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https://github.com/moses-smt/mosesdecoder.git
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fd577d7a65
Regression tests all check out, and kbmira seems to work fine on a Hansard French->English task. HypPackEnumerator class may be of interest to pro.cpp and future optimizers, as it abstracts a lot of the boilerplate involved in enumerating multiple k-best lists. MiraWeightVector is not really mira-specific - just a weight vector that enables efficient averaging. Could be useful to a perceptron as well. Same goes for MiraFeatureVector. Interaction with sparse features is written, but untested.
49 lines
1.5 KiB
C++
49 lines
1.5 KiB
C++
#include "Data.h"
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#include "Scorer.h"
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#include "ScorerFactory.h"
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#define BOOST_TEST_MODULE MertData
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#include <boost/test/unit_test.hpp>
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#include <boost/scoped_ptr.hpp>
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//very basic test of sharding
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BOOST_AUTO_TEST_CASE(shard_basic) {
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boost::scoped_ptr<Scorer> scorer(ScorerFactory::getScorer("BLEU", ""));
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Data data(scorer.get());
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FeatureArray fa1, fa2, fa3, fa4;
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ScoreArray sa1, sa2, sa3, sa4;
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fa1.setIndex("1");
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fa2.setIndex("2");
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fa3.setIndex("3");
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fa4.setIndex("4");
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sa1.setIndex("1");
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sa2.setIndex("2");
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sa3.setIndex("3");
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sa4.setIndex("4");
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data.getFeatureData()->add(fa1);
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data.getFeatureData()->add(fa2);
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data.getFeatureData()->add(fa3);
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data.getFeatureData()->add(fa4);
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data.getScoreData()->add(sa1);
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data.getScoreData()->add(sa2);
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data.getScoreData()->add(sa3);
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data.getScoreData()->add(sa4);
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std::vector<Data> shards;
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data.createShards(2,0,"",shards);
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BOOST_CHECK_EQUAL(shards.size(),(std::size_t)2);
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BOOST_CHECK_EQUAL(shards[1].getFeatureData()->size(),(std::size_t)2);
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}
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BOOST_AUTO_TEST_CASE(init_feature_map_test) {
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boost::scoped_ptr<Scorer> scorer(ScorerFactory::getScorer("BLEU", ""));
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Data data(scorer.get());
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std::string s = " d: 0 -7.66174 0 0 -3.51621 0 0 lm: -41.3435 -40.3647 tm: -67.6349 -100.438 -27.6817 -23.4685 8.99907 w: -9 ";
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std::string expected = "d_0 d_1 d_2 d_3 d_4 d_5 d_6 lm_0 lm_1 tm_0 tm_1 tm_2 tm_3 tm_4 w_0 ";
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data.InitFeatureMap(s);
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BOOST_CHECK_EQUAL(expected, data.Features());
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}
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