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linear psi2 statistics done, all gradients working

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Max Zwiessele 2013-05-03 13:36:04 +01:00
parent 914bdc73d8
commit ecf0dc0680
1 changed files with 6 additions and 12 deletions
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18
GPy/kern/linear.py
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@ -5,7 +5,6 @@
from kernpart import kernpart
import numpy as np
from ..util.linalg import tdot
from GPy.util.linalg import mdot
class linear(kernpart):
"""
@ -144,7 +143,7 @@ class linear(kernpart):
# psi2_old = self.ZZ * np.square(self.variances) * self.mu2_S[:, None, None, :]
# target += psi2.sum(-1)
# slow way of doing it, but right
# psi2_real = np.zeros((mu.shape[0], Z.shape[0], Z.shape[0]))
# psi2_real = rm np.zeros((mu.shape[0], Z.shape[0], Z.shape[0]))
# for n in range(mu.shape[0]):
# for m_prime in range(Z.shape[0]):
# for m in range(Z.shape[0]):
@ -171,14 +170,9 @@ class linear(kernpart):
"""Think N,M,M,Q """
self._psi_computations(Z, mu, S)
AZZA = self.ZA.T[:, None, :, None] * self.ZA[None, :, None, :]
AZZA += AZZA.swapaxes(1, 2)
tmp = self.ZZ * np.square(self.variances) # M,M,Q
dS_old = (dL_dpsi2[:, :, :, None] * tmp).sum(1).sum(1)
import ipdb;ipdb.set_trace()
target_S += dS_old
dpsi2_dmu = (dL_dpsi2[:, :, :, None] * np.tensordot(mu, AZZA, ((-1), (0)))).sum(1).sum(1)
# twomu = mu[:,None,None,:,None] + mu[:,None,None,None,:]
# t = (dL_dpsi2[:, :, :, None, None] * tmp[None, :, :, :, None] * twomu).sum(1).sum(1).sum(1)
AZZA = AZZA + AZZA.swapaxes(1, 2)
target_S += (dL_dpsi2[:, :, :, None] * self.ZA[None, :, None, :] * self.ZA[None, None, :, :]).sum(1).sum(1)
dpsi2_dmu = (dL_dpsi2[:, :, :, None] * np.tensordot(mu, AZZA, (-1, 0))).sum(1).sum(1)
target_mu += dpsi2_dmu
def dpsi2_dZ(self, dL_dpsi2, Z, mu, S, target):
@ -226,8 +220,8 @@ class linear(kernpart):
if Zv_changed:
# Z has changed, compute Z specific stuff
# self.ZZ = Z[:,None,:]*Z[None,:,:] # M,M,Q
self.ZZ = np.empty((Z.shape[0], Z.shape[0], Z.shape[1]), order='F')
[tdot(Z[:, i:i + 1], self.ZZ[:, :, i].T) for i in xrange(Z.shape[1])]
# self.ZZ = np.empty((Z.shape[0], Z.shape[0], Z.shape[1]), order='F')
# [tdot(Z[:, i:i + 1], self.ZZ[:, :, i].T) for i in xrange(Z.shape[1])]
self.ZA = Z * self.variances
self._Z = Z.copy()
self._variances = self.variances.copy()