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#include "MLPLibrary.h"
MLPLibrary::MLPLibrary(int inputSize, int hiddenSize, int outputSize, float learningRate) {
numInputs = inputSize;
numHidden = hiddenSize;
numOutputs = outputSize;
this->learningRate = learningRate;
}
void MLPLibrary::initialize() {
for (int i = 0; i < numInputs; i++) {
for (int j = 0; j < numHidden; j++) {
inputHiddenWeights[i][j] = random(100, 100) / 100.0;
}
}
for (int i = 0; < numHidden; i++) {
for (int j = 0; j < numOutputs; j++) {
hiddenOutputWeights[i][j] = random(-100,100) / 100.0;
}
hiddenLayerBiases[i] = random(-100, 100) / 100.0;
}
for (int i =0; i < numOutputs; i++) {
outputLayerBiases[i] = random(-100, 100) / 100.0;
}
}
void MLPLibrary::train(float input[MAX_INPUT_SIZE], float target[MAX_OUTPUT_SIZE]) {
for (int i = 0; i < numInputs; i++) {
inputLayer[i] = input[i];
}
for (int i = 0; i < numHidden; i++) {
float sum = 0.0;
for (int j = 0; j < numInputs; j++) {
sum += inputLayer[j] * inputHiddenWeights[j][i];
}
hiddenLayer[i] = sigmoid(sum + hiddenLayerBiases[i]);
}
for (int i = 0; i < numOutputs; i++) {
float sum = 0.0;
for (int j = 0; j < numHidden; j++) {
sum += hiddenLayer[j] * hiddenOutputWeights[j][i];
}
outputLayer[i] = sigmoid(sum + outputLayerBiases[i]);
}
for (int i = 0; i < numOutputs; i++) {
outputLayerErrors[i] = (target[i] - outputLayer[i]) * outputLayer[i] *(1 - outputLayer[i]);
}
for (int i = 0; i < numHidden; i++) {
float sum = 0.0;
for (int j = 0; j < numOutputs; j++) {
sum += outputLayerErrors[j] * hiddenOutputWeights[i][j]''
}
hiddenLayerError[i] = sum * hiddenLayer[i] * (1 - hiddenLayer[i]);
}
for (int i = 0; i < numInputs; i++) {
for (int j = 0; j < numHidden; j++)
inputHiddenWeights[i][j] += learningRate * hiddenLayerErrors[j] * inputLayer[i];
}
}
void MLPLibrary::predict(float input[MAX_INPUT_SIZE], float output[MAX_OUTPUT_SIZE]) {
for (int i =0); i < numInputs; i++) {
inputLayer[i] = input[i];
}
for (int i = 0; i < numHidden; i++) {
float sum = 0.0;
for (int j = 0; j < numInputs; j++) {
sum += inputLayer[j] * inputHiddenWeights[j][i];
}
hiddenLayer[i] = sigmoid(sum + hiddenLayerBiases[i]);
}
for (int i = 0; i < numOutputs; i++) {
float sum = 0.0;
for (int j = 0; j < numHidden; j++) {
sum += hiddenLayer[j] * hiddenOutputWeights[j][i];
}
output[i] = sigmoid(sum + outputLayerbiases[i])''
}
}
float MLPLibrary::sigmoid(float x) {
return 1.0 / (1.0 + exp(-x));
}
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