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

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mzwiessele 2015-07-29 10:48:27 +02:00
commit d6defa6645
9 changed files with 92 additions and 92 deletions
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26
GPy/plotting/matplot_dep/models_plots.py
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@ -107,11 +107,13 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
upper = m + 2*np.sqrt(v)
else:
if isinstance(model,GPCoregionalizedRegression) or isinstance(model,SparseGPCoregionalizedRegression):
meta = {'output_index': Xgrid[:,-1:].astype(np.int)}
else:
meta = None
m, v = model.predict(Xgrid, full_cov=False, Y_metadata=meta, **predict_kw)
lower, upper = model.predict_quantiles(Xgrid, Y_metadata=meta)
extra_data = Xgrid[:,-1:].astype(np.int)
if Y_metadata is None:
Y_metadata = {'output_index': extra_data}
else:
Y_metadata['output_index'] = extra_data
m, v = model.predict(Xgrid, full_cov=False, Y_metadata=Y_metadata, **predict_kw)
lower, upper = model.predict_quantiles(Xgrid, Y_metadata=Y_metadata)
for d in which_data_ycols:
@ -120,7 +122,9 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#optionally plot some samples
if samples: #NOTE not tested with fixed_inputs
Ysim = model.posterior_samples(Xgrid, samples)
Ysim = model.posterior_samples(Xgrid, samples, Y_metadata=Y_metadata)
print Ysim.shape
print Xnew.shape
for yi in Ysim.T:
plots['posterior_samples'] = ax.plot(Xnew, yi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
#ax.plot(Xnew, yi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.
@ -185,10 +189,12 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
m, _ = model._raw_predict(Xgrid, **predict_kw)
else:
if isinstance(model,GPCoregionalizedRegression) or isinstance(model,SparseGPCoregionalizedRegression):
meta = {'output_index': Xgrid[:,-1:].astype(np.int)}
else:
meta = None
m, v = model.predict(Xgrid, full_cov=False, Y_metadata=meta, **predict_kw)
extra_data = Xgrid[:,-1:].astype(np.int)
if Y_metadata is None:
Y_metadata = {'output_index': extra_data}
else:
Y_metadata['output_index'] = extra_data
m, v = model.predict(Xgrid, full_cov=False, Y_metadata=Y_metadata, **predict_kw)
for d in which_data_ycols:
m_d = m[:,d].reshape(resolution, resolution).T
plots['contour'] = ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)