Mods to visualize and dimensionality to make stick demos work for summer school.

GPy/examples/dimensionality_reduction.py

@@ -261,6 +261,7 @@ def bgplvm_simulation(optimize='scg',
 
     k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2)) # + kern.bias(Q)
     m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k, _debug=True)
+
     # m.constrain('variance|noise', logexp_clipped())
     m.ensure_default_constraints()
     m['noise'] = Y.var() / 100.
@@ -327,28 +328,56 @@ def brendan_faces():
     data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
     lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
     raw_input('Press enter to finish')
-    lvm_visualizer.close()
 
     return m
+def stick_play(range=None, frame_rate=15):
+    data = GPy.util.datasets.stick()
+    # optimize
+    if range==None:
+        Y = data['Y'].copy()
+    else:
+        Y = data['Y'][range[0]:range[1], :].copy()
+    y = Y[0, :]
+    data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
+    GPy.util.visualize.data_play(Y, data_show, frame_rate)
+    return Y
 
 def stick():
     data = GPy.util.datasets.stick()
-    m = GPy.models.GPLVM(data['Y'], 2)
-
     # optimize
+    m = GPy.models.GPLVM(data['Y'], 2)
     m.ensure_default_constraints()
     m.optimize(messages=1, max_f_eval=10000)
     m._set_params(m._get_params())
-
+    plt.clf
     ax = m.plot_latent()
     y = m.likelihood.Y[0, :]
     data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
     lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
     raw_input('Press enter to finish')
-    lvm_visualizer.close()
 
     return m
 
+def stick_bgplvm(model=None):
+    data = GPy.util.datasets.stick()
+    Q = 6
+    kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
+    m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=20,kernel=kernel)
+    # optimize
+    m.ensure_default_constraints()
+    m.optimize(messages=1, max_f_eval=3000,xtol=1e-300,ftol=1e-300)
+    m._set_params(m._get_params())
+    plt.clf, (latent_axes, sense_axes) = plt.subplots(1, 2)
+    plt.sca(latent_axes)
+    m.plot_latent()
+    y = m.likelihood.Y[0, :].copy()
+    data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
+    lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :].copy(), m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
+    raw_input('Press enter to finish')
+
+    return m
+
+
 def cmu_mocap(subject='35', motion=['01'], in_place=True):
 
     data = GPy.util.datasets.cmu_mocap(subject, motion)

GPy/util/visualize.py

@@ -5,7 +5,7 @@ import numpy as np
 import matplotlib as mpl
 import time
 import Image
-#import visual
+import visual
 
 class data_show:
     """
@@ -203,6 +203,7 @@ class lvm_dimselect(lvm):
             self.sense_axes = sense_axes
         self.labels = labels
         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"
 
 
@@ -506,5 +507,5 @@ def data_play(Y, visualizer, frame_rate=30):
     
 
     for y in Y:
-        visualizer.modify(y)
+        visualizer.modify(y[None, :])
         time.sleep(1./float(frame_rate))