Alakhator Alakhator

## fd2.py
from facenet_pytorch import MTCNN
import cv2
from PIL import Image
import numpy as np
from matplotlib import pyplot as plt
from tqdm.notebook import tqdm

## fd1.py
pip  install facenet-pytorch

## 2C++15.cpp
centroids.print("Centroids:");

## 2C++14.cpp
 mlpack_kmeans.Cluster(data, k, clusters, centroids);

## 2C++13.cpp
 mlpack::kmeans::KMeans<> mlpack_kmeans(max_iter);

## 2C++12.cpp
//cluster the data
arma::Row<size_t> clusters;
arma::mat centroids;

## 2C++11.cpp
// create data
    int i = 0;
    for(; i < samples / 2; ++i)
    {
        data.col(i) = arma::vec({1, 1}) + 0.25*arma::randn<arma::vec>(dim);
    }
    for(; i < samples; ++i)
    {
        data.col(i) = arma::vec({2, 3}) + 0.25*arma::randn<arma::vec>(dim);
    }

## 2C++10.cpp
arma::mat data(dim, samples, arma::fill::zeros);

## 2C++9.cpp
int k = 2; //amount of clusters
int dim = 2;//dimensionality of program
int samples = 50;
int max_iter = 10;//maximum number of iterations

## 2C++8.cpp
#include <bits/stdc++.h>// include basic c++ liraries
#include <mlpack/methods/kmeans/kmeans.hpp>// mlpack library for k-means
#include <armadillo>//ml pack is dependent on armadillo

Using namespace std;
	from facenet_pytorch import MTCNN
	import cv2
	from PIL import Image
	import numpy as np
	from matplotlib import pyplot as plt
	from tqdm.notebook import tqdm
	//cluster the data
	arma::Row<size_t> clusters;
	arma::mat centroids;
	// create data
	int i = 0;
	for(; i < samples / 2; ++i)
	{
	data.col(i) = arma::vec({1, 1}) + 0.25*arma::randn<arma::vec>(dim);
	}
	for(; i < samples; ++i)
	{
	data.col(i) = arma::vec({2, 3}) + 0.25*arma::randn<arma::vec>(dim);
	}
	int k = 2; //amount of clusters
	int dim = 2;//dimensionality of program
	int samples = 50;
	int max_iter = 10;//maximum number of iterations
	#include <bits/stdc++.h>// include basic c++ liraries
	#include <mlpack/methods/kmeans/kmeans.hpp>// mlpack library for k-means
	#include <armadillo>//ml pack is dependent on armadillo

	Using namespace std;