Added lab 3

lib/includes/NeuralNetwork.h

@@ -41,6 +41,15 @@ public:
     void vectorReLU(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
     void saveNetwork(const std::string& filename, int numOfFeatures, int numOfHiddenNodes, int numOfOutputNodes,
                      std::vector<std::vector<double>>& inputToHiddenWeights, std::vector<double>& hiddenLayerBias,

lib/src/NeuralNetwork.cpp

@@ -2,8 +2,10 @@
 #include <cmath>
 #include <vector>
 #include <fstream>
+#include <limits>
 #include "../includes/NeuralNetwork.h"
 
+
 double NeuralNetwork::singleNeuron(double input, double weight) 
 {
     return 0;
@@ -68,10 +70,51 @@ void NeuralNetwork::vectorSigmoid(std::vector<double>& inputVector, std::vector<
     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,
                                 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);
     if (!file.is_open()) {
@@ -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,
                                 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);
     if (!file.is_open()) {
@@ -123,30 +176,58 @@ void NeuralNetwork::loadNetwork(const std::string& filename, int numOfFeatures,
     }
 
     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 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++) {
-        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 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++) {
-        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();
     std::cout << "Network loaded from file: " << filename << "\n";
-}
+}
\ No newline at end of file