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golof

A Local Outlier Factor algorithm (Markus M. Breunig) implementation in Go. This implementation allows you to choose between updating nearest neighbors for all the samples or updating nearest neighbors only for nearest neighbors of the added sample.

It is not available yet with the go get command, so just use the source.

Example

Generate a slice of float64 points:

points := [][]float64{
    {-4.8447532242074978, -5.6869538132901658},
    {1.7265577109364076, -2.5446963280374302},
    {-1.9885982441038819, 1.705719643962865},
    {-1.999050026772494, -4.0367551415711844},
    {-2.0550860126898964, -3.6247409893236426},
    {-1.4456945632547327, -3.7669258809535102},
    {-4.6676062022635554, 1.4925324371089148},
    {-3.6526420667796877, -3.5582661345085662},
    {6.4551493172954029, -0.45434966683144573},
    {-0.56730591589443669, -5.5859532963153349},
    {-5.1400897823762239, -1.3359248994019064},
    {5.2586932439960243, 0.032431285797532586},
    {6.3610915734502838, -0.99059648246991894},
    {-0.31086913190231447, -2.8352818694180644},
    {1.2288582719783967, -1.1362795178325829},
    {-0.17986204466346614, -0.32813130288006365},
    {2.2532002509929216, -0.5142311840491649},
    {-0.75397166138399296, 2.2465141276038754},
    {1.9382517648161239, -1.7276112460593251},
    {1.6809250808549676, -2.3433636210337503},
    {0.68466572523884783, 1.4374914487477481},
    {2.0032364431791514, -2.9191062023123635},
    {-1.7565895138024741, 0.96995712544043267},
    {3.3809644295064505, 6.7497121359292684},
    {-4.2764152718650896, 5.6551328734397766},
    {-3.6347215445083019, -0.85149861984875741},
    {-5.6249411288060385, -3.9251965527768755},
    {4.6033708001912093, 1.3375110154658127},
    {-0.685421751407983, -0.73115552984211407},
    {-2.3744241805625044, 1.3443896265777866},
}

Convert them to ISample slice (golof works with ISample interface, see lof/samples.go for details):

samples   := lof.GetSamplesFromFloat64s(points)

Get a trained LOF type value:

lofGetter := lof.NewLOF(5)
lofGetter.Train(samples)

Obtain and print a mapping from samples to their LOF values:

mapping   := lofGetter.GetLOFs(samples, "fast")
for sample, factor := range mapping {
    fmt.Printf("Sample: %v,  \tLOF: %f\n", sample.GetPoint(), factor)
}

The second argument to GetLOFs(), namely "fast", tells LOF that we want to update the KNN table only for those samples that are the new sample's nearest neighbors. If you want to perform a full update each time, change "fast" to "strict".

Expected output:

Sample: [-2.3744241805625044 1.3443896265777866],       LOF: 1.428066
Sample: [-4.844753224207498 -5.686953813290166],        LOF: 0.765557
Sample: [1.7265577109364076 -2.54469632803743],         LOF: 1.436116
Sample: [-1.9885982441038819 1.705719643962865],        LOF: 1.518616
Sample: [-1.999050026772494 -4.036755141571184],        LOF: 1.128372
Sample: [-2.0550860126898964 -3.6247409893236426],      LOF: 1.184858
Sample: [-1.4456945632547327 -3.76692588095351],        LOF: 1.159083
Sample: [-4.667606202263555 1.4925324371089148],        LOF: 1.507649
Sample: [-3.6526420667796877 -3.558266134508566],       LOF: 0.963004
Sample: [6.455149317295403 -0.45434966683144573],       LOF: 1.950348
Sample: [-0.5673059158944367 -5.585953296315335],       LOF: 1.310592
Sample: [-5.140089782376224 -1.3359248994019064],       LOF: 1.516363
Sample: [5.258693243996024 0.032431285797532586],       LOF: 1.818367
Sample: [6.361091573450284 -0.9905964824699189],        LOF: 1.902978
Sample: [-0.31086913190231447 -2.8352818694180644],     LOF: 1.410723
Sample: [1.2288582719783967 -1.1362795178325829],       LOF: 1.455256
Sample: [-0.17986204466346614 -0.32813130288006365],    LOF: 1.403039
Sample: [2.2532002509929216 -0.5142311840491649],       LOF: 1.684117
Sample: [-0.753971661383993 2.2465141276038754],        LOF: 1.668804
Sample: [1.938251764816124 -1.7276112460593251],        LOF: 1.542820
Sample: [1.6809250808549676 -2.3433636210337503],       LOF: 1.445668
Sample: [0.6846657252388478 1.4374914487477481],        LOF: 1.712357
Sample: [2.0032364431791514 -2.9191062023123635],       LOF: 1.456276
Sample: [-1.756589513802474 0.9699571254404327],        LOF: 1.406158
Sample: [3.3809644295064505 6.749712135929268],         LOF: 2.458342
Sample: [-4.27641527186509 5.6551328734397766],         LOF: 2.128373
Sample: [-3.634721544508302 -0.8514986198487574],       LOF: 1.149214
Sample: [-5.6249411288060385 -3.9251965527768755],      LOF: 0.901403
Sample: [4.603370800191209 1.3375110154658127],         LOF: 1.843948
Sample: [-0.685421751407983 -0.7311555298421141],       LOF: 1.310516

The full example can be found in ./example.go.

TODO

  • Add normalization for samples
  • Write tests
  • Enable parallel mode for distances calculation