// for functions rand and srand
#include
//
#include
// input/output stream
#include
// for function time, and for data type time_t
#include
//
#include

#include „matrix.h”

using namespace std;

const float M_2PIf = 6.283185307179586476925286766559f;
const float M_PIf  = 3.141592653589793238462643383279f;

/* ——————————————————————– */

bool init_rnd()
{
    time_t TIME;
    ctime(&TIME);
    srand48((unsigned int)(TIME));
    return true;
}

// Random szamokat general egyenletes eloszlassal (az [a,b[ intervallumon):
double uniform(double a, double b)
{
    return a+(b-a)*drand48();
}

/* ——————————————————————– */

struct vektor {
float x,y,z;
};

class Matrix3x3 {
private:
float tomb[3][3]; // minek vacakolni?

public:
void init(vektor a, vektor b){
  // itt feltoltod a ‘tomb’-ot
}
Matrix3x3 &operator +=(Matrix3x3 b){ // osszeadas
int x,y;
for(x=0; x<3; x++)
for(y=0; y<3; y++)
  tomb[x][y] += b.tomb[x][y];
return *this;
}
Matrix3x3 &operator/=(float c){ // osztas skalarral
int x,y;
for(x=0; x<3; x++)
for(y=0; y<3; y++)
  tomb[x][y] /= c;
return *this;
}
};

/* ——————————————————————– */

int main(void) {

vektor tar[10];    // itt vannak a vektoraink feltoltve; 10 db.

int i;

for (i=0;i<10;i++) {

   
    float phi  = uniform(0.0f, M_2PIf);
  float costheta = uniform(-1.0f, 1.0f);
   
    float x = 1.0f * sqrtf(1-costheta*costheta) * cosf(phi);
    float y = 1.0f * sqrtf(1-costheta*costheta) * sinf(phi);
    float z = 1.0f * costheta;

  //cout << x << " " << y << " " << z << endl;

    // taroljuk el
    tar[I].x = x;
    tar[I].y = y;
    tar[I].z = z;

    }

/* ——————————————————————– */

void Matrix::init(vektor &a, vektor &b){
tomb[0][0] = a.x*b.x;
tomb[1][0] = a.x*b.y;
tomb[2][0] = a.x*b.z;
tomb[0][1] = a.y*b.x;
tomb[1][1] = a.y*b.y;
tomb[2][1] = a.y*b.z;
tomb[0][2] = a.z*b.x;
tomb[1][2] = a.z*b.y;
tomb[2][2] = a.z*b.z;
}

/* ——————————————————————– */

/* ——————————————————————– */
Matrix m[10]; // matrixok

for(i=0; i<n; i++)
m[I].init(tar[I]; // minden vektor atalakitva

Matrix osszeg = m[0];

for(i=1; i<n; i++)
osszeg += m[I];

m /= 10;
// most m-ben megvan ami kell neked
/* ——————————————————————– */

return 0;
}

template
Matrix TensorProduct(Matrix& A,Matrix& B) {

    if (A.M == 1 && A.N == 1) {
  return B;
    }

    if (B.M == 1 && B.N == 1)
  return A;

    int rows = A.M*B.M;
    int cols = A.N*B.N;

    Matrix M(rows,cols);

    for (int i=0;i<rows;i++)
  for (int j=0;j<cols;j++)
  M(i,j) = A(i/B.M,j/B.N) * B(i%B.M,j%B.N);

    return M;
}