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Merge remote-tracking branch 'gpy_real/devel' into merge_branch

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Alan Saul 2013-10-10 10:03:19 +01:00
commit 2e42b0b92d
5 changed files with 28 additions and 25 deletions
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17
GPy/kern/constructors.py
View file

@ -317,20 +317,23 @@ if sympy_available:
"""
Exponentiated quadratic with multiple outputs.
"""
X = sp.symbols('x_:' + str(input_dim))
Z = sp.symbols('z_:' + str(input_dim))
real_input_dim = input_dim
if output_dim>1:
real_input_dim -= 1
X = sp.symbols('x_:' + str(real_input_dim))
Z = sp.symbols('z_:' + str(real_input_dim))
variance = sp.var('variance',positive=True)
if ARD:
lengthscales = [sp.var('lengthscale%i_i lengthscale%i_j' % i, positive=True) for i in range(input_dim)]
dist_string = ' + '.join(['(x_%i-z_%i)**2/(lengthscale%i_i*lengthscale%i_j)' % (i, i, i) for i in range(input_dim)])
lengthscales = [sp.var('lengthscale%i_i lengthscale%i_j' % i, positive=True) for i in range(real_input_dim)]
dist_string = ' + '.join(['(x_%i-z_%i)**2/(lengthscale%i_i*lengthscale%i_j)' % (i, i, i) for i in range(real_input_dim)])
dist = parse_expr(dist_string)
f = variance*sp.exp(-dist/2.)
else:
lengthscale = sp.var('lengthscale_i lengthscale_j',positive=True)
dist_string = ' + '.join(['(x_%i-z_%i)**2' % (i, i) for i in range(input_dim)])
dist_string = ' + '.join(['(x_%i-z_%i)**2' % (i, i) for i in range(real_input_dim)])
dist = parse_expr(dist_string)
f = variance*sp.exp(-dist/(2*lengthscale**2))
return kern(input_dim, [spkern(input_dim, f, name='eq_sympy')])
f = variance*sp.exp(-dist/(2*lengthscale_i*lengthscale_j))
return kern(input_dim, [spkern(input_dim, f, output_dim=output_dim, name='eq_sympy')])
def sinc(input_dim, ARD=False, variance=1., lengthscale=1.):
"""

10
GPy/kern/kern.py
View file

@ -658,7 +658,7 @@ class Kern_check_dKdiag_dX(Kern_check_model):
def _set_params(self, x):
self.X=x.reshape(self.X.shape)
def kern_test(kern, X=None, X2=None, verbose=False):
def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False):
"""This function runs on kernels to check the correctness of their implementation. It checks that the covariance function is positive definite for a randomly generated data set.
:param kern: the kernel to be tested.
@ -672,12 +672,12 @@ def kern_test(kern, X=None, X2=None, verbose=False):
pass_checks = True
if X==None:
X = np.random.randn(10, kern.input_dim)
for ind in kern.output_indicator:
X[:, ind] = np.random.randint(kern.output_dim, X.shape[0])
if output_ind is not None:
X[:, output_ind] = np.random.randint(kern.output_dim, X.shape[0])
if X2==None:
X2 = np.random.randn(20, kern.input_dim)
for ind in kern.output_indicator:
X2[:, ind] = np.random.randint(kern.output_dim, X2.shape[0])
if output_ind is not None:
X2[:, output_ind] = np.random.randint(kern.output_dim, X2.shape[0])
if verbose:
print("Checking covariance function is positive definite.")

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

@ -12,8 +12,6 @@ class Kernpart(object):
Do not instantiate.
"""
# stores indices of any inputs that are for indicating outputs
self.output_indicator = []
# the input dimensionality for the covariance
self.input_dim = input_dim
# the number of optimisable parameters

22
GPy/kern/parts/sympykern.py
View file

@ -44,7 +44,6 @@ class spkern(Kernpart):
assert len(self._sp_x)==len(self._sp_z)
self.input_dim = len(self._sp_x)
if output_dim > 1:
self.output_indicator=[self.input_dim]
self.input_dim += 1
assert self.input_dim == input_dim
@ -84,7 +83,7 @@ class spkern(Kernpart):
if param is not None:
if param.has_key(theta):
val = param[theta]
setattr(self, theta, val)
setattr(self, theta.name, val)
#deal with param
self._set_params(self._get_params())
@ -146,7 +145,7 @@ class spkern(Kernpart):
reverse_arg_list = list(arg_list)
reverse_arg_list.reverse()
param_arg_list = ["param[%i]"%i for i in range(self.num_shared_params)]
param_arg_list = [shared_params.name for shared_params in self._sp_theta]
arg_list += param_arg_list
precompute_list=[]
@ -201,11 +200,12 @@ class spkern(Kernpart):
"""%(diag_precompute_string,diag_arg_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
# Code to compute gradients
func_list = ([' '*16 + 'target[%i] += partial[i*num_inducing+j]*dk_d%s(%s);'%(i,theta.name,arg_string) for i,theta in enumerate(self._sp_theta)])
func_list = []
if self.output_dim>1:
func_list += [' '*16 + "int %s=(int)%s[%s*input_dim+output_dim];"%(index, var, index2) for index, var, index2 in zip(['ii', 'jj'], ['X', 'Z'], ['i', 'j'])]
func_list += [' '*16 + 'target[%i+ii] += partial[i*num_inducing+j]*dk_d%s(%s);'%(self.num_shared_params+i*self.output_dim, theta.name, arg_string) for i, theta in enumerate(self._sp_theta_i)]
func_list += [' '*16 + 'target[%i+jj] += partial[i*num_inducing+j]*dk_d%s(%s);'%(self.num_shared_params+i*self.output_dim, theta.name, reverse_arg_string) for i, theta in enumerate(self._sp_theta_i)]
func_list += ([' '*16 + 'target[%i] += partial[i*num_inducing+j]*dk_d%s(%s);'%(i,theta.name,arg_string) for i,theta in enumerate(self._sp_theta)])
func_string = '\n'.join(func_list)
self._dK_dtheta_code =\
@ -290,7 +290,9 @@ class spkern(Kernpart):
#TODO: insert multiple functions here via string manipulation
#TODO: similar functions for psi_stats
def _get_arg_names(self, Z=None, partial=None):
arg_names = ['target','X','param']
arg_names = ['target','X']
for shared_params in self._sp_theta:
arg_names += [shared_params.name]
if Z is not None:
arg_names += ['Z']
if partial is not None:
@ -301,7 +303,9 @@ class spkern(Kernpart):
return arg_names
def _weave_inline(self, code, X, target, Z=None, partial=None):
param, output_dim = self._shared_params, self.output_dim
output_dim = self.output_dim
for shared_params in self._sp_theta:
locals()[shared_params.name] = getattr(self, shared_params.name)
# Need to extract parameters first
for split_params in self._split_theta_names:
@ -369,9 +373,7 @@ class spkern(Kernpart):
def _set_params(self,param):
assert param.size == (self.num_params)
for i, shared_params in enumerate(self._sp_theta):
start = i
end = i+1
setattr(self, shared_params, param[start:end])
setattr(self, shared_params.name, param[i])
if self.output_dim>1:
for i, split_params in enumerate(self._split_theta_names):
@ -383,7 +385,7 @@ class spkern(Kernpart):
def _get_params(self):
params = np.zeros(0)
for shared_params in self._sp_theta:
params = np.hstack((params, getattr(self, shared_params)))
params = np.hstack((params, getattr(self, shared_params.name)))
if self.output_dim>1:
for split_params in self._split_theta_names:
params = np.hstack((params, getattr(self, split_params).flatten()))

2
GPy/testing/kernel_tests.py
View file

@ -34,7 +34,7 @@ class KernelTests(unittest.TestCase):
self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
def test_eq_sympykernel(self):
kern = GPy.kern.eq_sympy(5, 3)
kern = GPy.kern.eq_sympy(5, 3, output_ind=4)
self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
def test_sinckernel(self):