public Perceptron(uint[] Neuron_layers_sizes)
{
if (Neuron_layers_sizes == null || Neuron_layers_sizes.Length < 2)
{
throw new Exception("bad args");
}
foreach (uint count in Neuron_layers_sizes)
{
if (count == 0)
{
throw new Exception("bad args");
}
}
NeuronData[][] neurons_data = new NeuronData[Neuron_layers_sizes.Length][];
for (int i = 0; i < Neuron_layers_sizes.Length; i++)
{
neurons_data[i] = new NeuronData[Neuron_layers_sizes[i]];
if (i > 0)
{
double[] weights = new double[Neuron_layers_sizes[i - 1]];
for (int j = 0; j < Neuron_layers_sizes[i]; j++)
{
neurons_data[i][j] = new NeuronData(weights, 1);
}
}
}
construct(neurons_data);
reinit();
}
public override string ToString()
{
NeuronData[][] neurons = new NeuronData[layers.Length][];
for (int i = 0; i < neurons.Length; i++)
{
neurons[i] = new NeuronData[layers[i].Length];
for (int j = 0; j < neurons[i].Length; j++)
{
var parents = layers[i][j].Parents;
double[] weights = new double[parents.Count - 1];
double bias_weight = 0;
int _i_ = 0;
foreach (var link in parents)
{
if (link.neuron == bias)
{
bias_weight = link.weight;
}
else
{
weights[_i_++] = link.weight;
}
}
neurons[i][j] = new NeuronData(weights, bias_weight);
}
}
return(JsonConvert.SerializeObject(neurons));
}
static void Main(string[] args)
{
//vybrat excel s hodnotami
//vyparsovat ho, ulozit si hodnoty
//rozbehat neuronku
//naucit neuronku
//testovat neuronku
//skusit pustit data on-the-fly
OpenFileDialog ofd = new OpenFileDialog()
{
Multiselect = false
};
string excelFileName;
if (ofd.ShowDialog() == DialogResult.OK)
{
excelFileName = ofd.FileName;
}
else
{
throw new Exception("Je nutne vybrat subor");
}
var excelManager = new ExcelParser(excelFileName);
excelManager.ParseSourceData();
double[][] input = NeuronData.GetInputMatrix();
double[][] output = NeuronData.GetOutputMatrix();
// create neural network
ActivationNetwork network = new ActivationNetwork(
new SigmoidFunction(3),
3, // two inputs in the network
3, // two neurons in the first layer
2); // one neuron in the second layer
// create teacher
BackPropagationLearning teacher =
new BackPropagationLearning(network);
bool needToStop = false;
// loop
while (!needToStop)
{
// run epoch of learning procedure
System.Threading.Thread.Sleep(1000);
double error = teacher.RunEpoch(input, output);
Console.WriteLine(error);
// check error value to see if we need to stop
// ...
}
}
/// <summary>
/// Рекурсивно удалить нейронные сети.
/// </summary>
/// <param name="unitOfWork">Модуль для работы с БД.</param>
/// <param name="neuronId">Идентификатор нейрона.</param>
private void DeleteNetworkRecursive(IUnitOfWork unitOfWork, NeuronData neuron)
{
if (neuron == null || neuron.Network == null)
{
return;
}
foreach (var currentNeuron in neuron.Network.Neurons)
{
DeleteNetworkRecursive(unitOfWork, currentNeuron);
}
neuron.Network.Weights.ForEach(w => unitOfWork.WeightRepository.Delete(w));
neuron.Network.InputAttributes.ForEach(i => unitOfWork.InputAttributeRepository.Delete(i));
neuron.Network.Neurons.ForEach(n => unitOfWork.NeuronRepository.Delete(n.Neuron));
unitOfWork.NetworkRepository.Delete(neuron.Network.Network);
}
void SetupNeuralNetworkData()
{
FirstLayer = new NeuronData[NumberOfKeywords];
for (int i = 0; i < FirstLayer.Length; i++)
{
FirstLayer[i] = new NeuronData();
}
foreach (var item in FirstLayer)
{
item.Weight = new float[Vertical * Horizontal];
for (int j = 0; j < Vertical * Horizontal; j++)
{
item.Weight[j] = 0.4f + UnityEngine.Random.value * 0.2f;
}
item.Bias = 0.0f;
}
RawData = new float[Vertical * Horizontal];
Output = new float[NumberOfKeywords];
Target = new float[NumberOfKeywords];
}
public void ParseSourceData()
{
var i = 1;
var cnt = 0;
using (ExcelPackage sourceFile = new ExcelPackage(new FileInfo(_fileName)))
{
var myWorksheet = sourceFile.Workbook.Worksheets.First(); //select sheet here
var totalRows = myWorksheet.Dimension.End.Row;
var totalColumns = myWorksheet.Dimension.End.Column;
cnt = totalRows;
var sb = new StringBuilder(); //this is your your data
for (int rowNum = 2; rowNum <= totalRows; rowNum++) //select starting row here
{
var row = myWorksheet.Cells[rowNum, 1, rowNum, totalColumns];
NeuronData.AddExcelRowData(row);
Console.WriteLine($"parsing: - {i}/{cnt}");
i++;
}
}
}
