diff --git a/MainTomasz.cpp b/MainTomasz.cpp
index 4dc233d..e4a96dc 100644
--- a/MainTomasz.cpp
+++ b/MainTomasz.cpp
@@ -27,6 +27,12 @@ int pozycjaTraktoraX = 1, pozycjaTraktoraY = 1;
 char currentWay = 'S';
 char underTraktor = '.';
 double timeToDest = 0.0;
+double **weightMatrix;
+double neuroOutputPole[25][25];
+double *inputNeurons;
+double **grad;
+double **avrGrad;
+double numberOfTests;
 
 void color(string foregroundColor, string backgroundColor)
 {
@@ -546,7 +552,21 @@ double countTimeToDest(int endX, int endY)
 
 double Sigmoid(double number)
 {
-	return (number / (1.0 + abs(number)));
+	int tempInt = 0;
+	if (number < 0)
+	{
+		tempInt = 1;
+	}
+	return tempInt + (number / (1.0 + abs(number)));
+}
+double pSigmoid(double number)
+{
+	int tempInt = 1;
+	if (number < 0)
+	{
+		tempInt = -1;
+	}
+	return tempInt * (number / ((1.0 + abs(number))*(1.0 + abs(number))));
 }
 double lookOfVege(int x, int y)
 {
@@ -585,18 +605,90 @@ double lookOfVege(int x, int y)
 		return 5.0;
 	}
 }
-double setValusesRange(double a, double b, double x)
+double setValusesRange(double a, double b, double num)
 {
-	double avr = ((a + b) / 2);
-	return Sigmoid(x - avr);
+	int temp = 1;
+	if (a > b)
+	{
+		temp = -1;
+	}
+	double avr = ((a + b) / 2)*temp;
+	return Sigmoid(num - avr);
+}
+void gradient(int desiredOutput[25][25])
+{
+	const int numberOfCellsInPole = (25 * 25);
+	const int inputNeuronsCount = numberOfCellsInPole * 4;
+	grad = (double **)malloc(numberOfCellsInPole * sizeof(double *));
+	for (int i = 0; i < numberOfCellsInPole; i++)
+	{
+		grad[i] = (double *)malloc(inputNeuronsCount * sizeof(double));
+	}
+	double z;
+	for (int i = 0; i < numberOfCellsInPole; i++)
+	{
+		for (int j = 0; j < inputNeuronsCount; j++)
+		{
+			if (weightMatrix[i][j] != 0)
+			{
+				int x, y;
+				y = i / 25;
+				x = i % 25;
+				grad[i][j] = 2 * pSigmoid(weightMatrix[i][j] * inputNeurons[j]) * inputNeurons[j] * (neuroOutputPole[y][x] - desiredOutput[y][x]);
+			}
+			else
+			{
+				grad[i][j] = 0;
+			}
+		}
+	}
+	//cout << "grad set" << endl;
 }
 
-void firstHiddenLayer()
+
+void buildMatrix()
 {
-	//25*25-1
+	const int numberOfCellsInPole = (25 * 25);
+	const int inputNeuronsCount = numberOfCellsInPole * 4;
+	weightMatrix = (double **)malloc(numberOfCellsInPole * sizeof(double *));
+	for (int i = 0; i < numberOfCellsInPole; i++)
+	{
+		weightMatrix[i] = (double *)malloc(inputNeuronsCount * sizeof(double));
+	}
+	for (int i = 0; i < numberOfCellsInPole; i++)
+	{
+		for (int j = 0; j < inputNeuronsCount; j++)
+		{
+			if (j >= (i * 4) && j < ((i + 1) * 4))
+			{
+				weightMatrix[i][j] = 1.0;
+			}
+			else
+			{
+				weightMatrix[i][j] = 0.0;
+			}
+		}
+	}
+}
+void buildAvrGrad()
+{
+	const int numberOfCellsInPole = (25 * 25);
+	const int inputNeuronsCount = numberOfCellsInPole * 4;
+	avrGrad = (double **)malloc(numberOfCellsInPole * sizeof(double *));
+	for (int i = 0; i < numberOfCellsInPole; i++)
+	{
+		avrGrad[i] = (double *)malloc(inputNeuronsCount * sizeof(double));
+	}
+	for (int i = 0; i < numberOfCellsInPole; i++)
+	{
+		for (int j = 0; j < inputNeuronsCount; j++)
+		{
+			avrGrad[i][j] = 0;
+		}
+	}
 }
 
-void neuronsInputBuild()
+double neuronsInputBuild(int desiredOutput[25][25])
 {
 	const int numberOfCellsInPole = (25 * 25);// -1;
 	const int inputNeuronsCount = numberOfCellsInPole * 4;
@@ -610,32 +702,193 @@ void neuronsInputBuild()
 	{
 		for (int j = 1; j <= 25; j++)
 		{
-			if (pole[i][j][0] != 'T')
+			int tempCell = (((i - 1) * 25) + (j - 1));
+			if (pole[i][j][0] == 'T')
 			{
-				int tempCell = (((i - 1) * 25) + (j - 1));
-				if (j >= pozycjaTraktoraX && i >= pozycjaTraktoraY)
+				/*if (j >= pozycjaTraktoraX && i >= pozycjaTraktoraY)
 				{
 					int tempCell = (((i - 1) * 25) + (j - 1))-1;
-				}
-				typeOfVege[tempCell] = setValusesRange(1, 9, pole[i][j][1]);//type after weight 1-9
-				timeToGetToVege[tempCell] = setValusesRange(0, 25 * 25 * 9, countTimeToDest(j, i));//time x.0
-				protectOrFertilize[tempCell] = setValusesRange(0, 3, poleInt[i][j][0]);//0.0 1.0 2.0 3.0
-				stateOfVege[tempCell] = setValusesRange(0, 5, lookOfVege(j, i));//0.0-5.0*/
+				}*/
+				typeOfVege[tempCell] = 0;//type after weight 1-9
+				timeToGetToVege[tempCell] = 0;//time x.0
+				protectOrFertilize[tempCell] = 0;//0.0 1.0 2.0 3.0
+				stateOfVege[tempCell] = 0;//0.0-5.0
+			}
+			else
+			{
+				typeOfVege[tempCell] = setValusesRange(1, 9, ((double)pole[i][j][1]-48));//type after weight 1-9
+				timeToGetToVege[tempCell] = setValusesRange(25 * 9, 0, countTimeToDest(j, i));//time x.0
+				protectOrFertilize[tempCell] = setValusesRange(3, 0, poleInt[i][j][0]);//0.0 1.0 2.0 3.0
+				stateOfVege[tempCell] = setValusesRange(0, 5, lookOfVege(j, i));//0.0-5.0
 			}
 		}
 	}
-	cout << "set neutrons";
-	double **weightMatrix = (double **)malloc(inputNeuronsCount * sizeof(double *));
-	for (int i = 0; i < inputNeuronsCount; i++)
+	//cout << "set neurons";
+	inputNeurons = (double *)malloc(inputNeuronsCount * sizeof(double));
+	for (int i = 0; i < numberOfCellsInPole; i++)
 	{
-		weightMatrix[i] = (double *)malloc(numberOfCellsInPole * sizeof(double));
+		inputNeurons[i * 4] = typeOfVege[i];
+		inputNeurons[(i * 4) + 1] = timeToGetToVege[i];
+		inputNeurons[(i * 4) + 2] = protectOrFertilize[i];
+		inputNeurons[(i * 4) + 3] = stateOfVege[i];
 	}
-
-	firstHiddenLayer();
-
 	
-}
+	/*double **weightMatrix = (double **)malloc(numberOfCellsInPole * sizeof(double *));
+	for (int i = 0; i < numberOfCellsInPole; i++)
+	{
+		weightMatrix[i] = (double *)malloc(inputNeuronsCount * sizeof(double));
+	}
+	for (int i = 0; i < numberOfCellsInPole; i++)
+	{
+		for (int j = 0; j < inputNeuronsCount; j++)
+		{
+			if (j >= (i * 4) && j < ((i + 1) * 4))
+			{
+				weightMatrix[i][j] = 1;
+			}
+			else
+			{
+				weightMatrix[i][j] = 0;
+			}
+		}
+	}*/
+	//0 1 2 inp
+	//1 
+	//2 
+	//num
 
+	//inp -> a inp(0-3)
+	//inp -> a1 inp(4-7)
+	//inp -> a2 inp(8-11)
+
+	//updatePola();
+	//cout << "matrix setup";
+	
+	//firstHiddenLayer();
+	//updatePola();
+	double *outputLayer = (double *)malloc(numberOfCellsInPole * sizeof(double));
+	for (int i = 0; i < numberOfCellsInPole; i++)
+	{
+		double sum = 0;
+		for (int j = 0; j < inputNeuronsCount; j++)
+		{
+			sum += weightMatrix[i][j] * inputNeurons[j];
+		}
+		outputLayer[i] = Sigmoid(sum);
+	}
+	for (int i = 0; i < 25; i++)
+	{
+		for (int j = 0; j < 25; j++)
+		{
+			int tempCell = ((i * 25) + j);
+			neuroOutputPole[i][j] = outputLayer[tempCell];
+		}
+	}
+	double cost = 0.0;
+	for (int i = 0; i < 25; i++)
+	{
+		for (int j = 0; j < 25; j++)
+		{
+			double tempNum = neuroOutputPole[i][j] - desiredOutput[i][j];
+			cost += (tempNum*tempNum);
+		}
+	}
+	//updatePola();
+	return cost;
+}
+void backProp(int desiredOuput[25][25])
+{
+	/*double node[25][25];
+	for (int i = 0; i < 25; i++)
+	{
+		for (int j = 0; j < 25; j++)
+		{
+			double tempNum = neuroOutputPole[i][j] - desiredOuput[i][j];
+			node[i][j] = (tempNum*tempNum);
+		}
+	}
+	cout << neuroOutputPole[1][2] << endl;//->0
+	cout << neuroOutputPole[4][3] << endl;//->1
+	updatePola();*/
+
+	const int numberOfCellsInPole = (25 * 25);
+	const int inputNeuronsCount = numberOfCellsInPole * 4;
+	double cost;
+	cost = neuronsInputBuild(desiredOuput);
+	int i = 0;
+	while (cost > 50 && i<30)
+	{
+		cout << i << " ";
+		gradient(desiredOuput);
+
+		for (int i = 0; i < numberOfCellsInPole; i++)
+		{
+			for (int j = 0; j < inputNeuronsCount; j++)
+			{
+				//cout << grad[i][j] << " ";
+				weightMatrix[i][j] -= grad[i][j];
+			}
+		}
+
+		cost = neuronsInputBuild(desiredOuput);
+		i++;
+	}
+	cout << "--END--" << endl;
+	for (int i = 0; i < numberOfCellsInPole; i++)
+	{
+		for (int j = 0; j < inputNeuronsCount; j++)
+		{
+			if (weightMatrix[i][j] != 0)
+			{
+				avrGrad[i][j] += 1 - weightMatrix[i][j]/numberOfTests;
+			}
+		}
+	}
+	//double cost = neuronsInputBuild(desiredOuput);
+	//cout << oldcost << endl;
+	//cout << cost << endl;
+	/*
+	for (int i = 0; i < 25; i++)
+	{
+		for (int j = 0; j < 25; j++)
+		{
+			if (desiredOuput[i][j] == 1)
+			{
+				cout << "!!" << node[i][j] << "!! ";
+			}
+			else
+			{
+				cout << node[i][j] << " ";
+			}
+		}
+	}*/
+}
+void network(int desiredX,int desiredY)
+{
+	int desiredPole[25][25];
+	for (int i = 0; i < 25; i++)
+	{
+		for (int j = 0; j < 25; j++)
+		{
+			desiredPole[i][j] = 0;
+		}
+	}
+	desiredPole[desiredY - 1][desiredX - 1] = 1;
+	//double cost = neuronsInputBuild(desiredPole);
+	backProp(desiredPole);
+}
+void bestMatrixBuild()
+{
+	const int numberOfCellsInPole = (25 * 25);
+	const int inputNeuronsCount = numberOfCellsInPole * 4;
+	for (int i = 0; i < numberOfCellsInPole; i++)
+	{
+		for (int j = 0; j < inputNeuronsCount; j++)
+		{
+			weightMatrix[i][j] -= avrGrad[i][j];
+		}
+	}
+}
 
 void test1()
 {
@@ -704,6 +957,46 @@ void start3()
 	gogo(goalX, goalY);
 }
 
+void neuroTest1(int bX,int bY)
+{
+	pole[bY][bX][0] = 'B';
+	pole[bY][bX][1] = '9';
+	poleInt[bY][bX][0] = 0;
+	poleInt[bY][bX][1] = 16;
+	updatePola();
+	network(bX, bY);
+	pole[bY][bX][0] = '.';
+	pole[bY][bX][1] = '1';
+	poleInt[bY][bX][0] = 0;
+	poleInt[bY][bX][1] = 0;
+	updatePola();
+}
+void neuroTest2()
+{
+	int bX[5] = { 4,24,24,25,25 }, bY[5] = {5,1,2,2,1};
+	for (int i = 0; i < 5; i++)
+	{
+		pole[bY[i]][bX[i]][0] = 'B';
+		pole[bY[i]][bX[i]][1] = '9';
+		poleInt[bY[i]][bX[i]][0] = 0;
+		poleInt[bY[i]][bX[i]][1] = 8;
+	}
+	poleInt[bY[4]][bX[4]][0] = 3;
+	poleInt[bY[4]][bX[4]][1] = 70;
+	updatePola();
+
+	network(bX[4], bY[4]);
+
+	for (int i = 0; i < 5; i++)
+	{
+		pole[bY[i]][bX[i]][0] = '.';
+		pole[bY[i]][bX[i]][1] = '1';
+		poleInt[bY[i]][bX[i]][0] = 0;
+		poleInt[bY[i]][bX[i]][1] = 0;
+	}
+	updatePola();
+}
+
 void neuroStart1()
 {
 	int b1X = 4, b1Y = 5;
@@ -715,10 +1008,87 @@ void neuroStart1()
 	pozycjaTraktoraX = 1, pozycjaTraktoraY = 1;
 	pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
 	pole[pozycjaTraktoraY][pozycjaTraktoraX][1] = '1';
+	//underTraktor='B'
+	//pole[pozycjaTraktoraY][pozycjaTraktoraX][1] = '9';
+	buildMatrix();
+	buildAvrGrad();
+	numberOfTests = 6;
 
+	neuroTest1(b1X, b1Y);
+	buildMatrix();
+	neuroTest1(b2X, b2Y);
+	buildMatrix();
+	neuroTest1(b3X, b3Y);
+	buildMatrix();
+	neuroTest1(b4X, b4Y);
+	buildMatrix();
+	neuroTest1(b5X, b5Y);
+	buildMatrix();
+	neuroTest2();
+	buildMatrix();
+
+	bestMatrixBuild();
+}
+
+void chousePath()
+{
+	int tempOut[25][25];
+	for (int i = 0; i < 25; i++)
+	{
+		for (int j = 0; j < 25; j++)
+		{
+			tempOut[i][j] = 0;
+		}
+	}
+	neuronsInputBuild(tempOut);
+	const int numberOfCellsInPole = (25 * 25);
+	const int inputNeuronsCount = numberOfCellsInPole * 4;
+	double bestX=0, bestY=0, bestChance=1;
+	for (int i = 0; i < 25; i++)
+	{
+		for (int j = 0; j < 25; j++)
+		{
+			//cout << neuroOutputPole[i][j] << " ";
+			
+			double tempChance;
+			if (pole[i + 1][j + 1][0] == 'T')
+			{
+				tempChance = 1;
+			}
+			else
+			{
+				tempChance = neuroOutputPole[i][j];
+			}
+			//cout << tempChance << " ";
+			if (tempChance < bestChance)
+			{
+				bestX = j;
+				bestY = i;
+				bestChance = tempChance;
+			}
+		}
+	}
+	//cout << bestChance << " " << bestX + 1 << " " << bestY + 1 << endl;
+	//Sleep(10000);
+	gogo(bestX+1, bestY+1);
+	//Sleep(100000);
+}
+
+void testOfNeuroMove()
+{
+	pole[1][2][0] = 'B';
+	pole[1][2][1] = '9';
+	poleInt[1][2][0] = 0;
+	poleInt[1][2][1] = 50;
+	pole[1][3][0] = 'B';
+	pole[1][3][1] = '9';
+	poleInt[1][3][0] = 0;
+	poleInt[1][3][1] = 60;
+	pole[1][4][0] = 'B';
+	pole[1][4][1] = '9';
+	poleInt[1][4][0] = 0;
+	poleInt[1][4][1] = 70;
 	updatePola();
-
-	neuronsInputBuild();
 }
 
 int main()
@@ -754,6 +1124,9 @@ int main()
 	//start3();  // testy start 1-3
 	neuroStart1();
 
+
+	testOfNeuroMove();
+
 	//---------start---------//
 	bool traktorDziala = true;
 
@@ -761,15 +1134,16 @@ int main()
 
 	do
 	{
-		akcja = _getch();
-		if (akcja == 'w' || akcja == 's' || akcja == 'a' || akcja == 'd')
+		chousePath();
+		/*akcja = _getch();
+		/if (akcja == 'w' || akcja == 's' || akcja == 'a' || akcja == 'd')
 		{
 			Move(akcja);
 		}
 		if (akcja == '0')
 		{
 			traktorDziala = false;
-		}
+		}*/
 	} while (traktorDziala);
 	//---------end---------//