//method for reading the values for the neural network(s)
static void Read(ref NeuralNetwork tanh, ref NeuralNetwork sigm, ref NeuralNetwork relu)
{
//list for data retrieved from .txt file
List <double> data2 = new List <double>();
//counter for the list of data
int counter = 0;
//will hold initial values retrieved from the .txt files
string[] text;
try
{
text = File.ReadAllText("sigmInput.txt").Trim().Split('\n');
foreach (string g in text)
{
data2.Add(double.Parse(g));
}
for (int i = 0; i < sigm.InputWeights.Rows; i++)
{
for (int j = 0; j < sigm.InputWeights.Columns; j++)
{
sigm.InputWeights.m[i, j] = data2[counter];
counter++;
}
}
//reset
counter = 0;
data2.Clear();
text = File.ReadAllText("sigmHidden.txt").Trim().Split('\n');
foreach (string g in text)
{
data2.Add(double.Parse(g));
}
for (int i = 0; i < sigm.HiddenWeights.Rows; i++)
{
for (int j = 0; j < sigm.HiddenWeights.Columns; j++)
{
sigm.HiddenWeights.m[i, j] = data2[counter];
counter++;
}
}
//reset
counter = 0;
data2.Clear();
text = File.ReadAllText("tanhHidden.txt").Trim().Split('\n');
foreach (string g in text)
{
data2.Add(double.Parse(g));
}
for (int i = 0; i < tanh.HiddenWeights.Rows; i++)
{
for (int j = 0; j < tanh.HiddenWeights.Columns; j++)
{
tanh.HiddenWeights.m[i, j] = data2[counter];
counter++;
}
}
//reset
counter = 0;
data2.Clear();
text = File.ReadAllText("tanhInput.txt").Trim().Split('\n');
foreach (string g in text)
{
data2.Add(double.Parse(g));
}
for (int i = 0; i < tanh.InputWeights.Rows; i++)
{
for (int j = 0; j < tanh.InputWeights.Columns; j++)
{
tanh.InputWeights.m[i, j] = data2[counter];
counter++;
}
}
//reset
counter = 0;
data2.Clear();
text = File.ReadAllText("reluInput.txt").Trim().Split('\n');
foreach (string g in text)
{
data2.Add(double.Parse(g));
}
for (int i = 0; i < relu.InputWeights.Rows; i++)
{
for (int j = 0; j < relu.InputWeights.Columns; j++)
{
relu.InputWeights.m[i, j] = data2[counter];
counter++;
}
}
//reset
counter = 0;
data2.Clear();
text = File.ReadAllText("reluHidden.txt").Trim().Split('\n');
foreach (string g in text)
{
data2.Add(double.Parse(g));
}
for (int i = 0; i < relu.HiddenWeights.Rows; i++)
{
for (int j = 0; j < relu.HiddenWeights.Columns; j++)
{
relu.HiddenWeights.m[i, j] = data2[counter];
counter++;
}
}
}
catch
{
}
}
//method for writing the current network(s) to a .txt file
static void Print(NeuralNetwork tanh, NeuralNetwork sigm, NeuralNetwork relu)
{
string docPath = @"C:\Users\morga\Desktop\Personal Projects\HeartDisease\HeartDisease\bin\Debug";
using (StreamWriter outputFile = new StreamWriter(Path.Combine(docPath, "sigmInput.txt"), false))
{
for (int i = 0; i < sigm.InputWeights.Rows; i++)
{
for (int j = 0; j < sigm.InputWeights.Columns; j++)
{
outputFile.WriteLine(sigm.InputWeights.m[i, j]);
}
}
}
using (StreamWriter outputFile = new StreamWriter(Path.Combine(docPath, "sigmHidden.txt"), false))
{
for (int i = 0; i < sigm.HiddenWeights.Rows; i++)
{
for (int j = 0; j < sigm.HiddenWeights.Columns; j++)
{
outputFile.WriteLine(sigm.HiddenWeights.m[i, j]);
}
}
}
using (StreamWriter outputFile = new StreamWriter(Path.Combine(docPath, "tanhInput.txt"), false))
{
for (int i = 0; i < tanh.InputWeights.Rows; i++)
{
for (int j = 0; j < tanh.InputWeights.Columns; j++)
{
outputFile.WriteLine(tanh.InputWeights.m[i, j]);
}
}
}
using (StreamWriter outputFile = new StreamWriter(Path.Combine(docPath, "tanhHidden.txt"), false))
{
for (int i = 0; i < tanh.HiddenWeights.Rows; i++)
{
for (int j = 0; j < tanh.HiddenWeights.Columns; j++)
{
outputFile.WriteLine(tanh.HiddenWeights.m[i, j]);
}
}
}
using (StreamWriter outputFile = new StreamWriter(Path.Combine(docPath, "reluInput.txt"), false))
{
for (int i = 0; i < relu.InputWeights.Rows; i++)
{
for (int j = 0; j < relu.InputWeights.Columns; j++)
{
outputFile.WriteLine(relu.InputWeights.m[i, j]);
}
}
}
using (StreamWriter outputFile = new StreamWriter(Path.Combine(docPath, "reluHidden.txt"), false))
{
for (int i = 0; i < relu.HiddenWeights.Rows; i++)
{
for (int j = 0; j < relu.HiddenWeights.Columns; j++)
{
outputFile.WriteLine(relu.HiddenWeights.m[i, j]);
}
}
}
}
static void Main(string[] args)
{
Random random = new Random();
//list for all the data
List <double[]> data = new List <double[]>();
//list for whether patient has heart disease
List <double> answer = new List <double>();
//read the values from the .csv into a comma delimited string
var reader = new StreamReader(File.OpenRead(@"heart.csv"));
//holds values from .csv file
List <string> listRows = new List <string>();
//retrieves the information
while (!reader.EndOfStream)
{
listRows.Add(reader.ReadLine());
}//end while
//remove the header row
listRows.RemoveAt(0);
for (int i = 0; i < listRows.Count; i++)
{
//holds values for rows from the .csv file
double[] holder = new double[14];
//splits the comma delimited rows
string[] values = listRows[i].Split(',');
//parses all values to doubles
for (int j = 0; j < holder.Length; j++)
{
holder[j] = double.Parse(values[j]);
}
//adds the row (array of doubles) just found to the data
data.Add(holder);
//adds whether the patient has heart disease or not
answer.Add(double.Parse(values[13]));
}
//neural network (learningrate, input nodes, hidden nodes, output nodes
//relu trains by relu as activation function
NeuralNetwork Relu = new NeuralNetwork(.00001, 13, 2, 1);
//sigmoid trains by sigmoid as activation function
NeuralNetwork Sigmoid = new NeuralNetwork(.00001, 13, 2, 1);
//tanh rains by relu as activaion function
NeuralNetwork Tanh = new NeuralNetwork(.00001, 13, 2, 1);
//user choice
int choice = 0;
//menu operations
while (choice != 6)
{
Menu();
choice = int.Parse(Console.ReadLine());
switch (choice)
{
//Train Network
case 1:
//prompt user for how many times they'd like to train the network
int num = 0;
while (num == 0)
{
Console.WriteLine("How many times would you like to train the network?");
try
{
num = int.Parse(Console.ReadLine());
}
catch
{
Console.WriteLine("Please enter an integer value.");
}
}
for (int i = 0; i < num; i++)
{
//trains the neural networks
Relu.TrainRelu(data, answer);
Sigmoid.TrainSigmoid(data, answer);
Tanh.TrainTanh(data, answer);
//clears the answer and shuffles the data
answer.Clear();
data = data.OrderBy(x => random.Next()).ToList();
//adds all the shuffled answers in order
for (int j = 0; j < data.Count; j++)
{
answer.Add(data[j][13]);
}
}
break;
//Retrain Network
case 2:
Tanh = new NeuralNetwork(.00001, 13, 2, 1);
Relu = new NeuralNetwork(.00001, 13, 2, 1);
Sigmoid = new NeuralNetwork(.00001, 13, 2, 1);
for (int i = 0; i < 100; i++)
{
//trains the neural networks
Relu.TrainRelu(data, answer);
Sigmoid.TrainSigmoid(data, answer);
Tanh.TrainTanh(data, answer);
//clears the answer and shuffles the data
answer.Clear();
data = data.OrderBy(x => random.Next()).ToList();
//adds all the shuffled answers in order
for (int j = 0; j < data.Count; j++)
{
answer.Add(data[j][13]);
}
}
break;
//read in a network saved previously
case 3:
Read(ref Tanh, ref Sigmoid, ref Relu);
break;
//print the average accuracy of the Neural Network
case 4:
double tanhAverage = 0;
double reluAverage = 0;
double sigmoidAverage = 0;
for (int i = 0; i < data.Count; i++)
{
double d = data[i][13];
double t = Tanh.Test(data[i]);
double r = Relu.Test(data[i]);
double s = Sigmoid.Test(data[i]);
tanhAverage += d - t;
reluAverage += d - r;
sigmoidAverage += d - s;
}
Console.WriteLine("Tanh=" + tanhAverage / 303);
Console.WriteLine("Relu=" + reluAverage / 303);
Console.WriteLine("Sigmoid=" + sigmoidAverage / 303);
break;
//saves the current network
case 5:
Print(Tanh, Sigmoid, Relu);
break;
case 6:
break;
default:
Console.WriteLine("Unknown Command");
break;
}
}
}
