scikit-learn · Dec 2, 2010 · Nov 27, 2010 · Nov 27, 2010 · Nov 27, 2010 · Nov 28, 2010
diff --git a/doc/modules/classes.rst b/doc/modules/classes.rst
@@ -122,6 +122,7 @@ Nearest Neighbors
    :template: class.rst
 
    neighbors.Neighbors
+   neighbors.NeighborsBarycenter
    ball_tree.BallTree
 
 .. autosummary::

diff --git a/doc/modules/neighbors.rst b/doc/modules/neighbors.rst
@@ -34,11 +34,20 @@ the k nearest neighbors of a point is assigned to this point.
 Regression
 ==========
 
-Nearest neighbor regression is not (yet) implemented, yet it should be
-straightforward using the BallTree class.
+The :class:`NeighborsBarycenter` estimator implements a nearest-neighbors
+regression method using barycenter weighting of the targets of the
+k-neighbors.
+
+.. figure:: ../auto_examples/images/plot_neighbors_regression.png
+   :target: ../auto_examples/plot_neighbors_regression.html
+   :align: center
+   :scale: 75
 
 
-.. currentmodule:: scikits.learn.ball_tree
+.. topic:: Examples:
+
+  * :ref:`example_plot_neighbors_regression.py`: an example of regression
+    using nearest neighbor.
 
 Efficient implementation: the ball tree
 ==========================================

diff --git a/examples/plot_neighbors.py b/examples/plot_neighbors.py
@@ -18,7 +18,7 @@
                      # avoid this ugly slicing by using a two-dim dataset
 Y = iris.target
 
-h=.02 # step size in the mesh
+h = .02 # step size in the mesh
 
 # we create an instance of SVM and fit out data. We do not scale our
 # data since we want to plot the support vectors
@@ -27,8 +27,8 @@
 
 # Plot the decision boundary. For that, we will asign a color to each
 # point in the mesh [x_min, m_max]x[y_min, y_max].
-x_min, x_max = X[:,0].min()-1, X[:,0].max()+1
-y_min, y_max = X[:,1].min()-1, X[:,1].max()+1
+x_min, x_max = X[:,0].min()-1, X[:,0].max() + 1
+y_min, y_max = X[:,1].min()-1, X[:,1].max() + 1
 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
 Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
 

diff --git a/examples/plot_neighbors_regression.py b/examples/plot_neighbors_regression.py
@@ -0,0 +1,44 @@
+"""
+==============================
+k-Nearest Neighbors regression
+==============================
+
+Demonstrate the resolution of a regression problem
+using a k-Nearest Neighbor and the interpolation of the
+target using barycenter computation.
+
+"""
+print __doc__
+
+###############################################################################
+# Generate sample data
+import numpy as np
+
+np.random.seed(0)
+X = np.sort(5*np.random.rand(40, 1), axis=0)
+T = np.linspace(0, 5, 500)
+y = np.sin(X).ravel()
+
+# Add noise to targets
+y[::5] += 1*(0.5 - np.random.rand(8))
+
+###############################################################################
+# Fit regression model
+
+from scikits.learn import neighbors
+
+knn_barycenter = neighbors.NeighborsBarycenter(n_neighbors=5)
+y_ = knn_barycenter.fit(X, y).predict(T)
+
+###############################################################################
+# look at the results
+import pylab as pl
+pl.scatter(X, y, c='k', label='data')
+pl.hold('on')
+pl.plot(T, y_, c='g', label='k-NN prediction')
+pl.xlabel('data')
+pl.ylabel('target')
+pl.legend()
+pl.title('k-NN Regression')
+pl.show()
+
diff --git a/examples/svm/plot_weighted_samples.py b/examples/svm/plot_weighted_samples.py
@@ -14,7 +14,7 @@
 
 # we create 20 points
 np.random.seed(0)
-X = np.r_[np.random.randn(10, 2) + [1,1], np.random.randn(10, 2)]
+X = np.r_[np.random.randn(10, 2) + [1, 1], np.random.randn(10, 2)]
 Y = [1]*10 + [-1]*10
 sample_weight = 100 * np.abs(np.random.randn(20))
 # and assign a bigger weight to the last 10 samples
@@ -27,16 +27,14 @@
 # get the separating hyperplane
 xx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500))
 
-
 # plot the line, the points, and the nearest vectors to the plane
 Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
 Z = Z.reshape(xx.shape)
 
 # plot the line, the points, and the nearest vectors to the plane
 pl.set_cmap(pl.cm.bone)
 pl.contourf(xx, yy, Z, alpha=0.75)
-pl.scatter(X[:,0], X[:,1], c=Y, s=sample_weight, alpha=0.9)
+pl.scatter(X[:, 0], X[:, 1], c=Y, s=sample_weight, alpha=0.9)
 
 pl.axis('tight')
 pl.show()
-