public Neuron(int inputCount, NeuronType type = NeuronType.Normal)
{
NeuronType = type;
Weights = new List <double>();
Inputs = new List <double>();
InitWeightsRandomValue(inputCount);
}
public OneNeuron(int inpCount, NeuronType nt = NeuronType.Normal)
{
NType = nt;
Inputs = new List <double>();
Weights = new List <double>();
InitWeights(inpCount);
}
/// <summary>
/// Initializes a new instance of the <see cref="Neuron"/> class.
/// </summary>
/// <param name="id">Id of the neuron.</param>
/// <param name="name">Name of the neuron.</param>
/// <param name="type">Type of the neuron.</param>
/// <param name="bias">Bias of the neuron.</param>
public Neuron(int id, string name, NeuronType type, double bias = 0)
{
this.Id = id;
this.Name = name;
this.Type = type;
this.Bias = bias;
}
/*
*
* METHODS
*
*/
// Constructor with configuration
public Dendrite(Random random, NeuronType neuronType, bool isSynapseInNeuronInInputLayer = false)
{
IsInInputLayer = isSynapseInNeuronInInputLayer;
Weight = IsInInputLayer ? 1 : getInitialDendriteWeight(random, neuronType);
SignalInput = 0;
SignalOutput = 0;
}
public Layer(List <Neuron> neurons, NeuronType type = NeuronType.Normal)
{
//TODO: проверить входные нейроны на соответствие
Neurons = neurons;
Type = type;
}
public Neuron(int id, CnnNet cnnNet,
IEnumerable<AxonGuidanceForceBase> axonGuidanceForces,
IEnumerable<SomaGuidanceForceBase> somaGuidanceForces,
NeuronType type)
{
Contract.Requires<ArgumentException>(id >= 0);
Contract.Requires<ArgumentNullException>(cnnNet != null);
Contract.Requires<ArgumentNullException>(axonGuidanceForces != null);
Contract.Requires<ArgumentNullException>(somaGuidanceForces != null);
Id = id;
Network = cnnNet;
AxonWayPoints = new List<NeuronAxonWaypoint>
{
new NeuronAxonWaypoint(1, this, new Point(PosX, PosY))
};
AxonGuidanceForces = new ReadOnlyCollection<AxonGuidanceForceBase>(axonGuidanceForces.ToList());
SomaGuidanceForces = new ReadOnlyCollection<SomaGuidanceForceBase>(somaGuidanceForces.ToList());
Type = type;
HasSomaReachedFinalPosition = Type == NeuronType.Input || Type == NeuronType.Output;
HasAxonReachedFinalPosition = Type == NeuronType.Output;
_synapses = new List<DendricSynapse>();
_synapsesActivationHistory = new FixedSizedQueue<DendricSynapse[]>(cnnNet.NeuronActivityHistoryLength);
_random = new Random();
}
public Neuron(int id, CnnNet cnnNet,
IEnumerable <AxonGuidanceForceBase> axonGuidanceForces,
IEnumerable <SomaGuidanceForceBase> somaGuidanceForces,
NeuronType type)
{
Contract.Requires <ArgumentException>(id >= 0);
Contract.Requires <ArgumentNullException>(cnnNet != null);
Contract.Requires <ArgumentNullException>(axonGuidanceForces != null);
Contract.Requires <ArgumentNullException>(somaGuidanceForces != null);
Id = id;
Network = cnnNet;
AxonWayPoints = new List <NeuronAxonWaypoint>
{
new NeuronAxonWaypoint(1, this, new Point(PosX, PosY))
};
AxonGuidanceForces = new ReadOnlyCollection <AxonGuidanceForceBase>(axonGuidanceForces.ToList());
SomaGuidanceForces = new ReadOnlyCollection <SomaGuidanceForceBase>(somaGuidanceForces.ToList());
Type = type;
HasSomaReachedFinalPosition = Type == NeuronType.Input || Type == NeuronType.Output;
HasAxonReachedFinalPosition = Type == NeuronType.Output;
_synapses = new List <DendricSynapse>();
_synapsesActivationHistory = new FixedSizedQueue <DendricSynapse[]>(cnnNet.NeuronActivityHistoryLength);
_random = new Random();
}
/// <summary>
/// Создание слоя нейронов
/// </summary>
/// <param name="neurons"></param> коллекция нейронов
/// <param name="type"></param> тип нейронов в этом слое
public Layer(List <Neuron> neurons, NeuronType type = NeuronType.Normal)
{
//Проверка на соответствие типу создаваемого слоя
bool ok = false;
for (int i = 0; i < neurons.Count; i++)
{
if (neurons[i].NeuronType != type)
{
MessageBox.Show($"Layer contains zero neurons.\n" +
$"Incorrect collection '{neurons}' neuron by index {i} type in constructor parameters", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
ok = false;
break;
}
else
{
ok = true;
}
}
if (ok)
{
Neurons = neurons;
Type = type;
}
else
{
Neurons = null;
}
}
} //Дельта
/// <summary>
/// Создание нейрона
/// </summary>
/// <param name="inputCount"></param> количество входных сигналов
/// <param name="type"></param> тип нейрна
public Neuron(int inputCount, NeuronType type = NeuronType.Normal)
{
//проверка входных параметров
try
{
NeuronType = type;
}
catch (InvalidCastException e)
{
MessageBox.Show(e.Message, "Error input", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
try
{
if (inputCount >= 1)
{
//Добаление весов
Weights = new List <double>();
Inputs = new List <double>();
InitWeghtsRandomValue(inputCount);
}
else
{
MessageBox.Show("The number of inputs can't be less than 1", "Error input", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
catch (InvalidCastException e)
{
MessageBox.Show(e.Message, "Error input", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
public Layer(List <Neuron> neurons, NeuronType type = NeuronType.Normal)
{
// RODO: Проверка на соответствие типу
Neurons = neurons;
this.Type = type;
}
// Конструктор
// Принимает как аргументы входное значение нейрона и тип нейрона
public Neuron(double input, NeuronType type)
{
this.input = input;
this.type = type;
this.output = Sigmoid(input);
this.weights = new List <double>();
}
public InputGUI(int rows, int cols, bool dontCount = false)
: base()
{
// Используемый тип нейрона
neuronType = NeuronType.Binary;
//
if(!dontCount)
++objectCounter;
titleLabel = new Button();
System.Windows.Forms.DataGridViewCellStyle dataGridViewCellStyle5 = new System.Windows.Forms.DataGridViewCellStyle();
((System.ComponentModel.ISupportInitialize)(this)).BeginInit();
//
// dataGridView
//
this.AllowUserToAddRows = false;
this.AllowUserToDeleteRows = false;
this.AllowUserToResizeColumns = false;
this.AllowUserToResizeRows = false;
this.ClipboardCopyMode = System.Windows.Forms.DataGridViewClipboardCopyMode.Disable;
this.ColumnHeadersBorderStyle = System.Windows.Forms.DataGridViewHeaderBorderStyle.None;
//this.ColumnHeadersHeight = 5;
this.ColumnHeadersHeightSizeMode = System.Windows.Forms.DataGridViewColumnHeadersHeightSizeMode.DisableResizing;
this.ColumnHeadersVisible = false;
this.Location = new System.Drawing.Point(0, 0);
this.MultiSelect = false;
this.Name = "dataGridView" + objectCounter;
this.ReadOnly = true;
this.RowHeadersVisible = false;
//this.RowHeadersWidth = 5;
this.RowHeadersWidthSizeMode = System.Windows.Forms.DataGridViewRowHeadersWidthSizeMode.DisableResizing;
//dataGridViewCellStyle5.BackColor = Color.FromArgb(0); //System.Drawing.Color.White;
//dataGridViewCellStyle5.ForeColor = Color.FromArgb(0); //System.Drawing.Color.White;
dataGridViewCellStyle5.SelectionBackColor = Color.FromArgb(0); //System.Drawing.Color.Red;
dataGridViewCellStyle5.SelectionForeColor = Color.FromArgb(0);// System.Drawing.Color.White;
this.RowsDefaultCellStyle = dataGridViewCellStyle5;
this.RowTemplate.Height = cellHeight;
this.RowTemplate.ReadOnly = true;
this.RowTemplate.Resizable = System.Windows.Forms.DataGridViewTriState.False;
this.ScrollBars = System.Windows.Forms.ScrollBars.None;
this.SelectionMode = System.Windows.Forms.DataGridViewSelectionMode.CellSelect;
this.Size = new System.Drawing.Size(cols * cellWidth + (cols - 1), rows * cellHeight + (rows - 2));
this.TabIndex = objectCounter;
this.CellMouseClick += new System.Windows.Forms.DataGridViewCellMouseEventHandler(this.dataGridView1_CellMouseClick);
this.CellMouseDown += new System.Windows.Forms.DataGridViewCellMouseEventHandler(this.dataGridView1_CellMouseDown);
this.CellMouseMove += new System.Windows.Forms.DataGridViewCellMouseEventHandler(this.dataGridView1_CellMouseMove);
this.CellMouseUp += new System.Windows.Forms.DataGridViewCellMouseEventHandler(this.dataGridView1_CellMouseUp);
this.MouseLeave += new System.EventHandler(this.dataGridView1_MouseLeave);
((System.ComponentModel.ISupportInitialize)(this)).EndInit();
///////////////////
///////////////////
///////////////////
this.RowCount = rows;
this.ColumnCount = cols;
drawMode = false;
selectionCellColor = Color.FromArgb(255, 255, 128, 255);
}
// Инициализирует список нейронов с входными значениями 0
// Если слой не выходной, то генерирует ещё и нейрон смещения (в конце списка) со значением 1
public void InitNeurons()
{
this.neurons = new List <Neuron>();
for (int i = 0; i < this.length; i++)
{
NeuronType type = NeuronType.Hidden;
switch (this.type)
{
case LayerType.Input:
type = NeuronType.Input;
break;
case LayerType.Output:
type = NeuronType.Output;
break;
case LayerType.Hidden:
type = NeuronType.Hidden;
break;
}
Neuron neuron = new Neuron(0, type);
this.neurons.Add(neuron);
}
// Если слой не является выходным, добавляем туда ещё нейрон смещения
if (this.type != LayerType.Output)
{
this.neurons.Add(new Neuron(1, NeuronType.Bias));
}
}
public Neuron(int weights, double value, NeuronType type)
{
Value = value;
Weights = new double[weights];
Type = type;
GenerateWeights(weights);
}
/// <summary>
/// Copy constructor.
/// </summary>
/// <param name="copyFrom"></param>
public NeuronGene(NeuronGene copyFrom)
{
this.innovationId = copyFrom.innovationId;
this.neuronType = copyFrom.neuronType;
this.activationFunction = copyFrom.activationFunction;
this.Layer = copyFrom.Layer;
}
/// <summary>
/// Copy constructor.
/// </summary>
/// <param name="copyFrom"></param>
public NeuronGene(NeuronGene copyFrom)
{
this.innovationId = copyFrom.innovationId;
this.neuronType = copyFrom.neuronType;
this.activationFunction = copyFrom.activationFunction;
this.Layer = copyFrom.Layer;
}
public static PropertyObject NodeWithProperties(long uniqueID, NeuronType nt)
{
return(new PropertyObject()
{
{ WINNode.SUniqueString, uniqueID.ToString() }, { WINNode.SNodeString, nt.ToString() }
});
}
public Neuron(NeuronType type, double result) : this(type)
{
if (type == NeuronType.Input)
{
_result = result;
}
}
public override WizardPanel GetNext()
{
NeuronType ntype =
uiUnipolar.Checked ? NeuronType.Unipolar : NeuronType.Bipolar;
return(new ExperimentPanel((int)uiInputCount.Value, ntype));
}
public NeuronType[][] CreateMaket(int[] network)
{
NeuronType[][] maket = new NeuronType[network.Length][];
for (int i = 0; i < network.Length; i++)
{
if (i != network.Length - 1)
{
maket[i] = new NeuronType[network[i] + 1];
}
else
{
maket[i] = new NeuronType[network[i]];
}
}
for (int i = 0; i < maket.Length; i++)
{
if (i == 0)
{
for (int j = 0; j < maket[i].Length; j++)
{
if (j == maket[i].Length - 1)
{
maket[i][j] = NeuronType.Bias;
}
else
{
maket[i][j] = NeuronType.Input;
}
}
continue;
}
if (i == maket.Length - 1)
{
for (int j = 0; j < maket[i].Length; j++)
{
maket[i][j] = NeuronType.Output;
}
continue;
}
for (int j = 0; j < maket[i].Length; j++)
{
if (j == maket[i].Length - 1)
{
maket[i][j] = NeuronType.Bias;
continue;
}
else
{
maket[i][j] = NeuronType.Hidden;
}
}
}
return(maket);
}
public NeuronGene(uint innovationId, NeuronType neuronType, IActivationFunction activationFunction)
{
this.innovationId = innovationId;
this.neuronType = neuronType;
this.activationFunction = activationFunction;
this.neuronBias = 0;
this.timeConstant = 1;
}
public Layer(IEnumerable <Neuron> neurons, NeuronType type = NeuronType.Normal)
{
if (AreAllNeuronsHaveSameType(neurons, type))
{
throw new TypeAccessException("all neurons should have same type");
}
Neurons = new List <Neuron>(neurons);
}
/// <summary>
/// [int] - Количество входящих нейронов, [NeuronType] - тип создаваемого нейрона
/// </summary>
public Neuron(int inputCountNeuron, NeuronType type = NeuronType.Hidden)
{
Weights = new List <float>();
InputSignals = new List <float>();
NeuronType = type;
InitWeightsRandomValues(inputCountNeuron);
}
public NeuronGene(uint innovationId, NeuronType neuronType, IActivationFunction activationFunction, float layer = -1)
{
this.innovationId = innovationId;
this.neuronType = neuronType;
this.activationFunction = activationFunction;
this.TimeConstant = 1;
this.Layer = layer;
}
public NeuronGene(NeuronType type, int id, double y, double x, bool r = false)
{
this.ID = id;
this.SplitY = y;
this.SplitX = x;
this.Recurrent = r;
this.NeuronType = type;
}
/// <summary>
/// 实例化
/// </summary>
/// <param name="nodeCount">当前层节点数量</param>
public NeuronLayer(int nodeCount, NeuronType layerType = NeuronType.Sigmoid)
{
this.LayerType = layerType;
for (var i = 0; i < nodeCount; i++)
{
NodeList.Add(new NeuronNode(layerType));
}
}
private static Neuron ReadNeuron(XmlElement xmlNeuron)
{
uint id = uint.Parse(XmlUtilities.GetAttributeValue(xmlNeuron, "id"));
NeuronType neuronType = XmlUtilities.GetNeuronType(XmlUtilities.GetAttributeValue(xmlNeuron, "type"));
string activationFn = XmlUtilities.GetAttributeValue(xmlNeuron, "activationFunction");
return(new Neuron(ActivationFunctionFactory.GetActivationFunction(activationFn), neuronType, id));
}
public NeuronGene(uint innovationId, NeuronType neuronType, IActivationFunction activationFunction, float layer)
{
this.innovationId = innovationId;
this.neuronType = neuronType;
this.activationFunction = activationFunction;
this.TimeConstant = 1;
this.Layer = layer;
//Console.WriteLine("LAYER LAYER LAYER!");
}
public Neuron(int inputCount, NeuronType type = NeuronType.Normal)
{
NeuronType = type;
Weights = new double[inputCount];
Inputs = new double[inputCount];
Deltas = new double[inputCount];
InitWeightsRandomValues(inputCount);
}
public Neuron(List <double> inputs, List <double> weights, NeuronType neuronType, int mapX, int mapY)
{
Inputs = inputs;
Weight = weights;
_NeuronType = neuronType;
MapX = mapX;
MapY = mapY;
LastNeurons = new List <Neuron>();
}
private static NeuronGene ReadNeuronGene(XmlElement xmlNeuronGene)
{
uint id = uint.Parse(XmlUtilities.GetAttributeValue(xmlNeuronGene, "id"));
float layer = (float)Convert.ToDouble(XmlUtilities.GetAttributeValue(xmlNeuronGene, "layer"));
NeuronType neuronType = XmlUtilities.GetNeuronType(XmlUtilities.GetAttributeValue(xmlNeuronGene, "type"));
string activationFn = XmlUtilities.GetAttributeValue(xmlNeuronGene, "activationFunction");
return(new NeuronGene(id, neuronType, ActivationFunctionFactory.GetActivationFunction(activationFn), layer));
}
private static NeuronGene ReadNeuronGene(XmlElement xmlNeuronGene)
{
uint id = uint.Parse(XmlUtilities.GetAttributeValue(xmlNeuronGene, "id"));
NeuronType neuronType = XmlUtilities.GetNeuronType(XmlUtilities.GetAttributeValue(xmlNeuronGene, "type"));
string activationFn = XmlUtilities.GetAttributeValue(xmlNeuronGene, "activationFunction");
double layer = double.Parse(XmlUtilities.GetAttributeValue(xmlNeuronGene, "layer"));
return(new NeuronGene(null, id, layer, neuronType, ActivationFunctionFactory.GetActivationFunction(activationFn)));
}
public OneNeuron(int inpCount, NeuronType nt = NeuronType.Normal)
{
NType = nt;
Weights = new List <double>();
for (var i = 0; i < inpCount; i++)
{
Weights.Add(1);
}
}
public Neuron(int inputCount, NeuronType type = NeuronType.Normal)
{
//TODO: check input data in Neuron class.
NeuronType = type;
Weights = new List <double>();
Inputs = new List <double>();
InitRandomWeights(inputCount);
}
public NeuronGene(uint innovationId, NeuronType neuronType, IActivationFunction activationFunction)
{
this.innovationId = innovationId;
this.neuronType = neuronType;
this.activationFunction = activationFunction;
this.TimeConstant = 1;
this.Layer = 10; // default value to signify that Layers are not being used. Normally Layer is between 0 and 1
//Console.WriteLine("wut, no layer?");
}
public NeuronGene(NeuronGene other)
{
this.type = other.type;
this.innovationId = other.innovationId;
this.a = other.a;
this.b = other.b;
this.c = other.c;
this.d = other.d;
this.mean = other.mean;
this.sigma = other.sigma;
}
public NeuronGene(int innovationId, NeuronType type, float a, float b, float c, float d, float mean, float sigma)
{
this.innovationId = innovationId;
this.type = type;
this.a = a;
this.b = b;
this.c = c;
this.d = d;
this.mean = mean;
this.sigma = sigma;
}
public NeuronGene(int innovationId, NeuronType type)
{
this.type = type;
this.innovationId = innovationId;
this.a = 0.5f;
this.b = 0.5f;
this.c = 0.5f;
this.d = 0.5f;
this.mean = 0.5f;
this.sigma = 1.0f;
}
public Neuron(IActivationFunction activationFn, NeuronType neuronType, uint id)
{
this.activationFn = activationFn;
this.neuronType = neuronType;
this.id = id;
connectionList = new ConnectionList();
if(neuronType == NeuronType.Bias)
this.outputValue = 1.0D;
else
this.outputValue = 0.0D;
}
public MP(string name, Point3D position, IHillock hillock, double initpotential, double startT)
{
id = Guid.NewGuid();
this.name = name;
this.position = position;
synapses = new Dictionary<Guid, ISynapse>();
currentsources = new Dictionary<Guid, ICurrentSource>();
this.hillock = hillock;
this.hillock.HostNeuron = this;
this.initpotential = initpotential;
ReSet(startT);
parentnetwork = null;
dynamicrule = null;
type = NeuronType.MP;
}
public static string GetNeuronTypeString(NeuronType type)
{
switch(type)
{
case NeuronType.Bias:
return "bias";
case NeuronType.Hidden:
return "hid";
case NeuronType.Input:
return "in";
case NeuronType.Output:
return "out";
default:
return string.Empty;
}
}
public NeuronGene(NeuronGene copyFrom, uint _innovationId, double _layer, NeuronType _neuronType, IActivationFunction _activationFunction)
{
if (copyFrom != null)
{
this.innovationId = copyFrom.InnovationId;
this.neuronType = copyFrom.NeuronType;
this.activationFunction = copyFrom.ActivationFunction;
this.Layer = copyFrom.Layer;
}
else
{
this.innovationId = _innovationId;
this.neuronType = _neuronType;
this.activationFunction = _activationFunction;
this.Layer = _layer;
}
}
public ICell Develop(NeuronType neurontype, double threshold = -50, double initpotential = -60, double restpotential = -60, double r = 5, double c = 2, double resetpotential = -60, double refractoryperiod = 1)
{
INeuron neuron = null;
switch (neurontype)
{
case NeuronType.MP:
neuron = new MP(threshold, initpotential);
break;
case NeuronType.LI:
neuron = new LI(threshold, initpotential, r, c, restpotential);
break;
case NeuronType.IF:
neuron = new IF(threshold, resetpotential, refractoryperiod, initpotential, r, c, restpotential);
break;
}
return Develop(neuron);
}
public static Color Dyes(NeuronType type)
{
Color color;
switch (type)
{
case NeuronType.HH:
color = Colors.Blue;
break;
case NeuronType.IF:
color = Colors.Red;
break;
case NeuronType.LI:
color = Colors.Green;
break;
default:
color = Colors.White;
break;
}
return color;
}
public ModelNeuron(NeuronType neuronType, uint id, IActivationFunction funct)
{
this.neuronType = neuronType;
this.id = id;
this.function = funct;
}
public Neuron(NeuronType type)
{
Type = type;
}
public NeuronGene(uint innovationId, NeuronType neuronType, IActivationFunction activationFunction)
{
this.innovationId = innovationId;
this.neuronType = neuronType;
this.activationFunction = activationFunction;
}
// Функция активации
int ActivateFunc(double sum, double threshold, NeuronType neuronType)
{
return (sum >= threshold ? NeuroActive(neuronType) : NeuroPassive(neuronType));
}
static int NeuroPassive(NeuronType nt)
{
switch (nt)
{
case NeuronType.Binary:
return 0;
case NeuronType.Bipolar:
return -1;
default:
return 0;
}
}
