Give some images, how to do classification after face recognition generate 128-D values

[fatalfeel]

//Here is share examples how to do it~~~

#include "iostream"
#include "unordered_map"
#include "vector"
#include "cmath"

//128-D can use dlib::length(descriptors128D0 - descriptors128D1); substitute this
double calculateDistance(const double& x1, const double& y1, const double& x2, const double& y2) {
double dx = x1 - x2;
double dy = y1 - y2;
return std::sqrt(dx * dx + dy * dy);
}

int main() {
int count = 0;
double vector[][2] = {{1, 1}, {2, 2}, {7, 7}, {8.2, 8.2}, {12, 12}, {13, 13}, {8.5, 8.5}};
std::unordered_map<int, std::vector> categoryMap;

int categoryIndex = 0;

for (int idx_current = 0; idx_current < sizeof(vector)/sizeof(vector[0]); ++idx_current) {
    bool foundCategory = false;

    // Compare the current vector with existing categories
    for (const auto& category : categoryMap)
    {
        for (const auto& idx_history : category.second)
        {
        	double distance = calculateDistance(vector[idx_current][0], vector[idx_current][1], vector[idx_history][0], vector[idx_history][1]);
            count++;

            if (distance <= 2.0) {
                // If distance is within the threshold, add the vector to the category
                categoryMap[category.first].push_back(idx_current);
                foundCategory = true;
                break;
            }
        }

        if (foundCategory) {
            break;
        }
    }

    // If no category found, create a new category for the current vector
    if (!foundCategory) {
        categoryMap[categoryIndex].push_back(idx_current);
        categoryIndex++;
    }
}

// Display the grouped categories
for (const auto& category : categoryMap) {
    std::cout << "Category " << category.first << ": ";
    for (const auto& idx_history : category.second) {
        std::cout << "(" << vector[idx_history][0] << ", " << vector[idx_history][1] << ") ";
    }
    std::cout << std::endl;
}

std::cout << count << std::endl;

return 0;

}

[fatalfeel]

result:
Category 2: (12, 12) (13, 13)
Category 1: (7, 7) (8.2, 8.2) (8.5, 8.5)
Category 0: (1, 1) (2, 2)