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deleted kernpart, prod and add seem to work okay.

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James Hensman 2014-02-19 17:37:18 +00:00
parent 493506408c
commit 92d71384b7
16 changed files with 95 additions and 238 deletions
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70
GPy/kern/_src/add.py
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@ -5,8 +5,8 @@ import sys
import numpy as np
import itertools
from linear import Linear
from ..core.parameterization import Parameterized
from GPy.core.parameterization.param import Param
from ...core.parameterization import Parameterized
from ...core.parameterization.param import Param
from kern import Kern
class Add(Kern):
@ -27,7 +27,7 @@ class Add(Kern):
self.add_parameters(*subkerns)
def K(self, X, X2=None, which_parts='all'):
def K(self, X, X2=None):
"""
Compute the kernel function.
@ -35,52 +35,22 @@ class Add(Kern):
:param X2: (optional) the second set of arguments to the kernel. If X2
is None, this is passed throgh to the 'part' object, which
handles this as X2 == X.
:param which_parts: a list of booleans detailing whether to include
each of the part functions. By default, 'all'
indicates all parts
"""
if which_parts == 'all':
which_parts = [True] * self.size
assert X.shape[1] == self.input_dim
if X2 is None:
target = np.zeros((X.shape[0], X.shape[0]))
[p.K(X[:, i_s], None, target=target) for p, i_s, part_i_used in zip(self._parameters_, self.input_slices, which_parts) if part_i_used]
return sum([p.K(X[:, i_s], None) for p, i_s in zip(self._parameters_, self.input_slices)])
else:
target = np.zeros((X.shape[0], X2.shape[0]))
[p.K(X[:, i_s], X2[:, i_s], target=target) for p, i_s, part_i_used in zip(self._parameters_, self.input_slices, which_parts) if part_i_used]
return target
return sum([p.K(X[:, i_s], X2[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)])
def update_gradients_full(self, dL_dK, X):
[p.update_gradients_full(dL_dK, X) for p in self._parameters_]
[p.update_gradients_full(dL_dK, X[:,i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
[p.update_gradients_sparse(dL_dKmm, dL_dKnm, dL_dKdiag, X, Z) for p in self._parameters_]
[p.update_gradients_sparse(dL_dKmm, dL_dKnm, dL_dKdiag, X[:,i_s], Z[:,i_s]) for p, i_s in zip(self._parameters_, i_s)]
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
[p.update_gradients_variational(dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z) for p in self._parameters_]
def _param_grad_helper(self, dL_dK, X, X2=None):
"""
Compute the gradient of the covariance function with respect to the parameters.
:param dL_dK: An array of gradients of the objective function with respect to the covariance function.
:type dL_dK: Np.ndarray (num_samples x num_inducing)
:param X: Observed data inputs
:type X: np.ndarray (num_samples x input_dim)
:param X2: Observed data inputs (optional, defaults to X)
:type X2: np.ndarray (num_inducing x input_dim)
returns: dL_dtheta
"""
assert X.shape[1] == self.input_dim
target = np.zeros(self.size)
if X2 is None:
[p._param_grad_helper(dL_dK, X[:, i_s], None, target[ps]) for p, i_s, ps, in zip(self._parameters_, self.input_slices, self._param_slices_)]
else:
[p._param_grad_helper(dL_dK, X[:, i_s], X2[:, i_s], target[ps]) for p, i_s, ps, in zip(self._parameters_, self.input_slices, self._param_slices_)]
return self._transform_gradients(target)
def gradients_X(self, dL_dK, X, X2=None):
"""Compute the gradient of the objective function with respect to X.
@ -93,33 +63,15 @@ class Add(Kern):
target = np.zeros_like(X)
if X2 is None:
[p.gradients_X(dL_dK, X[:, i_s], None, target[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
[np.add(target[:,i_s], p.gradients_X(dL_dK, X[:, i_s], None), target[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
else:
[p.gradients_X(dL_dK, X[:, i_s], X2[:, i_s], target[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
[np.add(target[:,i_s], p.gradients_X(dL_dK, X[:, i_s], X2[:,i_s]), target[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
return target
def Kdiag(self, X, which_parts='all'):
def Kdiag(self, X):
"""Compute the diagonal of the covariance function for inputs X."""
if which_parts == 'all':
which_parts = [True] * self.size
assert X.shape[1] == self.input_dim
target = np.zeros(X.shape[0])
[p.Kdiag(X[:, i_s], target=target) for p, i_s, part_on in zip(self._parameters_, self.input_slices, which_parts) if part_on]
return target
def dKdiag_dtheta(self, dL_dKdiag, X):
"""Compute the gradient of the diagonal of the covariance function with respect to the parameters."""
assert X.shape[1] == self.input_dim
assert dL_dKdiag.size == X.shape[0]
target = np.zeros(self.size)
[p.dKdiag_dtheta(dL_dKdiag, X[:, i_s], target[ps]) for p, i_s, ps in zip(self._parameters_, self.input_slices, self._param_slices_)]
return self._transform_gradients(target)
def dKdiag_dX(self, dL_dKdiag, X):
assert X.shape[1] == self.input_dim
target = np.zeros_like(X)
[p.dKdiag_dX(dL_dKdiag, X[:, i_s], target[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
return target
return sum([p.Kdiag(X[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)])
def psi0(self, Z, mu, S):
target = np.zeros(mu.shape[0])