GINI INDEX & INFO GAIN PRAC

Author: pronobkarmoker

Decision_tree_using_Entropy.c

@@ -221,7 +221,7 @@ int main()
         for (int j = 0; j < num_samples; j++)
         {
             int threshold = data[j][i]; // Calculate information gain for each value
-            double information_gain = calculate_information_gain(class_labels, data[i], num_samples, threshold, num_classes);
+            double information_gain = calculate_information_gain(class_labels, data[j], num_samples, threshold, num_classes);
 
             if (information_gain > max_information_gain)
             {

Information_gain_using_Gini_index.c

@@ -0,0 +1,111 @@
+#include <stdio.h>
+#include <math.h>
+#include <stdlib.h>
+
+#define num_sample 5
+#define num_class 2
+
+double calculate_gini_index(int cls_label[], int num_samples, int num_classes)
+{
+    double gini = 1.0;
+
+    int cls_count[num_classes];
+    for (int i = 0; i < num_classes; i++)
+    {
+        cls_count[i] = 0;
+    }
+
+    for (int i = 0; i < num_samples; i++)
+    {
+        cls_count[cls_label[i] - 1]++;
+    }
+    for (int i = 0; i < num_classes; i++)
+    {
+        double p = (double)cls_count[i] / num_samples;
+        gini -= p * p;
+    }
+
+    return gini;
+}
+
+double calculate_information_gain(int *class_labels, int *feature_values, int num_samples, int threshold, int num_classes)
+{
+    int left_count = 0;
+    int right_count = 0;
+    int *left_class_counts = (int *)malloc(num_classes * sizeof(int));
+    int *right_class_counts = (int *)malloc(num_classes * sizeof(int));
+
+    for (int i = 0; i < num_classes; ++i)
+    {
+        left_class_counts[i] = 0;
+        right_class_counts[i] = 0;
+    }
+
+    for (int i = 0; i < num_samples; i++)
+    {
+        if (feature_values[i] <= threshold)
+        {
+            left_count++;
+            left_class_counts[class_labels[i] - 1]++;
+        }
+        else
+        {
+            right_count++;
+            right_class_counts[class_labels[i] - 1]++;
+        }
+    }
+
+    double total_gini = calculate_gini_index(class_labels, num_samples, num_classes);
+    double left_gini = calculate_gini_index(left_class_counts, left_count, num_classes);
+    double right_gini = calculate_gini_index(right_class_counts, right_count, num_classes);
+
+    double p_left = (double)left_count / num_samples;
+    double p_right = (double)right_count / num_samples;
+
+    // Calculate information gain using Gini index
+    double information_gain = total_gini - (p_left * left_gini + p_right * right_gini);
+
+    // Free allocated memory
+    free(left_class_counts);
+    free(right_class_counts);
+
+    return information_gain;
+}
+
+int main()
+{
+    int data[4][3] = {
+        {5, 10, 15},
+        {50, 55, 60},
+        {100, 150, 200},
+        {250, 300, 350}};
+
+    int class_labels[4] = {1, 1, 2, 2};
+    int num_samples = 4;
+    int num_classes = 2;
+    int num_features = 3;
+
+    for (int i = 0; i < num_features; i++)
+    {
+        double gini_index = calculate_gini_index(class_labels, num_sample, num_class);
+        printf("attribute %d gini index : %lf\n", i, gini_index);
+    }
+
+    // for (int i = 0; i < num_features; i++)
+    // {
+    //     int *feature_values = (int *)malloc(num_samples * sizeof(int)); // Allocate memory for feature values
+    //     for (int j = 0; j < num_samples; j++)
+    //     {
+    //         feature_values[j] = data[j][i]; // Copy the values of the i-th column to the feature values array
+    //     }
+    //     for (int threshold = 0; threshold < num_samples; threshold++) // Iterate over thresholds for each feature
+    //     {
+    //         double info_gain = calculate_information_gain(class_labels, feature_values, num_samples, data[threshold][i], num_classes);
+
+    //         printf("Feature %d, Threshold %d: Information Gain = %lf\n", i, data[threshold][i], info_gain);
+    //     }
+    //     free(feature_values); // Free allocated memory
+    // }
+
+    return 0;
+}