diff --git a/scikits/learn/neighbors/benchmarks/bench_balltree.py b/scikits/learn/benchmarks/bench_balltree.py
similarity index 100%
rename from scikits/learn/neighbors/benchmarks/bench_balltree.py
rename to scikits/learn/benchmarks/bench_balltree.py
diff --git a/scikits/learn/datasets/iris.py b/scikits/learn/datasets/iris.py
deleted file mode 100644
index 59b8cac70e498e69fb093c42a17d8122c5a9dc6a..0000000000000000000000000000000000000000
--- a/scikits/learn/datasets/iris.py
+++ /dev/null
@@ -1,92 +0,0 @@
-#! /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. ...]
- """
diff --git a/scikits/learn/neighbors/neighbors.py b/scikits/learn/neighbors.py
similarity index 100%
rename from scikits/learn/neighbors/neighbors.py
rename to scikits/learn/neighbors.py
diff --git a/scikits/learn/neighbors/__init__.py b/scikits/learn/neighbors/__init__.py
deleted file mode 100644
index 2718fb385ed1b9b1d11fca8b7ff921f67d9d1679..0000000000000000000000000000000000000000
--- a/scikits/learn/neighbors/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from neighbors import Neighbors
diff --git a/scikits/learn/neighbors/examples/knn.py b/scikits/learn/neighbors/examples/knn.py
deleted file mode 100644
index 5f22a208fa82a5afcd9f2f9065ed611cd0a8f0f7..0000000000000000000000000000000000000000
--- a/scikits/learn/neighbors/examples/knn.py
+++ /dev/null
@@ -1,30 +0,0 @@
-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()
diff --git a/scikits/learn/neighbors/setup.py b/scikits/learn/neighbors/setup.py
deleted file mode 100644
index 03d5bffddc5cf92b34a82b45ae2cad001ffcce2f..0000000000000000000000000000000000000000
--- a/scikits/learn/neighbors/setup.py
+++ /dev/null
@@ -1,18 +0,0 @@
-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())
diff --git a/scikits/learn/neighbors/tests/__init__.py b/scikits/learn/neighbors/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/scikits/learn/setup.py b/scikits/learn/setup.py
index 7aa24dfcf682101341e3499d4d4b00d08a13deb8..dbcc203197b13ad9d90eeb267ac03dbb0acbba1c 100644
--- a/scikits/learn/setup.py
+++ b/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')
diff --git a/scikits/learn/neighbors/src/BallTree.cpp b/scikits/learn/src/BallTree.cpp
similarity index 100%
rename from scikits/learn/neighbors/src/BallTree.cpp
rename to scikits/learn/src/BallTree.cpp
diff --git a/scikits/learn/neighbors/src/BallTree.h b/scikits/learn/src/BallTree.h
similarity index 100%
rename from scikits/learn/neighbors/src/BallTree.h
rename to scikits/learn/src/BallTree.h
diff --git a/scikits/learn/neighbors/src/BallTreePoint.h b/scikits/learn/src/BallTreePoint.h
similarity index 100%
rename from scikits/learn/neighbors/src/BallTreePoint.h
rename to scikits/learn/src/BallTreePoint.h
diff --git a/scikits/learn/neighbors/tests/test_neighbors.py b/scikits/learn/tests/test_neighbors.py
similarity index 97%
rename from scikits/learn/neighbors/tests/test_neighbors.py
rename to scikits/learn/tests/test_neighbors.py
index 4e59fa068c51ee6043f99b54f9e898388ae7f3e5..8c50320022a28ca086b0824654d04df369918930 100644
--- a/scikits/learn/neighbors/tests/test_neighbors.py
+++ b/scikits/learn/tests/test_neighbors.py
@@ -1,5 +1,5 @@
import numpy as np
-from .. import neighbors
+from scikits.learn import neighbors
from numpy.testing import assert_array_equal