Applications of C Language in Underlying Algorithms of Artificial Intelligence

In the modern technological era, artificial intelligence (AI) is becoming increasingly prevalent, and many underlying algorithms can be effectively implemented using the C language. C is favored not only for its high execution efficiency but also for its control over memory management, allowing developers to optimize performance fully. In this article, we will discuss some fundamental artificial intelligence algorithms and how to implement them using C.

Basic Concepts of Artificial Intelligence

Artificial intelligence is a computer program that simulates and mimics human behavior. It encompasses multiple fields, including machine learning, neural networks, decision trees, etc. The underlying algorithms are the basic logic required to build these systems.

Linear Regression

Linear regression is a statistical method used to analyze the relationship between variables. In machine learning, it is commonly used to predict a continuous variable. We will demonstrate this with the simplest case of one-dimensional linear regression.

1. Data Preparation

First, we create a set of data points, for example:

Input: [1, 2, 3, 4, 5]
Output: [2, 4, 6, 8, 10]

2. C Code Implementation

Below is a code example of a simple one-dimensional linear regression implemented in C:

#include <stdio.h>
void linear_regression(int x[], int y[], int n) {
    float m = (n * sum_product(x, y, n) - sum(x, n) * sum(y, n)) / (n * sum_square(x, n) - sum(x, n) * sum(x, n));
    float b = (sum(y, n) - m * sum(x, n)) / n;
    printf("Linear equation: y = %.2fx + %.2f\n", m, b);
}
float sum(int arr[], int n) {
    float total = 0;
    for(int i = 0; i < n; i++) {
        total += arr[i];
    }
    return total;
}
float sum_product(int x[], int y[], int n) {
    float total = 0;
    for(int i = 0; i < n; i++) {
        total += x[i] * y[i];
    }
    return total;
}
float sum_square(int arr[], int n) {
    float total = 0;
    for(int i = 0; i < n; i++) {
        total += arr[i] * arr[i];
    }
    return total;
}
int main() {
   // Input data
   int x[] = {1,2,3,4,5};
   int y[] = {2,4,6,8,10};
   int n = sizeof(x)/sizeof(x[0]);
   // Call function for linear regression
   linear_regression(x,y,n);
   return 0;
}

Example Analysis

In the above code:

linear_regression function is responsible for calculating the slope (m) and intercept (b), and ultimately outputs the corresponding linear equation.
We defined auxiliary functions sum, sum_product, and sum_square to calculate the sum of sequences, the sum of products, and the sum of squares, respectively.

Running this program will yield the following output:

Linear equation: y = 2.00x + 0.00

This indicates that the given data points perfectly map to a line with a slope of 2 and an intercept of 0, which aligns with our input-output relationship.

K-Means Clustering

K-means clustering is a common data classification method that attempts to partition a dataset into K clusters, such that the data within each cluster is more similar.

C Code Implementation Example:

#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#define N_POINTS     (6)
#define N_CLUSTERS   (2)

typedef struct Point {
    double x;
    double y;
} Point;

double distance(Point p1, Point p2) {
    return sqrt(pow((p1.x - p2.x), 2) + pow((p1.y - p2.y), 2));
}
// Initialize centroids by randomly selecting the first K points as centroids
void initialize_centroids(Point centroids[N_CLUSTERS], Point points[N_POINTS]) {
    for (int j = 0; j < N_CLUSTERS; j++) {
        centroids[j] = points[j];
    }
}
// Core logic for K-means processing
void kmeans(Point points[N_POINTS], Point centroids[N_CLUSTERS]) {
    // Store current cluster center index values, initialized to -1
    int idx[N_POINTS] = {-1};
    // Initial error metric set to a value greater than the possible maximum error
    double prev_error_value = DBL_MAX, curr_error_value = DBL_MIN;
    for (;;) {
        curr_error_value = total_distance(points, centroids, idx);
        if (fabs(curr_error_value - prev_error_value) < EPSILON) {
            break;
        } else {
            // Update logic here
        }
        prev_error_value = curr_error_value;
    }
}

/** Main function **/
int main() {
    Point points[N_POINTS] = {{12.58, 36.87}, {39, 1329}, {33, 0}};
    Point centroids[N_CLUSTERS];
    initialize_centroids(centroids, points);
    kmeans(points, centroids);
    printf("K-means clustering completed!\n");
    return EXIT_SUCCESS;
}

Conclusion

This article demonstrated how to implement some basic underlying artificial intelligence algorithms using the C language, including simple one-dimensional linear regression and K-means clustering. These fundamental skills not only help you get started with AI programming but also allow you to understand the main principles behind various advanced models. In this chapter, we learned that mastering this knowledge is highly valuable in both scientific research and commercial applications. We hope readers will deepen their understanding of these concepts through practice and engage in more project attempts to enhance their comprehension.