// Generates permutations of reference and active window values to determine // whether or not data is anomalous. The number of permutations desired has // been set to 500 but can be increased for more precision. func DiffTest(vector govector.Vector, conf AnomalyzerConf) float64 { // Find the differences between neighboring elements and rank those differences. ranks := vector.RelDiff().Apply(math.Abs).Rank() // The indexing runs to length-1 because after applying .Diff(), We have // decreased the length of out vector by 1. _, active, err := extractWindows(ranks, conf.referenceSize-1, conf.ActiveSize, conf.ActiveSize) if err != nil { return NA } // Consider the sum of the ranks across the active data. This is the sum that // we will compare our permutations to. activeSum := active.Sum() i := 0 significant := 0 // Permute the active and reference data and compute the sums across the tail // (from the length of the reference data to the full length). for i < conf.PermCount { permRanks := vector.Shuffle().RelDiff().Apply(math.Abs).Rank() _, permActive, _ := extractWindows(permRanks, conf.referenceSize-1, conf.ActiveSize, conf.ActiveSize) // If we find a sum that is less than the initial sum across the active data, // this implies our initial sum might be uncharacteristically high. We increment // our count. if permActive.Sum() < activeSum { significant++ } i++ } // We return the percentage of the number of iterations where we found our initial // sum to be high. return float64(significant) / float64(conf.PermCount) }