/*******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();
}
CDataObject::CDataObject(int total_pts, enumStreetIndices br_ndx, int times_acted, int ndealt, eBetType action_type) :
m_npoints(total_pts),
m_br(br_ndx),
m_nacted(times_acted),
m_ndealt(ndealt),
m_action(action_type)
{
assert(br_ndx >= ePreflopIndex && br_ndx < eRoundIndices);
assert(times_acted >= 0);
m_ndims = times_acted + (br_ndx == ePreflopIndex ? num_prior_dims_preflop : num_prior_dims_postflop) + 1;
// this is only used for ann
m_data = new double*[m_npoints];
// allocate hand_ids for corresponding data points
m_hand_ids = new long[m_npoints];
for(long i = 0; i < m_npoints; i++)
m_hand_ids[i] = -1L;
m_profits = VectorXd::Zero(m_npoints);
m_points = Matrix<double, Dynamic, Dynamic, RowMajor>::Zero(m_npoints, m_ndims);
CDatabase p_db;
GetData(&p_db);
// Set the weights
diag_factor = Matrix<double, Dynamic, Dynamic, RowMajor>::Identity(m_ndims, m_ndims);
#ifdef KASPER_WEIGHTS
RowVectorXd tmp;
if(ePreflopIndex == br_ndx)
{
tmp = RowVectorXd::Zero(9);
tmp << 10, 20, 100, 10, 0, 0, 2, 2, 20;
}
else
{
tmp = RowVectorXd::Zero(11);
tmp << 10, 80, 10, 0, 0, 0, 2, 2, 50, 1, 1;
}
diag_factor.bottomRightCorner(tmp.cols(), tmp.cols()) = tmp.asDiagonal();
#else
FeatureNormalize();
CRegressionObject regress(m_points, m_profits);
diag_factor = regress.get_theta().asDiagonal();
#endif
gLog.WriteLog(eSeverityInfo, eCatPerformance, "br%d_%d: %8s - %8d points\n", br_ndx+1, times_acted, bets_str[action_type], m_npoints);
}
