mirror of
https://github.com/VictoriaMetrics/VictoriaMetrics.git
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vendor: update github.com/klauspost/compress from v1.9.7 to v1.9.8
New version should have better gzip compression. See https://github.com/klauspost/compress#changelog
This commit is contained in:
parent
0ef6f91410
commit
f8954c7250
9 changed files with 474 additions and 89 deletions
2
go.mod
2
go.mod
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@ -8,7 +8,7 @@ require (
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github.com/aws/aws-sdk-go v1.28.3
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github.com/cespare/xxhash/v2 v2.1.1
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github.com/golang/snappy v0.0.1
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github.com/klauspost/compress v1.9.7
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github.com/klauspost/compress v1.9.8
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github.com/valyala/fastjson v1.4.5
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github.com/valyala/fastrand v1.0.0
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github.com/valyala/gozstd v1.6.4
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4
go.sum
4
go.sum
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@ -85,8 +85,8 @@ github.com/jstemmer/go-junit-report v0.9.1/go.mod h1:Brl9GWCQeLvo8nXZwPNNblvFj/X
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github.com/kisielk/gotool v1.0.0/go.mod h1:XhKaO+MFFWcvkIS/tQcRk01m1F5IRFswLeQ+oQHNcck=
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github.com/klauspost/compress v1.4.0/go.mod h1:RyIbtBH6LamlWaDj8nUwkbUhJ87Yi3uG0guNDohfE1A=
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github.com/klauspost/compress v1.4.1/go.mod h1:RyIbtBH6LamlWaDj8nUwkbUhJ87Yi3uG0guNDohfE1A=
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github.com/klauspost/compress v1.9.7 h1:hYW1gP94JUmAhBtJ+LNz5My+gBobDxPR1iVuKug26aA=
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github.com/klauspost/compress v1.9.7/go.mod h1:RyIbtBH6LamlWaDj8nUwkbUhJ87Yi3uG0guNDohfE1A=
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github.com/klauspost/compress v1.9.8 h1:VMAMUUOh+gaxKTMk+zqbjsSjsIcUcL/LF4o63i82QyA=
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github.com/klauspost/compress v1.9.8/go.mod h1:RyIbtBH6LamlWaDj8nUwkbUhJ87Yi3uG0guNDohfE1A=
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github.com/klauspost/cpuid v0.0.0-20180405133222-e7e905edc00e/go.mod h1:Pj4uuM528wm8OyEC2QMXAi2YiTZ96dNQPGgoMS4s3ek=
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github.com/klauspost/cpuid v1.2.0/go.mod h1:Pj4uuM528wm8OyEC2QMXAi2YiTZ96dNQPGgoMS4s3ek=
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github.com/kr/pretty v0.1.0 h1:L/CwN0zerZDmRFUapSPitk6f+Q3+0za1rQkzVuMiMFI=
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6
vendor/github.com/klauspost/compress/flate/deflate.go
generated
vendored
6
vendor/github.com/klauspost/compress/flate/deflate.go
generated
vendored
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@ -644,7 +644,7 @@ func (d *compressor) init(w io.Writer, level int) (err error) {
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d.fill = (*compressor).fillBlock
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d.step = (*compressor).store
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case level == ConstantCompression:
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d.w.logReusePenalty = uint(4)
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d.w.logNewTablePenalty = 4
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d.window = make([]byte, maxStoreBlockSize)
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d.fill = (*compressor).fillBlock
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d.step = (*compressor).storeHuff
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@ -652,13 +652,13 @@ func (d *compressor) init(w io.Writer, level int) (err error) {
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level = 5
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fallthrough
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case level >= 1 && level <= 6:
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d.w.logReusePenalty = uint(level + 1)
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d.w.logNewTablePenalty = 6
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d.fast = newFastEnc(level)
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d.window = make([]byte, maxStoreBlockSize)
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d.fill = (*compressor).fillBlock
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d.step = (*compressor).storeFast
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case 7 <= level && level <= 9:
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d.w.logReusePenalty = uint(level)
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d.w.logNewTablePenalty = 10
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d.state = &advancedState{}
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d.compressionLevel = levels[level]
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d.initDeflate()
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42
vendor/github.com/klauspost/compress/flate/huffman_bit_writer.go
generated
vendored
42
vendor/github.com/klauspost/compress/flate/huffman_bit_writer.go
generated
vendored
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@ -93,7 +93,7 @@ type huffmanBitWriter struct {
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err error
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lastHeader int
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// Set between 0 (reused block can be up to 2x the size)
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logReusePenalty uint
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logNewTablePenalty uint
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lastHuffMan bool
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bytes [256]byte
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literalFreq [lengthCodesStart + 32]uint16
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@ -119,7 +119,7 @@ type huffmanBitWriter struct {
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// If lastHuffMan is set, a table for outputting literals has been generated and offsets are invalid.
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//
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// An incoming block estimates the output size of a new table using a 'fresh' by calculating the
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// optimal size and adding a penalty in 'logReusePenalty'.
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// optimal size and adding a penalty in 'logNewTablePenalty'.
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// A Huffman table is not optimal, which is why we add a penalty, and generating a new table
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// is slower both for compression and decompression.
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@ -349,6 +349,13 @@ func (w *huffmanBitWriter) headerSize() (size, numCodegens int) {
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int(w.codegenFreq[18])*7, numCodegens
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}
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// dynamicSize returns the size of dynamically encoded data in bits.
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func (w *huffmanBitWriter) dynamicReuseSize(litEnc, offEnc *huffmanEncoder) (size int) {
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size = litEnc.bitLength(w.literalFreq[:]) +
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offEnc.bitLength(w.offsetFreq[:])
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return size
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}
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// dynamicSize returns the size of dynamically encoded data in bits.
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func (w *huffmanBitWriter) dynamicSize(litEnc, offEnc *huffmanEncoder, extraBits int) (size, numCodegens int) {
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header, numCodegens := w.headerSize()
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@ -451,12 +458,12 @@ func (w *huffmanBitWriter) writeDynamicHeader(numLiterals int, numOffsets int, n
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i := 0
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for {
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var codeWord int = int(w.codegen[i])
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var codeWord = uint32(w.codegen[i])
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i++
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if codeWord == badCode {
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break
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}
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w.writeCode(w.codegenEncoding.codes[uint32(codeWord)])
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w.writeCode(w.codegenEncoding.codes[codeWord])
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switch codeWord {
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case 16:
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@ -602,14 +609,14 @@ func (w *huffmanBitWriter) writeBlockDynamic(tokens *tokens, eof bool, input []b
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var size int
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// Check if we should reuse.
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if w.lastHeader > 0 {
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// Estimate size for using a new table
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// Estimate size for using a new table.
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// Use the previous header size as the best estimate.
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newSize := w.lastHeader + tokens.EstimatedBits()
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newSize += newSize >> w.logNewTablePenalty
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// The estimated size is calculated as an optimal table.
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// We add a penalty to make it more realistic and re-use a bit more.
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newSize += newSize >> (w.logReusePenalty & 31)
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extra := w.extraBitSize()
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reuseSize, _ := w.dynamicSize(w.literalEncoding, w.offsetEncoding, extra)
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reuseSize := w.dynamicReuseSize(w.literalEncoding, w.offsetEncoding) + w.extraBitSize()
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// Check if a new table is better.
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if newSize < reuseSize {
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@ -801,21 +808,30 @@ func (w *huffmanBitWriter) writeBlockHuff(eof bool, input []byte, sync bool) {
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}
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// Add everything as literals
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estBits := histogramSize(input, w.literalFreq[:], !eof && !sync) + 15
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// We have to estimate the header size.
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// Assume header is around 70 bytes:
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// https://stackoverflow.com/a/25454430
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const guessHeaderSizeBits = 70 * 8
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estBits, estExtra := histogramSize(input, w.literalFreq[:], !eof && !sync)
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estBits += w.lastHeader + 15
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if w.lastHeader == 0 {
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estBits += guessHeaderSizeBits
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}
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estBits += estBits >> w.logNewTablePenalty
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// Store bytes, if we don't get a reasonable improvement.
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ssize, storable := w.storedSize(input)
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if storable && ssize < (estBits+estBits>>4) {
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if storable && ssize < estBits {
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w.writeStoredHeader(len(input), eof)
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w.writeBytes(input)
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return
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}
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if w.lastHeader > 0 {
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size, _ := w.dynamicSize(w.literalEncoding, huffOffset, w.lastHeader)
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estBits += estBits >> (w.logReusePenalty)
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reuseSize := w.literalEncoding.bitLength(w.literalFreq[:256])
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estBits += estExtra
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if estBits < size {
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if estBits < reuseSize {
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// We owe an EOB
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w.writeCode(w.literalEncoding.codes[endBlockMarker])
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w.lastHeader = 0
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70
vendor/github.com/klauspost/compress/flate/huffman_code.go
generated
vendored
70
vendor/github.com/klauspost/compress/flate/huffman_code.go
generated
vendored
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@ -7,7 +7,6 @@ package flate
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import (
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"math"
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"math/bits"
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"sort"
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)
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const (
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@ -25,8 +24,6 @@ type huffmanEncoder struct {
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codes []hcode
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freqcache []literalNode
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bitCount [17]int32
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lns byLiteral // stored to avoid repeated allocation in generate
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lfs byFreq // stored to avoid repeated allocation in generate
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}
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type literalNode struct {
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@ -270,7 +267,7 @@ func (h *huffmanEncoder) assignEncodingAndSize(bitCount []int32, list []literalN
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// assigned in literal order (not frequency order).
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chunk := list[len(list)-int(bits):]
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h.lns.sort(chunk)
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sortByLiteral(chunk)
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for _, node := range chunk {
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h.codes[node.literal] = hcode{code: reverseBits(code, uint8(n)), len: uint16(n)}
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code++
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@ -315,7 +312,7 @@ func (h *huffmanEncoder) generate(freq []uint16, maxBits int32) {
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}
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return
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}
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h.lfs.sort(list)
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sortByFreq(list)
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// Get the number of literals for each bit count
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bitCount := h.bitCounts(list, maxBits)
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@ -323,59 +320,44 @@ func (h *huffmanEncoder) generate(freq []uint16, maxBits int32) {
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h.assignEncodingAndSize(bitCount, list)
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}
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type byLiteral []literalNode
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func (s *byLiteral) sort(a []literalNode) {
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*s = byLiteral(a)
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sort.Sort(s)
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}
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func (s byLiteral) Len() int { return len(s) }
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func (s byLiteral) Less(i, j int) bool {
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return s[i].literal < s[j].literal
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}
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func (s byLiteral) Swap(i, j int) { s[i], s[j] = s[j], s[i] }
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type byFreq []literalNode
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func (s *byFreq) sort(a []literalNode) {
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*s = byFreq(a)
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sort.Sort(s)
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}
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func (s byFreq) Len() int { return len(s) }
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func (s byFreq) Less(i, j int) bool {
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if s[i].freq == s[j].freq {
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return s[i].literal < s[j].literal
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func atLeastOne(v float32) float32 {
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if v < 1 {
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return 1
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}
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return s[i].freq < s[j].freq
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return v
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}
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func (s byFreq) Swap(i, j int) { s[i], s[j] = s[j], s[i] }
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// histogramSize accumulates a histogram of b in h.
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// An estimated size in bits is returned.
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// Unassigned values are assigned '1' in the histogram.
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// len(h) must be >= 256, and h's elements must be all zeroes.
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func histogramSize(b []byte, h []uint16, fill bool) int {
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func histogramSize(b []byte, h []uint16, fill bool) (int, int) {
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h = h[:256]
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for _, t := range b {
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h[t]++
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}
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invTotal := 1.0 / float64(len(b))
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shannon := 0.0
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single := math.Ceil(-math.Log2(invTotal))
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invTotal := 1.0 / float32(len(b))
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shannon := float32(0.0)
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var extra float32
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if fill {
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oneBits := atLeastOne(-mFastLog2(invTotal))
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for i, v := range h[:] {
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if v > 0 {
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n := float64(v)
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shannon += math.Ceil(-math.Log2(n*invTotal) * n)
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} else if fill {
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shannon += single
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n := float32(v)
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shannon += atLeastOne(-mFastLog2(n*invTotal)) * n
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} else {
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h[i] = 1
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extra += oneBits
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}
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}
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return int(shannon + 0.99)
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} else {
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for _, v := range h[:] {
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if v > 0 {
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n := float32(v)
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shannon += atLeastOne(-mFastLog2(n*invTotal)) * n
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}
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}
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}
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return int(shannon + 0.99), int(extra + 0.99)
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}
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178
vendor/github.com/klauspost/compress/flate/huffman_sortByFreq.go
generated
vendored
Normal file
178
vendor/github.com/klauspost/compress/flate/huffman_sortByFreq.go
generated
vendored
Normal file
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@ -0,0 +1,178 @@
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// Copyright 2009 The Go Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style
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// license that can be found in the LICENSE file.
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package flate
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// Sort sorts data.
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// It makes one call to data.Len to determine n, and O(n*log(n)) calls to
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// data.Less and data.Swap. The sort is not guaranteed to be stable.
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func sortByFreq(data []literalNode) {
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n := len(data)
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quickSortByFreq(data, 0, n, maxDepth(n))
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}
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func quickSortByFreq(data []literalNode, a, b, maxDepth int) {
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for b-a > 12 { // Use ShellSort for slices <= 12 elements
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if maxDepth == 0 {
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heapSort(data, a, b)
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return
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}
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maxDepth--
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mlo, mhi := doPivotByFreq(data, a, b)
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// Avoiding recursion on the larger subproblem guarantees
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// a stack depth of at most lg(b-a).
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if mlo-a < b-mhi {
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quickSortByFreq(data, a, mlo, maxDepth)
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a = mhi // i.e., quickSortByFreq(data, mhi, b)
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} else {
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quickSortByFreq(data, mhi, b, maxDepth)
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b = mlo // i.e., quickSortByFreq(data, a, mlo)
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}
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}
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if b-a > 1 {
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// Do ShellSort pass with gap 6
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// It could be written in this simplified form cause b-a <= 12
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for i := a + 6; i < b; i++ {
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if data[i].freq == data[i-6].freq && data[i].literal < data[i-6].literal || data[i].freq < data[i-6].freq {
|
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data[i], data[i-6] = data[i-6], data[i]
|
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}
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}
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insertionSortByFreq(data, a, b)
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}
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}
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// siftDownByFreq implements the heap property on data[lo, hi).
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// first is an offset into the array where the root of the heap lies.
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func siftDownByFreq(data []literalNode, lo, hi, first int) {
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root := lo
|
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for {
|
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child := 2*root + 1
|
||||
if child >= hi {
|
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break
|
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}
|
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if child+1 < hi && (data[first+child].freq == data[first+child+1].freq && data[first+child].literal < data[first+child+1].literal || data[first+child].freq < data[first+child+1].freq) {
|
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child++
|
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}
|
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if data[first+root].freq == data[first+child].freq && data[first+root].literal > data[first+child].literal || data[first+root].freq > data[first+child].freq {
|
||||
return
|
||||
}
|
||||
data[first+root], data[first+child] = data[first+child], data[first+root]
|
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root = child
|
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}
|
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}
|
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func doPivotByFreq(data []literalNode, lo, hi int) (midlo, midhi int) {
|
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m := int(uint(lo+hi) >> 1) // Written like this to avoid integer overflow.
|
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if hi-lo > 40 {
|
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// Tukey's ``Ninther,'' median of three medians of three.
|
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s := (hi - lo) / 8
|
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medianOfThreeSortByFreq(data, lo, lo+s, lo+2*s)
|
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medianOfThreeSortByFreq(data, m, m-s, m+s)
|
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medianOfThreeSortByFreq(data, hi-1, hi-1-s, hi-1-2*s)
|
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}
|
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medianOfThreeSortByFreq(data, lo, m, hi-1)
|
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|
||||
// Invariants are:
|
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// data[lo] = pivot (set up by ChoosePivot)
|
||||
// data[lo < i < a] < pivot
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// data[a <= i < b] <= pivot
|
||||
// data[b <= i < c] unexamined
|
||||
// data[c <= i < hi-1] > pivot
|
||||
// data[hi-1] >= pivot
|
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pivot := lo
|
||||
a, c := lo+1, hi-1
|
||||
|
||||
for ; a < c && (data[a].freq == data[pivot].freq && data[a].literal < data[pivot].literal || data[a].freq < data[pivot].freq); a++ {
|
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}
|
||||
b := a
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for {
|
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for ; b < c && (data[pivot].freq == data[b].freq && data[pivot].literal > data[b].literal || data[pivot].freq > data[b].freq); b++ { // data[b] <= pivot
|
||||
}
|
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for ; b < c && (data[pivot].freq == data[c-1].freq && data[pivot].literal < data[c-1].literal || data[pivot].freq < data[c-1].freq); c-- { // data[c-1] > pivot
|
||||
}
|
||||
if b >= c {
|
||||
break
|
||||
}
|
||||
// data[b] > pivot; data[c-1] <= pivot
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||||
data[b], data[c-1] = data[c-1], data[b]
|
||||
b++
|
||||
c--
|
||||
}
|
||||
// If hi-c<3 then there are duplicates (by property of median of nine).
|
||||
// Let's be a bit more conservative, and set border to 5.
|
||||
protect := hi-c < 5
|
||||
if !protect && hi-c < (hi-lo)/4 {
|
||||
// Lets test some points for equality to pivot
|
||||
dups := 0
|
||||
if data[pivot].freq == data[hi-1].freq && data[pivot].literal > data[hi-1].literal || data[pivot].freq > data[hi-1].freq { // data[hi-1] = pivot
|
||||
data[c], data[hi-1] = data[hi-1], data[c]
|
||||
c++
|
||||
dups++
|
||||
}
|
||||
if data[b-1].freq == data[pivot].freq && data[b-1].literal > data[pivot].literal || data[b-1].freq > data[pivot].freq { // data[b-1] = pivot
|
||||
b--
|
||||
dups++
|
||||
}
|
||||
// m-lo = (hi-lo)/2 > 6
|
||||
// b-lo > (hi-lo)*3/4-1 > 8
|
||||
// ==> m < b ==> data[m] <= pivot
|
||||
if data[m].freq == data[pivot].freq && data[m].literal > data[pivot].literal || data[m].freq > data[pivot].freq { // data[m] = pivot
|
||||
data[m], data[b-1] = data[b-1], data[m]
|
||||
b--
|
||||
dups++
|
||||
}
|
||||
// if at least 2 points are equal to pivot, assume skewed distribution
|
||||
protect = dups > 1
|
||||
}
|
||||
if protect {
|
||||
// Protect against a lot of duplicates
|
||||
// Add invariant:
|
||||
// data[a <= i < b] unexamined
|
||||
// data[b <= i < c] = pivot
|
||||
for {
|
||||
for ; a < b && (data[b-1].freq == data[pivot].freq && data[b-1].literal > data[pivot].literal || data[b-1].freq > data[pivot].freq); b-- { // data[b] == pivot
|
||||
}
|
||||
for ; a < b && (data[a].freq == data[pivot].freq && data[a].literal < data[pivot].literal || data[a].freq < data[pivot].freq); a++ { // data[a] < pivot
|
||||
}
|
||||
if a >= b {
|
||||
break
|
||||
}
|
||||
// data[a] == pivot; data[b-1] < pivot
|
||||
data[a], data[b-1] = data[b-1], data[a]
|
||||
a++
|
||||
b--
|
||||
}
|
||||
}
|
||||
// Swap pivot into middle
|
||||
data[pivot], data[b-1] = data[b-1], data[pivot]
|
||||
return b - 1, c
|
||||
}
|
||||
|
||||
// Insertion sort
|
||||
func insertionSortByFreq(data []literalNode, a, b int) {
|
||||
for i := a + 1; i < b; i++ {
|
||||
for j := i; j > a && (data[j].freq == data[j-1].freq && data[j].literal < data[j-1].literal || data[j].freq < data[j-1].freq); j-- {
|
||||
data[j], data[j-1] = data[j-1], data[j]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// quickSortByFreq, loosely following Bentley and McIlroy,
|
||||
// ``Engineering a Sort Function,'' SP&E November 1993.
|
||||
|
||||
// medianOfThreeSortByFreq moves the median of the three values data[m0], data[m1], data[m2] into data[m1].
|
||||
func medianOfThreeSortByFreq(data []literalNode, m1, m0, m2 int) {
|
||||
// sort 3 elements
|
||||
if data[m1].freq == data[m0].freq && data[m1].literal < data[m0].literal || data[m1].freq < data[m0].freq {
|
||||
data[m1], data[m0] = data[m0], data[m1]
|
||||
}
|
||||
// data[m0] <= data[m1]
|
||||
if data[m2].freq == data[m1].freq && data[m2].literal < data[m1].literal || data[m2].freq < data[m1].freq {
|
||||
data[m2], data[m1] = data[m1], data[m2]
|
||||
// data[m0] <= data[m2] && data[m1] < data[m2]
|
||||
if data[m1].freq == data[m0].freq && data[m1].literal < data[m0].literal || data[m1].freq < data[m0].freq {
|
||||
data[m1], data[m0] = data[m0], data[m1]
|
||||
}
|
||||
}
|
||||
// now data[m0] <= data[m1] <= data[m2]
|
||||
}
|
201
vendor/github.com/klauspost/compress/flate/huffman_sortByLiteral.go
generated
vendored
Normal file
201
vendor/github.com/klauspost/compress/flate/huffman_sortByLiteral.go
generated
vendored
Normal file
|
@ -0,0 +1,201 @@
|
|||
// Copyright 2009 The Go Authors. All rights reserved.
|
||||
// Use of this source code is governed by a BSD-style
|
||||
// license that can be found in the LICENSE file.
|
||||
|
||||
package flate
|
||||
|
||||
// Sort sorts data.
|
||||
// It makes one call to data.Len to determine n, and O(n*log(n)) calls to
|
||||
// data.Less and data.Swap. The sort is not guaranteed to be stable.
|
||||
func sortByLiteral(data []literalNode) {
|
||||
n := len(data)
|
||||
quickSort(data, 0, n, maxDepth(n))
|
||||
}
|
||||
|
||||
func quickSort(data []literalNode, a, b, maxDepth int) {
|
||||
for b-a > 12 { // Use ShellSort for slices <= 12 elements
|
||||
if maxDepth == 0 {
|
||||
heapSort(data, a, b)
|
||||
return
|
||||
}
|
||||
maxDepth--
|
||||
mlo, mhi := doPivot(data, a, b)
|
||||
// Avoiding recursion on the larger subproblem guarantees
|
||||
// a stack depth of at most lg(b-a).
|
||||
if mlo-a < b-mhi {
|
||||
quickSort(data, a, mlo, maxDepth)
|
||||
a = mhi // i.e., quickSort(data, mhi, b)
|
||||
} else {
|
||||
quickSort(data, mhi, b, maxDepth)
|
||||
b = mlo // i.e., quickSort(data, a, mlo)
|
||||
}
|
||||
}
|
||||
if b-a > 1 {
|
||||
// Do ShellSort pass with gap 6
|
||||
// It could be written in this simplified form cause b-a <= 12
|
||||
for i := a + 6; i < b; i++ {
|
||||
if data[i].literal < data[i-6].literal {
|
||||
data[i], data[i-6] = data[i-6], data[i]
|
||||
}
|
||||
}
|
||||
insertionSort(data, a, b)
|
||||
}
|
||||
}
|
||||
func heapSort(data []literalNode, a, b int) {
|
||||
first := a
|
||||
lo := 0
|
||||
hi := b - a
|
||||
|
||||
// Build heap with greatest element at top.
|
||||
for i := (hi - 1) / 2; i >= 0; i-- {
|
||||
siftDown(data, i, hi, first)
|
||||
}
|
||||
|
||||
// Pop elements, largest first, into end of data.
|
||||
for i := hi - 1; i >= 0; i-- {
|
||||
data[first], data[first+i] = data[first+i], data[first]
|
||||
siftDown(data, lo, i, first)
|
||||
}
|
||||
}
|
||||
|
||||
// siftDown implements the heap property on data[lo, hi).
|
||||
// first is an offset into the array where the root of the heap lies.
|
||||
func siftDown(data []literalNode, lo, hi, first int) {
|
||||
root := lo
|
||||
for {
|
||||
child := 2*root + 1
|
||||
if child >= hi {
|
||||
break
|
||||
}
|
||||
if child+1 < hi && data[first+child].literal < data[first+child+1].literal {
|
||||
child++
|
||||
}
|
||||
if data[first+root].literal > data[first+child].literal {
|
||||
return
|
||||
}
|
||||
data[first+root], data[first+child] = data[first+child], data[first+root]
|
||||
root = child
|
||||
}
|
||||
}
|
||||
func doPivot(data []literalNode, lo, hi int) (midlo, midhi int) {
|
||||
m := int(uint(lo+hi) >> 1) // Written like this to avoid integer overflow.
|
||||
if hi-lo > 40 {
|
||||
// Tukey's ``Ninther,'' median of three medians of three.
|
||||
s := (hi - lo) / 8
|
||||
medianOfThree(data, lo, lo+s, lo+2*s)
|
||||
medianOfThree(data, m, m-s, m+s)
|
||||
medianOfThree(data, hi-1, hi-1-s, hi-1-2*s)
|
||||
}
|
||||
medianOfThree(data, lo, m, hi-1)
|
||||
|
||||
// Invariants are:
|
||||
// data[lo] = pivot (set up by ChoosePivot)
|
||||
// data[lo < i < a] < pivot
|
||||
// data[a <= i < b] <= pivot
|
||||
// data[b <= i < c] unexamined
|
||||
// data[c <= i < hi-1] > pivot
|
||||
// data[hi-1] >= pivot
|
||||
pivot := lo
|
||||
a, c := lo+1, hi-1
|
||||
|
||||
for ; a < c && data[a].literal < data[pivot].literal; a++ {
|
||||
}
|
||||
b := a
|
||||
for {
|
||||
for ; b < c && data[pivot].literal > data[b].literal; b++ { // data[b] <= pivot
|
||||
}
|
||||
for ; b < c && data[pivot].literal < data[c-1].literal; c-- { // data[c-1] > pivot
|
||||
}
|
||||
if b >= c {
|
||||
break
|
||||
}
|
||||
// data[b] > pivot; data[c-1] <= pivot
|
||||
data[b], data[c-1] = data[c-1], data[b]
|
||||
b++
|
||||
c--
|
||||
}
|
||||
// If hi-c<3 then there are duplicates (by property of median of nine).
|
||||
// Let's be a bit more conservative, and set border to 5.
|
||||
protect := hi-c < 5
|
||||
if !protect && hi-c < (hi-lo)/4 {
|
||||
// Lets test some points for equality to pivot
|
||||
dups := 0
|
||||
if data[pivot].literal > data[hi-1].literal { // data[hi-1] = pivot
|
||||
data[c], data[hi-1] = data[hi-1], data[c]
|
||||
c++
|
||||
dups++
|
||||
}
|
||||
if data[b-1].literal > data[pivot].literal { // data[b-1] = pivot
|
||||
b--
|
||||
dups++
|
||||
}
|
||||
// m-lo = (hi-lo)/2 > 6
|
||||
// b-lo > (hi-lo)*3/4-1 > 8
|
||||
// ==> m < b ==> data[m] <= pivot
|
||||
if data[m].literal > data[pivot].literal { // data[m] = pivot
|
||||
data[m], data[b-1] = data[b-1], data[m]
|
||||
b--
|
||||
dups++
|
||||
}
|
||||
// if at least 2 points are equal to pivot, assume skewed distribution
|
||||
protect = dups > 1
|
||||
}
|
||||
if protect {
|
||||
// Protect against a lot of duplicates
|
||||
// Add invariant:
|
||||
// data[a <= i < b] unexamined
|
||||
// data[b <= i < c] = pivot
|
||||
for {
|
||||
for ; a < b && data[b-1].literal > data[pivot].literal; b-- { // data[b] == pivot
|
||||
}
|
||||
for ; a < b && data[a].literal < data[pivot].literal; a++ { // data[a] < pivot
|
||||
}
|
||||
if a >= b {
|
||||
break
|
||||
}
|
||||
// data[a] == pivot; data[b-1] < pivot
|
||||
data[a], data[b-1] = data[b-1], data[a]
|
||||
a++
|
||||
b--
|
||||
}
|
||||
}
|
||||
// Swap pivot into middle
|
||||
data[pivot], data[b-1] = data[b-1], data[pivot]
|
||||
return b - 1, c
|
||||
}
|
||||
|
||||
// Insertion sort
|
||||
func insertionSort(data []literalNode, a, b int) {
|
||||
for i := a + 1; i < b; i++ {
|
||||
for j := i; j > a && data[j].literal < data[j-1].literal; j-- {
|
||||
data[j], data[j-1] = data[j-1], data[j]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// maxDepth returns a threshold at which quicksort should switch
|
||||
// to heapsort. It returns 2*ceil(lg(n+1)).
|
||||
func maxDepth(n int) int {
|
||||
var depth int
|
||||
for i := n; i > 0; i >>= 1 {
|
||||
depth++
|
||||
}
|
||||
return depth * 2
|
||||
}
|
||||
|
||||
// medianOfThree moves the median of the three values data[m0], data[m1], data[m2] into data[m1].
|
||||
func medianOfThree(data []literalNode, m1, m0, m2 int) {
|
||||
// sort 3 elements
|
||||
if data[m1].literal < data[m0].literal {
|
||||
data[m1], data[m0] = data[m0], data[m1]
|
||||
}
|
||||
// data[m0] <= data[m1]
|
||||
if data[m2].literal < data[m1].literal {
|
||||
data[m2], data[m1] = data[m1], data[m2]
|
||||
// data[m0] <= data[m2] && data[m1] < data[m2]
|
||||
if data[m1].literal < data[m0].literal {
|
||||
data[m1], data[m0] = data[m0], data[m1]
|
||||
}
|
||||
}
|
||||
// now data[m0] <= data[m1] <= data[m2]
|
||||
}
|
42
vendor/github.com/klauspost/compress/flate/token.go
generated
vendored
42
vendor/github.com/klauspost/compress/flate/token.go
generated
vendored
|
@ -184,9 +184,7 @@ func (t *tokens) indexTokens(in []token) {
|
|||
t.Reset()
|
||||
for _, tok := range in {
|
||||
if tok < matchType {
|
||||
t.tokens[t.n] = tok
|
||||
t.litHist[tok]++
|
||||
t.n++
|
||||
t.AddLiteral(tok.literal())
|
||||
continue
|
||||
}
|
||||
t.AddMatch(uint32(tok.length()), tok.offset())
|
||||
|
@ -211,43 +209,53 @@ func (t *tokens) AddLiteral(lit byte) {
|
|||
t.nLits++
|
||||
}
|
||||
|
||||
// from https://stackoverflow.com/a/28730362
|
||||
func mFastLog2(val float32) float32 {
|
||||
ux := int32(math.Float32bits(val))
|
||||
log2 := (float32)(((ux >> 23) & 255) - 128)
|
||||
ux &= -0x7f800001
|
||||
ux += 127 << 23
|
||||
uval := math.Float32frombits(uint32(ux))
|
||||
log2 += ((-0.34484843)*uval+2.02466578)*uval - 0.67487759
|
||||
return log2
|
||||
}
|
||||
|
||||
// EstimatedBits will return an minimum size estimated by an *optimal*
|
||||
// compression of the block.
|
||||
// The size of the block
|
||||
func (t *tokens) EstimatedBits() int {
|
||||
shannon := float64(0)
|
||||
shannon := float32(0)
|
||||
bits := int(0)
|
||||
nMatches := 0
|
||||
if t.nLits > 0 {
|
||||
invTotal := 1.0 / float64(t.nLits)
|
||||
invTotal := 1.0 / float32(t.nLits)
|
||||
for _, v := range t.litHist[:] {
|
||||
if v > 0 {
|
||||
n := float64(v)
|
||||
shannon += math.Ceil(-math.Log2(n*invTotal) * n)
|
||||
n := float32(v)
|
||||
shannon += -mFastLog2(n*invTotal) * n
|
||||
}
|
||||
}
|
||||
// Just add 15 for EOB
|
||||
shannon += 15
|
||||
for _, v := range t.extraHist[1 : literalCount-256] {
|
||||
for i, v := range t.extraHist[1 : literalCount-256] {
|
||||
if v > 0 {
|
||||
n := float64(v)
|
||||
shannon += math.Ceil(-math.Log2(n*invTotal) * n)
|
||||
bits += int(lengthExtraBits[v&31]) * int(v)
|
||||
n := float32(v)
|
||||
shannon += -mFastLog2(n*invTotal) * n
|
||||
bits += int(lengthExtraBits[i&31]) * int(v)
|
||||
nMatches += int(v)
|
||||
}
|
||||
}
|
||||
}
|
||||
if nMatches > 0 {
|
||||
invTotal := 1.0 / float64(nMatches)
|
||||
for _, v := range t.offHist[:offsetCodeCount] {
|
||||
invTotal := 1.0 / float32(nMatches)
|
||||
for i, v := range t.offHist[:offsetCodeCount] {
|
||||
if v > 0 {
|
||||
n := float64(v)
|
||||
shannon += math.Ceil(-math.Log2(n*invTotal) * n)
|
||||
bits += int(offsetExtraBits[v&31]) * int(n)
|
||||
n := float32(v)
|
||||
shannon += -mFastLog2(n*invTotal) * n
|
||||
bits += int(offsetExtraBits[i&31]) * int(v)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return int(shannon) + bits
|
||||
}
|
||||
|
||||
|
|
2
vendor/modules.txt
vendored
2
vendor/modules.txt
vendored
|
@ -83,7 +83,7 @@ github.com/jmespath/go-jmespath
|
|||
github.com/jstemmer/go-junit-report
|
||||
github.com/jstemmer/go-junit-report/formatter
|
||||
github.com/jstemmer/go-junit-report/parser
|
||||
# github.com/klauspost/compress v1.9.7
|
||||
# github.com/klauspost/compress v1.9.8
|
||||
github.com/klauspost/compress/flate
|
||||
github.com/klauspost/compress/fse
|
||||
github.com/klauspost/compress/gzip
|
||||
|
|
Loading…
Reference in a new issue