all parameterization stuff now in seperate module -> GPy.core.parameterization

GPy/core/parameterization/variational.py

@@ -0,0 +1,66 @@
+'''
+Created on 6 Nov 2013
+
+@author: maxz
+'''
+import numpy as np
+from parameterized import Parameterized
+from param import Param
+from ...util.misc import param_to_array
+
+class Normal(Parameterized):
+    '''
+    Normal distribution for variational approximations.
+    
+    holds the means and variances for a factorizing multivariate normal distribution
+    '''
+    def __init__(self, means, variances, name='latent space'):
+        Parameterized.__init__(self, name=name)
+        self.means = Param("mean", means)
+        self.variances = Param('variance', variances)
+        self.add_parameters(self.means, self.variances)
+
+    def plot(self, fignum=None, ax=None, colors=None):
+        """
+        Plot latent space X in 1D:
+
+            - if fig is given, create input_dim subplots in fig and plot in these
+            - if ax is given plot input_dim 1D latent space plots of X into each `axis`
+            - if neither fig nor ax is given create a figure with fignum and plot in there
+
+        colors:
+            colors of different latent space dimensions input_dim
+
+        """
+        import pylab
+        if ax is None:
+            fig = pylab.figure(num=fignum, figsize=(8, min(12, (2 * self.means.shape[1]))))
+        if colors is None:
+            colors = pylab.gca()._get_lines.color_cycle
+            pylab.clf()
+        else:
+            colors = iter(colors)
+        plots = []
+        means, variances = param_to_array(self.means, self.variances)
+        x = np.arange(means.shape[0])
+        for i in range(means.shape[1]):
+            if ax is None:
+                a = fig.add_subplot(means.shape[1], 1, i + 1)
+            elif isinstance(ax, (tuple, list)):
+                a = ax[i]
+            else:
+                raise ValueError("Need one ax per latent dimnesion input_dim")
+            a.plot(means, c='k', alpha=.3)
+            plots.extend(a.plot(x, means.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
+            a.fill_between(x,
+                            means.T[i] - 2 * np.sqrt(variances.T[i]),
+                            means.T[i] + 2 * np.sqrt(variances.T[i]),
+                            facecolor=plots[-1].get_color(),
+                            alpha=.3)
+            a.legend(borderaxespad=0.)
+            a.set_xlim(x.min(), x.max())
+            if i < means.shape[1] - 1:
+                a.set_xticklabels('')
+        pylab.draw()
+        fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
+        return fig