mirror of
https://github.com/moses-smt/mosesdecoder.git
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271 lines
7.0 KiB
C++
271 lines
7.0 KiB
C++
/*
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* Data.cpp
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* mert - Minimum Error Rate Training
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*
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* Created by Nicola Bertoldi on 13/05/08.
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*
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*/
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#include <algorithm>
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#include "util/check.hh"
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#include <cmath>
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#include <fstream>
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#include "Data.h"
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#include "FileStream.h"
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#include "Scorer.h"
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#include "ScorerFactory.h"
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#include "Util.h"
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Data::Data()
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: theScorer(NULL),
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number_of_scores(0),
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_sparse_flag(false),
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scoredata(),
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featdata() {}
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Data::Data(Scorer& ptr)
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: theScorer(&ptr),
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score_type(theScorer->getName()),
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number_of_scores(0),
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_sparse_flag(false),
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scoredata(new ScoreData(*theScorer)),
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featdata(new FeatureData)
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{
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TRACE_ERR("Data::score_type " << score_type << std::endl);
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TRACE_ERR("Data::Scorer type from Scorer: " << theScorer->getName() << endl);
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}
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//ADDED BY TS
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void Data::remove_duplicates() {
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size_t nSentences = featdata->size();
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assert(scoredata->size() == nSentences);
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for (size_t s=0; s < nSentences; s++) {
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FeatureArray& feat_array = featdata->get(s);
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ScoreArray& score_array = scoredata->get(s);
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assert(feat_array.size() == score_array.size());
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//serves as a hash-map:
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std::map<double, std::vector<size_t> > lookup;
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size_t end_pos = feat_array.size() - 1;
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size_t nRemoved = 0;
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for (size_t k=0; k <= end_pos; k++) {
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const FeatureStats& cur_feats = feat_array.get(k);
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double sum = 0.0;
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for (size_t l=0; l < cur_feats.size(); l++)
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sum += cur_feats.get(l);
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if (lookup.find(sum) != lookup.end()) {
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//std::cerr << "hit" << std::endl;
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std::vector<size_t>& cur_list = lookup[sum];
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size_t l=0;
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for (l=0; l < cur_list.size(); l++) {
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size_t j=cur_list[l];
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if (cur_feats == feat_array.get(j)
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&& score_array.get(k) == score_array.get(j)) {
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if (k < end_pos) {
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feat_array.swap(k,end_pos);
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score_array.swap(k,end_pos);
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k--;
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}
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end_pos--;
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nRemoved++;
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break;
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}
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}
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if (l == lookup[sum].size())
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cur_list.push_back(k);
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}
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else
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lookup[sum].push_back(k);
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// for (size_t j=0; j < k; j++) {
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// if (feat_array.get(k) == feat_array.get(j)
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// && score_array.get(k) == score_array.get(j)) {
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// if (k < end_pos) {
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// feat_array.swap(k,end_pos);
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// score_array.swap(k,end_pos);
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// k--;
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// }
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// end_pos--;
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// nRemoved++;
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// break;
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// }
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// }
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}
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if (nRemoved > 0) {
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feat_array.resize(end_pos+1);
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score_array.resize(end_pos+1);
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}
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}
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}
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//END_ADDED
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void Data::loadnbest(const std::string &file)
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{
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TRACE_ERR("loading nbest from " << file << std::endl);
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FeatureStats featentry;
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ScoreStats scoreentry;
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std::string sentence_index;
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inputfilestream inp(file); // matches a stream with a file. Opens the file
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if (!inp.good())
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throw runtime_error("Unable to open: " + file);
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std::string substring, subsubstring, stringBuf;
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std::string theSentence;
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std::string::size_type loc;
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while (getline(inp,stringBuf,'\n')) {
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if (stringBuf.empty()) continue;
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// TRACE_ERR("stringBuf: " << stringBuf << std::endl);
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getNextPound(stringBuf, substring, "|||"); //first field
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sentence_index = substring;
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getNextPound(stringBuf, substring, "|||"); //second field
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theSentence = substring;
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// adding statistics for error measures
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featentry.reset();
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scoreentry.clear();
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theScorer->prepareStats(sentence_index, theSentence, scoreentry);
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scoredata->add(scoreentry, sentence_index);
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getNextPound(stringBuf, substring, "|||"); //third field
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// examine first line for name of features
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if (!existsFeatureNames()) {
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std::string stringsupport=substring;
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std::string features="";
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std::string tmpname="";
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size_t tmpidx=0;
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while (!stringsupport.empty()) {
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// TRACE_ERR("Decompounding: " << substring << std::endl);
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getNextPound(stringsupport, subsubstring);
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// string ending with ":" are skipped, because they are the names of the features
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if ((loc = subsubstring.find_last_of(":")) != subsubstring.length()-1) {
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features+=tmpname+"_"+stringify(tmpidx)+" ";
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tmpidx++;
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}
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// ignore sparse feature name
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else if (subsubstring.find("_") != string::npos) {
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// also ignore its value
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getNextPound(stringsupport, subsubstring);
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}
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// update current feature name
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else {
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tmpidx=0;
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tmpname=subsubstring.substr(0,subsubstring.size() - 1);
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}
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}
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featdata->setFeatureMap(features);
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}
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// adding features
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while (!substring.empty()) {
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// TRACE_ERR("Decompounding: " << substring << std::endl);
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getNextPound(substring, subsubstring);
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// no ':' -> feature value that needs to be stored
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if ((loc = subsubstring.find_last_of(":")) != subsubstring.length()-1) {
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featentry.add(ConvertStringToFeatureStatsType(subsubstring));
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}
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// sparse feature name? store as well
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else if (subsubstring.find("_") != string::npos) {
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std::string name = subsubstring;
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getNextPound(substring, subsubstring);
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featentry.addSparse( name, atof(subsubstring.c_str()) );
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_sparse_flag = true;
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}
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}
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//cerr << "number of sparse features: " << featentry.getSparse().size() << endl;
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featdata->add(featentry,sentence_index);
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}
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inp.close();
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}
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// TODO
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void Data::mergeSparseFeatures() {
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std::cerr << "ERROR: sparse features can only be trained with pairwise ranked optimizer (PRO), not traditional MERT\n";
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exit(1);
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}
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void Data::createShards(size_t shard_count, float shard_size, const string& scorerconfig,
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std::vector<Data>& shards)
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{
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CHECK(shard_count);
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CHECK(shard_size >= 0);
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CHECK(shard_size <= 1);
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size_t data_size = scoredata->size();
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CHECK(data_size == featdata->size());
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shard_size *= data_size;
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for (size_t shard_id = 0; shard_id < shard_count; ++shard_id) {
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vector<size_t> shard_contents;
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if (shard_size == 0) {
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//split into roughly equal size shards
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const size_t shard_start = floor(0.5 + shard_id * static_cast<float>(data_size) / shard_count);
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const size_t shard_end = floor(0.5 + (shard_id + 1) * static_cast<float>(data_size) / shard_count);
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for (size_t i = shard_start; i < shard_end; ++i) {
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shard_contents.push_back(i);
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}
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} else {
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//create shards by randomly sampling
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for (size_t i = 0; i < floor(shard_size+0.5); ++i) {
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shard_contents.push_back(rand() % data_size);
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}
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}
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Scorer* scorer = ScorerFactory::getScorer(score_type, scorerconfig);
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shards.push_back(Data(*scorer));
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shards.back().score_type = score_type;
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shards.back().number_of_scores = number_of_scores;
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shards.back()._sparse_flag = _sparse_flag;
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for (size_t i = 0; i < shard_contents.size(); ++i) {
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shards.back().featdata->add(featdata->get(shard_contents[i]));
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shards.back().scoredata->add(scoredata->get(shard_contents[i]));
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}
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//cerr << endl;
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}
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}
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