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

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This commit is contained in:
Zhenwen Dai 2014-03-05 18:45:24 +00:00
commit 21ba9663b6
27 changed files with 684 additions and 352 deletions
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2
GPy/kern/_src/coregionalize.py
View file

@ -51,8 +51,6 @@ class Coregionalize(Kern):
assert kappa.shape==(self.output_dim, )
self.kappa = Param('kappa', kappa, Logexp())
self.add_parameters(self.W, self.kappa)
self.parameters_changed()
def parameters_changed(self):
self.B = np.dot(self.W, self.W.T) + np.diag(self.kappa)

4
GPy/kern/_src/kern.py
View file

@ -129,7 +129,7 @@ class Kern(Parameterized):
"""
return self.prod(other, tensor=True)
def prod(self, other, tensor=False):
def prod(self, other, tensor=False, name=None):
"""
Multiply two kernels (either on the same space, or on the tensor
product of the input space).
@ -142,4 +142,4 @@ class Kern(Parameterized):
"""
assert isinstance(other, Kern), "only kernels can be added to kernels..."
from prod import Prod
return Prod(self, other, tensor)
return Prod(self, other, tensor, name)

1
GPy/kern/_src/periodic.py
View file

@ -34,7 +34,6 @@ class Periodic(Kern):
self.lengthscale = Param('lengthscale', np.float64(lengthscale), Logexp())
self.period = Param('period', np.float64(period), Logexp())
self.add_parameters(self.variance, self.lengthscale, self.period)
self.parameters_changed()
def _cos(self, alpha, omega, phase):
def f(x):

20
GPy/kern/_src/prod.py
View file

@ -15,14 +15,16 @@ class Prod(Kern):
:rtype: kernel object
"""
def __init__(self, k1, k2, tensor=False):
def __init__(self, k1, k2, tensor=False,name=None):
if tensor:
super(Prod, self).__init__(k1.input_dim + k2.input_dim, k1.name + '_xx_' + k2.name)
name = k1.name + '_xx_' + k2.name if name is None else name
super(Prod, self).__init__(k1.input_dim + k2.input_dim, name)
self.slice1 = slice(0,k1.input_dim)
self.slice2 = slice(k1.input_dim,k1.input_dim+k2.input_dim)
else:
assert k1.input_dim == k2.input_dim, "Error: The input spaces of the kernels to multiply don't have the same dimension."
super(Prod, self).__init__(k1.input_dim, k1.name + '_x_' + k2.name)
name = k1.name + '_x_' + k2.name if name is None else name
super(Prod, self).__init__(k1.input_dim, name)
self.slice1 = slice(0, self.input_dim)
self.slice2 = slice(0, self.input_dim)
self.k1 = k1
@ -39,17 +41,17 @@ class Prod(Kern):
return self.k1.Kdiag(X[:,self.slice1]) * self.k2.Kdiag(X[:,self.slice2])
def update_gradients_full(self, dL_dK, X):
self.k1.update_gradients_full(dL_dK*self.k2(X[:,self.slice2]), X[:,self.slice1])
self.k2.update_gradients_full(dL_dK*self.k1(X[:,self.slice1]), X[:,self.slice2])
self.k1.update_gradients_full(dL_dK*self.k2.K(X[:,self.slice2]), X[:,self.slice1])
self.k2.update_gradients_full(dL_dK*self.k1.K(X[:,self.slice1]), X[:,self.slice2])
def gradients_X(self, dL_dK, X, X2=None):
target = np.zeros(X.shape)
if X2 is None:
target[:,self.slice1] += self.k1.gradients_X(dL_dK*self.k2(X[:,self.slice2]), X[:,self.slice1], None)
target[:,self.slice2] += self.k2.gradients_X(dL_dK*self.k1(X[:,self.slice1]), X[:,self.slice2], None)
target[:,self.slice1] += self.k1.gradients_X(dL_dK*self.k2.K(X[:,self.slice2]), X[:,self.slice1], None)
target[:,self.slice2] += self.k2.gradients_X(dL_dK*self.k1.K(X[:,self.slice1]), X[:,self.slice2], None)
else:
target[:,self.slice1] += self.k1.gradients_X(dL_dK*self.k2(X[:,self.slice2], X2[:,self.slice2]), X[:,self.slice1], X2[:,self.slice1])
target[:,self.slice2] += self.k2.gradients_X(dL_dK*self.k1(X[:,self.slice1], X2[:,self.slice1]), X[:,self.slice2], X2[:,self.slice2])
target[:,self.slice1] += self.k1.gradients_X(dL_dK*self.k2.K(X[:,self.slice2], X2[:,self.slice2]), X[:,self.slice1], X2[:,self.slice1])
target[:,self.slice2] += self.k2.gradients_X(dL_dK*self.k1.K(X[:,self.slice1], X2[:,self.slice1]), X[:,self.slice2], X2[:,self.slice2])
return target
def gradients_X_diag(self, dL_dKdiag, X):

32
GPy/kern/_src/sympykern.py
View file

@ -1,11 +1,10 @@
# Check Matthew Rocklin's blog post.
try:
try:
import sympy as sp
sympy_available=True
from sympy.utilities.lambdify import lambdify
except ImportError:
sympy_available=False
exit()
import numpy as np
from kern import Kern
@ -36,7 +35,7 @@ class Sympykern(Kern):
super(Sympykern, self).__init__(input_dim, name)
self._sp_k = k
# pull the variable names out of the symbolic covariance function.
sp_vars = [e for e in k.atoms() if e.is_Symbol]
self._sp_x= sorted([e for e in sp_vars if e.name[0:2]=='x_'],key=lambda x:int(x.name[2:]))
@ -51,7 +50,7 @@ class Sympykern(Kern):
self._sp_kdiag = k
for x, z in zip(self._sp_x, self._sp_z):
self._sp_kdiag = self._sp_kdiag.subs(z, x)
# If it is a multi-output covariance, add an input for indexing the outputs.
self._real_input_dim = x_dim
# Check input dim is number of xs + 1 if output_dim is >1
@ -73,7 +72,7 @@ class Sympykern(Kern):
# Extract names of shared parameters (those without a subscript)
self._sp_theta = [theta for theta in thetas if theta not in self._sp_theta_i and theta not in self._sp_theta_j]
self.num_split_params = len(self._sp_theta_i)
self._split_theta_names = ["%s"%theta.name[:-2] for theta in self._sp_theta_i]
# Add split parameters to the model.
@ -82,11 +81,11 @@ class Sympykern(Kern):
setattr(self, theta, Param(theta, np.ones(self.output_dim), None))
self.add_parameter(getattr(self, theta))
self.num_shared_params = len(self._sp_theta)
for theta_i, theta_j in zip(self._sp_theta_i, self._sp_theta_j):
self._sp_kdiag = self._sp_kdiag.subs(theta_j, theta_i)
else:
self.num_split_params = 0
self._split_theta_names = []
@ -107,10 +106,10 @@ class Sympykern(Kern):
derivative_arguments = self._sp_x + self._sp_theta
if self.output_dim > 1:
derivative_arguments += self._sp_theta_i
self.derivatives = {theta.name : sp.diff(self._sp_k,theta).simplify() for theta in derivative_arguments}
self.diag_derivatives = {theta.name : sp.diff(self._sp_kdiag,theta).simplify() for theta in derivative_arguments}
# This gives the parameters for the arg list.
self.arg_list = self._sp_x + self._sp_z + self._sp_theta
self.diag_arg_list = self._sp_x + self._sp_theta
@ -124,9 +123,6 @@ class Sympykern(Kern):
# generate the code for the covariance functions
self._gen_code()
self.parameters_changed() # initializes caches
def __add__(self,other):
return spkern(self._sp_k+other._sp_k)
@ -140,7 +136,7 @@ class Sympykern(Kern):
for key in self.derivatives.keys():
setattr(self, '_Kdiag_diff_' + key, lambdify(self.diag_arg_list, self.diag_derivatives[key], 'numpy'))
def K(self,X,X2=None):
def K(self,X,X2=None):
self._K_computations(X, X2)
return self._K_function(**self._arguments)
@ -148,11 +144,11 @@ class Sympykern(Kern):
def Kdiag(self,X):
self._K_computations(X)
return self._Kdiag_function(**self._diag_arguments)
def _param_grad_helper(self,partial,X,Z,target):
pass
def gradients_X(self, dL_dK, X, X2=None):
#if self._X is None or X.base is not self._X.base or X2 is not None:
self._K_computations(X, X2)
@ -171,7 +167,7 @@ class Sympykern(Kern):
gf = getattr(self, '_Kdiag_diff_' + x.name)
dX[:, i] = gf(**self._diag_arguments)*dL_dK
return dX
def update_gradients_full(self, dL_dK, X, X2=None):
# Need to extract parameters to local variables first
self._K_computations(X, X2)
@ -196,7 +192,7 @@ class Sympykern(Kern):
gradient += np.asarray([A[np.where(self._output_ind2==i)].T.sum()
for i in np.arange(self.output_dim)])
setattr(parameter, 'gradient', gradient)
def update_gradients_diag(self, dL_dKdiag, X):
self._K_computations(X)
@ -212,7 +208,7 @@ class Sympykern(Kern):
setattr(parameter, 'gradient',
np.asarray([a[np.where(self._output_ind==i)].sum()
for i in np.arange(self.output_dim)]))
def _K_computations(self, X, X2=None):
"""Set up argument lists for the derivatives."""
# Could check if this needs doing or not, there could