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gradient checker implemented

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Max Zwiessele 2013-07-23 12:10:32 +01:00
parent 33916b4d58
commit d5cb531d40
2 changed files with 28 additions and 7 deletions
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1
GPy/models/__init__.py
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@ -12,3 +12,4 @@ from sparse_gplvm import SparseGPLVM
from warped_gp import WarpedGP from warped_gp import WarpedGP
from bayesian_gplvm import BayesianGPLVM from bayesian_gplvm import BayesianGPLVM
from mrd import MRD from mrd import MRD
from gradient_checker import GradientChecker

34
GPy/models/gradient_checker.py
View file

@ -37,10 +37,29 @@ class GradientChecker(Model):
Names to print, when performing gradcheck. If a list was passed to x0 Names to print, when performing gradcheck. If a list was passed to x0
a list of names with the same length is expected. a list of names with the same length is expected.
:param args: Arguments passed as f(x, *args, **kwargs) and df(x, *args, **kwargs) :param args: Arguments passed as f(x, *args, **kwargs) and df(x, *args, **kwargs)
Examples:
---------
Sinusoid:
X = numpy.random.rand(N, Q)
f = lambda x: numpy.sin(x)
df = lambda x: numpy.cos(x)
grad = gc.GradientChecker(f,df,X,'x')
Using GPy:
N, M, Q = 10, 5, 3
X, Z = numpy.random.randn(N,Q), numpy.random.randn(M,Q)
kern = GPy.kern.linear(Q, ARD=True) + GPy.kern.rbf(Q, ARD=True)
import GPy.models.gradient_checker as gc
grad = gc.GradientChecker(kern.K,
lambda x: 2*kern.dK_dX(numpy.ones((1,1)), x),
x0 = X.copy(),
names='X')
""" """
Model.__init__(self) Model.__init__(self)
self.f = f
self.df = df
if isinstance(x0, (list, tuple)) and names is None: if isinstance(x0, (list, tuple)) and names is None:
self.shapes = [get_shape(xi) for xi in x0] self.shapes = [get_shape(xi) for xi in x0]
self.names = ['X{i}'.format(i=i) for i in range(len(x0))] self.names = ['X{i}'.format(i=i) for i in range(len(x0))]
@ -60,17 +79,19 @@ class GradientChecker(Model):
self._param_names.extend(map(lambda nameshape: ('_'.join(nameshape)).strip('_'), itertools.izip(itertools.repeat(name), itertools.imap(lambda t: '_'.join(map(str, t)), itertools.product(*map(lambda xi: range(xi), shape)))))) self._param_names.extend(map(lambda nameshape: ('_'.join(nameshape)).strip('_'), itertools.izip(itertools.repeat(name), itertools.imap(lambda t: '_'.join(map(str, t)), itertools.product(*map(lambda xi: range(xi), shape))))))
self.args = args self.args = args
self.kwargs = kwargs self.kwargs = kwargs
self.f = f
self.df = df
def _get_x(self): def _get_x(self):
if len(self.names) > 1: if len(self.names) > 1:
return [self.__getattribute__(name) for name in self.names] return [self.__getattribute__(name) for name in self.names] + list(self.args)
return self.__getattribute__(self.names[0]) return [self.__getattribute__(self.names[0])] + list(self.args)
def log_likelihood(self): def log_likelihood(self):
return numpy.atleast_1d(self.f(self._get_x(), *self.args, **self.kwargs)) return float(numpy.sum(self.f(*self._get_x(), **self.kwargs)))
def _log_likelihood_gradients(self): def _log_likelihood_gradients(self):
return numpy.atleast_1d(self.df(self._get_x(), *self.args, **self.kwargs)) return numpy.atleast_1d(self.df(*self._get_x(), **self.kwargs)).flatten()
def _get_params(self): def _get_params(self):
@ -86,4 +107,3 @@ class GradientChecker(Model):
def _get_param_names(self): def _get_param_names(self):
return self._param_names return self._param_names