kesavand | c71914f | 2022-03-25 11:19:03 +0530 | [diff] [blame^] | 1 | package compress |
| 2 | |
| 3 | import "math" |
| 4 | |
| 5 | // Estimate returns a normalized compressibility estimate of block b. |
| 6 | // Values close to zero are likely uncompressible. |
| 7 | // Values above 0.1 are likely to be compressible. |
| 8 | // Values above 0.5 are very compressible. |
| 9 | // Very small lengths will return 0. |
| 10 | func Estimate(b []byte) float64 { |
| 11 | if len(b) < 16 { |
| 12 | return 0 |
| 13 | } |
| 14 | |
| 15 | // Correctly predicted order 1 |
| 16 | hits := 0 |
| 17 | lastMatch := false |
| 18 | var o1 [256]byte |
| 19 | var hist [256]int |
| 20 | c1 := byte(0) |
| 21 | for _, c := range b { |
| 22 | if c == o1[c1] { |
| 23 | // We only count a hit if there was two correct predictions in a row. |
| 24 | if lastMatch { |
| 25 | hits++ |
| 26 | } |
| 27 | lastMatch = true |
| 28 | } else { |
| 29 | lastMatch = false |
| 30 | } |
| 31 | o1[c1] = c |
| 32 | c1 = c |
| 33 | hist[c]++ |
| 34 | } |
| 35 | |
| 36 | // Use x^0.6 to give better spread |
| 37 | prediction := math.Pow(float64(hits)/float64(len(b)), 0.6) |
| 38 | |
| 39 | // Calculate histogram distribution |
| 40 | variance := float64(0) |
| 41 | avg := float64(len(b)) / 256 |
| 42 | |
| 43 | for _, v := range hist { |
| 44 | Δ := float64(v) - avg |
| 45 | variance += Δ * Δ |
| 46 | } |
| 47 | |
| 48 | stddev := math.Sqrt(float64(variance)) / float64(len(b)) |
| 49 | exp := math.Sqrt(1 / float64(len(b))) |
| 50 | |
| 51 | // Subtract expected stddev |
| 52 | stddev -= exp |
| 53 | if stddev < 0 { |
| 54 | stddev = 0 |
| 55 | } |
| 56 | stddev *= 1 + exp |
| 57 | |
| 58 | // Use x^0.4 to give better spread |
| 59 | entropy := math.Pow(stddev, 0.4) |
| 60 | |
| 61 | // 50/50 weight between prediction and histogram distribution |
| 62 | return math.Pow((prediction+entropy)/2, 0.9) |
| 63 | } |
| 64 | |
| 65 | // ShannonEntropyBits returns the number of bits minimum required to represent |
| 66 | // an entropy encoding of the input bytes. |
| 67 | // https://en.wiktionary.org/wiki/Shannon_entropy |
| 68 | func ShannonEntropyBits(b []byte) int { |
| 69 | if len(b) == 0 { |
| 70 | return 0 |
| 71 | } |
| 72 | var hist [256]int |
| 73 | for _, c := range b { |
| 74 | hist[c]++ |
| 75 | } |
| 76 | shannon := float64(0) |
| 77 | invTotal := 1.0 / float64(len(b)) |
| 78 | for _, v := range hist[:] { |
| 79 | if v > 0 { |
| 80 | n := float64(v) |
| 81 | shannon += math.Ceil(-math.Log2(n*invTotal) * n) |
| 82 | } |
| 83 | } |
| 84 | return int(math.Ceil(shannon)) |
| 85 | } |