New files

GPy/plotting/matplot_dep/variational_plots.py

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+import pylab as pb
+
+def plot(parameterized, 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
+
+    """
+    if ax is None:
+        fig = pb.figure(num=fignum, figsize=(8, min(12, (2 * parameterized.means.shape[1]))))
+    if colors is None:
+        colors = pb.gca()._get_lines.color_cycle
+        pb.clf()
+    else:
+        colors = iter(colors)
+    plots = []
+    means, variances = param_to_array(parameterized.means, parameterized.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 dimension 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('')
+    pb.draw()
+    fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
+    return fig