Create Program.c

Machine_Learning_Algorithms/Naive Bayes Classifier Algorithm/Program.c

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+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+#define NUM_CLASSES 2 // Spam and Not Spam
+#define NUM_FEATURES 5 // Number of features (words)
+#define NUM_SAMPLES 6 // Number of training samples
+
+// Sample data: Each row represents a document with features indicating presence of words (1) or absence (0)
+int data[NUM_SAMPLES][NUM_FEATURES] = {
+    {1, 1, 0, 1, 0}, // Spam
+    {1, 0, 1, 1, 1}, // Spam
+    {0, 1, 1, 0, 0}, // Not Spam
+    {1, 1, 1, 0, 0}, // Not Spam
+    {0, 0, 1, 1, 1}, // Not Spam
+    {1, 0, 0, 0, 1}  // Spam
+};
+
+// Labels for the training data (0 = Not Spam, 1 = Spam)
+int labels[NUM_SAMPLES] = {1, 1, 0, 0, 0, 1};
+
+// Function to calculate prior probabilities
+void calculate_prior(double *prior) {
+    for (int i = 0; i < NUM_CLASSES; i++) {
+        prior[i] = 0;
+    }
+
+    for (int i = 0; i < NUM_SAMPLES; i++) {
+        prior[labels[i]]++;
+    }
+
+    for (int i = 0; i < NUM_CLASSES; i++) {
+        prior[i] /= NUM_SAMPLES; // Normalize
+    }
+}
+
+// Function to calculate likelihoods
+void calculate_likelihood(double likelihood[NUM_CLASSES][NUM_FEATURES]) {
+    for (int i = 0; i < NUM_CLASSES; i++) {
+        for (int j = 0; j < NUM_FEATURES; j++) {
+            likelihood[i][j] = 1; // Use Laplace smoothing
+        }
+    }
+
+    for (int i = 0; i < NUM_SAMPLES; i++) {
+        for (int j = 0; j < NUM_FEATURES; j++) {
+            likelihood[labels[i]][j] += data[i][j];
+        }
+    }
+
+    for (int i = 0; i < NUM_CLASSES; i++) {
+        for (int j = 0; j < NUM_FEATURES; j++) {
+            likelihood[i][j] /= (prior[i] * NUM_SAMPLES + 2); // Smooth with total features
+        }
+    }
+}
+
+// Function to predict the class for a given instance
+int predict(int instance[NUM_FEATURES], double prior[NUM_CLASSES], double likelihood[NUM_CLASSES][NUM_FEATURES]) {
+    double posteriors[NUM_CLASSES] = {0};
+
+    for (int i = 0; i < NUM_CLASSES; i++) {
+        posteriors[i] = prior[i];
+        for (int j = 0; j < NUM_FEATURES; j++) {
+            posteriors[i] *= likelihood[i][j] * instance[j];
+        }
+    }
+
+    return (posteriors[1] > posteriors[0]) ? 1 : 0; // Return class with higher probability
+}
+
+int main() {
+    double prior[NUM_CLASSES];
+    double likelihood[NUM_CLASSES][NUM_FEATURES];
+
+    // Calculate prior probabilities and likelihoods
+    calculate_prior(prior);
+    calculate_likelihood(likelihood);
+
+    // Test instance (example: a new document)
+    int test_instance[NUM_FEATURES] = {1, 0, 1, 1, 0}; // New document to classify
+
+    // Predict class for the test instance
+    int predicted_class = predict(test_instance, prior, likelihood);
+
+    // Output the result
+    printf("Predicted class: %s\n", (predicted_class == 1) ? "Spam" : "Not Spam");
+
+    return 0;
+}