[VOL-5292] Implementation for fetching the GEM port history Data from the ONT
Change-Id: I4cf22555cbd13bcd5e49e620c8aa8b67cbd2891c
Signed-off-by: Akash Reddy Kankanala <akash.kankanala@radisys.com>
diff --git a/vendor/github.com/klauspost/compress/compressible.go b/vendor/github.com/klauspost/compress/compressible.go
new file mode 100644
index 0000000..ea5a692
--- /dev/null
+++ b/vendor/github.com/klauspost/compress/compressible.go
@@ -0,0 +1,85 @@
+package compress
+
+import "math"
+
+// Estimate returns a normalized compressibility estimate of block b.
+// Values close to zero are likely uncompressible.
+// Values above 0.1 are likely to be compressible.
+// Values above 0.5 are very compressible.
+// Very small lengths will return 0.
+func Estimate(b []byte) float64 {
+ if len(b) < 16 {
+ return 0
+ }
+
+ // Correctly predicted order 1
+ hits := 0
+ lastMatch := false
+ var o1 [256]byte
+ var hist [256]int
+ c1 := byte(0)
+ for _, c := range b {
+ if c == o1[c1] {
+ // We only count a hit if there was two correct predictions in a row.
+ if lastMatch {
+ hits++
+ }
+ lastMatch = true
+ } else {
+ lastMatch = false
+ }
+ o1[c1] = c
+ c1 = c
+ hist[c]++
+ }
+
+ // Use x^0.6 to give better spread
+ prediction := math.Pow(float64(hits)/float64(len(b)), 0.6)
+
+ // Calculate histogram distribution
+ variance := float64(0)
+ avg := float64(len(b)) / 256
+
+ for _, v := range hist {
+ Δ := float64(v) - avg
+ variance += Δ * Δ
+ }
+
+ stddev := math.Sqrt(float64(variance)) / float64(len(b))
+ exp := math.Sqrt(1 / float64(len(b)))
+
+ // Subtract expected stddev
+ stddev -= exp
+ if stddev < 0 {
+ stddev = 0
+ }
+ stddev *= 1 + exp
+
+ // Use x^0.4 to give better spread
+ entropy := math.Pow(stddev, 0.4)
+
+ // 50/50 weight between prediction and histogram distribution
+ return math.Pow((prediction+entropy)/2, 0.9)
+}
+
+// ShannonEntropyBits returns the number of bits minimum required to represent
+// an entropy encoding of the input bytes.
+// https://en.wiktionary.org/wiki/Shannon_entropy
+func ShannonEntropyBits(b []byte) int {
+ if len(b) == 0 {
+ return 0
+ }
+ var hist [256]int
+ for _, c := range b {
+ hist[c]++
+ }
+ shannon := float64(0)
+ invTotal := 1.0 / float64(len(b))
+ for _, v := range hist[:] {
+ if v > 0 {
+ n := float64(v)
+ shannon += math.Ceil(-math.Log2(n*invTotal) * n)
+ }
+ }
+ return int(math.Ceil(shannon))
+}