From c0d514b6c094e282d33b43f1b936e9704921263e Mon Sep 17 00:00:00 2001
From: Max Zwiessele <ibinbei@gmail.com>
Date: Fri, 28 Jun 2013 11:01:31 +0100
Subject: [PATCH] optional plotting of inducing inputs added

---
 GPy/util/plot_latent.py | 59 +++++++++++++++++------------------------
 1 file changed, 24 insertions(+), 35 deletions(-)

diff --git a/GPy/util/plot_latent.py b/GPy/util/plot_latent.py
index 9c832769..d89d90d8 100644
--- a/GPy/util/plot_latent.py
+++ b/GPy/util/plot_latent.py
@@ -2,7 +2,7 @@ import pylab as pb
 import numpy as np
 from .. import util
 
-def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None, marker='o', s=40, fignum=None):
+def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None, marker='o', s=40, fignum=None, plot_inducing=False, legend=True):
     """
     :param labels: a np.array of size model.num_data containing labels for the points (can be number, strings, etc)
     :param resolution: the resolution of the grid on which to evaluate the predictive variance
@@ -15,11 +15,11 @@ def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None,
     if labels is None:
         labels = np.ones(model.num_data)
     if which_indices is None:
-        if model.input_dim==1:
+        if model.input_dim == 1:
             input_1 = 0
             input_2 = None
-        if model.input_dim==2:
-            input_1, input_2 = 0,1
+        if model.input_dim == 2:
+            input_1, input_2 = 0, 1
         else:
             try:
                 input_1, input_2 = np.argsort(model.input_sensitivity())[:2]
@@ -28,14 +28,14 @@ def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None,
     else:
         input_1, input_2 = which_indices
 
-    #first, plot the output variance as a function of the latent space
-    Xtest, xx,yy,xmin,xmax = util.plot.x_frame2D(model.X[:,[input_1, input_2]],resolution=resolution)
+    # first, plot the output variance as a function of the latent space
+    Xtest, xx, yy, xmin, xmax = util.plot.x_frame2D(model.X[:, [input_1, input_2]], resolution=resolution)
     Xtest_full = np.zeros((Xtest.shape[0], model.X.shape[1]))
     Xtest_full[:, :2] = Xtest
     mu, var, low, up = model.predict(Xtest_full)
     var = var[:, :1]
     ax.imshow(var.reshape(resolution, resolution).T,
-              extent=[xmin[0], xmax[0], xmin[1], xmax[1]], cmap=pb.cm.binary,interpolation='bilinear',origin='lower')
+              extent=[xmin[0], xmax[0], xmin[1], xmax[1]], cmap=pb.cm.binary, interpolation='bilinear', origin='lower')
 
     # make sure labels are in order of input:
     ulabels = []
@@ -47,46 +47,35 @@ def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None,
         if type(ul) is np.string_:
             this_label = ul
         elif type(ul) is np.int64:
-            this_label = 'class %i'%ul
+            this_label = 'class %i' % ul
         else:
-            this_label = 'class %i'%i
+            this_label = 'class %i' % i
         if len(marker) == len(ulabels):
             m = marker[i]
         else:
             m = marker
 
-        index = np.nonzero(labels==ul)[0]
-        if model.input_dim==1:
-            x = model.X[index,input_1]
+        index = np.nonzero(labels == ul)[0]
+        if model.input_dim == 1:
+            x = model.X[index, input_1]
             y = np.zeros(index.size)
         else:
-            x = model.X[index,input_1]
-            y = model.X[index,input_2]
+            x = model.X[index, input_1]
+            y = model.X[index, input_2]
         ax.scatter(x, y, marker=m, s=s, color=util.plot.Tango.nextMedium(), label=this_label)
 
-    ax.set_xlabel('latent dimension %i'%input_1)
-    ax.set_ylabel('latent dimension %i'%input_2)
+    ax.set_xlabel('latent dimension %i' % input_1)
+    ax.set_ylabel('latent dimension %i' % input_2)
 
-    if not np.all(labels==1.):
-        ax.legend(loc=0,numpoints=1)
+    if not np.all(labels == 1.) and legend:
+        ax.legend(loc=0, numpoints=1)
 
-    ax.set_xlim(xmin[0],xmax[0])
-    ax.set_ylim(xmin[1],xmax[1])
+    ax.set_xlim(xmin[0], xmax[0])
+    ax.set_ylim(xmin[1], xmax[1])
     ax.grid(b=False) # remove the grid if present, it doesn't look good
     ax.set_aspect('auto') # set a nice aspect ratio
-    return ax
-
-
-def plot_latent_indices(Model, which_indices=None, *args, **kwargs):
-
-    if which_indices is None:
-        try:
-            input_1, input_2 = np.argsort(Model.input_sensitivity())[:2]
-        except:
-            raise ValueError, "cannot Automatically determine which dimensions to plot, please pass 'which_indices'"
-    else:
-        input_1, input_2 = which_indices
-    ax = plot_latent(Model, which_indices=[input_1, input_2], *args, **kwargs)
-    # TODO: Here test if there are inducing points...
-    ax.plot(Model.Z[:, input_1], Model.Z[:, input_2], '^w')
+
+    if plot_inducing:
+        ax.plot(model.Z[:, input_1], model.Z[:, input_2], '^w')
+
     return ax