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Neil Lawrence 2013-05-14 09:23:48 +01:00
parent a500c526dd
commit c6ee7cd98f
2 changed files with 48 additions and 33 deletions
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2
GPy/models/GP.py
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@ -133,8 +133,8 @@ class GP(model):
""" """
Kx = self.kern.K(self.X, _Xnew,which_parts=which_parts) Kx = self.kern.K(self.X, _Xnew,which_parts=which_parts)
mu = np.dot(np.dot(Kx.T, self.Ki), self.likelihood.Y)
KiKx = np.dot(self.Ki, Kx) KiKx = np.dot(self.Ki, Kx)
mu = np.dot(KiKx.T, self.likelihood.Y)
if full_cov: if full_cov:
Kxx = self.kern.K(_Xnew, which_parts=which_parts) Kxx = self.kern.K(_Xnew, which_parts=which_parts)
var = Kxx - np.dot(KiKx.T, Kx) var = Kxx - np.dot(KiKx.T, Kx)

79
GPy/util/visualize.py
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@ -43,7 +43,7 @@ class vector_show(data_show):
class lvm(data_show): class lvm(data_show):
def __init__(self, vals, model, data_visualize, latent_axes=None, latent_index=[0,1]): def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0,1]):
"""Visualize a latent variable model """Visualize a latent variable model
:param model: the latent variable model to visualize. :param model: the latent variable model to visualize.
@ -70,7 +70,7 @@ class lvm(data_show):
self.data_visualize = data_visualize self.data_visualize = data_visualize
self.model = model self.model = model
self.latent_axes = latent_axes self.latent_axes = latent_axes
self.sense_axes = sense_axes
self.called = False self.called = False
self.move_on = False self.move_on = False
self.latent_index = latent_index self.latent_index = latent_index
@ -80,11 +80,13 @@ class lvm(data_show):
self.latent_values = vals self.latent_values = vals
self.latent_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0] self.latent_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0]
self.modify(vals) self.modify(vals)
self.show_sensitivities()
def modify(self, vals): def modify(self, vals):
"""When latent values are modified update the latent representation and ulso update the output visualization.""" """When latent values are modified update the latent representation and ulso update the output visualization."""
y = self.model.predict(vals)[0] y = self.model.predict(vals)[0]
print y
self.data_visualize.modify(y) self.data_visualize.modify(y)
self.latent_handle.set_data(vals[self.latent_index[0]], vals[self.latent_index[1]]) self.latent_handle.set_data(vals[self.latent_index[0]], vals[self.latent_index[1]])
self.axes.figure.canvas.draw() self.axes.figure.canvas.draw()
@ -99,6 +101,7 @@ class lvm(data_show):
if event.inaxes!=self.latent_axes: return if event.inaxes!=self.latent_axes: return
self.move_on = not self.move_on self.move_on = not self.move_on
self.called = True self.called = True
def on_move(self, event): def on_move(self, event):
if event.inaxes!=self.latent_axes: return if event.inaxes!=self.latent_axes: return
if self.called and self.move_on: if self.called and self.move_on:
@ -107,22 +110,54 @@ class lvm(data_show):
self.latent_values[self.latent_index[1]]=event.ydata self.latent_values[self.latent_index[1]]=event.ydata
self.modify(self.latent_values) self.modify(self.latent_values)
def show_sensitivities(self):
# 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.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()
class lvm_subplots(lvm): class lvm_subplots(lvm):
""" """
latent_axes is a np array of dimension np.ceil(Q/2) + 1, latent_axes is a np array of dimension np.ceil(Q/2),
one for each pair of the axes, and the last one for the sensitiity bar chart one for each pair of the latent dimensions.
""" """
def __init__(self, vals, model, data_visualize, latent_axes=None, latent_index=[0,1]): def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None):
lvm.__init__(self, vals, model,data_visualize,latent_axes,[0,1])
self.nplots = int(np.ceil(model.Q/2.))+1 self.nplots = int(np.ceil(model.Q/2.))+1
lvm.__init__(self,model,data_visualize,latent_axes,latent_index) assert len(latent_axes)==self.nplots
self.latent_values = np.zeros(2*np.ceil(self.model.Q/2.)) # possibly an extra dimension on this if vals==None:
assert latent_axes.size == self.nplots vals = model.X[0, :]
self.latent_values = vals
for i, axis in enumerate(latent_axes):
if i == self.nplots-1:
if self.nplots*2!=model.Q:
latent_index = [i*2, i*2]
lvm.__init__(self, self.latent_vals, model, data_visualize, axis, sense_axes, latent_index=latent_index)
else:
latent_index = [i*2, i*2+1]
lvm.__init__(self, self.latent_vals, model, data_visualize, axis, latent_index=latent_index)
class lvm_dimselect(lvm): 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. 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.
For an example of the visualizer's use try:
GPy.examples.dimensionality_reduction.BGPVLM_oil()
""" """
def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0, 1]): 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: if latent_axes==None and sense_axes==None:
@ -133,24 +168,9 @@ class lvm_dimselect(lvm):
else: else:
self.sense_axes = sense_axes self.sense_axes = sense_axes
lvm.__init__(self,vals,model,data_visualize,latent_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" 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): def on_click(self, event):
@ -177,12 +197,6 @@ class lvm_dimselect(lvm):
self.called = True 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): def on_leave(self,event):
latent_values = self.latent_values.copy() latent_values = self.latent_values.copy()
@ -305,7 +319,8 @@ class stick_show(mocap_data_show):
def process_values(self, vals): def process_values(self, vals):
self.vals = vals.reshape((3, vals.shape[1]/3)).T self.vals = vals.reshape((3, vals.shape[1]/3)).T
print vals
class skeleton_show(mocap_data_show): class skeleton_show(mocap_data_show):
"""data_show class for visualizing motion capture data encoded as a skeleton with angles.""" """data_show class for visualizing motion capture data encoded as a skeleton with angles."""
def __init__(self, vals, skel, padding=0, axes=None): def __init__(self, vals, skel, padding=0, axes=None):