Vector geometry

Canberra

canberra_distances

all-pairspairedcondensedsingledistanceVectors

d(x,y)=\sum_i\frac{|x_i-y_i|}{|x_i|+|y_i|}

A joint zero coordinate contributes zero. Optional w scales the coordinate terms.

Shapes and Modes

Input	Layout
One sample	`[D]`
Collection X	`[...,N,D]`
Collection Y	`[...,M,D]`

Mode	Selector	Output
all-pairs	`compare(X,Y,metric=...)`	`[...,N,M]`
paired	`compare(X,Y,metric=...,mode="paired")`	`[...,N]`; equal sample counts
condensed	`compare(X,metric=...,mode="condensed")`	`[...,N*(N-1)//2]`; strict upper triangle
single	`compare(x,y,metric=...,mode="single")`	`[...]`; leading batches only

Leading batch dimensions broadcast. See mode and shape conventions.

Entry Points

Named Python entry	Function
All-pairs	`canberra_distances`

The Python and C++ compare selectors cover every mode shown above. cdist / pairwise_distances and paired_distances select this comparison; pdist / condensed_distances select its condensed mode. single_comparison is shorthand for single mode.

Catalog aliases: canberra.

Accepted metric options: w. Omitted options keep the metric defaults; unused options are rejected.

Example

Choose Python or C++ in the top bar; the setting applies across this guide.

Python

import torch
import torchpairwise as tp

X = torch.rand(2, 2) + 0.2  # [N=2,D=2]
Y = torch.rand(3, 2) + 0.2  # [M=3,D=2]

metric = "canberra_distances"
all_pairs = tp.compare(X, Y, metric=metric)  # [2,3]
paired_Y = Y[:2]
paired = tp.compare(X, paired_Y, metric=metric, mode="paired")  # [2]
condensed = tp.compare(X, metric=metric, mode="condensed")      # [1]
x, y = X[0], Y[0]
single = tp.compare(x, y, metric=metric, mode="single")  # scalar

C++ / LibTorch

#include <torchpairwise.h>

namespace tp = torchpairwise::ops;
auto X = at::rand({2, 2}) + 0.2;  // [N=2,D=2]
auto Y = at::rand({3, 2}) + 0.2;  // [M=3,D=2]

const std::string metric = "canberra_distances";
tp::ComparisonOptions options;
auto all_pairs = tp::compare(X, Y, metric, "all_pairs", options);  // [2,3]
auto paired_Y = Y.narrow(0, 0, 2);
auto paired = tp::compare(X, paired_Y, metric, "paired", options);  // [2]
// [1]
auto condensed = tp::compare(X, c10::nullopt, metric, "condensed", options);
auto x = X[0], y = Y[0];
auto single = tp::compare(x, y, metric, "single", options);  // scalar

Streamed Evaluation

tp.lazy(x, y, name) binds this distance for reductions, selection and products without retaining its full generated comparison matrix. Native source factories depend on the sample family; see streamed operators and C++ use.

Numerical Contract

Floating CPU/CUDA inputs use matching float32 / float64, with lower precision promoted under autocast where supported. First derivatives follow the metric’s smooth and boundary conventions. See the detailed conventions and precision and deployment.

SciPy canberra.

Related APIs can differ in reduction, normalization, units or boundary behavior; match these before comparing values.

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