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[dim_reduce examples] updated plotting of brendan and bgplvm_oil

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mzwiessele 2014-05-22 15:08:36 +01:00
parent 6534e6606d
commit fa750d4ba4
2 changed files with 13 additions and 13 deletions
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20
GPy/examples/dimensionality_reduction.py
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@ -172,14 +172,14 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
m.data_labels = data['Y'][:N].argmax(axis=1)
if optimize:
m.optimize('scg', messages=verbose, max_iters=max_iters, gtol=.05)
m.optimize('bfgs', messages=verbose, max_iters=max_iters, gtol=.05)
if plot:
fig, (latent_axes, sense_axes) = plt.subplots(1, 2)
m.plot_latent(ax=latent_axes, labels=m.data_labels)
data_show = GPy.plotting.matplot_dep.visualize.vector_show((m.Y[0,:]))
lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(param_to_array(m.X.mean)[0:1,:], # @UnusedVariable
m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
m, data_show, latent_axes=latent_axes, sense_axes=sense_axes, labels=m.data_labels)
raw_input('Press enter to finish')
plt.close(fig)
return m
@ -386,18 +386,17 @@ def brendan_faces(optimize=True, verbose=True, plot=True):
Yn = Y - Y.mean()
Yn /= Yn.std()
m = GPy.models.GPLVM(Yn, Q)
m = GPy.models.BayesianGPLVM(Yn, Q, num_inducing=20)
# optimize
m.constrain('rbf|noise|white', GPy.transformations.LogexpClipped())
if optimize: m.optimize('scg', messages=verbose, max_iters=1000)
if optimize: m.optimize('bfgs', messages=verbose, max_iters=1000)
if plot:
ax = m.plot_latent(which_indices=(0, 1))
y = m.likelihood.Y[0, :]
y = m.Y[0, :]
data_show = GPy.plotting.matplot_dep.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, order='F', invert=False, scale=False)
GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
lvm = GPy.plotting.matplot_dep.visualize.lvm(m.X.mean[0, :].copy(), m, data_show, ax)
raw_input('Press enter to finish')
return m
@ -411,13 +410,14 @@ def olivetti_faces(optimize=True, verbose=True, plot=True):
Yn = Y - Y.mean()
Yn /= Yn.std()
m = GPy.models.GPLVM(Yn, Q)
if optimize: m.optimize('scg', messages=verbose, max_iters=1000)
m = GPy.models.BayesianGPLVM(Yn, Q, num_inducing=20)
if optimize: m.optimize('bfgs', messages=verbose, max_iters=1000)
if plot:
ax = m.plot_latent(which_indices=(0, 1))
y = m.likelihood.Y[0, :]
data_show = GPy.plotting.matplot_dep.visualize.image_show(y[None, :], dimensions=(112, 92), transpose=False, invert=False, scale=False)
GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
lvm = GPy.plotting.matplot_dep.visualize.lvm(m.X.mean[0, :].copy(), m, data_show, ax)
raw_input('Press enter to finish')
return m

6
GPy/plotting/matplot_dep/visualize.py
View file

@ -98,9 +98,9 @@ class lvm(matplotlib_show):
"""
if vals is None:
if isinstance(model.X, VariationalPosterior):
vals = param_to_array(model.X.mean)
vals = model.X.mean
else:
vals = param_to_array(model.X)
vals = model.X
if len(vals.shape)==1:
vals = vals[None,:]
matplotlib_show.__init__(self, vals, axes=latent_axes)
@ -133,7 +133,7 @@ class lvm(matplotlib_show):
def modify(self, vals):
"""When latent values are modified update the latent representation and ulso update the output visualization."""
self.vals = vals.copy()
self.vals = vals.view(np.ndarray).copy()
y = self.model.predict(self.vals)[0]
self.data_visualize.modify(y)
self.latent_handle.set_data(self.vals[0,self.latent_index[0]], self.vals[0,self.latent_index[1]])