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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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Max Zwiessele 2013-04-30 09:55:35 +01:00
commit 6a389493cd
5 changed files with 231 additions and 125 deletions
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3
.travis.yml
View file

@ -12,9 +12,10 @@ before_install:
- sudo apt-get install -qq python-matplotlib
install:
- pip install --upgrade numpy==1.7.1
- pip install sphinx
- pip install nose
- pip install . --use-mirrors
# command to run tests, e.g. python setup.py test
script:
- nosetests GPy/testing
- nosetests GPy/testing

2
GPy/core/model.py
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@ -255,7 +255,7 @@ class model(parameterised):
:max_f_eval: maximum number of function evaluations
:messages: whether to display during optimisation
:param optimzer: whice optimizer to use (defaults to self.preferred optimizer)
:param optimzer: which optimizer to use (defaults to self.preferred optimizer)
:type optimzer: string TODO: valid strings?
"""
if optimizer is None:

44
GPy/examples/dimensionality_reduction.py
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@ -81,11 +81,19 @@ def BGPLVM_oil(optimize=True, N=100, Q=10, M=15, max_f_eval=300):
else:
m.ensure_default_constraints()
# plot
print(m)
m.plot_latent(labels=m.data_labels)
pb.figure()
pb.bar(np.arange(m.kern.D), 1. / m.input_sensitivity())
y = m.likelihood.Y[0, :]
fig,(latent_axes,hist_axes) = plt.subplots(1,2)
plt.sca(latent_axes)
m.plot_latent()
data_show = GPy.util.visualize.vector_show(y)
lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :], m, data_show, latent_axes=latent_axes, hist_axes=hist_axes)
raw_input('Press enter to finish')
plt.close('all')
# # plot
# print(m)
# m.plot_latent(labels=m.data_labels)
# pb.figure()
# pb.bar(np.arange(m.kern.D), 1. / m.input_sensitivity())
return m
def oil_100():
@ -348,7 +356,7 @@ def brendan_faces():
ax = m.plot_latent()
y = m.likelihood.Y[0, :]
data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
lvm_visualizer = GPy.util.visualize.lvm(m, data_show, ax)
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :], m, data_show, ax)
raw_input('Press enter to finish')
plt.close('all')
@ -365,7 +373,29 @@ def stick():
ax = m.plot_latent()
y = m.likelihood.Y[0, :]
data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
lvm_visualizer = GPy.util.visualize.lvm(m, data_show, ax)
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :], m, data_show, ax)
raw_input('Press enter to finish')
plt.close('all')
return m
def cmu_mocap(subject='35', motion=['01'], in_place=True):
data = GPy.util.datasets.cmu_mocap(subject, motion)
Y = data['Y']
if in_place:
# Make figure move in place.
data['Y'][:, 0:3]=0.0
m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
# optimize
m.ensure_default_constraints()
m.optimize(messages=1, max_f_eval=10000)
ax = m.plot_latent()
y = m.likelihood.Y[0, :]
data_show = GPy.util.visualize.skeleton_show(y[None, :], data['skel'])
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :], m, data_show, ax)
raw_input('Press enter to finish')
plt.close('all')

1
GPy/util/mocap.py
View file

@ -532,7 +532,6 @@ class acclaim_skeleton(skeleton):
self.vertices[0].meta['orientation'] = [float(parts[1]),
float(parts[2]),
float(parts[3])]
print self.vertices[0].meta['orientation']
lin = self.read_line(fid)
return lin

306
GPy/util/visualize.py
View file

@ -3,121 +3,7 @@ from mpl_toolkits.mplot3d import Axes3D
import GPy
import numpy as np
import matplotlib as mpl
class lvm:
def __init__(self, model, data_visualize, latent_axes, latent_index=[0,1]):
if isinstance(latent_axes,mpl.axes.Axes):
self.cid = latent_axes.figure.canvas.mpl_connect('button_press_event', self.on_click)
self.cid = latent_axes.figure.canvas.mpl_connect('motion_notify_event', self.on_move)
self.cid = latent_axes.figure.canvas.mpl_connect('axes_leave_event', self.on_leave)
self.cid = latent_axes.figure.canvas.mpl_connect('axes_enter_event', self.on_enter)
else:
self.cid = latent_axes[0].figure.canvas.mpl_connect('button_press_event', self.on_click)
self.cid = latent_axes[0].figure.canvas.mpl_connect('motion_notify_event', self.on_move)
self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_leave_event', self.on_leave)
self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_enter_event', self.on_enter)
self.data_visualize = data_visualize
self.model = model
self.latent_axes = latent_axes
self.called = False
self.move_on = False
self.latent_index = latent_index
self.latent_dim = model.Q
def on_enter(self,event):
pass
def on_leave(self,event):
pass
def on_click(self, event):
#print 'click', event.xdata, event.ydata
if event.inaxes!=self.latent_axes: return
self.move_on = not self.move_on
# if self.called:
# self.xs.append(event.xdata)
# self.ys.append(event.ydata)
# self.line.set_data(self.xs, self.ys)
# self.line.figure.canvas.draw()
# else:
# self.xs = [event.xdata]
# self.ys = [event.ydata]
# self.line, = self.latent_axes.plot(event.xdata, event.ydata)
self.called = True
def on_move(self, event):
if event.inaxes!=self.latent_axes: return
if self.called and self.move_on:
# Call modify code on move
#print 'move', event.xdata, event.ydata
latent_values = np.zeros((1,self.latent_dim))
latent_values[0,self.latent_index] = np.array([event.xdata, event.ydata])
y = self.model.predict(latent_values)[0]
self.data_visualize.modify(y)
#print 'y', y
class lvm_subplots(lvm):
"""
latent_axes is a np array of dimension np.ceil(Q/2) + 1,
one for each pair of the axes, and the last one for the sensitiity histogram
"""
def __init__(self, model, data_visualize, latent_axes=None, latent_index=[0,1]):
self.nplots = int(np.ceil(model.Q/2.))+1
lvm.__init__(self,model,data_visualize,latent_axes,latent_index)
self.latent_values = np.zeros(2*np.ceil(self.model.Q/2.)) # possibly an extra dimension on this
assert latent_axes.size == self.nplots
class lvm_dimselect(lvm):
"""
A visualizer for latent variable models
with selection by clicking on the histogram
"""
def __init__(self, model, data_visualize):
self.fig,(latent_axes,self.hist_axes) = plt.subplots(1,2)
lvm.__init__(self,model,data_visualize,latent_axes,[0,1])
self.latent_values_clicked = np.zeros(model.Q)
self.clicked_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0]
print "use left and right mouse butons to select dimensions"
def on_click(self, event):
#print "click"
if event.inaxes==self.hist_axes:
self.hist_axes.cla()
self.hist_axes.bar(np.arange(self.model.Q),1./self.model.input_sensitivity(),color='b')
new_index = max(0,min(int(np.round(event.xdata-0.5)),self.model.Q-1))
self.latent_index[(0 if event.button==1 else 1)] = new_index
self.hist_axes.bar(np.array(self.latent_index),1./self.model.input_sensitivity()[self.latent_index],color='r')
self.latent_axes.cla()
self.model.plot_latent(which_indices = self.latent_index,ax=self.latent_axes)
self.clicked_handle = self.latent_axes.plot([self.latent_values_clicked[self.latent_index[0]]],self.latent_values_clicked[self.latent_index[1]],'rx',mew=2)[0]
if event.inaxes==self.latent_axes:
self.clicked_handle.set_visible(False)
self.latent_values_clicked[self.latent_index] = np.array([event.xdata,event.ydata])
self.clicked_handle = self.latent_axes.plot([self.latent_values_clicked[self.latent_index[0]]],self.latent_values_clicked[self.latent_index[1]],'rx',mew=2)[0]
self.fig.canvas.draw()
self.move_on=True
self.called = True
def on_move(self, event):
#print "move"
if event.inaxes!=self.latent_axes: return
if self.called and self.move_on:
latent_values = self.latent_values_clicked.copy()
latent_values[self.latent_index] = np.array([event.xdata, event.ydata])
y = self.model.predict(latent_values[None,:])[0]
self.data_visualize.modify(y)
def on_leave(self,event):
latent_values = self.latent_values_clicked.copy()
y = self.model.predict(latent_values[None,:])[0]
self.data_visualize.modify(y)
import time
class data_show:
"""
@ -155,6 +41,160 @@ class vector_show(data_show):
self.handle.set_data(xdata, self.vals)
self.axes.figure.canvas.draw()
class lvm(data_show):
def __init__(self, vals, model, data_visualize, latent_axes=None, latent_index=[0,1]):
"""Visualize a latent variable model
:param model: the latent variable model to visualize.
:param data_visualize: the object used to visualize the data which has been modelled.
:type data_visualize: visualize.data_show type.
:param latent_axes: the axes where the latent visualization should be plotted.
"""
if vals == None:
vals = model.X[0]
data_show.__init__(self, vals, axes=latent_axes)
if isinstance(latent_axes,mpl.axes.Axes):
self.cid = latent_axes.figure.canvas.mpl_connect('button_press_event', self.on_click)
self.cid = latent_axes.figure.canvas.mpl_connect('motion_notify_event', self.on_move)
self.cid = latent_axes.figure.canvas.mpl_connect('axes_leave_event', self.on_leave)
self.cid = latent_axes.figure.canvas.mpl_connect('axes_enter_event', self.on_enter)
else:
self.cid = latent_axes[0].figure.canvas.mpl_connect('button_press_event', self.on_click)
self.cid = latent_axes[0].figure.canvas.mpl_connect('motion_notify_event', self.on_move)
self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_leave_event', self.on_leave)
self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_enter_event', self.on_enter)
self.data_visualize = data_visualize
self.model = model
self.latent_axes = latent_axes
self.called = False
self.move_on = False
self.latent_index = latent_index
self.latent_dim = model.Q
# The red cross which shows current latent point.
self.latent_values = vals
self.latent_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0]
self.modify(vals)
def modify(self, vals):
"""When latent values are modified update the latent representation and ulso update the output visualization."""
y = self.model.predict(vals)[0]
self.data_visualize.modify(y)
self.latent_handle.set_data(vals[self.latent_index[0]], vals[self.latent_index[1]])
self.axes.figure.canvas.draw()
def on_enter(self,event):
pass
def on_leave(self,event):
pass
def on_click(self, event):
if event.inaxes!=self.latent_axes: return
self.move_on = not self.move_on
self.called = True
def on_move(self, event):
if event.inaxes!=self.latent_axes: return
if self.called and self.move_on:
# Call modify code on move
self.latent_values[self.latent_index[0]]=event.xdata
self.latent_values[self.latent_index[1]]=event.ydata
self.modify(self.latent_values)
class lvm_subplots(lvm):
"""
latent_axes is a np array of dimension np.ceil(Q/2) + 1,
one for each pair of the axes, and the last one for the sensitiity bar chart
"""
def __init__(self, vals, model, data_visualize, latent_axes=None, latent_index=[0,1]):
lvm.__init__(self, vals, model,data_visualize,latent_axes,[0,1])
self.nplots = int(np.ceil(model.Q/2.))+1
lvm.__init__(self,model,data_visualize,latent_axes,latent_index)
self.latent_values = np.zeros(2*np.ceil(self.model.Q/2.)) # possibly an extra dimension on this
assert latent_axes.size == self.nplots
class lvm_dimselect(lvm):
"""
A visualizer for latent variable models which allows selection of the latent dimensions to use by clicking on a bar chart of their length scales.
"""
def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0, 1]):
if latent_axes==None and sense_axes==None:
self.fig,(latent_axes,self.sense_axes) = plt.subplots(1,2)
elif sense_axes==None:
fig=plt.figure()
self.sense_axes = fig.add_subplot(111)
else:
self.sense_axes = sense_axes
lvm.__init__(self,vals,model,data_visualize,latent_axes,latent_index)
self.show_sensitivities()
print "use left and right mouse butons to select dimensions"
def show_sensitivities(self):
# A click in the bar chart axis for selection a dimension.
self.sense_axes.cla()
self.sense_axes.bar(np.arange(self.model.Q),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')
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.figure.canvas.draw()
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.Q-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
else:
# Make it green and x-axis (green=starboard=right) if it is a right button click
self.latent_index[0] = new_index
self.show_sensitivities()
self.latent_axes.cla()
self.model.plot_latent(which_indices=self.latent_index,
ax=self.latent_axes)
self.latent_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0]
self.modify(self.latent_values)
elif event.inaxes==self.latent_axes:
self.move_on = not self.move_on
self.called = True
def on_move(self, event):
if event.inaxes!=self.latent_axes: return
if self.called and self.move_on:
self.latent_values[self.latent_index[0]]=event.xdata
self.latent_values[self.latent_index[1]]=event.ydata
self.modify(self.latent_values)
def on_leave(self,event):
latent_values = self.latent_values.copy()
y = self.model.predict(latent_values[None,:])[0]
self.data_visualize.modify(y)
class image_show(data_show):
"""Show a data vector as an image."""
def __init__(self, vals, axes=None, dimensions=(16,16), transpose=False, invert=False, scale=False):
@ -269,12 +309,24 @@ class stick_show(mocap_data_show):
class skeleton_show(mocap_data_show):
"""data_show class for visualizing motion capture data encoded as a skeleton with angles."""
def __init__(self, vals, skel, padding=0, axes=None):
"""data_show class for visualizing motion capture data encoded as a skeleton with angles.
:param vals: set of modeled angles to use for printing in the axis when it's first created.
:type vals: np.array
:param skel: skeleton object that has the parameters of the motion capture skeleton associated with it.
:type skel: mocap.skeleton object
:param padding:
:type int
"""
self.skel = skel
self.padding = padding
connect = skel.connection_matrix()
mocap_data_show.__init__(self, vals, axes, connect)
def process_values(self, vals):
"""Takes a set of angles and converts them to the x,y,z coordinates in the internal prepresentation of the class, ready for plotting.
:param vals: the values that are being modelled."""
if self.padding>0:
channels = np.zeros((vals.shape[0], vals.shape[1]+self.padding))
channels[:, 0:vals.shape[0]] = vals
@ -296,3 +348,27 @@ class skeleton_show(mocap_data_show):
if nVals[i] != nVals[j]:
connect[i, j] = False
return vals, connect
def data_play(Y, visualizer, frame_rate=30):
"""Play a data set using the data_show object given.
:Y: the data set to be visualized.
:param visualizer: the data show objectwhether to display during optimisation
:type visualizer: data_show
Example usage:
This example loads in the CMU mocap database (http://mocap.cs.cmu.edu) subject number 35 motion number 01. It then plays it using the mocap_show visualize object.
data = GPy.util.datasets.cmu_mocap(subject='35', train_motions=['01'])
Y = data['Y']
Y[:, 0:3] = 0. # Make figure walk in place
visualize = GPy.util.visualize.skeleton_show(Y[0, :], data['skel'])
GPy.util.visualize.data_play(Y, visualize)
"""
for y in Y:
visualizer.modify(y)
time.sleep(1./float(frame_rate))