class sklearn.neighbors.DistanceMetric
DistanceMetric class
This class provides a uniform interface to fast distance metric functions. The various metrics can be accessed via the get_metric
class method and the metric string identifier (see below). For example, to use the Euclidean distance:
>>> dist = DistanceMetric.get_metric('euclidean') >>> X = [[0, 1, 2], [3, 4, 5]] >>> dist.pairwise(X) array([[ 0. , 5.19615242], [ 5.19615242, 0. ]])
Available Metrics The following lists the string metric identifiers and the associated distance metric classes:
Metrics intended for realvalued vector spaces:
identifier  class name  args  distance function 
“euclidean”  EuclideanDistance 
 sqrt(sum((x  y)^2)) 
“manhattan”  ManhattanDistance 
 sum(x  y) 
“chebyshev”  ChebyshevDistance 
 max(x  y) 
“minkowski”  MinkowskiDistance  p  sum(x  y^p)^(1/p) 
“wminkowski”  WMinkowskiDistance  p, w  sum(w * x  y^p)^(1/p) 
“seuclidean”  SEuclideanDistance  V  sqrt(sum((x  y)^2 / V)) 
“mahalanobis”  MahalanobisDistance  V or VI  sqrt((x  y)' V^1 (x  y)) 
Metrics intended for twodimensional vector spaces: Note that the haversine distance metric requires data in the form of [latitude, longitude] and both inputs and outputs are in units of radians.
identifier  class name  distance function 
“haversine”  HaversineDistance 

Metrics intended for integervalued vector spaces: Though intended for integervalued vectors, these are also valid metrics in the case of realvalued vectors.
identifier  class name  distance function 
“hamming”  HammingDistance  N_unequal(x, y) / N_tot 
“canberra”  CanberraDistance  sum(x  y / (x + y)) 
“braycurtis”  BrayCurtisDistance  sum(x  y) / (sum(x) + sum(y)) 
Metrics intended for booleanvalued vector spaces: Any nonzero entry is evaluated to “True”. In the listings below, the following abbreviations are used:
identifier  class name  distance function 
“jaccard”  JaccardDistance  NNEQ / NNZ 
“matching”  MatchingDistance  NNEQ / N 
“dice”  DiceDistance  NNEQ / (NTT + NNZ) 
“kulsinski”  KulsinskiDistance  (NNEQ + N  NTT) / (NNEQ + N) 
“rogerstanimoto”  RogersTanimotoDistance  2 * NNEQ / (N + NNEQ) 
“russellrao”  RussellRaoDistance  NNZ / N 
“sokalmichener”  SokalMichenerDistance  2 * NNEQ / (N + NNEQ) 
“sokalsneath”  SokalSneathDistance  NNEQ / (NNEQ + 0.5 * NTT) 
Userdefined distance:
identifier  class name  args 
“pyfunc”  PyFuncDistance  func 
Here func
is a function which takes two onedimensional numpy arrays, and returns a distance. Note that in order to be used within the BallTree, the distance must be a true metric: i.e. it must satisfy the following properties
Because of the Python object overhead involved in calling the python function, this will be fairly slow, but it will have the same scaling as other distances.
dist_to_rdist  Convert the true distance to the reduced distance. 
get_metric  Get the given distance metric from the string identifier. 
pairwise  Compute the pairwise distances between X and Y 
rdist_to_dist  Convert the Reduced distance to the true distance. 
__init__()
Initialize self. See help(type(self)) for accurate signature.
dist_to_rdist()
Convert the true distance to the reduced distance.
The reduced distance, defined for some metrics, is a computationally more efficient measure which preserves the rank of the true distance. For example, in the Euclidean distance metric, the reduced distance is the squaredeuclidean distance.
get_metric()
Get the given distance metric from the string identifier.
See the docstring of DistanceMetric for a list of available metrics.
Parameters: 
metric : string or class name The distance metric to use **kwargs : additional arguments will be passed to the requested metric 

pairwise()
Compute the pairwise distances between X and Y
This is a convenience routine for the sake of testing. For many metrics, the utilities in scipy.spatial.distance.cdist and scipy.spatial.distance.pdist will be faster.
Parameters: 
X : array_like Array of shape (Nx, D), representing Nx points in D dimensions. Y : array_like (optional) Array of shape (Ny, D), representing Ny points in D dimensions. If not specified, then Y=X. Returns : —— : dist : ndarray The shape (Nx, Ny) array of pairwise distances between points in X and Y. 

rdist_to_dist()
Convert the Reduced distance to the true distance.
The reduced distance, defined for some metrics, is a computationally more efficient measure which preserves the rank of the true distance. For example, in the Euclidean distance metric, the reduced distance is the squaredeuclidean distance.
© 2007–2017 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html