"""
=====================================================
Distance computations (:mod:`scipy.spatial.distance`)
=====================================================
.. sectionauthor:: Damian Eads
Function Reference
------------------
Distance matrix computation from a collection of raw observation vectors
stored in a rectangular array.
.. autosummary::
:toctree: generated/
pdist -- pairwise distances between observation vectors.
cdist -- distances between two collections of observation vectors
squareform -- convert distance matrix to a condensed one and vice versa
directed_hausdorff -- directed Hausdorff distance between arrays
Predicates for checking the validity of distance matrices, both
condensed and redundant. Also contained in this module are functions
for computing the number of observations in a distance matrix.
.. autosummary::
:toctree: generated/
is_valid_dm -- checks for a valid distance matrix
is_valid_y -- checks for a valid condensed distance matrix
num_obs_dm -- # of observations in a distance matrix
num_obs_y -- # of observations in a condensed distance matrix
Distance functions between two numeric vectors ``u`` and ``v``. Computing
distances over a large collection of vectors is inefficient for these
functions. Use ``pdist`` for this purpose.
.. autosummary::
:toctree: generated/
braycurtis -- the Bray-Curtis distance.
canberra -- the Canberra distance.
chebyshev -- the Chebyshev distance.
cityblock -- the Manhattan distance.
correlation -- the Correlation distance.
cosine -- the Cosine distance.
euclidean -- the Euclidean distance.
mahalanobis -- the Mahalanobis distance.
minkowski -- the Minkowski distance.
seuclidean -- the normalized Euclidean distance.
sqeuclidean -- the squared Euclidean distance.
wminkowski -- the weighted Minkowski distance.
Distance functions between two boolean vectors (representing sets) ``u`` and
``v``. As in the case of numerical vectors, ``pdist`` is more efficient for
computing the distances between all pairs.
.. autosummary::
:toctree: generated/
dice -- the Dice dissimilarity.
hamming -- the Hamming distance.
jaccard -- the Jaccard distance.
kulsinski -- the Kulsinski distance.
matching -- the matching dissimilarity.
rogerstanimoto -- the Rogers-Tanimoto dissimilarity.
russellrao -- the Russell-Rao dissimilarity.
sokalmichener -- the Sokal-Michener dissimilarity.
sokalsneath -- the Sokal-Sneath dissimilarity.
yule -- the Yule dissimilarity.
:func:`hamming` also operates over discrete numerical vectors.
"""
# Copyright (C) Damian Eads, 2007-2008. New BSD License.
from __future__ import division, print_function, absolute_import
__all__ = [
'braycurtis',
'canberra',
'cdist',
'chebyshev',
'cityblock',
'correlation',
'cosine',
'dice',
'directed_hausdorff',
'euclidean',
'hamming',
'is_valid_dm',
'is_valid_y',
'jaccard',
'kulsinski',
'mahalanobis',
'matching',
'minkowski',
'num_obs_dm',
'num_obs_y',
'pdist',
'rogerstanimoto',
'russellrao',
'seuclidean',
'sokalmichener',
'sokalsneath',
'sqeuclidean',
'squareform',
'wminkowski',
'yule'
]
import warnings
import numpy as np
from functools import partial
from scipy._lib.six import callable, string_types
from scipy._lib.six import xrange
from . import _distance_wrap
from . import _hausdorff
from ..linalg import norm
def _copy_array_if_base_present(a):
"""
Copies the array if its base points to a parent array.
"""
if a.base is not None:
return a.copy()
return a
def _convert_to_bool(X):
return np.ascontiguousarray(X, dtype=bool)
def _convert_to_double(X):
return np.ascontiguousarray(X, dtype=np.double)
def _filter_deprecated_kwargs(**kwargs):
# Filtering out old default keywords
for k in ["p", "V", "w", "VI"]:
kw = kwargs.pop(k, None)
if kw is not None:
warnings.warn('Got unexpected kwarg %s. This will raise an error'
' in a future version.' % k, DeprecationWarning)
def _nbool_correspond_all(u, v):
if u.dtype != v.dtype:
raise TypeError("Arrays being compared must be of the same data type.")
if u.dtype == int or u.dtype == np.float_ or u.dtype == np.double:
not_u = 1.0 - u
not_v = 1.0 - v
nff = (not_u * not_v).sum()
nft = (not_u * v).sum()
ntf = (u * not_v).sum()
ntt = (u * v).sum()
elif u.dtype == bool:
not_u = ~u
not_v = ~v
nff = (not_u & not_v).sum()
nft = (not_u & v).sum()
ntf = (u & not_v).sum()
ntt = (u & v).sum()
else:
raise TypeError("Arrays being compared have unknown type.")
return (nff, nft, ntf, ntt)
def _nbool_correspond_ft_tf(u, v):
if u.dtype == int or u.dtype == np.float_ or u.dtype == np.double:
not_u = 1.0 - u
not_v = 1.0 - v
nft = (not_u * v).sum()
ntf = (u * not_v).sum()
else:
not_u = ~u
not_v = ~v
nft = (not_u & v).sum()
ntf = (u & not_v).sum()
return (nft, ntf)
def _validate_mahalanobis_args(X, m, n, VI):
if VI is None:
if m <= n:
# There are fewer observations than the dimension of
# the observations.
raise ValueError("The number of observations (%d) is too "
"small; the covariance matrix is "
"singular. For observations with %d "
"dimensions, at least %d observations "
"are required." % (m, n, n + 1))
CV = np.atleast_2d(np.cov(X.astype(np.double).T))
VI = np.linalg.inv(CV).T.copy()
VI = _copy_array_if_base_present(_convert_to_double(VI))
return VI
def _validate_minkowski_args(p):
if p is None:
p = 2.
return p
def _validate_seuclidean_args(X, n, V):
if V is None:
V = np.var(X.astype(np.double), axis=0, ddof=1)
else:
V = np.asarray(V, order='c')
if V.dtype != np.double:
raise TypeError('Variance vector V must contain doubles.')
if len(V.shape) != 1:
raise ValueError('Variance vector V must '
'be one-dimensional.')
if V.shape[0] != n:
raise ValueError('Variance vector V must be of the same '
'dimension as the vectors on which the distances '
'are computed.')
return _convert_to_double(V)
def _validate_vector(u, dtype=None):
# XXX Is order='c' really necessary?
u = np.asarray(u, dtype=dtype, order='c').squeeze()
# Ensure values such as u=1 and u=[1] still return 1-D arrays.
u = np.atleast_1d(u)
if u.ndim > 1:
raise ValueError("Input vector should be 1-D.")
return u
def _validate_wminkowski_args(p, w):
if w is None:
raise ValueError('weighted minkowski requires a weight '
'vector `w` to be given.')
w = _convert_to_double(w)
if p is None:
p = 2.
return p, w
def directed_hausdorff(u, v, seed=0):
"""
Computes the directed Hausdorff distance between two N-D arrays.
Distances between pairs are calculated using a Euclidean metric.
Parameters
----------
u : (M,N) ndarray
Input array.
v : (O,N) ndarray
Input array.
seed : int or None
Local `np.random.RandomState` seed. Default is 0, a random shuffling of
u and v that guarantees reproducibility.
Returns
-------
d : double
The directed Hausdorff distance between arrays `u` and `v`,
index_1 : int
index of point contributing to Hausdorff pair in `u`
index_2 : int
index of point contributing to Hausdorff pair in `v`
Notes
-----
Uses the early break technique and the random sampling approach
described by [1]_. Although worst-case performance is ``O(m * o)``
(as with the brute force algorithm), this is unlikely in practice
as the input data would have to require the algorithm to explore
every single point interaction, and after the algorithm shuffles
the input points at that. The best case performance is O(m), which
is satisfied by selecting an inner loop distance that is less than
cmax and leads to an early break as often as possible. The authors
have formally shown that the average runtime is closer to O(m).
.. versionadded:: 0.19.0
References
----------
.. [1] A. A. Taha and A. Hanbury, "An efficient algorithm for
calculating the exact Hausdorff distance." IEEE Transactions On
Pattern Analysis And Machine Intelligence, vol. 37 pp. 2153-63,
2015.
See Also
--------
scipy.spatial.procrustes : Another similarity test for two data sets
Examples
--------
Find the directed Hausdorff distance between two 2-D arrays of
coordinates:
>>> from scipy.spatial.distance import directed_hausdorff
>>> u = np.array([(1.0, 0.0),
... (0.0, 1.0),
... (-1.0, 0.0),
... (0.0, -1.0)])
>>> v = np.array([(2.0, 0.0),
... (0.0, 2.0),
... (-2.0, 0.0),
... (0.0, -4.0)])
>>> directed_hausdorff(u, v)[0]
2.23606797749979
>>> directed_hausdorff(v, u)[0]
3.0
Find the general (symmetric) Hausdorff distance between two 2-D
arrays of coordinates:
>>> max(directed_hausdorff(u, v)[0], directed_hausdorff(v, u)[0])
3.0
Find the indices of the points that generate the Hausdorff distance
(the Hausdorff pair):
>>> directed_hausdorff(v, u)[1:]
(3, 3)
"""
u = np.asarray(u, dtype=np.float64, order='c')
v = np.asarray(v, dtype=np.float64, order='c')
result = _hausdorff.directed_hausdorff(u, v, seed)
return result
def minkowski(u, v, p):
"""
Computes the Minkowski distance between two 1-D arrays.
The Minkowski distance between 1-D arrays `u` and `v`,
is defined as
.. math::
{||u-v||}_p = (\\sum{|u_i - v_i|^p})^{1/p}.
Parameters
----------
u : (N,) array_like
Input array.
v : (N,) array_like
Input array.
p : int
The order of the norm of the difference :math:`{||u-v||}_p`.
Returns
-------
d : double
The Minkowski distance between vectors `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
if p < 1:
raise ValueError("p must be at least 1")
dist = norm(u - v, ord=p)
return dist
def wminkowski(u, v, p, w):
"""
Computes the weighted Minkowski distance between two 1-D arrays.
The weighted Minkowski distance between `u` and `v`, defined as
.. math::
\\left(\\sum{(|w_i (u_i - v_i)|^p)}\\right)^{1/p}.
Parameters
----------
u : (N,) array_like
Input array.
v : (N,) array_like
Input array.
p : int
The order of the norm of the difference :math:`{||u-v||}_p`.
w : (N,) array_like
The weight vector.
Returns
-------
wminkowski : double
The weighted Minkowski distance between vectors `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
w = _validate_vector(w)
if p < 1:
raise ValueError("p must be at least 1")
dist = norm(w * (u - v), ord=p)
return dist
def euclidean(u, v):
"""
Computes the Euclidean distance between two 1-D arrays.
The Euclidean distance between 1-D arrays `u` and `v`, is defined as
.. math::
{||u-v||}_2
Parameters
----------
u : (N,) array_like
Input array.
v : (N,) array_like
Input array.
Returns
-------
euclidean : double
The Euclidean distance between vectors `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
dist = norm(u - v)
return dist
def sqeuclidean(u, v):
"""
Computes the squared Euclidean distance between two 1-D arrays.
The squared Euclidean distance between `u` and `v` is defined as
.. math::
{||u-v||}_2^2.
Parameters
----------
u : (N,) array_like
Input array.
v : (N,) array_like
Input array.
Returns
-------
sqeuclidean : double
The squared Euclidean distance between vectors `u` and `v`.
"""
# Preserve float dtypes, but convert everything else to np.float64
# for stability.
utype, vtype = None, None
if not (hasattr(u, "dtype") and np.issubdtype(u.dtype, np.inexact)):
utype = np.float64
if not (hasattr(v, "dtype") and np.issubdtype(v.dtype, np.inexact)):
vtype = np.float64
u = _validate_vector(u, dtype=utype)
v = _validate_vector(v, dtype=vtype)
u_v = u - v
return np.dot(u_v, u_v)
def cosine(u, v):
"""
Computes the Cosine distance between 1-D arrays.
The Cosine distance between `u` and `v`, is defined as
.. math::
1 - \\frac{u \\cdot v}
{||u||_2 ||v||_2}.
where :math:`u \\cdot v` is the dot product of :math:`u` and
:math:`v`.
Parameters
----------
u : (N,) array_like
Input array.
v : (N,) array_like
Input array.
Returns
-------
cosine : double
The Cosine distance between vectors `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
dist = 1.0 - np.dot(u, v) / (norm(u) * norm(v))
return dist
def correlation(u, v):
"""
Computes the correlation distance between two 1-D arrays.
The correlation distance between `u` and `v`, is
defined as
.. math::
1 - \\frac{(u - \\bar{u}) \\cdot (v - \\bar{v})}
{{||(u - \\bar{u})||}_2 {||(v - \\bar{v})||}_2}
where :math:`\\bar{u}` is the mean of the elements of `u`
and :math:`x \\cdot y` is the dot product of :math:`x` and :math:`y`.
Parameters
----------
u : (N,) array_like
Input array.
v : (N,) array_like
Input array.
Returns
-------
correlation : double
The correlation distance between 1-D array `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
umu = u.mean()
vmu = v.mean()
um = u - umu
vm = v - vmu
dist = 1.0 - np.dot(um, vm) / (norm(um) * norm(vm))
return dist
def hamming(u, v):
"""
Computes the Hamming distance between two 1-D arrays.
The Hamming distance between 1-D arrays `u` and `v`, is simply the
proportion of disagreeing components in `u` and `v`. If `u` and `v` are
boolean vectors, the Hamming distance is
.. math::
\\frac{c_{01} + c_{10}}{n}
where :math:`c_{ij}` is the number of occurrences of
:math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for
:math:`k < n`.
Parameters
----------
u : (N,) array_like
Input array.
v : (N,) array_like
Input array.
Returns
-------
hamming : double
The Hamming distance between vectors `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
if u.shape != v.shape:
raise ValueError('The 1d arrays must have equal lengths.')
return (u != v).mean()
def jaccard(u, v):
"""
Computes the Jaccard-Needham dissimilarity between two boolean 1-D arrays.
The Jaccard-Needham dissimilarity between 1-D boolean arrays `u` and `v`,
is defined as
.. math::
\\frac{c_{TF} + c_{FT}}
{c_{TT} + c_{FT} + c_{TF}}
where :math:`c_{ij}` is the number of occurrences of
:math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for
:math:`k < n`.
Parameters
----------
u : (N,) array_like, bool
Input array.
v : (N,) array_like, bool
Input array.
Returns
-------
jaccard : double
The Jaccard distance between vectors `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
dist = (np.double(np.bitwise_and((u != v),
np.bitwise_or(u != 0, v != 0)).sum()) /
np.double(np.bitwise_or(u != 0, v != 0).sum()))
return dist
def kulsinski(u, v):
"""
Computes the Kulsinski dissimilarity between two boolean 1-D arrays.
The Kulsinski dissimilarity between two boolean 1-D arrays `u` and `v`,
is defined as
.. math::
\\frac{c_{TF} + c_{FT} - c_{TT} + n}
{c_{FT} + c_{TF} + n}
where :math:`c_{ij}` is the number of occurrences of
:math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for
:math:`k < n`.
Parameters
----------
u : (N,) array_like, bool
Input array.
v : (N,) array_like, bool
Input array.
Returns
-------
kulsinski : double
The Kulsinski distance between vectors `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
n = float(len(u))
(nff, nft, ntf, ntt) = _nbool_correspond_all(u, v)
return (ntf + nft - ntt + n) / (ntf + nft + n)
def seuclidean(u, v, V):
"""
Returns the standardized Euclidean distance between two 1-D arrays.
The standardized Euclidean distance between `u` and `v`.
Parameters
----------
u : (N,) array_like
Input array.
v : (N,) array_like
Input array.
V : (N,) array_like
`V` is an 1-D array of component variances. It is usually computed
among a larger collection vectors.
Returns
-------
seuclidean : double
The standardized Euclidean distance between vectors `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
V = _validate_vector(V, dtype=np.float64)
if V.shape[0] != u.shape[0] or u.shape[0] != v.shape[0]:
raise TypeError('V must be a 1-D array of the same dimension '
'as u and v.')
return np.sqrt(((u - v) ** 2 / V).sum())
def cityblock(u, v):
"""
Computes the City Block (Manhattan) distance.
Computes the Manhattan distance between two 1-D arrays `u` and `v`,
which is defined as
.. math::
\\sum_i {\\left| u_i - v_i \\right|}.
Parameters
----------
u : (N,) array_like
Input array.
v : (N,) array_like
Input array.
Returns
-------
cityblock : double
The City Block (Manhattan) distance between vectors `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
return abs(u - v).sum()
def mahalanobis(u, v, VI):
"""
Computes the Mahalanobis distance between two 1-D arrays.
The Mahalanobis distance between 1-D arrays `u` and `v`, is defined as
.. math::
\\sqrt{ (u-v) V^{-1} (u-v)^T }
where ``V`` is the covariance matrix. Note that the argument `VI`
is the inverse of ``V``.
Parameters
----------
u : (N,) array_like
Input array.
v : (N,) array_like
Input array.
VI : ndarray
The inverse of the covariance matrix.
Returns
-------
mahalanobis : double
The Mahalanobis distance between vectors `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
VI = np.atleast_2d(VI)
delta = u - v
m = np.dot(np.dot(delta, VI), delta)
return np.sqrt(m)
def chebyshev(u, v):
"""
Computes the Chebyshev distance.
Computes the Chebyshev distance between two 1-D arrays `u` and `v`,
which is defined as
.. math::
\\max_i {|u_i-v_i|}.
Parameters
----------
u : (N,) array_like
Input vector.
v : (N,) array_like
Input vector.
Returns
-------
chebyshev : double
The Chebyshev distance between vectors `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
return max(abs(u - v))
def braycurtis(u, v):
"""
Computes the Bray-Curtis distance between two 1-D arrays.
Bray-Curtis distance is defined as
.. math::
\\sum{|u_i-v_i|} / \\sum{|u_i+v_i|}
The Bray-Curtis distance is in the range [0, 1] if all coordinates are
positive, and is undefined if the inputs are of length zero.
Parameters
----------
u : (N,) array_like
Input array.
v : (N,) array_like
Input array.
Returns
-------
braycurtis : double
The Bray-Curtis distance between 1-D arrays `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v, dtype=np.float64)
return abs(u - v).sum() / abs(u + v).sum()
def canberra(u, v):
"""
Computes the Canberra distance between two 1-D arrays.
The Canberra distance is defined as
.. math::
d(u,v) = \\sum_i \\frac{|u_i-v_i|}
{|u_i|+|v_i|}.
Parameters
----------
u : (N,) array_like
Input array.
v : (N,) array_like
Input array.
Returns
-------
canberra : double
The Canberra distance between vectors `u` and `v`.
Notes
-----
When `u[i]` and `v[i]` are 0 for given i, then the fraction 0/0 = 0 is
used in the calculation.
"""
u = _validate_vector(u)
v = _validate_vector(v, dtype=np.float64)
olderr = np.seterr(invalid='ignore')
try:
d = np.nansum(abs(u - v) / (abs(u) + abs(v)))
finally:
np.seterr(**olderr)
return d
def yule(u, v):
"""
Computes the Yule dissimilarity between two boolean 1-D arrays.
The Yule dissimilarity is defined as
.. math::
\\frac{R}{c_{TT} * c_{FF} + \\frac{R}{2}}
where :math:`c_{ij}` is the number of occurrences of
:math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for
:math:`k < n` and :math:`R = 2.0 * c_{TF} * c_{FT}`.
Parameters
----------
u : (N,) array_like, bool
Input array.
v : (N,) array_like, bool
Input array.
Returns
-------
yule : double
The Yule dissimilarity between vectors `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
(nff, nft, ntf, ntt) = _nbool_correspond_all(u, v)
return float(2.0 * ntf * nft) / float(ntt * nff + ntf * nft)
def matching(u, v):
"""
Computes the Hamming distance between two boolean 1-D arrays.
This is a deprecated synonym for :func:`hamming`.
"""
return hamming(u, v)
def dice(u, v):
"""
Computes the Dice dissimilarity between two boolean 1-D arrays.
The Dice dissimilarity between `u` and `v`, is
.. math::
\\frac{c_{TF} + c_{FT}}
{2c_{TT} + c_{FT} + c_{TF}}
where :math:`c_{ij}` is the number of occurrences of
:math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for
:math:`k < n`.
Parameters
----------
u : (N,) ndarray, bool
Input 1-D array.
v : (N,) ndarray, bool
Input 1-D array.
Returns
-------
dice : double
The Dice dissimilarity between 1-D arrays `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
if u.dtype == bool:
ntt = (u & v).sum()
else:
ntt = (u * v).sum()
(nft, ntf) = _nbool_correspond_ft_tf(u, v)
return float(ntf + nft) / float(2.0 * ntt + ntf + nft)
def rogerstanimoto(u, v):
"""
Computes the Rogers-Tanimoto dissimilarity between two boolean 1-D arrays.
The Rogers-Tanimoto dissimilarity between two boolean 1-D arrays
`u` and `v`, is defined as
.. math::
\\frac{R}
{c_{TT} + c_{FF} + R}
where :math:`c_{ij}` is the number of occurrences of
:math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for
:math:`k < n` and :math:`R = 2(c_{TF} + c_{FT})`.
Parameters
----------
u : (N,) array_like, bool
Input array.
v : (N,) array_like, bool
Input array.
Returns
-------
rogerstanimoto : double
The Rogers-Tanimoto dissimilarity between vectors
`u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
(nff, nft, ntf, ntt) = _nbool_correspond_all(u, v)
return float(2.0 * (ntf + nft)) / float(ntt + nff + (2.0 * (ntf + nft)))
def russellrao(u, v):
"""
Computes the Russell-Rao dissimilarity between two boolean 1-D arrays.
The Russell-Rao dissimilarity between two boolean 1-D arrays, `u` and
`v`, is defined as
.. math::
\\frac{n - c_{TT}}
{n}
where :math:`c_{ij}` is the number of occurrences of
:math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for
:math:`k < n`.
Parameters
----------
u : (N,) array_like, bool
Input array.
v : (N,) array_like, bool
Input array.
Returns
-------
russellrao : double
The Russell-Rao dissimilarity between vectors `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
if u.dtype == bool:
ntt = (u & v).sum()
else:
ntt = (u * v).sum()
return float(len(u) - ntt) / float(len(u))
def sokalmichener(u, v):
"""
Computes the Sokal-Michener dissimilarity between two boolean 1-D arrays.
The Sokal-Michener dissimilarity between boolean 1-D arrays `u` and `v`,
is defined as
.. math::
\\frac{R}
{S + R}
where :math:`c_{ij}` is the number of occurrences of
:math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for
:math:`k < n`, :math:`R = 2 * (c_{TF} + c_{FT})` and
:math:`S = c_{FF} + c_{TT}`.
Parameters
----------
u : (N,) array_like, bool
Input array.
v : (N,) array_like, bool
Input array.
Returns
-------
sokalmichener : double
The Sokal-Michener dissimilarity between vectors `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
if u.dtype == bool:
ntt = (u & v).sum()
nff = (~u & ~v).sum()
else:
ntt = (u * v).sum()
nff = ((1.0 - u) * (1.0 - v)).sum()
(nft, ntf) = _nbool_correspond_ft_tf(u, v)
return float(2.0 * (ntf + nft)) / float(ntt + nff + 2.0 * (ntf + nft))
def sokalsneath(u, v):
"""
Computes the Sokal-Sneath dissimilarity between two boolean 1-D arrays.
The Sokal-Sneath dissimilarity between `u` and `v`,
.. math::
\\frac{R}
{c_{TT} + R}
where :math:`c_{ij}` is the number of occurrences of
:math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for
:math:`k < n` and :math:`R = 2(c_{TF} + c_{FT})`.
Parameters
----------
u : (N,) array_like, bool
Input array.
v : (N,) array_like, bool
Input array.
Returns
-------
sokalsneath : double
The Sokal-Sneath dissimilarity between vectors `u` and `v`.
"""
u = _validate_vector(u)
v = _validate_vector(v)
if u.dtype == bool:
ntt = (u & v).sum()
else:
ntt = (u * v).sum()
(nft, ntf) = _nbool_correspond_ft_tf(u, v)
denom = ntt + 2.0 * (ntf + nft)
if denom == 0:
raise ValueError('Sokal-Sneath dissimilarity is not defined for '
'vectors that are entirely false.')
return float(2.0 * (ntf + nft)) / denom
# Registry of "simple" distance metrics' pdist and cdist implementations,
# meaning the ones that accept one dtype and have no additional arguments.
_SIMPLE_CDIST = {}
_SIMPLE_PDIST = {}
for wrap_name, names, typ in [
("bray_curtis", ['braycurtis'], "double"),
("canberra", ['canberra'], "double"),
("chebyshev", ['chebychev', 'chebyshev', 'cheby', 'cheb', 'ch'], "double"),
("city_block", ["cityblock", "cblock", "cb", "c"], "double"),
("euclidean", ["euclidean", "euclid", "eu", "e"], "double"),
("sqeuclidean", ["sqeuclidean", "sqe", "sqeuclid"], "double"),
("dice", ["dice"], "bool"),
("kulsinski", ["kulsinski"], "bool"),
("rogerstanimoto", ["rogerstanimoto"], "bool"),
("russellrao", ["russellrao"], "bool"),
("sokalmichener", ["sokalmichener"], "bool"),
("sokalsneath", ["sokalsneath"], "bool"),
("yule", ["yule"], "bool"),
]:
converter = {"bool": _convert_to_bool,
"double": _convert_to_double}[typ]
fn_name = {"bool": "%s_bool_wrap",
"double": "%s_wrap"}[typ] % wrap_name
cdist_fn = getattr(_distance_wrap, "cdist_%s" % fn_name)
pdist_fn = getattr(_distance_wrap, "pdist_%s" % fn_name)
for name in names:
_SIMPLE_CDIST[name] = converter, cdist_fn
_SIMPLE_PDIST[name] = converter, pdist_fn
_METRICS_NAMES = ['braycurtis', 'canberra', 'chebyshev', 'cityblock',
'correlation', 'cosine', 'dice', 'euclidean', 'hamming',
'jaccard', 'kulsinski', 'mahalanobis', 'matching',
'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean',
'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule', 'wminkowski']
_TEST_METRICS = {'test_' + name: eval(name) for name in _METRICS_NAMES}
def pdist(X, metric='euclidean', p=None, w=None, V=None, VI=None):
"""
Pairwise distances between observations in n-dimensional space.
See Notes for common calling conventions.
Parameters
----------
X : ndarray
An m by n array of m original observations in an
n-dimensional space.
metric : str or function, optional
The distance metric to use. The distance function can
be 'braycurtis', 'canberra', 'chebyshev', 'cityblock',
'correlation', 'cosine', 'dice', 'euclidean', 'hamming',
'jaccard', 'kulsinski', 'mahalanobis', 'matching',
'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean',
'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'.
p : double, optional
The p-norm to apply
Only for Minkowski, weighted and unweighted. Default: 2.
w : ndarray, optional
The weight vector.
Only for weighted Minkowski. Mandatory
V : ndarray, optional
The variance vector
Only for standardized Euclidean. Default: var(X, axis=0, ddof=1)
VI : ndarray, optional
The inverse of the covariance matrix
Only for Mahalanobis. Default: inv(cov(X.T)).T
Returns
-------
Y : ndarray
Returns a condensed distance matrix Y. For
each :math:`i` and :math:`j` (where :math:`i<j<m`),where m is the number
of original observations. The metric ``dist(u=X[i], v=X[j])``
is computed and stored in entry ``ij``.
See Also
--------
squareform : converts between condensed distance matrices and
square distance matrices.
Notes
-----
See ``squareform`` for information on how to calculate the index of
this entry or to convert the condensed distance matrix to a
redundant square matrix.
The following are common calling conventions.
1. ``Y = pdist(X, 'euclidean')``
Computes the distance between m points using Euclidean distance
(2-norm) as the distance metric between the points. The points
are arranged as m n-dimensional row vectors in the matrix X.
2. ``Y = pdist(X, 'minkowski', p)``
Computes the distances using the Minkowski distance
:math:`||u-v||_p` (p-norm) where :math:`p \\geq 1`.
3. ``Y = pdist(X, 'cityblock')``
Computes the city block or Manhattan distance between the
points.
4. ``Y = pdist(X, 'seuclidean', V=None)``
Computes the standardized Euclidean distance. The standardized
Euclidean distance between two n-vectors ``u`` and ``v`` is
.. math::
\\sqrt{\\sum {(u_i-v_i)^2 / V[x_i]}}
V is the variance vector; V[i] is the variance computed over all
the i'th components of the points. If not passed, it is
automatically computed.
5. ``Y = pdist(X, 'sqeuclidean')``
Computes the squared Euclidean distance :math:`||u-v||_2^2` between
the vectors.
6. ``Y = pdist(X, 'cosine')``
Computes the cosine distance between vectors u and v,
.. math::
1 - \\frac{u \\cdot v}
{{||u||}_2 {||v||}_2}
where :math:`||*||_2` is the 2-norm of its argument ``*``, and
:math:`u \\cdot v` is the dot product of ``u`` and ``v``.
7. ``Y = pdist(X, 'correlation')``
Computes the correlation distance between vectors u and v. This is
.. math::
1 - \\frac{(u - \\bar{u}) \\cdot (v - \\bar{v})}
{{||(u - \\bar{u})||}_2 {||(v - \\bar{v})||}_2}
where :math:`\\bar{v}` is the mean of the elements of vector v,
and :math:`x \\cdot y` is the dot product of :math:`x` and :math:`y`.
8. ``Y = pdist(X, 'hamming')``
Computes the normalized Hamming distance, or the proportion of
those vector elements between two n-vectors ``u`` and ``v``
which disagree. To save memory, the matrix ``X`` can be of type
boolean.
9. ``Y = pdist(X, 'jaccard')``
Computes the Jaccard distance between the points. Given two
vectors, ``u`` and ``v``, the Jaccard distance is the
proportion of those elements ``u[i]`` and ``v[i]`` that
disagree.
10. ``Y = pdist(X, 'chebyshev')``
Computes the Chebyshev distance between the points. The
Chebyshev distance between two n-vectors ``u`` and ``v`` is the
maximum norm-1 distance between their respective elements. More
precisely, the distance is given by
.. math::
d(u,v) = \\max_i {|u_i-v_i|}
11. ``Y = pdist(X, 'canberra')``
Computes the Canberra distance between the points. The
Canberra distance between two points ``u`` and ``v`` is
.. math::
d(u,v) = \\sum_i \\frac{|u_i-v_i|}
{|u_i|+|v_i|}
12. ``Y = pdist(X, 'braycurtis')``
Computes the Bray-Curtis distance between the points. The
Bray-Curtis distance between two points ``u`` and ``v`` is
.. math::
d(u,v) = \\frac{\\sum_i {|u_i-v_i|}}
{\\sum_i {|u_i+v_i|}}
13. ``Y = pdist(X, 'mahalanobis', VI=None)``
Computes the Mahalanobis distance between the points. The
Mahalanobis distance between two points ``u`` and ``v`` is
:math:`\\sqrt{(u-v)(1/V)(u-v)^T}` where :math:`(1/V)` (the ``VI``
variable) is the inverse covariance. If ``VI`` is not None,
``VI`` will be used as the inverse covariance matrix.
14. ``Y = pdist(X, 'yule')``
Computes the Yule distance between each pair of boolean
vectors. (see yule function documentation)
15. ``Y = pdist(X, 'matching')``
Synonym for 'hamming'.
16. ``Y = pdist(X, 'dice')``
Computes the Dice distance between each pair of boolean
vectors. (see dice function documentation)
17. ``Y = pdist(X, 'kulsinski')``
Computes the Kulsinski distance between each pair of
boolean vectors. (see kulsinski function documentation)
18. ``Y = pdist(X, 'rogerstanimoto')``
Computes the Rogers-Tanimoto distance between each pair of
boolean vectors. (see rogerstanimoto function documentation)
19. ``Y = pdist(X, 'russellrao')``
Computes the Russell-Rao distance between each pair of
boolean vectors. (see russellrao function documentation)
20. ``Y = pdist(X, 'sokalmichener')``
Computes the Sokal-Michener distance between each pair of
boolean vectors. (see sokalmichener function documentation)
21. ``Y = pdist(X, 'sokalsneath')``
Computes the Sokal-Sneath distance between each pair of
boolean vectors. (see sokalsneath function documentation)
22. ``Y = pdist(X, 'wminkowski')``
Computes the weighted Minkowski distance between each pair of
vectors. (see wminkowski function documentation)
23. ``Y = pdist(X, f)``
Computes the distance between all pairs of vectors in X
using the user supplied 2-arity function f. For example,
Euclidean distance between the vectors could be computed
as follows::
dm = pdist(X, lambda u, v: np.sqrt(((u-v)**2).sum()))
Note that you should avoid passing a reference to one of
the distance functions defined in this library. For example,::
dm = pdist(X, sokalsneath)
would calculate the pair-wise distances between the vectors in
X using the Python function sokalsneath. This would result in
sokalsneath being called :math:`{n \\choose 2}` times, which
is inefficient. Instead, the optimized C version is more
efficient, and we call it using the following syntax.::
dm = pdist(X, 'sokalsneath')
"""
# You can also call this as:
# Y = pdist(X, 'test_abc')
# where 'abc' is the metric being tested. This computes the distance
# between all pairs of vectors in X using the distance metric 'abc' but
# with a more succinct, verifiable, but less efficient implementation.
X = np.asarray(X, order='c')
# The C code doesn't do striding.
X = _copy_array_if_base_present(X)
s = X.shape
if len(s) != 2:
raise ValueError('A 2-dimensional array must be passed.')
m, n = s
dm = np.zeros((m * (m - 1)) // 2, dtype=np.double)
# validate input for multi-args metrics
if(metric in ['minkowski', 'mi', 'm', 'pnorm', 'test_minkowski'] or
metric == minkowski):
p = _validate_minkowski_args(p)
_filter_deprecated_kwargs(w=w, V=V, VI=VI)
elif(metric in ['wminkowski', 'wmi', 'wm', 'wpnorm', 'test_wminkowski'] or
metric == wminkowski):
p, w = _validate_wminkowski_args(p, w)
_filter_deprecated_kwargs(V=V, VI=VI)
elif(metric in ['seuclidean', 'se', 's', 'test_seuclidean'] or
metric == seuclidean):
V = _validate_seuclidean_args(X, n, V)
_filter_deprecated_kwargs(p=p, w=w, VI=VI)
elif(metric in ['mahalanobis', 'mahal', 'mah', 'test_mahalanobis'] or
metric == mahalanobis):
VI = _validate_mahalanobis_args(X, m, n, VI)
_filter_deprecated_kwargs(p=p, w=w, V=V)
else:
_filter_deprecated_kwargs(p=p, w=w, V=V, VI=VI)
if callable(metric):
# metrics that expects only doubles:
if metric in [braycurtis, canberra, chebyshev, cityblock, correlation,
cosine, euclidean, mahalanobis, minkowski, sqeuclidean,
seuclidean, wminkowski]:
X = _convert_to_double(X)
# metrics that expects only bools:
elif metric in [dice, kulsinski, rogerstanimoto, russellrao,
sokalmichener, sokalsneath, yule]:
X = _convert_to_bool(X)
# metrics that may receive multiple types:
elif metric in [matching, hamming, jaccard]:
if X.dtype == bool:
X = _convert_to_bool(X)
else:
X = _convert_to_double(X)
# metrics that expects multiple args
if metric == minkowski:
metric = partial(minkowski, p=p)
elif metric == wminkowski:
metric = partial(wminkowski, p=p, w=w)
elif metric == seuclidean:
metric = partial(seuclidean, V=V)
elif metric == mahalanobis:
metric = partial(mahalanobis, VI=VI)
k = 0
for i in xrange(0, m - 1):
for j in xrange(i + 1, m):
dm[k] = metric(X[i], X[j])
k = k + 1
elif isinstance(metric, string_types):
mstr = metric.lower()
try:
validate, pdist_fn = _SIMPLE_PDIST[mstr]
X = validate(X)
pdist_fn(X, dm)
return dm
except KeyError:
pass
if mstr in ['matching', 'hamming', 'hamm', 'ha', 'h']:
if X.dtype == bool:
X = _convert_to_bool(X)
_distance_wrap.pdist_hamming_bool_wrap(X, dm)
else:
X = _convert_to_double(X)
_distance_wrap.pdist_hamming_wrap(X, dm)
elif mstr in ['jaccard', 'jacc', 'ja', 'j']:
if X.dtype == bool:
X = _convert_to_bool(X)
_distance_wrap.pdist_jaccard_bool_wrap(X, dm)
else:
X = _convert_to_double(X)
_distance_wrap.pdist_jaccard_wrap(X, dm)
elif mstr in ['minkowski', 'mi', 'm']:
X = _convert_to_double(X)
_distance_wrap.pdist_minkowski_wrap(X, dm, p=p)
elif mstr in ['wminkowski', 'wmi', 'wm', 'wpnorm']:
X = _convert_to_double(X)
_distance_wrap.pdist_weighted_minkowski_wrap(X, dm, p=p, w=w)
elif mstr in ['seuclidean', 'se', 's']:
X = _convert_to_double(X)
_distance_wrap.pdist_seuclidean_wrap(X, dm, V=V)
elif mstr in ['cosine', 'cos']:
X = _convert_to_double(X)
norms = _row_norms(X)
_distance_wrap.pdist_cosine_wrap(X, dm, norms)
elif mstr in ['old_cosine', 'old_cos']:
warnings.warn('"old_cosine" is deprecated and will be removed in '
'a future version. Use "cosine" instead.',
DeprecationWarning)
X = _convert_to_double(X)
norms = _row_norms(X)
nV = norms.reshape(m, 1)
# The numerator u * v
nm = np.dot(X, X.T)
# The denom. ||u||*||v||
de = np.dot(nV, nV.T)
dm = 1.0 - (nm / de)
dm[xrange(0, m), xrange(0, m)] = 0.0
dm = squareform(dm)
elif mstr in ['correlation', 'co']:
X = _convert_to_double(X)
X2 = X - X.mean(1)[:, np.newaxis]
norms = _row_norms(X2)
_distance_wrap.pdist_cosine_wrap(X2, dm, norms)
elif mstr in ['mahalanobis', 'mahal', 'mah']:
X = _convert_to_double(X)
_distance_wrap.pdist_mahalanobis_wrap(X, dm, VI=VI)
elif mstr.startswith("test_"):
if mstr in _TEST_METRICS:
kwargs = {"p":p, "w":w, "V":V, "VI":VI}
dm = pdist(X, _TEST_METRICS[mstr], **kwargs)
else:
raise ValueError('Unknown "Test" Distance Metric: %s' % mstr[5:])
else:
raise ValueError('Unknown Distance Metric: %s' % mstr)
else:
raise TypeError('2nd argument metric must be a string identifier '
'or a function.')
return dm
def squareform(X, force="no", checks=True):
"""
Converts a vector-form distance vector to a square-form distance
matrix, and vice-versa.
Parameters
----------
X : ndarray
Either a condensed or redundant distance matrix.
force : str, optional
As with MATLAB(TM), if force is equal to ``'tovector'`` or
``'tomatrix'``, the input will be treated as a distance matrix or
distance vector respectively.
checks : bool, optional
If set to False, no checks will be made for matrix
symmetry nor zero diagonals. This is useful if it is known that
``X - X.T1`` is small and ``diag(X)`` is close to zero.
These values are ignored any way so they do not disrupt the
squareform transformation.
Returns
-------
Y : ndarray
If a condensed distance matrix is passed, a redundant one is
returned, or if a redundant one is passed, a condensed distance
matrix is returned.
Notes
-----
1. v = squareform(X)
Given a square d-by-d symmetric distance matrix X,
``v = squareform(X)`` returns a ``d * (d-1) / 2`` (or
:math:`{n \\choose 2}`) sized vector v.
:math:`v[{n \\choose 2}-{n-i \\choose 2} + (j-i-1)]` is the distance
between points i and j. If X is non-square or asymmetric, an error
is returned.
2. X = squareform(v)
Given a ``d*(d-1)/2`` sized v for some integer ``d >= 2`` encoding
distances as described, ``X = squareform(v)`` returns a d by d distance
matrix X. The ``X[i, j]`` and ``X[j, i]`` values are set to
:math:`v[{n \\choose 2}-{n-i \\choose 2} + (j-i-1)]` and all
diagonal elements are zero.
In Scipy 0.19.0, ``squareform`` stopped casting all input types to
float64, and started returning arrays of the same dtype as the input.
"""
X = np.ascontiguousarray(X)
s = X.shape
if force.lower() == 'tomatrix':
if len(s) != 1:
raise ValueError("Forcing 'tomatrix' but input X is not a "
"distance vector.")
elif force.lower() == 'tovector':
if len(s) != 2:
raise ValueError("Forcing 'tovector' but input X is not a "
"distance matrix.")
# X = squareform(v)
if len(s) == 1:
if s[0] == 0:
return np.zeros((1, 1), dtype=X.dtype)
# Grab the closest value to the square root of the number
# of elements times 2 to see if the number of elements
# is indeed a binomial coefficient.
d = int(np.ceil(np.sqrt(s[0] * 2)))
# Check that v is of valid dimensions.
if d * (d - 1) != s[0] * 2:
raise ValueError('Incompatible vector size. It must be a binomial '
'coefficient n choose 2 for some integer n >= 2.')
# Allocate memory for the distance matrix.
M = np.zeros((d, d), dtype=X.dtype)
# Since the C code does not support striding using strides.
# The dimensions are used instead.
X = _copy_array_if_base_present(X)
# Fill in the values of the distance matrix.
_distance_wrap.to_squareform_from_vector_wrap(M, X)
# Return the distance matrix.
return M
elif len(s) == 2:
if s[0] != s[1]:
raise ValueError('The matrix argument must be square.')
if checks:
is_valid_dm(X, throw=True, name='X')
# One-side of the dimensions is set here.
d = s[0]
if d <= 1:
return np.array([], dtype=X.dtype)
# Create a vector.
v = np.zeros((d * (d - 1)) // 2, dtype=X.dtype)
# Since the C code does not support striding using strides.
# The dimensions are used instead.
X = _copy_array_if_base_present(X)
# Convert the vector to squareform.
_distance_wrap.to_vector_from_squareform_wrap(X, v)
return v
else:
raise ValueError(('The first argument must be one or two dimensional '
'array. A %d-dimensional array is not '
'permitted') % len(s))
def is_valid_dm(D, tol=0.0, throw=False, name="D", warning=False):
"""
Returns True if input array is a valid distance matrix.
Distance matrices must be 2-dimensional numpy arrays.
They must have a zero-diagonal, and they must be symmetric.
Parameters
----------
D : ndarray
The candidate object to test for validity.
tol : float, optional
The distance matrix should be symmetric. `tol` is the maximum
difference between entries ``ij`` and ``ji`` for the distance
metric to be considered symmetric.
throw : bool, optional
An exception is thrown if the distance matrix passed is not valid.
name : str, optional
The name of the variable to checked. This is useful if
throw is set to True so the offending variable can be identified
in the exception message when an exception is thrown.
warning : bool, optional
Instead of throwing an exception, a warning message is
raised.
Returns
-------
valid : bool
True if the variable `D` passed is a valid distance matrix.
Notes
-----
Small numerical differences in `D` and `D.T` and non-zeroness of
the diagonal are ignored if they are within the tolerance specified
by `tol`.
"""
D = np.asarray(D, order='c')
valid = True
try:
s = D.shape
if len(D.shape) != 2:
if name:
raise ValueError(('Distance matrix \'%s\' must have shape=2 '
'(i.e. be two-dimensional).') % name)
else:
raise ValueError('Distance matrix must have shape=2 (i.e. '
'be two-dimensional).')
if tol == 0.0:
if not (D == D.T).all():
if name:
raise ValueError(('Distance matrix \'%s\' must be '
'symmetric.') % name)
else:
raise ValueError('Distance matrix must be symmetric.')
if not (D[xrange(0, s[0]), xrange(0, s[0])] == 0).all():
if name:
raise ValueError(('Distance matrix \'%s\' diagonal must '
'be zero.') % name)
else:
raise ValueError('Distance matrix diagonal must be zero.')
else:
if not (D - D.T <= tol).all():
if name:
raise ValueError(('Distance matrix \'%s\' must be '
'symmetric within tolerance %5.5f.')
% (name, tol))
else:
raise ValueError('Distance matrix must be symmetric within'
' tolerance %5.5f.' % tol)
if not (D[xrange(0, s[0]), xrange(0, s[0])] <= tol).all():
if name:
raise ValueError(('Distance matrix \'%s\' diagonal must be'
' close to zero within tolerance %5.5f.')
% (name, tol))
else:
raise ValueError(('Distance matrix \'%s\' diagonal must be'
' close to zero within tolerance %5.5f.')
% tol)
except Exception as e:
if throw:
raise
if warning:
warnings.warn(str(e))
valid = False
return valid
def is_valid_y(y, warning=False, throw=False, name=None):
"""
Returns True if the input array is a valid condensed distance matrix.
Condensed distance matrices must be 1-dimensional numpy arrays.
Their length must be a binomial coefficient :math:`{n \\choose 2}`
for some positive integer n.
Parameters
----------
y : ndarray
The condensed distance matrix.
warning : bool, optional
Invokes a warning if the variable passed is not a valid
condensed distance matrix. The warning message explains why
the distance matrix is not valid. `name` is used when
referencing the offending variable.
throw : bool, optional
Throws an exception if the variable passed is not a valid
condensed distance matrix.
name : bool, optional
Used when referencing the offending variable in the
warning or exception message.
"""
y = np.asarray(y, order='c')
valid = True
try:
if len(y.shape) != 1:
if name:
raise ValueError(('Condensed distance matrix \'%s\' must '
'have shape=1 (i.e. be one-dimensional).')
% name)
else:
raise ValueError('Condensed distance matrix must have shape=1 '
'(i.e. be one-dimensional).')
n = y.shape[0]
d = int(np.ceil(np.sqrt(n * 2)))
if (d * (d - 1) / 2) != n:
if name:
raise ValueError(('Length n of condensed distance matrix '
'\'%s\' must be a binomial coefficient, i.e.'
'there must be a k such that '
'(k \\choose 2)=n)!') % name)
else:
raise ValueError('Length n of condensed distance matrix must '
'be a binomial coefficient, i.e. there must '
'be a k such that (k \\choose 2)=n)!')
except Exception as e:
if throw:
raise
if warning:
warnings.warn(str(e))
valid = False
return valid
def num_obs_dm(d):
"""
Returns the number of original observations that correspond to a
square, redundant distance matrix.
Parameters
----------
d : ndarray
The target distance matrix.
Returns
-------
num_obs_dm : int
The number of observations in the redundant distance matrix.
"""
d = np.asarray(d, order='c')
is_valid_dm(d, tol=np.inf, throw=True, name='d')
return d.shape[0]
def num_obs_y(Y):
"""
Returns the number of original observations that correspond to a
condensed distance matrix.
Parameters
----------
Y : ndarray
Condensed distance matrix.
Returns
-------
n : int
The number of observations in the condensed distance matrix `Y`.
"""
Y = np.asarray(Y, order='c')
is_valid_y(Y, throw=True, name='Y')
k = Y.shape[0]
if k == 0:
raise ValueError("The number of observations cannot be determined on "
"an empty distance matrix.")
d = int(np.ceil(np.sqrt(k * 2)))
if (d * (d - 1) / 2) != k:
raise ValueError("Invalid condensed distance matrix passed. Must be "
"some k where k=(n choose 2) for some n >= 2.")
return d
def _row_norms(X):
norms = np.einsum('ij,ij->i', X, X, dtype=np.double)
return np.sqrt(norms, out=norms)
def _cosine_cdist(XA, XB, dm):
XA = _convert_to_double(XA)
XB = _convert_to_double(XB)
np.dot(XA, XB.T, out=dm)
dm /= _row_norms(XA).reshape(-1, 1)
dm /= _row_norms(XB)
dm *= -1
dm += 1
def cdist(XA, XB, metric='euclidean', p=None, V=None, VI=None, w=None):
"""
Computes distance between each pair of the two collections of inputs.
See Notes for common calling conventions.
Parameters
----------
XA : ndarray
An :math:`m_A` by :math:`n` array of :math:`m_A`
original observations in an :math:`n`-dimensional space.
Inputs are converted to float type.
XB : ndarray
An :math:`m_B` by :math:`n` array of :math:`m_B`
original observations in an :math:`n`-dimensional space.
Inputs are converted to float type.
metric : str or callable, optional
The distance metric to use. If a string, the distance function can be
'braycurtis', 'canberra', 'chebyshev', 'cityblock', 'correlation',
'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'kulsinski',
'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao',
'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean',
'wminkowski', 'yule'.
p : double, optional
The p-norm to apply
Only for Minkowski, weighted and unweighted. Default: 2.
w : ndarray, optional
The weight vector.
Only for weighted Minkowski. Mandatory
V : ndarray, optional
The variance vector
Only for standardized Euclidean. Default: var(vstack([XA, XB]), axis=0, ddof=1)
VI : ndarray, optional
The inverse of the covariance matrix
Only for Mahalanobis. Default: inv(cov(vstack([XA, XB]).T)).T
Returns
-------
Y : ndarray
A :math:`m_A` by :math:`m_B` distance matrix is returned.
For each :math:`i` and :math:`j`, the metric
``dist(u=XA[i], v=XB[j])`` is computed and stored in the
:math:`ij` th entry.
Raises
------
ValueError
An exception is thrown if `XA` and `XB` do not have
the same number of columns.
Notes
-----
The following are common calling conventions:
1. ``Y = cdist(XA, XB, 'euclidean')``
Computes the distance between :math:`m` points using
Euclidean distance (2-norm) as the distance metric between the
points. The points are arranged as :math:`m`
:math:`n`-dimensional row vectors in the matrix X.
2. ``Y = cdist(XA, XB, 'minkowski', p)``
Computes the distances using the Minkowski distance
:math:`||u-v||_p` (:math:`p`-norm) where :math:`p \\geq 1`.
3. ``Y = cdist(XA, XB, 'cityblock')``
Computes the city block or Manhattan distance between the
points.
4. ``Y = cdist(XA, XB, 'seuclidean', V=None)``
Computes the standardized Euclidean distance. The standardized
Euclidean distance between two n-vectors ``u`` and ``v`` is
.. math::
\\sqrt{\\sum {(u_i-v_i)^2 / V[x_i]}}.
V is the variance vector; V[i] is the variance computed over all
the i'th components of the points. If not passed, it is
automatically computed.
5. ``Y = cdist(XA, XB, 'sqeuclidean')``
Computes the squared Euclidean distance :math:`||u-v||_2^2` between
the vectors.
6. ``Y = cdist(XA, XB, 'cosine')``
Computes the cosine distance between vectors u and v,
.. math::
1 - \\frac{u \\cdot v}
{{||u||}_2 {||v||}_2}
where :math:`||*||_2` is the 2-norm of its argument ``*``, and
:math:`u \\cdot v` is the dot product of :math:`u` and :math:`v`.
7. ``Y = cdist(XA, XB, 'correlation')``
Computes the correlation distance between vectors u and v. This is
.. math::
1 - \\frac{(u - \\bar{u}) \\cdot (v - \\bar{v})}
{{||(u - \\bar{u})||}_2 {||(v - \\bar{v})||}_2}
where :math:`\\bar{v}` is the mean of the elements of vector v,
and :math:`x \\cdot y` is the dot product of :math:`x` and :math:`y`.
8. ``Y = cdist(XA, XB, 'hamming')``
Computes the normalized Hamming distance, or the proportion of
those vector elements between two n-vectors ``u`` and ``v``
which disagree. To save memory, the matrix ``X`` can be of type
boolean.
9. ``Y = cdist(XA, XB, 'jaccard')``
Computes the Jaccard distance between the points. Given two
vectors, ``u`` and ``v``, the Jaccard distance is the
proportion of those elements ``u[i]`` and ``v[i]`` that
disagree where at least one of them is non-zero.
10. ``Y = cdist(XA, XB, 'chebyshev')``
Computes the Chebyshev distance between the points. The
Chebyshev distance between two n-vectors ``u`` and ``v`` is the
maximum norm-1 distance between their respective elements. More
precisely, the distance is given by
.. math::
d(u,v) = \\max_i {|u_i-v_i|}.
11. ``Y = cdist(XA, XB, 'canberra')``
Computes the Canberra distance between the points. The
Canberra distance between two points ``u`` and ``v`` is
.. math::
d(u,v) = \\sum_i \\frac{|u_i-v_i|}
{|u_i|+|v_i|}.
12. ``Y = cdist(XA, XB, 'braycurtis')``
Computes the Bray-Curtis distance between the points. The
Bray-Curtis distance between two points ``u`` and ``v`` is
.. math::
d(u,v) = \\frac{\\sum_i (|u_i-v_i|)}
{\\sum_i (|u_i+v_i|)}
13. ``Y = cdist(XA, XB, 'mahalanobis', VI=None)``
Computes the Mahalanobis distance between the points. The
Mahalanobis distance between two points ``u`` and ``v`` is
:math:`\\sqrt{(u-v)(1/V)(u-v)^T}` where :math:`(1/V)` (the ``VI``
variable) is the inverse covariance. If ``VI`` is not None,
``VI`` will be used as the inverse covariance matrix.
14. ``Y = cdist(XA, XB, 'yule')``
Computes the Yule distance between the boolean
vectors. (see `yule` function documentation)
15. ``Y = cdist(XA, XB, 'matching')``
Synonym for 'hamming'.
16. ``Y = cdist(XA, XB, 'dice')``
Computes the Dice distance between the boolean vectors. (see
`dice` function documentation)
17. ``Y = cdist(XA, XB, 'kulsinski')``
Computes the Kulsinski distance between the boolean
vectors. (see `kulsinski` function documentation)
18. ``Y = cdist(XA, XB, 'rogerstanimoto')``
Computes the Rogers-Tanimoto distance between the boolean
vectors. (see `rogerstanimoto` function documentation)
19. ``Y = cdist(XA, XB, 'russellrao')``
Computes the Russell-Rao distance between the boolean
vectors. (see `russellrao` function documentation)
20. ``Y = cdist(XA, XB, 'sokalmichener')``
Computes the Sokal-Michener distance between the boolean
vectors. (see `sokalmichener` function documentation)
21. ``Y = cdist(XA, XB, 'sokalsneath')``
Computes the Sokal-Sneath distance between the vectors. (see
`sokalsneath` function documentation)
22. ``Y = cdist(XA, XB, 'wminkowski')``
Computes the weighted Minkowski distance between the
vectors. (see `wminkowski` function documentation)
23. ``Y = cdist(XA, XB, f)``
Computes the distance between all pairs of vectors in X
using the user supplied 2-arity function f. For example,
Euclidean distance between the vectors could be computed
as follows::
dm = cdist(XA, XB, lambda u, v: np.sqrt(((u-v)**2).sum()))
Note that you should avoid passing a reference to one of
the distance functions defined in this library. For example,::
dm = cdist(XA, XB, sokalsneath)
would calculate the pair-wise distances between the vectors in
X using the Python function `sokalsneath`. This would result in
sokalsneath being called :math:`{n \\choose 2}` times, which
is inefficient. Instead, the optimized C version is more
efficient, and we call it using the following syntax::
dm = cdist(XA, XB, 'sokalsneath')
Examples
--------
Find the Euclidean distances between four 2-D coordinates:
>>> from scipy.spatial import distance
>>> coords = [(35.0456, -85.2672),
... (35.1174, -89.9711),
... (35.9728, -83.9422),
... (36.1667, -86.7833)]
>>> distance.cdist(coords, coords, 'euclidean')
array([[ 0. , 4.7044, 1.6172, 1.8856],
[ 4.7044, 0. , 6.0893, 3.3561],
[ 1.6172, 6.0893, 0. , 2.8477],
[ 1.8856, 3.3561, 2.8477, 0. ]])
Find the Manhattan distance from a 3-D point to the corners of the unit
cube:
>>> a = np.array([[0, 0, 0],
... [0, 0, 1],
... [0, 1, 0],
... [0, 1, 1],
... [1, 0, 0],
... [1, 0, 1],
... [1, 1, 0],
... [1, 1, 1]])
>>> b = np.array([[ 0.1, 0.2, 0.4]])
>>> distance.cdist(a, b, 'cityblock')
array([[ 0.7],
[ 0.9],
[ 1.3],
[ 1.5],
[ 1.5],
[ 1.7],
[ 2.1],
[ 2.3]])
"""
# You can also call this as:
# Y = cdist(XA, XB, 'test_abc')
# where 'abc' is the metric being tested. This computes the distance
# between all pairs of vectors in XA and XB using the distance metric 'abc'
# but with a more succinct, verifiable, but less efficient implementation.
# Store input arguments to check whether we can modify later.
input_XA, input_XB = XA, XB
XA = np.asarray(XA, order='c')
XB = np.asarray(XB, order='c')
# The C code doesn't do striding.
XA = _copy_array_if_base_present(XA)
XB = _copy_array_if_base_present(XB)
s = XA.shape
sB = XB.shape
if len(s) != 2:
raise ValueError('XA must be a 2-dimensional array.')
if len(sB) != 2:
raise ValueError('XB must be a 2-dimensional array.')
if s[1] != sB[1]:
raise ValueError('XA and XB must have the same number of columns '
'(i.e. feature dimension.)')
mA = s[0]
mB = sB[0]
n = s[1]
dm = np.zeros((mA, mB), dtype=np.double)
# validate input for multi-args metrics
if(metric in ['minkowski', 'mi', 'm', 'pnorm', 'test_minkowski'] or
metric == minkowski):
p = _validate_minkowski_args(p)
_filter_deprecated_kwargs(w=w, V=V, VI=VI)
elif(metric in ['wminkowski', 'wmi', 'wm', 'wpnorm', 'test_wminkowski'] or
metric == wminkowski):
p, w = _validate_wminkowski_args(p, w)
_filter_deprecated_kwargs(V=V, VI=VI)
elif(metric in ['seuclidean', 'se', 's', 'test_seuclidean'] or
metric == seuclidean):
V = _validate_seuclidean_args(np.vstack([XA, XB]), n, V)
_filter_deprecated_kwargs(p=p, w=w, VI=VI)
elif(metric in ['mahalanobis', 'mahal', 'mah', 'test_mahalanobis'] or
metric == mahalanobis):
VI = _validate_mahalanobis_args(np.vstack([XA, XB]), mA + mB, n, VI)
_filter_deprecated_kwargs(p=p, w=w, V=V)
else:
_filter_deprecated_kwargs(p=p, w=w, V=V, VI=VI)
if callable(metric):
# metrics that expects only doubles:
if metric in [braycurtis, canberra, chebyshev, cityblock, correlation,
cosine, euclidean, mahalanobis, minkowski, sqeuclidean,
seuclidean, wminkowski]:
XA = _convert_to_double(XA)
XB = _convert_to_double(XB)
# metrics that expects only bools:
elif metric in [dice, kulsinski, rogerstanimoto, russellrao,
sokalmichener, sokalsneath, yule]:
XA = _convert_to_bool(XA)
XB = _convert_to_bool(XB)
# metrics that may receive multiple types:
elif metric in [matching, hamming, jaccard]:
if XA.dtype == bool:
XA = _convert_to_bool(XA)
XB = _convert_to_bool(XB)
else:
XA = _convert_to_double(XA)
XB = _convert_to_double(XB)
# metrics that expects multiple args
if metric == minkowski:
metric = partial(minkowski, p=p)
elif metric == wminkowski:
metric = partial(wminkowski, p=p, w=w)
elif metric == seuclidean:
metric = partial(seuclidean, V=V)
elif metric == mahalanobis:
metric = partial(mahalanobis, VI=VI)
for i in xrange(0, mA):
for j in xrange(0, mB):
dm[i, j] = metric(XA[i, :], XB[j, :])
elif isinstance(metric, string_types):
mstr = metric.lower()
try:
validate, cdist_fn = _SIMPLE_CDIST[mstr]
XA = validate(XA)
XB = validate(XB)
cdist_fn(XA, XB, dm)
return dm
except KeyError:
pass
if mstr in ['matching', 'hamming', 'hamm', 'ha', 'h']:
if XA.dtype == bool:
XA = _convert_to_bool(XA)
XB = _convert_to_bool(XB)
_distance_wrap.cdist_hamming_bool_wrap(XA, XB, dm)
else:
XA = _convert_to_double(XA)
XB = _convert_to_double(XB)
_distance_wrap.cdist_hamming_wrap(XA, XB, dm)
elif mstr in ['jaccard', 'jacc', 'ja', 'j']:
if XA.dtype == bool:
XA = _convert_to_bool(XA)
XB = _convert_to_bool(XB)
_distance_wrap.cdist_jaccard_bool_wrap(XA, XB, dm)
else:
XA = _convert_to_double(XA)
XB = _convert_to_double(XB)
_distance_wrap.cdist_jaccard_wrap(XA, XB, dm)
elif mstr in ['minkowski', 'mi', 'm', 'pnorm']:
XA = _convert_to_double(XA)
XB = _convert_to_double(XB)
_distance_wrap.cdist_minkowski_wrap(XA, XB, dm, p=p)
elif mstr in ['wminkowski', 'wmi', 'wm', 'wpnorm']:
XA = _convert_to_double(XA)
XB = _convert_to_double(XB)
_distance_wrap.cdist_weighted_minkowski_wrap(XA, XB, dm, p=p, w=w)
elif mstr in ['seuclidean', 'se', 's']:
XA = _convert_to_double(XA)
XB = _convert_to_double(XB)
_distance_wrap.cdist_seuclidean_wrap(XA, XB, dm, V=V)
elif mstr in ['cosine', 'cos']:
XA = _convert_to_double(XA)
XB = _convert_to_double(XB)
_cosine_cdist(XA, XB, dm)
elif mstr in ['correlation', 'co']:
XA = _convert_to_double(XA)
XB = _convert_to_double(XB)
XA = XA.copy() if XA is input_XA else XA
XB = XB.copy() if XB is input_XB else XB
XA -= XA.mean(axis=1)[:, np.newaxis]
XB -= XB.mean(axis=1)[:, np.newaxis]
_cosine_cdist(XA, XB, dm)
elif mstr in ['mahalanobis', 'mahal', 'mah']:
XA = _convert_to_double(XA)
XB = _convert_to_double(XB)
# sqrt((u-v)V^(-1)(u-v)^T)
_distance_wrap.cdist_mahalanobis_wrap(XA, XB, dm, VI=VI)
elif mstr.startswith("test_"):
if mstr in _TEST_METRICS:
kwargs = {"p":p, "w":w, "V":V, "VI":VI}
dm = cdist(XA, XB, _TEST_METRICS[mstr], **kwargs)
else:
raise ValueError('Unknown "Test" Distance Metric: %s' % mstr[5:])
else:
raise ValueError('Unknown Distance Metric: %s' % mstr)
else:
raise TypeError('2nd argument metric must be a string identifier '
'or a function.')
return dm