/*******M step, maximazize log-likelihood*/
void Plsa::M_step(MatrixXd &data)
{
MatrixXd X;
for (int i=0;i<K;i++)
{
Pw_z.col(i)=(Pz_wd[i].cwiseProduct(data)).rowwise().sum();//suma de filas[vector(1XN)]
Pd_z.col(i)=(Pz_wd[i].cwiseProduct(data)).colwise().sum();//suma de columnas[vector(1XN)]
}
//normalize
RowVectorXd Temp;
RowVectorXd C;
VectorXd E;
VectorXd T;
Temp=RowVectorXd::Ones(K);
T=VectorXd::Ones(K); // vector of K with ones
P_z=Pd_z.colwise().sum();
cout<<P_z;
C=Pd_z.colwise().sum(); //suma de columnas[vector(1XN)]
Temp=Temp.cwiseQuotient(C);
Pd_z=Pd_z*(Temp.asDiagonal());
C=Pw_z.colwise().sum(); //
Temp=Temp.cwiseQuotient(C);
Pw_z=Pw_z*(Temp.asDiagonal());
E=P_z.rowwise().sum();
P_z=P_z.cwiseProduct(T.cwiseQuotient(E));
}
void Plsa::Normalize(MatrixXd &Mat)
{
RowVectorXd Temp;
Temp=RowVectorXd::Ones(K);
Temp=Temp.cwiseQuotient(Mat.colwise().sum());
Mat=Mat*Temp.asDiagonal();
}
