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[programming challenge, SSN] REPL, various "fixes"
This commit is contained in:
Michał W. Urbańczyk
parent
a900fe71c8
commit
82a6520feb
@ -94,6 +94,8 @@ struct Example
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}
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};
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struct SSN_Runner;
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class Framework
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{
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static CArtifactInstance *generateArtWithBonus(const Bonus &b);
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@ -113,6 +115,8 @@ public:
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static void buildLearningSet();
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static vector<Example> loadExamples(bool printInfo = true);
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friend SSN_Runner;
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};
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vector<string> Framework::getFileNames(const string &dirname, const std::string &ext)
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@ -149,9 +153,9 @@ vector<Example> Framework::loadExamples(bool printInfo)
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examples.push_back(ex);
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}
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tlog0 << "Found " << examples.size() << " examples.\n";
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if(printInfo)
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{
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tlog0 << "Found " << examples.size() << " examples.\n";
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BOOST_FOREACH(auto &ex, examples)
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{
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tlog0 << format("Battle on army %d for bonus %d of value %d has resultdiff %lf\n") % ex.i % ex.j % ex.k % ex.value;
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@ -471,11 +475,15 @@ public:
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};
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SSN();
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SSN(string filename);
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~SSN();
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//returns mse after learning
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double learn(const std::vector<Example> & input, const ParameterSet & params);
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double learn(bool adjustParams = false);
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SSN::ParameterSet getBestParams(vector<Example> &trainingSet);
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SSN::ParameterSet getBestParams();
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double test(const std::vector<Example> & input)
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{
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auto td = getTrainingData(input);
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@ -485,11 +493,17 @@ public:
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double run(const DuelParameters &dp, CArtifactInstance * inst);
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void save(const std::string &filename);
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void load(const std::string &filename);
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};
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SSN::SSN()
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{}
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SSN::SSN(string filename)
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{
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load(filename);
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}
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void SSN::init(const ParameterSet & params)
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{
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const float learning_rate = 0.7f;
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@ -517,7 +531,7 @@ double SSN::run(const DuelParameters &dp, CArtifactInstance * inst)
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double * input = genSSNinput(dp.sides[0], inst, dp.bfieldType, dp.terType);
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double * out = net.run(input);
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double ret = *out;
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free(out);
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//free(out);
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return ret;
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}
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@ -539,7 +553,6 @@ double SSN::learn(const std::vector<Example> & input, const ParameterSet & param
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net.set_callback(ANNCallback, NULL);
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net.train_on_data(*td, 1000, 1000, 0.01);
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// int exNum = 130;
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//
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// for(int exNum =0; exNum<input.size(); ++exNum)
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@ -553,6 +566,25 @@ double SSN::learn(const std::vector<Example> & input, const ParameterSet & param
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return net.test_data(*td);
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}
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double SSN::learn(bool adjustParams/* = false*/)
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{
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cout << "Loading examples...\n";
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auto trainingSet = Framework::loadExamples(false);
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cout << "Looking for best learning parameters...\n";
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auto params = adjustParams ? getBestParams(trainingSet) : getBestParams();
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cout << "Learning...\n";
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//saving of best network
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double finalLmse = learn(trainingSet, params);
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cout << "Learning done, LMSE=" << finalLmse << endl;
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save("last_network.net");
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return finalLmse;
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}
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double * SSN::genSSNinput(const DuelParameters::SideSettings & dp, CArtifactInstance * art, si32 bfieldType, si32 terType)
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{
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double * ret = new double[num_input];
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@ -633,16 +665,17 @@ FANN::training_data * SSN::getTrainingData( const std::vector<Example> &input )
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return ret;
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}
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void SSNRun()
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void SSN::load(const std::string &filename)
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{
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net.create_from_file(filename);
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cout << "Loaded a network from file " << filename << endl;
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}
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SSN::ParameterSet SSN::getBestParams(vector<Example> &trainingSet)
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{
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//Framework::buildLearningSet();
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double percentToTrain = 0.8;
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auto trainingSet = Framework::loadExamples(false);
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std::vector<Example> testSet;
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for(int i=0, maxi = trainingSet.size()*(1-percentToTrain); i<maxi; ++i)
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{
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int ind = rand()%trainingSet.size();
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@ -650,9 +683,6 @@ void SSNRun()
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trainingSet.erase(trainingSet.begin() + ind);
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}
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SSN network;
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SSN::ParameterSet bestParams;
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double besttMSE = 1e10;
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@ -661,12 +691,6 @@ void SSNRun()
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FANN::activation_function_enum possibleFuns[] = {FANN::SIGMOID_SYMMETRIC_STEPWISE, FANN::LINEAR,
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FANN::SIGMOID, FANN::SIGMOID_STEPWISE, FANN::SIGMOID_SYMMETRIC};
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//
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// bestParams.actSteepHidden = 0.346;
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// bestParams.actSteepnessOutput = 0.449;
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// bestParams.hiddenActFun = FANN::SIGMOID_SYMMETRIC;
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// bestParams.outActFun = FANN::SIGMOID_SYMMETRIC;
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// bestParams.neuronsInHidden = 23;
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for(int i=0; i<5000; i += 1)
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{
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@ -677,9 +701,9 @@ void SSNRun()
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ps.hiddenActFun = possibleFuns[rand()%ARRAY_COUNT(possibleFuns)];
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ps.outActFun = possibleFuns[rand()%ARRAY_COUNT(possibleFuns)];
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double lmse = network.learn(trainingSet, ps);
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double lmse = learn(trainingSet, ps);
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double tmse = network.test(testSet);
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double tmse = test(testSet);
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if(tmse < besttMSE)
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{
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besttMSE = tmse;
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@ -688,12 +712,199 @@ void SSNRun()
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cout << "hid:\t" << i << " lmse:\t" << lmse << " tmse:\t" << tmse << std::endl;
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}
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//saving of best network
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double debugMSE = network.learn(trainingSet, bestParams);
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network.save("network_config_file.net");
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return bestParams;
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}
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SSN::ParameterSet SSN::getBestParams()
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{
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// bestParams.actSteepHidden = 0.346;
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// bestParams.actSteepnessOutput = 0.449;
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// bestParams.hiddenActFun = FANN::SIGMOID_SYMMETRIC;
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// bestParams.outActFun = FANN::SIGMOID_SYMMETRIC;
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// bestParams.neuronsInHidden = 23;
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SSN::ParameterSet params;
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params.actSteepHidden = 1.18;
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params.actSteepnessOutput = 1.26;
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params.hiddenActFun = FANN::SIGMOID_STEPWISE;
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params.outActFun = FANN::SIGMOID_SYMMETRIC;
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params.neuronsInHidden = 47;
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return params;
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}
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struct SSN_Runner
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{
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unique_ptr<SSN> ssn;
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ArmyDescriptor ad;
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void printHelp()
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{
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const char *cmds[] = {"help - prints this info", "create - creates a new ANN, needs to be learned then", "load <file> - loads ANN from file", "save <file> - saves current ANN to file", "learn - runs learning process using examples set", "ask <id> - evaluates given art", "exit - closes application",
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"army clear - removes current army information", "army add <id> <count> - adds creature to army", "army remove <pos> - removes stack from position",
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"army print - prints current army state", "army random - generates random army"};
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cout << "Available commands:\n";
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BOOST_FOREACH(auto cmd, cmds)
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cout << "\t" << cmd << endl;
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}
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int run()
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{
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cout << "Welcome to the ANN interactive mode!\n";
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printHelp();
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while(1)
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{
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try
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{
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cout << "Please enter your command and press return.\n> ";
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stringstream ss;
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string input;
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getline(cin, input);
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ss.str(input);
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string command, secondWord;
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ss >> command >> secondWord;
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if(command == "exit")
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{
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cout << "Ending...\n";
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exit(0);
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}
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else if(command == "load")
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{
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if(secondWord.empty())
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secondWord = "last_network.net";
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ssn = unique_ptr<SSN>(new SSN(secondWord));
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}
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else if(command == "create")
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{
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ssn = unique_ptr<SSN>(new SSN());
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cout << "Network successfully created. It still needs to be learnt.\n";
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}
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else if(command == "help")
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{
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printHelp();
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}
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else if(command == "army" && secondWord.size())
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{
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if(secondWord == "clear")
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{
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ad.clear();
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cout << "Army is now empty.\n";
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}
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if(secondWord == "print")
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{
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cout << "Army contains " << ad.size() << " creatures.\n";
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BOOST_FOREACH(auto &itr, ad)
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{
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cout << itr.first << " => " << itr.second.count << " of " << itr.second.type->namePl << endl;
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}
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}
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if(secondWord == "erase")
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{
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int slot;
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ss >> slot;
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if(ad.find(slot) != ad.end())
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{
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ad.erase(slot);
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cout << "Slot " << slot << " successfully erased.\n";
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}
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}
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if(secondWord == "add")
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{
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int id, count;
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ss >> id >> count;
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int i = 0;
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if(id < 0 || id >= 118)
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{
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throw std::runtime_error("Id has to be in <0,118>");
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}
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if(count <= 0)
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{
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throw std::runtime_error("Count has to be > 0");
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}
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while(ad.find(i++) != ad.end());
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if(i >= ARMY_SIZE)
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{
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tlog1 << "Cannot add stack, army is full!\n";
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}
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else
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{
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ad[i] = CStackBasicDescriptor(id, count);
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tlog0 << "Creature successfully added to slot " << i << endl;;
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}
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}
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if(secondWord == "random")
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{
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srand(time(0));
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ad.clear();
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int stacks = rand() % 7 + 1;
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for(int i = 0; i < stacks; i++)
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{
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CCreature *c = VLC->creh->creatures[rand() % 118];
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ad[i] = CStackBasicDescriptor(c, c->growth);
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}
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cout << "Generated random army of " << stacks << " creatures.\n";
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}
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}
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else if(!ssn)
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{
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cout << "Error: you need to create or load ANN from file first!\n";
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continue;
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}
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else if(command == "learn")
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{
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ssn->learn();
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}
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else if(command == "save")
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{
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ssn->save(secondWord);
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}
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else if(command == "ask")
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{
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int artid = boost::lexical_cast<int>(secondWord);
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CArtifact *art = VLC->arth->artifacts.at(artid);
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DuelParameters dp = Framework::generateDuel(ad);
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CArtifactInstance * artInst = new CArtifactInstance(art);
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auto bonuses = art->getBonuses([](const Bonus*){return true;});
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if(!bonuses->size())
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{
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tlog1 << "This artifact deosn't provide any bonuses. Please pick another one.";
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}
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else
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{
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BOOST_FOREACH(auto b, *bonuses)
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artInst->addNewBonus(new Bonus(*b));
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auto val = ssn->run(dp, artInst);
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cout << "ANN rates " << art->Name() << " to value = " << val << endl;
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}
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}
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else
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tlog1 << "Unknown command \""<<command <<"\"!\n";
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}
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catch(std::exception &e)
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{
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tlog1 << "Encountered error: " << e.what() << endl;
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}
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catch(...)
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{
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tlog1 << "Encountered unknown error!" << endl;
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}
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}
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}
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};
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int main(int argc, char **argv)
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{
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std::cout << "VCMI Odpalarka\nMy path: " << argv[0] << std::endl;
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@ -764,7 +975,8 @@ int main(int argc, char **argv)
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VLC = new LibClasses();
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VLC->init();
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SSNRun();
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SSN_Runner runner;
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runner.run();
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return EXIT_SUCCESS;
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}
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