# scipy/scipy

2212 lines (1752 sloc) 67.2 KB
 """ ===================================================== 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 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', '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 scipy._lib.six import callable, string_types from scipy._lib.six import xrange from . import _distance_wrap 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() elif np.issubsctype(a, np.float32): return np.array(a, dtype=np.double) else: return a def _convert_to_bool(X): if X.dtype != bool: X = X.astype(bool) if not X.flags.contiguous: X = X.copy() return X def _convert_to_double(X): if X.dtype != np.double: X = X.astype(np.double) if not X.flags.contiguous: X = X.copy() return X 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 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 _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 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 names, wrap_name in [ (['braycurtis'], "bray_curtis"), (['canberra'], "canberra"), (['chebychev', 'chebyshev', 'cheby', 'cheb', 'ch'], "chebyshev"), (["cityblock", "cblock", "cb", "c"], "city_block"), (["euclidean", "euclid", "eu", "e"], "euclidean"), (["sqeuclidean", "sqe", "sqeuclid"], "sqeuclidean"), ]: cdist_fn = getattr(_distance_wrap, "cdist_%s_wrap" % wrap_name) pdist_fn = getattr(_distance_wrap, "pdist_%s_wrap" % wrap_name) for name in names: _SIMPLE_CDIST[name] = _convert_to_double, cdist_fn _SIMPLE_PDIST[name] = _convert_to_double, pdist_fn for name in ["dice", "kulsinski", "matching", "rogerstanimoto", "russellrao", "sokalmichener", "sokalsneath", "yule"]: wrap_name = "hamming" if name == "matching" else name cdist_fn = getattr(_distance_wrap, "cdist_%s_bool_wrap" % wrap_name) _SIMPLE_CDIST[name] = _convert_to_bool, cdist_fn pdist_fn = getattr(_distance_wrap, "pdist_%s_bool_wrap" % wrap_name) _SIMPLE_PDIST[name] = _convert_to_bool, pdist_fn def pdist(X, metric='euclidean', p=2, w=None, V=None, VI=None): """ Pairwise distances between observations in n-dimensional space. 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:`(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') 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'. w : ndarray, optional The weight vector (for weighted Minkowski). p : double, optional The p-norm to apply (for Minkowski, weighted and unweighted) V : ndarray, optional The variance vector (for standardized Euclidean). VI : ndarray, optional The inverse of the covariance matrix (for Mahalanobis). Returns ------- Y : ndarray Returns a condensed distance matrix Y. For each :math:`i` and :math:`j` (where :math:`i=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 v[{n \\choose 2}-{n-i \\choose 2} + (j-i-1)] and all diagonal elements are zero. """ X = _convert_to_double(np.asarray(X, order='c')) 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 X.shape[0] == 0: return np.zeros((1, 1), dtype=np.double) # 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(X.shape[0] * 2))) # Check that v is of valid dimensions. if d * (d - 1) / 2 != int(s[0]): 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=np.double) # 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=np.double) # Create a vector. v = np.zeros((d * (d - 1)) // 2, dtype=np.double) # 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 containing doubles. 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 D.dtype != np.double: if name: raise TypeError(('Distance matrix \'%s\' must contain doubles ' '(double).') % name) else: raise TypeError('Distance matrix must contain doubles ' '(double).') 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 containing doubles. 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 y.dtype != np.double: if name: raise TypeError(('Condensed distance matrix \'%s\' must ' 'contain doubles (double).') % name) else: raise TypeError('Condensed distance matrix must contain ' 'doubles (double).') 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=2, V=None, VI=None, w=None): """ Computes distance between each pair of the two collections of inputs. 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') 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'. w : ndarray, optional The weight vector (for weighted Minkowski). p : scalar, optional The p-norm to apply (for Minkowski, weighted and unweighted) V : ndarray, optional The variance vector (for standardized Euclidean). VI : ndarray, optional The inverse of the covariance matrix (for Mahalanobis). 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. 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. 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(_convert_to_double(XA)) XB = _copy_array_if_base_present(_convert_to_double(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) if callable(metric): if metric == minkowski: for i in xrange(0, mA): for j in xrange(0, mB): dm[i, j] = minkowski(XA[i, :], XB[j, :], p) elif metric == wminkowski: for i in xrange(0, mA): for j in xrange(0, mB): dm[i, j] = wminkowski(XA[i, :], XB[j, :], p, w) elif metric == seuclidean: for i in xrange(0, mA): for j in xrange(0, mB): dm[i, j] = seuclidean(XA[i, :], XB[j, :], V) elif metric == mahalanobis: for i in xrange(0, mA): for j in xrange(0, mB): dm[i, j] = mahalanobis(XA[i, :], XB[j, :], V) else: 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 ['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) elif mstr in ['wminkowski', 'wmi', 'wm', 'wpnorm']: XA = _convert_to_double(XA) XB = _convert_to_double(XB) w = _convert_to_double(w) _distance_wrap.cdist_weighted_minkowski_wrap(XA, XB, dm, p, w) elif mstr in ['seuclidean', 'se', 's']: XA = _convert_to_double(XA) XB = _convert_to_double(XB) if V is not None: 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.') # The C code doesn't do striding. VV = _copy_array_if_base_present(_convert_to_double(V)) else: VV = np.var(np.vstack([XA, XB]), axis=0, ddof=1) _distance_wrap.cdist_seuclidean_wrap(XA, XB, VV, dm) 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.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) if VI is not None: VI = _convert_to_double(np.asarray(VI, order='c')) VI = _copy_array_if_base_present(VI) else: m = mA + mB 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)) X = np.vstack([XA, XB]) V = np.atleast_2d(np.cov(X.T)) del X VI = np.linalg.inv(V).T.copy() # (u-v)V^(-1)(u-v)^T _distance_wrap.cdist_mahalanobis_wrap(XA, XB, VI, dm) elif metric == 'test_euclidean': dm = cdist(XA, XB, euclidean) elif metric == 'test_seuclidean': if V is None: V = np.var(np.vstack([XA, XB]), axis=0, ddof=1) else: V = np.asarray(V, order='c') dm = cdist(XA, XB, lambda u, v: seuclidean(u, v, V)) elif metric == 'test_sqeuclidean': dm = cdist(XA, XB, lambda u, v: sqeuclidean(u, v)) elif metric == 'test_braycurtis': dm = cdist(XA, XB, braycurtis) elif metric == 'test_mahalanobis': if VI is None: X = np.vstack([XA, XB]) V = np.cov(X.T) VI = np.linalg.inv(V) X = None del X else: VI = np.asarray(VI, order='c') VI = _copy_array_if_base_present(VI) # (u-v)V^(-1)(u-v)^T dm = cdist(XA, XB, (lambda u, v: mahalanobis(u, v, VI))) elif metric == 'test_canberra': dm = cdist(XA, XB, canberra) elif metric == 'test_cityblock': dm = cdist(XA, XB, cityblock) elif metric == 'test_minkowski': dm = cdist(XA, XB, minkowski, p=p) elif metric == 'test_wminkowski': dm = cdist(XA, XB, wminkowski, p=p, w=w) elif metric == 'test_correlation': dm = cdist(XA, XB, correlation) elif metric == 'test_hamming': dm = cdist(XA, XB, hamming) elif metric == 'test_jaccard': dm = cdist(XA, XB, jaccard) elif metric == 'test_chebyshev' or metric == 'test_chebychev': dm = cdist(XA, XB, chebyshev) elif metric == 'test_yule': dm = cdist(XA, XB, yule) elif metric == 'test_matching': dm = cdist(XA, XB, matching) elif metric == 'test_dice': dm = cdist(XA, XB, dice) elif metric == 'test_kulsinski': dm = cdist(XA, XB, kulsinski) elif metric == 'test_rogerstanimoto': dm = cdist(XA, XB, rogerstanimoto) elif metric == 'test_russellrao': dm = cdist(XA, XB, russellrao) elif metric == 'test_sokalsneath': dm = cdist(XA, XB, sokalsneath) elif metric == 'test_sokalmichener': dm = cdist(XA, XB, sokalmichener) else: raise ValueError('Unknown Distance Metric: %s' % mstr) else: raise TypeError('2nd argument metric must be a string identifier ' 'or a function.') return dm