//Function to generate a single list of two matrices (train or test), given an index
//  [[Rcpp::export]]
Rcpp::List MatSplit( const arma::mat & A, const arma::mat & B, const arma::uvec & splitind){
  arma::mat tA = A.rows(splitind);
  arma::mat tB = B.rows(splitind);
  return Rcpp::List::create(Rcpp::Named("A")=tA,
                            Rcpp::Named("B")=tB);

}
Exemplo n.º 2
0
// this function sample the loading parameters considering constant 
//  observational variance.
// [[Rcpp::export]]
arma::mat SampleDfmLoads(arma::mat y, arma::mat factors, arma::vec psi, 
  int s, double c0)
{
  /**
   * The data matrix 'y' is (T \times q).
   * Note that the factors matrix is (T+h \times k). In the models considered
   *  in the dissertation, h > s. This means that we always have the 
   *  VAR order, 'h', greater than the number of dynamic factors, 's', in the 
   *  observational equation. 
   * The argument 's' is the order of the dynamic factors present in the
   *  observational equation.
   * The prior mean is set to zero.
   */
   
  int T = y.n_rows;
  int q = y.n_cols;;
  int k = factors.n_cols;
  int TpH = factors.n_rows;
  int h = TpH - T;
  if (h < s+1){
    throw std::range_error(
      "Argument 's' greater than or equal to factors' VAR order in SampleDfmLoads.cpp"
      );
  }
  arma::mat I_q = eye(q, q);
//  std::cout << "\nh = " << h << "\n";
  arma::mat Xlambda;
  Xlambda = factors.rows(h, TpH-1);
//  std::cout << "\nNumber of rows in Xlambda is " << Xlambda.n_rows << "\n";
  if (s > 0){
    for (int i = 1; i <= s; i++){
    Xlambda = arma::join_rows(Xlambda, factors.rows(h-i, TpH-i-1));
    }
  }
  
  // posterior variance
  arma::mat Eigvec;
  arma::vec eigval;
  
  arma::eig_sym(eigval, Eigvec, arma::symmatu(Xlambda.t() * Xlambda));
  
  arma::mat kronEig = kron(I_q, Eigvec);  
  arma::vec L1eigval = 1.0/(arma::kron((1.0/psi), eigval) + 1/c0);
  arma::mat L1 = kronEig * diagmat(L1eigval) * kronEig.t();
  
  // posterior mean
  arma::vec yStar(y.begin(),  T*q);
  arma::vec l1 = L1 * (arma::kron(arma::diagmat(1.0/psi), Xlambda.t()) * yStar);
  arma::mat l1Mat(l1.begin(), k*(s+1), q); // mean in matrix form
  
  // random generation
  arma::vec vecnorm = sqrt(L1eigval) % randn(q*k*(s+1), 1);  
  arma::mat LambdaBar(vecnorm.begin(), k*(s+1), q, false);
  LambdaBar = l1Mat + Eigvec * LambdaBar;
  
  return LambdaBar.t();
}
// [[Rcpp::export]]
arma::mat BeQTL2(const arma::mat & A, const arma::mat & B, const arma::umat & Bootmat){
  int bsi= Bootmat.n_rows;
  arma::mat C(A.n_cols*B.n_cols,Bootmat.n_rows);
  arma::mat tC(A.n_rows,B.n_rows);
  for(int i=0; i<bsi; i++){
    tC = cor(A.rows(Bootmat.row(i)),B.rows(Bootmat.row(i)));
    C.col(i) = vectorise(tC,0);
  }
  C.elem(find_nonfinite(C)).zeros();

  return reshape(median(C,1),A.n_cols,B.n_cols);
}
Exemplo n.º 4
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/******************************************************************
 * 函数功能:几何校正需要调用的函数
 * 
 *
 */
arma::mat centering(arma::mat &x){
	arma::mat tmp = -mean(x.rows(0, 1), 1);
	arma::mat tmp2(2,2);
	tmp2.eye();
	arma::mat tmp1 = join_horiz(tmp2, tmp);
	arma::mat v;
	v << 0 << 0 << 1 << arma::endr;
	arma::mat T = join_vert(tmp1, v);
	//T.print("T =");
	arma::mat xm = T * x;
	//xm.print("xm =");
	
	//at least one pixel apart to avoid numerical problems
	//xm.print("xm =");
	double std11 = arma::stddev(xm.row(0));
	//cout << "std11:" << std11 << endl;
	double std22 = stddev(xm.row(1));
	//cout << "std22:" << std22 << endl;

    double std1 = std::max(std11, 1.0);
    double std2 = std::max(std22, 1.0);
	
	arma::mat S;
	S << 1/std1 << 0 << 0 << arma::endr
	  << 0 << 1/std2 << 0 << arma::endr
	  << 0 << 0 << 1 << arma::endr;
	arma::mat C = S * T ;
	//C.print("C =");
	return C;
}
Exemplo n.º 5
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// **********************************************************//
//                  Calculate Dyadic statistics              //
// **********************************************************//
// [[Rcpp::export]]
arma::cube degreestat (arma::cube cache, arma::mat intervals_d, int a, int A, arma::mat pd) {
	int nIP = pd.n_cols;
	arma::cube out = arma::zeros<arma::cube>(A, nIP, 6);
	for (unsigned int i = 0; i < 3; i++) {
		int mins = intervals_d(i, 0)-1;
		int maxs = intervals_d(i, 1)-1;
		arma::rowvec sendr = arma::zeros<arma::rowvec>(nIP);
		arma::mat receiver = arma::zeros<arma::mat>(A, nIP);
		arma::mat pdnew = pd.rows(mins, maxs);
		for (int r = 0; r < A; r++) {
			arma::colvec docsar = cache(arma::span(a), arma::span(r), arma::span(mins, maxs));
			sendr += docsar.t() * pdnew;
			for (int h = 0; h < A; h++) {
				arma::colvec docsra = cache(arma::span(h), arma::span(r), arma::span(mins, maxs));
				receiver.row(r) += docsra.t() * pdnew;	
				}	
		}
		for (int IP = 0; IP < nIP; IP++) {
			for (int r = 0; r < A; r++) {
				out(r, IP, i) = sendr[IP];
				out(r, IP, i+3) = receiver(r,IP);
			}
		}		
	}	
	return out;
}
//Script that takes two matrices, performs bootstrapped correlation, and returns the median
// [[Rcpp::export]]
arma::mat BeQTL(const arma::mat & A, const arma::mat & B, const arma::umat & Bootmat){
  int bsi= Bootmat.n_rows;
  Rcpp::Rcout<<"Starting Bootstrap!"<<std::endl;
  arma::mat C(A.n_cols*B.n_cols,Bootmat.n_rows);
  arma::mat tA(A.n_rows,A.n_cols);
  arma::mat tB(B.n_rows,B.n_cols);
  arma::mat tC(A.n_rows,B.n_rows);
  for(int i=0; i<bsi; i++){
    tA = A.rows(Bootmat.row(i));
    tB = B.rows(Bootmat.row(i));
    tC = cor(tA,tB);
    C.col(i) = vectorise(tC,0);
  }
  C.elem(find_nonfinite(C)).zeros();

 return reshape(median(C,1),A.n_cols,B.n_cols);
}
Exemplo n.º 7
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arma::colvec computeBetaJ_cpp(arma::colvec y, arma::mat X, int j, int m, int adjFinSample) {
  int bigT = X.n_rows;
  int numThrow = ceil((double)m / 2);
  if (adjFinSample == 0) numThrow = m ;
  mat XSparse; colvec ySparse;
  XSparse.zeros(bigT - numThrow, X.n_cols); ySparse.zeros(bigT - numThrow);
  if (j < 2) {
    XSparse.rows(0, bigT - numThrow - 1) = X.rows(numThrow, bigT - 1);
    ySparse.rows(0, bigT - numThrow - 1) = y.rows(numThrow, bigT - 1);
  } else {
    XSparse.rows(0, j - 2) = X.rows(0, j - 2); ySparse.rows(0, j - 2) = y.rows(0, j - 2);
    XSparse.rows(j - 1, bigT - numThrow - 1) = X.rows(j + numThrow - 1, bigT - 1);
    ySparse.rows(j - 1, bigT - numThrow - 1) = y.rows(j + numThrow - 1, bigT - 1);
  }
  colvec beta = runOLSFast_cpp(ySparse, XSparse);
  return(beta);
}
Exemplo n.º 8
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void
HMM::computeXiCached() {
  arma::mat temp = B_.rows(1,T_-1) % beta_.cols(1,T_-1).t();
  for(unsigned int i = 0; i < N_; ++i) {
    xi_.slice(i) = temp % 
      (alpha_(i,arma::span(0, T_-2)).t() * A_.row(i));
  }
}
Exemplo n.º 9
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static arma::vec s_recall(arma::mat &runMatrix, Qrels &qrels, int rank){
    // Use a greedy approach to find the max subtopics at rank k

    // Initialize working variables
    int numOfSubtopics = int(*qrels.subtopics.rbegin());
    int numOfRows = min(rank, qrels.numOfRelDocs);
    arma::vec maxSubtopics = arma::zeros(rank);
    arma::rowvec seenSubtopics = arma::zeros<arma::rowvec>(numOfSubtopics);
    arma::vec tmpVector;
    set<int> idealRankList;
    set<int>::iterator it;

    // Greedy approach searches for the best document at each rank
    for(int i = 0; i < numOfRows; i++){
        int maxNewSubtopics = 0;
        int pickedDoc = -1;
        // Iterate through the set of relevant documents to find
        //   the best document
        for(int j = 0; j < qrels.matrix.n_rows; j++){

            // Ignore the document already picked
            it = idealRankList.find(j);
            if(it != idealRankList.end() )
                continue;

            // Compute the number of new subtopics the document contains
            int numOfNewSubtopics = arma::sum((qrels.matrix.row(j) + seenSubtopics) >
                                arma::zeros<arma::vec>(numOfSubtopics));

            // Keep track of the best document/ max number of subtopics
            if(numOfNewSubtopics > maxNewSubtopics){
                maxNewSubtopics = numOfNewSubtopics;
                pickedDoc = j;
            }
        }

        // Add the best document to the ideal rank list
        //  and keep track of subtopics seen
        seenSubtopics += qrels.matrix.row(pickedDoc);
        idealRankList.insert(pickedDoc);
        maxSubtopics(i) = arma::sum(seenSubtopics >
                                        arma::zeros<arma::vec>(numOfSubtopics));
    }
    if(numOfRows < rank){
        for(int i=numOfRows; i < rank; i++)
            maxSubtopics(i) = maxSubtopics(numOfRows - 1);
    }

    // Consider only the top 'rank' number of documents
    arma::vec s_recall = arma::zeros(rank);
    for(int i=0; i < rank; i++){
        arma::mat matrix = runMatrix.rows(0,i);
        double retSubtopics = arma::sum(arma::sum(matrix) >
                                        arma::zeros<arma::vec>(numOfSubtopics));
        s_recall(i) = (retSubtopics/maxSubtopics(i));
    }
    return s_recall;
}
Exemplo n.º 10
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//[[Rcpp::export]]
double computeSStat_cpp(arma::colvec beta, arma::mat invS, arma::colvec y, arma::mat X,
                        int j, int m) {
  mat newX = X.rows(j - 1, j + m - 2); colvec newY = y.rows(j - 1, j + m - 2);
  mat A = trans(newX) * invS * (newY - newX * beta);
  mat V =  trans(newX) * invS * newX;
  mat S = trans(A) * inv(V) * A;
  double s = S(0, 0);
  return(s);
}
Exemplo n.º 11
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/*******************************************************************
 * 函数功能:几何校正需要调用的函数
 * 
 *
 */
arma::mat toAffinity(arma::mat &f){
	arma::mat A;
	arma::mat v;
	v << 0 << 0 << 1 << arma::endr;
	int flag = f.n_rows;
	switch(flag){
		case 6:{ // oriented ellipses
			arma::mat T = f.rows(0, 1);
			arma::mat tmp = join_horiz(f.rows(2, 3), f.rows(4, 5));
			arma::mat tmp1 = join_horiz(tmp, T);
			A = join_vert(tmp1, v);
			break;}
		case 4:{   // oriented discs
			arma::mat T = f.rows(0, 1);
			double s = f.at(2,0);
			double th = f.at(3,0);
			arma::mat S = arma::randu<arma::mat>(2,2);
			/*S.at(0, 0) = s*cos(th);
			S.at(0, 1) = -s*sin(th);
			S.at(1, 0) = s*sin(th);
			S.at(1, 1) = s*cos(th);*/
			S << s*cos(th) << -s*sin(th) << arma::endr
			  << s*sin(th) << s*cos(th)  << arma::endr;
			arma::mat tmp1 = join_horiz(S, T);
			A = join_vert(tmp1, v);
			//A.print("A =");
			break;}
		/*case 3:{    // discs
			mat T = f.rows(0, 1);
			mat s = f.row(2);
			int th = 0 ;
			A = [s*[cos(th) -sin(th) ; sin(th) cos(th)], T ; 0 0 1] ;
			   }
		case 5:{ // ellipses
			mat T = f.rows(0, 1);
			A = [mapFromS(f(3:5)), T ; 0 0 1] ;
			   }*/
		default:
            std::cout << "出错啦!" << std::endl;
			break;
	}
	return A;
}
Exemplo n.º 12
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// **********************************************************//
//                  Calculate Triadic statistics              //
// **********************************************************//
// [[Rcpp::export]]
arma::cube triadicstat (arma::cube cache, arma::mat intervals_d, int a, int A, arma::mat pd) {
	int nIP = pd.n_cols;
	arma::cube out = arma::zeros<arma::cube>(A, nIP, 36);
	int it = 0;
	for (unsigned int i = 0; i < 3; i++) {
		for (unsigned int j = 0; j < 3; j++) {
		int mins1 = intervals_d(i, 0)-1;
		int maxs1 = intervals_d(i, 1)-1;
		int mins2 = intervals_d(j, 0)-1;
		int maxs2 = intervals_d(j, 1)-1;
		arma::mat tsend = arma::zeros<arma::mat>(A, nIP);
		arma::mat treceive = arma::zeros<arma::mat>(A, nIP);
		arma::mat sibling = arma::zeros<arma::mat>(A, nIP);
		arma::mat cosibling = arma::zeros<arma::mat>(A, nIP);
		arma::mat pdnew1 = pd.rows(mins1, maxs1);
		arma::mat pdnew2 = pd.rows(mins2, maxs2);
		for (int r = 0; r < A; r++) {
			for (int h = 0; h < A; h++) {
				arma::colvec docsah = cache(arma::span(a), arma::span(h), arma::span(mins1, maxs1));
				arma::colvec docsrh = cache(arma::span(r), arma::span(h), arma::span(mins2, maxs2));
				arma::colvec docsha = cache(arma::span(h), arma::span(a), arma::span(mins1, maxs1));
				arma::colvec docshr = cache(arma::span(h), arma::span(r), arma::span(mins2, maxs2));
				tsend.row(r) += (docsah.t() * pdnew1) % (docshr.t() * pdnew2);				
				treceive.row(r) += (docsha.t() * pdnew1) % (docsrh.t() * pdnew2);	
				sibling.row(r) += (docsha.t() * pdnew1) % (docshr.t() * pdnew2);				
				cosibling.row(r) += (docsah.t() * pdnew1) % (docsrh.t() * pdnew2);	
				}	
		}
		for (int IP = 0; IP < nIP; IP++) {
			for (int r = 0; r < A; r++) {
				out(r, IP, it) = tsend(r, IP);
				out(r, IP, it+9) = treceive(r,IP);
				out(r, IP, it+18) = sibling(r, IP);
				out(r, IP, it+27) = cosibling(r,IP);
			}
		}
		it +=1;		
	}
	}
	arma::cube out2 = arma::zeros<arma::cube>(A, nIP, 12);
	for (int t = 0; t < 4; t++) {
		int add = 9*t;
	for (int IP = 0; IP < nIP; IP++) {
		for (int r = 0; r < A; r++) {
			out2(r, IP, 3*t+0) = out(r, IP, add+0);
			out2(r, IP, 3*t+1) = out(r, IP, add+1) + out(r, IP, add+3)+ out(r, IP, add+4);
			out2(r, IP, 3*t+2) = out(r, IP, add+2) + out(r, IP, add+5)+ out(r, IP, add+6)+ out(r, IP, add+7)+ out(r, IP, add+8);
			}
		}
	}		
	return out2 / 10;
}
// This piece of code computes the flowlength before taking into account 
// the dimensions of pixels/slope. 
//[[Rcpp::export]]
double fl_internal(arma::mat m) { 
  
  uword ny = m.n_cols; 
  uword nx = m.n_rows; 
  
  rowvec flcol = zeros(1, ny); 
  for ( uword r=0; r<nx; r++ ) { 
    rowvec a = sum(cumprod(m.rows(r, nx - 1), 0), 0); 
    flcol += a; 
  } 
  
  return( accu(flcol)/((double)nx * (double)ny) ); 
}
Exemplo n.º 14
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void RNN<
LayerTypes, OutputLayerType, InitializationRuleType, PerformanceFunction
>::Gradient(const arma::mat& /* unused */,
            const size_t i,
            arma::mat& gradient)
{
  if (gradient.is_empty())
  {
    gradient = arma::zeros<arma::mat>(parameter.n_rows, parameter.n_cols);
  }
  else
  {
    gradient.zeros();
  }

  Evaluate(parameter, i, false);

  arma::mat currentGradient = arma::mat(gradient.n_rows, gradient.n_cols);
  NetworkGradients(currentGradient, network);

  const arma::mat input = arma::mat(predictors.colptr(i), predictors.n_rows,
      1, false, true);

  // Iterate through the input sequence and perform the feed backward pass.
  for (seqNum = seqLen - 1; seqNum >= 0; seqNum--)
  {
    // Load the network activation for the upcoming backward pass.
    LoadActivations(input.rows(seqNum * inputSize, (seqNum + 1) *
        inputSize - 1), network);

    // Perform the backward pass.
    if (seqOutput)
    {
      arma::mat seqError = error.unsafe_col(seqNum);
      Backward(seqError, network);
    }
    else
    {
      Backward(error, network);
    }

    // Link the parameters and update the gradients.
    LinkParameter(network);
    UpdateGradients<>(network);

    // Update the overall gradient.
    gradient += currentGradient;

    if (seqNum == 0) break;
  }
}
Exemplo n.º 15
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arma::colvec computeTestStatDistribution_cpp(arma::colvec y, arma::mat X, int breakDate,
                                             int m, int adjFinSample, arma::mat invS) {
  int bigT = X.n_rows;
  colvec sJ = zeros(breakDate - m + 1);
  for (int j = 0; j < (breakDate - m + 1); j++) {
    colvec betaJ = computeBetaJ_cpp(y.rows(0, bigT - 1 - m), X.rows(0, bigT - 1 - m),
                                    j + 1, m, adjFinSample);
    sJ(j) = computeSStat_cpp(betaJ, invS, y, X, j + 1, m);
//     if (j == 44) {
//       Rcout << "betaJ = "<< betaJ << "sJ(j) = " << sJ(j) << endl;
//     }
  }
  return(sJ);
}
Exemplo n.º 16
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/**
 * Remove a certain set of rows in a matrix while copying to a second matrix.
 *
 * @param input Input matrix to copy.
 * @param rowsToRemove Vector containing indices of rows to be removed.
 * @param output Matrix to copy non-removed rows into.
 */
void mlpack::math::RemoveRows(const arma::mat& input,
                              const std::vector<size_t>& rowsToRemove,
                              arma::mat& output)
{
  const size_t nRemove = rowsToRemove.size();
  const size_t nKeep = input.n_rows - nRemove;

  if (nRemove == 0)
  {
    output = input; // Copy everything.
  }
  else
  {
    output.set_size(nKeep, input.n_cols);

    size_t curRow = 0;
    size_t removeInd = 0;
    // First, check 0 to first row to remove.
    if (rowsToRemove[0] > 0)
    {
      // Note that this implies that n_rows > 1.
      output.rows(0, rowsToRemove[0] - 1) = input.rows(0, rowsToRemove[0] - 1);
      curRow += rowsToRemove[0];
    }

    // Now, check i'th row to remove to (i + 1)'th row to remove, until i is the
    // penultimate row.
    while (removeInd < nRemove - 1)
    {
      const size_t height = rowsToRemove[removeInd + 1] -
          rowsToRemove[removeInd] - 1;

      if (height > 0)
      {
        output.rows(curRow, curRow + height - 1) =
            input.rows(rowsToRemove[removeInd] + 1,
                       rowsToRemove[removeInd + 1] - 1);
        curRow += height;
      }

      removeInd++;
    }

    // Now that i is the last row to remove, check last row to remove to last
    // row.
    if (rowsToRemove[removeInd] < input.n_rows - 1)
    {
      output.rows(curRow, nKeep - 1) = input.rows(rowsToRemove[removeInd] + 1,
          input.n_rows - 1);
    }
  }
}
Exemplo n.º 17
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// gradients -------------------------------------------------------------------
//' @title Gradient step for Latent Factors
//' @param x Either a single row or single column of X, the ratings matrix.
//' @param z A single matrix from the covariates array, associated with the
//' either a single row or column of X.
//' @param p_i The latent factors associated with a single user or track.
//' @param Q The latent factors associated with either all tracks or all users,
//' depending on the type of latent factor in p_i.
//' @param beta The learned regression coefficients.
//' @param lambda The regularization parameter for the current latent factor.
//' @param batch_samples The proportion of the observed entries to sample in the
//' gradient for each factor in the gradient descent.
//' @param gamma The step-size in the gradient descent.
//' @return The update latent factor for the current user / track.
//' @export
// [[Rcpp::export]]
arma::vec update_factor(arma::vec x, arma::mat z, arma::vec p_i, arma::mat Q,
			arma::vec beta, double lambda, double batch_samples,
			double gamma) {
  // subset to SGD rows with observed entries
  arma::uvec obs_ix = arma::conv_to<arma::uvec>::from(arma::find_finite(x));
  arma::mat Q_obs = Q.rows(obs_ix);
  arma::vec x_obs = x(obs_ix);

  arma::uvec batch_ix = get_batch_ix(x_obs.n_elem, batch_samples);

  if(batch_ix.n_elem > 0) {
    x_obs = x_obs(batch_ix);
    Q_obs = Q_obs.rows(batch_ix);
  }

  arma::vec resid = x_obs - Q_obs * p_i;
  arma::vec p_i_grad = 2 * (lambda * p_i - Q_obs.t() * resid);
  return  p_i - gamma * p_i_grad;
}
Exemplo n.º 18
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// [[Rcpp::export]]
List PCspatregMCMC(const arma::vec& y, const arma::mat& X, const arma::mat& K, const arma::uvec& sampleIndex,
const arma::vec& betaMean, const arma::mat& betaSig, const double& phiScale, const int& burn, const int& iter) {
  int na = K.n_cols;
  int nsite = X.n_rows;
  int nb = X.n_cols;
  int ns = y.n_elem;
  arma::uvec idx = sampleIndex-1;
  arma::mat Xsmp = X.rows(idx);
  arma::mat Ksmp = K.rows(idx);
  
  arma::mat betaStor(iter, nb, fill::zeros);
  arma::mat alphaStor(iter, na, fill::zeros);
  arma::vec phiStor(iter, fill::zeros);
  arma::vec sigStor(iter, fill::zeros);
  arma::mat predStor(iter, nsite);
  
  // Beta items
  arma::mat betaPrec = betaSig.i();
  arma::mat V_beta_inv(nb, nb);
  arma::vec v_beta(nb);
  // Alpha items
  arma::mat I(na,na, fill::eye);
  arma::mat V_alpha_inv(na, na);
  arma::vec v_alpha(na);
  // phi items
  arma::vec Kbar(ns);
  double V_phi_inv;
  double v_phi;
  
  // Initial values
  arma::vec beta(nb);
  beta = solve(Xsmp.t() * Xsmp, Xsmp.t()*y);
  double tau = (ns-1)/as_scalar((y - Xsmp*beta).t()*(y - Xsmp*beta));
  double phi = 0;
  arma::vec alpha(na, fill::zeros);
  
  for(int i=0; i<iter+burn; i++){
    
    // update beta
    V_beta_inv = tau*Xsmp.t()*Xsmp + betaPrec;
    v_beta = tau * Xsmp.t() * (y - phi*Ksmp*alpha) + betaPrec*betaMean;
    beta = GCN(V_beta_inv, v_beta);
    if(i>=burn){betaStor.row(i-burn) = beta.t();}
    
    // update alpha
    V_alpha_inv = phi*phi*tau*Ksmp.t()*Ksmp + I; 
    v_alpha = phi*tau*Ksmp.t()*(y-Xsmp*beta);
    alpha = GCN(V_alpha_inv, v_alpha);
    if(i>=burn){alphaStor.row(i-burn) = phi*alpha.t();}
    
    // update phi
    Kbar = Ksmp*alpha;
    V_phi_inv = tau*as_scalar(Kbar.t()*Kbar) + 1/(phiScale*phiScale);
    v_phi = tau*dot(Kbar, y-Xsmp*beta);
    phi = as<double>(rnorm(1, v_phi/V_phi_inv, 1/sqrt(V_phi_inv)));
    if(i>=burn) phiStor(i-burn) = phi; 
    
    // update tau
    tau = as<double>(rgamma(1, ns/2, as_scalar((y-Xsmp*beta-phi*Ksmp*alpha).t()*(y-Xsmp*beta-phi*Ksmp*alpha))/2));
    if(i>=burn) sigStor(i-burn) = 1/sqrt(tau);
    
    // make prediction
    if(i>=burn){
      predStor.row(i-burn) = (X*beta + phi*K*alpha).t();
    }
    
    
  }
  
  return Rcpp::List::create(
    Rcpp::Named("beta") = betaStor, 
    Rcpp::Named("alpha") = alphaStor, 
    Rcpp::Named("phi")=phiStor,
    Rcpp::Named("sigma")=sigStor,
    Rcpp::Named("pred")=predStor
    );  
}
Exemplo n.º 19
0
// [[Rcpp::export]]
Rcpp::List getWEXPcutoff(arma::mat data, arma::mat subdata, arma::mat Y, arma::mat subY, int N, arma::mat RT_out, double predictTime, arma::vec resid_sco, double fitvar, arma::colvec cutoffs){

   
   //create dataD
   arma::mat dataDuns = data.rows(find(data.col(1)==1));
   arma::mat dataD = dataDuns.rows(sort_index(dataDuns.col(0)));


   int n = data.n_rows, nD = dataD.n_rows, np = Y.n_cols, ncuts = subdata.n_rows; 
   //guide
    colvec times = data.col(0); 
   // status= data.col(1); 
   // Dtimes = dataD.col(0); 
   // weights = data.col(4); 
   //  Dweights = dataD.col(4); 

   uvec tmpind = find((data.col(0) <= predictTime)%data.col(1)); // indices of (data$times<=predict.time)&(data$status==1)
   mat myempty(1,1); 
   uvec tmpindT = conv_to<uvec>::from(CSumI(times.elem(tmpind), 4, dataD.col(0), myempty,  FALSE)); 


   colvec rrk =  exp(data.col(6)); //data.col(6)  is data$linearY
   
   // build riskmat
   mat riskmat(n, nD); 
   mat::iterator riskmat_it = riskmat.begin(); 
 
for(colvec::iterator j = dataD.begin_col(0); j != dataD.end_col(0); j++){   
   for( colvec::iterator i = data.begin_col(0); i != data.end_col(0); i++){
       *riskmat_it = *i >= *j; riskmat_it++;
      }  
   }   
   //s0 and s1
   colvec s0 = riskmat.t()*(rrk%data.col(4)); 
   colvec s1 = riskmat.t()*trans(Vec2Mat(rrk%data.col(4), np)%Y.t()); 

   //haz0 and cumhaz0
   
   colvec haz0 = dataD.col(4)/sum(riskmat%trans(Vec2Mat(rrk%data.col(4), nD))).t(); 
   colvec cumhaz0 = cumsum(haz0); 
   colvec ptvec(1); ptvec(0) = predictTime; 
   colvec cumhaz_t0 = CSumI(ptvec, 4, dataD.col(0), haz0, TRUE); 

   //Wexp
   colvec Wexp_beta = resid_sco*fitvar*N; 

   colvec WexpLam1(n); WexpLam1.zeros(n); 
   WexpLam1(tmpind) =  N/s0(tmpindT - 1);     
   WexpLam1 = WexpLam1 - CSumI( myPmin(data.col(0), predictTime), 4, dataD.col(0), haz0/s0, TRUE)%rrk*N; 
 
   colvec WexpLam2 = Wexp_beta*CSumI(ptvec, 4, dataD.col(0), haz0%s1/trans(Vec2Mat(s0, np)), TRUE); 
   colvec WexpLam = WexpLam1 - WexpLam2; 

   //Fyk = Pr(Sy < c)
   colvec Fyk = CSumI( cutoffs, 4, data.col(6), data.col(4), TRUE)/sum(data.col(4)); 

   colvec dFyk(Fyk.n_elem); dFyk(0) = 0;
   dFyk(span(1, Fyk.n_elem - 1)) = Fyk(span(0, Fyk.n_elem-2));
   dFyk = Fyk - dFyk; 
   
   colvec Sy = subdata.col(5); 
   colvec Syall = data.col(5); 
   colvec St0_Fyk = cumsum(Sy%dFyk); 
   double St0 = max(St0_Fyk); 
   colvec St0_Syk = St0 - St0_Fyk; 
 //
  mat Wexp_Cond_Stc =  -Vec2Mat(Sy%exp(subdata.col(6)), n)%(trans(Vec2Mat(WexpLam, ncuts)) +as_scalar(cumhaz_t0)*Wexp_beta*subY.t()); 

  mat tmpmat = conv_to<mat>::from(trans(Vec2Mat(data.col(6), ncuts)) > Vec2Mat(cutoffs, n)); 
  mat Wexp_Stc = trans(CSumI(cutoffs, 0, subdata.col(6), Wexp_Cond_Stc.t()%Vec2Mat(dFyk, n).t(), TRUE)) + trans(Vec2Mat(Syall,ncuts))%tmpmat - Vec2Mat(St0_Syk, n); 

  colvec Wexp_St = sum(trans(Wexp_Cond_Stc)%trans(Vec2Mat(dFyk, n))).t() + Syall - St0; 

   mat Wexp_Fc = 1-tmpmat - Vec2Mat(Fyk, n); 

   //assemble for classic performance measures, given linear predictor

   List out(8); 

   out[0] = -Wexp_Cond_Stc; 
   out[1] =  Wexp_Fc;
   mat Wexp_St_mat = Vec2Mat(Wexp_St, ncuts).t(); 

   out[2] = (-Wexp_St_mat%Vec2Mat(RT_out.col(3), n) + Wexp_Stc)/St0; 
   out[3] = (Wexp_St_mat%Vec2Mat(RT_out.col(4), n) -Wexp_Fc - Wexp_Stc)/(1-St0); 

   out[4] = -Wexp_St; 
   out[5] = (Wexp_St_mat - Wexp_Stc - Vec2Mat(RT_out.col(6), n)%Wexp_Fc)/Vec2Mat(Fyk, n); 
   out[6] = (Vec2Mat(RT_out.col(5)-1, n)%Wexp_Fc - Wexp_Stc)/Vec2Mat(1-Fyk, n);
   out[7] = Wexp_beta;

   return out; 
}
//' @export
// [[Rcpp::export]]
Rcpp::List affineObservationStateHandler_affineContracts(const arma::mat& stateMat, const Rcpp::List& modelParameters, const int iterCount){

  // In state mat, additionally, to state values, we carry the past filtered state
  // because it is necessary to calculate effects of stock return observation
  int Nf = stateMat.n_rows/2;
  
  // Evaluate divergence prices
  arma::cube cfCoeffs = Rcpp::as<arma::cube>(modelParameters["cfCoeffs"]);
  arma::vec pVecUnique = Rcpp::as<arma::vec>(modelParameters["pVecUnique"]);
  arma::vec tVecUnique = Rcpp::as<arma::vec>(modelParameters["tVecUnique"]);
  arma::vec pVec = Rcpp::as<arma::vec>(modelParameters["pVec"]);
  arma::vec tVec = Rcpp::as<arma::vec>(modelParameters["tVec"]);
  
  arma::cube divPrices = divergenceSwapRateCpp(pVecUnique, cfCoeffs, stateMat.rows(0,Nf-1).t());
  
  arma::cube skewPrices = skewnessSwapRateCpp(pVecUnique, cfCoeffs, stateMat.rows(0,Nf-1).t());
  
  // create return matrix
  arma::mat yhat(pVec.n_elem,stateMat.n_cols);
  
  // Cycle over state variable columns
  for(unsigned int kcol=0; kcol < stateMat.n_cols; kcol++){
    // Cycle through the representation of the contract specification matrix and construct final prices from priced single instruments
    for(unsigned int prow=0; prow < pVec.n_elem; prow++){
      // find p and t values in the unique vectors to know what to pick
      double loc_p = pVec(prow);
      double loc_t = tVec(prow);
      
      arma::vec loc_p_vec(pVecUnique.n_elem);
      loc_p_vec.fill(loc_p);
      arma::vec loc_t_vec(tVecUnique.n_elem);
      loc_t_vec.fill(loc_t);
      arma::uvec which_p = find(loc_p_vec == pVecUnique);
      arma::uvec which_t = find(loc_t_vec == tVecUnique);
      // Rcout << "loc_p= " << loc_p << ", loc_t= " << loc_t << "\n";
      // pVecUnique.print("pVecUnique");
      // which_p.print("which_p");
      // tVecUnique.print("tVecUnique");
      // which_t.print("which_t");
      
      // pick and calculate necessary affine swap rate
      double loc_div_price = divPrices(which_p(0L),which_t(0L),kcol);
      double loc_skew_price = skewPrices(which_p(0L),which_t(0L),kcol);
      if(loc_p == 0.0){
        yhat(prow, kcol) = loc_div_price;
      } else if(loc_p == 1.0){
        yhat(prow, kcol) = loc_skew_price/loc_div_price;
      } else {
        yhat(prow, kcol) = (loc_skew_price - (1.0 - 2.0*loc_p)/(loc_p*(loc_p-1.0))*loc_div_price) / (loc_div_price + 1.0/(loc_p*(loc_p-1.0)));
      }
    }
  }
  
  // Handle the noise covariance matrix. The stock ``observation'' noise is
  // uncorrelated with portfolio observation noise.
  arma::mat obsNoiseMat(pVec.n_elem,pVec.n_elem,arma::fill::zeros);
  
  // 
  // // extract observation noise params
  // arma::vec bVec = Rcpp::as<arma::vec>(modelParameters["bVec"]);
  // arma::vec cVec = Rcpp::as<arma::vec>(modelParameters["cVec"]);
  // // double spotVol = arma::accu(stateMat.col(0));
  // double spotVol = arma::accu(stateMat.submat(0,0,Nf-1,0));
  // obsNoiseMat(arma::span(1,obsNoiseMat.n_rows-1),arma::span(1,obsNoiseMat.n_cols-1)) = divModelObsNoiseMat(cVec,bVec,spotVol,tVecUnique,U);
  SEXP divBigMat_SEXP(modelParameters["divNoiseCube"]);
  arma::cube divBigMat = as<arma::cube>(divBigMat_SEXP);
  
  // var-cov matrix scaling
  arma::vec errSdParVec = as<arma::vec>(modelParameters["errSdParVec"]);
  
  obsNoiseMat(arma::span(0,obsNoiseMat.n_rows-1),arma::span(0,obsNoiseMat.n_cols-1)) = errSdParVec(0) * divBigMat.slice(iterCount);
  // Initialize return list
  Rcpp::List res = Rcpp::List::create(Rcpp::Named("yhat") = yhat, Rcpp::Named("obsNoiseMat") = obsNoiseMat);
  
  return res;
}