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Merge changes.
This commit is contained in:
Neil Lawrence
parent
a500c526dd
commit
c6ee7cd98f
2 changed files with 48 additions and 33 deletions
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@ -133,8 +133,8 @@ class GP(model):
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"""
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Kx = self.kern.K(self.X, _Xnew,which_parts=which_parts)
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mu = np.dot(np.dot(Kx.T, self.Ki), self.likelihood.Y)
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KiKx = np.dot(self.Ki, Kx)
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mu = np.dot(KiKx.T, self.likelihood.Y)
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if full_cov:
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Kxx = self.kern.K(_Xnew, which_parts=which_parts)
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var = Kxx - np.dot(KiKx.T, Kx)
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@ -43,7 +43,7 @@ class vector_show(data_show):
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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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def __init__(self, vals, model, data_visualize, latent_axes=None, sense_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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@ -70,7 +70,7 @@ class lvm(data_show):
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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.sense_axes = sense_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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@ -80,11 +80,13 @@ class lvm(data_show):
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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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self.show_sensitivities()
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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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print y
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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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@ -99,6 +101,7 @@ class lvm(data_show):
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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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@ -107,22 +110,54 @@ class lvm(data_show):
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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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def show_sensitivities(self):
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# A click in the bar chart axis for selection a dimension.
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if self.sense_axes != None:
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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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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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latent_axes is a np array of dimension np.ceil(Q/2),
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one for each pair of the latent dimensions.
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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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def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None):
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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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assert len(latent_axes)==self.nplots
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if vals==None:
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vals = model.X[0, :]
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self.latent_values = vals
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for i, axis in enumerate(latent_axes):
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if i == self.nplots-1:
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if self.nplots*2!=model.Q:
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latent_index = [i*2, i*2]
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lvm.__init__(self, self.latent_vals, model, data_visualize, axis, sense_axes, latent_index=latent_index)
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else:
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latent_index = [i*2, i*2+1]
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lvm.__init__(self, self.latent_vals, model, data_visualize, axis, latent_index=latent_index)
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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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For an example of the visualizer's use try:
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GPy.examples.dimensionality_reduction.BGPVLM_oil()
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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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@ -133,24 +168,9 @@ class lvm_dimselect(lvm):
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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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lvm.__init__(self,vals,model,data_visualize,latent_axes,sense_axes,latent_index)
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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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@ -177,12 +197,6 @@ class lvm_dimselect(lvm):
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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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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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def on_leave(self,event):
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latent_values = self.latent_values.copy()
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@ -305,7 +319,8 @@ class stick_show(mocap_data_show):
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def process_values(self, vals):
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self.vals = vals.reshape((3, vals.shape[1]/3)).T
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print vals
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class skeleton_show(mocap_data_show):
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"""data_show class for visualizing motion capture data encoded as a skeleton with angles."""
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def __init__(self, vals, skel, padding=0, axes=None):
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