diff --git a/examples/plot_neighbors.py b/examples/plot_neighbors.py
index b2c1322227d59c44151daec28057f48e51f263d2..4bdf51f44e778d217c7ac1b0d033b5450f786040 100644
--- a/examples/plot_neighbors.py
+++ b/examples/plot_neighbors.py
@@ -18,7 +18,7 @@ X = iris.data[:, :2] # we only take the first two features. We could
# 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 @@ clf.fit(X, Y)
# 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
index b5975a321f5a35b4130d0df63ab34775a45cd020..8bb31ea9167b27afec931891a88b3d2abfb95023 100644
--- a/examples/plot_neighbors_regression.py
+++ b/examples/plot_neighbors_regression.py
@@ -27,7 +27,7 @@ y[::5] += 1*(0.5 - np.random.rand(8))
from scikits.learn import neighbors
-knn_barycenter = neighbors.NeighborsBarycenter(k=5)
+knn_barycenter = neighbors.NeighborsBarycenter(n_neighbors=5)
y_ = knn_barycenter.fit(X, y).predict(T)
###############################################################################
diff --git a/scikits/learn/neighbors.py b/scikits/learn/neighbors.py
index 34948611b67391aae14ede65d8edd931580f86cb..50e8a0e088044ad7446e4f610592bdae5f675815 100644
--- a/scikits/learn/neighbors.py
+++ b/scikits/learn/neighbors.py
@@ -23,7 +23,7 @@ class Neighbors(BaseEstimator, ClassifierMixin):
labels : array
An array representing labels for the data (only arrays of
integers are supported).
- k : int
+ n_neighbors : int
default number of neighbors.
window_size : int
Window size passed to BallTree
@@ -33,9 +33,9 @@ class Neighbors(BaseEstimator, ClassifierMixin):
>>> 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 = Neighbors(n_neighbors=3)
>>> neigh.fit(samples, labels)
- Neighbors(k=3, window_size=1)
+ Neighbors(n_neighbors=3, window_size=1)
>>> print neigh.predict([[0,0,0]])
[ 0.]
@@ -44,11 +44,11 @@ class Neighbors(BaseEstimator, ClassifierMixin):
http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
"""
- def __init__(self, k=5, window_size=1):
+ def __init__(self, n_neighbors=5, window_size=1):
"""Internally uses the ball tree datastructure and algorithm for fast
neighbors lookups on high dimensional datasets.
"""
- self.k = k
+ self.n_neighbors = n_neighbors
self.window_size = window_size
def fit(self, X, Y=()):
@@ -57,14 +57,14 @@ class Neighbors(BaseEstimator, ClassifierMixin):
self.ball_tree = BallTree(X, self.window_size)
return self
- def kneighbors(self, data, k=None):
+ def kneighbors(self, data, n_neighbors=None):
"""Finds the K-neighbors of a point.
Parameters
----------
point : array-like
The new point.
- k : int
+ n_neighbors : int
Number of neighbors to get (default is the value
passed to the constructor).
@@ -85,9 +85,9 @@ class Neighbors(BaseEstimator, ClassifierMixin):
>>> 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 = Neighbors(n_neighbors=1)
>>> neigh.fit(samples, labels)
- Neighbors(k=1, window_size=1)
+ Neighbors(n_neighbors=1, window_size=1)
>>> print neigh.kneighbors([1., 1., 1.])
(array(0.5), array(2))
@@ -99,18 +99,18 @@ class Neighbors(BaseEstimator, ClassifierMixin):
(array([ 0.5 , 1.11803399]), array([1, 2]))
"""
- if k is None:
- k = self.k
- return self.ball_tree.query(data, k=k)
+ if n_neighbors is None:
+ n_neighbors = self.n_neighbors
+ return self.ball_tree.query(data, k=n_neighbors)
- def predict(self, T, k=None):
+ def predict(self, T, n_neighbors=None):
"""Predict the class labels for the provided data.
Parameters
----------
test: array
A 2-D array representing the test point.
- k : int
+ n_neighbors : int
Number of neighbors to get (default is the value
passed to the constructor).
@@ -124,28 +124,28 @@ class Neighbors(BaseEstimator, ClassifierMixin):
>>> 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 = Neighbors(n_neighbors=1)
>>> neigh.fit(samples, labels)
- Neighbors(k=1, window_size=1)
+ Neighbors(n_neighbors=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)
+ if n_neighbors is None:
+ n_neighbors = self.n_neighbors
+ return _predict_from_BallTree(self.ball_tree, self.Y, T, n_neighbors)
-def _predict_from_BallTree(ball_tree, Y, test, k):
+def _predict_from_BallTree(ball_tree, Y, test, n_neighbors):
"""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:
+ Y_ = Y[ball_tree.query(test, k=n_neighbors, return_distance=False)]
+ if n_neighbors == 1:
return Y_
return (stats.mode(Y_, axis=1)[0]).ravel()
@@ -167,7 +167,7 @@ class NeighborsBarycenter(BaseEstimator, RegressorMixin):
y : array
An array representing labels for the data (only arrays of
integers are supported).
- k : int
+ n_neighbors : int
default number of neighbors.
window_size : int
Window size passed to BallTree
@@ -177,9 +177,9 @@ class NeighborsBarycenter(BaseEstimator, RegressorMixin):
>>> X = [[0], [1], [2], [3]]
>>> y = [0, 0, 1, 1]
>>> from scikits.learn.neighbors import NeighborsBarycenter
- >>> neigh = NeighborsBarycenter(k=2)
+ >>> neigh = NeighborsBarycenter(n_neighbors=2)
>>> neigh.fit(X, y)
- NeighborsBarycenter(k=2, window_size=1)
+ NeighborsBarycenter(n_neighbors=2, window_size=1)
>>> print neigh.predict([[1.5]])
[ 0.5]
@@ -188,11 +188,11 @@ class NeighborsBarycenter(BaseEstimator, RegressorMixin):
http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
"""
- def __init__(self, k=5, window_size=1):
+ def __init__(self, n_neighbors=5, window_size=1):
"""Internally uses the ball tree datastructure and algorithm for fast
neighbors lookups on high dimensional datasets.
"""
- self.k = k
+ self.n_neighbors = n_neighbors
self.window_size = window_size
def fit(self, X, y, copy=True):
@@ -200,14 +200,14 @@ class NeighborsBarycenter(BaseEstimator, RegressorMixin):
self.ball_tree = BallTree(X, self.window_size)
return self
- def predict(self, T, k=None):
+ def predict(self, T, n_neighbors=None):
"""Predict the target for the provided data.
Parameters
----------
T : array
A 2-D array representing the test data.
- k : int
+ n_neighbors : int
Number of neighbors to get (default is the value
passed to the constructor).
@@ -221,18 +221,18 @@ class NeighborsBarycenter(BaseEstimator, RegressorMixin):
>>> X = [[0], [1], [2]]
>>> y = [0, 0, 1]
>>> from scikits.learn.neighbors import NeighborsBarycenter
- >>> neigh = NeighborsBarycenter(k=2)
+ >>> neigh = NeighborsBarycenter(n_neighbors=2)
>>> neigh.fit(X, y)
- NeighborsBarycenter(k=2, window_size=1)
+ NeighborsBarycenter(n_neighbors=2, window_size=1)
>>> print neigh.predict([[.5], [1.5]])
[ 0. 0.5]
"""
T = np.asanyarray(T)
if T.ndim == 1:
T = T[:,None]
- if k is None:
- k = self.k
- A = kneighbors_graph(T, k=k, weight="barycenter",
+ if n_neighbors is None:
+ n_neighbors = self.n_neighbors
+ A = kneighbors_graph(T, n_neighbors=n_neighbors, weight="barycenter",
ball_tree=self.ball_tree).tocsr()
return A * self._y
@@ -286,7 +286,8 @@ def barycenter_weights(x, X_neighbors, tol=1e-3):
return w
-def kneighbors_graph(X, k, weight=None, ball_tree=None, window_size=1):
+def kneighbors_graph(X, n_neighbors, weight=None, ball_tree=None,
+ window_size=1):
"""Computes the (weighted) graph of k-Neighbors
Parameters
@@ -294,7 +295,7 @@ def kneighbors_graph(X, k, weight=None, ball_tree=None, window_size=1):
X : array-like, shape = [n_samples, n_features]
Coordinates of samples. One sample per row.
- k : int
+ n_neighbors : int
Number of neighbors for each sample.
weight : None (default)
@@ -319,7 +320,7 @@ def kneighbors_graph(X, k, weight=None, ball_tree=None, window_size=1):
Examples
--------
>>> X = [[0], [2], [1]]
- >>> A = kneighbors_graph(X, k=2, weight=None)
+ >>> A = kneighbors_graph(X, n_neighbors=2, weight=None)
>>> print A.todense()
[[ 1. 0. 1.]
[ 0. 1. 1.]
@@ -331,16 +332,16 @@ def kneighbors_graph(X, k, weight=None, ball_tree=None, window_size=1):
if ball_tree is None:
ball_tree = BallTree(X, window_size)
A = sparse.lil_matrix((n_samples, ball_tree.size))
- dist, ind = ball_tree.query(X, k=k)
+ dist, ind = ball_tree.query(X, k=n_neighbors)
if weight is None:
for i, li in enumerate(ind):
- if k > 1:
- A[i, list(li)] = np.ones(k)
+ if n_neighbors > 1:
+ A[i, list(li)] = np.ones(n_neighbors)
else:
A[i, li] = 1.0
elif weight is "distance":
for i, li in enumerate(ind):
- if k > 1:
+ if n_neighbors > 1:
A[i, list(li)] = dist[i, :]
else:
A[i, li] = dist[i, 0]
@@ -348,7 +349,7 @@ def kneighbors_graph(X, k, weight=None, ball_tree=None, window_size=1):
# XXX : the next loop could be done in parallel
# by parallelizing groups of indices
for i, li in enumerate(ind):
- if k > 1:
+ if n_neighbors > 1:
X_i = ball_tree.data[li]
A[i, list(li)] = barycenter_weights(X[i], X_i)
else:
diff --git a/scikits/learn/tests/test_neighbors.py b/scikits/learn/tests/test_neighbors.py
index 42f9ab2029c7c842bbcd9e7de175df661bd6d24d..42836da6e0c2d7742a5fd784e99e041358290f03 100644
--- a/scikits/learn/tests/test_neighbors.py
+++ b/scikits/learn/tests/test_neighbors.py
@@ -17,16 +17,16 @@ def test_neighbors_1D():
X = [[x] for x in range(0, n)]
Y = [0]*n_2 + [1]*n_2
- # k = 1
- knn = neighbors.Neighbors(k=1)
+ # n_neighbors = 1
+ knn = neighbors.Neighbors(n_neighbors=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])
# same as before, but using predict() instead of Neighbors object
- # k = 3
- knn = neighbors.Neighbors(k=3)
+ # n_neighbors = 3
+ knn = neighbors.Neighbors(n_neighbors=3)
knn.fit(X, Y)
assert_array_equal(knn.predict([[i +0.01] for i in range(0, n_2)]),
[0 for i in range(n_2)])
@@ -59,7 +59,7 @@ def test_neighbors_barycenter():
"""
X = [[0], [1], [2], [3]]
y = [0, 0, 1, 1]
- neigh = neighbors.NeighborsBarycenter(k=2)
+ neigh = neighbors.NeighborsBarycenter(n_neighbors=2)
neigh.fit(X, y)
assert_equal(neigh.predict([[1.5]]), 0.5)