diff --git a/sklearn/datasets/__init__.py b/sklearn/datasets/__init__.py
index dfdb4f6b5d0af0dcdb88e77ecc1241c079223a08..41fd97dacb2f23aec37d6c6934b974388af444ba 100644
--- a/sklearn/datasets/__init__.py
+++ b/sklearn/datasets/__init__.py
@@ -10,6 +10,7 @@ from .base import load_files
from .base import load_iris
from .base import load_linnerud
from .base import load_boston
+from .base import load_cities
from .base import get_data_home
from .base import clear_data_home
from .base import load_sample_images
diff --git a/sklearn/datasets/base.py b/sklearn/datasets/base.py
index 90b42fc02422046d9e418b3309c882bad19d0f77..a4b78ade3a18a8a1e7a27bbc886d4d17b7082d70 100644
--- a/sklearn/datasets/base.py
+++ b/sklearn/datasets/base.py
@@ -334,6 +334,32 @@ def load_diabetes():
return Bunch(data=data, target=target)
+def load_cities():
+ """Load and return the travelling distances between major
+ cities in france.
+
+ ============== ==================
+ Samples total 17 cities
+ Dimensionality
+ Features real, 0 < x < 1300
+ Targets 3D positions
+ ==============
+
+ Returns
+ -------
+ data: Bunch
+ Dictionary-like object, the interesting attributes are: 'data', the
+ distance matrix to learn and 'header', labels for the 14 cities.
+ """
+ base_dir = join(dirname(__file__), 'data/')
+ # Read data
+ data = np.loadtxt(base_dir + 'france_distances.csv',
+ skiprows=1)
+ with open(base_dir + 'france_distances.csv') as f:
+ header = f.readline().split(',')
+ return Bunch(data=data, header=header)
+
+
def load_linnerud():
"""Load and return the linnerud dataset (multivariate regression).
diff --git a/sklearn/datasets/data/france_distances.csv b/sklearn/datasets/data/france_distances.csv
new file mode 100644
index 0000000000000000000000000000000000000000..31e8ec646fb3e3323f2ff57e057b0e57834853e1
--- /dev/null
+++ b/sklearn/datasets/data/france_distances.csv
@@ -0,0 +1,19 @@
+Poitiers,Tours,Lille,Dijon,Le Mans,Toulouse,Bordeaux,Paris,Clermont Ferrand,Limoges,Rennes,Nantes,Brest,Le Havre,Caen,Lyon,Marseille,Grenoble
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diff --git a/sklearn/manifold/nmds.py b/sklearn/manifold/mds.py
similarity index 93%
rename from sklearn/manifold/nmds.py
rename to sklearn/manifold/mds.py
index 1b30b5514ed8a470b7bc8f6e6d61920dffa93a89..98fdfb055aa291ff4b6ceeb8d94c9c9759354fe4 100644
--- a/sklearn/manifold/nmds.py
+++ b/sklearn/manifold/mds.py
@@ -184,16 +184,25 @@ class MDS(BaseEstimator):
Parameters
----------
-
Notes
-----
"""
- def __init__(self, p=2, init=None, max_iter=300, eps=1e-3):
+ # TODO
+ def __init__(self, p=2, metric=True, init=None, max_iter=300, eps=1e-3):
self.p = p
self.init = init
self.max_iter = max_iter
self.eps = eps
- def fit():
+ def fit(self, X, y=None):
+ """
+ """
+ self.X = smacof(X, metric=self.metric, p=self.p, init=self.init,
+ max_iter=self.max_iter, verbose=self.verbose,
+ eps=self.eps)
+ return self
+
+ def predict(self, X):
"""
"""
+ # TODO
diff --git a/sklearn/manifold/tests/test_nmds.py b/sklearn/manifold/tests/test_mds.py
similarity index 84%
rename from sklearn/manifold/tests/test_nmds.py
rename to sklearn/manifold/tests/test_mds.py
index 3072a3dacdede5ac96dfc7a14d53082c3ee38da0..04f6621032e851035a5365346efdff825b81e5e6 100644
--- a/sklearn/manifold/tests/test_nmds.py
+++ b/sklearn/manifold/tests/test_mds.py
@@ -2,13 +2,13 @@ import numpy as np
from numpy.testing import assert_array_almost_equal
from nose.tools import assert_raises
-from sklearn.manifold import nmds
+from sklearn.manifold import mds
def test_pav():
distances = np.array([10., 8, 11, 5, 13, 11, 9, 14, 6, 16])
similarities = np.arange(10)
- distances_fit = nmds.PAV(distances, similarities)
+ distances_fit = mds.PAV(distances, similarities)
assert_array_almost_equal(distances_fit,
np.array([8.5, 8.5, 8.5, 8.5, 10.6, 10.6, 10.6, 10.6,
@@ -26,7 +26,7 @@ def test_smacof():
[.451, .252],
[.016, -.238],
[-.200, .524]])
- X = nmds.smacof(sim, init=Z, p=2, max_iter=1)
+ X = mds.smacof(sim, init=Z, p=2, max_iter=1)
X_true = np.array([[-1.415, -2.471],
[1.633, 1.107],
[.249, -.067],
@@ -41,14 +41,14 @@ def test_smacof_error():
[3, 2, 0, 1],
[4, 2, 1, 0]])
- assert_raises(ValueError, nmds.smacof, sim)
+ assert_raises(ValueError, mds.smacof, sim)
# Not squared similarity matrix:
sim = np.array([[0, 5, 9, 4],
[5, 0, 2, 2],
[4, 2, 1, 0]])
- assert_raises(ValueError, nmds.smacof, sim)
+ assert_raises(ValueError, mds.smacof, sim)
# init not None and not correct format:
sim = np.array([[0, 5, 3, 4],
@@ -59,4 +59,4 @@ def test_smacof_error():
Z = np.array([[-.266, -.539],
[.016, -.238],
[-.200, .524]])
- assert_raises(ValueError, nmds.smacof, sim, init=Z)
+ assert_raises(ValueError, mds.smacof, sim, init=Z)