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plot_latent bug-fix of creating no figure

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Max Zwiessele 2013-06-27 11:17:58 +01:00
parent 8cdedf2edb
commit 9e9131b90d
3 changed files with 20 additions and 19 deletions
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10
GPy/testing/examples_tests.py
View file

@ -19,14 +19,14 @@ class ExamplesTests(unittest.TestCase):
self.assertTrue(isinstance(Model, GPy.models))
"""
def model_instance_generator(Model):
def model_instance_generator(model):
def check_model_returned(self):
self._model_instance(Model)
self._model_instance(model)
return check_model_returned
def checkgrads_generator(Model):
def checkgrads_generator(model):
def model_checkgrads(self):
self._checkgrad(Model)
self._checkgrad(model)
return model_checkgrads
"""
@ -37,7 +37,7 @@ def model_checkgrads(model):
def model_instance(model):
#assert isinstance(model, GPy.core.model)
return isinstance(model, GPy.core.Model)
return isinstance(model, GPy.core.model)
@nottest
def test_models():

5
GPy/util/plot_latent.py
View file

@ -2,13 +2,14 @@ import pylab as pb
import numpy as np
from .. import util
def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None, marker='o', s=40):
def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None, marker='o', s=40, fignum=None):
"""
:param labels: a np.array of size model.num_data containing labels for the points (can be number, strings, etc)
:param resolution: the resolution of the grid on which to evaluate the predictive variance
"""
if ax is None:
ax = pb.gca()
fig = pb.figure(num=fignum)
ax = fig.add_subplot(111)
util.plot.Tango.reset()
if labels is None:

24
GPy/util/visualize.py
View file

@ -103,7 +103,7 @@ class lvm(matplotlib_show):
self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_enter_event', self.on_enter)
self.data_visualize = data_visualize
self.Model = model
self.model = model
self.latent_axes = latent_axes
self.sense_axes = sense_axes
self.called = False
@ -120,7 +120,7 @@ class lvm(matplotlib_show):
def modify(self, vals):
"""When latent values are modified update the latent representation and ulso update the output visualization."""
self.vals = vals.copy()
y = self.Model.predict(self.vals)[0]
y = self.model.predict(self.vals)[0]
self.data_visualize.modify(y)
self.latent_handle.set_data(self.vals[self.latent_index[0]], self.vals[self.latent_index[1]])
self.axes.figure.canvas.draw()
@ -148,15 +148,15 @@ class lvm(matplotlib_show):
# A click in the bar chart axis for selection a dimension.
if self.sense_axes != None:
self.sense_axes.cla()
self.sense_axes.bar(np.arange(self.Model.input_dim),1./self.Model.input_sensitivity(),color='b')
self.sense_axes.bar(np.arange(self.model.input_dim),1./self.model.input_sensitivity(),color='b')
if self.latent_index[1] == self.latent_index[0]:
self.sense_axes.bar(np.array(self.latent_index[0]),1./self.Model.input_sensitivity()[self.latent_index[0]],color='y')
self.sense_axes.bar(np.array(self.latent_index[1]),1./self.Model.input_sensitivity()[self.latent_index[1]],color='y')
self.sense_axes.bar(np.array(self.latent_index[0]),1./self.model.input_sensitivity()[self.latent_index[0]],color='y')
self.sense_axes.bar(np.array(self.latent_index[1]),1./self.model.input_sensitivity()[self.latent_index[1]],color='y')
else:
self.sense_axes.bar(np.array(self.latent_index[0]),1./self.Model.input_sensitivity()[self.latent_index[0]],color='g')
self.sense_axes.bar(np.array(self.latent_index[1]),1./self.Model.input_sensitivity()[self.latent_index[1]],color='r')
self.sense_axes.bar(np.array(self.latent_index[0]),1./self.model.input_sensitivity()[self.latent_index[0]],color='g')
self.sense_axes.bar(np.array(self.latent_index[1]),1./self.model.input_sensitivity()[self.latent_index[1]],color='r')
self.sense_axes.figure.canvas.draw()
@ -193,7 +193,7 @@ class lvm_dimselect(lvm):
GPy.examples.dimensionality_reduction.BGPVLM_oil()
"""
def __init__(self, vals, Model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0, 1], labels=None):
def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0, 1], labels=None):
if latent_axes==None and sense_axes==None:
self.fig,(latent_axes,self.sense_axes) = plt.subplots(1,2)
elif sense_axes==None:
@ -202,7 +202,7 @@ class lvm_dimselect(lvm):
else:
self.sense_axes = sense_axes
self.labels = labels
lvm.__init__(self,vals,Model,data_visualize,latent_axes,sense_axes,latent_index)
lvm.__init__(self,vals,model,data_visualize,latent_axes,sense_axes,latent_index)
self.show_sensitivities()
print "use left and right mouse butons to select dimensions"
@ -210,7 +210,7 @@ class lvm_dimselect(lvm):
def on_click(self, event):
if event.inaxes==self.sense_axes:
new_index = max(0,min(int(np.round(event.xdata-0.5)),self.Model.input_dim-1))
new_index = max(0,min(int(np.round(event.xdata-0.5)),self.model.input_dim-1))
if event.button == 1:
# Make it red if and y-axis (red=port=left) if it is a left button click
self.latent_index[1] = new_index
@ -221,7 +221,7 @@ class lvm_dimselect(lvm):
self.show_sensitivities()
self.latent_axes.cla()
self.Model.plot_latent(which_indices=self.latent_index,
self.model.plot_latent(which_indices=self.latent_index,
ax=self.latent_axes, labels=self.labels)
self.latent_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0]
self.modify(self.latent_values)
@ -235,7 +235,7 @@ class lvm_dimselect(lvm):
def on_leave(self,event):
latent_values = self.latent_values.copy()
y = self.Model.predict(latent_values[None,:])[0]
y = self.model.predict(latent_values[None,:])[0]
self.data_visualize.modify(y)