/// <summary>
/// Сохранить нейронную сеть.
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void Btn_SaveNetwork_Click(object sender, EventArgs e)
{
try
{
var network = _networkBase ?? new NetworkBase();
network.Name = tbNetworkName.Text;
if (string.IsNullOrEmpty(network.Name))
{
throw new Exception("Введите название сети на вкладке \"Обучение\".");
}
var networkData = new NeuralNetworkData
{
Network = network,
InputAttributes = _neuralNetwork.InputAttributes,
Neurons = _neuralNetwork.Neurons.Select(n => new NeuronData(n, null)).ToList(),
Weights = _neuralNetwork.Weights
};
_networkService.SaveNetworkData(networkData);
DialogResult = DialogResult.OK;
Close();
}
catch (Exception ex)
{
MessageBox.Show(ex.Message, "Ошибка", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
public void Init(NeuralNetworkData nnd)
{
this.sizes = nnd.sizes;
this.biases = nnd.biases;
this.weights = nnd.weights;
this.nnd = nnd;
}
public void InitNeuralNetwork(NeuralNetworkData nnd)
{
nn = new NeuralNetwork();
nn.Init(nnd);
nnctlr = GetComponent <NNSphereController>();
isInitialised = true;
}
public static NeuralNetworkData Copy(NeuralNetworkData data)
{
NeuralNetworkData newData = new NeuralNetworkData();
newData.sizes = data.sizes;
newData.biases = data.biases;
newData.weights = data.weights;
return(newData);
}
public NeuralNetworkData GenerateIntentionRecognitionNeuralNetworkData(
int inputLayer, int outputLayer)
{
var neuralNetwork = this.GenerateNetwork(inputLayer, outputLayer);
var neuralNetworkData = new NeuralNetworkData()
{
Type = NeuralNetworkType.IntentionRecognition.ToString(),
Data = neuralNetwork.GetNetworkBytes()
};
return(neuralNetworkData);
}
private void CheckLoadOrSave()
{
if (m_save)
{
SaveContainer(m_dataFileName);
m_save = false;
}
if (m_loadPath)
{
LoadContainer(NeuralNetworkData.Load(m_dataFileName));
m_loadPath = false;
}
if (m_loadFile)
{
LoadContainer(NeuralNetworkData.Load(m_dataFile));
m_loadFile = false;
}
}
public long RegisterNewNetwork(NeuralNetwork network, long botId, string createdBy, NeuralNetworkType networkType)
{
var bot = this.FindBotById(botId);
var networkBytes = network.GetNetworkBytes();
var networkDataModel = new NeuralNetworkData()
{
CreatedBy = createdBy,
Data = networkBytes,
Bot = bot,
BotId = bot.Id,
Type = networkType.ToString()
};
this.Data.NeuralNetworkDataRepository.Add(networkDataModel);
this.Data.SaveChanges();
return(networkDataModel.Id);
}
public void AutomaticallyEncodedNeuralNetworkDataTest()
{
string[] rawData = new[]
{
"Sex Age Locale Income Politics",
"==============================================",
"Male 25 Rural 63,000.00 Conservative",
"Female 36 Suburban 55,000.00 Liberal",
"Male 40 Urban 74,000.00 Moderate",
"Female 23 Rural 28,000.00 Liberal"
};
char separator = ' ';
var networkData = new NeuralNetworkData(rawData.Skip(2).ToArray(), separator);
var convertedData = networkData.ConvertedData;
Assert.AreEqual("Moderate", networkData.DecodeOutput(0, 0, 1));
Assert.AreEqual("Liberal", networkData.DecodeOutput(0, 1, 0));
Assert.AreEqual("Conservative", networkData.DecodeOutput(1, 0, 0));
Assert.AreEqual("Moderate", ParsingUtilities.Decode(rawData.Skip(2).ToArray(), 4, separator, new double[] { 0, 0, 1 }));
}
private void SaveContainer(string fileName)
{
NNCSaveData data = new NNCSaveData {
m_trainingData = m_trainingManager.SaveData(),
m_sampleData = m_sampleManager.SaveData(),
m_visuilizationNetworkData = m_visualizationNetwork.SaveData(),
//m_visuilizationSampleData = m_visualizationSample.SaveData(),
m_networkData = m_network.SaveData(),
m_dropoutSeed = m_initDropoutSeed,
m_layerLengths = m_layerLengths,
m_dataFileName = fileName,
m_activisionType = m_activisionType,
m_activisionTypeOutput = m_activisionTypeOutput,
m_costType = m_costType,
m_initializationType = m_initializationType,
m_activisionConstant = m_activisionConstant,
};
NeuralNetworkData.Save(data, fileName);
}
/// <summary>
/// Сохранить данные о сети.
/// </summary>
/// <param name="networkData">Данные о сети.</param>
public void SaveNetworkData(NeuralNetworkData networkData)
{
var isEdit = networkData.Network.NetworkId > 0;
using (IUnitOfWork unitOfWork = _unitOfWorkFactory.Create(_configuration))
{
unitOfWork.BeginTransaction();
try
{
if (!isEdit)
{
// Сеть
networkData.Network.CreatedOn = DateTime.Now;
networkData.Network.NetworkId = unitOfWork.NetworkRepository.Insert(networkData.Network);
// Нейроны
foreach (var neuron in networkData.Neurons)
{
neuron.Neuron.NetworkId = networkData.Network.NetworkId;
neuron.Neuron.NeuronId = unitOfWork.NeuronRepository.Insert(neuron.Neuron);
}
// Входные параметры
foreach (var inputAttribute in networkData.InputAttributes)
{
inputAttribute.NetworkId = networkData.Network.NetworkId;
inputAttribute.InputAttributeId = unitOfWork.InputAttributeRepository.Insert(inputAttribute);
}
// Веса
foreach (var weight in networkData.Weights)
{
weight.InputAttributeId = networkData.InputAttributes
.First(e => e.InputAttributeNumber == weight.InputAttributeNumber)
.InputAttributeId;
weight.NeuronId = networkData.Neurons
.First(e => e.Neuron.NeuronNumber == weight.NeuronNumber)
.Neuron.NeuronId;
weight.WeightId = unitOfWork.WeightRepository.Insert(weight);
}
}
else
{
// Сеть
unitOfWork.NetworkRepository.Update(networkData.Network);
// Нейроны
foreach (var neuron in networkData.Neurons)
{
unitOfWork.NeuronRepository.Update(neuron.Neuron);
}
// Входные параметры
foreach (var inputAttribute in networkData.InputAttributes)
{
unitOfWork.InputAttributeRepository.Update(inputAttribute);
}
// Веса
foreach (var weight in networkData.Weights)
{
unitOfWork.WeightRepository.Update(weight);
}
}
unitOfWork.CommitTransaction();
}
catch
{
unitOfWork.RollbackTransaction();
throw;
}
}
}
public void IrisClasificationTest()
{
#region Training Data
//Encoded
// Iris setosa = 1 0 0
// Iris versicolor = 0 1 0
// Iris virginica = 0 0 1
string[] rawData = new[]
{
"setosa",
"versicolor",
"virginica"
};
double[][] encodedData = new double[150][];
encodedData[0] = new double[] { 5.1, 3.5, 1.4, 0.2, 0, 0, 1 };
encodedData[1] = new double[] { 4.9, 3.0, 1.4, 0.2, 0, 0, 1 };
encodedData[2] = new double[] { 4.7, 3.2, 1.3, 0.2, 0, 0, 1 };
encodedData[3] = new double[] { 4.6, 3.1, 1.5, 0.2, 0, 0, 1 };
encodedData[4] = new double[] { 5.0, 3.6, 1.4, 0.2, 0, 0, 1 };
encodedData[5] = new double[] { 5.4, 3.9, 1.7, 0.4, 0, 0, 1 };
encodedData[6] = new double[] { 4.6, 3.4, 1.4, 0.3, 0, 0, 1 };
encodedData[7] = new double[] { 5.0, 3.4, 1.5, 0.2, 0, 0, 1 };
encodedData[8] = new double[] { 4.4, 2.9, 1.4, 0.2, 0, 0, 1 };
encodedData[9] = new double[] { 4.9, 3.1, 1.5, 0.1, 0, 0, 1 };
encodedData[10] = new double[] { 5.4, 3.7, 1.5, 0.2, 0, 0, 1 };
encodedData[11] = new double[] { 4.8, 3.4, 1.6, 0.2, 0, 0, 1 };
encodedData[12] = new double[] { 4.8, 3.0, 1.4, 0.1, 0, 0, 1 };
encodedData[13] = new double[] { 4.3, 3.0, 1.1, 0.1, 0, 0, 1 };
encodedData[14] = new double[] { 5.8, 4.0, 1.2, 0.2, 0, 0, 1 };
encodedData[15] = new double[] { 5.7, 4.4, 1.5, 0.4, 0, 0, 1 };
encodedData[16] = new double[] { 5.4, 3.9, 1.3, 0.4, 0, 0, 1 };
encodedData[17] = new double[] { 5.1, 3.5, 1.4, 0.3, 0, 0, 1 };
encodedData[18] = new double[] { 5.7, 3.8, 1.7, 0.3, 0, 0, 1 };
encodedData[19] = new double[] { 5.1, 3.8, 1.5, 0.3, 0, 0, 1 };
encodedData[20] = new double[] { 5.4, 3.4, 1.7, 0.2, 0, 0, 1 };
encodedData[21] = new double[] { 5.1, 3.7, 1.5, 0.4, 0, 0, 1 };
encodedData[22] = new double[] { 4.6, 3.6, 1.0, 0.2, 0, 0, 1 };
encodedData[23] = new double[] { 5.1, 3.3, 1.7, 0.5, 0, 0, 1 };
encodedData[24] = new double[] { 4.8, 3.4, 1.9, 0.2, 0, 0, 1 };
encodedData[25] = new double[] { 5.0, 3.0, 1.6, 0.2, 0, 0, 1 };
encodedData[26] = new double[] { 5.0, 3.4, 1.6, 0.4, 0, 0, 1 };
encodedData[27] = new double[] { 5.2, 3.5, 1.5, 0.2, 0, 0, 1 };
encodedData[28] = new double[] { 5.2, 3.4, 1.4, 0.2, 0, 0, 1 };
encodedData[29] = new double[] { 4.7, 3.2, 1.6, 0.2, 0, 0, 1 };
encodedData[30] = new double[] { 4.8, 3.1, 1.6, 0.2, 0, 0, 1 };
encodedData[31] = new double[] { 5.4, 3.4, 1.5, 0.4, 0, 0, 1 };
encodedData[32] = new double[] { 5.2, 4.1, 1.5, 0.1, 0, 0, 1 };
encodedData[33] = new double[] { 5.5, 4.2, 1.4, 0.2, 0, 0, 1 };
encodedData[34] = new double[] { 4.9, 3.1, 1.5, 0.1, 0, 0, 1 };
encodedData[35] = new double[] { 5.0, 3.2, 1.2, 0.2, 0, 0, 1 };
encodedData[36] = new double[] { 5.5, 3.5, 1.3, 0.2, 0, 0, 1 };
encodedData[37] = new double[] { 4.9, 3.1, 1.5, 0.1, 0, 0, 1 };
encodedData[38] = new double[] { 4.4, 3.0, 1.3, 0.2, 0, 0, 1 };
encodedData[39] = new double[] { 5.1, 3.4, 1.5, 0.2, 0, 0, 1 };
encodedData[40] = new double[] { 5.0, 3.5, 1.3, 0.3, 0, 0, 1 };
encodedData[41] = new double[] { 4.5, 2.3, 1.3, 0.3, 0, 0, 1 };
encodedData[42] = new double[] { 4.4, 3.2, 1.3, 0.2, 0, 0, 1 };
encodedData[43] = new double[] { 5.0, 3.5, 1.6, 0.6, 0, 0, 1 };
encodedData[44] = new double[] { 5.1, 3.8, 1.9, 0.4, 0, 0, 1 };
encodedData[45] = new double[] { 4.8, 3.0, 1.4, 0.3, 0, 0, 1 };
encodedData[46] = new double[] { 5.1, 3.8, 1.6, 0.2, 0, 0, 1 };
encodedData[47] = new double[] { 4.6, 3.2, 1.4, 0.2, 0, 0, 1 };
encodedData[48] = new double[] { 5.3, 3.7, 1.5, 0.2, 0, 0, 1 };
encodedData[49] = new double[] { 5.0, 3.3, 1.4, 0.2, 0, 0, 1 };
encodedData[50] = new double[] { 7.0, 3.2, 4.7, 1.4, 0, 1, 0 };
encodedData[51] = new double[] { 6.4, 3.2, 4.5, 1.5, 0, 1, 0 };
encodedData[52] = new double[] { 6.9, 3.1, 4.9, 1.5, 0, 1, 0 };
encodedData[53] = new double[] { 5.5, 2.3, 4.0, 1.3, 0, 1, 0 };
encodedData[54] = new double[] { 6.5, 2.8, 4.6, 1.5, 0, 1, 0 };
encodedData[55] = new double[] { 5.7, 2.8, 4.5, 1.3, 0, 1, 0 };
encodedData[56] = new double[] { 6.3, 3.3, 4.7, 1.6, 0, 1, 0 };
encodedData[57] = new double[] { 4.9, 2.4, 3.3, 1.0, 0, 1, 0 };
encodedData[58] = new double[] { 6.6, 2.9, 4.6, 1.3, 0, 1, 0 };
encodedData[59] = new double[] { 5.2, 2.7, 3.9, 1.4, 0, 1, 0 };
encodedData[60] = new double[] { 5.0, 2.0, 3.5, 1.0, 0, 1, 0 };
encodedData[61] = new double[] { 5.9, 3.0, 4.2, 1.5, 0, 1, 0 };
encodedData[62] = new double[] { 6.0, 2.2, 4.0, 1.0, 0, 1, 0 };
encodedData[63] = new double[] { 6.1, 2.9, 4.7, 1.4, 0, 1, 0 };
encodedData[64] = new double[] { 5.6, 2.9, 3.6, 1.3, 0, 1, 0 };
encodedData[65] = new double[] { 6.7, 3.1, 4.4, 1.4, 0, 1, 0 };
encodedData[66] = new double[] { 5.6, 3.0, 4.5, 1.5, 0, 1, 0 };
encodedData[67] = new double[] { 5.8, 2.7, 4.1, 1.0, 0, 1, 0 };
encodedData[68] = new double[] { 6.2, 2.2, 4.5, 1.5, 0, 1, 0 };
encodedData[69] = new double[] { 5.6, 2.5, 3.9, 1.1, 0, 1, 0 };
encodedData[70] = new double[] { 5.9, 3.2, 4.8, 1.8, 0, 1, 0 };
encodedData[71] = new double[] { 6.1, 2.8, 4.0, 1.3, 0, 1, 0 };
encodedData[72] = new double[] { 6.3, 2.5, 4.9, 1.5, 0, 1, 0 };
encodedData[73] = new double[] { 6.1, 2.8, 4.7, 1.2, 0, 1, 0 };
encodedData[74] = new double[] { 6.4, 2.9, 4.3, 1.3, 0, 1, 0 };
encodedData[75] = new double[] { 6.6, 3.0, 4.4, 1.4, 0, 1, 0 };
encodedData[76] = new double[] { 6.8, 2.8, 4.8, 1.4, 0, 1, 0 };
encodedData[77] = new double[] { 6.7, 3.0, 5.0, 1.7, 0, 1, 0 };
encodedData[78] = new double[] { 6.0, 2.9, 4.5, 1.5, 0, 1, 0 };
encodedData[79] = new double[] { 5.7, 2.6, 3.5, 1.0, 0, 1, 0 };
encodedData[80] = new double[] { 5.5, 2.4, 3.8, 1.1, 0, 1, 0 };
encodedData[81] = new double[] { 5.5, 2.4, 3.7, 1.0, 0, 1, 0 };
encodedData[82] = new double[] { 5.8, 2.7, 3.9, 1.2, 0, 1, 0 };
encodedData[83] = new double[] { 6.0, 2.7, 5.1, 1.6, 0, 1, 0 };
encodedData[84] = new double[] { 5.4, 3.0, 4.5, 1.5, 0, 1, 0 };
encodedData[85] = new double[] { 6.0, 3.4, 4.5, 1.6, 0, 1, 0 };
encodedData[86] = new double[] { 6.7, 3.1, 4.7, 1.5, 0, 1, 0 };
encodedData[87] = new double[] { 6.3, 2.3, 4.4, 1.3, 0, 1, 0 };
encodedData[88] = new double[] { 5.6, 3.0, 4.1, 1.3, 0, 1, 0 };
encodedData[89] = new double[] { 5.5, 2.5, 4.0, 1.3, 0, 1, 0 };
encodedData[90] = new double[] { 5.5, 2.6, 4.4, 1.2, 0, 1, 0 };
encodedData[91] = new double[] { 6.1, 3.0, 4.6, 1.4, 0, 1, 0 };
encodedData[92] = new double[] { 5.8, 2.6, 4.0, 1.2, 0, 1, 0 };
encodedData[93] = new double[] { 5.0, 2.3, 3.3, 1.0, 0, 1, 0 };
encodedData[94] = new double[] { 5.6, 2.7, 4.2, 1.3, 0, 1, 0 };
encodedData[95] = new double[] { 5.7, 3.0, 4.2, 1.2, 0, 1, 0 };
encodedData[96] = new double[] { 5.7, 2.9, 4.2, 1.3, 0, 1, 0 };
encodedData[97] = new double[] { 6.2, 2.9, 4.3, 1.3, 0, 1, 0 };
encodedData[98] = new double[] { 5.1, 2.5, 3.0, 1.1, 0, 1, 0 };
encodedData[99] = new double[] { 5.7, 2.8, 4.1, 1.3, 0, 1, 0 };
encodedData[100] = new double[] { 6.3, 3.3, 6.0, 2.5, 1, 0, 0 };
encodedData[101] = new double[] { 5.8, 2.7, 5.1, 1.9, 1, 0, 0 };
encodedData[102] = new double[] { 7.1, 3.0, 5.9, 2.1, 1, 0, 0 };
encodedData[103] = new double[] { 6.3, 2.9, 5.6, 1.8, 1, 0, 0 };
encodedData[104] = new double[] { 6.5, 3.0, 5.8, 2.2, 1, 0, 0 };
encodedData[105] = new double[] { 7.6, 3.0, 6.6, 2.1, 1, 0, 0 };
encodedData[106] = new double[] { 4.9, 2.5, 4.5, 1.7, 1, 0, 0 };
encodedData[107] = new double[] { 7.3, 2.9, 6.3, 1.8, 1, 0, 0 };
encodedData[108] = new double[] { 6.7, 2.5, 5.8, 1.8, 1, 0, 0 };
encodedData[109] = new double[] { 7.2, 3.6, 6.1, 2.5, 1, 0, 0 };
encodedData[110] = new double[] { 6.5, 3.2, 5.1, 2.0, 1, 0, 0 };
encodedData[111] = new double[] { 6.4, 2.7, 5.3, 1.9, 1, 0, 0 };
encodedData[112] = new double[] { 6.8, 3.0, 5.5, 2.1, 1, 0, 0 };
encodedData[113] = new double[] { 5.7, 2.5, 5.0, 2.0, 1, 0, 0 };
encodedData[114] = new double[] { 5.8, 2.8, 5.1, 2.4, 1, 0, 0 };
encodedData[115] = new double[] { 6.4, 3.2, 5.3, 2.3, 1, 0, 0 };
encodedData[116] = new double[] { 6.5, 3.0, 5.5, 1.8, 1, 0, 0 };
encodedData[117] = new double[] { 7.7, 3.8, 6.7, 2.2, 1, 0, 0 };
encodedData[118] = new double[] { 7.7, 2.6, 6.9, 2.3, 1, 0, 0 };
encodedData[119] = new double[] { 6.0, 2.2, 5.0, 1.5, 1, 0, 0 };
encodedData[120] = new double[] { 6.9, 3.2, 5.7, 2.3, 1, 0, 0 };
encodedData[121] = new double[] { 5.6, 2.8, 4.9, 2.0, 1, 0, 0 };
encodedData[122] = new double[] { 7.7, 2.8, 6.7, 2.0, 1, 0, 0 };
encodedData[123] = new double[] { 6.3, 2.7, 4.9, 1.8, 1, 0, 0 };
encodedData[124] = new double[] { 6.7, 3.3, 5.7, 2.1, 1, 0, 0 };
encodedData[125] = new double[] { 7.2, 3.2, 6.0, 1.8, 1, 0, 0 };
encodedData[126] = new double[] { 6.2, 2.8, 4.8, 1.8, 1, 0, 0 };
encodedData[127] = new double[] { 6.1, 3.0, 4.9, 1.8, 1, 0, 0 };
encodedData[128] = new double[] { 6.4, 2.8, 5.6, 2.1, 1, 0, 0 };
encodedData[129] = new double[] { 7.2, 3.0, 5.8, 1.6, 1, 0, 0 };
encodedData[130] = new double[] { 7.4, 2.8, 6.1, 1.9, 1, 0, 0 };
encodedData[131] = new double[] { 7.9, 3.8, 6.4, 2.0, 1, 0, 0 };
encodedData[132] = new double[] { 6.4, 2.8, 5.6, 2.2, 1, 0, 0 };
encodedData[133] = new double[] { 6.3, 2.8, 5.1, 1.5, 1, 0, 0 };
encodedData[134] = new double[] { 6.1, 2.6, 5.6, 1.4, 1, 0, 0 };
encodedData[135] = new double[] { 7.7, 3.0, 6.1, 2.3, 1, 0, 0 };
encodedData[136] = new double[] { 6.3, 3.4, 5.6, 2.4, 1, 0, 0 };
encodedData[137] = new double[] { 6.4, 3.1, 5.5, 1.8, 1, 0, 0 };
encodedData[138] = new double[] { 6.0, 3.0, 4.8, 1.8, 1, 0, 0 };
encodedData[139] = new double[] { 6.9, 3.1, 5.4, 2.1, 1, 0, 0 };
encodedData[140] = new double[] { 6.7, 3.1, 5.6, 2.4, 1, 0, 0 };
encodedData[141] = new double[] { 6.9, 3.1, 5.1, 2.3, 1, 0, 0 };
encodedData[142] = new double[] { 5.8, 2.7, 5.1, 1.9, 1, 0, 0 };
encodedData[143] = new double[] { 6.8, 3.2, 5.9, 2.3, 1, 0, 0 };
encodedData[144] = new double[] { 6.7, 3.3, 5.7, 2.5, 1, 0, 0 };
encodedData[145] = new double[] { 6.7, 3.0, 5.2, 2.3, 1, 0, 0 };
encodedData[146] = new double[] { 6.3, 2.5, 5.0, 1.9, 1, 0, 0 };
encodedData[147] = new double[] { 6.5, 3.0, 5.2, 2.0, 1, 0, 0 };
encodedData[148] = new double[] { 6.2, 3.4, 5.4, 2.3, 1, 0, 0 };
encodedData[149] = new double[] { 5.9, 3.0, 5.1, 1.8, 1, 0, 0 };
#endregion
var networkData = new NeuralNetworkData(rawData, ' ');
networkData.PrepareTrainingSet(
encodedData: encodedData,
trainDataPercentage: 0.80d,
seed: 72);
ANeuralNetwork nn = new ANeuralNetwork(
inputNodes: 4,
hiddenNodes: 7,
outputNodes: 3,
hiddenNodeActivation: Activation.HyperTan,
outputNodeActivation: Activation.Softmax);
nn.Train(
networkData.TrainingData,
maxEpochs: 1000,
learnRate: 0.05d,
momentum: 0.01d,
meanSquaredErrorThreshold: 0.040d);
Assert.IsTrue(nn.GetAccuracyFor(networkData.TrainingData) > 0.95d);
Assert.IsTrue(nn.GetAccuracyFor(networkData.TestData) > 0.95d);
Assert.AreEqual("virginica", networkData.DecodeOutput(nn.Predict(new double[] { 5.1, 3.5, 1.4, 0.2 })));
Assert.AreEqual("versicolor", networkData.DecodeOutput(nn.Predict(new double[] { 7.0, 3.2, 4.7, 1.4 })));
Assert.AreEqual("setosa", networkData.DecodeOutput(nn.Predict(new double[] { 7.4, 2.8, 6.1, 1.9 })));
}
private void SpawnNewGeneration(int generationId, NeuralNetworkData bestObjectNnd = null)
{
generationTxt.text = "Generation: " + generationId;
for (int i = 0; i < objects.Count; i++)
{
Destroy(objects[i].gameObject);
}
objects = new List <GameObject>();
// Children of best parent
for (int i = 0; i < ChildrenOfBest; i++)
{
float[][] newBias = bestObjectNnd != null?CopyNewBiasArrayByValue(bestObjectNnd.biases) : null;
float[][,] newWeights = bestObjectNnd != null?CopyNewWeightArrayByValue(bestObjectNnd.weights) : null;
NeuralNetworkData nnd = new NeuralNetworkData
{
sizes = this.sizes,
biases = bestObjectNnd != null?GenerateBiasesFrom(newBias) : GenerateNewBiases(),
weights = bestObjectNnd != null?GenerateWeightsFrom(newWeights) : GenerateNewWeights()
};
GameObject obj = Instantiate(ObjectToSpawn, SpawnPosition.position, Quaternion.identity);
obj.name = "ObjectsA" + i;
obj.GetComponent <NeuralNetworkObjectInterfacer>().InitNeuralNetwork(nnd);
obj.transform.parent = this.transform;
objects.Add(obj);
}
// Random new genes
for (int i = 0; i < NewDNA / 2; i++)
{
NeuralNetworkData nnd = new NeuralNetworkData
{
sizes = this.sizes,
biases = GenerateNewBiases(),
weights = GenerateNewWeights()
};
GameObject obj = Instantiate(ObjectToSpawn, SpawnPosition.position, Quaternion.identity);
obj.name = "ObjectsB" + i;
obj.GetComponent <NeuralNetworkObjectInterfacer>().InitNeuralNetwork(nnd);
obj.transform.parent = this.transform;
objects.Add(obj);
}
if (bestObjectNnd != null)
{
NeuralNetworkData bestNndCopy = NeuralNetworkData.Copy(bestObjectNnd);
GameObject obj = Instantiate(ObjectToSpawn, SpawnPosition.position, Quaternion.identity);
obj.name = "Former Best";
obj.GetComponent <NeuralNetworkObjectInterfacer>().InitNeuralNetwork(bestNndCopy);
obj.transform.parent = this.transform;
objects.Add(obj);
}
timeLeft = timePerGeneration;
isTimeUp = false;
}
