diff --git a/doc/modules/neighbors.rst b/doc/modules/neighbors.rst
index 49ab552e9e93fc63d24affe123e602a4feab2039..5e4bf87e42620e5403f606c910128e6aa67e5848 100644
--- a/doc/modules/neighbors.rst
+++ b/doc/modules/neighbors.rst
@@ -33,8 +33,8 @@ handwritten digits or satellite image scenes. It is often successful
in classification situations where the decision boundary is very irregular.
The classes in :mod:`sklearn.neighbors` can handle either Numpy arrays or
-`scipy.sparse` matrices as input. It currently supports only the Euclidean
-distance metric.
+`scipy.sparse` matrices as input. Arbitrary Minkowski metrics are supported
+for searches.
Unsupervised Nearest Neighbors
diff --git a/doc/tutorial/statistical_inference/supervised_learning.rst b/doc/tutorial/statistical_inference/supervised_learning.rst
index 5c1171fe063b1043573c72251ab0dc03be635d5b..1088214755d5e382307b1cc9357dadfcf9d5c246 100644
--- a/doc/tutorial/statistical_inference/supervised_learning.rst
+++ b/doc/tutorial/statistical_inference/supervised_learning.rst
@@ -95,7 +95,7 @@ Scikit-learn documentation for more information about this type of classifier.)
>>> from sklearn.neighbors import KNeighborsClassifier
>>> knn = KNeighborsClassifier()
>>> knn.fit(iris_X_train, iris_y_train)
- KNeighborsClassifier(algorithm='auto', leaf_size=30, n_neighbors=5,
+ KNeighborsClassifier(algorithm='auto', leaf_size=30, n_neighbors=5, p=2,
warn_on_equidistant=True, weights='uniform')
>>> knn.predict(iris_X_test)
array([1, 2, 1, 0, 0, 0, 2, 1, 2, 0])
diff --git a/sklearn/neighbors/base.py b/sklearn/neighbors/base.py
index 87c3407c99b01423bcfe2df5720da9b29464ca14..87820d51f505b2984781955e92aaa4660d861bc9 100644
--- a/sklearn/neighbors/base.py
+++ b/sklearn/neighbors/base.py
@@ -13,7 +13,7 @@ from scipy.spatial.ckdtree import cKDTree
from .ball_tree import BallTree
from ..base import BaseEstimator
-from ..metrics import euclidean_distances
+from ..metrics import pairwise_distances
from ..utils import safe_asarray, atleast2d_or_csr
@@ -71,15 +71,18 @@ class NeighborsBase(BaseEstimator):
# rely on soon-to-be-updated functionality in the pairwise module.
def _init_params(self, n_neighbors=None, radius=None,
algorithm='auto', leaf_size=30,
- warn_on_equidistant=True):
+ warn_on_equidistant=True, p=2):
self.n_neighbors = n_neighbors
self.radius = radius
self.algorithm = algorithm
self.leaf_size = leaf_size
self.warn_on_equidistant = warn_on_equidistant
+ self.p = p
if algorithm not in ['auto', 'brute', 'kd_tree', 'ball_tree']:
raise ValueError("unrecognized algorithm: '%s'" % algorithm)
+ if p < 1:
+ raise ValueError("p must be greater than or equal to 1")
self._fit_X = None
self._tree = None
@@ -131,7 +134,7 @@ class NeighborsBase(BaseEstimator):
if self._fit_method == 'kd_tree':
self._tree = cKDTree(X, self.leaf_size)
elif self._fit_method == 'ball_tree':
- self._tree = BallTree(X, self.leaf_size)
+ self._tree = BallTree(X, self.leaf_size, p=self.p)
elif self._fit_method == 'brute':
self._tree = None
else:
@@ -202,7 +205,16 @@ class KNeighborsMixin(object):
n_neighbors = self.n_neighbors
if self._fit_method == 'brute':
- dist = euclidean_distances(X, self._fit_X, squared=True)
+ if self.p == 1:
+ dist = pairwise_distances(X, self._fit_X, 'manhattan')
+ elif self.p == 2:
+ dist = pairwise_distances(X, self._fit_X, 'euclidean',
+ squared=True)
+ elif self.p == np.inf:
+ dist = pairwise_distances(X, self._fit_X, 'chebyshev')
+ else:
+ dist = pairwise_distances(X, self._fit_X, 'minkowski',
+ p=self.p)
# XXX: should be implemented with a partial sort
neigh_ind = dist.argsort(axis=1)
if self.warn_on_equidistant and n_neighbors < self._fit_X.shape[0]:
@@ -214,7 +226,10 @@ class KNeighborsMixin(object):
neigh_ind = neigh_ind[:, :n_neighbors]
if return_distance:
j = np.arange(neigh_ind.shape[0])[:, None]
- return np.sqrt(dist[j, neigh_ind]), neigh_ind
+ if self.p == 2:
+ return np.sqrt(dist[j, neigh_ind]), neigh_ind
+ else:
+ return dist[j, neigh_ind], neigh_ind
else:
return neigh_ind
elif self._fit_method == 'ball_tree':
@@ -224,7 +239,7 @@ class KNeighborsMixin(object):
warn_equidistant()
return result
elif self._fit_method == 'kd_tree':
- dist, ind = self._tree.query(X, n_neighbors)
+ dist, ind = self._tree.query(X, n_neighbors, p=self.p)
# kd_tree returns a 1D array for n_neighbors = 1
if n_neighbors == 1:
dist = dist[:, None]
@@ -366,10 +381,19 @@ class RadiusNeighborsMixin(object):
radius = self.radius
if self._fit_method == 'brute':
- dist = euclidean_distances(X, self._fit_X, squared=True)
- rad2 = radius ** 2
+ if self.p == 1:
+ dist = pairwise_distances(X, self._fit_X, 'manhattan')
+ elif self.p == 2:
+ dist = pairwise_distances(X, self._fit_X, 'euclidean',
+ squared=True)
+ radius *= radius
+ elif self.p == np.inf:
+ dist = pairwise_distances(X, self._fit_X, 'chebyshev')
+ else:
+ dist = pairwise_distances(X, self._fit_X, 'minkowski',
+ p=self.p)
- neigh_ind = [np.where(d < rad2)[0] for d in dist]
+ neigh_ind = [np.where(d < radius)[0] for d in dist]
# if there are the same number of neighbors for each point,
# we can do a normal array. Otherwise, we return an object
@@ -382,9 +406,14 @@ class RadiusNeighborsMixin(object):
dtype_F = object
if return_distance:
- dist = np.array([np.sqrt(d[neigh_ind[i]]) \
- for i, d in enumerate(dist)],
- dtype=dtype_F)
+ if self.p == 2:
+ dist = np.array([np.sqrt(d[neigh_ind[i]]) \
+ for i, d in enumerate(dist)],
+ dtype=dtype_F)
+ else:
+ dist = np.array([d[neigh_ind[i]] \
+ for i, d in enumerate(dist)],
+ dtype=dtype_F)
return dist, neigh_ind
else:
return neigh_ind
@@ -400,7 +429,8 @@ class RadiusNeighborsMixin(object):
elif self._fit_method == 'kd_tree':
Npts = self._fit_X.shape[0]
dist, ind = self._tree.query(X, Npts,
- distance_upper_bound=radius)
+ distance_upper_bound=radius,
+ p=self.p)
ind = [ind_i[:ind_i.searchsorted(Npts)] for ind_i in ind]
diff --git a/sklearn/neighbors/classification.py b/sklearn/neighbors/classification.py
index f8ded2b1f87935046b6567e499d04169e4ee9f80..90aebfa35e5ecc4340339aaf25cbdfff2c47f243 100644
--- a/sklearn/neighbors/classification.py
+++ b/sklearn/neighbors/classification.py
@@ -68,6 +68,12 @@ class KNeighborsClassifier(NeighborsBase, KNeighborsMixin,
ordering of the training data.
If the fit method is ``'kd_tree'``, no warnings will be generated.
+ p: integer, optional (default = 2)
+ Parameter for the Minkowski metric from
+ sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is
+ equivalent to using manhattan_distance, and euclidean_distance for
+ p = 2. For arbitrary p, minkowski_distance is used.
+
Examples
--------
>>> X = [[0], [1], [2], [3]]
@@ -97,11 +103,12 @@ class KNeighborsClassifier(NeighborsBase, KNeighborsMixin,
def __init__(self, n_neighbors=5,
weights='uniform',
algorithm='auto', leaf_size=30,
- warn_on_equidistant=True):
+ warn_on_equidistant=True, p=2):
self._init_params(n_neighbors=n_neighbors,
algorithm=algorithm,
leaf_size=leaf_size,
- warn_on_equidistant=warn_on_equidistant)
+ warn_on_equidistant=warn_on_equidistant,
+ p=p)
self.weights = _check_weights(weights)
def predict(self, X):
@@ -174,6 +181,12 @@ class RadiusNeighborsClassifier(NeighborsBase, RadiusNeighborsMixin,
required to store the tree. The optimal value depends on the
nature of the problem.
+ p: integer, optional (default = 2)
+ Parameter for the Minkowski metric from
+ sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is
+ equivalent to using manhattan_distance, and euclidean_distance for
+ p = 2. For arbitrary p, minkowski_distance is used.
+
Examples
--------
>>> X = [[0], [1], [2], [3]]
@@ -201,10 +214,11 @@ class RadiusNeighborsClassifier(NeighborsBase, RadiusNeighborsMixin,
"""
def __init__(self, radius=1.0, weights='uniform',
- algorithm='auto', leaf_size=30):
+ algorithm='auto', leaf_size=30, p=2):
self._init_params(radius=radius,
algorithm=algorithm,
- leaf_size=leaf_size)
+ leaf_size=leaf_size,
+ p=p)
self.weights = _check_weights(weights)
def predict(self, X):
diff --git a/sklearn/neighbors/regression.py b/sklearn/neighbors/regression.py
index 9f918db7810892d9494fdd0ef4aea428dbf948b0..a1acd28e93640c228d7498e262be1d51af064f8b 100644
--- a/sklearn/neighbors/regression.py
+++ b/sklearn/neighbors/regression.py
@@ -70,6 +70,12 @@ class KNeighborsRegressor(NeighborsBase, KNeighborsMixin,
ordering of the training data.
If the fit method is ``'kd_tree'``, no warnings will be generated.
+ p: integer, optional (default = 2)
+ Parameter for the Minkowski metric from
+ sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is
+ equivalent to using manhattan_distance, and euclidean_distance for
+ p = 2. For arbitrary p, minkowski_distance is used.
+
Examples
--------
>>> X = [[0], [1], [2], [3]]
@@ -97,11 +103,13 @@ class KNeighborsRegressor(NeighborsBase, KNeighborsMixin,
"""
def __init__(self, n_neighbors=5, weights='uniform',
- algorithm='auto', leaf_size=30, warn_on_equidistant=True):
+ algorithm='auto', leaf_size=30, warn_on_equidistant=True,
+ p=2):
self._init_params(n_neighbors=n_neighbors,
algorithm=algorithm,
leaf_size=leaf_size,
- warn_on_equidistant=warn_on_equidistant)
+ warn_on_equidistant=warn_on_equidistant,
+ p=p)
self.weights = _check_weights(weights)
def predict(self, X):
@@ -177,6 +185,12 @@ class RadiusNeighborsRegressor(NeighborsBase, RadiusNeighborsMixin,
required to store the tree. The optimal value depends on the
nature of the problem.
+ p: integer, optional (default = 2)
+ Parameter for the Minkowski metric from
+ sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is
+ equivalent to using manhattan_distance, and euclidean_distance for
+ p = 2. For arbitrary p, minkowski_distance is used.
+
Examples
--------
>>> X = [[0], [1], [2], [3]]
@@ -204,10 +218,11 @@ class RadiusNeighborsRegressor(NeighborsBase, RadiusNeighborsMixin,
"""
def __init__(self, radius=1.0, weights='uniform',
- algorithm='auto', leaf_size=30):
+ algorithm='auto', leaf_size=30, p=2):
self._init_params(radius=radius,
algorithm=algorithm,
- leaf_size=leaf_size)
+ leaf_size=leaf_size,
+ p=p)
self.weights = _check_weights(weights)
def predict(self, X):
diff --git a/sklearn/neighbors/tests/test_neighbors.py b/sklearn/neighbors/tests/test_neighbors.py
index 810d6358a9564e4005a91ab224d5495011fee7f7..86934f5353f2edd06cb84e9cbf4a342d31ca133a 100644
--- a/sklearn/neighbors/tests/test_neighbors.py
+++ b/sklearn/neighbors/tests/test_neighbors.py
@@ -19,6 +19,7 @@ SPARSE_TYPES = (bsr_matrix, coo_matrix, csc_matrix, csr_matrix, dok_matrix,
SPARSE_OR_DENSE = SPARSE_TYPES + (np.asarray,)
ALGORITHMS = ('ball_tree', 'brute', 'kd_tree', 'auto')
+P = (1, 2, 3, 4, np.inf)
def test_warn_on_equidistant(n_samples=100, n_features=3, k=3):
@@ -73,21 +74,24 @@ def test_unsupervised_kneighbors(n_samples=20, n_features=5,
test = rng.rand(n_query_pts, n_features)
- results_nodist = []
- results = []
- for algorithm in ALGORITHMS:
- neigh = neighbors.NearestNeighbors(n_neighbors=n_neighbors,
- algorithm=algorithm)
- neigh.fit(X)
+ for p in P:
+ results_nodist = []
+ results = []
+
+ for algorithm in ALGORITHMS:
+ neigh = neighbors.NearestNeighbors(n_neighbors=n_neighbors,
+ algorithm=algorithm,
+ p=p)
+ neigh.fit(X)
- results_nodist.append(neigh.kneighbors(test, return_distance=False))
- results.append(neigh.kneighbors(test, return_distance=True))
+ results_nodist.append(neigh.kneighbors(test, return_distance=False))
+ results.append(neigh.kneighbors(test, return_distance=True))
- for i in range(len(results) - 1):
- assert_array_almost_equal(results_nodist[i], results[i][1])
- assert_array_almost_equal(results[i][0], results[i + 1][0])
- assert_array_almost_equal(results[i][1], results[i + 1][1])
+ for i in range(len(results) - 1):
+ assert_array_almost_equal(results_nodist[i], results[i][1])
+ assert_array_almost_equal(results[i][0], results[i + 1][0])
+ assert_array_almost_equal(results[i][1], results[i + 1][1])
def test_unsupervised_inputs():
@@ -119,32 +123,35 @@ def test_unsupervised_radius_neighbors(n_samples=20, n_features=5,
test = rng.rand(n_query_pts, n_features)
- results = []
-
- for algorithm in ALGORITHMS:
- neigh = neighbors.NearestNeighbors(radius=radius,
- algorithm=algorithm)
- neigh.fit(X)
-
- ind1 = neigh.radius_neighbors(test, return_distance=False)
-
- # sort the results: this is not done automatically for
- # radius searches
- dist, ind = neigh.radius_neighbors(test, return_distance=True)
- for (d, i, i1) in zip(dist, ind, ind1):
- j = d.argsort()
- d[:] = d[j]
- i[:] = i[j]
- results.append((dist, ind))
-
- assert_array_almost_equal(np.concatenate(list(ind)),
- np.concatenate(list(ind1)))
-
- for i in range(len(results) - 1):
- assert_array_almost_equal(np.concatenate(list(results[i][0])),
- np.concatenate(list(results[i + 1][0]))),
- assert_array_almost_equal(np.concatenate(list(results[i][1])),
- np.concatenate(list(results[i + 1][1])))
+ for p in P:
+ results = []
+
+ for algorithm in ALGORITHMS:
+ neigh = neighbors.NearestNeighbors(radius=radius,
+ algorithm=algorithm,
+ p=p)
+ neigh.fit(X)
+
+ ind1 = neigh.radius_neighbors(test, return_distance=False)
+
+ # sort the results: this is not done automatically for
+ # radius searches
+ dist, ind = neigh.radius_neighbors(test, return_distance=True)
+ for (d, i, i1) in zip(dist, ind, ind1):
+ j = d.argsort()
+ d[:] = d[j]
+ i[:] = i[j]
+ i1[:] = i1[j]
+ results.append((dist, ind))
+
+ assert_array_almost_equal(np.concatenate(list(ind)),
+ np.concatenate(list(ind1)))
+
+ for i in range(len(results) - 1):
+ assert_array_almost_equal(np.concatenate(list(results[i][0])),
+ np.concatenate(list(results[i + 1][0]))),
+ assert_array_almost_equal(np.concatenate(list(results[i][1])),
+ np.concatenate(list(results[i + 1][1])))
def test_kneighbors_classifier(n_samples=40,
diff --git a/sklearn/neighbors/unsupervised.py b/sklearn/neighbors/unsupervised.py
index 6149bd69bd4a89ec980c4f859b28c1dd973220c2..0b53270f6ddb9f0c593e45104ab69fb6bd1af9b6 100644
--- a/sklearn/neighbors/unsupervised.py
+++ b/sklearn/neighbors/unsupervised.py
@@ -45,6 +45,12 @@ class NearestNeighbors(NeighborsBase, KNeighborsMixin,
ordering of the training data.
If the fit method is ``'kd_tree'``, no warnings will be generated.
+ p: integer, optional (default = 2)
+ Parameter for the Minkowski metric from
+ sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is
+ equivalent to using manhattan_distance, and euclidean_distance for
+ p = 2. For arbitrary p, minkowski_distance is used.
+
Examples
--------
>>> from sklearn.neighbors import NearestNeighbors
@@ -78,9 +84,10 @@ class NearestNeighbors(NeighborsBase, KNeighborsMixin,
def __init__(self, n_neighbors=5, radius=1.0,
algorithm='auto', leaf_size=30,
- warn_on_equidistant=True):
+ warn_on_equidistant=True, p=2):
self._init_params(n_neighbors=n_neighbors,
radius=radius,
algorithm=algorithm,
leaf_size=leaf_size,
- warn_on_equidistant=warn_on_equidistant)
+ warn_on_equidistant=warn_on_equidistant,
+ p=p)