as pb
import GPy import GPy
np.random.seed(123344) np.random.seed(123344)
N = 10 N = 3
M = 3 M = 2
Q = 4 Q = 2
D = 5 D = 4
#generate GPLVM-like data #generate GPLVM-like data
X = np.random.rand(N, Q) X = np.random.rand(N, Q)
k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001) k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X) K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T Y = np.random.multivariate_normal(np.zeros(N),K,D).T
<<<<<<< HEAD k = GPy.kern.bias(Q) #+ GPy.kern.white(Q)
k = GPy.kern.rbf(Q) + GPy.kern.rbf(Q) + GPy.kern.white(Q) # k = GPy.kern.rbf(Q) + GPy.kern.rbf(Q) + GPy.kern.white(Q)
# k = GPy.kern.linear(Q, ARD = True) + GPy.kern.white(Q, 0.00001)
=======
# k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001) # k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
k = GPy.kern.rbf(Q, ARD = False) + GPy.kern.white(Q, 0.00001) # k = GPy.kern.rbf(Q, ARD = False) + GPy.kern.white(Q, 0.00001)
>>>>>>> master
m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M) m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M)
m.constrain_positive('(rbf|bias|noise|white|S)') m.constrain_positive('(rbf|bias|noise|white|S)')
# m.constrain_fixed('S', 1) # m.constrain_fixed('S', 1)

4
GPy/examples/oil_flow_demo.py
View file

@ -41,9 +41,9 @@ m.constrain_positive('(rbf|bias|S|linear|white|noise)')
# m.unconstrain('white') # m.unconstrain('white')
# m.constrain_bounded('white', 1e-6, 10.0) # m.constrain_bounded('white', 1e-6, 10.0)
# plot_oil(m.X, np.array([1,1]), labels, 'PCA initialization') # plot_oil(m.X, np.array([1,1]), labels, 'PCA initialization')
m.optimize(messages = True) # m.optimize(messages = True)
# m.optimize('tnc', messages = True) # m.optimize('tnc', messages = True)
plot_oil(m.X, m.kern.parts[0].lengthscale, labels, 'B-GPLVM') # plot_oil(m.X, m.kern.parts[0].lengthscale, labels, 'B-GPLVM')
# # pb.figure() # # pb.figure()
# m.plot() # m.plot()
# pb.title('PCA initialisation') # pb.title('PCA initialisation')

18
GPy/kern/bias.py
View file

@ -47,6 +47,10 @@ class bias(kernpart):
def dKdiag_dX(self,partial,X,target): def dKdiag_dX(self,partial,X,target):
pass pass
#---------------------------------------#
# PSI statistics #
#---------------------------------------#
def psi0(self, Z, mu, S, target): def psi0(self, Z, mu, S, target):
target += self.variance target += self.variance
@ -59,27 +63,27 @@ class bias(kernpart):
def dpsi0_dtheta(self, partial, Z, mu, S, target): def dpsi0_dtheta(self, partial, Z, mu, S, target):
target += partial.sum() target += partial.sum()
def dpsi1_dtheta(self, partial, Z, mu, S, target):
target += partial.sum()
def dpsi2_dtheta(self, partial, Z, mu, S, target):
target += 2.*self.variance*partial.sum()
def dpsi0_dZ(self, partial, Z, mu, S, target): def dpsi0_dZ(self, partial, Z, mu, S, target):
pass pass
def dpsi0_dmuS(self, partial, Z, mu, S, target_mu, target_S): def dpsi0_dmuS(self, partial, Z, mu, S, target_mu, target_S):
pass pass
def dpsi1_dtheta(self, partial, Z, mu, S, target):
target += partial.sum()
def dpsi1_dZ(self, partial, Z, mu, S, target): def dpsi1_dZ(self, partial, Z, mu, S, target):
pass pass
def dpsi1_dmuS(self, partial, Z, mu, S, target_mu, target_S): def dpsi1_dmuS(self, partial, Z, mu, S, target_mu, target_S):
pass pass
def dpsi2_dtheta(self, partial, Z, mu, S, target):
target += np.sum(2.*self.variance*partial)
def dpsi2_dZ(self, partial, Z, mu, S, target): def dpsi2_dZ(self, partial, Z, mu, S, target):
pass pass
def dpsi2_dmuS(self, partial, Z, mu, S, target_mu, target_S): def dpsi2_dmuS(self, partial, Z, mu, S, target_mu, target_S):
pass pass