A Julia package for evaluating distances(metrics) between vectors.



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A Julia package for evaluating distances(metrics) between vectors.

This package also provides optimized functions to compute column-wise and pairwise distances, which are often substantially faster than a straightforward loop implementation. (See the benchmark section below for details).

Supported distances

  • Euclidean distance
  • Squared Euclidean distance
  • Cityblock distance
  • Jaccard distance
  • Rogers-Tanimoto distance
  • Chebyshev distance
  • Minkowski distance
  • Hamming distance
  • Cosine distance
  • Correlation distance
  • Chi-square distance
  • Kullback-Leibler divergence
  • Generalized Kullback-Leibler divergence
  • Rényi divergence
  • Jensen-Shannon divergence
  • Mahalanobis distance
  • Squared Mahalanobis distance
  • Bhattacharyya distance
  • Hellinger distance
  • Haversine distance
  • Mean absolute deviation
  • Mean squared deviation
  • Root mean squared deviation
  • Normalized root mean squared deviation
  • Bray-Curtis dissimilarity

For Euclidean distance, Squared Euclidean distance, Cityblock distance, Minkowski distance, and Hamming distance, a weighted version is also provided.

Basic Use

The library supports three ways of computation: computing the distance between two vectors, column-wise computation, and pairwise computation.

Computing the distance between two vectors

Each distance corresponds to a distance type. You can always compute a certain distance between two vectors using the following syntax

r = evaluate(dist, x, y)

Here, dist is an instance of a distance type. For example, the type for Euclidean distance is Euclidean (more distance types will be introduced in the next section), then you can compute the Euclidean distance between x and y as

r = evaluate(Euclidean(), x, y)

Common distances also come with convenient functions for distance evaluation. For example, you may also compute Euclidean distance between two vectors as below

r = euclidean(x, y)

Computing distances between corresponding columns

Suppose you have two m-by-n matrix X and Y, then you can compute all distances between corresponding columns of X and Y in one batch, using the colwise function, as

r = colwise(dist, X, Y)

The output r is a vector of length n. In particular, r[i] is the distance between X[:,i] and Y[:,i]. The batch computation typically runs considerably faster than calling evaluate column-by-column.

Note that either of X and Y can be just a single vector -- then the colwise function will compute the distance between this vector and each column of the other parameter.

Computing pairwise distances

Let X and Y respectively have m and n columns. Then the pairwise function computes distances between each pair of columns in X and Y:

R = pairwise(dist, X, Y)

In the output, R is a matrix of size (m, n), such that R[i,j] is the distance between X[:,i] and Y[:,j]. Computing distances for all pairs using pairwise function is often remarkably faster than evaluting for each pair individually.

If you just want to just compute distances between columns of a matrix X, you can write

R = pairwise(dist, X)

This statement will result in an m-by-m matrix, where R[i,j] is the distance between X[:,i] and X[:,j]. pairwise(dist, X) is typically more efficient than pairwise(dist, X, X), as the former will take advantage of the symmetry when dist is a semi-metric (including metric).

Computing column-wise and pairwise distances inplace

If the vector/matrix to store the results are pre-allocated, you may use the storage (without creating a new array) using the following syntax:

colwise!(r, dist, X, Y)
pairwise!(R, dist, X, Y)
pairwise!(R, dist, X)

Please pay attention to the difference, the functions for inplace computation are colwise! and pairwise! (instead of colwise and pairwise).

Distance type hierarchy

The distances are organized into a type hierarchy.

At the top of this hierarchy is an abstract class PreMetric, which is defined to be a function d that satisfies

d(x, x) == 0  for all x
d(x, y) >= 0  for all x, y

SemiMetric is a abstract type that refines PreMetric. Formally, a semi-metric is a pre-metric that is also symmetric, as

d(x, y) == d(y, x)  for all x, y

Metric is a abstract type that further refines SemiMetric. Formally, a metric is a semi-metric that also satisfies triangle inequality, as

d(x, z) <= d(x, y) + d(y, z)  for all x, y, z

This type system has practical significance. For example, when computing pairwise distances between a set of vectors, you may only perform computation for half of the pairs, and derive the values immediately for the remaining halve by leveraging the symmetry of semi-metrics.

Each distance corresponds to a distance type. The type name and the corresponding mathematical definitions of the distances are listed in the following table.

type name convenient syntax math definition
Euclidean euclidean(x, y) sqrt(sum((x - y) .^ 2))
SqEuclidean sqeuclidean(x, y) sum((x - y).^2)
Cityblock cityblock(x, y) sum(abs(x - y))
Chebyshev chebyshev(x, y) max(abs(x - y))
Minkowski minkowski(x, y, p) sum(abs(x - y).^p) ^ (1/p)
Hamming hamming(k, l) sum(k .!= l)
RogersTanimoto rogerstanimoto(a, b) 2(sum(a&!b) + sum(!a&b)) / (2(sum(a&!b) + sum(!a&b)) + sum(a&b) + sum(!a&!b))
Jaccard jaccard(x, y) 1 - sum(min(x, y)) / sum(max(x, y))
BrayCurtis braycurtis(x, y) sum(abs(x - y)) / sum(abs(x + y))
CosineDist cosine_dist(x, y) 1 - dot(x, y) / (norm(x) * norm(y))
CorrDist corr_dist(x, y) cosine_dist(x - mean(x), y - mean(y))
ChiSqDist chisq_dist(x, y) sum((x - y).^2 / (x + y))
KLDivergence kl_divergence(p, q) sum(p .* log(p ./ q))
GenKLDivergence gkl_divergence(x, y) sum(p .* log(p ./ q) - p + q)
RenyiDivergence renyi_divergence(p, q, k) log(sum( p .* (p ./ q) .^ (k - 1))) / (k - 1)
JSDivergence js_divergence(p, q) KL(p, m) / 2 + KL(p, m) / 2 with m = (p + q) / 2
SpanNormDist spannorm_dist(x, y) max(x - y) - min(x - y)
BhattacharyyaDist bhattacharyya(x, y) -log(sum(sqrt(x .* y) / sqrt(sum(x) * sum(y)))
HellingerDist hellinger(x, y) sqrt(1 - sum(sqrt(x .* y) / sqrt(sum(x) * sum(y))))
Haversine haversine(x, y, r) Haversine formula
Mahalanobis mahalanobis(x, y, Q) sqrt((x - y)' * Q * (x - y))
SqMahalanobis sqmahalanobis(x, y, Q) (x - y)' * Q * (x - y)
MeanAbsDeviation meanad(x, y) mean(abs.(x - y))
MeanSqDeviation msd(x, y) mean(abs2.(x - y))
RMSDeviation rmsd(x, y) sqrt(msd(x, y))
NormRMSDeviation nrmsd(x, y) rmsd(x, y) / (maximum(x) - minimum(x))
WeightedEuclidean weuclidean(x, y, w) sqrt(sum((x - y).^2 .* w))
WeightedSqEuclidean wsqeuclidean(x, y, w) sum((x - y).^2 .* w)
WeightedCityblock wcityblock(x, y, w) sum(abs(x - y) .* w)
WeightedMinkowski wminkowski(x, y, w, p) sum(abs(x - y).^p .* w) ^ (1/p)
WeightedHamming whamming(x, y, w) sum((x .!= y) .* w)

Note: The formulas above are using Julia's functions. These formulas are mainly for conveying the math concepts in a concise way. The actual implementation may use a faster way. The arguments x and y are arrays of real numbers; k and l are arrays of distinct elements of any kind; a and b are arrays of Bools; and finally, p and q are arrays forming a discrete probability distribution and are therefore both expected to sum to one.

Precision for Euclidean and SqEuclidean

For efficiency (see the benchmarks below), Euclidean and SqEuclidean make use of BLAS3 matrix-matrix multiplication to calculate distances. This corresponds to the following expansion:

(x-y)^2 == x^2 - 2xy + y^2

However, equality is not precise in the presence of roundoff error, and particularly when x and y are nearby points this may not be accurate. Consequently, Euclidean and SqEuclidean allow you to supply a relative tolerance to force recalculation:

julia> x = reshape([0.1, 0.3, -0.1], 3, 1);

julia> pairwise(Euclidean(), x, x)
1×1 Array{Float64,2}:

julia> pairwise(Euclidean(1e-12), x, x)
1×1 Array{Float64,2}:


The implementation has been carefully optimized based on benchmarks. The script in benchmark/benchmarks.jl defines a benchmark suite for a variety of distances, under column-wise and pairwise settings.

Here are benchmarks obtained running Julia 0.6 on a computer with a quad-core Intel Core i5-2500K processor @ 3.3 GHz. The tables below can be replicated using the script in benchmark/print_table.jl.

Column-wise benchmark

The table below compares the performance (measured in terms of average elapsed time of each iteration) of a straightforward loop implementation and an optimized implementation provided in Distances.jl. The task in each iteration is to compute a specific distance between corresponding columns in two 200-by-10000 matrices.

distance loop colwise gain
SqEuclidean 0.007467s 0.002171s 3.4393
Euclidean 0.007421s 0.002185s 3.3961
Cityblock 0.007442s 0.002168s 3.4328
Chebyshev 0.011494s 0.005846s 1.9662
Minkowski 0.174122s 0.143938s 1.2097
Hamming 0.007586s 0.002249s 3.3739
CosineDist 0.008581s 0.002853s 3.0076
CorrDist 0.014991s 0.011402s 1.3148
ChiSqDist 0.012990s 0.006910s 1.8800
KLDivergence 0.051694s 0.047433s 1.0898
RenyiDivergence 0.021406s 0.017845s 1.1996
RenyiDivergence 0.031397s 0.027801s 1.1294
JSDivergence 0.115657s 0.495861s 0.2332
BhattacharyyaDist 0.019273s 0.013195s 1.4606
HellingerDist 0.018883s 0.012921s 1.4613
WeightedSqEuclidean 0.007559s 0.002256s 3.3504
WeightedEuclidean 0.007624s 0.002325s 3.2796
WeightedCityblock 0.007803s 0.002248s 3.4709
WeightedMinkowski 0.154231s 0.147579s 1.0451
WeightedHamming 0.009042s 0.003182s 2.8417
SqMahalanobis 0.070869s 0.019199s 3.6913
Mahalanobis 0.070980s 0.019305s 3.6768
Haversine 0.006549s 0.000809s 8.0967

We can see that using colwise instead of a simple loop yields considerable gain (2x - 4x), especially when the internal computation of each distance is simple. Nonetheless, when the computation of a single distance is heavy enough (e.g. KLDivergence, RenyiDivergence), the gain is not as significant.

Pairwise benchmark

The table below compares the performance (measured in terms of average elapsed time of each iteration) of a straightforward loop implementation and an optimized implementation provided in Distances.jl. The task in each iteration is to compute a specific distance in a pairwise manner between columns in a 100-by-200 and 100-by-250 matrices, which will result in a 200-by-250 distance matrix.

distance loop pairwise gain
SqEuclidean 0.019217s 0.000196s 97.9576
Euclidean 0.019287s 0.000505s 38.1874
Cityblock 0.019376s 0.002532s 7.6521
Chebyshev 0.032814s 0.014811s 2.2155
Minkowski 0.382199s 0.361059s 1.0586
Hamming 0.019826s 0.003047s 6.5072
CosineDist 0.024012s 0.000367s 65.3661
CorrDist 0.041356s 0.000421s 98.3049
ChiSqDist 0.035105s 0.017882s 1.9632
KLDivergence 0.131773s 0.117640s 1.1201
RenyiDivergence 0.057569s 0.042694s 1.3484
RenyiDivergence 0.082862s 0.067811s 1.2220
JSDivergence 0.292014s 0.276898s 1.0546
BhattacharyyaDist 0.051302s 0.033043s 1.5526
HellingerDist 0.049518s 0.031856s 1.5545
WeightedSqEuclidean 0.019959s 0.000218s 91.7298
WeightedEuclidean 0.020336s 0.000557s 36.5405
WeightedCityblock 0.020391s 0.003118s 6.5404
WeightedMinkowski 0.387738s 0.366898s 1.0568
WeightedHamming 0.024456s 0.007403s 3.3033
SqMahalanobis 0.113107s 0.000366s 309.3621
Mahalanobis 0.114646s 0.000686s 167.0595
Haversine 0.015267s 0.003656s 4.1763

For distances of which a major part of the computation is a quadratic form (e.g. Euclidean, CosineDist, Mahalanobis), the performance can be drastically improved by restructuring the computation and delegating the core part to GEMM in BLAS. The use of this strategy can easily lead to 100x performance gain over simple loops (see the highlighted part of the table above).

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