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 real-valued 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 two-dimensional 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 | 2 arcsin(sqrt(sin^2(0.5*dx) + cos(x1)cos(x2)sin^2(0.5*dy))) |
Metrics intended for integer-valued vector spaces: Though intended for integer-valued vectors, these are also valid metrics in the case of real-valued 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 boolean-valued 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) |
User-defined distance:
identifier | class name | args |
“pyfunc” | PyFuncDistance | func |
Here func
is a function which takes two one-dimensional 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__($self, /, *args, **kwargs)
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 squared-euclidean distance.
get_metric()
Get the given distance metric from the string identifier.
See the docstring of DistanceMetric for a list of available metrics.
Parameters: |
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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: |
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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 squared-euclidean distance.
© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html