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Make the histogram clustering function more generic.

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Change the template parameter to be the histogram class
instead of the alphabet size of the histogram.
This commit is contained in:
Zoltan Szabadka 2014-10-28 13:36:21 +01:00
parent c6c08e492e
commit f321ba1964
1 changed files with 19 additions and 18 deletions
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37
enc/cluster.h
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@ -20,6 +20,7 @@
#include <math.h>
#include <stdint.h>
#include <stdio.h>
#include <algorithm>
#include <complex>
#include <map>
#include <set>
@ -59,8 +60,8 @@ inline double ClusterCostDiff(int size_a, int size_b) {
// 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 heap.
template<int kSize>
void CompareAndPushToHeap(const Histogram<kSize>* out,
template<typename HistogramType>
void CompareAndPushToHeap(const HistogramType* out,
const int* cluster_size,
int idx1, int idx2,
std::vector<HistogramPair>* pairs) {
@ -90,7 +91,7 @@ void CompareAndPushToHeap(const Histogram<kSize>* out,
} else {
double threshold = pairs->empty() ? 1e99 :
std::max(0.0, (*pairs)[0].cost_diff);
Histogram<kSize> combo = out[idx1];
HistogramType combo = out[idx1];
combo.AddHistogram(out[idx2]);
double cost_combo = PopulationCost(combo);
if (cost_combo < threshold - p.cost_diff) {
@ -105,8 +106,8 @@ void CompareAndPushToHeap(const Histogram<kSize>* out,
}
}
template<int kSize>
void HistogramCombine(Histogram<kSize>* out,
template<typename HistogramType>
void HistogramCombine(HistogramType* out,
int* cluster_size,
int* symbols,
int symbols_size,
@ -178,22 +179,22 @@ void HistogramCombine(Histogram<kSize>* out,
// Histogram refinement
// What is the bit cost of moving histogram from cur_symbol to candidate.
template<int kSize>
double HistogramBitCostDistance(const Histogram<kSize>& histogram,
const Histogram<kSize>& candidate) {
template<typename HistogramType>
double HistogramBitCostDistance(const HistogramType& histogram,
const HistogramType& candidate) {
if (histogram.total_count_ == 0) {
return 0.0;
}
Histogram<kSize> tmp = histogram;
HistogramType tmp = histogram;
tmp.AddHistogram(candidate);
return PopulationCost(tmp) - candidate.bit_cost_;
}
// Find the best 'out' histogram for each of the 'in' histograms.
// Note: we assume that out[]->bit_cost_ is already up-to-date.
template<int kSize>
void HistogramRemap(const Histogram<kSize>* in, int in_size,
Histogram<kSize>* out, int* symbols) {
template<typename HistogramType>
void HistogramRemap(const HistogramType* in, int in_size,
HistogramType* out, int* symbols) {
std::set<int> all_symbols;
for (int i = 0; i < in_size; ++i) {
all_symbols.insert(symbols[i]);
@ -224,10 +225,10 @@ void HistogramRemap(const Histogram<kSize>* in, int in_size,
// Reorder histograms in *out so that the new symbols in *symbols come in
// increasing order.
template<int kSize>
void HistogramReindex(std::vector<Histogram<kSize> >* out,
template<typename HistogramType>
void HistogramReindex(std::vector<HistogramType>* out,
std::vector<int>* symbols) {
std::vector<Histogram<kSize> > tmp(*out);
std::vector<HistogramType> tmp(*out);
std::map<int, int> new_index;
int next_index = 0;
for (int i = 0; i < symbols->size(); ++i) {
@ -246,11 +247,11 @@ void HistogramReindex(std::vector<Histogram<kSize> >* out,
// 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<int kSize>
void ClusterHistograms(const std::vector<Histogram<kSize> >& in,
template<typename HistogramType>
void ClusterHistograms(const std::vector<HistogramType>& in,
int num_contexts, int num_blocks,
int max_histograms,
std::vector<Histogram<kSize> >* out,
std::vector<HistogramType>* out,
std::vector<int>* histogram_symbols) {
const int in_size = num_contexts * num_blocks;
std::vector<int> cluster_size(in_size, 1);