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a little work on mappings

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This commit is contained in:
James Hensman 2015-03-23 12:38:15 +00:00
parent 1b46a99e75
commit 99545500b1
2 changed files with 24 additions and 68 deletions
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68
GPy/core/mapping.py
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@ -74,72 +74,4 @@ class Bijective_mapping(Mapping):
"""Inverse mapping from output domain of the function to the inputs."""
raise NotImplementedError
from model import Model
class Mapping_check_model(Model):
"""
This is a dummy model class used as a base class for checking that the
gradients of a given mapping are implemented correctly. It enables
checkgradient() to be called independently on each mapping.
"""
def __init__(self, mapping=None, dL_df=None, X=None):
num_samples = 20
if mapping==None:
mapping = GPy.mapping.linear(1, 1)
if X==None:
X = np.random.randn(num_samples, mapping.input_dim)
if dL_df==None:
dL_df = np.ones((num_samples, mapping.output_dim))
self.mapping=mapping
self.X = X
self.dL_df = dL_df
self.num_params = self.mapping.num_params
Model.__init__(self)
def _get_params(self):
return self.mapping._get_params()
def _get_param_names(self):
return self.mapping._get_param_names()
def _set_params(self, x):
self.mapping._set_params(x)
def log_likelihood(self):
return (self.dL_df*self.mapping.f(self.X)).sum()
def _log_likelihood_gradients(self):
raise NotImplementedError, "This needs to be implemented to use the Mapping_check_model class."
class Mapping_check_df_dtheta(Mapping_check_model):
"""This class allows gradient checks for the gradient of a mapping with respect to parameters. """
def __init__(self, mapping=None, dL_df=None, X=None):
Mapping_check_model.__init__(self,mapping=mapping,dL_df=dL_df, X=X)
def _log_likelihood_gradients(self):
return self.mapping.df_dtheta(self.dL_df, self.X)
class Mapping_check_df_dX(Mapping_check_model):
"""This class allows gradient checks for the gradient of a mapping with respect to X. """
def __init__(self, mapping=None, dL_df=None, X=None):
Mapping_check_model.__init__(self,mapping=mapping,dL_df=dL_df, X=X)
if dL_df==None:
dL_df = np.ones((self.X.shape[0],self.mapping.output_dim))
self.num_params = self.X.shape[0]*self.mapping.input_dim
def _log_likelihood_gradients(self):
return self.mapping.df_dX(self.dL_df, self.X).flatten()
def _get_param_names(self):
return ['X_' +str(i) + ','+str(j) for j in range(self.X.shape[1]) for i in range(self.X.shape[0])]
def _get_params(self):
return self.X.flatten()
def _set_params(self, x):
self.X=x.reshape(self.X.shape)

24
GPy/old_tests/mapping_tests.py
View file

@ -5,6 +5,30 @@ import unittest
import numpy as np
import GPy
class MappingGradChecker(GPy.core.Model):
"""
This class has everything we need to check the gradient of a mapping. It
implement a simple likelihood which is the sum of the outputs of the
mapping. the gradients are checked against the parameters of the mapping
and the input.
"""
def __init__(self, mapping, X, name):
super(MappingChecker).__init__(self, name)
self.mapping = mapping
self.add_parameter(self.mapping)
self.X = GPy.core.Param('X',X)
self.add_parameter(self.X)
self.dL_dY = np.ones((self.X.shape[0]. self.mapping.output_dim))
def log_likelihood(self):
return np.sum(self.mapping.f(X))
def parameters_changed(self):
self.X.gradient = self.mapping.gradients_X(self.dL_dY, self.X)
self.mapping.update_gradients(self.dL_dY, self.X)
class MappingTests(unittest.TestCase):