1 files changed, 0 insertions, 85 deletions
diff --git a/vendor/github.com/klauspost/compress/compressible.go b/vendor/github.com/klauspost/compress/compressible.go
deleted file mode 100644
index ea5a692d5..000000000
--- a/vendor/github.com/klauspost/compress/compressible.go
+++ /dev/null
@@ -1,85 +0,0 @@
-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))
-}