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MainTomasz.cpp
430
MainTomasz.cpp
@ -27,6 +27,12 @@ int pozycjaTraktoraX = 1, pozycjaTraktoraY = 1;
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char currentWay = 'S';
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char underTraktor = '.';
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double timeToDest = 0.0;
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double **weightMatrix;
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double neuroOutputPole[25][25];
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double *inputNeurons;
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double **grad;
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double **avrGrad;
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double numberOfTests;
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void color(string foregroundColor, string backgroundColor)
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{
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@ -546,7 +552,21 @@ double countTimeToDest(int endX, int endY)
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double Sigmoid(double number)
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{
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return (number / (1.0 + abs(number)));
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int tempInt = 0;
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if (number < 0)
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{
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tempInt = 1;
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}
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return tempInt + (number / (1.0 + abs(number)));
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}
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double pSigmoid(double number)
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{
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int tempInt = 1;
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if (number < 0)
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{
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tempInt = -1;
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}
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return tempInt * (number / ((1.0 + abs(number))*(1.0 + abs(number))));
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}
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double lookOfVege(int x, int y)
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{
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@ -585,18 +605,90 @@ double lookOfVege(int x, int y)
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return 5.0;
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}
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}
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double setValusesRange(double a, double b, double x)
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double setValusesRange(double a, double b, double num)
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{
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double avr = ((a + b) / 2);
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return Sigmoid(x - avr);
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int temp = 1;
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if (a > b)
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{
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temp = -1;
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}
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double avr = ((a + b) / 2)*temp;
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return Sigmoid(num - avr);
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}
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void gradient(int desiredOutput[25][25])
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{
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const int numberOfCellsInPole = (25 * 25);
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const int inputNeuronsCount = numberOfCellsInPole * 4;
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grad = (double **)malloc(numberOfCellsInPole * sizeof(double *));
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for (int i = 0; i < numberOfCellsInPole; i++)
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{
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grad[i] = (double *)malloc(inputNeuronsCount * sizeof(double));
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}
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double z;
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for (int i = 0; i < numberOfCellsInPole; i++)
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{
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for (int j = 0; j < inputNeuronsCount; j++)
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{
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if (weightMatrix[i][j] != 0)
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{
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int x, y;
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y = i / 25;
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x = i % 25;
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grad[i][j] = 2 * pSigmoid(weightMatrix[i][j] * inputNeurons[j]) * inputNeurons[j] * (neuroOutputPole[y][x] - desiredOutput[y][x]);
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}
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else
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{
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grad[i][j] = 0;
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}
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}
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}
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//cout << "grad set" << endl;
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}
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void firstHiddenLayer()
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void buildMatrix()
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{
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//25*25-1
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const int numberOfCellsInPole = (25 * 25);
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const int inputNeuronsCount = numberOfCellsInPole * 4;
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weightMatrix = (double **)malloc(numberOfCellsInPole * sizeof(double *));
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for (int i = 0; i < numberOfCellsInPole; i++)
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{
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weightMatrix[i] = (double *)malloc(inputNeuronsCount * sizeof(double));
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}
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for (int i = 0; i < numberOfCellsInPole; i++)
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{
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for (int j = 0; j < inputNeuronsCount; j++)
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{
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if (j >= (i * 4) && j < ((i + 1) * 4))
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{
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weightMatrix[i][j] = 1.0;
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}
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else
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{
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weightMatrix[i][j] = 0.0;
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}
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}
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}
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}
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void buildAvrGrad()
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{
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const int numberOfCellsInPole = (25 * 25);
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const int inputNeuronsCount = numberOfCellsInPole * 4;
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avrGrad = (double **)malloc(numberOfCellsInPole * sizeof(double *));
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for (int i = 0; i < numberOfCellsInPole; i++)
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{
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avrGrad[i] = (double *)malloc(inputNeuronsCount * sizeof(double));
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}
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for (int i = 0; i < numberOfCellsInPole; i++)
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{
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for (int j = 0; j < inputNeuronsCount; j++)
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{
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avrGrad[i][j] = 0;
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}
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}
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}
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void neuronsInputBuild()
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double neuronsInputBuild(int desiredOutput[25][25])
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{
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const int numberOfCellsInPole = (25 * 25);// -1;
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const int inputNeuronsCount = numberOfCellsInPole * 4;
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@ -610,32 +702,193 @@ void neuronsInputBuild()
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{
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for (int j = 1; j <= 25; j++)
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{
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if (pole[i][j][0] != 'T')
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int tempCell = (((i - 1) * 25) + (j - 1));
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if (pole[i][j][0] == 'T')
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{
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int tempCell = (((i - 1) * 25) + (j - 1));
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if (j >= pozycjaTraktoraX && i >= pozycjaTraktoraY)
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/*if (j >= pozycjaTraktoraX && i >= pozycjaTraktoraY)
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{
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int tempCell = (((i - 1) * 25) + (j - 1))-1;
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}
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typeOfVege[tempCell] = setValusesRange(1, 9, pole[i][j][1]);//type after weight 1-9
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timeToGetToVege[tempCell] = setValusesRange(0, 25 * 25 * 9, countTimeToDest(j, i));//time x.0
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protectOrFertilize[tempCell] = setValusesRange(0, 3, poleInt[i][j][0]);//0.0 1.0 2.0 3.0
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stateOfVege[tempCell] = setValusesRange(0, 5, lookOfVege(j, i));//0.0-5.0*/
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}*/
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typeOfVege[tempCell] = 0;//type after weight 1-9
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timeToGetToVege[tempCell] = 0;//time x.0
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protectOrFertilize[tempCell] = 0;//0.0 1.0 2.0 3.0
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stateOfVege[tempCell] = 0;//0.0-5.0
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}
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else
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{
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typeOfVege[tempCell] = setValusesRange(1, 9, ((double)pole[i][j][1]-48));//type after weight 1-9
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timeToGetToVege[tempCell] = setValusesRange(25 * 9, 0, countTimeToDest(j, i));//time x.0
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protectOrFertilize[tempCell] = setValusesRange(3, 0, poleInt[i][j][0]);//0.0 1.0 2.0 3.0
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stateOfVege[tempCell] = setValusesRange(0, 5, lookOfVege(j, i));//0.0-5.0
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}
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}
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}
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cout << "set neutrons";
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double **weightMatrix = (double **)malloc(inputNeuronsCount * sizeof(double *));
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for (int i = 0; i < inputNeuronsCount; i++)
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//cout << "set neurons";
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inputNeurons = (double *)malloc(inputNeuronsCount * sizeof(double));
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for (int i = 0; i < numberOfCellsInPole; i++)
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{
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weightMatrix[i] = (double *)malloc(numberOfCellsInPole * sizeof(double));
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inputNeurons[i * 4] = typeOfVege[i];
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inputNeurons[(i * 4) + 1] = timeToGetToVege[i];
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inputNeurons[(i * 4) + 2] = protectOrFertilize[i];
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inputNeurons[(i * 4) + 3] = stateOfVege[i];
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}
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firstHiddenLayer();
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}
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/*double **weightMatrix = (double **)malloc(numberOfCellsInPole * sizeof(double *));
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for (int i = 0; i < numberOfCellsInPole; i++)
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{
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weightMatrix[i] = (double *)malloc(inputNeuronsCount * sizeof(double));
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}
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for (int i = 0; i < numberOfCellsInPole; i++)
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{
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for (int j = 0; j < inputNeuronsCount; j++)
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{
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if (j >= (i * 4) && j < ((i + 1) * 4))
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{
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weightMatrix[i][j] = 1;
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}
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else
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{
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weightMatrix[i][j] = 0;
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}
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}
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}*/
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//0 1 2 inp
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//1
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//2
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//num
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//inp -> a inp(0-3)
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//inp -> a1 inp(4-7)
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//inp -> a2 inp(8-11)
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//updatePola();
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//cout << "matrix setup";
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//firstHiddenLayer();
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//updatePola();
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double *outputLayer = (double *)malloc(numberOfCellsInPole * sizeof(double));
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for (int i = 0; i < numberOfCellsInPole; i++)
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{
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double sum = 0;
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for (int j = 0; j < inputNeuronsCount; j++)
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{
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sum += weightMatrix[i][j] * inputNeurons[j];
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}
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outputLayer[i] = Sigmoid(sum);
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}
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for (int i = 0; i < 25; i++)
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{
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for (int j = 0; j < 25; j++)
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{
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int tempCell = ((i * 25) + j);
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neuroOutputPole[i][j] = outputLayer[tempCell];
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}
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}
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double cost = 0.0;
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for (int i = 0; i < 25; i++)
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{
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for (int j = 0; j < 25; j++)
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{
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double tempNum = neuroOutputPole[i][j] - desiredOutput[i][j];
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cost += (tempNum*tempNum);
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}
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}
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//updatePola();
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return cost;
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}
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void backProp(int desiredOuput[25][25])
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{
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/*double node[25][25];
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for (int i = 0; i < 25; i++)
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{
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for (int j = 0; j < 25; j++)
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{
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double tempNum = neuroOutputPole[i][j] - desiredOuput[i][j];
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node[i][j] = (tempNum*tempNum);
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}
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}
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cout << neuroOutputPole[1][2] << endl;//->0
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cout << neuroOutputPole[4][3] << endl;//->1
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updatePola();*/
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const int numberOfCellsInPole = (25 * 25);
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const int inputNeuronsCount = numberOfCellsInPole * 4;
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double cost;
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cost = neuronsInputBuild(desiredOuput);
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int i = 0;
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while (cost > 50 && i<30)
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{
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cout << i << " ";
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gradient(desiredOuput);
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for (int i = 0; i < numberOfCellsInPole; i++)
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{
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for (int j = 0; j < inputNeuronsCount; j++)
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{
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//cout << grad[i][j] << " ";
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weightMatrix[i][j] -= grad[i][j];
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}
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}
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cost = neuronsInputBuild(desiredOuput);
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i++;
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}
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cout << "--END--" << endl;
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for (int i = 0; i < numberOfCellsInPole; i++)
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{
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for (int j = 0; j < inputNeuronsCount; j++)
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{
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if (weightMatrix[i][j] != 0)
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{
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avrGrad[i][j] += 1 - weightMatrix[i][j]/numberOfTests;
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}
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}
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}
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//double cost = neuronsInputBuild(desiredOuput);
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//cout << oldcost << endl;
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//cout << cost << endl;
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/*
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for (int i = 0; i < 25; i++)
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{
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for (int j = 0; j < 25; j++)
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{
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if (desiredOuput[i][j] == 1)
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{
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cout << "!!" << node[i][j] << "!! ";
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}
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else
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{
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cout << node[i][j] << " ";
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}
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}
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}*/
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}
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void network(int desiredX,int desiredY)
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{
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int desiredPole[25][25];
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for (int i = 0; i < 25; i++)
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{
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for (int j = 0; j < 25; j++)
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{
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desiredPole[i][j] = 0;
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}
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}
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desiredPole[desiredY - 1][desiredX - 1] = 1;
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//double cost = neuronsInputBuild(desiredPole);
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backProp(desiredPole);
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}
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void bestMatrixBuild()
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{
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const int numberOfCellsInPole = (25 * 25);
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const int inputNeuronsCount = numberOfCellsInPole * 4;
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for (int i = 0; i < numberOfCellsInPole; i++)
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{
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for (int j = 0; j < inputNeuronsCount; j++)
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{
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weightMatrix[i][j] -= avrGrad[i][j];
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}
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}
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}
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void test1()
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{
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@ -704,6 +957,46 @@ void start3()
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gogo(goalX, goalY);
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}
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void neuroTest1(int bX,int bY)
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{
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pole[bY][bX][0] = 'B';
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pole[bY][bX][1] = '9';
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poleInt[bY][bX][0] = 0;
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poleInt[bY][bX][1] = 16;
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updatePola();
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network(bX, bY);
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pole[bY][bX][0] = '.';
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pole[bY][bX][1] = '1';
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poleInt[bY][bX][0] = 0;
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poleInt[bY][bX][1] = 0;
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updatePola();
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}
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void neuroTest2()
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{
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int bX[5] = { 4,24,24,25,25 }, bY[5] = {5,1,2,2,1};
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for (int i = 0; i < 5; i++)
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{
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pole[bY[i]][bX[i]][0] = 'B';
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pole[bY[i]][bX[i]][1] = '9';
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poleInt[bY[i]][bX[i]][0] = 0;
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poleInt[bY[i]][bX[i]][1] = 8;
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}
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poleInt[bY[4]][bX[4]][0] = 3;
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poleInt[bY[4]][bX[4]][1] = 70;
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updatePola();
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network(bX[4], bY[4]);
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for (int i = 0; i < 5; i++)
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{
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pole[bY[i]][bX[i]][0] = '.';
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pole[bY[i]][bX[i]][1] = '1';
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poleInt[bY[i]][bX[i]][0] = 0;
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poleInt[bY[i]][bX[i]][1] = 0;
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}
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updatePola();
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}
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void neuroStart1()
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{
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int b1X = 4, b1Y = 5;
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@ -715,10 +1008,87 @@ void neuroStart1()
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pozycjaTraktoraX = 1, pozycjaTraktoraY = 1;
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pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
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pole[pozycjaTraktoraY][pozycjaTraktoraX][1] = '1';
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//underTraktor='B'
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//pole[pozycjaTraktoraY][pozycjaTraktoraX][1] = '9';
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buildMatrix();
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buildAvrGrad();
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numberOfTests = 6;
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neuroTest1(b1X, b1Y);
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buildMatrix();
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neuroTest1(b2X, b2Y);
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buildMatrix();
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neuroTest1(b3X, b3Y);
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buildMatrix();
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neuroTest1(b4X, b4Y);
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buildMatrix();
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neuroTest1(b5X, b5Y);
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buildMatrix();
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neuroTest2();
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buildMatrix();
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bestMatrixBuild();
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}
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void chousePath()
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{
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int tempOut[25][25];
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for (int i = 0; i < 25; i++)
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{
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for (int j = 0; j < 25; j++)
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{
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tempOut[i][j] = 0;
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}
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}
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neuronsInputBuild(tempOut);
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const int numberOfCellsInPole = (25 * 25);
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const int inputNeuronsCount = numberOfCellsInPole * 4;
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double bestX=0, bestY=0, bestChance=1;
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for (int i = 0; i < 25; i++)
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{
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for (int j = 0; j < 25; j++)
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{
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//cout << neuroOutputPole[i][j] << " ";
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double tempChance;
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if (pole[i + 1][j + 1][0] == 'T')
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{
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tempChance = 1;
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}
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else
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{
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tempChance = neuroOutputPole[i][j];
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}
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//cout << tempChance << " ";
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if (tempChance < bestChance)
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{
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bestX = j;
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bestY = i;
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bestChance = tempChance;
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}
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}
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}
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//cout << bestChance << " " << bestX + 1 << " " << bestY + 1 << endl;
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//Sleep(10000);
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gogo(bestX+1, bestY+1);
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//Sleep(100000);
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}
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void testOfNeuroMove()
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{
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pole[1][2][0] = 'B';
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pole[1][2][1] = '9';
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poleInt[1][2][0] = 0;
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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---------//
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user