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X caching is not yet done, parameter caching working fine. X cache must be adjusted to update at the right times

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Max Zwiessele 2013-10-27 17:04:46 +00:00
parent d3721b76a8
commit d8151eee61
12 changed files with 249 additions and 109 deletions
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3
GPy/kern/kern.py
View file

@ -52,6 +52,9 @@ class kern(Parameterized):
def parameters_changed(self):
[p.parameters_changed() for p in self._parameters_]
def connect_input(self, Xparam):
[p.connect_input(Xparam) for p in self._parameters_]
def getstate(self):
"""
Get the current state of the class,

2
GPy/kern/parts/bias.py
View file

@ -16,7 +16,7 @@ class Bias(Kernpart):
:type variance: float
"""
super(Bias, self).__init__(input_dim, 'bias')
self.variance = Param("variance", variance, None)
self.variance = Param("variance", variance)
self.add_parameter(self.variance)
#self._set_params(np.array([variance]).flatten())

15
GPy/kern/parts/kernpart.py
View file

@ -21,7 +21,22 @@ class Kernpart(Parameterized):
# the name of the covariance function.
# link to parameterized objects
self._parameters_ = []
self._X = None
def connect_input(self, X):
X.add_observer(self, self.on_input_change)
self._X = X
def on_input_change(self, X):
"""
During optimization this function will be called when
the inputs X changed. Use this to update caches dependent
on the inputs X.
"""
# overwrite this to update kernel when inputs X change
pass
# def set_as_parameter_named(self, name, gradient, index=None, *args, **kwargs):
# """
# :param names: name of parameter to set as parameter

19
GPy/kern/parts/linear.py
View file

@ -39,16 +39,23 @@ class Linear(Kernpart):
else:
if variances is not None:
variances = np.asarray(variances)
assert variances.size == self.input_dim, "bad number of lengthscales"
assert variances.size == self.input_dim, "bad number of variances, need one ARD variance per input_dim"
else:
variances = np.ones(self.input_dim)
self.variances = Param('variances', variances)
self.add_parameters(self.variances)
self.variances.add_observer(self, self.update_variance)
# initialize cache
self._Z, self._mu, self._S = np.empty(shape=(3, 1))
self._X, self._X2 = np.empty(shape=(2, 1))
def update_variance(self, v):
self.variances2 = np.square(self.variances)
def on_input_change(self, X):
self._K_computations(X, None)
# def _get_params(self):
# return self.variances
@ -56,8 +63,8 @@ class Linear(Kernpart):
# 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 parameters_changed(self):
# self.variances2 = np.square(self.variances)
#
# def _get_param_names(self):
# if self.num_params == 1:
@ -74,7 +81,8 @@ class Linear(Kernpart):
XX2 = X2 * np.sqrt(self.variances)
target += np.dot(XX, XX2.T)
else:
self._K_computations(X, X2)
if X is not self._X or X2 is not None:
self._K_computations(X, X2)
target += self.variances * self._dot_product
def Kdiag(self, X, target):
@ -88,7 +96,8 @@ class Linear(Kernpart):
product = X[:, None, :] * X2[None, :, :]
target += (dL_dK[:, :, None] * product).sum(0).sum(0)
else:
self._K_computations(X, X2)
if X is not self._X or X2 is not None:
self._K_computations(X, X2)
target += np.sum(self._dot_product * dL_dK)
def dKdiag_dtheta(self, dL_dKdiag, X, target):

38
GPy/kern/parts/rbf.py
View file

@ -50,32 +50,34 @@ class RBF(Kernpart):
else:
lengthscale = np.ones(self.input_dim)
#self._set_params(np.hstack((variance, lengthscale.flatten())))
self.variance = Param('variance', variance, None)
self.lengthscale = Param('lengthscale', lengthscale, None)
self.variance = Param('variance', variance)
self.lengthscale = Param('lengthscale', lengthscale)
self.lengthscale.add_observer(self, self.update_lengthscale)
self.add_parameters(self.variance, self.lengthscale)
# self.set_as_parameter('variance', self.variance, None)
# self.set_as_parameter('lengthscale', self.lengthscale, None)
# initialize cache
self._Z, self._mu, self._S = np.empty(shape=(3, 1))
self._X, self._X2, self._params_save = np.empty(shape=(3, 1))
#self._Z, self._mu, self._S = np.empty(shape=(3, 1))
#self._X, self._X2, self._params_save = np.empty(shape=(3, 1))
# a set of optional args to pass to weave
self.weave_options = {'headers' : ['<omp.h>'],
'extra_compile_args': ['-fopenmp -O3'], # -march=native'],
'extra_link_args' : ['-lgomp']}
def on_input_change(self, X):
import pdb;pdb.set_trace()
self._K_computations(X, None)
def update_lengthscale(self, l):
self.lengthscale2 = np.square(self.lengthscale)
def parameters_changed(self):
self.lengthscale2 = np.square(self.lengthscale)
# reset cached results
#self._X, self._X2, self._params_save = np.empty(shape=(3, 1))
#self._Z, self._mu, self._S = np.empty(shape=(3, 1)) # cached versions of Z,mu,S
self._X, self._X2 = np.empty(shape=(2, 1))
self._Z, self._mu, self._S = np.empty(shape=(3, 1)) # cached versions of Z,mu,S
#self._X, self._X2 = np.empty(shape=(2, 1))
#self._Z, self._mu, self._S = np.empty(shape=(3, 1)) # cached versions of Z,mu,S
pass
# def _get_params(self):
# return np.hstack((self.variance, self.lengthscale))
# #
@ -97,14 +99,17 @@ class RBF(Kernpart):
# return ['variance'] + ['lengthscale_%i' % i for i in range(self.lengthscale.size)]
def K(self, X, X2, target):
self._K_computations(X, X2)
if self._X is None or X.base is not self._X.base or X2 is not None:
import pdb;pdb.set_trace()
self._K_computations(X, X2)
target += self.variance * self._K_dvar
def Kdiag(self, X, target):
np.add(target, self.variance, target)
def dK_dtheta(self, dL_dK, X, X2, target):
self._K_computations(X, X2)
if self._X is None or X.base is not self._X.base or X2 is not None:
self._K_computations(X, X2)
target[0] += np.sum(self._K_dvar * dL_dK)
if self.ARD:
dvardLdK = self._K_dvar * dL_dK
@ -152,7 +157,8 @@ class RBF(Kernpart):
target[0] += np.sum(dL_dKdiag)
def dK_dX(self, dL_dK, X, X2, target):
self._K_computations(X, X2)
if self._X is None or X.base is not self._X.base or X2 is not None:
self._K_computations(X, X2)
if X2 is None:
_K_dist = 2*(X[:, None, :] - X[None, :, :])
else:
@ -241,7 +247,7 @@ class RBF(Kernpart):
def _K_computations(self, X, X2):
#params = self._get_params()
if not (fast_array_equal(X, self._X) and fast_array_equal(X2, self._X2)):# and fast_array_equal(self._params_save , params)):
self._X = X.copy()
#self._X = X.copy()
#self._params_save = params.copy()
if X2 is None:
self._X2 = None

2
GPy/kern/parts/white.py
View file

@ -17,7 +17,7 @@ class White(Kernpart):
def __init__(self,input_dim,variance=1.):
super(White, self).__init__(input_dim, 'white')
self.input_dim = input_dim
self.variance = Param('variance', variance, None)
self.variance = Param('variance', variance)
self.add_parameters(self.variance)
# self._set_params(np.array([variance]).flatten())
self._psi1 = 0 # TODO: more elegance here