// [[Rcpp::export]]
SEXP FilterIndepOld (Rcpp::NumericVector y_rcpp,
Rcpp::NumericMatrix y_lagged_rcpp,
Rcpp::NumericMatrix z_dependent_rcpp,
Rcpp::NumericMatrix z_independent_rcpp,
Rcpp::NumericVector beta_rcpp,
Rcpp::NumericVector mu_rcpp,
Rcpp::NumericVector sigma_rcpp,
Rcpp::NumericMatrix gamma_dependent_rcpp,
Rcpp::NumericVector gamma_independent_rcpp,
Rcpp::NumericMatrix transition_probs_rcpp,
Rcpp::NumericVector initial_dist_rcpp
)
{
int n = y_rcpp.size();
int M = mu_rcpp.size();
arma::mat xi_k_t(M, n); // make a transpose first for easier column operations.
arma::colvec y(y_rcpp.begin(), y_rcpp.size(), false);
arma::mat y_lagged(y_lagged_rcpp.begin(),
y_lagged_rcpp.nrow(), y_lagged_rcpp.ncol(), false);
arma::mat z_dependent(z_dependent_rcpp.begin(),
z_dependent_rcpp.nrow(), z_dependent_rcpp.ncol(), false);
arma::mat z_independent(z_independent_rcpp.begin(),
z_independent_rcpp.nrow(), z_independent_rcpp.ncol(), false);
arma::colvec beta(beta_rcpp.begin(), beta_rcpp.size(), false);
arma::colvec mu(mu_rcpp.begin(), mu_rcpp.size(), false);
arma::colvec sigma(sigma_rcpp.begin(), sigma_rcpp.size(), false);
arma::mat gamma_dependent(gamma_dependent_rcpp.begin(),
gamma_dependent_rcpp.nrow(), gamma_dependent_rcpp.ncol(), false);
arma::colvec gamma_independent(gamma_independent_rcpp.begin(),
gamma_independent_rcpp.size(), false);
arma::mat transition_probs(transition_probs_rcpp.begin(),
transition_probs_rcpp.nrow(), transition_probs_rcpp.ncol(), false);
arma::colvec initial_dist(initial_dist_rcpp.begin(), initial_dist_rcpp.size(), false);
double likelihood = 0;
SEXP eta_rcpp = EtaIndep(y_rcpp, y_lagged_rcpp,
z_dependent_rcpp, z_independent_rcpp,
beta_rcpp, mu_rcpp, sigma_rcpp,
gamma_dependent_rcpp, gamma_independent_rcpp);
arma::mat eta_t = (Rcpp::as<arma::mat>(eta_rcpp)).t();
xi_k_t.col(0) = eta_t.col(0) % initial_dist;
double total = sum(xi_k_t.col(0));
xi_k_t.col(0) = xi_k_t.col(0) / total;
likelihood += log(total);
for (int k = 1; k < n; k++)
{
xi_k_t.col(k) = eta_t.col(k) % (transition_probs * xi_k_t.col(k-1));
total = sum(xi_k_t.col(k));
xi_k_t.col(k) = xi_k_t.col(k) / total;
likelihood += log(total);
}
return Rcpp::List::create(Named("xi.k") = wrap(xi_k_t.t()),
Named("likelihood") = wrap(likelihood));
}
// [[Rcpp::export]]
Rcpp::NumericMatrix rcpp_sweep_(Rcpp::NumericMatrix x, Rcpp::NumericVector vec)
{
Rcpp::NumericMatrix ret(x.nrow(), x.ncol());
#pragma omp parallel for default(shared)
for (int j=0; j<x.ncol(); j++)
{
#pragma omp simd
for (int i=0; i<x.nrow(); i++)
ret(i, j) = x(i, j) - vec(i);
}
return ret;
}
std::vector<std::vector<float> > matrix_to_array_v(Rcpp::NumericMatrix& mat)
{
std::vector<std::vector<float> > v;
if(!mat.ncol() || !mat.nrow())
return(v);
v.resize(mat.nrow());
std::vector<float> v_row(mat.ncol()); // = new float[ mat.ncol() * mat.nrow() ];
for(unsigned int i=0; i < (unsigned int)mat.nrow(); ++i){
for(unsigned int j=0; j < (unsigned int)mat.ncol(); ++j){
v_row[j] = (float)mat(i, j);
}
v[i] = v_row;
}
return(v);
}
static double bic_linear(Rcpp::NumericMatrix X,
Rcpp::NumericVector y,
Rcpp::NumericMatrix beta_new,
double eps,
Rcpp::IntegerVector nk)
{
int n_tot = X.nrow();
int p = X.ncol();
int K = nk.size();
/*calculate SSe*/
double SSe = 0.0;
int idx = 0;
for (int k = 0; k < K; k++) {
int n = nk[k];
for (int i = 0; i < n; i++) {
double Xrow_betacol = 0.0;
for (int j = 0; j < p; j++) {
Xrow_betacol += elem(X, idx+i, j) * elem(beta_new, j, k);
}
SSe += pow(y[idx+i] - Xrow_betacol, 2);
}
idx += n;
}
double ll = -n_tot / 2.0 * (log(SSe) - log(n_tot) + log(2.0 * M_PI) + 1);
double bic = -2 * ll + df(beta_new, eps) * log(n_tot);
return bic;
}
static Rcpp::IntegerVector nz_vec(Rcpp::NumericMatrix alpha_new,
Rcpp::NumericMatrix eta_new,
Rcpp::NumericVector d_new,
double eps)
{
int K = alpha_new.ncol();
int p = alpha_new.nrow();
int L = eta_new.nrow();
Rcpp::IntegerVector result(p*K + L*K + L);
for (int i = 0; i < p*K; i++) {
result[i] = nz(alpha_new[i],eps);
}
for (int i = 0; i < L*K; i++) {
result[p*K + i] = nz(eta_new[i],eps);
}
for (int i = 0; i < L; i++) {
result[p*K + L*K + i] = nz(d_new[i],eps);
}
return result;
}
static Rcpp::NumericMatrix x_tilde_3(Rcpp::NumericMatrix X,
Rcpp::IntegerVector nk,
Rcpp::IntegerMatrix groups,
Rcpp::NumericMatrix alpha_new,
Rcpp::NumericVector d_new)
{
int K = nk.size();
int n_tot = X.nrow();
int p = X.ncol();
int L = groups.ncol();
Rcpp::NumericMatrix result(n_tot, L * K);
int idx = 0;
for (int k = 0; k < K; k++) {
int n = nk[k];
for (int l = 0; l < L; l++) {
for (int i = 0; i < n; i++) {
double sum = 0.0;
for (int j = 0; j < p; j++) {
if (elem(groups, j, l)) {
sum += elem(X, idx + i, j) * elem(alpha_new, j, k);
}
}
elem(result, idx + i, L * k + l) = d_new[l] * sum;
}
}
idx += n;
}
return result;
}
static double bic_logistic(Rcpp::NumericMatrix X,
Rcpp::NumericVector y,
Rcpp::NumericMatrix beta_new,
double eps,
Rcpp::IntegerVector nk)
{
int n_tot = X.nrow();
int p = X.ncol();
int K = nk.size();
int idx = 0;
double ll = 0.0;
for (int k = 0; k < K; k++) {
int n = nk[k];
for (int i = 0; i < n; i++) {
double lp = 0.0;
for (int j = 0; j < p; j++) {
lp += elem(X, idx+i, j) * elem(beta_new, j, k);
}
ll += y[idx+i] * lp - log(1.0 + exp(lp));
}
idx += n;
}
double bic = -2.0 * ll + df(beta_new, eps) * log(n_tot);
return bic;
}
//' Marginal correlation matrix
//'
//' Various workhorse functions to compute the marginal (or unconditional)
//' correlations (and cross-correlation) estimates efficiently.
//' They are (almost)
//' equivalent implementations of \code{\link[stats]{cor}} in Rcpp,
//' RcppArmadillo, and RcppEigen.
//'
//' @rdname corFamily
//' @aliases corFamily
//' corRcpp xcorRcpp corArma xcorArma corEigen xcorEigen
//' @param X A numeric matrix.
//' @param Y A numeric matrix of compatible dimension with the \code{X}, i.e.
//' \code{nrow(X)} equals \code{nrow(Y)}.
//' @return
//' The \code{corXX} family returns a numeric correlation matrix of size
//' \code{ncol(X)} times \code{ncol(X)}.
//'
//' The \code{xcorXX} family returns a numeric cross-correlation matrix
//' of size \code{ncol(X)} times \code{ncol(Y)}.
//' @details
//' Functions almost like \code{\link{cor}}.
//' For the \code{xcorXX} functions, the \code{i}'th and \code{j}'th
//' entry of the output matrix is the correlation between \code{X[i, ]} and
//' \code{X[j, ]}.
//' Likewise, for the \code{xcorXX} functions, the \code{i}'th and
//' \code{j}'th entry of the output is the correlation between \code{X[i, ]}
//' and \code{Y[j, ]}.
//' @note
//' \code{NA}s in \code{X} or \code{Y} will yield \code{NA}s in the correlation matrix.
//' This also includes the diagonal unlike the behavior of
//' \code{\link[stats]{cor}}.
//' @author Anders Ellern Bilgrau <anders.ellern.bilgrau (at) gmail.com>
//' @export
// [[Rcpp::export]]
Rcpp::NumericMatrix corRcpp(Rcpp::NumericMatrix & X) {
const int m = X.ncol();
const int n = X.nrow();
// Centering the matrix
X = centerNumericMatrix(X);
Rcpp::NumericMatrix cor(m, m);
// Degenerate case
if (n == 0) {
std::fill(cor.begin(), cor.end(), Rcpp::NumericVector::get_na());
return cor;
}
// Compute 1 over the sample standard deviation
Rcpp::NumericVector inv_sqrt_ss(m);
for (int i = 0; i < m; ++i) {
inv_sqrt_ss(i) = 1/sqrt(Rcpp::sum(X(Rcpp::_, i)*X(Rcpp::_, i)));
}
// Computing the correlation matrix
for (int i = 0; i < m; ++i) {
for (int j = 0; j <= i; ++j) {
cor(i, j) = Rcpp::sum(X(Rcpp::_,i)*X(Rcpp::_,j)) *
inv_sqrt_ss(i) * inv_sqrt_ss(j);
cor(j, i) = cor(i, j);
}
}
return cor;
}
///********************************************************************
///** cdm_rcpp_irt_classify_individuals
// [[Rcpp::export]]
Rcpp::List cdm_rcpp_irt_classify_individuals( Rcpp::NumericMatrix like )
{
int N = like.nrow();
int TP = like.ncol();
Rcpp::IntegerVector class_index(N);
Rcpp::NumericVector class_maxval(N);
double val=0;
int ind=0;
for (int nn=0; nn<N; nn++){
val=0;
ind=0;
for (int tt=0; tt<TP; tt++){
if ( like(nn,tt) > val ){
val = like(nn,tt);
ind = tt;
}
}
class_index[nn] = ind + 1;
class_maxval[nn] = val;
}
//---- OUTPUT:
return Rcpp::List::create(
Rcpp::Named("class_index") = class_index,
Rcpp::Named("class_maxval") = class_maxval
);
}
// [[Rcpp::export]]
Rcpp::NumericMatrix update_sigma2_batch(Rcpp::S4 xmod){
Rcpp::RNGScope scope;
// get model
Rcpp::S4 model(xmod);
// get parameter estimates
Rcpp::NumericMatrix theta = model.slot("theta");
Rcpp::IntegerVector z = model.slot("z");
double nu_0 = model.slot("nu.0");
double sigma2_0 = model.slot("sigma2.0");
// get data and size attributes
Rcpp::NumericVector x = model.slot("data");
int n = x.size();
int K = theta.ncol();
int B = theta.nrow();
//IntegerVector nn = model.slot("zfreq");
// get batch info
Rcpp::NumericMatrix tabz = tableBatchZ(xmod);
Rcpp::IntegerVector batch = model.slot("batch");
Rcpp::IntegerVector ub = uniqueBatch(batch);
Rcpp::NumericMatrix ss(B, K);
for (int i = 0; i < n; ++i) {
for (int b = 0; b < B; ++b) {
if (batch[i] != ub[b]) {
continue;
}
for (int k = 0; k < K; ++k){
if (z[i] == k+1){
ss(b, k) += pow(x[i] - theta(b, k), 2);
}
}
}
}
//NumericMatrix sigma2_nh(B, K);
double shape;
double rate;
double sigma2_nh;
double nu_n;
Rcpp::NumericMatrix sigma2_tilde(B, K);
Rcpp::NumericMatrix sigma2_(B, K);
for (int b = 0; b < B; ++b) {
for (int k = 0; k < K; ++k) {
nu_n = nu_0 + tabz(b, k);
sigma2_nh = 1.0/nu_n*(nu_0*sigma2_0 + ss(b, k));
shape = 0.5 * nu_n;
rate = shape * sigma2_nh;
sigma2_tilde(b, k) = Rcpp::as<double>(rgamma(1, shape, 1.0/rate));
sigma2_(b, k) = 1.0 / sigma2_tilde(b, k);
}
}
return sigma2_;
}
/**
* Update V, Vtr and fac
*
* Note: May want to update fac in a separate operation. For the
* fixed-effects modules this will update the factor twice because
* it is separately updated in updateRzxRx.
*
* @param Xwt square root of the weights for the model matrices
* @param wtres weighted residuals
*/
void sPredModule::reweight(Rcpp::NumericMatrix const& Xwt,
Rcpp::NumericVector const& wtres) throw(std::runtime_error) {
if (d_coef.size() == 0) return;
double one = 1., zero = 0.;
int Wnc = Xwt.ncol();//, Wnr = Xwt.nrow(),
// Xnc = d_X.ncol, Xnr = d_X.nrow;
if ((Xwt.rows() * Xwt.cols()) != (int)d_X.nrow)
throw std::runtime_error("dimension mismatch");
// Rf_error("%s: dimension mismatch %s(%d,%d), %s(%d,%d)",
// "deFeMod::reweight", "X", Xnr, Xnc,
// "Xwt", Wnr, Wnc);
if (Wnc == 1) {
if (d_V) M_cholmod_free_sparse(&d_V, &c);
d_V = M_cholmod_copy_sparse(&d_X, &c);
chmDn csqrtX(Xwt);
M_cholmod_scale(&csqrtX, CHOLMOD_ROW, d_V, &c);
} else throw runtime_error("sPredModule::reweight: multiple columns in Xwt");
// FIXME write this combination using the triplet representation
chmDn cVtr(d_Vtr);
const chmDn cwtres(wtres);
M_cholmod_sdmult(d_V, 'T', &one, &zero, &cwtres, &cVtr, &c);
CHM_SP Vt = M_cholmod_transpose(d_V, 1/*values*/, &c);
d_fac.update(*Vt);
M_cholmod_free_sparse(&Vt, &c);
}
static Rcpp::NumericMatrix x_tilde_2(Rcpp::NumericMatrix X,
Rcpp::IntegerVector nk,
Rcpp::IntegerMatrix groups,
Rcpp::NumericMatrix alpha_new,
Rcpp::NumericMatrix eta_cur)
{
int K = nk.size();
int n_tot = X.nrow();
int p = X.ncol();
int L = groups.ncol();
Rcpp::NumericMatrix result(n_tot, L);
for (int l = 0; l < L; l++) {
int k = -1;
int n = 0;
for (int i = 0; i < n_tot; i++) {
if (i == n){
k +=1;
n += nk[k];
}
double sum = 0.0;
for (int j = 0; j < p; j++) {
if (elem(groups, j, l)) {
sum += elem(X, i, j) * elem(alpha_new, j, k);
}
}
elem(result, i, l) = elem(eta_cur, l, k) * sum;
}
}
return result;
}
///********************************************************************
///** scale2_NA_C
// [[Rcpp::export]]
Rcpp::NumericMatrix scale2_NA_C( Rcpp::NumericMatrix x )
{
int n = x.nrow();
int p = x.ncol();
Rcpp::NumericMatrix y(n,p);
double mvv=0;
double sdvv=0;
double nvv=0;
double eps_add = 1e-10;
for (int vv=0;vv<p;vv++){
mvv=0;
sdvv=0;
nvv=0;
for (int ii=0;ii<n;ii++){
if (! R_IsNA(x(ii,vv)) ) {
mvv += x(ii,vv);
sdvv += std::pow( x(ii,vv), 2.0 );
nvv ++;
}
}
mvv = mvv / nvv;
sdvv = std::sqrt( ( sdvv - nvv * mvv*mvv )/(nvv - 1.0 ) );
// define standardization
y(_,vv) = ( x(_,vv) - mvv ) / ( sdvv + eps_add );
}
//--- output
return y;
}
//' Check whether there are any non-finite values in a matrix
//'
//' The C++ functions will not work with NA values, and the calculation of the
//' summary profile will take a long time to run before crashing.
//'
//' @param matPtr matrix to check.
//'
//' @return
//' Throws an error if any \code{NA}, \code{NaN}, \code{Inf}, or \code{-Inf}
//' values are found, otherwise returns silently.
//'
// [[Rcpp::export]]
void CheckFinite(Rcpp::NumericMatrix matPtr) {
arma::mat mat = arma::mat(matPtr.begin(), matPtr.nrow(), matPtr.ncol(), false, true);
arma::uvec nonFiniteIdx = arma::find_nonfinite(mat);
if (nonFiniteIdx.n_elem > 0) {
throw Rcpp::exception("matrices cannot have non-finite or missing values");
}
}
void ScoreGaussL0PenScatter::setData(Rcpp::List& data)
{
std::vector<int>::iterator vi;
//uint i;
// Cast preprocessed data from R list
dout.level(2) << "Casting preprocessed data...\n";
_dataCount = Rcpp::as<std::vector<int> >(data["data.count"]);
dout.level(3) << "# samples per vertex: " << _dataCount << "\n";
_totalDataCount = Rcpp::as<uint>(data["total.data.count"]);
dout.level(3) << "Total # samples: " << _totalDataCount << "\n";
Rcpp::List scatter = data["scatter"];
Rcpp::NumericMatrix scatterMat;
_disjointScatterMatrices.resize(scatter.size());
dout.level(3) << "# disjoint scatter matrices: " << scatter.size() << "\n";
for (R_len_t i = 0; i < scatter.size(); ++i) {
scatterMat = Rcpp::NumericMatrix((SEXP)(scatter[i]));
_disjointScatterMatrices[i] = arma::mat(scatterMat.begin(), scatterMat.nrow(), scatterMat.ncol(), false);
}
// Cast index of scatter matrices, adjust R indexing convention to C++
std::vector<int> scatterIndex = Rcpp::as<std::vector<int> >(data["scatter.index"]);
for (std::size_t i = 0; i < scatterIndex.size(); ++i)
_scatterMatrices[i] = &(_disjointScatterMatrices[scatterIndex[i] - 1]);
// Cast lambda: penalty constant
_lambda = Rcpp::as<double>(data["lambda"]);
dout.level(3) << "Penalty parameter lambda: " << _lambda << "\n";
// Check whether an intercept should be calculated
_allowIntercept = Rcpp::as<bool>(data["intercept"]);
dout.level(3) << "Include intercept: " << _allowIntercept << "\n";
}
static Rcpp::NumericMatrix x_tilde(Rcpp::NumericMatrix X,
Rcpp::IntegerVector nk,
Rcpp::IntegerMatrix groups,
Rcpp::NumericVector d_cur,
Rcpp::NumericMatrix eta_cur)
{
int K = nk.size();
int n_tot = X.nrow();
int p = X.ncol();
int L = groups.ncol();
Rcpp::NumericMatrix result(n_tot, p * K);
int idx = 0;
for (int k = 0; k < K; k++) {
int n = nk[k];
for (int j = 0; j < p; j++) {
//calculate sum for column j
double sum = 0.0;
for (int l = 0; l < L; l++) {
if (elem(groups, j, l)) {
sum += d_cur[l] * elem(eta_cur, l, k);
}
}
//multiply column j in submatrix k of X with sum
for (int i = 0; i < n; i++) {
elem(result, idx + i, p * k + j) = elem(X, idx + i, j) * sum;
}
}
idx += n;
}
return result;
}
//' @title Calculating validation scores between two adjacency matrices
//'
//' @description
//' This function calculates the validation scores between two adjacency matrices.
//'
//' @param inf_mat matrix. It should be adjacency matrix of inferred network.
//' @param true_mat matrix. It should be adjacency matrix of true network.
// [[Rcpp::export]]
Rcpp::NumericVector rcpp_validate(Rcpp::NumericMatrix inf_mat, Rcpp::NumericMatrix true_mat) {
if(inf_mat.ncol() != true_mat.ncol()) {
throw std::invalid_argument( "Two input matrices should have the same number of columns." );
}
if(inf_mat.nrow() != true_mat.nrow()) {
throw std::invalid_argument( "Two input matrices should have the same number of rows." );
}
int tp=0;
int tn=0;
int fp=0;
int fn=0;
for(signed int i=0; i<inf_mat.nrow(); i++) {
//Convert R objects into C++ objects.
Rcpp::NumericVector xr = inf_mat.row(i);
Rcpp::NumericVector yr = true_mat.row(i);
std::vector<int> x = Rcpp::as<std::vector <int> >(xr);
std::vector<int> y = Rcpp::as<std::vector <int> >(yr);
std::vector<int> z;
//Calculate the frequency of numbers.
//tp=true positive [1,1], tn=true negative [0,0], fp=false positive [1,0], fn=false negative [0,1].
for(unsigned int k=0; k<x.size(); k++) {
z.push_back(x[k] + y[k]); //Calculate the summation of x and y between each element.
if(z[k] == 2) {
tp += 1;
} else if(z[k] == 0) {
tn += 1;
} else if(z[k] == 1) {
if(x[k] == 0) {
fp += 1;
} else {
fn += 1;
}
} else {
throw std::invalid_argument("Error in calculating the contigency table.");
}
}
}
//std::vector<int> output{tp, tn, fp, fn}; c++11 only
int tmp_arr[4] = {tp, tn, fp, fn};
std::vector<int> output(&tmp_arr[0], &tmp_arr[0]+4);
return Rcpp::wrap(output);
}
// This calculates lambda from a lookup table index by transition (row) and rounded quality (col)
double compute_lambda(Raw *raw, Sub *sub, Rcpp::NumericMatrix errMat, bool use_quals) {
// use_quals does nothing in this function, just here for backwards compatability for now
int s, pos0, pos1, nti0, nti1, len1, ncol;
double lambda;
float prefactor, fqmin;
int tvec[SEQLEN];
int qind[SEQLEN];
if(!sub) { // NULL Sub, outside Kmer threshold
return 0.0;
}
// Make vector that indexes as integers the transitions at each position in seq1
// Index is 0: exclude, 1: A->A, 2: A->C, ..., 5: C->A, ...
len1 = raw->length;
ncol = errMat.ncol();
prefactor = ((float) (ncol-1))/((float) QMAX-QMIN);
fqmin = (float) QMIN;
for(pos1=0;pos1<len1;pos1++) {
nti1 = ((int) raw->seq[pos1]) - 1;
if(nti1 == 0 || nti1 == 1 || nti1 == 2 || nti1 == 3) {
tvec[pos1] = nti1*4 + nti1;
} else {
Rcpp::stop("Error: Can't handle non ACGT sequences in CL3.");
}
if(raw->qual) {
// Turn quality into the index in the array
qind[pos1] = round(prefactor * (raw->qual[pos1] - fqmin));
} else {
qind[pos1] = 0;
}
if( qind[pos1] > (ncol-1) ) {
Rcpp::stop("Error: rounded quality exceeded err lookup table.");
}
}
// Now fix the ones where subs occurred
for(s=0;s<sub->nsubs;s++) {
pos0 = sub->pos[s];
if(pos0 < 0 || pos0 >= len1) { Rcpp::stop("CL: Bad pos0."); }
pos1 = sub->map[sub->pos[s]];
if(pos1 < 0 || pos1 >= len1) { Rcpp::stop("CL: Bad pos1."); }
nti0 = ((int) sub->nt0[s]) - 1;
nti1 = ((int) sub->nt1[s]) - 1;
tvec[pos1] = nti0*4 + nti1;
}
// And calculate lambda
lambda = 1.0;
for(pos1=0;pos1<len1;pos1++) {
lambda = lambda * errMat(tvec[pos1], qind[pos1]);
}
if(lambda < 0 || lambda > 1) { Rcpp::stop("Bad lambda."); }
return lambda;
}
densePred::densePred(Rcpp::NumericMatrix x,
Rcpp::NumericVector coef0)
throw (std::runtime_error)
: predMod(coef0), d_X(x),
a_X(x.begin(), x.nrow(), x.ncol(), false, true) {
if (d_coef0.size() != d_X.ncol())
throw std::runtime_error("length(coef0) != ncol(X)");
}
//[[Rcpp::export]]
Rcpp::NumericMatrix ZERO(Rcpp::NumericMatrix x) {
int i=0, j=0;
for(i=0; i < x.ncol(); i++) {
for(j=0; j < x.nrow(); j++) {
x(i,j) = 0;
}
}
return(x);
}
static int df(Rcpp::NumericMatrix beta_new, double eps)
{
int result = 0;
int n = beta_new.nrow() * beta_new.ncol();
for(int i=0; i < n; i++){
result += nz(beta_new[i],eps);
}
return result;
}
// kernel Dist function on a Grid
// [[Rcpp::export]]
Rcpp::NumericVector
KdeDist(const Rcpp::NumericMatrix & X
, const Rcpp::NumericMatrix & Grid
, const double h
, const Rcpp::NumericVector & weight
, const bool printProgress
) {
const unsigned sampleNum = X.nrow();
const unsigned dimension = Grid.ncol();
const unsigned gridNum = Grid.nrow();
// first = sum K_h(X_i, X_j), second = K_h(x, x), third = sum K_h(x, X_i)
std::vector< double > firstValue;
const double second = 1.0;
std::vector< double > thirdValue;
double firstmean;
Rcpp::NumericVector kdeDistValue(gridNum);
int counter = 0, percentageFloor = 0;
int totalCount = sampleNum + gridNum;
if (printProgress) {
printProgressFrame(Rprintf);
}
firstValue = computeKernel< std::vector< double > >(
X, X, h, weight, printProgress, Rprintf, counter, totalCount,
percentageFloor);
if (dimension <= 1) {
thirdValue = computeKernel< std::vector< double > >(
X, Grid, h, weight, printProgress, Rprintf, counter, totalCount,
percentageFloor);
}
else {
thirdValue = computeGaussOuter< std::vector< double > >(
X, Grid, h, weight, printProgress, Rprintf, counter, totalCount,
percentageFloor);
}
if (weight.size() == 1) {
firstmean = std::accumulate(firstValue.begin(), firstValue.end(), 0.0) / sampleNum;
}
else {
firstmean = std::inner_product(
firstValue.begin(), firstValue.end(), weight.begin(), 0.0) /
std::accumulate(weight.begin(), weight.end(), 0.0);
}
for (unsigned gridIdx = 0; gridIdx < gridNum; ++gridIdx) {
kdeDistValue[gridIdx] = std::sqrt(firstmean + second - 2 * thirdValue[gridIdx]);
}
if (printProgress) {
Rprintf("\n");
}
return kdeDistValue;
}
static void print(Rcpp::NumericMatrix A)
{
for (int i = 0; i < A.nrow(); i++) {
for (int j = 0; j < A.ncol(); j++) {
printf("%9f ", elem(A, i, j));
}
putchar('\n');
}
}
//[[Rcpp::export]]
void decorr(Rcpp::NumericMatrix x) {
unsigned int i = 1, j=1, n=x.nrow();
if(n != x.ncol()) Rcpp::stop("matrix is not square");
for(i=0; i < n; i++) {
for(j=0; j < n; j++) {
if(j!=i) x(i,j) = x(i,j)*sqrt(x(i,i)*x(j,j));
}
}
}
// [[Rcpp::export]]
Rcpp::NumericMatrix testColPost(Rcpp::NumericMatrix post, Rcpp::List m2u, int nthreads){
Rcpp::IntegerVector values = Rcpp::as<Rcpp::IntegerVector>(m2u["values"]);
Rcpp::IntegerVector map = Rcpp::as<Rcpp::IntegerVector>(m2u["map"]);
if (post.ncol() != map.length()) Rcpp::stop("posteriors doesn't match with m2u");
Rcpp::NumericMatrix smallerPost(post.nrow(), values.length());
Vec<double> foo; NMPreproc preproc(asVec(values), asVec(map), foo);
collapsePosteriors_core(asMat(smallerPost), asMat(post), preproc);
return smallerPost;
}
static Rcpp::IntegerMatrix nz(Rcpp::NumericMatrix m, double eps)
{
int nr = m.nrow();
int nc = m.ncol();
Rcpp::IntegerMatrix result(nr, nc);
for(int i=0; i < nr*nc; i++){
result[i] = nz(m[i],eps);
}
return result;
}
// gradient-descent ------------------------------------------------------------
//' @title Regular gradient descent for latent factor model with covariates
//' @param X The ratings matrix. Unobserved entries must be marked NA. Users
//' must be along rows, and tracks must be along columns.
//' @param Z The covariates associated with each pair. This must be shaped as an
//' array with users along rows, tracks along columns, and features along
//' slices.
//' @param k_factors The number of latent factors for the problem.
//' @param lambdas The regularization parameters for P, Q, and beta, respectively.
//' @param n_iter The number of gradient descent iterations to run.
//' @param batch_samples The proportion of the observed entries to sample in the
//' gradient for each factor in the gradient descent.
//' @param batch_factors The proportion of latent factors to update at each
//' iteration.
//' @param gamma The step-size in the gradient descent.
//' @param verbose Print the objective at each iteration of the descent?
//' @return A list with the following elements, \cr
//' $P The learned user latent factors. \cr
//' $Q The learned track latent factors. \cr
//' $beta The learned regression coefficients.
//' @export
// [[Rcpp::export]]
Rcpp::List svd_cov(Rcpp::NumericMatrix X, Rcpp::NumericVector Z_vec,
int k_factors, Rcpp::NumericVector lambdas, int n_iter,
double batch_samples, double batch_factors,
double gamma_pq, double gamma_beta, bool verbose) {
// convert to arma
Rcpp::IntegerVector Z_dim = Z_vec.attr("dim");
arma::cube Z(Z_vec.begin(), Z_dim[0], Z_dim[1], Z_dim[2], false);
// initialize results
int m = X.nrow();
int n = X.ncol();
int l = Z_dim[2];
arma::mat P = 1.0 / sqrt(m) * arma::randn(m, k_factors);
arma::mat Q = 1.0 / sqrt(n) * arma::randn(n, k_factors);
arma::vec beta = 1.0 / sqrt(l) * arma::randn(l);
arma::vec objs = arma::zeros(n_iter);
// perform gradient descent steps
for(int cur_iter = 0; cur_iter < n_iter; cur_iter++) {
// update user factors
arma::uvec cur_factors = get_batch_ix(m, batch_factors);
for(int i = 0; i < cur_factors.n_elem; i++) {
int cur_ix = cur_factors(i);
P.row(cur_ix) = update_factor(X.row(cur_ix),
Z(arma::span(cur_ix), arma::span(), arma::span()),
arma::conv_to<arma::vec>::from(P.row(cur_ix)), Q, beta,
batch_samples, lambdas[0], gamma_pq).t();
}
// update track factors
cur_factors = get_batch_ix(n, batch_factors);
for(int j = 0; j < cur_factors.n_elem; j++) {
int cur_ix = cur_factors(j);
Q.row(cur_ix) = update_factor(X.column(cur_ix),
Z(arma::span(), arma::span(cur_ix), arma::span()),
arma::conv_to<arma::vec>::from(Q.row(cur_ix)), P, beta,
batch_samples, lambdas[1], gamma_pq).t();
}
// update regression coefficients
beta = update_beta(Rcpp::as<arma::mat>(X), Z, P, Q, beta, lambdas[2], gamma_beta);
objs[cur_iter] = objective_fun(Rcpp::as<arma::mat>(X), Z, P, Q, beta, lambdas);
if(verbose) {
printf("iteration %d \n", cur_iter);
printf("Objective: %g \n", objs[cur_iter]);
}
}
return Rcpp::List::create(Rcpp::Named("P") = P,
Rcpp::Named("Q") = Q,
Rcpp::Named("beta") = beta,
Rcpp::Named("objs") = objs);
}
// [[Rcpp::export]]
SEXP EtaIndep (Rcpp::NumericVector y_rcpp,
Rcpp::NumericMatrix y_lagged_rcpp,
Rcpp::NumericMatrix z_dependent_rcpp,
Rcpp::NumericMatrix z_independent_rcpp,
Rcpp::NumericVector beta_rcpp,
Rcpp::NumericVector mu_rcpp,
Rcpp::NumericVector sigma_rcpp,
Rcpp::NumericMatrix gamma_dependent_rcpp,
Rcpp::NumericVector gamma_independent_rcpp)
{
int M = mu_rcpp.size();
int n = y_rcpp.size();
arma::mat eta(n, M);
arma::colvec y(y_rcpp.begin(), y_rcpp.size(), false);
arma::mat y_lagged(y_lagged_rcpp.begin(),
y_lagged_rcpp.nrow(), y_lagged_rcpp.ncol(), false);
arma::mat z_dependent(z_dependent_rcpp.begin(),
z_dependent_rcpp.nrow(), z_dependent_rcpp.ncol(), false);
arma::mat z_independent(z_independent_rcpp.begin(),
z_independent_rcpp.nrow(), z_independent_rcpp.ncol(), false);
arma::colvec beta(beta_rcpp.begin(), beta_rcpp.size(), false);
arma::colvec mu(mu_rcpp.begin(), mu_rcpp.size(), false);
arma::colvec sigma(sigma_rcpp.begin(), sigma_rcpp.size(), false);
arma::mat gamma_dependent(gamma_dependent_rcpp.begin(),
gamma_dependent_rcpp.nrow(), gamma_dependent_rcpp.ncol(), false);
arma::colvec gamma_independent(gamma_independent_rcpp.begin(),
gamma_independent_rcpp.size(), false);
for (int j = 0; j < M; j++)
{
eta.col(j) = y - y_lagged * beta -
z_dependent * gamma_dependent.col(j) -
z_independent * gamma_independent - mu(j);
eta.col(j) = eta.col(j) % eta.col(j); // element-wise multiplication
eta.col(j) = exp(-eta.col(j) / (2 * (sigma(j) * sigma(j))));
eta.col(j) = eta.col(j) / (SQRT2PI * sigma(j));
}
return (wrap(eta));
}
///********************************************************************
///** tam_rcpp_irt_likelihood_cfa
// [[Rcpp::export]]
Rcpp::List tam_rcpp_irt_likelihood_cfa( Rcpp::NumericMatrix data,
Rcpp::NumericVector nu, Rcpp::NumericMatrix psi,
Rcpp::NumericMatrix L, Rcpp::NumericMatrix theta )
{
int N = data.nrow();
int I = data.ncol();
int D = L.ncol();
int TP= theta.nrow();
Rcpp::NumericMatrix hwt(N,TP);
std::fill( hwt.begin(), hwt.end(), 1 );
double term=0;
double val=0;
double sdii=0;
for (int tt=0;tt<TP;tt++){
for (int ii=0;ii<I;ii++){
term = nu[ii];
for (int dd=0; dd < D; dd++){
term += L(ii,dd) * theta(tt,dd);
}
sdii = sqrt( psi(ii,ii) );
for (int nn=0;nn<N;nn++){
if ( ! R_IsNA( data(nn,ii) ) ){
val = ::Rf_dnorm4( data(nn,ii), term, sdii, false );
hwt(nn,tt) = hwt(nn,tt) * val;
} // end if not missing
} // end nn
} // end ii
} // end tt
//*************************************************
// OUTPUT
return Rcpp::List::create(
Rcpp::Named("hwt") = hwt,
Rcpp::Named("N") = N,
Rcpp::Named("I") = I,
Rcpp::Named("TP")= TP,
Rcpp::Named("D") = D
);
}
void sampleRho(Rcpp::NumericVector& rho, Rcpp::NumericMatrix& A, arma::mat A_restrict, double rhoa, double rhob){
int k = A.ncol();
int p = A.nrow();
arma::rowvec A_maxnnz_col = arma::sum(A_restrict, 0);
for (int i=0; i<k; i++) {
NumericMatrix::Column col = A.column(i) ;
NumericVector As = ifelse(abs(col)<1e-10, 0.0, 1.0);
double nnz = std::accumulate(As.begin(), As.end(), 0.0);
double maxnnz = A_maxnnz_col(i);
rho(i) = Rf_rbeta(rhoa + nnz, rhob + maxnnz-nnz);
}
}