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#include "precomputation.h"
#include <iostream>
#include <queue>
#include "data_array.h"
#include "suffix_array.h"
#include "vocabulary.h"
using namespace std;
namespace extractor {
int Precomputation::NONTERMINAL = -1;
Precomputation::Precomputation(
shared_ptr<Vocabulary> vocabulary, 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<vector<int>> frequent_patterns = FindMostFrequentPatterns(
suffix_array, num_frequent_patterns, max_frequent_phrase_len,
min_frequency);
// Construct sets containing the frequent and superfrequent contiguous
// collocations.
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]);
}
}
shared_ptr<DataArray> data_array = suffix_array->GetData();
vector<tuple<int, int, int>> matchings;
for (size_t i = 0; i < data_array->GetSize(); ++i) {
// If the sentence is over, add all the discontiguous frequent patterns to
// the index.
if (data_array->AtIndex(i) == DataArray::END_OF_LINE) {
UpdateIndex(data_array, vocabulary, matchings, max_rule_span,
min_gap_size, max_rule_symbols);
matchings.clear();
continue;
}
// Find all the contiguous frequent patterns starting at position i.
vector<int> pattern;
for (int j = 1;
j <= max_frequent_phrase_len && i + j <= data_array->GetSize();
++j) {
pattern.push_back(data_array->AtIndex(i + j - 1));
if (!frequent_patterns_set.count(pattern)) {
// If the current pattern is not frequent, any longer pattern having the
// current pattern as prefix will not be frequent.
break;
}
int is_super_frequent = super_frequent_patterns_set.count(pattern);
matchings.push_back(make_tuple(i, j, is_super_frequent));
}
}
}
Precomputation::Precomputation() {}
Precomputation::~Precomputation() {}
vector<vector<int>> Precomputation::FindMostFrequentPatterns(
shared_ptr<SuffixArray> suffix_array, 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);
// Find all the patterns occurring at least min_frequency times.
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];
if (frequency >= min_frequency) {
heap.push(make_pair(frequency,
make_pair(suffix_array->GetSuffix(run_start[len]), len + 1)));
}
run_start[len] = i;
}
}
shared_ptr<DataArray> data_array = suffix_array->GetData();
// Extract the most frequent patterns.
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_array->GetWordIds(start, len);
if (find(pattern.begin(), pattern.end(), DataArray::END_OF_LINE) ==
pattern.end()) {
frequent_patterns.push_back(pattern);
}
}
return frequent_patterns;
}
void Precomputation::UpdateIndex(
shared_ptr<DataArray> data_array, shared_ptr<Vocabulary> vocabulary,
const vector<tuple<int, int, int>>& matchings,
int max_rule_span, int min_gap_size, int max_rule_symbols) {
// Select the leftmost subpattern.
for (size_t i = 0; i < matchings.size(); ++i) {
int start1, size1, is_super1;
tie(start1, size1, is_super1) = matchings[i];
// Select the second (middle) subpattern
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;
AppendSubpattern(pattern, data_array, vocabulary, start1, size1);
pattern.push_back(Precomputation::NONTERMINAL);
AppendSubpattern(pattern, data_array, vocabulary, start2, size2);
AppendCollocation(index[pattern], {start1, start2});
// Try extending the binary collocation to a ternary collocation.
if (is_super2) {
pattern.push_back(Precomputation::NONTERMINAL);
// Select the rightmost subpattern.
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)) {
AppendSubpattern(pattern, data_array, vocabulary, start3, size3);
AppendCollocation(index[pattern], {start1, start2, start3});
pattern.erase(pattern.end() - size3);
}
}
}
}
}
}
}
void Precomputation::AppendSubpattern(
vector<int>& pattern, shared_ptr<DataArray> data_array,
shared_ptr<Vocabulary> vocabulary, int start, int size) {
vector<string> words = data_array->GetWords(start, size);
for (const string& word: words) {
pattern.push_back(vocabulary->GetTerminalIndex(word));
}
}
void Precomputation::AppendCollocation(
vector<int>& collocations, const vector<int>& collocation) {
copy(collocation.begin(), collocation.end(), back_inserter(collocations));
}
bool Precomputation::Contains(const vector<int>& pattern) const {
return index.count(pattern);
}
vector<int> Precomputation::GetCollocations(const vector<int>& pattern) const {
return index.at(pattern);
}
bool Precomputation::operator==(const Precomputation& other) const {
return index == other.index;
}
} // namespace extractor
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