diff --git a/scikits/learn/neighbors.py b/scikits/learn/neighbors.py
index 31230b5d285d0ab1a767a398b469598fa9363f69..e72b58172f02c933665ad3fd8c8168aa6fec6c05 100644
--- a/scikits/learn/neighbors.py
+++ b/scikits/learn/neighbors.py
@@ -10,140 +10,183 @@ from scipy import stats
from .base import BaseEstimator, ClassifierMixin
from .ball_tree import BallTree
-class Neighbors(BaseEstimator, ClassifierMixin):
- """
- Classifier implementing k-Nearest Neighbor Algorithm.
-
- Parameters
- ----------
- data : array-like, shape (n, k)
- The data points to be indexed. This array is not copied, and so
- modifying this data will result in bogus results.
- labels : array
- An array representing labels for the data (only arrays of
- integers are supported).
- k : int
- default number of neighbors.
- window_size : int
- the default window size.
-
- Examples
- --------
- >>> samples = [[0.,0.,1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]]
- >>> labels = [0,0,1,1]
- >>> from scikits.learn.neighbors import Neighbors
- >>> neigh = Neighbors(k=3)
- >>> neigh.fit(samples, labels)
- Neighbors(k=3, window_size=1)
- >>> print neigh.predict([[0,0,0]])
- [ 0.]
- """
-
- def __init__(self, k=5, window_size=1):
- """
- Internally uses the ball tree datastructure and algorithm for fast
- neighbors lookups on high dimensional datasets.
- """
- self.k = k
- self.window_size = window_size
-
- def fit(self, X, Y=()):
- # we need Y to be an integer, because after we'll use it an index
- self.Y = np.asanyarray(Y, dtype=np.int)
- self.ball_tree = BallTree(X, self.window_size)
- return self
- def kneighbors(self, data, k=None):
- """
- Finds the K-neighbors of a point.
+class Neighbors(BaseEstimator, ClassifierMixin):
+ """Classifier implementing k-Nearest Neighbor Algorithm.
Parameters
----------
- point : array-like
- The new point.
+ data : array-like, shape (n, k)
+ The data points to be indexed. This array is not copied, and so
+ modifying this data will result in bogus results.
+ labels : array
+ An array representing labels for the data (only arrays of
+ integers are supported).
k : int
- Number of neighbors to get (default is the value
- passed to the constructor).
-
- Returns
- -------
- dist : array
- Array representing the lenghts to point.
- ind : array
- Array representing the indices of the nearest points in the
- population matrix.
+ default number of neighbors.
+ window_size : int
+ the default window size.
Examples
--------
- In the following example, we construnct a Neighbors class from an
- array representing our data set and ask who's the closest point to
- [1,1,1]
-
- >>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
- >>> labels = [0, 0, 1]
+ >>> samples = [[0.,0.,1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]]
+ >>> labels = [0,0,1,1]
>>> from scikits.learn.neighbors import Neighbors
- >>> neigh = Neighbors(k=1)
+ >>> neigh = Neighbors(k=3)
>>> neigh.fit(samples, labels)
- Neighbors(k=1, window_size=1)
- >>> print neigh.kneighbors([1., 1., 1.])
- (array(0.5), array(2))
+ Neighbors(k=3, window_size=1)
+ >>> print neigh.predict([[0,0,0]])
+ [ 0.]
+ """
+
+ def __init__(self, k=5, window_size=1):
+ """Internally uses the ball tree datastructure and algorithm for fast
+ neighbors lookups on high dimensional datasets.
+ """
+ self.k = k
+ self.window_size = window_size
+
+ def fit(self, X, Y=()):
+ # we need Y to be an integer, because after we'll use it an index
+ self.Y = np.asanyarray(Y, dtype=np.int)
+ self.ball_tree = BallTree(X, self.window_size)
+ return self
+
+ def kneighbors(self, data, k=None):
+ """Finds the K-neighbors of a point.
+
+ Parameters
+ ----------
+ point : array-like
+ The new point.
+ k : int
+ Number of neighbors to get (default is the value
+ passed to the constructor).
+
+ Returns
+ -------
+ dist : array
+ Array representing the lengths to point.
+ ind : array
+ Array representing the indices of the nearest points in the
+ population matrix.
+
+ Examples
+ --------
+ In the following example, we construnct a Neighbors class from an
+ array representing our data set and ask who's the closest point to
+ [1,1,1]
+
+ >>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
+ >>> labels = [0, 0, 1]
+ >>> from scikits.learn.neighbors import Neighbors
+ >>> neigh = Neighbors(k=1)
+ >>> neigh.fit(samples, labels)
+ Neighbors(k=1, window_size=1)
+ >>> print neigh.kneighbors([1., 1., 1.])
+ (array(0.5), array(2))
+
+ As you can see, it returns [0.5], and [2], which means that the
+ element is at distance 0.5 and is the third element of samples
+ (indexes start at 0). You can also query for multiple points:
+
+ >>> print neigh.kneighbors([[0., 1., 0.], [1., 0., 1.]])
+ (array([ 0.5 , 1.11803399]), array([1, 2]))
+
+ """
+ if k is None:
+ k = self.k
+ return self.ball_tree.query(data, k=k)
+
+ def predict(self, T, k=None):
+ """Predict the class labels for the provided data.
+
+ Parameters
+ ----------
+ test: array
+ A 2-D array representing the test point.
+ k : int
+ Number of neighbors to get (default is the value
+ passed to the constructor).
+
+ Returns
+ -------
+ labels: array
+ List of class labels (one for each data sample).
+
+ Examples
+ --------
+ >>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
+ >>> labels = [0, 0, 1]
+ >>> from scikits.learn.neighbors import Neighbors
+ >>> neigh = Neighbors(k=1)
+ >>> neigh.fit(samples, labels)
+ Neighbors(k=1, window_size=1)
+ >>> print neigh.predict([.2, .1, .2])
+ 0
+ >>> print neigh.predict([[0., -1., 0.], [3., 2., 0.]])
+ [0 1]
+ """
+ T = np.asanyarray(T)
+ if k is None:
+ k = self.k
+ return _predict_from_BallTree(self.ball_tree, self.Y, T, k=k)
- As you can see, it returns [0.5], and [2], which means that the
- element is at distance 0.5 and is the third element of samples
- (indexes start at 0). You can also query for multiple points:
- >>> print neigh.kneighbors([[0., 1., 0.], [1., 0., 1.]])
- (array([ 0.5 , 1.11803399]), array([1, 2]))
+def _predict_from_BallTree(ball_tree, Y, test, k):
+ """Predict target from BallTree object containing the data points.
+ This is a helper method, not meant to be used directly. It will
+ not check that input is of the correct type.
"""
- if k is None:
- k=self.k
- return self.ball_tree.query(data, k=k)
+ Y_ = Y[ball_tree.query(test, k=k, return_distance=False)]
+ if k == 1:
+ return Y_
+ return (stats.mode(Y_, axis=1)[0]).ravel()
- def predict(self, T, k=None):
- """
- Predict the class labels for the provided data.
+def kneighbors_graph(X, k, with_dist=True):
+ """Computes the graph of k-Neighbors
Parameters
----------
- test: array
- A 2-D array representing the test point.
+ X : array-like, shape = [n_samples, n_features]
+ Coordinates of samples. One sample per row.
+
k : int
- Number of neighbors to get (default is the value
- passed to the constructor).
+ Number of neighbors for each sample.
Returns
-------
- labels: array
- List of class labels (one for each data sample).
+ A : sparse matrix, shape = [n_samples, n_samples]
+ A is returned as LInked List Sparse matrix
+ A[i,j] = 1 if sample j is a neighbor of sample i
Examples
--------
- >>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
- >>> labels = [0, 0, 1]
- >>> from scikits.learn.neighbors import Neighbors
- >>> neigh = Neighbors(k=1)
- >>> neigh.fit(samples, labels)
- Neighbors(k=1, window_size=1)
- >>> print neigh.predict([.2, .1, .2])
- 0
- >>> print neigh.predict([[0., -1., 0.], [3., 2., 0.]])
- [0 1]
+ >>> X = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
+ >>> A = kneighbors_graph(X, k=2, with_dist=False)
+ >>> print A
+ (0, 1) 1.0
+ (1, 0) 1.0
+ (2, 1) 1.0
"""
- T = np.asanyarray(T)
- if k is None:
- k=self.k
- return _predict_from_BallTree(self.ball_tree, self.Y, T, k=k)
-
-
-def _predict_from_BallTree(ball_tree, Y, test, k):
- """
- Predict target from BallTree object containing the data points.
-
- This is a helper method, not meant to be used directly. It will
- not check that input is of the correct type.
- """
- Y_ = Y[ball_tree.query(test, k=k, return_distance=False)]
- if k == 1: return Y_
- return (stats.mode(Y_, axis=1)[0]).ravel()
+ from scipy import sparse
+ X = np.asanyarray(X)
+ n_samples = X.shape[0]
+ A = sparse.lil_matrix((n_samples, n_samples))
+ knn = Neighbors(k=k)
+ dist, ind = knn.fit(X).kneighbors(X)
+ if with_dist:
+ for i, li in enumerate(ind):
+ if k > 2:
+ A[i, list(li[1:])] = dist[i, 1:]
+ else:
+ A[i, li[1]] = dist[i, 1]
+ else:
+ for i, li in enumerate(ind):
+ if k > 2:
+ A[i, list(li[1:])] = np.ones(k-1)
+ else:
+ A[i, li[1]] = 1.0
+ return A
diff --git a/scikits/learn/tests/test_neighbors.py b/scikits/learn/tests/test_neighbors.py
index a7dbc990fcd1a15a1ac87147b8a176a51e79be13..501c5fc28063bdcdac4f81bb3c833ca0106f039d 100644
--- a/scikits/learn/tests/test_neighbors.py
+++ b/scikits/learn/tests/test_neighbors.py
@@ -1,8 +1,9 @@
-from numpy.testing import assert_array_equal
+from numpy.testing import assert_array_equal, assert_array_almost_equal
from .. import neighbors
+
def test_neighbors_1D():
"""
Nearest Neighbors in a line.
@@ -13,21 +14,22 @@ def test_neighbors_1D():
n = 6
n_2 = n/2
X = [[x] for x in range(0, n)]
- Y = [0]*n_2 + [1]*n_2
+ Y = [0]*n_2 + [1]*n_2
# k = 1
knn = neighbors.Neighbors(k=1)
knn.fit(X, Y)
- test = [[i +0.01] for i in range(0,n_2)] + [[i-0.01] for i in range(n_2, n)]
- assert_array_equal( knn.predict(test), [0, 0, 0, 1, 1, 1])
+ test = [[i + 0.01] for i in range(0, n_2)] + \
+ [[i - 0.01] for i in range(n_2, n)]
+ assert_array_equal(knn.predict(test), [0, 0, 0, 1, 1, 1])
# same as before, but using predict() instead of Neighbors object
# k = 3
knn = neighbors.Neighbors(k=3)
knn.fit(X, Y)
- assert_array_equal( knn.predict([ [i +0.01] for i in range(0,n_2)]),
+ assert_array_equal(knn.predict([[i +0.01] for i in range(0, n_2)]),
[0 for i in range(n_2)])
- assert_array_equal( knn.predict([ [i-0.01] for i in range(n_2, n)]),
+ assert_array_equal(knn.predict([[i-0.01] for i in range(n_2, n)]),
[1 for i in range(n_2)])
@@ -40,12 +42,28 @@ def test_neighbors_2D():
"""
X = (
(0, 1), (1, 1), (1, 0), # label 0
- (-1,0), (-1,-1),(0,-1)) # label 1
+ (-1, 0), (-1, -1), (0, -1)) # label 1
n_2 = len(X)/2
Y = [0]*n_2 + [1]*n_2
knn = neighbors.Neighbors()
knn.fit(X, Y)
- prediction = knn.predict([[0, .1], [0, -.1], [.1, 0], [-.1, 0]])
+ prediction = knn.predict([[0, .1], [0, -.1], [.1, 0], [-.1, 0]])
assert_array_equal(prediction, [0, 1, 0, 1])
+
+def test_kneighbors_graph():
+ """
+ Test kneighbors_graph to build the k-Nearest Neighbor graph.
+ """
+ X = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
+ A = neighbors.kneighbors_graph(X, 2, with_dist=False)
+ assert_array_equal(A.todense(),
+ [[0, 1, 0], [1, 0, 0], [0, 1, 0]])
+ A = neighbors.kneighbors_graph(X, 2)
+ assert_array_almost_equal(A.todense(),
+ [[0, 0.5, 0], [0.5, 0, 0], [0, 1.2247, 0]], 4)
+
+ # Also check corner cases
+ A = neighbors.kneighbors_graph(X, 3, with_dist=False)
+ A = neighbors.kneighbors_graph(X, 3)