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merge chagnes from devel

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This commit is contained in:
Zhenwen Dai 2014-05-15 18:02:33 +01:00
commit 4ec8f464e2
64 changed files with 2592 additions and 10697 deletions
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6
GPy/plotting/matplot_dep/dim_reduction_plots.py
View file

@ -97,7 +97,7 @@ def plot_latent(model, labels=None, which_indices=None,
elif type(ul) is np.int64:
this_label = 'class %i' % ul
else:
this_label = 'class %i' % i
this_label = unicode(ul)
m = marker.next()
index = np.nonzero(labels == ul)[0]
@ -121,7 +121,7 @@ def plot_latent(model, labels=None, which_indices=None,
if plot_inducing:
Z = param_to_array(model.Z)
ax.plot(Z[:, input_1], Z[:, input_2], '^w')
ax.set_xlim((xmin, xmax))
ax.set_ylim((ymin, ymax))
@ -132,7 +132,7 @@ def plot_latent(model, labels=None, which_indices=None,
except Exception as e:
print "Could not invoke tight layout: {}".format(e)
pass
if updates:
try:
ax.figure.canvas.show()

2
GPy/plotting/matplot_dep/kernel_plots.py
View file

@ -68,7 +68,7 @@ def plot_ARD(kernel, fignum=None, ax=None, title='', legend=False):
for i in range(ard_params.shape[0]):
c = Tango.nextMedium()
bars.append(plot_bars(fig, ax, x, ard_params[i,:], c, kernel._parameters_[i].name, bottom=bottom))
bars.append(plot_bars(fig, ax, x, ard_params[i,:], c, kernel.parameters[i].name, bottom=bottom))
bottom += ard_params[i,:]
ax.set_xlim(-.5, kernel.input_dim - .5)

4
GPy/plotting/matplot_dep/models_plots.py
View file

@ -14,7 +14,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
which_data_ycols='all', fixed_inputs=[],
levels=20, samples=0, fignum=None, ax=None, resolution=None,
plot_raw=False,
linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue'], Y_metadata=None):
linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue'], Y_metadata=None, data_symbol='kx'):
"""
Plot the posterior of the GP.
- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
@ -97,7 +97,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
for d in which_data_ycols:
plots['gpplot'] = gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], ax=ax, edgecol=linecol, fillcol=fillcol)
plots['dataplot'] = ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], 'kx', mew=1.5)
plots['dataplot'] = ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], data_symbol, mew=1.5)
#optionally plot some samples
if samples: #NOTE not tested with fixed_inputs

31
GPy/plotting/matplot_dep/visualize.py
View file

@ -74,13 +74,16 @@ class vector_show(matplotlib_show):
"""
def __init__(self, vals, axes=None):
matplotlib_show.__init__(self, vals, axes)
self.handle = self.axes.plot(np.arange(0, len(vals))[:, None], self.vals)
#assert vals.ndim == 2, "Please give a vector in [n x 1] to plot"
#assert vals.shape[1] == 1, "only showing a vector in one dimension"
self.size = vals.size
self.handle = self.axes.plot(np.arange(0, vals.size)[:, None], vals)[0]
def modify(self, vals):
self.vals = vals.copy()
for handle, vals in zip(self.handle, self.vals.T):
xdata, ydata = handle.get_data()
handle.set_data(xdata, vals)
xdata, ydata = self.handle.get_data()
assert vals.size == self.size, "values passed into modify changed size! vals.size:{} != in.size:{}".format(vals.size, self.size)
self.handle.set_data(xdata, self.vals)
self.axes.figure.canvas.draw()
@ -94,13 +97,13 @@ class lvm(matplotlib_show):
:type data_visualize: visualize.data_show type.
:param latent_axes: the axes where the latent visualization should be plotted.
"""
if vals == None:
if vals is None:
if isinstance(model.X, VariationalPosterior):
vals = param_to_array(model.X.mean)
else:
vals = param_to_array(model.X)
vals = param_to_array(vals)
if len(vals.shape)==1:
vals = vals[None,:]
matplotlib_show.__init__(self, vals, axes=latent_axes)
if isinstance(latent_axes,mpl.axes.Axes):
@ -391,14 +394,13 @@ class mocap_data_show_vpython(vpython_show):
def process_values(self):
raise NotImplementedError, "this needs to be implemented to use the data_show class"
class mocap_data_show(matplotlib_show):
"""Base class for visualizing motion capture data."""
def __init__(self, vals, axes=None, connect=None):
if axes==None:
fig = plt.figure()
axes = fig.add_subplot(111, projection='3d')
axes = fig.add_subplot(111, projection='3d',aspect='equal')
matplotlib_show.__init__(self, vals, axes)
self.connect = connect
@ -443,11 +445,12 @@ class mocap_data_show(matplotlib_show):
def process_values(self):
raise NotImplementedError, "this needs to be implemented to use the data_show class"
def initialize_axes(self):
def initialize_axes(self, boundary=0.05):
"""Set up the axes with the right limits and scaling."""
self.x_lim = np.array([self.vals[:, 0].min(), self.vals[:, 0].max()])
self.y_lim = np.array([self.vals[:, 1].min(), self.vals[:, 1].max()])
self.z_lim = np.array([self.vals[:, 2].min(), self.vals[:, 2].max()])
bs = [(self.vals[:, i].max()-self.vals[:, i].min())*boundary for i in xrange(3)]
self.x_lim = np.array([self.vals[:, 0].min()-bs[0], self.vals[:, 0].max()+bs[0]])
self.y_lim = np.array([self.vals[:, 1].min()-bs[1], self.vals[:, 1].max()+bs[1]])
self.z_lim = np.array([self.vals[:, 2].min()-bs[2], self.vals[:, 2].max()+bs[2]])
def initialize_axes_modify(self):
self.points_handle.remove()
@ -470,6 +473,8 @@ class mocap_data_show(matplotlib_show):
class stick_show(mocap_data_show):
"""Show a three dimensional point cloud as a figure. Connect elements of the figure together using the matrix connect."""
def __init__(self, vals, connect=None, axes=None):
if len(vals.shape)==1:
vals = vals[None,:]
mocap_data_show.__init__(self, vals, axes=axes, connect=connect)
def process_values(self):