current/deps/brotli/c/enc/cluster_inc.h
/* NOLINT(build/header_guard) */
/* Copyright 2013 Google Inc. All Rights Reserved.
Distributed under MIT license.
See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
*/
/* template parameters: FN, CODE */
#define HistogramType FN(Histogram)
/* 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. */
BROTLI_INTERNAL void FN(BrotliCompareAndPushToQueue)(
const HistogramType* out, const uint32_t* cluster_size, uint32_t idx1,
uint32_t idx2, size_t max_num_pairs, HistogramPair* pairs,
size_t* num_pairs) CODE({
BROTLI_BOOL is_good_pair = BROTLI_FALSE;
HistogramPair p;
p.idx1 = p.idx2 = 0;
p.cost_diff = p.cost_combo = 0;
if (idx1 == idx2) {
return;
}
if (idx2 < idx1) {
uint32_t t = idx2;
idx2 = idx1;
idx1 = t;
}
p.idx1 = idx1;
p.idx2 = idx2;
p.cost_diff = 0.5 * ClusterCostDiff(cluster_size[idx1], 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 = BROTLI_TRUE;
} else if (out[idx2].total_count_ == 0) {
p.cost_combo = out[idx1].bit_cost_;
is_good_pair = BROTLI_TRUE;
} else {
double threshold = *num_pairs == 0 ? 1e99 :
BROTLI_MAX(double, 0.0, pairs[0].cost_diff);
HistogramType combo = out[idx1];
double cost_combo;
FN(HistogramAddHistogram)(&combo, &out[idx2]);
cost_combo = FN(BrotliPopulationCost)(&combo);
if (cost_combo < threshold - p.cost_diff) {
p.cost_combo = cost_combo;
is_good_pair = BROTLI_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);
}
}
})
BROTLI_INTERNAL size_t FN(BrotliHistogramCombine)(HistogramType* out,
uint32_t* cluster_size,
uint32_t* symbols,
uint32_t* clusters,
HistogramPair* pairs,
size_t num_clusters,
size_t symbols_size,
size_t max_clusters,
size_t max_num_pairs) CODE({
double cost_diff_threshold = 0.0;
size_t min_cluster_size = 1;
size_t num_pairs = 0;
{
/* We maintain a vector of histogram pairs, with the property that the pair
with the maximum bit cost reduction is the first. */
size_t idx1;
for (idx1 = 0; idx1 < num_clusters; ++idx1) {
size_t idx2;
for (idx2 = idx1 + 1; idx2 < num_clusters; ++idx2) {
FN(BrotliCompareAndPushToQueue)(out, cluster_size, clusters[idx1],
clusters[idx2], max_num_pairs, &pairs[0], &num_pairs);
}
}
}
while (num_clusters > min_cluster_size) {
uint32_t best_idx1;
uint32_t best_idx2;
size_t i;
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;
FN(HistogramAddHistogram)(&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) {
memmove(&clusters[i], &clusters[i + 1],
(num_clusters - i - 1) * sizeof(clusters[0]));
break;
}
}
--num_clusters;
{
/* Remove pairs intersecting the just combined best pair. */
size_t copy_to_idx = 0;
for (i = 0; i < num_pairs; ++i) {
HistogramPair* p = &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. */
HistogramPair front = 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) {
FN(BrotliCompareAndPushToQueue)(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. */
BROTLI_INTERNAL double FN(BrotliHistogramBitCostDistance)(
const HistogramType* histogram, const HistogramType* candidate) CODE({
if (histogram->total_count_ == 0) {
return 0.0;
} else {
HistogramType tmp = *histogram;
FN(HistogramAddHistogram)(&tmp, candidate);
return FN(BrotliPopulationCost)(&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. */
BROTLI_INTERNAL void FN(BrotliHistogramRemap)(const HistogramType* in,
size_t in_size, const uint32_t* clusters, size_t num_clusters,
HistogramType* out, uint32_t* symbols) CODE({
size_t i;
for (i = 0; i < in_size; ++i) {
uint32_t best_out = i == 0 ? symbols[0] : symbols[i - 1];
double best_bits =
FN(BrotliHistogramBitCostDistance)(&in[i], &out[best_out]);
size_t j;
for (j = 0; j < num_clusters; ++j) {
const double cur_bits =
FN(BrotliHistogramBitCostDistance)(&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) {
FN(HistogramClear)(&out[clusters[i]]);
}
for (i = 0; i < in_size; ++i) {
FN(HistogramAddHistogram)(&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[]. */
BROTLI_INTERNAL size_t FN(BrotliHistogramReindex)(MemoryManager* m,
HistogramType* out, uint32_t* symbols, size_t length) CODE({
static const uint32_t kInvalidIndex = BROTLI_UINT32_MAX;
uint32_t* new_index = BROTLI_ALLOC(m, uint32_t, length);
uint32_t next_index;
HistogramType* tmp;
size_t i;
if (BROTLI_IS_OOM(m)) return 0;
for (i = 0; i < length; ++i) {
new_index[i] = kInvalidIndex;
}
next_index = 0;
for (i = 0; i < length; ++i) {
if (new_index[symbols[i]] == 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 = BROTLI_ALLOC(m, HistogramType, next_index);
if (BROTLI_IS_OOM(m)) return 0;
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]];
}
BROTLI_FREE(m, new_index);
for (i = 0; i < next_index; ++i) {
out[i] = tmp[i];
}
BROTLI_FREE(m, tmp);
return next_index;
})
BROTLI_INTERNAL void FN(BrotliClusterHistograms)(
MemoryManager* m, const HistogramType* in, const size_t in_size,
size_t max_histograms, HistogramType* out, size_t* out_size,
uint32_t* histogram_symbols) CODE({
uint32_t* cluster_size = BROTLI_ALLOC(m, uint32_t, in_size);
uint32_t* clusters = BROTLI_ALLOC(m, uint32_t, in_size);
size_t num_clusters = 0;
const size_t max_input_histograms = 64;
size_t pairs_capacity = max_input_histograms * max_input_histograms / 2;
/* For the first pass of clustering, we allow all pairs. */
HistogramPair* pairs = BROTLI_ALLOC(m, HistogramPair, pairs_capacity + 1);
size_t i;
if (BROTLI_IS_OOM(m)) return;
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_ = FN(BrotliPopulationCost)(&in[i]);
histogram_symbols[i] = (uint32_t)i;
}
for (i = 0; i < in_size; i += max_input_histograms) {
size_t num_to_combine =
BROTLI_MIN(size_t, in_size - i, max_input_histograms);
size_t num_new_clusters;
size_t j;
for (j = 0; j < num_to_combine; ++j) {
clusters[num_clusters + j] = (uint32_t)(i + j);
}
num_new_clusters =
FN(BrotliHistogramCombine)(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. */
size_t max_num_pairs = BROTLI_MIN(size_t,
64 * num_clusters, (num_clusters / 2) * num_clusters);
BROTLI_ENSURE_CAPACITY(
m, HistogramPair, pairs, pairs_capacity, max_num_pairs + 1);
if (BROTLI_IS_OOM(m)) return;
/* Collapse similar histograms. */
num_clusters = FN(BrotliHistogramCombine)(out, cluster_size,
histogram_symbols, clusters,
pairs, num_clusters, in_size,
max_histograms, max_num_pairs);
}
BROTLI_FREE(m, pairs);
BROTLI_FREE(m, cluster_size);
/* Find the optimal map from original histograms to the final ones. */
FN(BrotliHistogramRemap)(in, in_size, clusters, num_clusters,
out, histogram_symbols);
BROTLI_FREE(m, clusters);
/* Convert the context map to a canonical form. */
*out_size = FN(BrotliHistogramReindex)(m, out, histogram_symbols, in_size);
if (BROTLI_IS_OOM(m)) return;
})
#undef HistogramType