From dc956b4d0250a67459af145934a6583c71526820 Mon Sep 17 00:00:00 2001
From: Fabian Pedregosa <fabian.pedregosa@inria.fr>
Date: Thu, 16 Dec 2010 17:32:42 +0100
Subject: [PATCH] FIX: second argument in euclidean_distances.
This method failed when second argument was not given. Slightly
changed the API to always take a second argument without speed
penalization and add optional argument axis.
Added test.
---
scikits/learn/metrics/pairwise.py | 43 +++++++++++++-------
scikits/learn/metrics/tests/test_pairwise.py | 1 +
2 files changed, 29 insertions(+), 15 deletions(-)
diff --git a/scikits/learn/metrics/pairwise.py b/scikits/learn/metrics/pairwise.py
index ef32cae797..4dc2c05e76 100644
--- a/scikits/learn/metrics/pairwise.py
+++ b/scikits/learn/metrics/pairwise.py
@@ -9,36 +9,49 @@ sets of points.
import numpy as np
-def euclidian_distances(X, Y=None):
+def euclidian_distances(X, Y):
"""
Considering the rows of X (and Y=X) as vectors, compute the
- distance matrix between each pair of vector
+ distance matrix between each pair of vectors.
Parameters
----------
- X, array of shape (n_samples_1, n_features)
+ X: array of shape (n_samples_1, n_features)
- Y, array of shape (n_samples_2, n_features), default None
- if Y is None, then Y=X is used instead
+ Y: array of shape (n_samples_2, n_features)
Returns
-------
- distances, array of shape (n_samples_1, n_samples_2)
- """
+ distances: array of shape (n_samples_1, n_samples_2)
+
+ Examples
+ --------
+ >>> X = [[0, 1], [1, 1]]
+ >>> # distrance between rows of X
+ >>> euclidian_distances(X, X)
+ array([[ 0., 1.],
+ [ 1., 0.]])
+ >>> # get distance to origin
+ >>> euclidian_distances(X, [[0, 0]])
+ array([[ 1. ],
+ [ 1.41421356]])
+ """
+ # shortcut in the common case euclidean_distances(X, X)
+ compute_Y = X is not Y
+
X = np.asanyarray(X)
Y = np.asanyarray(Y)
- if Y is None:
- Y = X
+
if X.shape[1] != Y.shape[1]:
- raise ValueError, "incompatible dimension for X and Y matrices"
+ raise ValueError("Incompatible dimension for X and Y matrices")
XX = np.sum(X * X, axis=1)[:, np.newaxis]
- if Y is None:
- YY = XX.T
- else:
+ if compute_Y:
YY = np.sum(Y * Y, axis=1)[np.newaxis, :]
+ else:
+ YY = XX.T
+
distances = XX + YY # Using broadcasting
distances -= 2 * np.dot(X, Y.T)
distances = np.maximum(distances, 0)
- distances = np.sqrt(distances)
- return distances
+ return np.sqrt(distances)
diff --git a/scikits/learn/metrics/tests/test_pairwise.py b/scikits/learn/metrics/tests/test_pairwise.py
index 1d8b1dc54e..26b1c200b5 100644
--- a/scikits/learn/metrics/tests/test_pairwise.py
+++ b/scikits/learn/metrics/tests/test_pairwise.py
@@ -9,3 +9,4 @@ def test_euclidian_distances():
Y = [[1], [2]]
D = euclidian_distances(X, Y)
assert_array_almost_equal(D, [[1., 2.]])
+
--
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