IMPISD_BEGIN_NAMESPACE
MultivariateFNormalSufficientSparse::MultivariateFNormalSufficientSparse(
const MatrixXd& FX, double JF, const VectorXd& FM,
const SparseMatrix<double>& Sigma, cholmod_common *c, double cutoff) :
Object("Multivariate Normal distribution %1%")
{
c_ = c;
W_=nullptr;
Sigma_=nullptr;
P_=nullptr;
PW_=nullptr;
epsilon_=nullptr;
L_=nullptr;
N_=FX.rows();
M_=FX.cols();
IMP_LOG(TERSE, "MVNsparse: direct init with N=" << N_
<< " and M=" << M_ << std::endl);
IMP_USAGE_CHECK( N_ > 0,
"please provide at least one observation per dimension");
IMP_USAGE_CHECK( M_ > 0,
"please provide at least one variable");
FM_=FM;
set_FX(FX,cutoff); //also computes W, Fbar and epsilon.
set_JF(JF);
set_Sigma(Sigma); //computes the Cholesky decomp.
}
MultivariateFNormalSufficientSparse::MultivariateFNormalSufficientSparse(
const VectorXd& Fbar, double JF, const VectorXd& FM, int Nobs,
const SparseMatrix<double>& W, const SparseMatrix<double>& Sigma,
cholmod_common *c)
: Object("Multivariate Normal distribution %1%")
{
c_ = c;
W_=nullptr;
Sigma_=nullptr;
P_=nullptr;
PW_=nullptr;
epsilon_=nullptr;
L_=nullptr;
N_=Nobs;
M_=Fbar.rows();
IMP_LOG(TERSE, "MVNsparse: sufficient statistics init with N=" << N_
<< " and M=" << M_ << std::endl);
IMP_USAGE_CHECK( N_ > 0,
"please provide at least one observation per dimension");
IMP_USAGE_CHECK( M_ > 0,
"please provide at least one variable");
FM_=FM;
set_Fbar(Fbar); //also computes epsilon
set_W(W);
set_JF(JF);
set_Sigma(Sigma);
}
void ZMMM::mle(){
double n = suf()->n();
if(n < 1) {
report_error("Too few degrees of freedom to compute ML in "
"ZeroMeanGaussianModel::mle()");
}
set_Sigma( suf()->center_sumsq(mu_) / (n-1));
}
MultivariateFNormalSufficient::MultivariateFNormalSufficient(
const MatrixXd& FX, double JF, const VectorXd& FM,
const MatrixXd& Sigma, double factor)
{
//O(1)
reset_flags();
N_=FX.rows();
M_=FX.cols();
LOG( "MVN: direct init with N=" << N_
<< " and M=" << M_ << std::endl);
CHECK( N_ > 0,
"please provide at least one observation per dimension");
CHECK( M_ > 0,
"please provide at least one variable");
set_factor(factor);
set_FM(FM);
set_FX(FX);
set_jacobian(JF);
set_Sigma(Sigma);
}
MultivariateFNormalSufficient::MultivariateFNormalSufficient(
const VectorXd& Fbar, double JF, const VectorXd& FM, int Nobs,
const MatrixXd& W, const MatrixXd& Sigma, double factor)
{
reset_flags();
N_=Nobs;
M_=Fbar.rows();
LOG( "MVN: sufficient statistics init with N=" << N_
<< " and M=" << M_ << std::endl);
CHECK( N_ > 0,
"please provide at least one observation per dimension");
CHECK( M_ > 0,
"please provide at least one variable");
set_factor(factor);
set_FM(FM);
set_Fbar(Fbar);
set_W(W);
set_jacobian(JF);
set_Sigma(Sigma);
}
MVTR::MvtRegModel(const Mat &X,const Mat &Y, bool add_intercept)
: ParamPolicy(new MatrixParams(X.ncol() + add_intercept,Y.ncol()),
new SpdParams(Y.ncol()),
new UnivParams(default_df))
{
Mat XX(add_intercept? cbind(1.0,X) : X);
QR qr(XX);
Mat Beta(qr.solve(qr.QtY(Y)));
Mat resid = Y - XX* Beta;
uint n = XX.nrow();
Spd Sig = resid.t() * resid/n;
set_Beta(Beta);
set_Sigma(Sig);
for(uint i=0; i<n; ++i){
Vec y = Y.row(i);
Vec x = XX.row(i);
NEW(MvRegData, dp)(y,x);
DataPolicy::add_data(dp);
}
}
MultivariateFNormalSufficient::MultivariateFNormalSufficient(
const VectorXd& Fbar, double JF, const VectorXd& FM, int Nobs,
const MatrixXd& W, const MatrixXd& Sigma, double factor)
: base::Object("Multivariate Normal distribution %1%")
{
reset_flags();
N_=Nobs;
M_=Fbar.rows();
IMP_LOG_TERSE( "MVN: sufficient statistics init with N=" << N_
<< " and M=" << M_ << std::endl);
IMP_USAGE_CHECK( N_ > 0,
"please provide at least one observation per dimension");
IMP_USAGE_CHECK( M_ > 0,
"please provide at least one variable");
set_factor(factor);
set_FM(FM);
set_Fbar(Fbar);
set_W(W);
set_jacobian(JF);
set_Sigma(Sigma);
use_cg_=false;
}
MultivariateFNormalSufficient::MultivariateFNormalSufficient(
const MatrixXd& FX, double JF, const VectorXd& FM,
const MatrixXd& Sigma, double factor) :
base::Object("Multivariate Normal distribution %1%")
{
//O(1)
reset_flags();
N_=FX.rows();
M_=FX.cols();
IMP_LOG_TERSE( "MVN: direct init with N=" << N_
<< " and M=" << M_ << std::endl);
IMP_USAGE_CHECK( N_ > 0,
"please provide at least one observation per dimension");
IMP_USAGE_CHECK( M_ > 0,
"please provide at least one variable");
set_factor(factor);
set_FM(FM);
set_FX(FX);
set_jacobian(JF);
set_Sigma(Sigma);
use_cg_=false;
}
void WeightedMvnModel::mle(){
set_mu(suf()->ybar());
set_Sigma(suf()->var_hat());
}
void MvReg::mle() {
set_Beta(suf()->beta_hat());
set_Sigma(suf()->SSE(Beta()) / suf()->n());
}
