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https://github.com/SheffieldML/GPy.git
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Merge branch 'devel' of github.com:SheffieldML/GPy into devel
This commit is contained in:
Max Zwiessele
commit
29790e327a
4 changed files with 34 additions and 31 deletions
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@ -148,7 +148,7 @@ def BGPLVM_oil(optimize=True, N=100, Q=5, M=25, max_f_eval=4e3, plot=False, **k)
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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, :].copy(), m, data_show, latent_axes=latent_axes) # , sense_axes=sense_axes)
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lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :], m, data_show, latent_axes=latent_axes) # , sense_axes=sense_axes)
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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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@ -185,7 +185,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
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return self._clipped(np.hstack((self.dbound_dmuS.flatten(), self.dbound_dZtheta)))
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def plot_latent(self, *args, **kwargs):
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plot_latent.plot_latent_indices(self, *args, **kwargs)
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return plot_latent.plot_latent_indices(self, *args, **kwargs)
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def do_test_latents(self, Y):
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"""
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@ -64,4 +64,4 @@ class GPLVM(GP):
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pb.plot(mu[:,0], mu[:,1],'k',linewidth=1.5)
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def plot_latent(self, *args, **kwargs):
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util.plot_latent.plot_latent(self, *args, **kwargs)
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return util.plot_latent.plot_latent(self, *args, **kwargs)
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@ -33,13 +33,12 @@ class vector_show(data_show):
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"""
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def __init__(self, vals, axes=None):
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data_show.__init__(self, vals, axes)
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self.vals = vals.T.copy()
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self.handle = self.axes.plot(np.arange(0, len(vals))[:, None], self.vals)[0]
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self.handle = self.axes.plot(np.arange(0, len(vals))[:, None], self.vals.T)[0]
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def modify(self, vals):
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self.vals = vals.copy()
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xdata, ydata = self.handle.get_data()
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self.vals = vals.T.copy()
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self.handle.set_data(xdata, self.vals)
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self.handle.set_data(xdata, self.vals.T)
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self.axes.figure.canvas.draw()
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@ -53,7 +52,7 @@ class lvm(data_show):
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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].copy()
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vals = model.X[0]
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data_show.__init__(self, vals, axes=latent_axes)
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@ -85,9 +84,10 @@ class lvm(data_show):
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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.vals = vals.copy()
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y = self.model.predict(self.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.latent_handle.set_data(self.vals[self.latent_index[0]], self.vals[self.latent_index[1]])
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self.axes.figure.canvas.draw()
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@ -217,7 +217,7 @@ class image_show(data_show):
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self.presetSTD = presetSTD
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self.selectImage = selectImage # This is used when the y vector contains multiple images concatenated.
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self.set_image(vals)
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self.set_image(self.vals)
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if not self.palette == []: # Can just show the image (self.set_image() took care of setting the palette)
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self.handle = self.axes.imshow(self.vals, interpolation='nearest')
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else: # Use a boring gray map.
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@ -225,7 +225,7 @@ class image_show(data_show):
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plt.show()
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def modify(self, vals):
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self.set_image(vals)
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self.set_image(vals.copy())
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self.handle.set_array(self.vals)
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self.axes.figure.canvas.draw()
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@ -248,7 +248,7 @@ class image_show(data_show):
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else:
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self.vals = np.reshape(vals[0,dim*self.selectImage+np.array(range(dim))], self.dimensions, order='F')
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if self.transpose:
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self.vals = self.vals.T.copy()
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self.vals = self.vals.T
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# if not self.scale:
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# self.vals = self.vals
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if self.invert:
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@ -276,7 +276,7 @@ class mocap_data_show(data_show):
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data_show.__init__(self, vals, axes)
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self.connect = connect
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self.process_values(vals)
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self.process_values()
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self.initialize_axes()
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self.draw_vertices()
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self.finalize_axes()
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@ -306,14 +306,15 @@ class mocap_data_show(data_show):
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self.line_handle = self.axes.plot(np.array(x), np.array(y), np.array(z), 'b-')
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def modify(self, vals):
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self.process_values(vals)
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self.vals = vals.copy()
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self.process_values()
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self.initialize_axes_modify()
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self.draw_vertices()
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self.finalize_axes_modify()
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self.draw_edges()
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self.axes.figure.canvas.draw()
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def process_values(self, vals):
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def process_values(self):
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raise NotImplementedError, "this needs to be implemented to use the data_show class"
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def initialize_axes(self):
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@ -330,7 +331,9 @@ class mocap_data_show(data_show):
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self.axes.set_xlim(self.x_lim)
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self.axes.set_ylim(self.y_lim)
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self.axes.set_zlim(self.z_lim)
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self.axes.set_aspect(1)
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self.axes.auto_scale_xyz([-1., 1.], [-1., 1.], [-1.5, 1.5])
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#self.axes.set_aspect('equal')
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self.axes.autoscale(enable=False)
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def finalize_axes_modify(self):
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@ -344,8 +347,8 @@ class stick_show(mocap_data_show):
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def __init__(self, vals, axes=None, connect=None):
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mocap_data_show.__init__(self, vals, axes, connect)
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def process_values(self, vals):
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self.vals = vals.reshape((3, vals.shape[1]/3)).T.copy()
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def process_values(self):
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self.vals = self.vals.reshape((3, self.vals.shape[1]/3)).T
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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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@ -363,32 +366,32 @@ class skeleton_show(mocap_data_show):
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connect = skel.connection_matrix()
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mocap_data_show.__init__(self, vals, axes, connect)
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def process_values(self, vals):
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def process_values(self):
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"""Takes a set of angles and converts them to the x,y,z coordinates in the internal prepresentation of the class, ready for plotting.
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:param vals: the values that are being modelled."""
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if self.padding>0:
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channels = np.zeros((vals.shape[0], vals.shape[1]+self.padding))
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channels[:, 0:vals.shape[0]] = vals
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channels = np.zeros((self.vals.shape[0], self.vals.shape[1]+self.padding))
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channels[:, 0:self.vals.shape[0]] = self.vals
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else:
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channels = vals
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channels = self.vals
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vals_mat = self.skel.to_xyz(channels.flatten())
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self.vals = vals_mat
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self.vals = np.zeros_like(vals_mat)
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# Flip the Y and Z axes
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self.vals[:, 0] = vals_mat[:, 0]
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self.vals[:, 1] = vals_mat[:, 2]
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self.vals[:, 2] = vals_mat[:, 1]
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self.vals[:, 0] = vals_mat[:, 0].copy()
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self.vals[:, 1] = vals_mat[:, 2].copy()
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self.vals[:, 2] = vals_mat[:, 1].copy()
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def wrap_around(vals, lim, connect):
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def wrap_around(self, lim, connect):
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quot = lim[1] - lim[0]
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vals = rem(vals, quot)+lim[0]
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nVals = floor(vals/quot)
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self.vals = rem(self.vals, quot)+lim[0]
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nVals = floor(self.vals/quot)
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for i in range(connect.shape[0]):
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for j in find(connect[i, :]):
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if nVals[i] != nVals[j]:
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connect[i, j] = False
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return vals, connect
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return connect
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def data_play(Y, visualizer, frame_rate=30):
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