Update brotli to v1.0.0-snapshot.

third_party/brotli/enc/cluster.h

 /* Copyright 2013 Google Inc. All Rights Reserved.
 
    Distributed under MIT license.
    See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
 */
 
-// Functions for clustering similar histograms together.
+/* Functions for clustering similar histograms together. */
 
 #ifndef BROTLI_ENC_CLUSTER_H_
 #define BROTLI_ENC_CLUSTER_H_
 
-#include <math.h>
-#include <algorithm>
-#include <utility>
-#include <vector>
+#include <brotli/types.h>
+#include "./histogram.h"
+#include "./memory.h"
+#include "./port.h"
 
-#include "./bit_cost.h"
-#include "./entropy_encode.h"
-#include "./fast_log.h"
-#include "./histogram.h"
-#include "./port.h"
-#include "./types.h"
+#if defined(__cplusplus) || defined(c_plusplus)
+extern "C" {
+#endif
 
-namespace brotli {
-
-struct HistogramPair {
+typedef struct HistogramPair {
   uint32_t idx1;
   uint32_t idx2;
   double cost_combo;
   double cost_diff;
-};
+} HistogramPair;
 
-inline bool operator<(const HistogramPair& p1, const HistogramPair& p2) {
-  if (p1.cost_diff != p2.cost_diff) {
-    return p1.cost_diff > p2.cost_diff;
-  }
-  return (p1.idx2 - p1.idx1) > (p2.idx2 - p2.idx1);
-}
+#define CODE(X) /* Declaration */;
 
-// Returns entropy reduction of the context map when we combine two clusters.
-inline double ClusterCostDiff(size_t size_a, size_t size_b) {
-  size_t size_c = size_a + size_b;
-  return static_cast<double>(size_a) * FastLog2(size_a) +
-      static_cast<double>(size_b) * FastLog2(size_b) -
-      static_cast<double>(size_c) * FastLog2(size_c);
-}
+#define FN(X) X ## Literal
+#include "./cluster_inc.h"  /* NOLINT(build/include) */
+#undef FN
 
-// 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.
-template<typename HistogramType>
-void CompareAndPushToQueue(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) {
-  if (idx1 == idx2) {
-    return;
-  }
-  if (idx2 < idx1) {
-    uint32_t t = idx2;
-    idx2 = idx1;
-    idx1 = t;
-  }
-  bool store_pair = false;
-  HistogramPair p = {};
-  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_;
+#define FN(X) X ## Command
+#include "./cluster_inc.h"  /* NOLINT(build/include) */
+#undef FN
 
-  if (out[idx1].total_count_ == 0) {
-    p.cost_combo = out[idx2].bit_cost_;
-    store_pair = true;
-  } else if (out[idx2].total_count_ == 0) {
-    p.cost_combo = out[idx1].bit_cost_;
-    store_pair = true;
-  } else {
-    double threshold = *num_pairs == 0 ? 1e99 :
-        std::max(0.0, pairs[0].cost_diff);
-    HistogramType combo = out[idx1];
-    combo.AddHistogram(out[idx2]);
-    double cost_combo = PopulationCost(combo);
-    if (cost_combo < threshold - p.cost_diff) {
-      p.cost_combo = cost_combo;
-      store_pair = true;
-    }
-  }
-  if (store_pair) {
-    p.cost_diff += p.cost_combo;
-    if (*num_pairs > 0 && 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);
-    }
-  }
-}
+#define FN(X) X ## Distance
+#include "./cluster_inc.h"  /* NOLINT(build/include) */
+#undef FN
 
-template<typename HistogramType>
-size_t HistogramCombine(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) {
-  double cost_diff_threshold = 0.0;
-  size_t min_cluster_size = 1;
+#undef CODE
 
-  // We maintain a vector of histogram pairs, with the property that the pair
-  // with the maximum bit cost reduction is the first.
-  size_t num_pairs = 0;
-  for (size_t idx1 = 0; idx1 < num_clusters; ++idx1) {
-    for (size_t idx2 = idx1 + 1; idx2 < num_clusters; ++idx2) {
-      CompareAndPushToQueue(out, cluster_size, clusters[idx1], clusters[idx2],
-                            max_num_pairs, &pairs[0], &num_pairs);
-    }
-  }
+#if defined(__cplusplus) || defined(c_plusplus)
+}  /* extern "C" */
+#endif
 
-  while (num_clusters > min_cluster_size) {
-    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.
-    uint32_t best_idx1 = pairs[0].idx1;
-    uint32_t best_idx2 = pairs[0].idx2;
-    out[best_idx1].AddHistogram(out[best_idx2]);
-    out[best_idx1].bit_cost_ = pairs[0].cost_combo;
-    cluster_size[best_idx1] += cluster_size[best_idx2];
-    for (size_t i = 0; i < symbols_size; ++i) {
-      if (symbols[i] == best_idx2) {
-        symbols[i] = best_idx1;
-      }
-    }
-    for (size_t 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 (size_t 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 (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 (size_t i = 0; i < num_clusters; ++i) {
-      CompareAndPushToQueue(out, cluster_size, best_idx1, clusters[i],
-                            max_num_pairs, &pairs[0], &num_pairs);
-    }
-  }
-  return num_clusters;
-}
-
-// -----------------------------------------------------------------------------
-// Histogram refinement
-
-// What is the bit cost of moving histogram from cur_symbol to candidate.
-template<typename HistogramType>
-double HistogramBitCostDistance(const HistogramType& histogram,
-                                const HistogramType& candidate) {
-  if (histogram.total_count_ == 0) {
-    return 0.0;
-  }
-  HistogramType tmp = histogram;
-  tmp.AddHistogram(candidate);
-  return PopulationCost(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.
-template<typename HistogramType>
-void HistogramRemap(const HistogramType* in, size_t in_size,
-                    const uint32_t* clusters, size_t num_clusters,
-                    HistogramType* out, uint32_t* symbols) {
-  for (size_t i = 0; i < in_size; ++i) {
-    uint32_t best_out = i == 0 ? symbols[0] : symbols[i - 1];
-    double best_bits = HistogramBitCostDistance(in[i], out[best_out]);
-    for (size_t j = 0; j < num_clusters; ++j) {
-      const double cur_bits = HistogramBitCostDistance(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 (size_t j = 0; j < num_clusters; ++j) {
-    out[clusters[j]].Clear();
-  }
-  for (size_t i = 0; i < in_size; ++i) {
-    out[symbols[i]].AddHistogram(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[].
-template<typename HistogramType>
-size_t HistogramReindex(HistogramType* out, uint32_t* symbols, size_t length) {
-  static const uint32_t kInvalidIndex = std::numeric_limits<uint32_t>::max();
-  std::vector<uint32_t> new_index(length, kInvalidIndex);
-  uint32_t next_index = 0;
-  for (size_t i = 0; i < length; ++i) {
-    if (new_index[symbols[i]] == kInvalidIndex) {
-      new_index[symbols[i]] = next_index;
-      ++next_index;
-    }
-  }
-  std::vector<HistogramType> tmp(next_index);
-  next_index = 0;
-  for (size_t 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]];
-  }
-  for (size_t i = 0; i < next_index; ++i) {
-    out[i] = tmp[i];
-  }
-  return next_index;
-}
-
-// Clusters similar histograms in 'in' together, the selected histograms are
-// placed in 'out', and for each index in 'in', *histogram_symbols will
-// indicate which of the 'out' histograms is the best approximation.
-template<typename HistogramType>
-void ClusterHistograms(const std::vector<HistogramType>& in,
-                       size_t num_contexts, size_t num_blocks,
-                       size_t max_histograms,
-                       std::vector<HistogramType>* out,
-                       std::vector<uint32_t>* histogram_symbols) {
-  const size_t in_size = num_contexts * num_blocks;
-  assert(in_size == in.size());
-  std::vector<uint32_t> cluster_size(in_size, 1);
-  std::vector<uint32_t> clusters(in_size);
-  size_t num_clusters = 0;
-  out->resize(in_size);
-  histogram_symbols->resize(in_size);
-  for (size_t i = 0; i < in_size; ++i) {
-    (*out)[i] = in[i];
-    (*out)[i].bit_cost_ = PopulationCost(in[i]);
-    (*histogram_symbols)[i] = static_cast<uint32_t>(i);
-  }
-
-  const size_t max_input_histograms = 64;
-  // For the first pass of clustering, we allow all pairs.
-  size_t max_num_pairs = max_input_histograms * max_input_histograms / 2;
-  std::vector<HistogramPair> pairs(max_num_pairs + 1);
-
-  for (size_t i = 0; i < in_size; i += max_input_histograms) {
-    size_t num_to_combine = std::min(in_size - i, max_input_histograms);
-    for (size_t j = 0; j < num_to_combine; ++j) {
-      clusters[num_clusters + j] = static_cast<uint32_t>(i + j);
-    }
-    size_t num_new_clusters =
-        HistogramCombine(&(*out)[0], &cluster_size[0],
-                         &(*histogram_symbols)[i],
-                         &clusters[num_clusters], &pairs[0],
-                         num_to_combine, num_to_combine,
-                         max_histograms, max_num_pairs);
-    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.
-  max_num_pairs =
-      std::min(64 * num_clusters, (num_clusters / 2) * num_clusters);
-  pairs.resize(max_num_pairs + 1);
-
-  // Collapse similar histograms.
-  num_clusters = HistogramCombine(&(*out)[0], &cluster_size[0],
-                                  &(*histogram_symbols)[0], &clusters[0],
-                                  &pairs[0], num_clusters, in_size,
-                                  max_histograms, max_num_pairs);
-
-  // Find the optimal map from original histograms to the final ones.
-  HistogramRemap(&in[0], in_size, &clusters[0], num_clusters,
-                 &(*out)[0], &(*histogram_symbols)[0]);
-
-  // Convert the context map to a canonical form.
-  size_t num_histograms =
-      HistogramReindex(&(*out)[0], &(*histogram_symbols)[0], in_size);
-  out->resize(num_histograms);
-}
-
-}  // namespace brotli
-
-#endif  // BROTLI_ENC_CLUSTER_H_
+#endif  /* BROTLI_ENC_CLUSTER_H_ */