public static void SaveNeuralNetwork(NeuralNetwork.Network neuralNetwork)
{
Stream stream = null;
try
{
stream = File.Open(Config.Default.FilesLocation + NETWORK, FileMode.Create);
BinaryFormatter bFormatter = new BinaryFormatter();
if (neuralNetwork != null)
{
bFormatter.Serialize(stream, neuralNetwork);
}
}
catch (Exception ex)
{
Debug.WriteLine(ex.Message);
}
finally
{
if (stream != null)
{
stream.Close();
}
}
}
static void Main(string[] args)
{
var nn = new NeuralNetwork.Network(784, 200, 10, 0.2);
TrainNetwork(ref nn);
TestNetwork(ref nn);
Console.WriteLine("\nDone!\nPress Enter to exit");
Console.ReadLine();
}
public static NetworkResult guessHTTPService()
{
NetworkResult result = new NetworkResult("Unknown", 100.0f);
NeuralNetwork.Network net = new NeuralNetwork.Network();
net.addInput("Windows");
net.addInput("Linux");
net.addInput("FreeBSD");
net.addInput("PHP");
net.addInput("ASP.NET");
net.addInput("Static");
net.addInput("Ruby/Node.js");
return(result);
}
private static void WithFile(string fileName, NeuralNetwork.Network network, Action <List <Int32>, NeuralNetwork.Network> action)
{
using (var file = File.OpenRead(fileName))
{
using (var streamReader = new StreamReader(file))
{
while (!streamReader.EndOfStream)
{
var line = streamReader.ReadLine();
var parts = line.Split(',').Select(v => int.Parse(v)).ToList();
action(parts, network);
}
}
}
}
private static void TrainNetwork(ref NeuralNetwork.Network network)
{
var fileName = ConfigurationManager.AppSettings["trainData"];
Stopwatch sw = Stopwatch.StartNew();
Console.WriteLine("Training Neural Network.");
WithFile(fileName, network, (parts, nn) => {
var inputs = GetInputs(parts);
var targets = GetTargets(parts[0]);
nn.Train(inputs, targets);
});
sw.Stop();
Console.WriteLine("Training is finished!\n");
Console.WriteLine("Training took {0:0.000} ms", sw.ElapsedMilliseconds);
}
private static void TestNetwork(ref NeuralNetwork.Network network)
{
var fileName = ConfigurationManager.AppSettings["testData"];
Console.WriteLine("\nTesting Neural Network.");
Stopwatch sw = Stopwatch.StartNew();
List <short> results = new List <short>();
var counter = 0;
WithFile(fileName, network, (parts, nn) =>
{
var expectedValue = parts[0];
var inputs = GetInputs(parts);
var result = nn.Query(inputs).MaxValueIndex();
results.Add((short)((expectedValue == result) ? 1 : 0));
counter++;
});
sw.Stop();
Console.WriteLine("Testing is finished!\n");
Console.WriteLine("Testing took {0:0.000} ms", sw.ElapsedMilliseconds);
var foregroundColor = Console.ForegroundColor;
Console.ForegroundColor = ConsoleColor.DarkGreen;
Console.WriteLine("\nSuccess rate is {0:0.000}%", (double)results.Sum(v => v) / counter);
Console.ForegroundColor = foregroundColor;
}
public static NeuralNetwork.Network LoadNeuralNetwork()
{
NeuralNetwork.Network neuralNetwork = null;
Stream stream = null;
try
{
stream = File.Open(Config.Default.FilesLocation + NETWORK, FileMode.Open);
BinaryFormatter bFormatter = new BinaryFormatter();
neuralNetwork = (NeuralNetwork.Network)bFormatter.Deserialize(stream);
}
catch (Exception ex)
{
Debug.WriteLine(ex.Message);
}
finally
{
if (stream != null)
{
stream.Close();
}
}
return(neuralNetwork);
}
public static OSResult guessOS(string[] foundServices)
{
OSResult result;
// Initialize objects
NeuralNetwork.Network net = new NeuralNetwork.Network();
// Load in data to memory
if (!File.Exists(Path.Combine(Environment.ExpandEnvironmentVariables("%userprofile%"), "Documents", "Gerbil", "memstore", "OSServiceTraining.ini")))
{
return(new OSResult("ERROR", 0.0f));
}
string[] trainingData = File.ReadAllLines(Path.Combine(Environment.ExpandEnvironmentVariables("%userprofile%"), "Documents", "Gerbil", "memstore", "OSServiceTraining.ini"));
// Calculate weights
PairCounter pc = new PairCounter();
foreach (string i in trainingData)
{
string sName = i.Split('=')[0];
string fOS = i.Split('=')[1];
pc.Add(new Pair(sName, fOS));
}
Dictionary <Pair, float> connectionWeights = getPercentagesFromPair(pc.getResults());
// TODO: Train network
foreach (KeyValuePair <Pair, float> i in connectionWeights)
{
net.addInput(i.Key.item1);
net.addOutput(i.Key.item2, i.Key.item1 + "Connector", i.Value, i.Key.item1);
}
// Feed data into tranined neural network
foreach (string i in foundServices)
{
try
{
net.fireInput(i);
}
catch (NeuralNetwork.NodeNotFoundException e)
{
// Serive does not exist, since we are not in training mode, ignore.
}
catch
{
// A serious engine error occured. Throw fatal error.
throw new FatalEngineException();
}
}
// Get outputs
Dictionary <string, float> results = net.getResults();
string resultName = "Unknown";
float resultCertainty = 0.0f;
float maxCertainty = 0.0f;
// Find most likely answer
foreach (KeyValuePair <string, float> i in results)
{
if (i.Value > resultCertainty)
{
resultName = i.Key;
resultCertainty = i.Value;
maxCertainty += i.Value;
}
}
resultCertainty = resultCertainty / maxCertainty;
result = new OSResult(resultName, resultCertainty);
return(result);
}
