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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.
41 lines
1.2 KiB
C++
41 lines
1.2 KiB
C++
#include "FeatureData.h"
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#define BOOST_TEST_MODULE FeatureData
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#include <boost/test/unit_test.hpp>
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#include <sstream>
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namespace {
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void CheckFeatureMap(const FeatureData* feature_data,
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const char* str, int num_feature, int* cnt) {
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for (int i = 0; i < num_feature; ++i) {
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std::stringstream ss;
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ss << str << "_" << i;
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const std::string& s = ss.str();
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BOOST_CHECK_EQUAL(feature_data->getFeatureIndex(s), (std::size_t)(*cnt));
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BOOST_CHECK_EQUAL(feature_data->getFeatureName(*cnt).c_str(), s);
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++(*cnt);
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}
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}
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} // namespace
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BOOST_AUTO_TEST_CASE(set_feature_map) {
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std::string str("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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FeatureData feature_data;
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feature_data.setFeatureMap(str);
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BOOST_REQUIRE(feature_data.Features() == str);
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BOOST_REQUIRE(feature_data.NumberOfFeatures() == 15);
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int cnt = 0;
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CheckFeatureMap(&feature_data, "d", 7, &cnt);
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CheckFeatureMap(&feature_data, "lm", 2, &cnt);
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CheckFeatureMap(&feature_data, "tm", 5, &cnt);
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BOOST_CHECK_EQUAL(feature_data.getFeatureIndex("w_0"), (std::size_t)cnt);
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BOOST_CHECK_EQUAL(feature_data.getFeatureName(cnt).c_str(), "w_0");
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}
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