mirror of
https://github.com/SheffieldML/GPy.git
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Merge branch 'devel' into new_constraints
Conflicts: GPy/core/model.py GPy/core/parameterised.py
This commit is contained in:
James Hensman
commit
db3646c3f3
6 changed files with 331 additions and 219 deletions
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@ -12,6 +12,7 @@ before_install:
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- sudo apt-get install -qq python-matplotlib
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install:
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- pip install --upgrade numpy==1.7.1
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- pip install sphinx
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- pip install nose
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- pip install . --use-mirrors
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@ -2,17 +2,19 @@
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as np
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from scipy import optimize
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import sys, pdb
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import multiprocessing as mp
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from GPy.util.misc import opt_wrapper
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#import numdifftools as ndt
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from parameterised import parameterised
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import priors
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from ..util.linalg import jitchol
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from ..inference import optimization
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from .. import likelihoods
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from ..inference import optimization
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from ..util.linalg import jitchol
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from GPy.util.misc import opt_wrapper
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from parameterised import parameterised
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from scipy import optimize
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import multiprocessing as mp
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import numpy as np
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import priors
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import re
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import sys
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import pdb
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# import numdifftools as ndt
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class model(parameterised):
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def __init__(self):
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@ -65,9 +67,14 @@ class model(parameterised):
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# check constraints are okay
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if isinstance(what, (priors.gamma, priors.log_Gaussian)):
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assert not np.any(which[:,None]==self.constrained_negative_indices), "constraint and prior incompatible"
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assert not np.any(which[:,None]==self.constrained_bounded_indices), "constraint and prior incompatible"
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unconst = np.setdiff1d(which, self.constrained_positive_indices)
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constrained_positive_indices = [i for i,t in zip(self.constrained_indices, self.constraints) if t.domain=='positive']
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if len(constrained_positive_indices):
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constrained_positive_indices = np.hstack(constrained_positive_indices)
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else:
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constrained_positive_indices = np.zeros(shape=(0,))
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bad_constraints = np.setdiff1d(self.all_constrained_indices(),constrained_positive_indices)
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assert not np.any(which[:, None] == bad_constraints), "constraint and prior incompatible"
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unconst = np.setdiff1d(which, constrained_positive_indices)
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if len(unconst):
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print "Warning: constraining parameters to be positive:"
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print '\n'.join([n for i, n in enumerate(self._get_param_names()) if i in unconst])
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@ -78,7 +85,6 @@ class model(parameterised):
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else:
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raise ValueError, "prior not recognised"
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# store the prior in a local list
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for w in which:
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self.priors[w] = what
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@ -207,9 +213,9 @@ class model(parameterised):
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for s in positive_strings:
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for i in self.grep_param_names(s):
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if not (i in currently_constrained):
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to_make_positive.append(param_names[i])
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to_make_positive.append(re.escape(param_names[i]))
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if warn:
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print "Warning! constraining %s postive"%name
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print "Warning! constraining %s positive" % s
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if len(to_make_positive):
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self.constrain_positive('(' + '|'.join(to_make_positive) + ')')
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@ -246,7 +252,7 @@ class model(parameterised):
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:max_f_eval: maximum number of function evaluations
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:messages: whether to display during optimisation
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:param optimzer: whice optimizer to use (defaults to self.preferred optimizer)
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:param optimzer: which optimizer to use (defaults to self.preferred optimizer)
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:type optimzer: string TODO: valid strings?
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"""
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if optimizer is None:
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@ -81,11 +81,19 @@ def BGPLVM_oil(optimize=True, N=100, Q=10, M=15, max_f_eval=300):
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else:
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m.ensure_default_constraints()
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# plot
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print(m)
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m.plot_latent(labels=m.data_labels)
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pb.figure()
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pb.bar(np.arange(m.kern.D), 1. / m.input_sensitivity())
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y = m.likelihood.Y[0, :]
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fig,(latent_axes,hist_axes) = plt.subplots(1,2)
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plt.sca(latent_axes)
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m.plot_latent()
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data_show = GPy.util.visualize.vector_show(y)
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lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :], m, data_show, latent_axes=latent_axes, hist_axes=hist_axes)
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raw_input('Press enter to finish')
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plt.close('all')
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# # plot
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# print(m)
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# m.plot_latent(labels=m.data_labels)
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# pb.figure()
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# pb.bar(np.arange(m.kern.D), 1. / m.input_sensitivity())
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return m
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def oil_100():
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@ -348,7 +356,7 @@ def brendan_faces():
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ax = m.plot_latent()
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
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lvm_visualizer = GPy.util.visualize.lvm(m, data_show, ax)
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lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :], m, data_show, ax)
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raw_input('Press enter to finish')
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plt.close('all')
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@ -365,7 +373,29 @@ def stick():
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ax = m.plot_latent()
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
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lvm_visualizer = GPy.util.visualize.lvm(m, data_show, ax)
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lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :], m, data_show, ax)
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raw_input('Press enter to finish')
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plt.close('all')
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return m
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def cmu_mocap(subject='35', motion=['01'], in_place=True):
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data = GPy.util.datasets.cmu_mocap(subject, motion)
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Y = data['Y']
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if in_place:
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# Make figure move in place.
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data['Y'][:, 0:3]=0.0
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m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
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# optimize
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m.ensure_default_constraints()
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m.optimize(messages=1, max_f_eval=10000)
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ax = m.plot_latent()
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.skeleton_show(y[None, :], data['skel'])
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lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :], m, data_show, ax)
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raw_input('Press enter to finish')
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plt.close('all')
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@ -532,7 +532,6 @@ class acclaim_skeleton(skeleton):
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self.vertices[0].meta['orientation'] = [float(parts[1]),
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float(parts[2]),
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float(parts[3])]
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print self.vertices[0].meta['orientation']
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lin = self.read_line(fid)
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return lin
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@ -3,121 +3,7 @@ from mpl_toolkits.mplot3d import Axes3D
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import GPy
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import numpy as np
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import matplotlib as mpl
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class lvm:
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def __init__(self, model, data_visualize, latent_axes, latent_index=[0,1]):
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if isinstance(latent_axes,mpl.axes.Axes):
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self.cid = latent_axes.figure.canvas.mpl_connect('button_press_event', self.on_click)
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self.cid = latent_axes.figure.canvas.mpl_connect('motion_notify_event', self.on_move)
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self.cid = latent_axes.figure.canvas.mpl_connect('axes_leave_event', self.on_leave)
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self.cid = latent_axes.figure.canvas.mpl_connect('axes_enter_event', self.on_enter)
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else:
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self.cid = latent_axes[0].figure.canvas.mpl_connect('button_press_event', self.on_click)
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self.cid = latent_axes[0].figure.canvas.mpl_connect('motion_notify_event', self.on_move)
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self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_leave_event', self.on_leave)
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self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_enter_event', self.on_enter)
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self.data_visualize = data_visualize
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self.model = model
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self.latent_axes = latent_axes
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self.called = False
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self.move_on = False
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self.latent_index = latent_index
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self.latent_dim = model.Q
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def on_enter(self,event):
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pass
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def on_leave(self,event):
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pass
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def on_click(self, event):
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#print 'click', event.xdata, event.ydata
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if event.inaxes!=self.latent_axes: return
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self.move_on = not self.move_on
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# if self.called:
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# self.xs.append(event.xdata)
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# self.ys.append(event.ydata)
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# self.line.set_data(self.xs, self.ys)
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# self.line.figure.canvas.draw()
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# else:
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# self.xs = [event.xdata]
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# self.ys = [event.ydata]
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# self.line, = self.latent_axes.plot(event.xdata, event.ydata)
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self.called = True
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def on_move(self, event):
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if event.inaxes!=self.latent_axes: return
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if self.called and self.move_on:
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# Call modify code on move
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#print 'move', event.xdata, event.ydata
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latent_values = np.zeros((1,self.latent_dim))
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latent_values[0,self.latent_index] = np.array([event.xdata, event.ydata])
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y = self.model.predict(latent_values)[0]
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self.data_visualize.modify(y)
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#print 'y', y
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class lvm_subplots(lvm):
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"""
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latent_axes is a np array of dimension np.ceil(Q/2) + 1,
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one for each pair of the axes, and the last one for the sensitiity histogram
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"""
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def __init__(self, model, data_visualize, latent_axes=None, latent_index=[0,1]):
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self.nplots = int(np.ceil(model.Q/2.))+1
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lvm.__init__(self,model,data_visualize,latent_axes,latent_index)
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self.latent_values = np.zeros(2*np.ceil(self.model.Q/2.)) # possibly an extra dimension on this
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assert latent_axes.size == self.nplots
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class lvm_dimselect(lvm):
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"""
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A visualizer for latent variable models
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with selection by clicking on the histogram
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"""
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def __init__(self, model, data_visualize):
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self.fig,(latent_axes,self.hist_axes) = plt.subplots(1,2)
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lvm.__init__(self,model,data_visualize,latent_axes,[0,1])
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self.latent_values_clicked = np.zeros(model.Q)
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self.clicked_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0]
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print "use left and right mouse butons to select dimensions"
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def on_click(self, event):
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#print "click"
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if event.inaxes==self.hist_axes:
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self.hist_axes.cla()
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self.hist_axes.bar(np.arange(self.model.Q),1./self.model.input_sensitivity(),color='b')
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new_index = max(0,min(int(np.round(event.xdata-0.5)),self.model.Q-1))
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self.latent_index[(0 if event.button==1 else 1)] = new_index
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self.hist_axes.bar(np.array(self.latent_index),1./self.model.input_sensitivity()[self.latent_index],color='r')
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self.latent_axes.cla()
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self.model.plot_latent(which_indices = self.latent_index,ax=self.latent_axes)
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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]
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if event.inaxes==self.latent_axes:
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self.clicked_handle.set_visible(False)
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self.latent_values_clicked[self.latent_index] = np.array([event.xdata,event.ydata])
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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]
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self.fig.canvas.draw()
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self.move_on=True
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self.called = True
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def on_move(self, event):
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#print "move"
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if event.inaxes!=self.latent_axes: return
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if self.called and self.move_on:
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latent_values = self.latent_values_clicked.copy()
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latent_values[self.latent_index] = np.array([event.xdata, event.ydata])
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y = self.model.predict(latent_values[None,:])[0]
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self.data_visualize.modify(y)
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def on_leave(self,event):
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latent_values = self.latent_values_clicked.copy()
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y = self.model.predict(latent_values[None,:])[0]
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self.data_visualize.modify(y)
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import time
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class data_show:
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"""
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@ -155,6 +41,160 @@ class vector_show(data_show):
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self.handle.set_data(xdata, self.vals)
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self.axes.figure.canvas.draw()
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class lvm(data_show):
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def __init__(self, vals, model, data_visualize, latent_axes=None, latent_index=[0,1]):
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"""Visualize a latent variable model
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:param model: the latent variable model to visualize.
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:param data_visualize: the object used to visualize the data which has been modelled.
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:type data_visualize: visualize.data_show type.
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:param latent_axes: the axes where the latent visualization should be plotted.
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"""
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if vals == None:
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vals = model.X[0]
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data_show.__init__(self, vals, axes=latent_axes)
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if isinstance(latent_axes,mpl.axes.Axes):
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self.cid = latent_axes.figure.canvas.mpl_connect('button_press_event', self.on_click)
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self.cid = latent_axes.figure.canvas.mpl_connect('motion_notify_event', self.on_move)
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self.cid = latent_axes.figure.canvas.mpl_connect('axes_leave_event', self.on_leave)
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self.cid = latent_axes.figure.canvas.mpl_connect('axes_enter_event', self.on_enter)
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else:
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self.cid = latent_axes[0].figure.canvas.mpl_connect('button_press_event', self.on_click)
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self.cid = latent_axes[0].figure.canvas.mpl_connect('motion_notify_event', self.on_move)
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self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_leave_event', self.on_leave)
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self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_enter_event', self.on_enter)
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self.data_visualize = data_visualize
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self.model = model
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self.latent_axes = latent_axes
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self.called = False
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self.move_on = False
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self.latent_index = latent_index
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self.latent_dim = model.Q
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# The red cross which shows current latent point.
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self.latent_values = vals
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self.latent_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0]
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self.modify(vals)
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def modify(self, vals):
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"""When latent values are modified update the latent representation and ulso update the output visualization."""
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y = self.model.predict(vals)[0]
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self.data_visualize.modify(y)
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self.latent_handle.set_data(vals[self.latent_index[0]], vals[self.latent_index[1]])
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self.axes.figure.canvas.draw()
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def on_enter(self,event):
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pass
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def on_leave(self,event):
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pass
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def on_click(self, event):
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if event.inaxes!=self.latent_axes: return
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self.move_on = not self.move_on
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self.called = True
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def on_move(self, event):
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if event.inaxes!=self.latent_axes: return
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if self.called and self.move_on:
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# Call modify code on move
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self.latent_values[self.latent_index[0]]=event.xdata
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self.latent_values[self.latent_index[1]]=event.ydata
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self.modify(self.latent_values)
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class lvm_subplots(lvm):
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"""
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latent_axes is a np array of dimension np.ceil(Q/2) + 1,
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one for each pair of the axes, and the last one for the sensitiity bar chart
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"""
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def __init__(self, vals, model, data_visualize, latent_axes=None, latent_index=[0,1]):
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lvm.__init__(self, vals, model,data_visualize,latent_axes,[0,1])
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self.nplots = int(np.ceil(model.Q/2.))+1
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lvm.__init__(self,model,data_visualize,latent_axes,latent_index)
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self.latent_values = np.zeros(2*np.ceil(self.model.Q/2.)) # possibly an extra dimension on this
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assert latent_axes.size == self.nplots
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class lvm_dimselect(lvm):
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"""
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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.
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"""
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def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0, 1]):
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if latent_axes==None and sense_axes==None:
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self.fig,(latent_axes,self.sense_axes) = plt.subplots(1,2)
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elif sense_axes==None:
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fig=plt.figure()
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self.sense_axes = fig.add_subplot(111)
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else:
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self.sense_axes = sense_axes
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lvm.__init__(self,vals,model,data_visualize,latent_axes,latent_index)
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self.show_sensitivities()
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print "use left and right mouse butons to select dimensions"
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def show_sensitivities(self):
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# A click in the bar chart axis for selection a dimension.
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self.sense_axes.cla()
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self.sense_axes.bar(np.arange(self.model.Q),1./self.model.input_sensitivity(),color='b')
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if self.latent_index[1] == self.latent_index[0]:
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self.sense_axes.bar(np.array(self.latent_index[0]),1./self.model.input_sensitivity()[self.latent_index[0]],color='y')
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self.sense_axes.bar(np.array(self.latent_index[1]),1./self.model.input_sensitivity()[self.latent_index[1]],color='y')
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else:
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self.sense_axes.bar(np.array(self.latent_index[0]),1./self.model.input_sensitivity()[self.latent_index[0]],color='g')
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self.sense_axes.bar(np.array(self.latent_index[1]),1./self.model.input_sensitivity()[self.latent_index[1]],color='r')
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self.sense_axes.figure.canvas.draw()
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def on_click(self, event):
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if event.inaxes==self.sense_axes:
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new_index = max(0,min(int(np.round(event.xdata-0.5)),self.model.Q-1))
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if event.button == 1:
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# Make it red if and y-axis (red=port=left) if it is a left button click
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self.latent_index[1] = new_index
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else:
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# Make it green and x-axis (green=starboard=right) if it is a right button click
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self.latent_index[0] = new_index
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|
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self.show_sensitivities()
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self.latent_axes.cla()
|
||||
self.model.plot_latent(which_indices=self.latent_index,
|
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ax=self.latent_axes)
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self.latent_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0]
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self.modify(self.latent_values)
|
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|
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elif event.inaxes==self.latent_axes:
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self.move_on = not self.move_on
|
||||
|
||||
self.called = True
|
||||
|
||||
|
||||
def on_move(self, event):
|
||||
if event.inaxes!=self.latent_axes: return
|
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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))
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue