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/ neighbors / _dist_metrics.pypy3-71-x86_64-linux-gnu.so
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return a list of metric IDs which map to those classes.
Example:
>>> L = get_valid_metric_ids([EuclideanDistance, 'ManhattanDistance'])
>>> sorted(L)
['cityblock', 'euclidean', 'l1', 'l2', 'manhattan']
Format string allocated too short, see comment in numpy.pxd Empty shape tuple for cython.array DistanceMetric is an abstract class Custom distance function must accept two vectors and return a float. Cannot create writable memory view from read-only memoryview Cannot assign to read-only memoryview Can only create a buffer that is contiguous in memory. Buffer view does not expose strides unknown dtype code in numpy.pxd (%d) sklearn/neighbors/_dist_metrics.pyx numpy.core.multiarray failed to import WMinkowskiDistance dist: size of w does not match Must provide either V or VI for Mahalanobis distance Mahalanobis dist: size of V does not match <strided and direct or indirect> unable to allocate array data. get_valid_metric_ids (line 92) ndarray is not C contiguous itemsize <= 0 for cython.array Invalid shape in axis %d: %d. p must be greater than 1 Cannot index with type '%s' <contiguous and indirect> <MemoryView of %r at 0x%x> RogersTanimotoDistance <MemoryView of %r object> <contiguous and direct> Unrecognized metric '%s' SokalMichenerDistance <strided and indirect> get_valid_metric_ids V/VI must be square SokalSneathDistance NotImplementedError MahalanobisDistance <strided and direct> cline_in_traceback WMinkowskiDistance SEuclideanDistance RussellRaoDistance BrayCurtisDistance __pyx_unpickle_Enum MinkowskiDistance ManhattanDistance KulsinskiDistance HaversineDistance EuclideanDistance ChebyshevDistance MatchingDistance CanberraDistance __setstate_cython__ __pyx_PickleError dtype_is_object allocate_buffer View.MemoryView JaccardDistance HammingDistance rogerstanimoto PyFuncDistance METRIC_MAPPING DistanceMetric sokalmichener __reduce_cython__ __pyx_getbuffer stringsource __pyx_checksum RuntimeError DiceDistance sokalsneath mahalanobis PickleError MemoryError ImportError wminkowski seuclidean russellrao __pyx_vtable__ __pyx_result get_metric braycurtis ValueError IndexError __reduce_ex__ __pyx_state minkowski manhattan kulsinski haversine euclidean enumerate cityblock chebyshev TypeError _typedefs __setstate__ __pyx_type matching itemsize infinity __getstate__ canberra Ellipsis memview jaccard hamming fortran asarray update unpack struct __reduce__ pyfunc pickle newObj name metric linalg __import__ format encode arcsin zeros start shape range order numpy items isinf flags error dtype __class__ ITYPE DTYPE ASCII __test__ stop step sqrt size pack ndim __name__ mode __main__ join func __dict__ dice base val sin pop obj __new__ key inv inf , } : ^ np l2 l1 id VI w (%s) p c Y X V T{ O L C %s() got multiple values for keyword argument '%U' %.200s() keywords must be strings %s() got an unexpected keyword argument '%U' Buffer dtype mismatch, expected %s%s%s but got %s Buffer dtype mismatch, expected '%s' but got %s in '%s.%s' memviewslice is already initialized! Unable to initialize pickling for %s sklearn/neighbors/_dist_metrics.pyx %.200s.%.200s is not a type object %.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject %s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject Does not understand character buffer dtype format string ('%c') sklearn/neighbors/_dist_metrics.c View.MemoryView.memoryview.__str__ View.MemoryView.memoryview.size.__get__ View.MemoryView.memoryview.nbytes.__get__ View.MemoryView.memoryview.itemsize.__get__ View.MemoryView.memoryview.ndim.__get__ View.MemoryView.memoryview.suboffsets.__get__ View.MemoryView.memoryview.shape.__get__ View.MemoryView.memoryview.assign_item_from_object View.MemoryView._memoryviewslice.assign_item_from_object View.MemoryView.memoryview.setitem_indexed %.200s() takes %.8s %zd positional argument%.1s (%zd given) View.MemoryView.array.memview.__get__ View.MemoryView.array.__getattr__ View.MemoryView.array.__getitem__ sklearn.neighbors._dist_metrics.PyFuncDistance.__init__ BufferFormatFromTypeInfo.format_from_typeinfo sklearn.neighbors._dist_metrics.DistanceMetric.__getstate__ sklearn.neighbors._dist_metrics.DistanceMetric.pdist View.MemoryView.array.__setitem__ Subscript deletion not supported by %.200s sklearn.neighbors._dist_metrics.MahalanobisDistance.dist_to_rdist sklearn.neighbors._dist_metrics.WMinkowskiDistance.dist_to_rdist sklearn.neighbors._dist_metrics.WMinkowskiDistance.rdist_to_dist sklearn.neighbors._dist_metrics.MinkowskiDistance.dist_to_rdist sklearn.neighbors._dist_metrics.MinkowskiDistance.rdist_to_dist sklearn.neighbors._dist_metrics.MinkowskiDistance.dist sklearn.neighbors._dist_metrics.SEuclideanDistance.dist_to_rdist sklearn.neighbors._dist_metrics.EuclideanDistance.dist_to_rdist sklearn.neighbors._dist_metrics.EuclideanDistance.rdist sklearn.neighbors._dist_metrics.EuclideanDistance.dist sklearn.neighbors._dist_metrics.DistanceMetric.rdist __int__ returned non-int (type %.200s). 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A user-defined distance
Parameters
----------
func : function
func should take two numpy arrays as input, and return a distance.
sklearn.neighbors._dist_metrics.HaversineDistance Haversine (Spherical) Distance
The Haversine distance is the angular distance between two points on
the surface of a sphere. The first distance of each point is assumed
to be the latitude, the second is the longitude, given in radians.
The dimension of the points must be 2:
.. math::
D(x, y) = 2\arcsin[\sqrt{\sin^2((x1 - y1) / 2)
+ \cos(x1)\cos(y1)\sin^2((x2 - y2) / 2)}]
sklearn.neighbors._dist_metrics.SokalSneathDistance Sokal-Sneath Distance
Sokal-Sneath Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
.. math::
D(x, y) = \frac{N_{TF} + N_{FT}}{N_{TT} / 2 + N_{TF} + N_{FT}}
sklearn.neighbors._dist_metrics.SokalMichenerDistance Sokal-Michener Distance
Sokal-Michener Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
.. math::
D(x, y) = \frac{2 (N_{TF} + N_{FT})}{N + N_{TF} + N_{FT}}
sklearn.neighbors._dist_metrics.RussellRaoDistance Russell-Rao Distance
Russell-Rao Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
.. math::
D(x, y) = \frac{N - N_{TT}}{N}
sklearn.neighbors._dist_metrics.RogersTanimotoDistance Rogers-Tanimoto Distance
Rogers-Tanimoto Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
.. math::
D(x, y) = \frac{2 (N_{TF} + N_{FT})}{N + N_{TF} + N_{FT}}
sklearn.neighbors._dist_metrics.KulsinskiDistance Kulsinski Distance
Kulsinski Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
.. math::
D(x, y) = 1 - \frac{N_{TT}}{N + N_{TF} + N_{FT}}
sklearn.neighbors._dist_metrics.DiceDistance Dice Distance
Dice Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
.. math::
D(x, y) = \frac{N_{TF} + N_{FT}}{2 * N_{TT} + N_{TF} + N_{FT}}
sklearn.neighbors._dist_metrics.MatchingDistance Matching Distance
Matching Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
.. math::
D(x, y) = \frac{N_{TF} + N_{FT}}{N}
sklearn.neighbors._dist_metrics.JaccardDistance Jaccard Distance
Jaccard Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
.. math::
D(x, y) = \frac{N_{TF} + N_{FT}}{N_{TT} + N_{TF} + N_{FT}}
sklearn.neighbors._dist_metrics.BrayCurtisDistance Bray-Curtis Distance
Bray-Curtis distance is meant for discrete-valued vectors, though it is
a valid metric for real-valued vectors.
.. math::
D(x, y) = \frac{\sum_i |x_i - y_i|}{\sum_i(|x_i| + |y_i|)}
sklearn.neighbors._dist_metrics.CanberraDistance Canberra Distance
Canberra distance is meant for discrete-valued vectors, though it is
a valid metric for real-valued vectors.
.. math::
D(x, y) = \sum_i \frac{|x_i - y_i|}{|x_i| + |y_i|}
sklearn.neighbors._dist_metrics.HammingDistance Hamming Distance
Hamming distance is meant for discrete-valued vectors, though it is
a valid metric for real-valued vectors.
.. math::
D(x, y) = \frac{1}{N} \sum_i \delta_{x_i, y_i}
sklearn.neighbors._dist_metrics.MahalanobisDistance Mahalanobis Distance
.. math::
D(x, y) = \sqrt{ (x - y)^T V^{-1} (x - y) }
Parameters
----------
V : array_like
Symmetric positive-definite covariance matrix.
The inverse of this matrix will be explicitly computed.
VI : array_like
optionally specify the inverse directly. If VI is passed,
then V is not referenced.
sklearn.neighbors._dist_metrics.WMinkowskiDistance Weighted Minkowski Distance
.. math::
D(x, y) = [\sum_i |w_i * (x_i - y_i)|^p] ^ (1/p)
Weighted Minkowski Distance requires p >= 1 and finite.
Parameters
----------
p : int
The order of the norm of the difference :math:`{||u-v||}_p`.
w : (N,) array_like
The weight vector.
sklearn.neighbors._dist_metrics.MinkowskiDistance Minkowski Distance
.. math::
D(x, y) = [\sum_i (x_i - y_i)^p] ^ (1/p)
Minkowski Distance requires p >= 1 and finite. For p = infinity,
use ChebyshevDistance.
Note that for p=1, ManhattanDistance is more efficient, and for
p=2, EuclideanDistance is more efficient.
sklearn.neighbors._dist_metrics.ChebyshevDistance Chebyshev/Infinity Distance
.. math::
D(x, y) = max_i (|x_i - y_i|)
sklearn.neighbors._dist_metrics.ManhattanDistance Manhattan/City-block Distance metric
.. math::
D(x, y) = \sum_i |x_i - y_i|
sklearn.neighbors._dist_metrics.SEuclideanDistance Standardized Euclidean Distance metric
.. math::
D(x, y) = \sqrt{ \sum_i \frac{ (x_i - y_i) ^ 2}{V_i} }
sklearn.neighbors._dist_metrics.EuclideanDistance Euclidean Distance metric
.. math::
D(x, y) = \sqrt{ \sum_i (x_i - y_i) ^ 2 }
sklearn.neighbors._dist_metrics.DistanceMetric DistanceMetric class
This class provides a uniform interface to fast distance metric
functions. The various metrics can be accessed via the :meth:`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:
- N : number of dimensions
- NTT : number of dims in which both values are True
- NTF : number of dims in which the first value is True, second is False
- NFT : number of dims in which the first value is False, second is True
- NFF : number of dims in which both values are False
- NNEQ : number of non-equal dimensions, NNEQ = NTF + NFT
- NNZ : number of nonzero dimensions, NNZ = NTF + NFT + NTT
================= ======================= ===============================
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
1) Non-negativity: d(x, y) >= 0
2) Identity: d(x, y) = 0 if and only if x == y
3) Symmetry: d(x, y) = d(y, x)
4) Triangle Inequality: d(x, y) + d(y, z) >= d(x, z)
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.
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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.
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.
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.
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
set state for pickling
get state for pickling
reduce method used for pickling
Given an iterable of metric class names or class identifiers,
return a list of metric IDs which map to those classes.
Example:
>>> L = get_valid_metric_ids([EuclideanDistance, 'ManhattanDistance'])
>>> sorted(L)
['cityblock', 'euclidean', 'l1', 'l2', 'manhattan']
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