Alakhator Alakhator

## 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;

## 2C++7.py
SquaredLoss<> loss;   //initializing square loss object
Data<RealVector> prediction = model(data.inputs());  //prediction is calculated based on data inputs
cout << "squared loss: " << loss(data.labels(), prediction) << endl; // In end we ouptut the loss

## 2C++6.cpp
// show model parameters
cout << "intercept: " << model.offset() << endl;
cout << "matrix: " << model.matrix() << endl;

## 2C++5.cpp
//training the model
trainer.train(model, data);// train function ro training the model.
	//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;
	SquaredLoss<> loss; //initializing square loss object
	Data<RealVector> prediction = model(data.inputs()); //prediction is calculated based on data inputs
	cout << "squared loss: " << loss(data.labels(), prediction) << endl; // In end we ouptut the loss
	// show model parameters
	cout << "intercept: " << model.offset() << endl;
	cout << "matrix: " << model.matrix() << endl;
	//training the model
	trainer.train(model, data);// train function ro training the model.