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[dim red plots] plotting big models

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Conflicts:
	GPy/plotting/matplot_dep/dim_reduction_plots.py
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mzwiessele 2014-08-28 15:53:33 -07:00
parent 313a238b15
commit c0bf227985
1 changed files with 51 additions and 0 deletions
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51
GPy/plotting/matplot_dep/dim_reduction_plots.py
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@ -56,6 +56,57 @@ def plot_latent(model, labels=None, which_indices=None,
X = param_to_array(X)
if X.shape[0] > 1000:
print "Warning: subsampling X, as it has more samples then 1000. X.shape={!s}".format(X.shape)
subsample = np.random.choice(X.shape[0], size=1000, replace=False)
X = X[subsample]
labels = labels[subsample]
#=======================================================================
# <<<WORK IN PROGRESS>>>
# <<<DO NOT DELETE>>>
# plt.close('all')
# fig, ax = plt.subplots(1,1)
# from GPy.plotting.matplot_dep.dim_reduction_plots import most_significant_input_dimensions
# import matplotlib.patches as mpatches
# i1, i2 = most_significant_input_dimensions(m, None)
# xmin, xmax = 100, -100
# ymin, ymax = 100, -100
# legend_handles = []
#
# X = m.X.mean[:, [i1, i2]]
# X = m.X.variance[:, [i1, i2]]
#
# xmin = X[:,0].min(); xmax = X[:,0].max()
# ymin = X[:,1].min(); ymax = X[:,1].max()
# range_ = [[xmin, xmax], [ymin, ymax]]
# ul = np.unique(labels)
#
# for i, l in enumerate(ul):
# #cdict = dict(red =[(0., colors[i][0], colors[i][0]), (1., colors[i][0], colors[i][0])],
# # green=[(0., colors[i][0], colors[i][1]), (1., colors[i][1], colors[i][1])],
# # blue =[(0., colors[i][0], colors[i][2]), (1., colors[i][2], colors[i][2])],
# # alpha=[(0., 0., .0), (.5, .5, .5), (1., .5, .5)])
# #cmap = LinearSegmentedColormap('{}'.format(l), cdict)
# cmap = LinearSegmentedColormap.from_list('cmap_{}'.format(str(l)), [colors[i], colors[i]], 255)
# cmap._init()
# #alphas = .5*(1+scipy.special.erf(np.linspace(-2,2, cmap.N+3)))#np.log(np.linspace(np.exp(0), np.exp(1.), cmap.N+3))
# alphas = (scipy.special.erf(np.linspace(0,2.4, cmap.N+3)))#np.log(np.linspace(np.exp(0), np.exp(1.), cmap.N+3))
# cmap._lut[:, -1] = alphas
# print l
# x, y = X[labels==l].T
#
# heatmap, xedges, yedges = np.histogram2d(x, y, bins=300, range=range_)
# #heatmap, xedges, yedges = np.histogram2d(x, y, bins=100)
#
# im = ax.imshow(heatmap, extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]], cmap=cmap, aspect='auto', interpolation='nearest', label=str(l))
# legend_handles.append(mpatches.Patch(color=colors[i], label=l))
# ax.set_xlim(xmin, xmax)
# ax.set_ylim(ymin, ymax)
# plt.legend(legend_handles, [l.get_label() for l in legend_handles])
# plt.draw()
# plt.show()
#=======================================================================
# create a function which computes the shading of latent space according to the output variance
def plot_function(x):
Xtest_full = np.zeros((x.shape[0], model.X.shape[1]))