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Commit c32b1643 by Nazrul_being
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Added lab 3

parent 41f3c9b1
Inline Side-by-side
Showing with 102 additions and 12 deletions
lib/includes/NeuralNetwork.h
...@@ -41,6 +41,15 @@ public: ...@@ -41,6 +41,15 @@ public:
void vectorReLU(std::vector<double>& inputVector, std::vector<double>& outputVector); void vectorReLU(std::vector<double>& inputVector, std::vector<double>& outputVector);
void vectorSigmoid(std::vector<double>& inputVector, std::vector<double>& outputVector); void vectorSigmoid(std::vector<double>& inputVector, std::vector<double>& outputVector);
// Print a 2D matrix
void printMatrix(int rows, int cols, const std::vector<std::vector<double>>& matrix);
// Compute cost for logistic regression
double computeCost(int m, const std::vector<std::vector<double>>& yhat, const std::vector<std::vector<double>>& y);
// Normalize a 2D matrix
int normalizeData2D(const std::vector<std::vector<double>>& inputMatrix, std::vector<std::vector<double>>& outputMatrix);
// Save network // Save network
void saveNetwork(const std::string& filename, int numOfFeatures, int numOfHiddenNodes, int numOfOutputNodes, void saveNetwork(const std::string& filename, int numOfFeatures, int numOfHiddenNodes, int numOfOutputNodes,
std::vector<std::vector<double>>& inputToHiddenWeights, std::vector<double>& hiddenLayerBias, std::vector<std::vector<double>>& inputToHiddenWeights, std::vector<double>& hiddenLayerBias,
......
lib/src/NeuralNetwork.cpp
...@@ -2,8 +2,10 @@ ...@@ -2,8 +2,10 @@
#include <cmath> #include <cmath>
#include <vector> #include <vector>
#include <fstream> #include <fstream>
#include <limits>
#include "../includes/NeuralNetwork.h" #include "../includes/NeuralNetwork.h"
double NeuralNetwork::singleNeuron(double input, double weight) double NeuralNetwork::singleNeuron(double input, double weight)
{ {
return 0; return 0;
...@@ -68,10 +70,51 @@ void NeuralNetwork::vectorSigmoid(std::vector<double>& inputVector, std::vector< ...@@ -68,10 +70,51 @@ void NeuralNetwork::vectorSigmoid(std::vector<double>& inputVector, std::vector<
return; return;
} }
void NeuralNetwork::printMatrix(int rows, int cols, const std::vector<std::vector<double>>& matrix)
{
return;
}
double NeuralNetwork::computeCost(int m, const std::vector<std::vector<double>>& yhat, const std::vector<std::vector<double>>& y)
{
return 0;
}
int NeuralNetwork::normalizeData2D(const std::vector<std::vector<double>>& inputMatrix, std::vector<std::vector<double>>& outputMatrix)
{
int rows = inputMatrix.size();
int cols = inputMatrix[0].size();
if (rows <= 1) {
std::cerr << "ERROR: At least 2 examples are required. Current dataset length is " << rows << std::endl;
return 1;
} else {
for (int j = 0; j < cols; j++) {
double max = -9999999;
double min = 9999999;
// Find MIN and MAX values in the given column
for (int i = 0; i < rows; i++) {
if (inputMatrix[i][j] > max) {
max = inputMatrix[i][j];
}
if (inputMatrix[i][j] < min) {
min = inputMatrix[i][j];
}
}
// Normalization
for (int i = 0; i < rows; i++) {
outputMatrix[i][j] = (inputMatrix[i][j] - min) / (max - min);
}
}
}
return 0;
}
void NeuralNetwork::saveNetwork(const std::string& filename, int numOfFeatures, int numOfHiddenNodes, int numOfOutputNodes, void NeuralNetwork::saveNetwork(const std::string& filename, int numOfFeatures, int numOfHiddenNodes, int numOfOutputNodes,
std::vector<std::vector<double>>& inputToHiddenWeights, std::vector<double>& hiddenLayerBias, std::vector<std::vector<double>>& inputToHiddenWeights, std::vector<double>& hiddenLayerBias,
std::vector<std::vector<double>>& hiddenToOutputWeights, std::vector<double>& outputLayerBias) std::vector<std::vector<double>>& hiddenToOutputWeights, std::vector<double>& outputLayerBias) {
{
std::ofstream file(filename); std::ofstream file(filename);
if (!file.is_open()) { if (!file.is_open()) {
...@@ -113,8 +156,18 @@ void NeuralNetwork::saveNetwork(const std::string& filename, int numOfFeatures, ...@@ -113,8 +156,18 @@ void NeuralNetwork::saveNetwork(const std::string& filename, int numOfFeatures,
void NeuralNetwork::loadNetwork(const std::string& filename, int numOfFeatures, int numOfHiddenNodes, int numOfOutputNodes, void NeuralNetwork::loadNetwork(const std::string& filename, int numOfFeatures, int numOfHiddenNodes, int numOfOutputNodes,
std::vector<std::vector<double>>& inputToHiddenWeights, std::vector<double>& hiddenLayerBias, std::vector<std::vector<double>>& inputToHiddenWeights, std::vector<double>& hiddenLayerBias,
std::vector<std::vector<double>>& hiddenToOutputWeights, std::vector<double>& outputLayerBias) std::vector<std::vector<double>>& hiddenToOutputWeights, std::vector<double>& outputLayerBias) {
{
// Clear vectors and resize to the correct dimensions
inputToHiddenWeights.clear();
hiddenLayerBias.clear();
hiddenToOutputWeights.clear();
outputLayerBias.clear();
inputToHiddenWeights.resize(numOfHiddenNodes, std::vector<double>(numOfFeatures));
hiddenLayerBias.resize(numOfHiddenNodes);
hiddenToOutputWeights.resize(numOfOutputNodes, std::vector<double>(numOfHiddenNodes));
outputLayerBias.resize(numOfOutputNodes);
std::ifstream file(filename); std::ifstream file(filename);
if (!file.is_open()) { if (!file.is_open()) {
...@@ -123,28 +176,56 @@ void NeuralNetwork::loadNetwork(const std::string& filename, int numOfFeatures, ...@@ -123,28 +176,56 @@ void NeuralNetwork::loadNetwork(const std::string& filename, int numOfFeatures,
} }
std::string temp; std::string temp;
file >> temp >> temp; // Skip "Hidden Layer Weights:"
// Read "Hidden Layer Weights:" line and skip to the next line
std::getline(file, temp);
for (int i = 0; i < numOfHiddenNodes; i++) { for (int i = 0; i < numOfHiddenNodes; i++) {
for (int j = 0; j < numOfFeatures; j++) { for (int j = 0; j < numOfFeatures; j++) {
file >> inputToHiddenWeights[i][j]; if (!(file >> inputToHiddenWeights[i][j])) {
std::cerr << "Error reading input-to-hidden weight at (" << i << ", " << j << ")\n";
return;
}
} }
} }
// Flush remaining newline characters
file.ignore(std::numeric_limits<std::streamsize>::max(), '\n');
file >> temp >> temp; // Skip "Hidden Layer Biases:" // Read "Hidden Layer Biases:" line and skip to the next line
std::getline(file, temp);
for (int i = 0; i < numOfHiddenNodes; i++) { for (int i = 0; i < numOfHiddenNodes; i++) {
file >> hiddenLayerBias[i]; if (!(file >> hiddenLayerBias[i])) {
std::cerr << "Error reading hidden layer bias at index " << i << "\n";
return;
}
} }
// Flush remaining newline characters
file.ignore(std::numeric_limits<std::streamsize>::max(), '\n');
file >> temp >> temp; // Skip "Output Layer Weights:" // Read "Output Layer Weights:" line and skip to the next line
std::getline(file, temp);
for (int i = 0; i < numOfOutputNodes; i++) { for (int i = 0; i < numOfOutputNodes; i++) {
for (int j = 0; j < numOfHiddenNodes; j++) { for (int j = 0; j < numOfHiddenNodes; j++) {
file >> hiddenToOutputWeights[i][j]; if (!(file >> hiddenToOutputWeights[i][j])) {
std::cerr << "Error reading hidden-to-output weight at (" << i << ", " << j << ")\n";
return;
} }
} }
}
// Flush remaining newline characters
file.ignore(std::numeric_limits<std::streamsize>::max(), '\n');
file >> temp >> temp; // Skip "Output Layer Biases:" // Read "Output Layer Biases:" line and skip to the next line
std::getline(file, temp);
for (int i = 0; i < numOfOutputNodes; i++) { for (int i = 0; i < numOfOutputNodes; i++) {
file >> outputLayerBias[i]; if (!(file >> outputLayerBias[i])) {
std::cerr << "Error reading output layer bias at index " << i << "\n";
return;
}
}
if (file.fail()) {
std::cerr << "File stream encountered an error.\n";
return;
} }
file.close(); file.close();
......
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