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gitea/vendor/github.com/andybalholm/brotli/cluster_distance.go

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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
*/
/* Computes the bit cost reduction by combining out[idx1] and out[idx2] and if
it is below a threshold, stores the pair (idx1, idx2) in the *pairs queue. */
func compareAndPushToQueueDistance(out []histogramDistance, cluster_size []uint32, idx1 uint32, idx2 uint32, max_num_pairs uint, pairs []histogramPair, num_pairs *uint) {
var is_good_pair bool = false
var p histogramPair
p.idx2 = 0
p.idx1 = p.idx2
p.cost_combo = 0
p.cost_diff = p.cost_combo
if idx1 == idx2 {
return
}
if idx2 < idx1 {
var t uint32 = idx2
idx2 = idx1
idx1 = t
}
p.idx1 = idx1
p.idx2 = idx2
p.cost_diff = 0.5 * clusterCostDiff(uint(cluster_size[idx1]), uint(cluster_size[idx2]))
p.cost_diff -= out[idx1].bit_cost_
p.cost_diff -= out[idx2].bit_cost_
if out[idx1].total_count_ == 0 {
p.cost_combo = out[idx2].bit_cost_
is_good_pair = true
} else if out[idx2].total_count_ == 0 {
p.cost_combo = out[idx1].bit_cost_
is_good_pair = true
} else {
var threshold float64
if *num_pairs == 0 {
threshold = 1e99
} else {
threshold = brotli_max_double(0.0, pairs[0].cost_diff)
}
var combo histogramDistance = out[idx1]
var cost_combo float64
histogramAddHistogramDistance(&combo, &out[idx2])
cost_combo = populationCostDistance(&combo)
if cost_combo < threshold-p.cost_diff {
p.cost_combo = cost_combo
is_good_pair = true
}
}
if is_good_pair {
p.cost_diff += p.cost_combo
if *num_pairs > 0 && histogramPairIsLess(&pairs[0], &p) {
/* Replace the top of the queue if needed. */
if *num_pairs < max_num_pairs {
pairs[*num_pairs] = pairs[0]
(*num_pairs)++
}
pairs[0] = p
} else if *num_pairs < max_num_pairs {
pairs[*num_pairs] = p
(*num_pairs)++
}
}
}
func histogramCombineDistance(out []histogramDistance, cluster_size []uint32, symbols []uint32, clusters []uint32, pairs []histogramPair, num_clusters uint, symbols_size uint, max_clusters uint, max_num_pairs uint) uint {
var cost_diff_threshold float64 = 0.0
var min_cluster_size uint = 1
var num_pairs uint = 0
{
/* We maintain a vector of histogram pairs, with the property that the pair
with the maximum bit cost reduction is the first. */
var idx1 uint
for idx1 = 0; idx1 < num_clusters; idx1++ {
var idx2 uint
for idx2 = idx1 + 1; idx2 < num_clusters; idx2++ {
compareAndPushToQueueDistance(out, cluster_size, clusters[idx1], clusters[idx2], max_num_pairs, pairs[0:], &num_pairs)
}
}
}
for num_clusters > min_cluster_size {
var best_idx1 uint32
var best_idx2 uint32
var i uint
if pairs[0].cost_diff >= cost_diff_threshold {
cost_diff_threshold = 1e99
min_cluster_size = max_clusters
continue
}
/* Take the best pair from the top of heap. */
best_idx1 = pairs[0].idx1
best_idx2 = pairs[0].idx2
histogramAddHistogramDistance(&out[best_idx1], &out[best_idx2])
out[best_idx1].bit_cost_ = pairs[0].cost_combo
cluster_size[best_idx1] += cluster_size[best_idx2]
for i = 0; i < symbols_size; i++ {
if symbols[i] == best_idx2 {
symbols[i] = best_idx1
}
}
for i = 0; i < num_clusters; i++ {
if clusters[i] == best_idx2 {
copy(clusters[i:], clusters[i+1:][:num_clusters-i-1])
break
}
}
num_clusters--
{
/* Remove pairs intersecting the just combined best pair. */
var copy_to_idx uint = 0
for i = 0; i < num_pairs; i++ {
var p *histogramPair = &pairs[i]
if p.idx1 == best_idx1 || p.idx2 == best_idx1 || p.idx1 == best_idx2 || p.idx2 == best_idx2 {
/* Remove invalid pair from the queue. */
continue
}
if histogramPairIsLess(&pairs[0], p) {
/* Replace the top of the queue if needed. */
var front histogramPair = pairs[0]
pairs[0] = *p
pairs[copy_to_idx] = front
} else {
pairs[copy_to_idx] = *p
}
copy_to_idx++
}
num_pairs = copy_to_idx
}
/* Push new pairs formed with the combined histogram to the heap. */
for i = 0; i < num_clusters; i++ {
compareAndPushToQueueDistance(out, cluster_size, best_idx1, clusters[i], max_num_pairs, pairs[0:], &num_pairs)
}
}
return num_clusters
}
/* What is the bit cost of moving histogram from cur_symbol to candidate. */
func histogramBitCostDistanceDistance(histogram *histogramDistance, candidate *histogramDistance) float64 {
if histogram.total_count_ == 0 {
return 0.0
} else {
var tmp histogramDistance = *histogram
histogramAddHistogramDistance(&tmp, candidate)
return populationCostDistance(&tmp) - candidate.bit_cost_
}
}
/* Find the best 'out' histogram for each of the 'in' histograms.
When called, clusters[0..num_clusters) contains the unique values from
symbols[0..in_size), but this property is not preserved in this function.
Note: we assume that out[]->bit_cost_ is already up-to-date. */
func histogramRemapDistance(in []histogramDistance, in_size uint, clusters []uint32, num_clusters uint, out []histogramDistance, symbols []uint32) {
var i uint
for i = 0; i < in_size; i++ {
var best_out uint32
if i == 0 {
best_out = symbols[0]
} else {
best_out = symbols[i-1]
}
var best_bits float64 = histogramBitCostDistanceDistance(&in[i], &out[best_out])
var j uint
for j = 0; j < num_clusters; j++ {
var cur_bits float64 = histogramBitCostDistanceDistance(&in[i], &out[clusters[j]])
if cur_bits < best_bits {
best_bits = cur_bits
best_out = clusters[j]
}
}
symbols[i] = best_out
}
/* Recompute each out based on raw and symbols. */
for i = 0; i < num_clusters; i++ {
histogramClearDistance(&out[clusters[i]])
}
for i = 0; i < in_size; i++ {
histogramAddHistogramDistance(&out[symbols[i]], &in[i])
}
}
/* Reorders elements of the out[0..length) array and changes values in
symbols[0..length) array in the following way:
* when called, symbols[] contains indexes into out[], and has N unique
values (possibly N < length)
* on return, symbols'[i] = f(symbols[i]) and
out'[symbols'[i]] = out[symbols[i]], for each 0 <= i < length,
where f is a bijection between the range of symbols[] and [0..N), and
the first occurrences of values in symbols'[i] come in consecutive
increasing order.
Returns N, the number of unique values in symbols[]. */
var histogramReindexDistance_kInvalidIndex uint32 = math.MaxUint32
func histogramReindexDistance(out []histogramDistance, symbols []uint32, length uint) uint {
var new_index []uint32 = make([]uint32, length)
var next_index uint32
var tmp []histogramDistance
var i uint
for i = 0; i < length; i++ {
new_index[i] = histogramReindexDistance_kInvalidIndex
}
next_index = 0
for i = 0; i < length; i++ {
if new_index[symbols[i]] == histogramReindexDistance_kInvalidIndex {
new_index[symbols[i]] = next_index
next_index++
}
}
/* TODO: by using idea of "cycle-sort" we can avoid allocation of
tmp and reduce the number of copying by the factor of 2. */
tmp = make([]histogramDistance, next_index)
next_index = 0
for i = 0; i < length; i++ {
if new_index[symbols[i]] == next_index {
tmp[next_index] = out[symbols[i]]
next_index++
}
symbols[i] = new_index[symbols[i]]
}
new_index = nil
for i = 0; uint32(i) < next_index; i++ {
out[i] = tmp[i]
}
tmp = nil
return uint(next_index)
}
func clusterHistogramsDistance(in []histogramDistance, in_size uint, max_histograms uint, out []histogramDistance, out_size *uint, histogram_symbols []uint32) {
var cluster_size []uint32 = make([]uint32, in_size)
var clusters []uint32 = make([]uint32, in_size)
var num_clusters uint = 0
var max_input_histograms uint = 64
var pairs_capacity uint = max_input_histograms * max_input_histograms / 2
var pairs []histogramPair = make([]histogramPair, (pairs_capacity + 1))
var i uint
/* For the first pass of clustering, we allow all pairs. */
for i = 0; i < in_size; i++ {
cluster_size[i] = 1
}
for i = 0; i < in_size; i++ {
out[i] = in[i]
out[i].bit_cost_ = populationCostDistance(&in[i])
histogram_symbols[i] = uint32(i)
}
for i = 0; i < in_size; i += max_input_histograms {
var num_to_combine uint = brotli_min_size_t(in_size-i, max_input_histograms)
var num_new_clusters uint
var j uint
for j = 0; j < num_to_combine; j++ {
clusters[num_clusters+j] = uint32(i + j)
}
num_new_clusters = histogramCombineDistance(out, cluster_size, histogram_symbols[i:], clusters[num_clusters:], pairs, num_to_combine, num_to_combine, max_histograms, pairs_capacity)
num_clusters += num_new_clusters
}
{
/* For the second pass, we limit the total number of histogram pairs.
After this limit is reached, we only keep searching for the best pair. */
var max_num_pairs uint = brotli_min_size_t(64*num_clusters, (num_clusters/2)*num_clusters)
if pairs_capacity < (max_num_pairs + 1) {
var _new_size uint
if pairs_capacity == 0 {
_new_size = max_num_pairs + 1
} else {
_new_size = pairs_capacity
}
var new_array []histogramPair
for _new_size < (max_num_pairs + 1) {
_new_size *= 2
}
new_array = make([]histogramPair, _new_size)
if pairs_capacity != 0 {
copy(new_array, pairs[:pairs_capacity])
}
pairs = new_array
pairs_capacity = _new_size
}
/* Collapse similar histograms. */
num_clusters = histogramCombineDistance(out, cluster_size, histogram_symbols, clusters, pairs, num_clusters, in_size, max_histograms, max_num_pairs)
}
pairs = nil
cluster_size = nil
/* Find the optimal map from original histograms to the final ones. */
histogramRemapDistance(in, in_size, clusters, num_clusters, out, histogram_symbols)
clusters = nil
/* Convert the context map to a canonical form. */
*out_size = histogramReindexDistance(out, histogram_symbols, in_size)
}