private Dictionary <int, int> byKmeans(double[][] v)
{
K_means k_mean = new K_means();
//k_mean.addSetting(k, maxLoop, Normalized(x), epsilon, sigma, T, M);
k_mean.addSetting(k, maxLoop, x, epsilon, sigma, T, M);
k_mean.run(v);
v_Last = k_mean.getV();
return(k_mean.get_Clustered_Data());
}
public void run()
{
int n = x.Length; // n is number of user of group
int d = x[0].Length; // d is number of Item of rating matrix
double[][] v = new double[k][];
v = CREATE_V_Init_01(x, k);
for (int i = 0; i < 3; i++)
{
int loop = 1;
K_means k_mean = new K_means();
k_mean.addSetting(k, loop, x, epsilon, sigma, T, M);
k_mean.run(v);
v = k_mean.getV();
//K_means_DCA k_mean_DCA = new K_means_DCA();
//k_mean_DCA.addSetting(k, loop, x, epsilon, sigma, T, M);
//k_mean_DCA.run(v);
//v = k_mean_DCA.getV();
}
Repeat(n, d, v);
}
private Dictionary<int, int> byKmeans(double[][] v)
{
K_means k_mean = new K_means();
//k_mean.addSetting(k, maxLoop, Normalized(x), epsilon, sigma, T, M);
k_mean.addSetting(k, maxLoop, x, epsilon, sigma, T, M);
k_mean.run(v);
v_Last = k_mean.getV();
return k_mean.get_Clustered_Data();
}
public void run()
{
int n = x.Length; // n is number of user of group
int d = x[0].Length; // d is number of Item of rating matrix
double[][] v = new double[k][];
v = CREATE_V_Init_01(x, k);
for (int i = 0; i < 3; i++)
{
int loop = 1;
K_means k_mean = new K_means();
k_mean.addSetting(k, loop, x, epsilon, sigma, T, M);
k_mean.run(v);
v = k_mean.getV();
//K_means_DCA k_mean_DCA = new K_means_DCA();
//k_mean_DCA.addSetting(k, loop, x, epsilon, sigma, T, M);
//k_mean_DCA.run(v);
//v = k_mean_DCA.getV();
}
Repeat(n, d, v);
}