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#include "precomputation.h"
#include <iostream>
#include <queue>
#include "data_array.h"
#include "suffix_array.h"
using namespace std;
namespace extractor {
int Precomputation::FIRST_NONTERMINAL = -1;
int Precomputation::SECOND_NONTERMINAL = -2;
Precomputation::Precomputation(
shared_ptr<SuffixArray> suffix_array, int num_frequent_patterns,
int num_super_frequent_patterns, int max_rule_span,
int max_rule_symbols, int min_gap_size,
int max_frequent_phrase_len, int min_frequency) {
vector<int> data = suffix_array->GetData()->GetData();
vector<vector<int> > frequent_patterns = FindMostFrequentPatterns(
suffix_array, data, num_frequent_patterns, max_frequent_phrase_len,
min_frequency);
unordered_set<vector<int>, VectorHash> frequent_patterns_set;
unordered_set<vector<int>, VectorHash> super_frequent_patterns_set;
for (size_t i = 0; i < frequent_patterns.size(); ++i) {
frequent_patterns_set.insert(frequent_patterns[i]);
if (i < num_super_frequent_patterns) {
super_frequent_patterns_set.insert(frequent_patterns[i]);
}
}
vector<tuple<int, int, int> > matchings;
for (size_t i = 0; i < data.size(); ++i) {
if (data[i] == DataArray::END_OF_LINE) {
AddCollocations(matchings, data, max_rule_span, min_gap_size,
max_rule_symbols);
matchings.clear();
continue;
}
vector<int> pattern;
for (int j = 1; j <= max_frequent_phrase_len && i + j <= data.size(); ++j) {
pattern.push_back(data[i + j - 1]);
if (frequent_patterns_set.count(pattern)) {
int is_super_frequent = super_frequent_patterns_set.count(pattern);
matchings.push_back(make_tuple(i, j, is_super_frequent));
} else {
// If the current pattern is not frequent, any longer pattern having the
// current pattern as prefix will not be frequent.
break;
}
}
}
}
Precomputation::Precomputation() {}
Precomputation::~Precomputation() {}
vector<vector<int> > Precomputation::FindMostFrequentPatterns(
shared_ptr<SuffixArray> suffix_array, const vector<int>& data,
int num_frequent_patterns, int max_frequent_phrase_len, int min_frequency) {
vector<int> lcp = suffix_array->BuildLCPArray();
vector<int> run_start(max_frequent_phrase_len);
priority_queue<pair<int, pair<int, int> > > heap;
for (size_t i = 1; i < lcp.size(); ++i) {
for (int len = lcp[i]; len < max_frequent_phrase_len; ++len) {
int frequency = i - run_start[len];
// TODO(pauldb): Only add patterns that don't span across multiple
// sentences.
if (frequency >= min_frequency) {
heap.push(make_pair(frequency,
make_pair(suffix_array->GetSuffix(run_start[len]), len + 1)));
}
run_start[len] = i;
}
}
vector<vector<int> > frequent_patterns;
while (frequent_patterns.size() < num_frequent_patterns && !heap.empty()) {
int start = heap.top().second.first;
int len = heap.top().second.second;
heap.pop();
vector<int> pattern(data.begin() + start, data.begin() + start + len);
if (find(pattern.begin(), pattern.end(), DataArray::END_OF_LINE) ==
pattern.end()) {
frequent_patterns.push_back(pattern);
}
}
return frequent_patterns;
}
void Precomputation::AddCollocations(
const vector<tuple<int, int, int> >& matchings, const vector<int>& data,
int max_rule_span, int min_gap_size, int max_rule_symbols) {
for (size_t i = 0; i < matchings.size(); ++i) {
int start1, size1, is_super1;
tie(start1, size1, is_super1) = matchings[i];
for (size_t j = i + 1; j < matchings.size(); ++j) {
int start2, size2, is_super2;
tie(start2, size2, is_super2) = matchings[j];
if (start2 - start1 >= max_rule_span) {
break;
}
if (start2 - start1 - size1 >= min_gap_size
&& start2 + size2 - start1 <= max_rule_span
&& size1 + size2 + 1 <= max_rule_symbols) {
vector<int> pattern(data.begin() + start1,
data.begin() + start1 + size1);
pattern.push_back(Precomputation::FIRST_NONTERMINAL);
pattern.insert(pattern.end(), data.begin() + start2,
data.begin() + start2 + size2);
AddStartPositions(collocations[pattern], start1, start2);
if (is_super2) {
pattern.push_back(Precomputation::SECOND_NONTERMINAL);
for (size_t k = j + 1; k < matchings.size(); ++k) {
int start3, size3, is_super3;
tie(start3, size3, is_super3) = matchings[k];
if (start3 - start1 >= max_rule_span) {
break;
}
if (start3 - start2 - size2 >= min_gap_size
&& start3 + size3 - start1 <= max_rule_span
&& size1 + size2 + size3 + 2 <= max_rule_symbols
&& (is_super1 || is_super3)) {
pattern.insert(pattern.end(), data.begin() + start3,
data.begin() + start3 + size3);
AddStartPositions(collocations[pattern], start1, start2, start3);
pattern.erase(pattern.end() - size3);
}
}
}
}
}
}
}
void Precomputation::AddStartPositions(
vector<int>& positions, int pos1, int pos2) {
positions.push_back(pos1);
positions.push_back(pos2);
}
void Precomputation::AddStartPositions(
vector<int>& positions, int pos1, int pos2, int pos3) {
positions.push_back(pos1);
positions.push_back(pos2);
positions.push_back(pos3);
}
void Precomputation::WriteBinary(const fs::path& filepath) const {
FILE* file = fopen(filepath.string().c_str(), "w");
// TODO(pauldb): Refactor this code.
int size = collocations.size();
fwrite(&size, sizeof(int), 1, file);
for (auto entry: collocations) {
size = entry.first.size();
fwrite(&size, sizeof(int), 1, file);
fwrite(entry.first.data(), sizeof(int), size, file);
size = entry.second.size();
fwrite(&size, sizeof(int), 1, file);
fwrite(entry.second.data(), sizeof(int), size, file);
}
}
const Index& Precomputation::GetCollocations() const {
return collocations;
}
} // namespace extractor
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