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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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This commit is contained in:
mzwiessele 2015-09-30 08:22:37 +01:00
commit 75ccd468ef
74 changed files with 759 additions and 3197 deletions
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4
GPy/core/gp.py
View file

@ -640,7 +640,7 @@ class GP(Model):
fignum, ax, data_symbol, **kw)
def errorbars_trainset(self, which_data_rows='all',
def plot_errorbars_trainset(self, which_data_rows='all',
which_data_ycols='all', fixed_inputs=[], fignum=None, ax=None,
linecol=None, data_symbol='kx', predict_kw=None, plot_training_data=True,lw=None):
@ -669,7 +669,7 @@ class GP(Model):
kw = {}
if lw is not None:
kw['lw'] = lw
return models_plots.errorbars_trainset(self, which_data_rows, which_data_ycols, fixed_inputs,
return models_plots.plot_errorbars_trainset(self, which_data_rows, which_data_ycols, fixed_inputs,
fignum, ax, linecol, data_symbol,
predict_kw, plot_training_data, **kw)

2
GPy/core/model.py
View file

@ -255,7 +255,7 @@ class Model(Parameterized):
opt.model = self
else:
optimizer = optimization.get_optimizer(optimizer)
opt = optimizer(start, model=self, max_iters=max_iters, **kwargs)
opt = optimizer(x_init=start, model=self, max_iters=max_iters, **kwargs)
with VerboseOptimization(self, opt, maxiters=max_iters, verbose=messages, ipython_notebook=ipython_notebook, clear_after_finish=clear_after_finish) as vo:
opt.run(f_fp=self._objective_grads, f=self._objective, fp=self._grads)

4
GPy/core/parameterization/parameterized.py
View file

@ -314,8 +314,8 @@ class Parameterized(Parameterizable):
if name in pnames:
param = self.parameters[pnames.index(name)]
param[:] = val; return
except AttributeError:
pass
except AttributeError as a:
raise
return object.__setattr__(self, name, val);
#===========================================================================

1
GPy/core/sparse_gp.py
View file

@ -115,6 +115,7 @@ class SparseGP(GP):
#gradients wrt Z
self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
self.Z.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'].T, self.Z, self.X)
self._Zgrad = self.Z.gradient.copy()
def _raw_predict(self, Xnew, full_cov=False, kern=None):