数字识别-C代码示例

以下是一个简单的数字识别C代码示例：

#include <stdio.h>
#include <stdlib.h>
#include <math.h>

// 训练集
int training_set[10][7][5] = {
    {{1,1,1,1,1},{1,0,0,0,1},{1,0,0,0,1},{1,0,0,0,1},{1,1,1,1,1}}, // 0
    {{0,0,1,0,0},{0,1,1,0,0},{0,0,1,0,0},{0,0,1,0,0},{0,1,1,1,0}}, // 1
    {{1,1,1,0,0},{0,0,0,1,0},{0,0,1,0,0},{0,1,0,0,0},{1,1,1,1,1}}, // 2
    {{1,1,1,0,0},{0,0,0,1,0},{0,1,1,0,0},{0,0,0,1,0},{1,1,1,0,0}}, // 3
    {{1,0,0,1,0},{1,0,0,1,0},{1,1,1,1,1},{0,0,0,1,0},{0,0,0,1,0}}, // 4
    {{1,1,1,1,1},{1,0,0,0,0},{1,1,1,1,0},{0,0,0,1,0},{1,1,1,0,0}}, // 5
    {{0,1,1,1,0},{1,0,0,0,0},{1,1,1,1,0},{1,0,0,1,0},{0,1,1,0,0}}, // 6
    {{1,1,1,1,1},{0,0,0,1,0},{0,0,1,0,0},{0,1,0,0,0},{1,0,0,0,0}}, // 7
    {{0,1,1,0,0},{1,0,0,1,0},{0,1,1,0,0},{1,0,0,1,0},{0,1,1,0,0}}, // 8
    {{0,1,1,0,0},{1,0,0,1,0},{0,1,1,1,0},{0,0,0,1,0},{0,1,1,0,0}}  // 9
};

// 测试集
int test_set[7][5] = {
    {1,1,1,1,1},
    {1,0,0,0,1},
    {1,1,1,1,1},
    {0,0,0,1,0},
    {0,0,0,1,0},
    {0,0,0,1,0},
    {0,0,0,1,0}
};

// 按位比较两个数字
int compare_numbers(int num1[], int num2[], int size) {
    int i;
    for (i = 0; i < size; i++) {
        if (num1[i] != num2[i]) {
            return 0;
        }
    }
    return 1;
}

int main() {
    int i, j, k;
    double similarity, max_similarity = 0.0;
    int max_index = -1;

    // 遍历所有训练集中的数字
    for (i = 0; i < 10; i++) {
        similarity = 0.0;
        // 计算测试集和训练集中数字的相似度
        for (j = 0; j < 7; j++) {
            for (k = 0; k < 5; k++) {
                if (test_set[j][k] == training_set[i][j][k]) {
                    similarity += 1.0;
                }
            }
        }
        // 找到相似度最高的数字
        if (similarity > max_similarity) {
            max_similarity = similarity;
            max_index = i;
        }
    }

    // 输出结果
    if (max_index >= 0 && max_index <= 9) {
        printf("The number is %d\n", max_index);
    } else {
        printf("The number cannot be recognized\n");
    }

    return 0;
}

该代码中使用了一个简单的训练集和测试集，训练集中包含0到9这10个数字的图像数据，而测试集中包含了一个待识别的数字。代码会遍历训练集中的每个数字，计算测试集和训练集中数字的相似度，最终输出相似度最高的数字。相似度的计算方式很简单，就是按位比较测试集和训练集中数字的每个像素值是否相同，相同则增加相似度值。