Distance matrix computation from a collection of raw observation vectors stored in a rectangular array.

pdist(X[, metric, p, w, V, VI]) |
Computes the pairwise distances between m original observations in n-dimensional space. |

cdist(XA, XB[, metric, p, V, VI, w]) |
Computes distance between each pair of the two collections of inputs. |

squareform(X[, force, checks]) |
Converts a vector-form distance vector to a square-form distance matrix, 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.

is_valid_dm(D[, tol, throw, name, warning]) |
Returns True if the variable D passed is a valid distance matrix. |

is_valid_y(y[, warning, throw, name]) |
Returns True if the variable y passed is a valid condensed |

num_obs_dm(d) |
Returns the number of original observations that correspond to a |

num_obs_y(Y) |
Returns the number of original observations that correspond to a |

Distance functions between two vectors `u` and `v`. Computing
distances over a large collection of vectors is inefficient for these
functions. Use `pdist` for this purpose.

braycurtis(u, v) |
Computes the Bray-Curtis distance between two n-vectors u and |

canberra(u, v) |
Computes the Canberra distance between two n-vectors u and v, |

chebyshev(u, v) |
Computes the Chebyshev distance between two n-vectors u and v, |

cityblock(u, v) |
Computes the Manhattan distance between two n-vectors u and v, |

correlation(u, v) |
Computes the correlation distance between two n-vectors u and v, which is defined as .. |

cosine(u, v) |
Computes the Cosine distance between two n-vectors u and v, which |

dice(u, v) |
Computes the Dice dissimilarity between two boolean n-vectors |

euclidean(u, v) |
Computes the Euclidean distance between two n-vectors u and v, |

hamming(u, v) |
Computes the Hamming distance between two n-vectors u and |

jaccard(u, v) |
Computes the Jaccard-Needham dissimilarity between two boolean |

kulsinski(u, v) |
Computes the Kulsinski dissimilarity between two boolean n-vectors |

mahalanobis(u, v, VI) |
Computes the Mahalanobis distance between two n-vectors u and v, |

matching(u, v) |
Computes the Matching dissimilarity between two boolean n-vectors |

minkowski(u, v, p) |
Computes the Minkowski distance between two vectors u and v, |

rogerstanimoto(u, v) |
Computes the Rogers-Tanimoto dissimilarity between two boolean |

russellrao(u, v) |
Computes the Russell-Rao dissimilarity between two boolean n-vectors |

seuclidean(u, v, V) |
Returns the standardized Euclidean distance between two n-vectors |

sokalmichener(u, v) |
Computes the Sokal-Michener dissimilarity between two boolean vectors |

sokalsneath(u, v) |
Computes the Sokal-Sneath dissimilarity between two boolean vectors |

sqeuclidean(u, v) |
Computes the squared Euclidean distance between two n-vectors u and v, |

yule(u, v) |
Computes the Yule dissimilarity between two boolean n-vectors u and v, |

Functions

braycurtis(u, v) |
Computes the Bray-Curtis distance between two n-vectors u and |

canberra(u, v) |
Computes the Canberra distance between two n-vectors u and v, |

cdist(XA, XB[, metric, p, V, VI, w]) |
Computes distance between each pair of the two collections of inputs. |

chebyshev(u, v) |
Computes the Chebyshev distance between two n-vectors u and v, |

cityblock(u, v) |
Computes the Manhattan distance between two n-vectors u and v, |

correlation(u, v) |
Computes the correlation distance between two n-vectors u and v, which is defined as .. |

cosine(u, v) |
Computes the Cosine distance between two n-vectors u and v, which |

dice(u, v) |
Computes the Dice dissimilarity between two boolean n-vectors |

euclidean(u, v) |
Computes the Euclidean distance between two n-vectors u and v, |

hamming(u, v) |
Computes the Hamming distance between two n-vectors u and |

is_valid_dm(D[, tol, throw, name, warning]) |
Returns True if the variable D passed is a valid distance matrix. |

is_valid_y(y[, warning, throw, name]) |
Returns True if the variable y passed is a valid condensed |

jaccard(u, v) |
Computes the Jaccard-Needham dissimilarity between two boolean |

kulsinski(u, v) |
Computes the Kulsinski dissimilarity between two boolean n-vectors |

mahalanobis(u, v, VI) |
Computes the Mahalanobis distance between two n-vectors u and v, |

matching(u, v) |
Computes the Matching dissimilarity between two boolean n-vectors |

minkowski(u, v, p) |
Computes the Minkowski distance between two vectors u and v, |

norm(x[, ord]) |
Matrix or vector norm. |

num_obs_dm(d) |
Returns the number of original observations that correspond to a |

num_obs_y(Y) |
Returns the number of original observations that correspond to a |

pdist(X[, metric, p, w, V, VI]) |
Computes the pairwise distances between m original observations in n-dimensional space. |

rogerstanimoto(u, v) |
Computes the Rogers-Tanimoto dissimilarity between two boolean |

russellrao(u, v) |
Computes the Russell-Rao dissimilarity between two boolean n-vectors |

seuclidean(u, v, V) |
Returns the standardized Euclidean distance between two n-vectors |

sokalmichener(u, v) |
Computes the Sokal-Michener dissimilarity between two boolean vectors |

sokalsneath(u, v) |
Computes the Sokal-Sneath dissimilarity between two boolean vectors |

sqeuclidean(u, v) |
Computes the squared Euclidean distance between two n-vectors u and v, |

squareform(X[, force, checks]) |
Converts a vector-form distance vector to a square-form distance matrix, and vice-versa. |

wminkowski(u, v, p, w) |
Computes the weighted Minkowski distance between two vectors u |

yule(u, v) |
Computes the Yule dissimilarity between two boolean n-vectors u and v, |