public XorNeuralNet(bool predefined)
{
if (predefined)
{
const double
w1 = 0.5d,
w2 = 0.3d,
w3 = 0.4d,
w4 = 0.2d,
b0 = 0.5d,
b1 = 0.3d,
w5 = 0.8d,
w6 = 0.9d,
b2 = 0.8d;
net0 = new Perceptron(new[] { w1, w3 }, b0);
net1 = new Perceptron(new[] { w2, w4 }, b1);
output = new OutputPerceptron(new[] { w5, w6 }, b2);
}
else
{
var r = new Random(42);
net0 = new Perceptron(new[] { r.NextDouble(), r.NextDouble() }, r.NextDouble());
net1 = new Perceptron(new[] { r.NextDouble(), r.NextDouble() }, r.NextDouble());
output = new OutputPerceptron(new[] { r.NextDouble(), r.NextDouble() }, r.NextDouble());
}
}
private void loadDataBasesToolStripMenuItem_Click(object sender, EventArgs e)
{
try
{
using (var open = new OpenFileDialog {
Title = Resources.Form1_saveDataBasesToolStripMenuItem_Click_Выберете_базу_данных, Filter = "База Данных|*.IIDB"
})
{
if (open.ShowDialog() == DialogResult.OK)
{
using (var str = File.OpenRead(open.FileName))
{
var bf = new BinaryFormatter();
var temp = (SaveStructure)bf.Deserialize(str);
_perceptron = temp.Perceptron;
_teacher = new Teacher(_perceptron);
_imageHeight = temp.ImageHeight;
_imageWidth = temp.ImageWidth;
_neuronCount = temp.NeuronCount;
}
}
}
}
catch (Exception ex)
{
MessageBox.Show(ex.ToString());
}
}
private Brain()
{
random = new Random();
xBall = new Perceptron();
yBall = new Perceptron();
myPaddle = new Perceptron();
theirPaddle = new Perceptron();
}
private void CreatePerceptronWithTeacher()
{
try
{
perceptron = new Perceptron(width * height, layerSizes);
teacher = new Teacher(perceptron);
}
catch (Exception ex)
{
MessageBox.Show("В ходе выполнения программы произошла ошибка.\n" + ex.Message);
}
}
public Form1()
{
InitializeComponent();
//Первая цифра колличество нейронов.
//Вторая цифра колличество элементов векторном представлении картинки
//Для размера картинки 64x64 будет произведение
_perceptron = new Perceptron(_neuronCount, _imageWidth * _imageHeight);
_teacher = new Teacher(_perceptron);
pictureBox1.Image = new Bitmap(_imageWidth, _imageHeight);
_formThread = new Thread(UpdateForm);
_formThread.Start();
label6.Text = "v: " + ProductVersion;
}
private void settingsToolStripMenuItem_Click(object sender, EventArgs e)
{
var sett = new settingsForm(_neuronCount, _imageWidth, _imageHeight);
sett.ShowDialog();
if (!sett.ItsClosed)
{
_imageHeight = sett.GetImageHeight;
_imageWidth = sett.GetImageWidth;
_neuronCount = sett.GetNeuronCount;
_perceptron = new Perceptron(_neuronCount, _imageWidth * _imageHeight);
_teacher = new Teacher(_perceptron);
}
}
public PerceptronDirectParametersVisualizer(IPerceptron perceptron, DirectParametersVisualizationType visualizationType)
{
VisualizationType = visualizationType;
Unit = perceptron;
switch (VisualizationType)
{
case DirectParametersVisualizationType.SquareOfRGB:
if (Math.Sqrt(Unit.InputsNumber / 3) % 1 != 0)
{
throw new ArgumentException("Visualization Impossible because of wrong size", "perceptron");
}
ImageWidth = (int)Math.Sqrt(Unit.InputsNumber / 3);
ImageHeight = (int)Math.Sqrt(Unit.InputsNumber / 3);
break;
case DirectParametersVisualizationType.SquareOfGray:
if (Math.Sqrt(Unit.InputsNumber) % 1 != 0)
{
throw new ArgumentException("Visualization Impossible because of wrong size", "perceptron");
}
ImageWidth = (int)Math.Sqrt(Unit.InputsNumber);
ImageHeight = (int)Math.Sqrt(Unit.InputsNumber);
break;
case DirectParametersVisualizationType.BeltOfRGB:
if ((Unit.InputsNumber / 3) % 1 != 0)
{
throw new ArgumentException("Visualization Impossible because of wrong size", "perceptron");
}
ImageWidth = 1;
ImageHeight = (int)Unit.InputsNumber / 3;
break;
case DirectParametersVisualizationType.BeltOfGray:
ImageWidth = 1;
ImageHeight = (int)Unit.InputsNumber;
break;
}
}
/// <summary>
/// Конструктор
/// </summary>
/// <param name="perceptron">Персептрон</param>
public Teacher(IPerceptron perceptron)
{
_perceptron = perceptron;
}
public PerceptronUnderTraining(IEnumerable <ITraversableUnitReadOnly <IUnitUnderTraining, IConnectionUnderTraining, IUnitActivationTraining> > inputUnits, IPerceptron <IUnit, IConnection, IUnitActivation <IUnit> > perceptron = null)
{
_perceptron = perceptron ?? new Perceptron(inputUnits);
Network = new LayeredUnitCollection <IUnitUnderTraining, IConnectionUnderTraining, IUnitActivationTraining>(inputUnits).GetLayeredUnits();
}
/// <summary>
/// Serializes a perceptron as JSON and returns the result.
/// </summary>
/// <typeparam name="TUnit">The declared type of the Unit</typeparam>
/// <typeparam name="TConnection">The declared type of the connection</typeparam>
/// <typeparam name="TUnitActivation">The declared type of the unit activation</typeparam>
/// <param name="perceptron">The perceptron to be serialized</param>
/// <returns>A string representation of the perceptron that can later be deserialized back into an IPerceptron</returns>
public string SerializeJson <TUnit, TConnection, TUnitActivation>(IPerceptron <TUnit, TConnection, TUnitActivation> perceptron)
where TUnit : IUnit
where TConnection : IConnection
where TUnitActivation : IUnitActivation <TUnit>
{
var unitMapping = new Dictionary <ITraversableUnitReadOnly <TUnit, TConnection, TUnitActivation>, SimpleUnit>();
var simpleUnits = new List <List <SimpleUnit> >();
foreach (var layer in perceptron.Network)
{
var simpleUnitLayer = new List <SimpleUnit>();
foreach (var unit in layer)
{
var simpleUnit = new SimpleUnit
{
UnitActivationType = unit.UnitActivation.GetType(),
UnitType = unit.UnitActivation.Properties.GetType(),
UnitPropertiesType = unit.UnitActivation.Properties.GetType()
};
MapPropertiesToDictionary(unit.UnitActivation.Properties, simpleUnit.Properties);
simpleUnitLayer.Add(simpleUnit);
unitMapping.Add(unit, simpleUnit);
}
simpleUnits.Add(simpleUnitLayer);
}
var simpleConnections = new List <SimpleConnection>();
foreach (var connection in unitMapping.SelectMany(u => u.Key.IncomingConnections))
{
var inSimple = unitMapping[connection.InputUnit];
var outSimple = unitMapping[connection.OutputUnit];
var simpleConnection = new SimpleConnection
{
InputUnit = inSimple,
OutputUnit = outSimple,
Weight = connection.Properties.Weight,
Name = connection.Properties.Name,
ConnectionType = connection.Properties.GetType()
};
MapPropertiesToDictionary(connection.Properties, simpleConnection.Properties);
simpleConnections.Add(simpleConnection);
}
var data = new PerceptronJson
{
SimpleUnits = simpleUnits.Select(layer => layer.ToArray()).ToArray(),
SimpleConnections = simpleConnections.ToArray(),
PerceptronType = perceptron.GetType()
};
return(JsonConvert.SerializeObject(data, new JsonSerializerSettings
{
ReferenceLoopHandling = ReferenceLoopHandling.Serialize,
PreserveReferencesHandling = PreserveReferencesHandling.Objects,
}));
}
public void SetUp()
{
_wrappedPerceptronMock = new Mock <IPerceptron <IUnit, IConnection, IUnitActivation <IUnit> > >();
_perceptron = new OneHotPerceptron(_wrappedPerceptronMock.Object);
}
/// <param name="perceptron"> Перцептрон. </param> public Teacher(IPerceptron perceptron) => (Perceptron, PrecisionLog) = (perceptron, new List <float>());
protected OneHotPerceptron()
{
_perceptron = new Perceptron();
}
public OneHotPerceptron(IEnumerable <ITraversableUnitReadOnly <IUnit, IConnection, IUnitActivation <IUnit> > > inputUnits)
{
_perceptron = new Perceptron(inputUnits);
}
public OneHotPerceptron(IPerceptron <IUnit, IConnection, IUnitActivation <IUnit> > wrapped)
{
_perceptron = wrapped;
}
public PerceptronProblemPair(IPerceptron perceptron, IPerceptronProblem problem)
{
Perceptron = perceptron;
Problem = problem;
}
