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naming and pil changes

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Max Zwiessele 2013-12-07 18:45:24 +00:00
parent b502efb1c5
commit e1bb3e508e
11 changed files with 72 additions and 63 deletions
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4
GPy/kern/kern.py
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@ -59,7 +59,7 @@ class kern(Parameterized):
Get the current state of the class,
here just all the indices, rest can get recomputed
"""
return Parameterized.getstate(self) + [self._parameters_,
return Parameterized.getstate(self) + [#self._parameters_,
#self.num_params,
self.input_dim,
self.input_slices,
@ -71,7 +71,7 @@ class kern(Parameterized):
self.input_slices = state.pop()
self.input_dim = state.pop()
#self.num_params = state.pop()
self._parameters_ = state.pop()
#self._parameters_ = state.pop()
Parameterized.setstate(self, state)

13
GPy/kern/parts/rbf.py
View file

@ -63,10 +63,11 @@ class RBF(Kernpart):
#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']}
# self.weave_options = {'headers' : ['<omp.h>'],
# 'extra_compile_args': ['-fopenmp -O3'], # -march=native'],
# 'extra_link_args' : ['-lgomp']}
self.weave_options = {}
def on_input_change(self, X):
#self._K_computations(X, None)
pass
@ -133,7 +134,8 @@ class RBF(Kernpart):
}
"""
num_data, num_inducing, input_dim = X.shape[0], X.shape[0], self.input_dim
weave.inline(code, arg_names=['num_data', 'num_inducing', 'input_dim', 'X', 'X2', 'target', 'dvardLdK', 'var_len3'], type_converters=weave.converters.blitz, **self.weave_options)
X = np.asarray(X)
weave.inline(code, arg_names=['num_data', 'num_inducing', 'input_dim', 'X', 'target', 'dvardLdK', 'var_len3'], type_converters=weave.converters.blitz, **self.weave_options)
else:
code = """
int q,i,j;
@ -150,6 +152,7 @@ class RBF(Kernpart):
"""
num_data, num_inducing, input_dim = X.shape[0], X2.shape[0], self.input_dim
# [np.add(target[1+q:2+q],var_len3[q]*np.sum(dvardLdK*np.square(X[:,q][:,None]-X2[:,q][None,:])),target[1+q:2+q]) for q in range(self.input_dim)]
X,X2 = np.asarray(X), numpy.asarray(X2)
weave.inline(code, arg_names=['num_data', 'num_inducing', 'input_dim', 'X', 'X2', 'target', 'dvardLdK', 'var_len3'], type_converters=weave.converters.blitz, **self.weave_options)
else:
target[1] += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dK)