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[testing] BGPLVM

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This commit is contained in:
mzwiessele 2015-10-06 14:22:29 +01:00
parent a6ad9c33a6
commit 133a815950
5 changed files with 33 additions and 19 deletions
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7
GPy/plotting/__init__.py
View file

@ -41,9 +41,7 @@ if config.get('plotting', 'library') is not 'none':
from ..core import SparseGP
SparseGP.plot_inducing = gpy_plot.data_plots.plot_inducing
from ..models import GPLVM
from ..models import BayesianGPLVM
from ..models import bayesian_gplvm_minibatch
from ..models import GPLVM, BayesianGPLVM, bayesian_gplvm_minibatch, SSGPLVM, SSMRD
GPLVM.plot_prediction_fit = gpy_plot.latent_plots.plot_prediction_fit
GPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
GPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
@ -53,6 +51,9 @@ if config.get('plotting', 'library') is not 'none':
bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_latent = gpy_plot.latent_plots.plot_latent
bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_prediction_fit = gpy_plot.latent_plots.plot_prediction_fit
bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
SSGPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
SSGPLVM.plot_prediction_fit = gpy_plot.latent_plots.plot_prediction_fit
SSGPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
from ..kern import Kern
Kern.plot_covariance = gpy_plot.kernel_plots.plot_covariance

9
GPy/plotting/gpy_plot/latent_plots.py
View file

@ -72,7 +72,7 @@ def plot_prediction_fit(self, plot_limits=None,
:param dict sactter_kwargs: kwargs for the scatter plot, specific for the plotting library you are using
:param kwargs plot_kwargs: kwargs for the data plot for the plotting library you are using
"""
canvas, kwargs = pl.get_new_canvas(plot_kwargs)
canvas, kwargs = pl.get_new_canvas(**plot_kwargs)
plots = _plot_prediction_fit(self, canvas, plot_limits, which_data_rows, which_data_ycols,
fixed_inputs, resolution, plot_raw,
apply_link, visible_dims,
@ -289,9 +289,10 @@ def _plot_steepest_gradient_map(self, canvas, input_1, input_2, Xgrid,
Y, annotation = plot_function(Xgrid[:, [input_1, input_2]])
annotation_kwargs = update_not_existing_kwargs(annotation_kwargs or {}, pl.defaults.annotation)
imshow_kwargs = update_not_existing_kwargs(imshow_kwargs or {}, pl.defaults.gradient)
imshow, annotation = pl.annotation_heatmap(canvas, Y, annotation, (xmin[0], xmax[0], xmin[1], xmax[1]),
None, plot_function, resolution, imshow_kwargs=imshow_kwargs, **annotation_kwargs)
imshow_kwargs = update_not_existing_kwargs(imshow_kwargs, pl.defaults.gradient)
imshow, annotation = pl.annotation_heatmap(canvas, Y, annotation,
(xmin[0], xmax[0], xmin[1], xmax[1]),
None, plot_function, resolution,
imshow_kwargs=imshow_kwargs, **annotation_kwargs)
return dict(heatmap=imshow, annotation=annotation)
def plot_steepest_gradient_map(self, output_labels=None, data_labels=None, which_indices=None,

4
GPy/plotting/matplot_dep/plot_definitions.py
View file

@ -71,7 +71,7 @@ class MatplotlibPlots(AbstractPlottingLibrary):
fontdict=dict(family='sans-serif', weight='light', size=9)
if legend >= 1:
#ax.legend(prop=fontdict)
legend_ontop(ax, ncol=legend)
legend_ontop(ax, ncol=legend, fontdict=fontdict)
if zlim is not None:
ax.set_zlim(zlim)
ax.figure.canvas.draw()
@ -144,7 +144,7 @@ class MatplotlibPlots(AbstractPlottingLibrary):
annotations = []
for [i, x], [j, y] in itertools.product(enumerate(xlin), enumerate(ylin[::-1])):
annotations.append(ax.text(x + xoffset, y + yoffset, "{}".format(annotation[j, i]), **annotation_kwargs))
return [imshow, annotations]
return imshow, annotations
def contour(self, ax, X, Y, C, levels=20, label=None, **kwargs):
return ax.contour(X, Y, C, levels=np.linspace(C.min(), C.max(), levels), label=label, **kwargs)