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parameterized now supports deleting of parameters

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Max Zwiessele 2014-02-21 17:53:44 +00:00
parent 2da256fa93
commit 659643038f
12 changed files with 113 additions and 83 deletions
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20
GPy/plotting/matplot_dep/dim_reduction_plots.py
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@ -1,8 +1,8 @@
import pylab as pb
import numpy as np
from ... import util
from latent_space_visualizations.controllers.imshow_controller import ImshowController,ImAnnotateController
from GPy.util.misc import param_to_array
from ...util.misc import param_to_array
from .base_plots import x_frame2D
import itertools
import Tango
from matplotlib.cm import get_cmap
@ -37,7 +37,7 @@ def plot_latent(model, labels=None, which_indices=None,
if ax is None:
fig = pb.figure(num=fignum)
ax = fig.add_subplot(111)
util.plot.Tango.reset()
Tango.reset()
if labels is None:
labels = np.ones(model.num_data)
@ -46,7 +46,7 @@ def plot_latent(model, labels=None, which_indices=None,
X = param_to_array(model.X)
# first, plot the output variance as a function of the latent space
Xtest, xx, yy, xmin, xmax = util.plot.x_frame2D(X[:, [input_1, input_2]], resolution=resolution)
Xtest, xx, yy, xmin, xmax = x_frame2D(X[:, [input_1, input_2]], resolution=resolution)
Xtest_full = np.zeros((Xtest.shape[0], model.X.shape[1]))
def plot_function(x):
@ -87,7 +87,7 @@ def plot_latent(model, labels=None, which_indices=None,
else:
x = X[index, input_1]
y = X[index, input_2]
ax.scatter(x, y, marker=m, s=s, color=util.plot.Tango.nextMedium(), label=this_label)
ax.scatter(x, y, marker=m, s=s, color=Tango.nextMedium(), label=this_label)
ax.set_xlabel('latent dimension %i' % input_1)
ax.set_ylabel('latent dimension %i' % input_2)
@ -120,7 +120,7 @@ def plot_magnification(model, labels=None, which_indices=None,
if ax is None:
fig = pb.figure(num=fignum)
ax = fig.add_subplot(111)
util.plot.Tango.reset()
Tango.reset()
if labels is None:
labels = np.ones(model.num_data)
@ -128,7 +128,7 @@ def plot_magnification(model, labels=None, which_indices=None,
input_1, input_2 = most_significant_input_dimensions(model, which_indices)
# first, plot the output variance as a function of the latent space
Xtest, xx, yy, xmin, xmax = util.plot.x_frame2D(model.X[:, [input_1, input_2]], resolution=resolution)
Xtest, xx, yy, xmin, xmax = x_frame2D(model.X[:, [input_1, input_2]], resolution=resolution)
Xtest_full = np.zeros((Xtest.shape[0], model.X.shape[1]))
def plot_function(x):
@ -165,7 +165,7 @@ def plot_magnification(model, labels=None, which_indices=None,
else:
x = model.X[index, input_1]
y = model.X[index, input_2]
ax.scatter(x, y, marker=m, s=s, color=util.plot.Tango.nextMedium(), label=this_label)
ax.scatter(x, y, marker=m, s=s, color=Tango.nextMedium(), label=this_label)
ax.set_xlabel('latent dimension %i' % input_1)
ax.set_ylabel('latent dimension %i' % input_2)
@ -205,7 +205,7 @@ def plot_steepest_gradient_map(model, fignum=None, ax=None, which_indices=None,
return dmu_dX[indices, argmax], np.array(labels)[argmax]
if ax is None:
fig = pyplot.figure(num=fignum)
fig = pb.figure(num=fignum)
ax = fig.add_subplot(111)
if data_labels is None:
@ -241,7 +241,7 @@ def plot_steepest_gradient_map(model, fignum=None, ax=None, which_indices=None,
ax.legend()
ax.figure.tight_layout()
if updates:
pyplot.show()
pb.show()
clear = raw_input('Enter to continue')
if clear.lower() in 'yes' or clear == '':
controller.deactivate()