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Merge branch 'params' of github.com:SheffieldML/GPy into params

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Max Zwiessele 2014-03-24 11:22:39 +00:00
commit d27733bf5f
3 changed files with 20 additions and 10 deletions
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12
GPy/kern/_src/linear.py
View file

@ -121,7 +121,7 @@ class Linear(Kern):
gamma = variational_posterior.binary_prob gamma = variational_posterior.binary_prob
mu = variational_posterior.mean mu = variational_posterior.mean
return np.einsum('nq,q,mq,nq->nm',gamma,self.variances,Z,mu) return np.einsum('nq,q,mq,nq->nm',gamma,self.variances,Z,mu)
# return (self.variances*gamma*mu).sum(axis=1) # return (self.variances*gamma*mu).sum(axis=1)
else: else:
return self.K(variational_posterior.mean, Z) #the variance, it does nothing return self.K(variational_posterior.mean, Z) #the variance, it does nothing
@ -177,7 +177,7 @@ class Linear(Kern):
grad = np.einsum('nm,nq,q,nq->mq',dL_dpsi1,gamma, self.variances,mu) +\ grad = np.einsum('nm,nq,q,nq->mq',dL_dpsi1,gamma, self.variances,mu) +\
np.einsum('nmo,noq->mq',dL_dpsi2,_dpsi2_dZ) np.einsum('nmo,noq->mq',dL_dpsi2,_dpsi2_dZ)
return grad return grad
else: else:
#psi1 #psi1
@ -191,15 +191,15 @@ class Linear(Kern):
gamma = variational_posterior.binary_prob gamma = variational_posterior.binary_prob
mu = variational_posterior.mean mu = variational_posterior.mean
S = variational_posterior.variance S = variational_posterior.variance
mu2S = np.square(mu)+S mu2S = np.square(mu)+S
_, _dpsi2_dgamma, _dpsi2_dmu, _dpsi2_dS, _ = linear_psi_comp._psi2computations(self.variances, Z, mu, S, gamma) _, _dpsi2_dgamma, _dpsi2_dmu, _dpsi2_dS, _ = linear_psi_comp._psi2computations(self.variances, Z, mu, S, gamma)
grad_gamma = np.einsum('n,q,nq->nq',dL_dpsi0,self.variances,mu2S) + np.einsum('nm,q,mq,nq->nq',dL_dpsi1,self.variances,Z,mu) +\ grad_gamma = np.einsum('n,q,nq->nq',dL_dpsi0,self.variances,mu2S) + np.einsum('nm,q,mq,nq->nq',dL_dpsi1,self.variances,Z,mu) +\
np.einsum('nmo,nmoq->nq',dL_dpsi2,_dpsi2_dgamma) np.einsum('nmo,nmoq->nq',dL_dpsi2,_dpsi2_dgamma)
grad_mu = np.einsum('n,nq,q,nq->nq',dL_dpsi0,gamma,2.*self.variances,mu) + np.einsum('nm,nq,q,mq->nq',dL_dpsi1,gamma,self.variances,Z) +\ grad_mu = np.einsum('n,nq,q,nq->nq',dL_dpsi0,gamma,2.*self.variances,mu) + np.einsum('nm,nq,q,mq->nq',dL_dpsi1,gamma,self.variances,Z) +\
np.einsum('nmo,nmoq->nq',dL_dpsi2,_dpsi2_dmu) np.einsum('nmo,nmoq->nq',dL_dpsi2,_dpsi2_dmu)
grad_S = np.einsum('n,nq,q->nq',dL_dpsi0,gamma,self.variances) + np.einsum('nmo,nmoq->nq',dL_dpsi2,_dpsi2_dS) grad_S = np.einsum('n,nq,q->nq',dL_dpsi0,gamma,self.variances) + np.einsum('nmo,nmoq->nq',dL_dpsi2,_dpsi2_dS)
return grad_mu, grad_S, grad_gamma return grad_mu, grad_S, grad_gamma
else: else:
grad_mu, grad_S = np.zeros(variational_posterior.mean.shape), np.zeros(variational_posterior.mean.shape) grad_mu, grad_S = np.zeros(variational_posterior.mean.shape), np.zeros(variational_posterior.mean.shape)
@ -210,7 +210,7 @@ class Linear(Kern):
grad_mu += (dL_dpsi1[:, :, None] * (Z * self.variances)).sum(1) grad_mu += (dL_dpsi1[:, :, None] * (Z * self.variances)).sum(1)
# psi2 # psi2
self._weave_dpsi2_dmuS(dL_dpsi2, Z, variational_posterior, grad_mu, grad_S) self._weave_dpsi2_dmuS(dL_dpsi2, Z, variational_posterior, grad_mu, grad_S)
return grad_mu, grad_S return grad_mu, grad_S
#--------------------------------------------------# #--------------------------------------------------#

6
GPy/kern/_src/prod.py
View file

@ -58,8 +58,6 @@ class Prod(CombinationKernel):
def gradients_X_diag(self, dL_dKdiag, X): def gradients_X_diag(self, dL_dKdiag, X):
target = np.zeros(X.shape) target = np.zeros(X.shape)
for k1,k2 in itertools.combinations(self.parts, 2): for k1,k2 in itertools.combinations(self.parts, 2):
target += k1.gradients_X(dL_dKdiag*k2.Kdiag(X), X) target += k1.gradients_X_diag(dL_dKdiag*k2.Kdiag(X), X)
target += k2.gradients_X(dL_dKdiag*k1.Kdiag(X), X) target += k2.gradients_X_diag(dL_dKdiag*k1.Kdiag(X), X)
return target return target

12
GPy/testing/kernel_tests.py
View file

@ -8,6 +8,7 @@ import sys
verbose = 0 verbose = 0
np.random.seed(50)
class Kern_check_model(GPy.core.Model): class Kern_check_model(GPy.core.Model):
@ -243,6 +244,17 @@ class KernelGradientTestsContinuous(unittest.TestCase):
k.randomize() k.randomize()
self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose)) self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
def test_Prod2(self):
k = (GPy.kern.RBF(2, active_dims=[0,4]) * GPy.kern.Linear(self.D))
k.randomize()
self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
def test_Prod3(self):
k = GPy.kern.Matern32(2, active_dims=[2,3]) * (GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D))
k = (GPy.kern.RBF(2, active_dims=[0,4]) * GPy.kern.Linear(self.D))
k.randomize()
self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
def test_Add(self): def test_Add(self):
k = GPy.kern.Matern32(2, active_dims=[2,3]) + GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D) k = GPy.kern.Matern32(2, active_dims=[2,3]) + GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D)
k += GPy.kern.Matern32(2, active_dims=[2,3]) + GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D) k += GPy.kern.Matern32(2, active_dims=[2,3]) + GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D)