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deleted kernpart, prod and add seem to work okay.

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James Hensman 2014-02-19 17:37:18 +00:00
parent 493506408c
commit 92d71384b7
16 changed files with 95 additions and 238 deletions
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16
GPy/plotting/matplot_dep/models_plots.py
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@ -9,7 +9,7 @@ from ...util.misc import param_to_array
def plot_fit(model, plot_limits=None, which_data_rows='all',
which_data_ycols='all', which_parts='all', fixed_inputs=[],
which_data_ycols='all', fixed_inputs=[],
levels=20, samples=0, fignum=None, ax=None, resolution=None,
plot_raw=False,
linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue']):
@ -20,7 +20,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
- In higher dimensions, use fixed_inputs to plot the GP with some of the inputs fixed.
Can plot only part of the data and part of the posterior functions
using which_data_rowsm which_data_ycols and which_parts
using which_data_rowsm which_data_ycols.
:param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
:type plot_limits: np.array
@ -28,8 +28,6 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
:type which_data_rows: 'all' or a slice object to slice model.X, model.Y
:param which_data_ycols: when the data has several columns (independant outputs), only plot these
:type which_data_rows: 'all' or a list of integers
:param which_parts: which of the kernel functions to plot (additively)
:type which_parts: 'all', or list of bools
:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
:type fixed_inputs: a list of tuples
:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
@ -76,12 +74,12 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#make a prediction on the frame and plot it
if plot_raw:
m, v = model._raw_predict(Xgrid, which_parts=which_parts)
m, v = model._raw_predict(Xgrid)
lower = m - 2*np.sqrt(v)
upper = m + 2*np.sqrt(v)
Y = model.Y
else:
m, v, lower, upper = model.predict(Xgrid, which_parts=which_parts)
m, v, lower, upper = model.predict(Xgrid)
Y = model.Y
for d in which_data_ycols:
gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax, edgecol=linecol, fillcol=fillcol)
@ -89,7 +87,7 @@ 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, which_parts=which_parts)
Ysim = model.posterior_samples(Xgrid, samples)
for yi in Ysim.T:
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.
@ -131,10 +129,10 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#predict on the frame and plot
if plot_raw:
m, _ = model._raw_predict(Xgrid, which_parts=which_parts)
m, _ = model._raw_predict(Xgrid)
Y = model.Y
else:
m, _, _, _ = model.predict(Xgrid, which_parts=which_parts)
m, _, _, _ = model.predict(Xgrid)
Y = model.data
for d in which_data_ycols:
m_d = m[:,d].reshape(resolution, resolution).T