Flat is better than nested.

Denest the neighbors module. For practical purposes the API will
remain the same.

From: Fabian Pedregosa <fabian.pedregosa@inria.fr>

git-svn-id: https://scikit-learn.svn.sourceforge.net/svnroot/scikit-learn/trunk@529 22fbfee3-77ab-4535-9bad-27d1bd3bc7d8

scikits/learn/datasets/iris.py

-#! /usr/bin/env python
-# -*- coding: utf-8 -*-
-
-# The code and descriptive text is copyrighted and offered under the terms of
-# the BSD License from the authors; see below. However, the actual dataset may
-# have a different origin and intellectual property status. See the SOURCE and
-# COPYRIGHT variables for this information.
-
-# Copyright (c) 2007 David Cournapeau <cournape@gmail.com>
-#               2010 Fabian Pedregosa <fabian.pedregosa@inria.fr>
-#
-Iris Plants Database
-
-Creator: R.A. Fisher 
-Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
-Date: July, 1988
-
-This is a copy of UCI ML iris datasets.
-
-References: 
-   - Fisher,R.A. 'The use of multiple measurements in taxonomic problems'
-     Annual Eugenics, 7, Part II, 179-188 (1936); also in 'Contributions to
-     Mathematical Statistics' (John Wiley, NY, 1950).
-   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
-     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
-   - Dasarathy, B.V. (1980) 'Nosing Around the Neighborhood: A New System
-     Structure and Classification Rule for Recognition in Partially Exposed
-     Environments'.  IEEE Transactions on Pattern Analysis and Machine
-     Intelligence, Vol. PAMI-2, No. 1, 67-71.
-   - Gates, G.W. (1972) 'The Reduced Nearest Neighbor Rule'.  IEEE Transactions
-     on Information Theory, May 1972, 431-433.
-   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al's AUTOCLASS II
-     conceptual clustering system finds 3 classes in the data.
-   - Many, many more
-     """
-
-DESCR   = """
-The famous Iris database, first used by Sir R.A Fisher
-
-This is perhaps the best known database to be found in the
-pattern recognition literature.  Fisher's paper is a classic in the field and
-is referenced frequently to this day.  (See Duda & Hart, for example.)  The
-data set contains 3 classes of 50 instances each, where each class refers to a
-type of iris plant.  One class is linearly separable from the other 2; the
-latter are NOT linearly separable from each other.
-
-Number of Instances: 150 (50 in each of three classes)
-
-Number of Attributes: 4 numeric, predictive attributes and the class
-
-Attribute Information:
-   - sepal length in cm
-   - sepal width in cm
-   - petal length in cm
-   - petal width in cm
-   - class: 
-        - Iris-Setosa
-        - Iris-Versicolour
-        - Iris-Virginica
-
-Summary Statistics:
-                 Min  Max   Mean    SD   Class Correlation
-   sepal length: 4.3  7.9   5.84  0.83    0.7826   
-    sepal width: 2.0  4.4   3.05  0.43   -0.4194
-   petal length: 1.0  6.9   3.76  1.76    0.9490  (high!)
-    petal width: 0.1  2.5   1.20  0.76    0.9565  (high!)
-
-Missing Attribute Values: None
-
-Class Distribution: 33.3% for each of 3 classes.
-"""
-
-import numpy as np
-from .base import Bunch
-
-def load():
-    """load the iris data and returns them.
-    
-    Returns
-    -------
-        iris : Bunch
-            See docstring of bunch for a complete description of its members.
-
-    Example
-    -------
-    Let's say you are interested in the samples 10, 25, and 50, and want to
-    know their class name.
-
-    >>> data = load()
-    >>> print data.label #doctest: +ELLIPSIS
-    [ 0. 0. ...][ 0.  0. ...]
-    """

scikits/learn/neighbors/__init__.py

-from neighbors import Neighbors

scikits/learn/neighbors/examples/knn.py

-import numpy as np
-import matplotlib.pyplot as plt
-from scikits.learn.neighbors import Neighbors
-
-n = 100 # number of points
-data1 = np.random.randn(n,2) + 3.0
-data2 = np.random.randn(n, 2) + 5.0
-data = np.concatenate((data1, data2))
-labels = [0]*n + [1]*n
-
-# we create the mesh
-h = .1 # step size
-x = np.arange(-2, 12, h)
-y = np.arange(-2, 12, h)
-X, Y = np.meshgrid(x, y)
-
-neigh = Neighbors(data, labels=labels, k=3)
-
-points= [(x_i, y_j) for x_i in x for y_j in y]
-Z =  neigh.predict(points)
-Z =  Z.reshape(np.shape(X))
-
-ax = plt.subplot(111)
-plt.pcolormesh(X, Y, Z.T)
-
-# print the population points
-plt.scatter(data1[:,0], data1[:,1], c='blue')
-plt.scatter(data2[:,0], data2[:,1], c='red')
-
-plt.show()

scikits/learn/neighbors/setup.py

-import numpy
-from os.path import join
-
-def configuration(parent_package='', top_path=None):
-    from numpy.distutils.misc_util import Configuration
-    config = Configuration('neighbors',parent_package,top_path)
-
-    config.add_extension('BallTree',
-                         sources=[join('src', 'BallTree.cpp')],
-                         include_dirs=[numpy.get_include()]
-                         )
-    config.add_data_dir('tests')
-    config.add_data_dir('benchmarks')
-    return config
-
-if __name__ == '__main__':
-    from numpy.distutils.core import setup
-    setup(**configuration(top_path='').todict())

scikits/learn/setup.py

@@ -17,6 +17,13 @@ def configuration(parent_package='',top_path=None):
                          depends=[join('src', 'svm.h'),
                                  join('src', 'libsvm_helper.c'),
                                   ])
+
+    config.add_extension('BallTree',
+                         sources=[join('src', 'BallTree.cpp')],
+                         include_dirs=[numpy.get_include()]
+                         )
+
+
     return config
 
     config.add_subpackage('utils')

scikits/learn/neighbors/tests/test_neighbors.py → scikits/learn/tests/test_neighbors.py

 import numpy as np
-from .. import neighbors
+from scikits.learn import neighbors
 
 from numpy.testing import assert_array_equal