Java implementation of Local Outlier Factor algorithm by Markus M. Breunig. The implementation accepts a collection double[] arrays, where each array corresponds to an instance.
The following example illustrates the simple use case of computing LOF value of an example based on the training data. First, we initialize LOF constructor by passing data variable. Next, we call getScore(double[] example, int kNN) with an array of doubles and kNN value.
ArrayList<double[]> data = new ArrayList<double[]>();
data.add(new double[]{0, 0});
data.add(new double[]{0, 1});
data.add(new double[]{1, 0});
data.add(new double[]{1, 1});
data.add(new double[]{1, 2});
data.add(new double[]{2, 1});
data.add(new double[]{2, 2});
data.add(new double[]{2, 0});
data.add(new double[]{2, 0});
data.add(new double[]{2, 0});
data.add(new double[]{2, 0});
LOF model = new LOF(data);
int kNN = 5;
System.out.println(model.getScore(new double[]{2, 0}, kNN));
System.out.println(model.getScore(new double[]{0, 0}, kNN));
System.out.println(model.getScore(new double[]{10, 4}, kNN));
The output should be:
0.9439752132155199
1.4115350452823745
7.845854226211409
To get a list of k-nearest neighbors, call getNeighbors(double[] example, int kNN) method:
double[] testSample = new double[]{2, 0};
System.out.println("\nNeighbors of "+Arrays.toString(testSample));
ArrayList<double[]> neighbors = model.getNeighbors(testSample, kNN);
for(double[] n : neighbors){
System.out.println(Arrays.toString(n));
}
The output is:
Neighbors of [2.0, 0.0]
[0.0, 0.0]
[1.0, 0.0]
[2.0, 1.0]
[2.0, 0.0]
[2.0, 0.0]
[2.0, 0.0]
The get the LOF values for existing training data, simply call getTrainingScores(int knn) method:
double[] scores = model.getTrainingScores(kNN);
for(int i = 0; i < scores.length; i++){
System.out.println(Arrays.toString(data.get(i)) + "\t" + scores[i]);
}
The output should be:
[0.0, 0.0] 1.5237323873815085
[0.0, 1.0] 1.196228163810988
[1.0, 0.0] 1.071470776748656
[1.0, 1.0] 1.2335968200610736
[1.0, 2.0] 1.196228163810988
[2.0, 1.0] 1.071470776748656
[2.0, 2.0] 1.5237323873815085
[2.0, 0.0] 0.9237025720677815
[2.0, 0.0] 0.9237025720677815
[2.0, 0.0] 0.9237025720677815
[2.0, 0.0] 0.9237025720677815
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