mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-24 14:15:14 +02:00
linear kern variational updates
This commit is contained in:
Max Zwiessele
parent
ed87e6bbd2
commit
9e1546524e
1 changed files with 26 additions and 58 deletions
|
|
@ -8,6 +8,7 @@ from kernpart import Kernpart
|
|||
from ...util.linalg import tdot
|
||||
from ...util.misc import fast_array_equal, param_to_array
|
||||
from ...core.parameterization import Param
|
||||
from ...core.parameterization.transformations import Logexp
|
||||
|
||||
class Linear(Kernpart):
|
||||
"""
|
||||
|
|
@ -43,8 +44,9 @@ class Linear(Kernpart):
|
|||
else:
|
||||
variances = np.ones(self.input_dim)
|
||||
|
||||
self.variances = Param('variances', variances)
|
||||
self.add_parameters(self.variances)
|
||||
self.variances = Param('variances', variances, Logexp())
|
||||
self.variances.gradient = np.zeros(self.variances.shape)
|
||||
self.add_parameter(self.variances)
|
||||
self.variances.add_observer(self, self.update_variance)
|
||||
|
||||
# initialize cache
|
||||
|
|
@ -57,42 +59,35 @@ class Linear(Kernpart):
|
|||
def on_input_change(self, X):
|
||||
self._K_computations(X, None)
|
||||
|
||||
def update_gradients_full(self, dL_dK, X):
|
||||
#self.variances.gradient[:] = 0
|
||||
self._param_grad_helper(dL_dK, X, self.variances.gradient)
|
||||
|
||||
def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
|
||||
tmp = dL_dKdiag[:, None] * X ** 2
|
||||
if self.ARD:
|
||||
self.variances.gradient = tmp.sum(0)
|
||||
else:
|
||||
self.variances.gradient = tmp.sum()
|
||||
self._param_grad_helper(dL_dKmm, Z, None, self.variances.gradient)
|
||||
self._param_grad_helper(dL_dKnm, X, Z, self.variances.gradient)
|
||||
|
||||
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
|
||||
self._psi_computations(Z, mu, S)
|
||||
|
||||
# psi0:
|
||||
tmp = dL_dpsi0[:, None] * self.mu2_S
|
||||
if self.ARD: self.variances.gradient = tmp.sum(0)
|
||||
else: self.variances.gradient = tmp.sum()
|
||||
|
||||
if self.ARD: self.variances.gradient[:] = tmp.sum(0)
|
||||
else: self.variances.gradient[:] = tmp.sum()
|
||||
#psi1
|
||||
self.dK_dtheta(dL_dpsi1, mu, Z, self.variances.gradient)
|
||||
|
||||
#from psi2
|
||||
self._param_grad_helper(dL_dpsi1, mu, Z, self.variances.gradient)
|
||||
#psi2
|
||||
tmp = dL_dpsi2[:, :, :, None] * (self.ZAinner[:, :, None, :] * (2 * Z)[None, None, :, :])
|
||||
if self.ARD: self.variances.gradient += tmp.sum(0).sum(0).sum(0)
|
||||
else: self.variances.gradient += tmp.sum()
|
||||
|
||||
#from Kmm
|
||||
self._K_computations(Z, None)
|
||||
self.dK_dtheta(dL_dKmm, Z, None, self.variances.gradient)
|
||||
self._param_grad_helper(dL_dKmm, Z, None, self.variances.gradient)
|
||||
|
||||
|
||||
# def _get_params(self):
|
||||
# return self.variances
|
||||
#
|
||||
# def _set_params(self, x):
|
||||
# assert x.size == (self.num_params)
|
||||
# self.variances = x
|
||||
#def parameters_changed(self):
|
||||
# self.variances2 = np.square(self.variances)
|
||||
#
|
||||
# def _get_param_names(self):
|
||||
# if self.num_params == 1:
|
||||
# return ['variance']
|
||||
# else:
|
||||
# return ['variance_%i' % i for i in range(self.variances.size)]
|
||||
|
||||
def K(self, X, X2, target):
|
||||
if self.ARD:
|
||||
XX = X * np.sqrt(self.variances)
|
||||
|
|
@ -109,7 +104,7 @@ class Linear(Kernpart):
|
|||
def Kdiag(self, X, target):
|
||||
np.add(target, np.sum(self.variances * np.square(X), -1), target)
|
||||
|
||||
def dK_dtheta(self, dL_dK, X, X2, target):
|
||||
def _param_grad_helper(self, dL_dK, X, X2, target):
|
||||
if self.ARD:
|
||||
if X2 is None:
|
||||
[np.add(target[i:i + 1], np.sum(dL_dK * tdot(X[:, i:i + 1])), target[i:i + 1]) for i in range(self.input_dim)]
|
||||
|
|
@ -121,13 +116,6 @@ class Linear(Kernpart):
|
|||
self._K_computations(X, X2)
|
||||
target += np.sum(self._dot_product * dL_dK)
|
||||
|
||||
def dKdiag_dtheta(self, dL_dKdiag, X, target):
|
||||
tmp = dL_dKdiag[:, None] * X ** 2
|
||||
if self.ARD:
|
||||
target += tmp.sum(0)
|
||||
else:
|
||||
target += tmp.sum()
|
||||
|
||||
def gradients_X(self, dL_dK, X, X2, target):
|
||||
if X2 is None:
|
||||
target += 2*(((X[None,:, :] * self.variances)) * dL_dK[:, :, None]).sum(1)
|
||||
|
|
@ -145,14 +133,6 @@ class Linear(Kernpart):
|
|||
self._psi_computations(Z, mu, S)
|
||||
target += np.sum(self.variances * self.mu2_S, 1)
|
||||
|
||||
def dpsi0_dtheta(self, dL_dpsi0, Z, mu, S, target):
|
||||
self._psi_computations(Z, mu, S)
|
||||
tmp = dL_dpsi0[:, None] * self.mu2_S
|
||||
if self.ARD:
|
||||
target += tmp.sum(0)
|
||||
else:
|
||||
target += tmp.sum()
|
||||
|
||||
def dpsi0_dmuS(self, dL_dpsi0, Z, mu, S, target_mu, target_S):
|
||||
target_mu += dL_dpsi0[:, None] * (2.0 * mu * self.variances)
|
||||
target_S += dL_dpsi0[:, None] * self.variances
|
||||
|
|
@ -161,10 +141,6 @@ class Linear(Kernpart):
|
|||
"""the variance, it does nothing"""
|
||||
self._psi1 = self.K(mu, Z, target)
|
||||
|
||||
def dpsi1_dtheta(self, dL_dpsi1, Z, mu, S, target):
|
||||
"""the variance, it does nothing"""
|
||||
self.dK_dtheta(dL_dpsi1, mu, Z, target)
|
||||
|
||||
def dpsi1_dmuS(self, dL_dpsi1, Z, mu, S, target_mu, target_S):
|
||||
"""Do nothing for S, it does not affect psi1"""
|
||||
self._psi_computations(Z, mu, S)
|
||||
|
|
@ -185,21 +161,13 @@ class Linear(Kernpart):
|
|||
def dpsi2_dtheta_new(self, dL_dpsi2, Z, mu, S, target):
|
||||
tmp = np.zeros((mu.shape[0], Z.shape[0]))
|
||||
self.K(mu,Z,tmp)
|
||||
self.dK_dtheta(2.*np.sum(dL_dpsi2*tmp[:,None,:],2),mu,Z,target)
|
||||
self._param_grad_helper(2.*np.sum(dL_dpsi2*tmp[:,None,:],2),mu,Z,target)
|
||||
result= 2.*(dL_dpsi2[:,:,:,None]*S[:,None,None,:]*self.variances*Z[None,:,None,:]*Z[None,None,:,:]).sum(0).sum(0).sum(0)
|
||||
if self.ARD:
|
||||
target += result.sum(0).sum(0).sum(0)
|
||||
else:
|
||||
target += result.sum()
|
||||
|
||||
def dpsi2_dtheta(self, dL_dpsi2, Z, mu, S, target):
|
||||
self._psi_computations(Z, mu, S)
|
||||
tmp = dL_dpsi2[:, :, :, None] * (self.ZAinner[:, :, None, :] * (2 * Z)[None, None, :, :])
|
||||
if self.ARD:
|
||||
target += tmp.sum(0).sum(0).sum(0)
|
||||
else:
|
||||
target += tmp.sum()
|
||||
|
||||
def dpsi2_dmuS_new(self, dL_dpsi2, Z, mu, S, target_mu, target_S):
|
||||
tmp = np.zeros((mu.shape[0], Z.shape[0]))
|
||||
self.K(mu,Z,tmp)
|
||||
|
|
@ -309,11 +277,11 @@ class Linear(Kernpart):
|
|||
if not (fast_array_equal(X, self._X) and fast_array_equal(X2, self._X2)):
|
||||
self._X = X.copy()
|
||||
if X2 is None:
|
||||
self._dot_product = tdot(X)
|
||||
self._dot_product = tdot(param_to_array(X))
|
||||
self._X2 = None
|
||||
else:
|
||||
self._X2 = X2.copy()
|
||||
self._dot_product = np.dot(X, X2.T)
|
||||
self._dot_product = np.dot(param_to_array(X), param_to_array(X2.T))
|
||||
|
||||
def _psi_computations(self, Z, mu, S):
|
||||
# here are the "statistics" for psi1 and psi2
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue