static void Main(string[] args)
{
//Kreiranje neuronske mreze
NeuralNetwork network = new NeuralNetwork();
network.Add(new NeuralLayer(4, 3, "sigmoid"));
network.Add(new NeuralLayer(3, 2, "sigmoid"));
network.Add(new NeuralLayer(2, 1, "sigmoid"));
List <double> col_1 = new List <double>();
List <double> col_2 = new List <double>();
List <double> col_3 = new List <double>();
List <double> col_4 = new List <double>();
List <double> col_5 = new List <double>();
List <double> col_1Test = new List <double>();
List <double> col_2Test = new List <double>();
List <double> col_3Test = new List <double>();
List <double> col_4Test = new List <double>();
List <double> col_5Test = new List <double>();
String[] dataLines = File.ReadAllLines(@"./../../train.csv");
dataLines = dataLines.Skip(1).ToArray(); //Skipujemo heder u CSV fajlu
foreach (String linija in dataLines)
{
string[] parts = linija.Split(',');
col_1.Add(double.Parse(parts[(int)Header.col_1], CultureInfo.InvariantCulture));
col_2.Add(double.Parse(parts[(int)Header.col_2], CultureInfo.InvariantCulture));
col_3.Add(double.Parse(parts[(int)Header.col_3], CultureInfo.InvariantCulture));
col_4.Add(double.Parse(parts[(int)Header.col_4], CultureInfo.InvariantCulture));
String type = (parts[(int)Header.col_5]);
if (type.Equals("type_1"))
{
col_5.Add(0);
}
else if (type.Equals("type_2"))
{
col_5.Add(0.5);
}
else
{
col_5.Add(1);
}
}
List <double> col_1Normalizovani = normalizacijaInt(col_1);
List <double> col_2Normalizovani = normalizacijaInt(col_2);
List <double> col_3Normalizovani = normalizacijaInt(col_3);
List <double> col_4Normalizovani = normalizacijaInt(col_4);
List <List <double> > X = new List <List <double> >();
List <List <double> > Y = new List <List <double> >();
for (int i = 0; i < dataLines.Length; i++)
{
double[] xTemp = { col_1Normalizovani[i], col_2Normalizovani[i], col_3Normalizovani[i], col_4Normalizovani[i] };
X.Add(xTemp.ToList());
double[] yTemp = { col_5[i] };
Y.Add(yTemp.ToList());
}
Console.WriteLine("Training started");
network.fit(X, Y, 0.1, 0.9, 500);
Console.WriteLine("Training ended");
// String[] dataLinesTest = File.ReadAllLines(@"./../../test.csv");
String[] dataLinesTest = File.ReadAllLines(@"./../../test.csv");
dataLinesTest = dataLinesTest.Skip(1).ToArray(); //Skipujemo heder u CSV fajlu
foreach (String linija in dataLinesTest)
{
string[] parts = linija.Split(',');
col_1Test.Add(double.Parse(parts[(int)Header.col_1], CultureInfo.InvariantCulture));
col_2Test.Add(double.Parse(parts[(int)Header.col_2], CultureInfo.InvariantCulture));
col_3Test.Add(double.Parse(parts[(int)Header.col_3], CultureInfo.InvariantCulture));
col_4Test.Add(double.Parse(parts[(int)Header.col_4], CultureInfo.InvariantCulture));
// Console.WriteLine(double.Parse(parts[(int)Header.col_4], CultureInfo.InvariantCulture));
String type = (parts[(int)Header.col_5]);
if (type.Equals("type_1"))
{
col_5Test.Add(0);
}
else if (type.Equals("type_2"))
{
col_5Test.Add(0.5);
}
else
{
col_5Test.Add(1);
}
}
List <double> col_1TestNormalizovani = normalizacijaInt(col_1Test);
List <double> col_2TestNormalizovani = normalizacijaInt(col_2Test);
List <double> col_3TestNormalizovani = normalizacijaInt(col_3Test);
List <double> col_4TestNormalizovani = normalizacijaInt(col_4Test);
/* foreach (double d in col_4TestNormalizovani)
* {
* Console.WriteLine(d);
* }*/
double dobrih = 0;
double broj = 0;
for (int i = 0; i < dataLinesTest.Length; i++)
{
double[] x1 = { col_1TestNormalizovani[i], col_2TestNormalizovani[i], col_3TestNormalizovani[i], col_4TestNormalizovani[i] };
double type = 0;
if (network.predict(x1.ToList())[0] < 0.25)
{
type = 0;
}
else if (network.predict(x1.ToList())[0] > 0.25 && network.predict(x1.ToList())[0] < 0.75)
{
type = 0.5;
}
else
{
type = 1;
}
if (col_5Test[i] == type)
{
dobrih++;
}
Console.WriteLine("PRediktovana vrednost je " + network.predict(x1.ToList())[0]);
Console.WriteLine("Stvarna vrednost je " + col_5Test[i]);
broj++;
}
Console.WriteLine("Dobro prediktovanih je " + dobrih + " od " + broj);
double procenattacnosti = (dobrih / broj) * 100;
Console.WriteLine("Tačnost je = " + procenattacnosti + " %");
Console.ReadLine();
// Pomocne funckije
List <double> normalizacijaInt(List <double> listaVrednosti)
{
List <double> noramlizovanaLista = new List <double>();
double dataMax = listaVrednosti.Max();
double dataMin = listaVrednosti.Min();
foreach (double vrednost in listaVrednosti)
{
noramlizovanaLista.Add((vrednost - dataMin) / (dataMax - dataMin));
// Console.WriteLine("Minimalna vrendost je" + (vrednost - dataMin) / (dataMax - dataMin));
}
return(noramlizovanaLista);
}
}
static void Main(string[] args)
{
#region Ucitavanje data seta
List <int> indeksKolonaInputa = new List <int>();
// ulazi od col_1 do col_5
indeksKolonaInputa.Add(1);
indeksKolonaInputa.Add(2);
indeksKolonaInputa.Add(3);
indeksKolonaInputa.Add(4);
List <int> indeksKolonaOutputa = new List <int>();
// col_5 izlaz
indeksKolonaOutputa.Add(5);
FileDAO fileDAO = new FileDAO(indeksKolonaInputa, indeksKolonaOutputa, 0);
// fileDAO.imaKategorickihAtributa
// fileDAO.getKategorickeAtribute
#endregion
#region Kreiranje neuronske mreze
NeuralNetwork network = new NeuralNetwork();
network.Add(new NeuralLayer(indeksKolonaInputa.Count, 2, "sigmoid"));
//network.Add(new NeuralLayer(3, 2, "sigmoid"));
network.Add(new NeuralLayer(2, 1, "sigmoid"));
#endregion
#region Vektori za inpute
List <List <double> > X = new List <List <double> >();
X = fileDAO.X;
#endregion
#region Vektori za outpute
List <List <double> > Y = new List <List <double> >();
Y = fileDAO.Y;
#endregion
#region Fitovanje
Console.WriteLine("Obuka pocela.");
network.fit(X, Y, 0.1, 0.9, 3000);
Console.WriteLine("Kraj obuke.");
#endregion
#region Predikcija
int ukupnoPogodjenih = 0;
for (int i = 0; i < fileDAO.XTest.Count; ++i)
{
List <Double> prediction = network.predict(fileDAO.XTest[i]);
Console.WriteLine("Pravi rezultat u test podacima je: {0}, Po proracunu i predikciji dobijam {1}", fileDAO.YTest[i][0], prediction[0]);
// pravi rezultat mi moze biti 0, 0.5 ili 1 pa onda ove sto sam dobio, moram pretvoriti u tip
// i proveriti da li su odgovarajuceg tipa
double tip = -1;
if (prediction[0] <= 0.33)
{
tip = 0;
}
else if (prediction[0] > 0.33 && prediction[0] <= 0.66)
{
tip = 0.5;
}
else
{
tip = 1;
}
if (fileDAO.YTest[i][0] == tip)
{
++ukupnoPogodjenih;
}
}
Console.Write("Pogodjeno {0} od {1} ", ukupnoPogodjenih, fileDAO.YTest.Count);
Console.Write("Tacnost {0} %", ukupnoPogodjenih * 100 / fileDAO.YTest.Count);
Console.ReadLine();
#endregion
}
static void Main(string[] args)
{
string[] lines;
lines = File.ReadAllLines(@"./../../data/heart.csv");
lines = lines.Skip(1).ToArray();
List <int> age = new List <int>();
List <int> pol = new List <int>();
List <int> chestPain = new List <int>();
List <int> RestingBloodP = new List <int>();
List <int> cholesterol = new List <int>();
List <int> fastingBloodSugar = new List <int>();
List <int> restingECG = new List <int>();
List <int> thalach = new List <int>();
List <int> excerciseAngina = new List <int>();
List <double> oldPeak = new List <double>();
List <int> slope = new List <int>();
List <int> ca = new List <int>();
List <int> thai = new List <int>();
List <int> isTarget = new List <int>();
Dictionary <String, int> myMap = new Dictionary <String, int>();
foreach (string line in lines)
{
string[] parts = line.Split(',');
age.Add(int.Parse((parts[(int)HeaderDisese.Age])));
pol.Add(int.Parse((parts[(int)HeaderDisese.Sex])));
chestPain.Add(int.Parse((parts[(int)HeaderDisese.Cp])));
RestingBloodP.Add(int.Parse((parts[(int)HeaderDisese.TrestBps])));
cholesterol.Add(int.Parse((parts[(int)HeaderDisese.Chol])));
fastingBloodSugar.Add(int.Parse((parts[(int)HeaderDisese.Fbs])));
restingECG.Add(int.Parse((parts[(int)HeaderDisese.Restecg])));
thalach.Add(int.Parse((parts[(int)HeaderDisese.Thalach])));
excerciseAngina.Add(int.Parse((parts[(int)HeaderDisese.Exang])));
oldPeak.Add(double.Parse((parts[(int)HeaderDisese.Oldpeak])));
slope.Add(int.Parse((parts[(int)HeaderDisese.Slope])));
ca.Add(int.Parse((parts[(int)HeaderDisese.Ca])));
thai.Add(int.Parse((parts[(int)HeaderDisese.Thal])));
isTarget.Add(int.Parse((parts[(int)HeaderDisese.Target])));
}
NeuralNetwork network = new NeuralNetwork();
// Two activation functions are supported
// sigmoid
// relu
network.Add(new NeuralLayer(13, 7, "relu"));
network.Add(new NeuralLayer(7, 4, "relu"));
network.Add(new NeuralLayer(4, 2, "relu"));
network.Add(new NeuralLayer(2, 1, "relu"));
List <List <double> > X = new List <List <double> >();
List <List <double> > Y = new List <List <double> >();
// 303 - total
// 0-30 - test case
// 30-270 - training data
// 270-303 - test case
for (int i = 30; i < 270; i++)
{
double[] xTemp = { (double)age[i], (double)pol[i], (double)chestPain[i], (double)RestingBloodP[i], (double)cholesterol[i], (double)fastingBloodSugar[i], (double)restingECG[i], (double)thalach[i], (double)excerciseAngina[i], oldPeak[i], (double)slope[i], (double)ca[i], (double)thai[i] };
X.Add(xTemp.ToList());
double[] yTemp = { (double)isTarget[i] };
Y.Add(yTemp.ToList());
}
Console.WriteLine("Training started...");
network.fit(X, Y, 0.1, 0.9, 500);
Console.WriteLine("Training finished");
int hit = 0;
for (int i = 0; i < 303; i++)
{
if (i > 29 && i < 270)
{
continue;
}
double[] x1 = { age[i], pol[i], chestPain[i], RestingBloodP[i], cholesterol[i], fastingBloodSugar[i], restingECG[i], thalach[i], excerciseAngina[i], oldPeak[i], slope[i], ca[i], thai[i] };
int predict = -1;
if (network.predict(x1.ToList())[0] < 0.5)
{
predict = 0;
}
else
{
predict = 1;
}
if (isTarget[i] == predict)
{
hit++;
}
}
Console.WriteLine("Result: " + hit + "/60");
Console.ReadLine();
}
static void Main(string[] args)
{
#region Ucitavanje data seta
List <int> indeksKolonaInputa = new List <int>();
// popularity
indeksKolonaInputa.Add(31);
//male
indeksKolonaInputa.Add(7);
// books
indeksKolonaInputa.Add(16);
indeksKolonaInputa.Add(17);
indeksKolonaInputa.Add(18);
indeksKolonaInputa.Add(19);
indeksKolonaInputa.Add(20);
// isNoble
indeksKolonaInputa.Add(26);
// numDeadRelations
indeksKolonaInputa.Add(28);
// house
indeksKolonaInputa.Add(14);
List <int> indeksKolonaOutputa = new List <int>();
// isAlive
indeksKolonaOutputa.Add(32);
FileDAO fileDAO = new FileDAO(indeksKolonaInputa, indeksKolonaOutputa, 20);
#endregion
#region Vektori za inpute
List <List <double> > X = new List <List <double> >();
X = fileDAO.X;
#endregion
#region Vektori za outpute
List <List <double> > Y = new List <List <double> >();
Y = fileDAO.Y;
#endregion
#region Kreiranje neuronske mreze
NeuralNetwork network = new NeuralNetwork();
network.Add(new NeuralLayer(X[0].Count, 2, "sigmoid")); // X[0], ali moze i X[bilo koji idx], bitno je da tako dobijem broj ulaza
network.Add(new NeuralLayer(2, 1, "sigmoid"));
#endregion
#region Fitovanje
Console.WriteLine("Obuka pocela.");
network.fit(X, Y, 0.1, 0.9, 500);
Console.WriteLine("Kraj obuke.");
#endregion
#region Skor koji postize mreza
Score score = new Score();
score.giveMeScore(fileDAO, network);
#endregion
}
static void Main(string[] args)
{
NeuralNetwork network = new NeuralNetwork();
network.Add(new NeuralLayer(4, 2, "sigmoid"));
network.Add(new NeuralLayer(2, 1, "sigmoid"));
List <int> lotArea = new List <int>();
List <int> yearBuilt = new List <int>();
Dictionary <String, int> mapNeighbourhood = new Dictionary <String, int>();
List <int> listNeighbourhood = new List <int>();
List <double> listBldgType = new List <double>();//5 razlicitih vrsta
List <int> salePrice = new List <int>();
String[] dataLines = File.ReadAllLines(@"./../../train.csv");
dataLines = dataLines.Skip(1).ToArray();
foreach (String linija in dataLines)
{
string[] parts = linija.Split(',');
lotArea.Add(int.Parse(parts[(int)Header.LotArea]));
yearBuilt.Add(int.Parse(parts[(int)Header.YearBuilt]));
String neighbourhood = parts[(int)Header.Neighborhood];
if (!neighbourhood.Equals(""))
{
if (!mapNeighbourhood.ContainsKey(neighbourhood))
{
mapNeighbourhood.Add(neighbourhood, mapNeighbourhood.Count);
listNeighbourhood.Add(mapNeighbourhood.Count);
}
else
{
listNeighbourhood.Add(mapNeighbourhood[neighbourhood]);
}
}
else
{
listNeighbourhood.Add(mapNeighbourhood.Count);
}
//listBldgType
String BldgType = parts[(int)Header.BldgType];
if (BldgType.Equals("1Fam"))
{
listBldgType.Add(0);
}
else if (BldgType.Equals("2fmCon"))
{
listBldgType.Add(0.25);
}
else if (BldgType.Equals("Duplex"))
{
listBldgType.Add(0.5);
}
else if (BldgType.Equals("Twnhs"))
{
listBldgType.Add(0.75);
}
else if (BldgType.Equals("TwnhsE"))
{
listBldgType.Add(1);
}
salePrice.Add(int.Parse(parts[(int)Header.SalePrice]));
}
List <double> lotAreaNormalizovani = normalizacijaInt(lotArea);
List <double> yearBuiltNormalizovani = normalizacijaInt(yearBuilt);
List <double> salePriceNormalizovani = normalizacijaInt(salePrice);
List <double> listNeighbourhoodNormalizovani = normalizacijaInt(listNeighbourhood);
List <List <double> > X = new List <List <double> >();
List <List <double> > Y = new List <List <double> >();
// prolazimo kroz sve likove
for (int i = 0; i < 1167; i++)
{
double[] xTemp = { lotAreaNormalizovani[i], yearBuiltNormalizovani[i], listNeighbourhoodNormalizovani[i], listBldgType[i] };
X.Add(xTemp.ToList());
double[] yTemp = { (double)salePriceNormalizovani[i] };
Y.Add(yTemp.ToList());
}
Console.WriteLine("Obuka pocela");
network.fit(X, Y, 0.1, 0.9, 250);
Console.WriteLine("Gotova obuka");
int tacnoPrediktovanih = 0;
int brojPrediktovanih = 0;
for (int i = 1167; i < 1459; i++)
{
brojPrediktovanih++;
double[] x1 = { lotAreaNormalizovani[i], yearBuiltNormalizovani[i], listNeighbourhoodNormalizovani[i], listBldgType[i] };
double predictedPrice = network.predict(x1.ToList())[0];
double realPrice = salePriceNormalizovani[i];
Console.WriteLine("prediktovana vrednost je " + predictedPrice + " stvarna je " + salePriceNormalizovani[i]);
double tacnost = (predictedPrice * 100) / realPrice;
Console.WriteLine(tacnost);
if (tacnost >= 80 && tacnost <= 120)
{
tacnoPrediktovanih++;
}
}
Console.WriteLine("Tacnost: " + (tacnoPrediktovanih * 100) / brojPrediktovanih + "%");
Console.ReadLine();
List <double> normalizacijaInt(List <int> listaVrednosti)
{
List <double> noramlizovanaLista = new List <double>();
double dataMax = listaVrednosti.Max();
double dataMin = listaVrednosti.Min();
// Console.WriteLine("Maksimalna vrendost je" + dataMax);
//Console.WriteLine("Minimalna vrendost je" + dataMin);
foreach (double vrednost in listaVrednosti)
{
noramlizovanaLista.Add((vrednost - dataMin) / (dataMax - dataMin));
// Console.WriteLine("Minimalna vrendost je" + (vrednost - dataMin) / (dataMax - dataMin));
}
return(noramlizovanaLista);
}
}
static void Main(string[] args)
{
#region Podesavanja koje cemo kolone ucitavati
List <int> indeksKolonaInputa = new List <int>();
// ulazi za pojavljivanje u knjigama
indeksKolonaInputa.Add(16);
indeksKolonaInputa.Add(17);
indeksKolonaInputa.Add(18);
indeksKolonaInputa.Add(19);
indeksKolonaInputa.Add(20);
// muski ili zensko ulaz
indeksKolonaInputa.Add(7);
// popularnost ulaz
indeksKolonaInputa.Add(31);
// plemicko poreklo ulaz
indeksKolonaInputa.Add(26);
// num dead relations ulaz
indeksKolonaInputa.Add(28);
// porodica kojoj pripada
indeksKolonaInputa.Add(14);
List <int> indeksKolonaOutputa = new List <int>();
// isAlive ulaz
indeksKolonaOutputa.Add(32);
FileDAO fileDAO = new FileDAO(indeksKolonaInputa, indeksKolonaOutputa, 20);
// fileDAO.imaKategorickihAtributa
// fileDAO.getKategorickeAtribute
#endregion
#region Vektori za inpute
List <List <double> > X = new List <List <double> >();
X = fileDAO.X;
#endregion
#region Vektori za outpute
List <List <double> > Y = new List <List <double> >();
Y = fileDAO.Y;
#endregion
#region Kreiranje neuronske mreze
NeuralNetwork network = new NeuralNetwork();
network.Add(new NeuralLayer(X[0].Count, 4, "sigmoid")); // X[0], ali moze i X[bilo koji idx], bitno je da tako dobijem broj ulaza
network.Add(new NeuralLayer(4, 2, "sigmoid"));
network.Add(new NeuralLayer(2, 1, "sigmoid"));
#endregion
#region Fitovanje
Console.WriteLine("Obuka pocela.");
network.fit(X, Y, 0.1, 0.9, 10);
Console.WriteLine("Kraj obuke.");
#endregion
#region Predikcija
Score score = new Score();
score.scoreModel(fileDAO, network);
#endregion
}
static void Main(string[] args)
{
string[] lines = File.ReadAllLines("@./../../../../../novi.csv");
lines = lines.Skip(1).ToArray( );
//ulazi
List <int> typeList = new List <int>( );
Dictionary <String, int> TypeDict = new Dictionary <String, int>( );
List <double> oldBalanceOrgList = new List <double>( );
List <double> newbalanceOrigList = new List <double>( );
List <double> oldBalanceDestList = new List <double>( );
List <double> newbalanceDestList = new List <double>( );
List <double> amountList = new List <double>( );
//izlaz
List <int> isFraudList = new List <int>( );
foreach (string line in lines)
{
string[] attr = line.Split(',');
String p = attr[(int)FirstLine.type];
if (TypeDict.ContainsKey(p))
{
typeList.Add(TypeDict[p]);
}
else
{
TypeDict[p] = TypeDict.Count;
typeList.Add(TypeDict[p]);
}
oldBalanceOrgList.Add(double.Parse(attr[(int)FirstLine.oldbalanceOrg]));
newbalanceOrigList.Add(double.Parse(attr[(int)FirstLine.newbalanceOrig]));
oldBalanceDestList.Add(double.Parse(attr[(int)FirstLine.oldbalanceDest]));
newbalanceDestList.Add(double.Parse(attr[(int)FirstLine.newbalanceDest]));
amountList.Add(double.Parse(attr[(int)FirstLine.amount]));
isFraudList.Add(int.Parse(attr[(int)FirstLine.isFraud]));
}
//normalizacije
double max = oldBalanceOrgList.Max( );
for (int i = 0; i < oldBalanceOrgList.Count; i++)
{
oldBalanceOrgList[i] /= max;
}
max = newbalanceOrigList.Max( );
for (int i = 0; i < newbalanceOrigList.Count; i++)
{
newbalanceOrigList[i] /= max;
}
max = oldBalanceDestList.Max( );
for (int i = 0; i < oldBalanceDestList.Count; i++)
{
oldBalanceDestList[i] /= max;
}
max = newbalanceDestList.Max( );
for (int i = 0; i < newbalanceDestList.Count; i++)
{
newbalanceDestList[i] /= max;
}
max = amountList.Max( );
for (int i = 0; i < amountList.Count; i++)
{
amountList[i] /= max;
}
NeuralNetwork network = new NeuralNetwork( );
network.Add(new NeuralLayer(6, 6, "sigmoid"));
network.Add(new NeuralLayer(6, 1, "sigmoid"));
List <List <double> > X = new List <List <double> >( );
List <List <double> > Y = new List <List <double> >( );
for (int i = 0; i < oldBalanceOrgList.Count; i++)
{
if (i % 5 != 0)
{
double[] xTemp = { (double)typeList[i], (double)oldBalanceOrgList[i], (double)newbalanceOrigList[i], (double)oldBalanceDestList[i], (double)newbalanceDestList[i], (double)amountList[i] };
X.Add(xTemp.ToList( ));
double[] yTemp = { (double)isFraudList[i] };
Y.Add(yTemp.ToList( ));
}
}
Console.WriteLine("obuka");
network.fit(X, Y, 0.1, 0.9, 100);
Console.WriteLine("done");
//int x = (int)Math.Ceiling(oldBalanceOrgList.Count * 0.5);
int x = 0;
int ok1 = 0;
int ok0 = 0;
int uk1 = 0;
int uk0 = 0;
for (int i = x; i < oldBalanceDestList.Count; i++)
{
if (i % 5 == 0)
{
double[] x1 = { typeList[i], oldBalanceOrgList[i], newbalanceOrigList[i], oldBalanceDestList[i], newbalanceDestList[i], amountList[i] };
if (isFraudList[i] == 0)
{
uk0++;
if (network.predict(x1.ToList( ))[0] < 0.5)
{
ok0++;
}
}
else if (isFraudList[i] == 1)
{
uk1++;
if (network.predict(x1.ToList( ))[0] >= 0.5)
{
ok1++;
}
}
}
}
Console.WriteLine("OK 0: " + ok0 + "\nUkupno: " + uk0 + "\nProcenat pogodaka:" + 100 * ((double)ok0 / uk0) + "\n");
Console.WriteLine("OK 1: " + ok1 + "\nUkupno: " + uk1 + "\nProcenat pogodaka:" + 100 * ((double)ok1 / uk1) + "\n");
Console.WriteLine("OK UkUPNO: " + (ok1 + ok0) + "\nUkupno: " + (uk0 + uk1) + "\nProcenat pogodaka:" + 100 * ((double)(ok1 + ok0) / (uk0 + uk1)) + "\n");
Console.ReadLine( );
}
static void Main(string[] args)
{
#region Ucitavanje data seta
List <int> indeksKolonaInputa = new List <int>();
// Gender
indeksKolonaInputa.Add(1);
// Married
indeksKolonaInputa.Add(2);
// education
indeksKolonaInputa.Add(4);
// ApplicantIncome
indeksKolonaInputa.Add(6);
//CoapplicantIncome
indeksKolonaInputa.Add(7);
//LoanAmount
indeksKolonaInputa.Add(8);
List <int> indeksKolonaOutputa = new List <int>();
// Loan_Status
indeksKolonaOutputa.Add(12);
FileDAO fileDAO = new FileDAO(indeksKolonaInputa, indeksKolonaOutputa, 30); // 30 jer toliko zelim za test podatke
#endregion
#region Vektori za inpute
List <List <double> > X = new List <List <double> >();
X = fileDAO.X;
#endregion
#region Vektori za outpute
List <List <double> > Y = new List <List <double> >();
Y = fileDAO.Y;
#endregion
#region Kreiranje neuronske mreze
NeuralNetwork network = new NeuralNetwork();
network.Add(new NeuralLayer(X[0].Count, 2, "tanh")); // X[0], ali moze i X[bilo koji idx], bitno je da tako dobijem broj ulaza
network.Add(new NeuralLayer(2, 1, "tanh"));
#endregion
#region Fitovanje
Console.WriteLine("Obuka pocela.");
network.fit(X, Y, 0.1, 0.9, 300);
Console.WriteLine("Kraj obuke.");
#endregion
#region Skor koji postize mreza
Score score = new Score();
score.giveMeScore(fileDAO, network);
#endregion
}