/// <summary>
/// Creating a equialent matrix
/// </summary>
/// <returns></returns>
public static bool[,] some()
{
bool[,] temp = new bool[8, 8];
VectorGenerator vg = new VectorGenerator();
for (int i = 0; i < 8; i++)
{
int c = 0;
foreach (bool k in vg.CreateVector(4))
{
temp[c, i] = k;
c++;
}
}
// Displays matrix of input vectors
for (int i = 0; i < 8; i++)
{
// columns
for (int k = 0; k < 8; k++)
{
Console.Write(Convert.ToInt32(temp[i, k]).ToString() + " ");
}
Console.Write('\n');
}
bool[,] ttm = new bool[8, 28];
int ctr = 0;
// Creates equi matrix
for (int i = 0; i < 8; i++)
{
// columns
for (int k = 0; k < 8; k++)
{
for (int y = k + 1; y < 8; y++)
{
ttm[i, ctr] = temp[i, k] == temp[i, y];
ctr++;
}
}
ctr = 0;
}
// Displays equi matrix
for (int i = 0; i < 8; i++)
{
for (int k = 0; k < 28; k++)
{
Console.Write(Convert.ToInt32(ttm[i, k]).ToString() + " ");
}
Console.Write('\n');
}
return(ttm);
}
static void Main(string[] args)
{
int numberOfProps = 5;
Tree tree = new Tree();
VectorGenerator vg = new VectorGenerator();
NeuronGraph <int> neurons = new NeuronGraph <int>();
/****************
* Very important, below we are instantiating neurons as input level and
* their indexes are equal to the indexes of features they presenting
*****************/
for (int i = 0; i < numberOfProps; i++)
{
neurons.AddVertex(neurons.Matrix.VertexCounter);
}
// Vector of weights according to neurons
float[] weights = new float[numberOfProps];
// Initializing
for (int i = 0; i < weights.Length; i++)
{
weights[i] = 0.1f;
}
// Calibrating weights
for (int i = 0; i < 1000; i++)
{
List <bool> input = vg.CreateVector(numberOfProps);
float[] tempWeights = weights;
for (int k = 0; k < weights.Length; k++)
{
tempWeights[k] = Func(input, weights, k);
}
weights = tempWeights;
}
foreach (float f in weights)
{
Console.WriteLine(f);
}
/*********
* Below is implementation of another algorythm which uses tree
* **********/
// Simple loop of vectors input
/*List<bool> t;
* for (int i = 0; i < 0; i++)
* {
* // Vectror processing by tree
* tree.Add(vg.CreateVector(numberOfProps));
* // Checks for activated vertices
* t = tree.CheckOver(tree.Root);
* // Then creating a new Neuron if activated
* if (t != null)
* {
* Console.WriteLine("Start");
* foreach (bool k in t)
* {
* Console.WriteLine(Convert.ToInt32(k));
* }
* Console.WriteLine("End");
* int currentIndex = neurons.Matrix.VertexCounter;
* neurons.AddVertex(currentIndex);
* // Inherits from features
* for(int counter = 0; counter < t.Count; counter++)
* {
* neurons.MakeNeighbours(currentIndex, counter);
* }
* }
* }
* //neurons.Matrix.Display();
* bool[,] ttmm = some();
* for (int i = 0; i < 28; i++)
* {
* List<bool> tmp = new List<bool>();
* for(int k = 0; k<8; k++)
* {
* tmp.Add(ttmm[k, i]);
* }
* // Vectroe processing by tree
* tree.Add(tmp);
* // Checks for activated vertices
* t = tree.CheckOver(tree.Root);
* // Then creating a new Neuron if activated
* if (t != null)
* {
* Console.WriteLine("Start");
* foreach (bool k in t)
* {
* Console.WriteLine(Convert.ToInt32(k));
* }
* Console.WriteLine("End");
* int currentIndex = neurons.Matrix.VertexCounter;
* neurons.AddVertex(currentIndex);
* // Inherits from features
* for (int counter = 0; counter < t.Count; counter++)
* {
* neurons.MakeNeighbours(currentIndex, counter);
* }
* }
* }*/
}