source: code/trunk/vendor/github.com/andybalholm/brotli/block_splitter_distance.go@ 145

package brotli

import "math"

/* Copyright 2013 Google Inc. All Rights Reserved.

   Distributed under MIT license.
   See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
*/

func initialEntropyCodesDistance(data []uint16, length uint, stride uint, num_histograms uint, histograms []histogramDistance) {
        var seed uint32 = 7
        var block_length uint = length / num_histograms
        var i uint
        clearHistogramsDistance(histograms, num_histograms)
        for i = 0; i < num_histograms; i++ {
                var pos uint = length * i / num_histograms
                if i != 0 {
                        pos += uint(myRand(&seed) % uint32(block_length))
                }

                if pos+stride >= length {
                        pos = length - stride - 1
                }

                histogramAddVectorDistance(&histograms[i], data[pos:], stride)
        }
}

func randomSampleDistance(seed *uint32, data []uint16, length uint, stride uint, sample *histogramDistance) {
        var pos uint = 0
        if stride >= length {
                stride = length
        } else {
                pos = uint(myRand(seed) % uint32(length-stride+1))
        }

        histogramAddVectorDistance(sample, data[pos:], stride)
}

func refineEntropyCodesDistance(data []uint16, length uint, stride uint, num_histograms uint, histograms []histogramDistance) {
        var iters uint = kIterMulForRefining*length/stride + kMinItersForRefining
        var seed uint32 = 7
        var iter uint
        iters = ((iters + num_histograms - 1) / num_histograms) * num_histograms
        for iter = 0; iter < iters; iter++ {
                var sample histogramDistance
                histogramClearDistance(&sample)
                randomSampleDistance(&seed, data, length, stride, &sample)
                histogramAddHistogramDistance(&histograms[iter%num_histograms], &sample)
        }
}

/* Assigns a block id from the range [0, num_histograms) to each data element
   in data[0..length) and fills in block_id[0..length) with the assigned values.
   Returns the number of blocks, i.e. one plus the number of block switches. */
func findBlocksDistance(data []uint16, length uint, block_switch_bitcost float64, num_histograms uint, histograms []histogramDistance, insert_cost []float64, cost []float64, switch_signal []byte, block_id []byte) uint {
        var data_size uint = histogramDataSizeDistance()
        var bitmaplen uint = (num_histograms + 7) >> 3
        var num_blocks uint = 1
        var i uint
        var j uint
        assert(num_histograms <= 256)
        if num_histograms <= 1 {
                for i = 0; i < length; i++ {
                        block_id[i] = 0
                }

                return 1
        }

        for i := 0; i < int(data_size*num_histograms); i++ {
                insert_cost[i] = 0
        }
        for i = 0; i < num_histograms; i++ {
                insert_cost[i] = fastLog2(uint(uint32(histograms[i].total_count_)))
        }

        for i = data_size; i != 0; {
                i--
                for j = 0; j < num_histograms; j++ {
                        insert_cost[i*num_histograms+j] = insert_cost[j] - bitCost(uint(histograms[j].data_[i]))
                }
        }

        for i := 0; i < int(num_histograms); i++ {
                cost[i] = 0
        }
        for i := 0; i < int(length*bitmaplen); i++ {
                switch_signal[i] = 0
        }

        /* After each iteration of this loop, cost[k] will contain the difference
           between the minimum cost of arriving at the current byte position using
           entropy code k, and the minimum cost of arriving at the current byte
           position. This difference is capped at the block switch cost, and if it
           reaches block switch cost, it means that when we trace back from the last
           position, we need to switch here. */
        for i = 0; i < length; i++ {
                var byte_ix uint = i
                var ix uint = byte_ix * bitmaplen
                var insert_cost_ix uint = uint(data[byte_ix]) * num_histograms
                var min_cost float64 = 1e99
                var block_switch_cost float64 = block_switch_bitcost
                var k uint
                for k = 0; k < num_histograms; k++ {
                        /* We are coding the symbol in data[byte_ix] with entropy code k. */
                        cost[k] += insert_cost[insert_cost_ix+k]

                        if cost[k] < min_cost {
                                min_cost = cost[k]
                                block_id[byte_ix] = byte(k)
                        }
                }

                /* More blocks for the beginning. */
                if byte_ix < 2000 {
                        block_switch_cost *= 0.77 + 0.07*float64(byte_ix)/2000
                }

                for k = 0; k < num_histograms; k++ {
                        cost[k] -= min_cost
                        if cost[k] >= block_switch_cost {
                                var mask byte = byte(1 << (k & 7))
                                cost[k] = block_switch_cost
                                assert(k>>3 < bitmaplen)
                                switch_signal[ix+(k>>3)] |= mask
                                /* Trace back from the last position and switch at the marked places. */
                        }
                }
        }
        {
                var byte_ix uint = length - 1
                var ix uint = byte_ix * bitmaplen
                var cur_id byte = block_id[byte_ix]
                for byte_ix > 0 {
                        var mask byte = byte(1 << (cur_id & 7))
                        assert(uint(cur_id)>>3 < bitmaplen)
                        byte_ix--
                        ix -= bitmaplen
                        if switch_signal[ix+uint(cur_id>>3)]&mask != 0 {
                                if cur_id != block_id[byte_ix] {
                                        cur_id = block_id[byte_ix]
                                        num_blocks++
                                }
                        }

                        block_id[byte_ix] = cur_id
                }
        }

        return num_blocks
}

var remapBlockIdsDistance_kInvalidId uint16 = 256

func remapBlockIdsDistance(block_ids []byte, length uint, new_id []uint16, num_histograms uint) uint {
        var next_id uint16 = 0
        var i uint
        for i = 0; i < num_histograms; i++ {
                new_id[i] = remapBlockIdsDistance_kInvalidId
        }

        for i = 0; i < length; i++ {
                assert(uint(block_ids[i]) < num_histograms)
                if new_id[block_ids[i]] == remapBlockIdsDistance_kInvalidId {
                        new_id[block_ids[i]] = next_id
                        next_id++
                }
        }

        for i = 0; i < length; i++ {
                block_ids[i] = byte(new_id[block_ids[i]])
                assert(uint(block_ids[i]) < num_histograms)
        }

        assert(uint(next_id) <= num_histograms)
        return uint(next_id)
}

func buildBlockHistogramsDistance(data []uint16, length uint, block_ids []byte, num_histograms uint, histograms []histogramDistance) {
        var i uint
        clearHistogramsDistance(histograms, num_histograms)
        for i = 0; i < length; i++ {
                histogramAddDistance(&histograms[block_ids[i]], uint(data[i]))
        }
}

var clusterBlocksDistance_kInvalidIndex uint32 = math.MaxUint32

func clusterBlocksDistance(data []uint16, length uint, num_blocks uint, block_ids []byte, split *blockSplit) {
        var histogram_symbols []uint32 = make([]uint32, num_blocks)
        var block_lengths []uint32 = make([]uint32, num_blocks)
        var expected_num_clusters uint = clustersPerBatch * (num_blocks + histogramsPerBatch - 1) / histogramsPerBatch
        var all_histograms_size uint = 0
        var all_histograms_capacity uint = expected_num_clusters
        var all_histograms []histogramDistance = make([]histogramDistance, all_histograms_capacity)
        var cluster_size_size uint = 0
        var cluster_size_capacity uint = expected_num_clusters
        var cluster_size []uint32 = make([]uint32, cluster_size_capacity)
        var num_clusters uint = 0
        var histograms []histogramDistance = make([]histogramDistance, brotli_min_size_t(num_blocks, histogramsPerBatch))
        var max_num_pairs uint = histogramsPerBatch * histogramsPerBatch / 2
        var pairs_capacity uint = max_num_pairs + 1
        var pairs []histogramPair = make([]histogramPair, pairs_capacity)
        var pos uint = 0
        var clusters []uint32
        var num_final_clusters uint
        var new_index []uint32
        var i uint
        var sizes = [histogramsPerBatch]uint32{0}
        var new_clusters = [histogramsPerBatch]uint32{0}
        var symbols = [histogramsPerBatch]uint32{0}
        var remap = [histogramsPerBatch]uint32{0}

        for i := 0; i < int(num_blocks); i++ {
                block_lengths[i] = 0
        }
        {
                var block_idx uint = 0
                for i = 0; i < length; i++ {
                        assert(block_idx < num_blocks)
                        block_lengths[block_idx]++
                        if i+1 == length || block_ids[i] != block_ids[i+1] {
                                block_idx++
                        }
                }

                assert(block_idx == num_blocks)
        }

        for i = 0; i < num_blocks; i += histogramsPerBatch {
                var num_to_combine uint = brotli_min_size_t(num_blocks-i, histogramsPerBatch)
                var num_new_clusters uint
                var j uint
                for j = 0; j < num_to_combine; j++ {
                        var k uint
                        histogramClearDistance(&histograms[j])
                        for k = 0; uint32(k) < block_lengths[i+j]; k++ {
                                histogramAddDistance(&histograms[j], uint(data[pos]))
                                pos++
                        }

                        histograms[j].bit_cost_ = populationCostDistance(&histograms[j])
                        new_clusters[j] = uint32(j)
                        symbols[j] = uint32(j)
                        sizes[j] = 1
                }

                num_new_clusters = histogramCombineDistance(histograms, sizes[:], symbols[:], new_clusters[:], []histogramPair(pairs), num_to_combine, num_to_combine, histogramsPerBatch, max_num_pairs)
                if all_histograms_capacity < (all_histograms_size + num_new_clusters) {
                        var _new_size uint
                        if all_histograms_capacity == 0 {
                                _new_size = all_histograms_size + num_new_clusters
                        } else {
                                _new_size = all_histograms_capacity
                        }
                        var new_array []histogramDistance
                        for _new_size < (all_histograms_size + num_new_clusters) {
                                _new_size *= 2
                        }
                        new_array = make([]histogramDistance, _new_size)
                        if all_histograms_capacity != 0 {
                                copy(new_array, all_histograms[:all_histograms_capacity])
                        }

                        all_histograms = new_array
                        all_histograms_capacity = _new_size
                }

                brotli_ensure_capacity_uint32_t(&cluster_size, &cluster_size_capacity, cluster_size_size+num_new_clusters)
                for j = 0; j < num_new_clusters; j++ {
                        all_histograms[all_histograms_size] = histograms[new_clusters[j]]
                        all_histograms_size++
                        cluster_size[cluster_size_size] = sizes[new_clusters[j]]
                        cluster_size_size++
                        remap[new_clusters[j]] = uint32(j)
                }

                for j = 0; j < num_to_combine; j++ {
                        histogram_symbols[i+j] = uint32(num_clusters) + remap[symbols[j]]
                }

                num_clusters += num_new_clusters
                assert(num_clusters == cluster_size_size)
                assert(num_clusters == all_histograms_size)
        }

        histograms = nil

        max_num_pairs = brotli_min_size_t(64*num_clusters, (num_clusters/2)*num_clusters)
        if pairs_capacity < max_num_pairs+1 {
                pairs = nil
                pairs = make([]histogramPair, (max_num_pairs + 1))
        }

        clusters = make([]uint32, num_clusters)
        for i = 0; i < num_clusters; i++ {
                clusters[i] = uint32(i)
        }

        num_final_clusters = histogramCombineDistance(all_histograms, cluster_size, histogram_symbols, clusters, pairs, num_clusters, num_blocks, maxNumberOfBlockTypes, max_num_pairs)
        pairs = nil
        cluster_size = nil

        new_index = make([]uint32, num_clusters)
        for i = 0; i < num_clusters; i++ {
                new_index[i] = clusterBlocksDistance_kInvalidIndex
        }
        pos = 0
        {
                var next_index uint32 = 0
                for i = 0; i < num_blocks; i++ {
                        var histo histogramDistance
                        var j uint
                        var best_out uint32
                        var best_bits float64
                        histogramClearDistance(&histo)
                        for j = 0; uint32(j) < block_lengths[i]; j++ {
                                histogramAddDistance(&histo, uint(data[pos]))
                                pos++
                        }

                        if i == 0 {
                                best_out = histogram_symbols[0]
                        } else {
                                best_out = histogram_symbols[i-1]
                        }
                        best_bits = histogramBitCostDistanceDistance(&histo, &all_histograms[best_out])
                        for j = 0; j < num_final_clusters; j++ {
                                var cur_bits float64 = histogramBitCostDistanceDistance(&histo, &all_histograms[clusters[j]])
                                if cur_bits < best_bits {
                                        best_bits = cur_bits
                                        best_out = clusters[j]
                                }
                        }

                        histogram_symbols[i] = best_out
                        if new_index[best_out] == clusterBlocksDistance_kInvalidIndex {
                                new_index[best_out] = next_index
                                next_index++
                        }
                }
        }

        clusters = nil
        all_histograms = nil
        brotli_ensure_capacity_uint8_t(&split.types, &split.types_alloc_size, num_blocks)
        brotli_ensure_capacity_uint32_t(&split.lengths, &split.lengths_alloc_size, num_blocks)
        {
                var cur_length uint32 = 0
                var block_idx uint = 0
                var max_type byte = 0
                for i = 0; i < num_blocks; i++ {
                        cur_length += block_lengths[i]
                        if i+1 == num_blocks || histogram_symbols[i] != histogram_symbols[i+1] {
                                var id byte = byte(new_index[histogram_symbols[i]])
                                split.types[block_idx] = id
                                split.lengths[block_idx] = cur_length
                                max_type = brotli_max_uint8_t(max_type, id)
                                cur_length = 0
                                block_idx++
                        }
                }

                split.num_blocks = block_idx
                split.num_types = uint(max_type) + 1
        }

        new_index = nil
        block_lengths = nil
        histogram_symbols = nil
}

func splitByteVectorDistance(data []uint16, length uint, literals_per_histogram uint, max_histograms uint, sampling_stride_length uint, block_switch_cost float64, params *encoderParams, split *blockSplit) {
        var data_size uint = histogramDataSizeDistance()
        var num_histograms uint = length/literals_per_histogram + 1
        var histograms []histogramDistance
        if num_histograms > max_histograms {
                num_histograms = max_histograms
        }

        if length == 0 {
                split.num_types = 1
                return
        } else if length < kMinLengthForBlockSplitting {
                brotli_ensure_capacity_uint8_t(&split.types, &split.types_alloc_size, split.num_blocks+1)
                brotli_ensure_capacity_uint32_t(&split.lengths, &split.lengths_alloc_size, split.num_blocks+1)
                split.num_types = 1
                split.types[split.num_blocks] = 0
                split.lengths[split.num_blocks] = uint32(length)
                split.num_blocks++
                return
        }

        histograms = make([]histogramDistance, num_histograms)

        /* Find good entropy codes. */
        initialEntropyCodesDistance(data, length, sampling_stride_length, num_histograms, histograms)

        refineEntropyCodesDistance(data, length, sampling_stride_length, num_histograms, histograms)
        {
                var block_ids []byte = make([]byte, length)
                var num_blocks uint = 0
                var bitmaplen uint = (num_histograms + 7) >> 3
                var insert_cost []float64 = make([]float64, (data_size * num_histograms))
                var cost []float64 = make([]float64, num_histograms)
                var switch_signal []byte = make([]byte, (length * bitmaplen))
                var new_id []uint16 = make([]uint16, num_histograms)
                var iters uint
                if params.quality < hqZopflificationQuality {
                        iters = 3
                } else {
                        iters = 10
                }
                /* Find a good path through literals with the good entropy codes. */

                var i uint
                for i = 0; i < iters; i++ {
                        num_blocks = findBlocksDistance(data, length, block_switch_cost, num_histograms, histograms, insert_cost, cost, switch_signal, block_ids)
                        num_histograms = remapBlockIdsDistance(block_ids, length, new_id, num_histograms)
                        buildBlockHistogramsDistance(data, length, block_ids, num_histograms, histograms)
                }

                insert_cost = nil
                cost = nil
                switch_signal = nil
                new_id = nil
                histograms = nil
                clusterBlocksDistance(data, length, num_blocks, block_ids, split)
                block_ids = nil
        }
}