added bibtex references draft

doc/sphinx/source/conf.py

@@ -28,7 +28,8 @@
 # ones.
 extensions = [
     'sphinx.ext.pngmath',
-    'sgexample'
+    'sgexample',
+    'sphinxcontrib.bibtex',
 ]
 
 # Add any paths that contain templates here, relative to this directory.

doc/sphinx/source/examples/classifier/knn.rst

@@ -4,24 +4,37 @@ K Nearest neighbours
 
 KNN classifies data according to the majority of labels in the nearest neighbourhood, according to some underlying distance function :math:`d(x,x')`.
 
-For :math:`k=1`, the label for point :math:`x_i` is :math:`y_{k^*}`, where :math:`k^*=\argmin_n d(x_i, x_n)`.  
+For :math:`k=1`, the label for point :math:`x_i` is :math:`y_{k^*}`, where
+
+.. math::
+
+   k^*=\argmin_n d(x_i, x_n).  
+   
+See Chapter 14 in :cite:`barber2012bayesian` for a detailed introduction.
 
 -------
 Example
 -------
 
-Imagine you have some training and test data.
+Load some training and test data.
 
 .. sgexample:: knn.sg:load_data
 
-In order to run KNN, we need to choose a distance, for example CEuclideanDistance, or other sub-classes of CDistance. The distance needs the data we want to classify.
+In order to run KNN, we need to choose a distance, for example CEuclideanDistance, or other sub-classes of CDistance. The distance is initialized with the data we want to classify.
 
 .. sgexample:: knn.sg:choose_distance
 
-Once you have chosen a distance, create an instance of the CKNN classifier, passing it training data and labels
+Once we have chosen a distance, we create an instance of the CKNN classifier, passing it training data and labels.
 
 .. sgexample:: knn.sg:create_instance
 
-Now we run the KNN algorithm and apply it to test data
+Then we run the KNN algorithm and apply it to test data.
 
 .. sgexample:: knn.sg:train_and_apply
+
+----------
+References
+----------
+`KNN on Wikipedia <https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm>`_
+
+.. bibliography:: ../../references.bib

doc/sphinx/source/references.bib

@@ -0,0 +1,6 @@
+@book{barber2012bayesian,
+  title={Bayesian reasoning and machine learning},
+  author={Barber, David},
+  year={2012},
+  publisher={Cambridge University Press}
+}