// [[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));
}
double sPredModule::solveCoef(double wrss) {
Rcpp::NumericMatrix
cc = d_fac.solve(CHOLMOD_L, d_fac.solve(CHOLMOD_P, d_Vtr));
double ans = sqrt(inner_product(cc.begin(), cc.end(),
cc.begin(), double())/wrss);
Rcpp::NumericMatrix
bb = d_fac.solve(CHOLMOD_Pt, d_fac.solve(CHOLMOD_Lt, cc));
copy(bb.begin(), bb.end(), d_coef.begin());
return ans;
}
Rcpp::List RadiusSearch(Rcpp::NumericMatrix query_,
Rcpp::NumericMatrix ref_,
double radius,
int max_neighbour,
std::string build,
int cores,
int checks) {
const std::size_t n_dim = query_.ncol();
const std::size_t n_query = query_.nrow();
const std::size_t n_ref = ref_.nrow();
// Column major to row major
arma::mat query(n_dim, n_query);
{
arma::mat temp_q(query_.begin(), n_query, n_dim, false);
query = arma::trans(temp_q);
}
flann::Matrix<double> q_flann(query.memptr(), n_query, n_dim);
arma::mat ref(n_dim, n_ref);
{
arma::mat temp_r(ref_.begin(), n_ref, n_dim, false);
ref = arma::trans(temp_r);
}
flann::Matrix<double> ref_flann(ref.memptr(), n_ref, n_dim);
// Setting the flann index params
flann::IndexParams params;
if (build == "kdtree") {
params = flann::KDTreeSingleIndexParams(1);
} else if (build == "kmeans") {
params = flann::KMeansIndexParams(2, 10, flann::FLANN_CENTERS_RANDOM, 0.2);
} else if (build == "linear") {
params = flann::LinearIndexParams();
}
// Perform the radius search
flann::Index<flann::L2<double> > index(ref_flann, params);
index.buildIndex();
std::vector< std::vector<int> >
indices_flann(n_query, std::vector<int>(max_neighbour));
std::vector< std::vector<double> >
dists_flann(n_query, std::vector<double>(max_neighbour));
flann::SearchParams search_params;
search_params.cores = cores;
search_params.checks = checks;
search_params.max_neighbors = max_neighbour;
index.radiusSearch(q_flann, indices_flann, dists_flann, radius,
search_params);
return Rcpp::List::create(Rcpp::Named("indices") = indices_flann,
Rcpp::Named("distances") = dists_flann);
}
//' 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 eigs_sym_shift_c(
mat_op op, int n, int k, double sigma,
const spectra_opts *opts, void *data,
int *nconv, int *niter, int *nops,
double *evals, double *evecs, int *info
)
{
BEGIN_RCPP
CRealShift cmat_op(op, n, data);
Rcpp::List res;
try {
res = run_eigs_shift_sym((RealShift*) &cmat_op, n, k, opts->ncv, opts->rule,
sigma, opts->maxitr, opts->tol, opts->retvec != 0);
*info = 0;
} catch(...) {
*info = 1; // indicates error
}
*nconv = Rcpp::as<int>(res["nconv"]);
*niter = Rcpp::as<int>(res["niter"]);
*nops = Rcpp::as<int>(res["nops"]);
Rcpp::NumericVector val = res["values"];
std::copy(val.begin(), val.end(), evals);
if(opts->retvec != 0)
{
Rcpp::NumericMatrix vec = res["vectors"];
std::copy(vec.begin(), vec.end(), evecs);
}
VOID_END_RCPP
}
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";
}
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]]
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));
}
// [[Rcpp::export]]
arma::mat MVGAUSS(Rcpp::NumericMatrix OMEGA_, int n, int seed) {
// std::srand(12523);
arma::mat OMEGA( OMEGA_.begin(), OMEGA_.nrow(), OMEGA_.ncol(), false );
arma::vec eigval;
arma::mat eigvec;
arma::eig_sym(eigval,eigvec, OMEGA);
int ncol = OMEGA.n_cols;
arma::mat X = arma::randn<arma::mat>(n,ncol);
eigval = arma::sqrt(eigval);
arma::mat Z = arma::diagmat(eigval);
X = eigvec * Z * X.t();
return(X.t());
}
RcppExport SEXP rthxpos(SEXP m)
{
Rcpp::NumericMatrix tmpm = Rcpp::NumericMatrix(m);
int nr = tmpm.nrow();
int nc = tmpm.ncol();
thrust::device_vector<double> dmat(tmpm.begin(),tmpm.end());
// make space for the transpose
thrust::device_vector<double> dxp(nr*nc);
// iterator to march through the matrix elements
thrust::counting_iterator<int> seqb(0);
thrust::counting_iterator<int> seqe = seqb + nr*nc;
// for each i in seq, copy the matrix elt to its spot in the
// transpose
thrust::for_each(seqb,seqe,
copyelt2xp(dmat.begin(),dxp.begin(),nr,nc));
// prepare the R output, and return it
Rcpp::NumericVector routmat(nc*nr);
thrust::copy(dxp.begin(),dxp.end(),routmat.begin());
return routmat;
}
/**
* Update L, Ut and cu for new weights.
*
* Update Ut from Zt and sqrtXwt, then L from Lambda and Ut
* Update cu from wtres, Lambda and Ut.
*
* @param Xwt Matrix of weights for the model matrix
* @param wtres weighted residuals
*/
void reModule::reweight(Rcpp::NumericMatrix const& Xwt,
Rcpp::NumericVector const& wtres) {
double mone = -1., one = 1.;
int Wnc = Xwt.ncol();
if (d_Ut) M_cholmod_free_sparse(&d_Ut, &c);
if (Wnc == 1) {
d_Ut = M_cholmod_copy_sparse(&d_Zt, &c);
chmDn csqrtX(Xwt);
M_cholmod_scale(&csqrtX, CHOLMOD_COL, d_Ut, &c);
} else {
int n = Xwt.nrow();
CHM_TR tr = M_cholmod_sparse_to_triplet(&d_Zt, &c);
int *j = (int*)tr->j, nnz = tr->nnz;
double *x = (double*)tr->x, *W = Xwt.begin();
for (int k = 0; k < nnz; k++) {
x[k] *= W[j[k]];
j[k] = j[k] % n;
}
tr->ncol = (size_t)n;
d_Ut = M_cholmod_triplet_to_sparse(tr, nnz, &c);
M_cholmod_free_triplet(&tr, &c);
}
// update the factor L
CHM_SP LambdatUt = d_Lambda.crossprod(d_Ut);
d_L.update(*LambdatUt, 1.);
d_ldL2 = d_L.logDet2();
// update cu
chmDn ccu(d_cu), cwtres(wtres);
copy(d_u0.begin(), d_u0.end(), d_cu.begin());
M_cholmod_sdmult(LambdatUt, 0/*trans*/, &one, &mone, &cwtres, &ccu, &c);
M_cholmod_free_sparse(&LambdatUt, &c);
NumericMatrix
ans = d_L.solve(CHOLMOD_L, d_L.solve(CHOLMOD_P, d_cu));
copy(ans.begin(), ans.end(), d_cu.begin());
d_CcNumer = inner_product(d_cu.begin(), d_cu.end(), d_cu.begin(), double());
}
///' Calculate the network properties, data matrix not provided
///'
///' @details
///' \subsection{Input expectations:}{
///' Note that this function expects all inputs to be sensible, as checked by
///' the R function 'checkUserInput' and processed by 'networkProperties'.
///'
///' These requirements are:
///' \itemize{
///' \item{'net' is a square matrix, and its rownames are identical to its
///' column names.}
///' \item{'moduleAssigments' is a named character vector, where the names
///' represent node labels found in the discovery dataset. Unlike
///' 'PermutationProcedure', these may include nodes that are not
///' present in 'data' and 'net'.}
///' \item{The module labels specified in 'modules' must occur in
///' 'moduleAssignments'.}
///' }
///' }
///'
///' @param net adjacency matrix of network edge weights between all pairs of
///' nodes in the dataset in which to calculate the network properties.
///' @param moduleAssignments a named character vector containing the module
///' each node belongs to in the discovery dataset.
///' @param modules a character vector of modules for which to calculate the
///' network properties for.
///'
///' @return a list containing the summary profile, node contribution, module
///' coherence, weighted degree, and average edge weight for each 'module'.
///'
///' @keywords internal
// [[Rcpp::export]]
Rcpp::List NetPropsNoData (
Rcpp::NumericMatrix net,
Rcpp::CharacterVector moduleAssignments,
Rcpp::CharacterVector modules
) {
// convert the colnames / rownames to C++ equivalents
const std::vector<std::string> nodeNames (Rcpp::as<std::vector<std::string>>(colnames(net)));
unsigned int nNodes = net.ncol();
R_CheckUserInterrupt();
/* Next, we need to create two mappings:
* - From node IDs to indices in the dataset of interest
* - From modules to node IDs
* - From modules to only node IDs present in the dataset of interest
*/
const namemap nodeIdxMap = MakeIdxMap(nodeNames);
const stringmap modNodeMap = MakeModMap(moduleAssignments);
const stringmap modNodePresentMap = MakeModMap(moduleAssignments, nodeIdxMap);
// What modules do we actually want to analyse?
const std::vector<std::string> mods (Rcpp::as<std::vector<std::string>>(modules));
R_CheckUserInterrupt();
// Calculate the network properties for each module
std::string mod; // iterators
unsigned int mNodesPresent, mNodes;
arma::uvec nodeIdx, propIdx, nodeRank;
namemap propIdxMap;
std::vector<std::string> modNodeNames;
arma::vec WD; // results containers
double avgWeight;
Rcpp::NumericVector degree; // for casting to R equivalents
Rcpp::List results; // final storage container
for (auto mi = mods.begin(); mi != mods.end(); ++mi) {
// What nodes are in this module?
// modNodeNames = names(moduleAssignments[moduleAssignments == mod])
mod = *mi;
modNodeNames = GetModNodeNames(mod, modNodeMap);
// initialise results containers with NA values for nodes not present in
// the dataset we're calculating the network properties in.
degree = Rcpp::NumericVector(modNodeNames.size(), NA_REAL);
avgWeight = NA_REAL;
degree.names() = modNodeNames;
// Create a mapping between node names and the result vectors
propIdxMap = MakeIdxMap(modNodeNames);
// Get just the indices of nodes that are present in the requested dataset
nodeIdx = GetNodeIdx(mod, modNodePresentMap, nodeIdxMap);
mNodesPresent = nodeIdx.n_elem;
// And a mapping of those nodes to the initialised vectors
propIdx = GetNodeIdx(mod, modNodePresentMap, propIdxMap);
mNodes = propIdx.n_elem;
// Calculate the properties if the module has nodes in the test dataset
if (nodeIdx.n_elem > 0) {
// sort the node indices for sequential memory access
nodeRank = SortNodes(nodeIdx.memptr(), mNodesPresent);
WD = WeightedDegree(net.begin(), nNodes, nodeIdx.memptr(), mNodesPresent);
WD = WD(nodeRank); // reorder results
avgWeight = AverageEdgeWeight(WD.memptr(), WD.n_elem);
R_CheckUserInterrupt();
// Fill the results vectors appropriately
Fill(degree, WD.memptr(), mNodesPresent, propIdx.memptr(), mNodes);
}
results.push_back(
Rcpp::List::create(
Rcpp::Named("degree") = degree,
Rcpp::Named("avgWeight") = avgWeight
)
);
}
results.names() = mods;
return(results);
}
densePred::densePred(Rcpp::NumericMatrix x)
throw (std::runtime_error)
: predMod(x.ncol()), d_X(x),
a_X(x.begin(), x.nrow(), x.ncol(), false, true) {
}
// [[Rcpp::export]]
Rcpp::List rangerCpp(uint treetype, std::string dependent_variable_name,
Rcpp::NumericMatrix input_data, std::vector<std::string> variable_names, uint mtry, uint num_trees, bool verbose,
uint seed, uint num_threads, bool write_forest, uint importance_mode_r, uint min_node_size,
std::vector<std::vector<double>>& split_select_weights, bool use_split_select_weights,
std::vector<std::string>& always_split_variable_names, bool use_always_split_variable_names,
std::string status_variable_name, bool prediction_mode, Rcpp::List loaded_forest, Rcpp::RawMatrix sparse_data,
bool sample_with_replacement, bool probability, std::vector<std::string>& unordered_variable_names,
bool use_unordered_variable_names, bool save_memory, uint splitrule_r,
std::vector<double>& case_weights, bool use_case_weights, bool predict_all,
bool keep_inbag, double sample_fraction, double alpha, double minprop, bool holdout, uint prediction_type_r) {
Rcpp::List result;
Forest* forest = 0;
Data* data = 0;
try {
// Empty split select weights and always split variables if not used
if (!use_split_select_weights) {
split_select_weights.clear();
}
if (!use_always_split_variable_names) {
always_split_variable_names.clear();
}
if (!use_unordered_variable_names) {
unordered_variable_names.clear();
}
if (!use_case_weights) {
case_weights.clear();
}
std::ostream* verbose_out;
if (verbose) {
verbose_out = &Rcpp::Rcout;
} else {
verbose_out = new std::stringstream;
}
size_t num_rows = input_data.nrow();
size_t num_cols = input_data.ncol();
// Initialize data with double memmode
data = new DataDouble(input_data.begin(), variable_names, num_rows, num_cols);
// If there is sparse data, add it
if (sparse_data.nrow() > 1) {
data->addSparseData(sparse_data.begin(), sparse_data.ncol());
}
switch (treetype) {
case TREE_CLASSIFICATION:
if (probability) {
forest = new ForestProbability;
} else {
forest = new ForestClassification;
}
break;
case TREE_REGRESSION:
forest = new ForestRegression;
break;
case TREE_SURVIVAL:
forest = new ForestSurvival;
break;
case TREE_PROBABILITY:
forest = new ForestProbability;
break;
}
ImportanceMode importance_mode = (ImportanceMode) importance_mode_r;
SplitRule splitrule = (SplitRule) splitrule_r;
PredictionType prediction_type = (PredictionType) prediction_type_r;
// Init Ranger
forest->initR(dependent_variable_name, data, mtry, num_trees, verbose_out, seed, num_threads,
importance_mode, min_node_size, split_select_weights, always_split_variable_names, status_variable_name,
prediction_mode, sample_with_replacement, unordered_variable_names, save_memory, splitrule, case_weights,
predict_all, keep_inbag, sample_fraction, alpha, minprop, holdout, prediction_type);
// Load forest object if in prediction mode
if (prediction_mode) {
size_t dependent_varID = loaded_forest["dependent.varID"];
//size_t num_trees = loaded_forest["num.trees"];
std::vector<std::vector<std::vector<size_t>> > child_nodeIDs = loaded_forest["child.nodeIDs"];
std::vector<std::vector<size_t>> split_varIDs = loaded_forest["split.varIDs"];
std::vector<std::vector<double>> split_values = loaded_forest["split.values"];
std::vector<bool> is_ordered = loaded_forest["is.ordered"];
if (treetype == TREE_CLASSIFICATION) {
std::vector<double> class_values = loaded_forest["class.values"];
((ForestClassification*) forest)->loadForest(dependent_varID, num_trees, child_nodeIDs, split_varIDs,
split_values, class_values, is_ordered);
} else if (treetype == TREE_REGRESSION) {
((ForestRegression*) forest)->loadForest(dependent_varID, num_trees, child_nodeIDs, split_varIDs, split_values,
is_ordered);
} else if (treetype == TREE_SURVIVAL) {
size_t status_varID = loaded_forest["status.varID"];
std::vector<std::vector<std::vector<double>> > chf = loaded_forest["chf"];
std::vector<double> unique_timepoints = loaded_forest["unique.death.times"];
((ForestSurvival*) forest)->loadForest(dependent_varID, num_trees, child_nodeIDs, split_varIDs, split_values,
status_varID, chf, unique_timepoints, is_ordered);
} else if (treetype == TREE_PROBABILITY) {
std::vector<double> class_values = loaded_forest["class.values"];
std::vector<std::vector<std::vector<double>>>terminal_class_counts =
loaded_forest["terminal.class.counts"];
((ForestProbability*) forest)->loadForest(dependent_varID, num_trees, child_nodeIDs, split_varIDs, split_values,
class_values, terminal_class_counts, is_ordered);
}
}
// Run Ranger
forest->run(false);
if (use_split_select_weights && importance_mode != IMP_NONE) {
*verbose_out
<< "Warning: Split select weights used. Variable importance measures are only comparable for variables with equal weights."
<< std::endl;
}
// Use first non-empty dimension of predictions
const std::vector<std::vector<std::vector<double>>>& predictions = forest->getPredictions();
if (predictions.size() == 1) {
if (predictions[0].size() == 1) {
result.push_back(forest->getPredictions()[0][0], "predictions");
} else {
result.push_back(forest->getPredictions()[0], "predictions");
}
} else {
result.push_back(forest->getPredictions(), "predictions");
}
// Return output
result.push_back(forest->getNumTrees(), "num.trees");
result.push_back(forest->getNumIndependentVariables(), "num.independent.variables");
if (treetype == TREE_SURVIVAL) {
ForestSurvival* temp = (ForestSurvival*) forest;
result.push_back(temp->getUniqueTimepoints(), "unique.death.times");
}
if (!verbose) {
std::stringstream temp;
temp << verbose_out->rdbuf();
result.push_back(temp.str(), "log");
}
if (!prediction_mode) {
result.push_back(forest->getMtry(), "mtry");
result.push_back(forest->getMinNodeSize(), "min.node.size");
if (importance_mode != IMP_NONE) {
result.push_back(forest->getVariableImportance(), "variable.importance");
}
result.push_back(forest->getOverallPredictionError(), "prediction.error");
}
if (keep_inbag) {
result.push_back(forest->getInbagCounts(), "inbag.counts");
}
// Save forest if needed
if (write_forest) {
Rcpp::List forest_object;
forest_object.push_back(forest->getDependentVarId(), "dependent.varID");
forest_object.push_back(forest->getNumTrees(), "num.trees");
forest_object.push_back(forest->getChildNodeIDs(), "child.nodeIDs");
forest_object.push_back(forest->getSplitVarIDs(), "split.varIDs");
forest_object.push_back(forest->getSplitValues(), "split.values");
forest_object.push_back(forest->getIsOrderedVariable(), "is.ordered");
if (treetype == TREE_CLASSIFICATION) {
ForestClassification* temp = (ForestClassification*) forest;
forest_object.push_back(temp->getClassValues(), "class.values");
} else if (treetype == TREE_PROBABILITY) {
ForestProbability* temp = (ForestProbability*) forest;
forest_object.push_back(temp->getClassValues(), "class.values");
forest_object.push_back(temp->getTerminalClassCounts(), "terminal.class.counts");
} else if (treetype == TREE_SURVIVAL) {
ForestSurvival* temp = (ForestSurvival*) forest;
forest_object.push_back(temp->getStatusVarId(), "status.varID");
forest_object.push_back(temp->getChf(), "chf");
forest_object.push_back(temp->getUniqueTimepoints(), "unique.death.times");
}
result.push_back(forest_object, "forest");
}
delete forest;
delete data;
} catch (std::exception& e) {
if (strcmp(e.what(), "User interrupt.") != 0) {
Rcpp::Rcerr << "Error: " << e.what() << " Ranger will EXIT now." << std::endl;
}
delete forest;
delete data;
return result;
}
return result;
}
RcppExport SEXP DEoptim(SEXP lowerS, SEXP upperS, SEXP fnS, SEXP controlS, SEXP rhoS) {
try {
Rcpp::NumericVector f_lower(lowerS), f_upper(upperS); // User-defined bounds
Rcpp::List control(controlS); // named list of params
double VTR = Rcpp::as<double>(control["VTR"]); // value to reach
int i_strategy = Rcpp::as<int>(control["strategy"]); // chooses DE-strategy
int i_itermax = Rcpp::as<int>(control["itermax"]); // Maximum number of generations
long l_nfeval = 0; // nb of function evals (NOT passed in)
int i_D = Rcpp::as<int>(control["npar"]); // Dimension of parameter vector
int i_NP = Rcpp::as<int>(control["NP"]); // Number of population members
int i_storepopfrom = Rcpp::as<int>(control["storepopfrom"]) - 1; // When to start storing populations
int i_storepopfreq = Rcpp::as<int>(control["storepopfreq"]); // How often to store populations
int i_specinitialpop = Rcpp::as<int>(control["specinitialpop"]);// User-defined inital population
Rcpp::NumericMatrix initialpopm = Rcpp::as<Rcpp::NumericMatrix>(control["initialpop"]);
double f_weight = Rcpp::as<double>(control["F"]); // stepsize
double f_cross = Rcpp::as<double>(control["CR"]); // crossover probability
int i_bs_flag = Rcpp::as<int>(control["bs"]); // Best of parent and child
int i_trace = Rcpp::as<int>(control["trace"]); // Print progress?
int i_check_winner = Rcpp::as<int>(control["checkWinner"]); // Re-evaluate best parameter vector?
int i_av_winner = Rcpp::as<int>(control["avWinner"]); // Average
double i_pPct = Rcpp::as<double>(control["p"]); // p to define the top 100p% best solutions
arma::colvec minbound(f_lower.begin(), f_lower.size(), false); // convert Rcpp vectors to arma vectors
arma::colvec maxbound(f_upper.begin(), f_upper.size(), false);
arma::mat initpopm(initialpopm.begin(), initialpopm.rows(), initialpopm.cols(), false);
arma::mat ta_popP(i_D, i_NP*2); // Data structures for parameter vectors
arma::mat ta_oldP(i_D, i_NP);
arma::mat ta_newP(i_D, i_NP);
arma::colvec t_bestP(i_D);
arma::colvec ta_popC(i_NP*2); // Data structures for obj. fun. values
arma::colvec ta_oldC(i_NP);
arma::colvec ta_newC(i_NP);
double t_bestC;
arma::colvec t_bestitP(i_D);
arma::colvec t_tmpP(i_D);
int i_nstorepop = ceil((i_itermax - i_storepopfrom) / i_storepopfreq);
arma::mat d_pop(i_D, i_NP);
Rcpp::List d_storepop(i_nstorepop);
arma::mat d_bestmemit(i_D, i_itermax);
arma::colvec d_bestvalit(i_itermax);
int i_iter = 0;
// call actual Differential Evolution optimization given the parameters
devol(VTR, f_weight, f_cross, i_bs_flag, minbound, maxbound, fnS, rhoS, i_trace, i_strategy, i_D, i_NP,
i_itermax, initpopm, i_storepopfrom, i_storepopfreq, i_specinitialpop, i_check_winner, i_av_winner,
ta_popP, ta_oldP, ta_newP, t_bestP, ta_popC, ta_oldC, ta_newC, t_bestC, t_bestitP, t_tmpP,
d_pop, d_storepop, d_bestmemit, d_bestvalit, i_iter, i_pPct, l_nfeval);
return Rcpp::List::create(Rcpp::Named("bestmem") = t_bestP, // and return a named list with results to R
Rcpp::Named("bestval") = t_bestC,
Rcpp::Named("nfeval") = l_nfeval,
Rcpp::Named("iter") = i_iter,
Rcpp::Named("bestmemit") = trans(d_bestmemit),
Rcpp::Named("bestvalit") = d_bestvalit,
Rcpp::Named("pop") = trans(d_pop),
Rcpp::Named("storepop") = d_storepop);
} catch( std::exception& ex) {
forward_exception_to_r(ex);
} catch(...) {
::Rf_error( "c++ exception (unknown reason)");
}
return R_NilValue;
}
IndepTestGauss(uint sampleSize, Rcpp::NumericMatrix& cor) :
_sampleSize(sampleSize),
_correlation(cor.begin(), cor.nrow(), cor.ncol(), false) {}
//' rcpp_get_polygons
//'
//' Extracts all polygons from an overpass API query
//'
//' @param st Text contents of an overpass API query
//' @return A \code{SpatialLinesDataFrame} contains all polygons and associated data
// [[Rcpp::export]]
Rcpp::S4 rcpp_get_polygons (const std::string& st)
{
#ifdef DUMP_INPUT
{
std::ofstream dump ("./get-polygons.xml");
if (dump.is_open())
{
dump.write (st.c_str(), st.size());
}
}
#endif
XmlPolys xml (st);
const std::map <osmid_t, Node>& nodes = xml.nodes ();
const std::map <osmid_t, OneWay>& ways = xml.ways ();
const std::vector <Relation>& rels = xml.relations ();
int count = 0;
float xmin = FLOAT_MAX, xmax = -FLOAT_MAX,
ymin = FLOAT_MAX, ymax = -FLOAT_MAX;
std::vector <float> lons, lats;
std::unordered_set <std::string> idset; // see TODO below
std::vector <std::string> colnames, rownames, polynames;
std::set <std::string> varnames;
Rcpp::List dimnames (0), dummy_list (0);
Rcpp::NumericMatrix nmat (Rcpp::Dimension (0, 0));
idset.clear ();
colnames.push_back ("lon");
colnames.push_back ("lat");
varnames.insert ("name");
// other varnames added below
/*
* NOTE: Nodes are first loaded into the 2 vectors of (lon, lat), and these
* are then copied into nmat. This intermediate can be avoided by loading
* directly into nmat using direct indexing rather than iterators, however
* this does *NOT* make the routine any faster, and so the current version
* which more safely uses iterators is kept instead.
*/
Rcpp::Environment sp_env = Rcpp::Environment::namespace_env ("sp");
Rcpp::Function Polygon = sp_env ["Polygon"];
Rcpp::Language polygons_call ("new", "Polygons");
Rcpp::S4 polygons;
/*
* Polygons are extracted from the XmlPolys class in three setps:
* 1. Get the names of all polygons that are part of multipolygon relations
* 2. Get the names of any remaining ways that are polygonal (start == end)
* 3. From the resultant list, extract the actual polygonal ways
*
* NOTE: OSM polygons are stored as ways, and thus all objects in the class
* xmlPolys are rightly referred to as ways. Here within this Rcpp function,
* these are referred to as Polygons, but the iteration is over the actual
* polygonal ways.
*/
// Step#1
std::set <osmid_t> the_ways; // Set will only insert unique values
for (auto it = rels.begin (); it != rels.end (); ++it)
for (auto itw = (*it).ways.begin (); itw != (*it).ways.end (); ++itw)
{
assert (ways.find (itw->first) != ways.end ());
the_ways.insert (itw->first);
}
// Step#2
//const std::map <osmid_t, OneWay>& ways = xml.ways ();
for (auto it = ways.begin (); it != ways.end (); ++it)
{
if (the_ways.find ((*it).first) == the_ways.end ())
if ((*it).second.nodes.begin () == (*it).second.nodes.end ())
the_ways.insert ((*it).first);
}
// Step#2b - Erase any ways that contain no data (should not happen).
for (auto it = the_ways.begin (); it != the_ways.end (); )
{
auto itw = ways.find (*it);
if (itw->second.nodes.size () == 0)
it = the_ways.erase (it);
else
++it;
}
Rcpp::List polyList (the_ways.size ());
// Step#3 - Extract and store the_ways
for (auto it = the_ways.begin (); it != the_ways.end (); ++it)
{
auto itw = ways.find (*it);
// Collect all unique keys
std::for_each (itw->second.key_val.begin (),
itw->second.key_val.end (),
[&](const std::pair <std::string, std::string>& p)
{
varnames.insert (p.first);
});
/*
* The following lines check for duplicate way IDs -- which do very
* occasionally occur -- and ensures unique values as required by 'sp'
* through appending decimal digits to <osmid_t> OSM IDs.
*/
std::string id = std::to_string (itw->first);
int tempi = 0;
while (idset.find (id) != idset.end ())
id = std::to_string (itw->first) + "." + std::to_string (tempi++);
idset.insert (id);
polynames.push_back (id);
// Then iterate over nodes of that way and store all lat-lons
size_t n = itw->second.nodes.size ();
lons.clear ();
lats.clear ();
rownames.clear ();
lons.reserve (n);
lats.reserve (n);
rownames.reserve (n);
for (auto itn = itw->second.nodes.begin ();
itn != itw->second.nodes.end (); ++itn)
{
// TODO: Propoer exception handler
assert (nodes.find (*itn) != nodes.end ());
lons.push_back (nodes.find (*itn)->second.lon);
lats.push_back (nodes.find (*itn)->second.lat);
rownames.push_back (std::to_string (*itn));
}
xmin = std::min (xmin, *std::min_element (lons.begin(), lons.end()));
xmax = std::max (xmax, *std::max_element (lons.begin(), lons.end()));
ymin = std::min (ymin, *std::min_element (lats.begin(), lats.end()));
ymax = std::max (ymax, *std::max_element (lats.begin(), lats.end()));
nmat = Rcpp::NumericMatrix (Rcpp::Dimension (lons.size (), 2));
std::copy (lons.begin (), lons.end (), nmat.begin ());
std::copy (lats.begin (), lats.end (), nmat.begin () + lons.size ());
// This only works with push_back, not with direct re-allocation
dimnames.push_back (rownames);
dimnames.push_back (colnames);
nmat.attr ("dimnames") = dimnames;
dimnames.erase (0, dimnames.size());
//Rcpp::S4 poly = Rcpp::Language ("Polygon", nmat).eval ();
Rcpp::S4 poly = Polygon (nmat);
dummy_list.push_back (poly);
polygons = polygons_call.eval ();
polygons.slot ("Polygons") = dummy_list;
polygons.slot ("ID") = id;
polyList [count++] = polygons;
dummy_list.erase (0);
} // end for it over the_ways
polyList.attr ("names") = polynames;
// Store all key-val pairs in one massive DF
int nrow = the_ways.size (), ncol = varnames.size ();
Rcpp::CharacterVector kv_vec (nrow * ncol, Rcpp::CharacterVector::get_na ());
int namecoli = std::distance (varnames.begin (), varnames.find ("name"));
for (auto it = the_ways.begin (); it != the_ways.end (); ++it)
{
int rowi = std::distance (the_ways.begin (), it);
auto itw = ways.find (*it);
kv_vec (namecoli * nrow + rowi) = itw->second.name;
for (auto kv_iter = itw->second.key_val.begin ();
kv_iter != itw->second.key_val.end (); ++kv_iter)
{
const std::string& key = (*kv_iter).first;
auto ni = varnames.find (key); // key must exist in varnames!
int coli = std::distance (varnames.begin (), ni);
kv_vec (coli * nrow + rowi) = (*kv_iter).second;
}
}
Rcpp::Language sp_polys_call ("new", "SpatialPolygonsDataFrame");
Rcpp::S4 sp_polys = sp_polys_call.eval ();
sp_polys.slot ("polygons") = polyList;
sp_polys.slot ("bbox") = rcpp_get_bbox (xmin, xmax, ymin, ymax);
Rcpp::Language crs_call ("new", "CRS");
Rcpp::S4 crs = crs_call.eval ();
crs.slot ("projargs") = "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +towgs84=0,0,0";
sp_polys.slot ("proj4string") = crs;
Rcpp::CharacterMatrix kv_mat (nrow, ncol, kv_vec.begin());
Rcpp::DataFrame kv_df = kv_mat;
kv_df.attr ("names") = varnames;
sp_polys.slot ("data") = kv_df;
return sp_polys;
}
///' Calculate the network properties
///'
///' @details
///' \subsection{Input expectations:}{
///' Note that this function expects all inputs to be sensible, as checked by
///' the R function 'checkUserInput' and processed by 'networkProperties'.
///'
///' These requirements are:
///' \itemize{
///' \item{The ordering of node names across 'data' and 'net' is consistent.}
///' \item{The columns of 'data' are the nodes.}
///' \item{'net' is a square matrix, and its rownames are identical to its
///' column names.}
///' \item{'moduleAssigments' is a named character vector, where the names
///' represent node labels found in the discovery dataset. Unlike
///' 'PermutationProcedure', these may include nodes that are not
///' present in 'data' and 'net'.}
///' \item{The module labels specified in 'modules' must occur in
///' 'moduleAssignments'.}
///' }
///' }
///'
///' @param data data matrix from the dataset in which to calculate the network
///' properties.
///' @param net adjacency matrix of network edge weights between all pairs of
///' nodes in the dataset in which to calculate the network properties.
///' @param moduleAssignments a named character vector containing the module
///' each node belongs to in the discovery dataset.
///' @param modules a character vector of modules for which to calculate the
///' network properties for.
///'
///' @return a list containing the summary profile, node contribution, module
///' coherence, weighted degree, and average edge weight for each 'module'.
///'
///' @keywords internal
// [[Rcpp::export]]
Rcpp::List NetProps (
Rcpp::NumericMatrix data, Rcpp::NumericMatrix net,
Rcpp::CharacterVector moduleAssignments,
Rcpp::CharacterVector modules
) {
// First, scale the matrix data
unsigned int nSamples = data.nrow();
unsigned int nNodes = data.ncol();
arma::mat scaledData = Scale(data.begin(), nSamples, nNodes);
R_CheckUserInterrupt();
// convert the colnames / rownames to C++ equivalents
const std::vector<std::string> nodeNames (Rcpp::as<std::vector<std::string>>(colnames(net)));
const std::vector<std::string> sampleNames (Rcpp::as<std::vector<std::string>>(rownames(data)));
/* Next, we need to create two mappings:
* - From node IDs to indices in the dataset of interest
* - From modules to node IDs
* - From modules to only node IDs present in the dataset of interest
*/
const namemap nodeIdxMap = MakeIdxMap(nodeNames);
const stringmap modNodeMap = MakeModMap(moduleAssignments);
const stringmap modNodePresentMap = MakeModMap(moduleAssignments, nodeIdxMap);
// What modules do we actually want to analyse?
const std::vector<std::string> mods (Rcpp::as<std::vector<std::string>>(modules));
R_CheckUserInterrupt();
// Calculate the network properties for each module
std::string mod; // iterators
unsigned int mNodesPresent, mNodes;
arma::uvec nodeIdx, propIdx, nodeRank;
namemap propIdxMap;
std::vector<std::string> modNodeNames;
arma::vec WD, SP, NC; // results containers
double avgWeight, coherence;
Rcpp::NumericVector degree, summary, contribution; // for casting to R equivalents
Rcpp::List results; // final storage container
for (auto mi = mods.begin(); mi != mods.end(); ++mi) {
// What nodes are in this module?
mod = *mi;
modNodeNames = GetModNodeNames(mod, modNodeMap);
// initialise results containers with NA values for nodes not present in
// the dataset we're calculating the network properties in.
degree = Rcpp::NumericVector(modNodeNames.size(), NA_REAL);
contribution = Rcpp::NumericVector(modNodeNames.size(), NA_REAL);
summary = Rcpp::NumericVector(nSamples, NA_REAL);
avgWeight = NA_REAL;
coherence = NA_REAL;
degree.names() = modNodeNames;
contribution.names() = modNodeNames;
// Create a mapping between node names and the result vectors
propIdxMap = MakeIdxMap(modNodeNames);
// Get just the indices of nodes that are present in the requested dataset
nodeIdx = GetNodeIdx(mod, modNodePresentMap, nodeIdxMap);
mNodesPresent = nodeIdx.n_elem;
// And a mapping of those nodes to the initialised vectors
propIdx = GetNodeIdx(mod, modNodePresentMap, propIdxMap);
mNodes = propIdx.n_elem;
// Calculate the properties if the module has nodes in the test dataset
if (nodeIdx.n_elem > 0) {
// sort the node indices for sequential memory access
nodeRank = SortNodes(nodeIdx.memptr(), mNodesPresent);
WD = WeightedDegree(net.begin(), nNodes, nodeIdx.memptr(), mNodesPresent);
WD = WD(nodeRank); // reorder results
avgWeight = AverageEdgeWeight(WD.memptr(), WD.n_elem);
R_CheckUserInterrupt();
SP = SummaryProfile(scaledData.memptr(), nSamples, nNodes,
nodeIdx.memptr(), mNodesPresent);
R_CheckUserInterrupt();
NC = NodeContribution(scaledData.memptr(), nSamples, nNodes,
nodeIdx.memptr(), mNodesPresent, SP.memptr());
NC = NC(nodeRank); // reorder results
coherence = ModuleCoherence(NC.memptr(), mNodesPresent);
R_CheckUserInterrupt();
// Convert NaNs to NAs
SP.elem(arma::find_nonfinite(SP)).fill(NA_REAL);
NC.elem(arma::find_nonfinite(NC)).fill(NA_REAL);
if (!arma::is_finite(coherence)) {
coherence = NA_REAL;
}
// Fill results vectors
Fill(degree, WD.memptr(), mNodesPresent, propIdx.memptr(), mNodes);
Fill(contribution, NC.memptr(), mNodesPresent, propIdx.memptr(), mNodes);
summary = Rcpp::NumericVector(SP.begin(), SP.end());
}
summary.names() = sampleNames;
results.push_back(
Rcpp::List::create(
Rcpp::Named("summary") = summary,
Rcpp::Named("contribution") = contribution,
Rcpp::Named("coherence") = coherence,
Rcpp::Named("degree") = degree,
Rcpp::Named("avgWeight") = avgWeight
)
);
}
results.names() = mods;
return(results);
}
// [[Rcpp::export]]
Rcpp::List DEoptim_impl(const arma::colvec & minbound, // user-defined lower bounds
const arma::colvec & maxbound, // user-defined upper bounds
SEXP fnS, // function to be optimized, either R or C++
const Rcpp::List & control, // parameters
SEXP rhoS) { // optional environment
double VTR = Rcpp::as<double>(control["VTR"]); // value to reach
int i_strategy = Rcpp::as<int>(control["strategy"]); // chooses DE-strategy
int i_itermax = Rcpp::as<int>(control["itermax"]); // Maximum number of generations
long l_nfeval = 0; // nb of function evals (NOT passed in)
int i_D = Rcpp::as<int>(control["npar"]); // Dimension of parameter vector
int i_NP = Rcpp::as<int>(control["NP"]); // Number of population members
int i_storepopfrom = Rcpp::as<int>(control["storepopfrom"]) - 1; // When to start storing populations
int i_storepopfreq = Rcpp::as<int>(control["storepopfreq"]); // How often to store populations
int i_specinitialpop = Rcpp::as<int>(control["specinitialpop"]); // User-defined inital population
double f_weight = Rcpp::as<double>(control["F"]); // stepsize
double f_cross = Rcpp::as<double>(control["CR"]); // crossover probability
int i_bs_flag = Rcpp::as<int>(control["bs"]); // Best of parent and child
int i_trace = Rcpp::as<int>(control["trace"]); // Print progress?
double i_pPct = Rcpp::as<double>(control["p"]); // p to define the top 100p% best solutions
double d_c = Rcpp::as<double>(control["c"]); // c as a trigger of the JADE algorithm
double d_reltol = Rcpp::as<double>(control["reltol"]); // tolerance for relative convergence test, default to be sqrt(.Machine$double.eps)
int i_steptol = Rcpp::as<double>(control["steptol"]); // maximum of iteration after relative convergence test is passed, default to be itermax
// as above, doing it in two steps is faster
Rcpp::NumericMatrix initialpopm = Rcpp::as<Rcpp::NumericMatrix>(control["initialpop"]);
arma::mat initpopm(initialpopm.begin(), initialpopm.rows(), initialpopm.cols(), false);
arma::mat ta_popP(i_D, i_NP*2); // Data structures for parameter vectors
arma::mat ta_oldP(i_D, i_NP);
arma::mat ta_newP(i_D, i_NP);
arma::colvec t_bestP(i_D);
arma::colvec ta_popC(i_NP*2); // Data structures for obj. fun. values
arma::colvec ta_oldC(i_NP);
arma::colvec ta_newC(i_NP);
double t_bestC;
arma::colvec t_bestitP(i_D);
arma::colvec t_tmpP(i_D);
int i_nstorepop = static_cast<int>(ceil(static_cast<double>((i_itermax - i_storepopfrom) / i_storepopfreq)));
arma::mat d_pop(i_D, i_NP);
Rcpp::List d_storepop(i_nstorepop);
arma::mat d_bestmemit(i_D, i_itermax);
arma::colvec d_bestvalit(i_itermax);
int i_iter = 0;
// call actual Differential Evolution optimization given the parameters
devol(VTR, f_weight, f_cross, i_bs_flag, minbound, maxbound, fnS, rhoS, i_trace, i_strategy, i_D, i_NP,
i_itermax, initpopm, i_storepopfrom, i_storepopfreq, i_specinitialpop,
ta_popP, ta_oldP, ta_newP, t_bestP, ta_popC, ta_oldC, ta_newC, t_bestC, t_bestitP, t_tmpP,
d_pop, d_storepop, d_bestmemit, d_bestvalit, i_iter, i_pPct, d_c, l_nfeval,
d_reltol, i_steptol);
return Rcpp::List::create(Rcpp::Named("bestmem") = t_bestP, // and return a named list with results to R
Rcpp::Named("bestval") = t_bestC,
Rcpp::Named("nfeval") = l_nfeval,
Rcpp::Named("iter") = i_iter,
Rcpp::Named("bestmemit") = trans(d_bestmemit),
Rcpp::Named("bestvalit") = d_bestvalit,
Rcpp::Named("pop") = trans(d_pop),
Rcpp::Named("storepop") = d_storepop);
}
// [[Rcpp::export]]
SEXP EMcppARMSIQuZhuo (Rcpp::NumericVector y_rcpp,
Rcpp::NumericMatrix y_lagged_rcpp,
Rcpp::NumericMatrix z_dependent_rcpp,
Rcpp::NumericMatrix z_independent_rcpp,
Rcpp::NumericMatrix beta0_rcpp,
Rcpp::NumericVector mu0_rcpp,
Rcpp::NumericVector sigma0_rcpp,
Rcpp::NumericMatrix gamma_dependent0_rcpp,
Rcpp::NumericMatrix gamma_independent0_rcpp,
Rcpp::NumericMatrix transition_probs0_rcpp,
Rcpp::NumericVector initial_dist0_rcpp,
int maxit,
double epsilon,
double transition_probs_min,
double transition_probs_max,
double sigma_min)
{
// conversion first
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::mat beta0(beta0_rcpp.begin(),
beta0_rcpp.nrow(), beta0_rcpp.ncol(), false);
arma::colvec mu0(mu0_rcpp.begin(), mu0_rcpp.size(), false);
arma::colvec sigma0(sigma0_rcpp.begin(), sigma0_rcpp.size(), false);
arma::mat gamma_dependent0(gamma_dependent0_rcpp.begin(),
gamma_dependent0_rcpp.nrow(),
gamma_dependent0_rcpp.ncol(), false);
arma::mat gamma_independent0(gamma_independent0_rcpp.begin(),
gamma_independent0_rcpp.nrow(),
gamma_independent0_rcpp.ncol(), false);
arma::mat transition_probs0(transition_probs0_rcpp.begin(),
transition_probs0_rcpp.nrow(),
transition_probs0_rcpp.ncol(), false);
arma::colvec initial_dist0(initial_dist0_rcpp.begin(),
initial_dist0_rcpp.size(), false);
arma::mat y_lagged_t = y_lagged.t();
arma::mat z_dependent_t = z_dependent.t();
arma::mat z_independent_t = z_independent.t();
arma::colvec likelihoods(maxit, arma::fill::zeros);
int index_exit = -1;
Theta theta_original(beta0, mu0, sigma0,
gamma_dependent0, gamma_independent0,
transition_probs0, initial_dist0);
Theta theta_updated = theta_original;
Theta theta = theta_updated;
theta.likelihood = -std::numeric_limits<double>::infinity();
likelihoods(0) = -std::numeric_limits<double>::infinity();
// 1. EM iterations
for (int i = 1; i < maxit; i++)
{
index_exit++;
Xi e_step = ExpectationStepARMSIQuZhuo(&y, &y_lagged, &z_dependent, &z_independent,
&theta_updated);
likelihoods(i) = theta_updated.likelihood;
// Stop if 1. the difference in likelihoods is small enough
// or 2. likelihood decreases (a decrease is due to locating local max.
// out of hard constraints in maximization step, which suggests that this
// is not a good candidate anyway.)
// Use negative condition to stop if 3. the updated likelihood is NaN.
if (!((theta_updated.likelihood - theta.likelihood) >= epsilon))
break;
theta = theta_updated;
theta_updated = MaximizationStepARMSIQuZhuo(&y, &y_lagged,
&z_dependent, &z_independent,
&y_lagged_t, &z_dependent_t, &z_independent_t,
&theta,
&(e_step.xi_k), &(e_step.xi_past_t),
&(e_step.xi_n),
transition_probs_min,
transition_probs_max,
sigma_min);
// printf("sigma updated at %d: %f", i, theta_updated.sigma(0));
}
likelihoods = likelihoods.subvec(0, std::min((index_exit + 1), (maxit - 1))); // remove unused elements
Rcpp::List theta_R = Rcpp::List::create(Named("beta") = wrap(theta.beta),
Named("mu") = wrap(theta.mu),
Named("sigma") = wrap(theta.sigma),
Named("gamma.dependent") =
wrap(theta.gamma_dependent),
Named("gamma.independent") =
wrap(theta.gamma_independent),
Named("transition.probs") =
wrap(theta.transition_probs),
Named("initial.dist") =
wrap(theta.initial_dist)
);
return Rcpp::List::create(Named("theta") = wrap(theta_R),
Named("likelihood") = wrap(theta.likelihood),
Named("likelihoods") = wrap(likelihoods));
}
///' Calculate the intermediate network properties in the discovery dataset
///'
///' These properties are need at every permutation: so they will be computed
///' once.
///'
///' @details
///' \subsection{Input expectations:}{
///' Note that this function expects all inputs to be sensible, as checked by
///' the R function 'checkUserInput' and processed by 'modulePreservation'.
///'
///' These requirements are:
///' \itemize{
///' \item{The ordering of node names across 'dData', 'dCorr', and 'dNet' is
///' consistent.}
///' \item{The columns of 'dData' are the nodes.}
///' \item{'dData' has been scaled by 'Scale'.}
///' \item{'dCorr' and 'dNet' are square matrices, and their rownames are
///' identical to their column names.}
///' \item{'moduleAssigments' is a named character vector, where the names
///' represent node labels found in the discovery dataset (e.g. 'dNet').}
///' }
///' }
///'
///' @param dData scaled data matrix from the \emph{discovery} dataset.
///' @param dCorr matrix of correlation coefficients between all pairs of
///' variables/nodes in the \emph{discovery} dataset.
///' @param dNet adjacency matrix of network edge weights between all pairs of
///' nodes in the \emph{discovery} dataset.
///' @param tNodeNames a character vector of node names in the test dataset
///' @param moduleAssignments a named character vector containing the module
///' each node belongs to in the discovery dataset.
///' @param modules a character vector of modules for which to calculate the
///' module preservation statistics.
///'
///' @return a list containing three lists: a list of weighted degree vectors,
///' a list of correlation coefficient vectors, and a list of node
///' contribution vectors. There is one vector for each module in each list.
///'
///' @keywords internal
// [[Rcpp::export]]
Rcpp::List IntermediateProperties (
Rcpp::NumericMatrix dData, Rcpp::NumericMatrix dCorr, Rcpp::NumericMatrix dNet,
Rcpp::CharacterVector tNodeNames, Rcpp::CharacterVector moduleAssignments,
Rcpp::CharacterVector modules
) {
// First, scale the matrix data
unsigned int nSamples = dData.nrow();
unsigned int nNodes = dData.ncol();
R_CheckUserInterrupt();
// convert the colnames / rownames to C++ equivalents
const std::vector<std::string> dNames (Rcpp::as<std::vector<std::string>>(colnames(dNet)));
const std::vector<std::string> tNames (Rcpp::as<std::vector<std::string>>(tNodeNames));
/* Next, we need to create three mappings:
* - From node IDs to indices in the discovery dataset.
* - From modules to all node IDs.
* - From modules to just node IDs present in the test dataset.
*/
const namemap dIdxMap = MakeIdxMap(dNames);
const stringmap modNodeMap = MakeModMap(moduleAssignments);
const namemap tIdxMap = MakeIdxMap(tNames);
const stringmap modNodePresentMap = MakeModMap(moduleAssignments, tIdxMap);
// What modules do we actually want to analyse?
const std::vector<std::string> mods (Rcpp::as<std::vector<std::string>>(modules));
// We only need to iterate through modules which have nodes in the test
// dataset
std::vector<std::string> modsPresent;
for (auto it = mods.begin(); it != mods.end(); ++it) {
if (modNodePresentMap.count(*it) > 0) {
modsPresent.push_back(*it);
}
}
R_CheckUserInterrupt();
Rcpp::List degree;
Rcpp::List corr;
Rcpp::List contribution;
// Calculate the network properties in the discovery dataset.
std::string mod;
unsigned int mNodes;
arma::uvec dIdx, dRank;
arma::vec dSP, dWD, dCV, dNC;
for (auto mi = modsPresent.begin(); mi != modsPresent.end(); ++mi) {
// Get the node indices in the discovery dataset for this module
mod = *mi;
dIdx = GetNodeIdx(mod, modNodePresentMap, dIdxMap);
mNodes = dIdx.n_elem;
R_CheckUserInterrupt();
// Calculate the network properties and insert into their storage containers
dCV = CorrVector(dCorr.begin(), nNodes, dIdx.memptr(), mNodes);
R_CheckUserInterrupt();
// Sort node indices for sequential memory access
dRank = SortNodes(dIdx.memptr(), mNodes);
dWD = WeightedDegree(dNet.begin(), nNodes, dIdx.memptr(), mNodes);
dWD = dWD(dRank); // reorder
R_CheckUserInterrupt();
dSP = SummaryProfile(dData.begin(), nSamples, nNodes, dIdx.memptr(), mNodes);
R_CheckUserInterrupt();
dNC = NodeContribution(dData.begin(), nSamples, nNodes,
dIdx.memptr(), mNodes, dSP.memptr());
dNC = dNC(dRank); // reorder results
R_CheckUserInterrupt();
// Cast to R-vectors and add to results lists
corr.push_back(Rcpp::NumericVector(dCV.begin(), dCV.end()));
degree.push_back(Rcpp::NumericVector(dWD.begin(), dWD.end()));
contribution.push_back(Rcpp::NumericVector(dNC.begin(), dNC.end()));
}
degree.names() = modsPresent;
corr.names() = modsPresent;
contribution.names() = modsPresent;
return Rcpp::List::create(
Rcpp::Named("degree") = degree,
Rcpp::Named("corr") = corr,
Rcpp::Named("contribution") = contribution
);
}
// Returns an n by 1 column that represents the likelihoods of
// an estimated univariate MS-AR model for each k where 1 \leq k \leq n where
// beta is switching.
// Note that even if beta is non-switching, setting beta as a s by M matrix with
// repeated column of the original beta will give you the likelihood for
// MS-AR model with non-switching beta.
// TODO: Implement the version where z_dependent/z_independent exist(lagged variables should be implemented for MSM models)
// [[Rcpp::export]]
SEXP LikelihoodsMSMAR (Rcpp::NumericVector y_rcpp,
Rcpp::NumericMatrix y_lagged_rcpp,
Rcpp::NumericMatrix z_dependent_rcpp,
Rcpp::NumericMatrix z_independent_rcpp,
Rcpp::NumericMatrix z_dependent_lagged_rcpp,
Rcpp::NumericMatrix z_independent_lagged_rcpp,
Rcpp::NumericMatrix transition_probs_rcpp,
Rcpp::NumericVector initial_dist_extended_rcpp,
Rcpp::NumericMatrix beta_rcpp,
Rcpp::NumericVector mu_rcpp,
Rcpp::NumericVector sigma_rcpp,
Rcpp::NumericMatrix gamma_dependent_rcpp,
Rcpp::NumericVector gamma_independent_rcpp,
Rcpp::IntegerMatrix state_conversion_mat_rcpp
)
{
int n = y_rcpp.size();
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_dependent_lagged(z_dependent_lagged_rcpp.begin(),
z_dependent_lagged_rcpp.nrow(),
z_dependent_lagged_rcpp.ncol(), false);
arma::mat z_independent(z_independent_rcpp.begin(),
z_independent_rcpp.nrow(),
z_independent_rcpp.ncol(), false);
arma::mat z_independent_lagged(z_independent_lagged_rcpp.begin(),
z_independent_lagged_rcpp.nrow(),
z_independent_lagged_rcpp.ncol(), false);
arma::mat transition_probs(transition_probs_rcpp.begin(),
transition_probs_rcpp.nrow(),
transition_probs_rcpp.ncol(), false);
arma::colvec initial_dist_extended(initial_dist_extended_rcpp.begin(),
initial_dist_extended_rcpp.size(), false);
arma::mat beta(beta_rcpp.begin(),
beta_rcpp.nrow(), beta_rcpp.ncol(), 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::imat state_conversion_mat(state_conversion_mat_rcpp.begin(),
state_conversion_mat_rcpp.nrow(),
state_conversion_mat_rcpp.ncol(), false);
arma::mat transition_probs_extended = GetExtendedTransitionProbs(
transition_probs, state_conversion_mat);
arma::mat transition_probs_extended_t = transition_probs_extended.t();
arma::colvec likelihoods(n, arma::fill::zeros);
int M_extended = transition_probs_extended_t.n_rows;
int M = gamma_dependent.n_cols;
int s = beta.n_rows;
int M_extended_block = IntPower(M, s);
arma::mat* xi_k_t = new arma::mat(M_extended, n, arma::fill::zeros); // make a transpose first for col operations.
// partition blocks
int p = gamma_dependent.n_rows;
int q = gamma_independent.size();
arma::mat* z_dependent_lagged_blocks = new arma::mat[s];
arma::mat* z_independent_lagged_blocks = new arma::mat[s];
for (int i = 0; i < s; i++)
{
int z_dependent_block_first = i * p;
int z_independent_block_first = i * q;
z_dependent_lagged_blocks[i] = z_dependent_lagged.cols(z_dependent_block_first,
z_dependent_block_first + p - 1);
z_independent_lagged_blocks[i] = z_independent_lagged.cols(z_independent_block_first,
z_independent_block_first + q - 1);
}
for (int k = 0; k < n; k++)
{
// initial setting; keep track of minimum value and its index
// to divide everything by the min. value in order to prevent
// possible numerical errors when computing posterior probs.
int min_index = -1;
double min_value = std::numeric_limits<double>::infinity();
double* ratios = new double[M_extended];
double row_sum = 0;
arma::colvec xi_past;
if (k > 0)
xi_past = transition_probs_extended_t * exp(xi_k_t->col(k-1));
else
xi_past = initial_dist_extended;
xi_past /= arma::sum(xi_past);
for (int j_M = 0; j_M < M; j_M++)
{
for (int j_extra = 0; j_extra < M_extended_block; j_extra++)
{
int j = j_M * M_extended_block + j_extra;
arma::colvec xi_k_t_jk = y.row(k);
// arma::colvec xi_k_t_jk = y.row(k) -
// z_dependent.row(k) * gamma_dependent.col(j_M) -
// z_independent.row(k) * gamma_independent - mu(j_M);
for (int lag = 0; lag < s; lag++)
{
int lagged_index = state_conversion_mat.at((lag + 1), j);
xi_k_t_jk -= beta.at(lag, j_M) *
(y_lagged.at(k, lag) -
mu(lagged_index));
xi_k_t_jk -= beta.at(lag, j_M) *
(y_lagged.at(k, lag) -
z_dependent_lagged_blocks[lag].row(k) * gamma_dependent.col(lagged_index) -
z_independent_lagged_blocks[lag].row(k) * gamma_independent -
mu(lagged_index));
}
xi_k_t->at(j,k) = xi_k_t_jk(0); // explicit gluing
xi_k_t->at(j,k) *= xi_k_t->at(j,k);
xi_k_t->at(j,k) = xi_k_t->at(j,k) / (2 * (sigma(j_M) * sigma(j_M)));
if (min_value > xi_k_t->at(j,k))
{
min_value = xi_k_t->at(j,k);
min_index = j;
}
// SQRT2PI only matters in calculation of eta;
// you can remove it in the final log-likelihood.
ratios[j] = xi_past(j) / sigma(j_M);
}
}
for (int j = 0; j < M_extended; j++)
{
if (j == min_index)
row_sum += 1.0;
else
row_sum += (ratios[j] / ratios[min_index]) *
exp(min_value - xi_k_t->at(j,k));
xi_k_t->at(j,k) += log(ratios[j]);
}
likelihoods(k) = log(row_sum) - min_value + log(ratios[min_index]) - LOG2PI_OVERTWO;
delete[] ratios; // clear memory
}
arma::exp(xi_k_t->cols(1,4)).t().print();
// clear memory for blocks
delete[] z_dependent_lagged_blocks;
delete[] z_independent_lagged_blocks;
delete xi_k_t;
return (wrap(likelihoods));
}