1 files changed, 223 insertions, 0 deletions
diff --git a/Master/Modellbildung_und_Simulation/Aufgabenblatt3/MLP/src/MLPConfig.cpp b/Master/Modellbildung_und_Simulation/Aufgabenblatt3/MLP/src/MLPConfig.cpp
new file mode 100644
index 0000000..067a0d8
--- /dev/null
+++ b/Master/Modellbildung_und_Simulation/Aufgabenblatt3/MLP/src/MLPConfig.cpp
@@ -0,0 +1,223 @@
+/*
+ * MLPConfig.cpp
+ *
+ *  Created on: 09.06.2011
+ *      Author: sven
+ */
+
+#include "MLPConfig.h"
+
+#include <iostream>
+#include <string>
+#include <sstream>
+#include <iomanip>
+#include <boost/tokenizer.hpp>
+#include <boost/foreach.hpp>
+#include <time.h>
+
+typedef tokenizer<boost::char_separator<char> > MLP_Tokenizer;
+const uint32_t DEFAULT_NUMCYCLES = 20000U;
+const double DEFAULT_ERROR_THRESHOLD = 0.01;
+const double DEFAULT_CONFIGACCEPTANCE_THRESHOLD = 0.3;
+
+MLPConfig::MLPConfig()
+:mNumInputNeurons(0),mNumHiddenNeurons(0),mNumOutputNeurons(0),mValid(false)
+,mErrorThreshold(DEFAULT_ERROR_THRESHOLD),mNumTrainingCycles(DEFAULT_NUMCYCLES)
+,mConfigAcceptanceErrorThreshold(DEFAULT_CONFIGACCEPTANCE_THRESHOLD)
+{
+
+}
+MLPConfig::MLPConfig(const char* configFileName)
+:mNumInputNeurons(0),mNumHiddenNeurons(0),mNumOutputNeurons(0),mValid(false)
+,mErrorThreshold(DEFAULT_ERROR_THRESHOLD),mNumTrainingCycles(DEFAULT_NUMCYCLES)
+{
+	parseConfigfile(configFileName);
+}
+
+MLPConfig::~MLPConfig() {
+	// TODO Auto-generated destructor stub
+}
+bool MLPConfig::parseConfigfile(ifstream& configFile)
+{
+	string line;
+	while( configFile.good() )
+	{
+		getline(configFile,line);
+		if( !parseLine(line) )
+		{
+			return false;
+		}
+	}
+	return true;
+}
+
+bool MLPConfig::parseLine(string& line)
+{
+	vector<string> tokens;
+	char_separator<char> sep(":");
+	MLP_Tokenizer tok(line,sep);
+	MLP_Tokenizer::iterator it;
+	BOOST_FOREACH(string t, tok)
+	{
+		tokens.push_back(t);
+	}
+	if (tokens.empty()) {
+		return true;
+	}
+	if (tokens.at(0).find("Bias") != string::npos) {
+		return true;
+	}
+	if (tokens.at(0).find("Threshold") != string::npos) {
+		return true;
+	}
+	if (tokens.at(0).find("Input -> Hidden") != string::npos) {
+		return true;
+	}
+	if (tokens.at(0).find("Hidden -> Output") != string::npos) {
+		return true;
+	}
+	if (tokens.at(0).find("Input") != string::npos) {
+		MLP_Tokenizer inpTok(tokens.at(1));
+		for (it = inpTok.begin() ;it != inpTok.end() ; ++it) {
+			mNumInputNeurons++;
+		}
+		return true;
+	}
+	if (tokens.at(0).find("Hidden") != string::npos) {
+		MLP_Tokenizer hiddenTok(tokens.at(1));
+		for (it = hiddenTok.begin() ;it != hiddenTok.end() ; ++it) {
+			mNumHiddenNeurons++;
+		}
+		return true;
+	}
+	if (tokens.at(0).find("Output") != string::npos) {
+		MLP_Tokenizer outputTok(tokens.at(1));
+		for (it = outputTok.begin() ;it != outputTok.end() ; ++it) {
+			mNumOutputNeurons++;
+		}
+		return true;
+	}
+	if (mWeights.empty()) {
+		initWeights(false);
+	}
+	char_separator<char> weightSep(" ");
+	MLP_Tokenizer weightTok(tokens.at(0),weightSep);
+	it = weightTok.begin();
+	uint32_t from = toUint32(*it);
+	it++;
+	uint32_t to = toUint32(*it);
+	it++;
+	double w = toDouble(*it);
+	mWeights.at(from).at(to) = w;
+	return true;
+}
+
+void MLPConfig::initWeights(bool randomWeights)
+{
+	uint32_t i,j;
+	uint32_t numNeurons = 1 /*Bias*/ + mNumInputNeurons + mNumHiddenNeurons + mNumOutputNeurons;
+	for (i = 0; i< numNeurons; i++) {
+		mWeights.push_back(vector<double>(numNeurons));
+	}
+	double randWeight = 0.0;
+	if (randomWeights) {
+		srand(time(NULL));
+		uint32_t startHidden = 1 + mNumInputNeurons;
+		uint32_t startOutput = startHidden + mNumHiddenNeurons;
+		uint32_t noNeuron = startOutput + mNumOutputNeurons;
+		// Bias -> Hidden
+		for (i = startHidden; i < startOutput; i++) {
+			randWeight = getRandomWeight();
+			if ( (randWeight < -0.5) || (randWeight > 0.5) ) {
+				cout << randWeight << endl;
+			}
+			mWeights.at(0).at(i) = randWeight;
+		}
+		// Bias -> Output
+		for (i = startOutput; i < noNeuron; i++) {
+			randWeight = getRandomWeight();
+			if ( (randWeight < -0.5) || (randWeight > 0.5) ) {
+				cout << randWeight << endl;
+			}
+			mWeights.at(0).at(i) = randWeight;
+		}
+		// Input -> Hidden
+		for (i=1; i < startHidden; i++) {
+			for (j=startHidden ; j<startOutput; j++) {
+				randWeight = getRandomWeight();
+				if ( (randWeight < -0.5) || (randWeight > 0.5) ) {
+					cout << randWeight << endl;
+				}
+				mWeights.at(i).at(j) = randWeight;
+			}
+		}
+		// Hidden -> Output
+		for (i=startHidden; i < startOutput; i++) {
+			for (j=startOutput ; j<noNeuron; j++) {
+				randWeight = getRandomWeight();
+				if ( (randWeight < -0.5) || (randWeight > 0.5) ) {
+					cout << randWeight << endl;
+				}
+				mWeights.at(i).at(j) = randWeight;
+			}
+		}
+	}
+}
+
+uint32_t MLPConfig::toUint32(const string& t)
+{
+	uint32_t res;
+	istringstream ss(t);
+	ss >> res;
+	return res;
+}
+
+double MLPConfig::toDouble(const string& t)
+{
+	double res;
+	istringstream ss(t);
+	ss >> res;
+	return res;
+}
+
+void MLPConfig::dump()
+{
+	int width = 12;
+	int prec = 8;
+	cout << "============ MLP config==========" << endl;
+    cout << "Lernrate: " << mLernrate << endl;
+    cout << "Momentum: " << mMomentum << endl;
+    cout << "Updatemode: " << (mUpdateMode==UPDATE_MODE_BATCH?"batch":"single") << endl;
+	cout << "Input neurons: " << mNumInputNeurons << endl;
+	cout << "Hidden neurons: " << mNumHiddenNeurons << endl;
+	cout << "Output neurons: " << mNumOutputNeurons << endl;
+
+	cout << "Weights:" << endl;
+	BOOST_FOREACH(vector<double> i, mWeights)
+	{
+		BOOST_FOREACH(double w, i)
+		{
+			cout << setw(width) << setprecision(prec)  << w;
+		}
+		cout << endl;
+	}
+	cout << "=================================" << endl;
+}
+bool MLPConfig::parseConfigfile(const char* configfilename)
+{
+	ifstream inFile(configfilename);
+	if (!inFile.is_open()) {
+		mValid = false;
+		return mValid;
+	}
+	mValid = parseConfigfile(inFile);
+	inFile.close();
+	return mValid;
+}
+double MLPConfig::getRandomWeight()
+{
+	int prec = 10000;
+	int x = rand() % prec;
+	x -= prec / 2;
+	return ((double) x) / ((double) prec);
+}