C++ (Cpp) set_Sigma示例

示例#1

文件： MultivariateFNormalSufficientSparse.cpp 项目： andreyto/imp-fork-proddl

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.
}

示例#2

文件： MultivariateFNormalSufficientSparse.cpp 项目： andreyto/imp-fork-proddl

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);
}

示例#3

文件： ZeroMeanMvnModel.cpp 项目： Hkey1/boom

 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));
 }

示例#4

文件： MultivariateFNormalSufficient.cpp 项目： yannickspill/gp

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);
}

示例#5

文件： MultivariateFNormalSufficient.cpp 项目： yannickspill/gp

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);
}

示例#6

文件： MvtRegModel.cpp 项目： Hkey1/boom

  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);
    }
  }

示例#7

文件： MultivariateFNormalSufficient.cpp 项目： apolitis/imp

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;
}

示例#8

文件： MultivariateFNormalSufficient.cpp 项目： apolitis/imp

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;
}

示例#9

文件： WeightedMvnModel.cpp 项目： MarkEdmondson1234/Boom

 void WeightedMvnModel::mle(){
   set_mu(suf()->ybar());
   set_Sigma(suf()->var_hat());
 }

示例#10

文件： MultivariateRegression.cpp 项目： steve-the-bayesian/BOOM

 void MvReg::mle() {
   set_Beta(suf()->beta_hat());
   set_Sigma(suf()->SSE(Beta()) / suf()->n());
 }