void Awake()
{
Target = (NeuralNetwork)target;
}
static void Main()
{
List <string> Accuracy = new List <string>();
var EngWord = File.ReadAllLines("EN.filtered");
var ITWord = File.ReadAllLines("IT.filtered");
List <CharSpace> Wordspace = new List <CharSpace>();
for (int i = 0; i < 8; i++)
{
Wordspace.Add(new CharSpace(i));
}
var allinputs = new List <ConstantArgumentValue>();
foreach (var i in Wordspace)
{
allinputs.AddRange(i.letters);
}
OutputData English, Italian;
English = new OutputData();
Italian = new OutputData();
var outputs = new List <OutputData>();
outputs.Add(English);
outputs.Add(Italian);
NeuralNetwork LanguageNeuralNet = new NeuralNetwork(allinputs, outputs, new int[] { 20 });
int counterEN, counterIT;
counterEN = 0;
counterIT = 0;
var random = new Random();
int Right, Wrong;
Right = 0;
Wrong = 0;
{
var accur = CalculateAccurary(Wordspace, LanguageNeuralNet, English, Italian, EngWord, ITWord);
Accuracy.Add((counterEN + counterIT).ToString() + ";" + accur.ToString());
}
while (counterEN + counterIT < EngWord.Length + ITWord.Length)
{
string word;
if (random.Next() % 2 == 0)
{
if (counterEN != EngWord.Length)
{
word = EngWord[counterEN];
counterEN++;
English.MustBeHigh = true;
Italian.MustBeHigh = false;
}
else
{
word = ITWord[counterIT];
counterIT++;
English.MustBeHigh = false;
Italian.MustBeHigh = true;
}
}
else
{
if (counterIT != ITWord.Length)
{
word = ITWord[counterIT];
counterIT++;
English.MustBeHigh = false;
Italian.MustBeHigh = true;
}
else
{
word = EngWord[counterEN];
counterEN++;
English.MustBeHigh = true;
Italian.MustBeHigh = false;
}
}
for (int characternum = 0; characternum < 8; characternum++)
{
Wordspace[characternum].SetLetter(word[characternum]);
}
LanguageNeuralNet.Learn();
LanguageNeuralNet.CalculateResults();
if (English.Value > Italian.Value)
{
if (English.MustBeHigh)
{
Right++;
}
else
{
Wrong++;
}
}
else
{
if (Italian.MustBeHigh)
{
Right++;
}
else
{
Wrong++;
}
}
if ((counterEN + counterIT) % 50 == 0)
{
var accur = CalculateAccurary(Wordspace, LanguageNeuralNet, English, Italian, EngWord, ITWord);
Accuracy.Add((counterEN + counterIT).ToString() + ";" + accur.ToString());
}
//if ((counterEN + counterIT) == 7000)
//{
// double percentageright = (double)Right / (double)(Right + Wrong) * 100f;
// int lol = 9;
//}
}
File.WriteAllLines("Results20NodesAccurary.txt", Accuracy.ToArray());
}
static void Main()
{
var EngWord = File.ReadAllLines("EN.filtered");
var ITWord = File.ReadAllLines("IT.filtered");
List <CharSpace> Wordspace = new List <CharSpace>();
for (int i = 0; i < 8; i++)
{
Wordspace.Add(new CharSpace(i));
}
var allinputs = new List <ArgumentValue>();
foreach (var i in Wordspace)
{
allinputs.AddRange(i.letters);
}
OutputData English, Italian;
English = new OutputData();
Italian = new OutputData();
var outputs = new List <OutputData>();
outputs.Add(English);
outputs.Add(Italian);
Italian.MustBeHigh = true;
bool makeNew = true;
NeuralNetwork LanguageNeuralNet;
if (!makeNew)
{
//LanguageNeuralNet = NeuralNetwork.Load(allinputs, outputs);
LanguageNeuralNet = NeuralNetwork.LoadMatrix(allinputs, outputs);
}
else
{
System.Diagnostics.Stopwatch builder = new System.Diagnostics.Stopwatch();
builder.Start();
LanguageNeuralNet = new NeuralNetwork(allinputs, outputs, new int[] { 10 });
builder.Stop();
System.Diagnostics.Stopwatch timer = new System.Diagnostics.Stopwatch();
timer.Start();
int counterEN, counterIT;
counterEN = 0;
counterIT = 0;
var random = new Random();
int Right, Wrong;
Right = 0;
Wrong = 0;
while (counterEN + counterIT < EngWord.Length + ITWord.Length)
{
string word;
if (random.Next() % 2 == 0)
{
if (counterEN != EngWord.Length)
{
word = EngWord[counterEN];
counterEN++;
English.MustBeHigh = true;
Italian.MustBeHigh = false;
}
else
{
word = ITWord[counterIT];
counterIT++;
English.MustBeHigh = false;
Italian.MustBeHigh = true;
}
}
else
{
if (counterIT != ITWord.Length)
{
word = ITWord[counterIT];
counterIT++;
English.MustBeHigh = false;
Italian.MustBeHigh = true;
}
else
{
word = EngWord[counterEN];
counterEN++;
English.MustBeHigh = true;
Italian.MustBeHigh = false;
}
}
for (int characternum = 0; characternum < 8; characternum++)
{
Wordspace[characternum].SetLetter(word[characternum]);
}
LanguageNeuralNet.Learn();
LanguageNeuralNet.CalculateResults();
if (English.Value > Italian.Value)
{
if (English.MustBeHigh)
{
Right++;
}
else
{
Wrong++;
}
}
else
{
if (Italian.MustBeHigh)
{
Right++;
}
else
{
Wrong++;
}
}
}
timer.Stop();
LanguageNeuralNet.SaveMatrix();
}
int definitiveRight = 0;
int definitiveWrong = 0;
foreach (var i in EngWord)
{
for (int characternum = 0; characternum < 8; characternum++)
{
Wordspace[characternum].SetLetter(i[characternum]);
}
LanguageNeuralNet.CalculateResults();
if (English.Value > Italian.Value)
{
definitiveRight++;
}
else
{
definitiveWrong++;
}
}
foreach (var i in ITWord)
{
for (int characternum = 0; characternum < 8; characternum++)
{
Wordspace[characternum].SetLetter(i[characternum]);
}
LanguageNeuralNet.CalculateResults();
if (Italian.Value > English.Value)
{
definitiveRight++;
}
else
{
definitiveWrong++;
}
}
double endPercentage = (double)definitiveRight / (double)(definitiveRight + definitiveWrong) * 100.0;
}
public static NeuralNetwork LoadMatrix(List <ArgumentValue> Inputs, List <OutputData> Outputs)
{
NeuralNetwork newNeuralNet = new NeuralNetwork();
System.Windows.Forms.OpenFileDialog open = new System.Windows.Forms.OpenFileDialog();
open.Filter = "Comma Seperated Values (*.csv)|*.csv";
//open.FileName = "matrixtest.csv";
open.ShowDialog();
StreamReader reader = new StreamReader(open.FileName);
string wholeThing = reader.ReadToEnd();
reader.Close();
var lines = wholeThing.Split('\n');
var splitElements = from line in lines select line.Split(';');
int lastSize = 1;
List <List <List <string> > > stringMatrices = new List <List <List <string> > >();
foreach (var line in splitElements)
{
if (line.Length <= 1)
{
lastSize = line.Length;
}
else
{
if (line.Length != lastSize)
{
stringMatrices.Add(new List <List <string> >());
}
lastSize = line.Length;
stringMatrices.Last().Add(line.ToList());
}
}
List <double[, ]> matrices = new List <double[, ]>();
foreach (var i in stringMatrices)
{
var matrix = new double[i[0].Count, i.Count];
for (int x = 0; x < matrix.GetLength(0); x++)
{
for (int y = 0; y < matrix.GetLength(1); y++)
{
matrix[x, y] = double.Parse(i[y][x], CultureInfo.InvariantCulture);
}
}
matrices.Add(matrix);
}
if (Inputs.Count != matrices[0].GetLength(0))
{
throw new Exception("Inputs do not match with loaded matrix");
}
if (Outputs.Count != matrices.Last().GetLength(1))
{
throw new Exception("Outputs do not match with loaded matrix");
}
foreach (var i in Inputs)//Fill the network with nodes
{
newNeuralNet.Input.Add(new InputNeuron(i));
}
foreach (var i in Outputs)//Fill the network with nodes
{
newNeuralNet.Output.Add(new OutputNeuron(i));
}
//Adds inbetweenlayers for all the inbetween layers
for (int i = 0; i < matrices.Count - 1; i++)
{
newNeuralNet.InbetweenLayers.Add(new List <Neuron>());
for (int y = 0; y < matrices[i].GetLength(1); y++)
{
newNeuralNet.InbetweenLayers[i].Add(new Neuron());
}
}
for (int x = 0; x < matrices[0].GetLength(0); x++)
{
for (int y = 0; y < matrices[0].GetLength(1); y++)
{
newNeuralNet.Input[x].LinkTo(newNeuralNet.InbetweenLayers[0][y], new ArgumentValue(matrices[0][x, y]));
}
}
for (int i = 0; i < newNeuralNet.InbetweenLayers.Count - 1; i++)
{
for (int y = 0; y < matrices[i + 1].GetLength(1); y++)
{
for (int x = 0; x < matrices[i + 1].GetLength(0); x++)
{
newNeuralNet.InbetweenLayers[i][x].LinkTo(newNeuralNet.InbetweenLayers[i + 1][y], new ArgumentValue(matrices[i][x, y]));
}
}
}
for (int x = 0; x < matrices.Last().GetLength(0); x++)
{
for (int y = 0; y < matrices.Last().GetLength(1); y++)
{
newNeuralNet.InbetweenLayers.Last()[x].LinkTo(newNeuralNet.Output[y], new ArgumentValue(matrices.Last()[x, y]));
}
}
newNeuralNet.BuildEquations();
return(newNeuralNet);
}
public Trainer(NeuralNetwork neuralNetwork, TrainData trainData)
{
NeuralNetwork = neuralNetwork;
TrainData = trainData;
}