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).

- Euclidean distance
- Squared Euclidean distance
- Periodic Euclidean distance
- Cityblock distance
- Total variation 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
- Bregman divergence

For `Euclidean distance`

, `Squared Euclidean distance`

, `Cityblock distance`

, `Minkowski distance`

, and `Hamming distance`

, a weighted version is also provided.

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

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)
r = 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)
r = 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)
```

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.

Let `X`

and `Y`

respectively have `m`

and `n`

columns. Then the `pairwise`

function with the `dims=2`

argument computes distances between each pair of columns in `X`

and `Y`

:

```
R = pairwise(dist, X, Y, dims=2)
```

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, dims=2)
```

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).

For performance reasons, it is recommended to use matrices with observations in columns (as shown above). Indeed,
the `Array`

type in Julia is column-major, making it more efficient to access memory column by column. However,
matrices with observations stored in rows are also supported via the argument `dims=1`

.

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 (`i`

being either `1`

or `2`

):

```
colwise!(r, dist, X, Y)
pairwise!(R, dist, X, Y, dims=i)
pairwise!(R, dist, X, dims=i)
```

Please pay attention to the difference, the functions for inplace computation are `colwise!`

and `pairwise!`

(instead of `colwise`

and `pairwise`

).

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)` |

PeriodicEuclidean | `peuclidean(x, y, p)` |
`sqrt(sum(min(mod(abs(x - y), p), p - mod(abs(x - y), p)).^2))` |

Cityblock | `cityblock(x, y)` |
`sum(abs(x - y))` |

TotalVariation | `totalvariation(x, y)` |
`sum(abs(x - y)) / 2` |

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)` |

Bregman | `bregman(F, ∇, x, y; inner = LinearAlgebra.dot)` |
`F(x) - F(y) - inner(∇(y), x - y)` |

**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.

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}:
7.45058e-9
julia> pairwise(Euclidean(1e-12), x, x)
1×1 Array{Float64,2}:
0.0
```

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 1.0 on a computer with a dual-core Intel Core i5-2300K processor @ 2.3 GHz.
The tables below can be replicated using the script in `benchmark/print_table.jl`

.

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 | 0.004432s | 0.001049s | 4.2270 |

Euclidean | 0.004537s | 0.001054s | 4.3031 |

PeriodicEuclidean | 0.012092s | 0.006714s | 1.8011 |

Cityblock | 0.004515s | 0.001060s | 4.2585 |

TotalVariation | 0.004496s | 0.001062s | 4.2337 |

Chebyshev | 0.009123s | 0.005034s | 1.8123 |

Minkowski | 0.047573s | 0.042508s | 1.1191 |

Hamming | 0.004355s | 0.001099s | 3.9638 |

CosineDist | 0.006432s | 0.002282s | 2.8185 |

CorrDist | 0.010273s | 0.012500s | 0.8219 |

ChiSqDist | 0.005291s | 0.001271s | 4.1635 |

KLDivergence | 0.031491s | 0.025643s | 1.2281 |

RenyiDivergence | 0.052420s | 0.048075s | 1.0904 |

RenyiDivergence | 0.017317s | 0.009023s | 1.9193 |

JSDivergence | 0.047905s | 0.044006s | 1.0886 |

BhattacharyyaDist | 0.007761s | 0.003796s | 2.0445 |

HellingerDist | 0.007636s | 0.003665s | 2.0836 |

WeightedSqEuclidean | 0.004550s | 0.001151s | 3.9541 |

WeightedEuclidean | 0.004687s | 0.001168s | 4.0125 |

WeightedCityblock | 0.004493s | 0.001157s | 3.8849 |

WeightedMinkowski | 0.049442s | 0.042145s | 1.1732 |

WeightedHamming | 0.004431s | 0.001153s | 3.8440 |

SqMahalanobis | 0.082493s | 0.019843s | 4.1574 |

Mahalanobis | 0.082180s | 0.019618s | 4.1891 |

BrayCurtis | 0.004464s | 0.001121s | 3.9809 |

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.

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 | 0.012498s | 0.000170s | 73.6596 |

Euclidean | 0.012583s | 0.000257s | 48.9628 |

PeriodicEuclidean | 0.030935s | 0.017572s | 1.7605 |

Cityblock | 0.012416s | 0.000910s | 13.6464 |

TotalVariation | 0.012763s | 0.000959s | 13.3080 |

Chebyshev | 0.023800s | 0.012042s | 1.9763 |

Minkowski | 0.121388s | 0.107333s | 1.1310 |

Hamming | 0.012171s | 0.000689s | 17.6538 |

CosineDist | 0.017474s | 0.000214s | 81.6546 |

CorrDist | 0.028195s | 0.000259s | 108.7360 |

ChiSqDist | 0.014372s | 0.003129s | 4.5932 |

KLDivergence | 0.079669s | 0.063491s | 1.2548 |

RenyiDivergence | 0.134093s | 0.117737s | 1.1389 |

RenyiDivergence | 0.047658s | 0.024960s | 1.9094 |

JSDivergence | 0.121999s | 0.110984s | 1.0993 |

BhattacharyyaDist | 0.021788s | 0.009414s | 2.3145 |

HellingerDist | 0.020735s | 0.008784s | 2.3606 |

WeightedSqEuclidean | 0.012671s | 0.000186s | 68.0345 |

WeightedEuclidean | 0.012867s | 0.000276s | 46.6634 |

WeightedCityblock | 0.012803s | 0.001539s | 8.3200 |

WeightedMinkowski | 0.127386s | 0.107257s | 1.1877 |

WeightedHamming | 0.012240s | 0.001462s | 8.3747 |

SqMahalanobis | 0.214285s | 0.000330s | 650.0722 |

Mahalanobis | 0.197294s | 0.000420s | 470.2354 |

BrayCurtis | 0.012872s | 0.001489s | 8.6456 |

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).

08/26/2014

11 days ago

117 commits