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49 lines
2.3 KiB
C++
49 lines
2.3 KiB
C++
#include "src/Application.hpp"
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#include "gtest/gtest.h"
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using namespace CppJieba;
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TEST(ApplicationTest, Test1) {
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CppJieba::Application app("../dict/jieba.dict.utf8",
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"../dict/hmm_model.utf8",
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"../dict/user.dict.utf8",
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"../dict/idf.utf8",
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"../dict/stop_words.utf8");
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vector<string> words;
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string result;
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app.cut("我来自北京邮电大学。", words, METHOD_MP);
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result << words;
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ASSERT_EQ("[\"我\", \"来自\", \"北京邮电大学\", \"。\"]", result);
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app.cut("我来自北京邮电大学。。。学号123456", words, METHOD_HMM);
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result << words;
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ASSERT_EQ("[\"我来\", \"自北京\", \"邮电大学\", \"。\", \"。\", \"。\", \"学号\", \"123456\"]", result);
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app.cut("我来自北京邮电大学。。。学号123456，用AK47", words, METHOD_MIX);
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result << words;
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ASSERT_EQ("[\"我\", \"来自\", \"北京邮电大学\", \"。\", \"。\", \"。\", \"学号\", \"123456\", \"，\", \"用\", \"AK47\"]", result);
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app.cut("他来到了网易杭研大厦", words, METHOD_MIX);
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result << words;
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ASSERT_EQ("[\"他\", \"来到\", \"了\", \"网易\", \"杭研\", \"大厦\"]", result);
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app.cut("我来自北京邮电大学", words, METHOD_FULL);
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result << words;
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ASSERT_EQ(result, "[\"我\", \"来自\", \"北京\", \"北京邮电\", \"北京邮电大学\", \"邮电\", \"邮电大学\", \"电大\", \"大学\"]");
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app.cut("他来到了网易杭研大厦", words, METHOD_QUERY);
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result << words;
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ASSERT_EQ("[\"他\", \"来到\", \"了\", \"网易\", \"杭研\", \"大厦\"]", result);
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vector<pair<string, string> > tagres;
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app.tag("iPhone6手机的最大特点是很容易弯曲。", tagres);
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result << tagres;
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ASSERT_EQ("[\"iPhone6:eng\", \"手机:n\", \"的:uj\", \"最大:a\", \"特点:n\", \"是:v\", \"很:zg\", \"容易:a\", \"弯曲:v\", \"。:x\"]", result);
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vector<pair<string, double> > keywordres;
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app.extract("我是拖拉机学院手扶拖拉机专业的。不用多久，我就会升职加薪，当上CEO，走上人生巅峰。", keywordres, 5);
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result << keywordres;
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ASSERT_EQ(result, "[\"CEO:11.7392\", \"升职:10.8562\", \"加薪:10.6426\", \"手扶拖拉机:10.0089\", \"巅峰:9.49396\"]");
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}
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