Format workspace

R/RcppExports.R

@@ -1,6 +1,35 @@
 # Generated by using Rcpp::compileAttributes() -> do not edit by hand
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
+#' Function to calculate the gradient at the current data.
+#' This function is not meant to be called directly by the user.
+#'
+#' @param data A data frame containing the data to be segmented.
+#' @param theta Estimated theta from the previous iteration.
+#' @param family Family of the model.
+#' @keywords internal
+#'
+#' @noRd
+#' @return Gradient at the current data.
+cost_update_gradient <- function(data, theta, family) {
+    .Call(`_fastcpd_cost_update_gradient`, data, theta, family)
+}
+
+#' Function to calculate the Hessian matrix at the current data.
+#' This function is not meant to be called directly by the user.
+#'
+#' @param data A data frame containing the data to be segmented.
+#' @param theta Estimated theta from the previous iteration.
+#' @param family Family of the model.
+#' @param min_prob Minimum probability to avoid numerical issues.
+#' @keywords internal
+#'
+#' @noRd
+#' @return Hessian at the current data.
+cost_update_hessian <- function(data, theta, family, min_prob) {
+    .Call(`_fastcpd_cost_update_hessian`, data, theta, family, min_prob)
+}
+
 #' Update the cost values for the segmentation.
 #' This function is not meant to be called directly by the user.
 #'
@@ -36,33 +65,26 @@ cost_update <- function(data, theta_hat, theta_sum, hessian, tau, i, k, family,
     .Call(`_fastcpd_cost_update`, data, theta_hat, theta_sum, hessian, tau, i, k, family, momentum, momentum_coef, epsilon, min_prob, winsorise_minval, winsorise_maxval, lambda, cost_gradient, cost_hessian)
 }
 
-#' Function to calculate the gradient at the current data.
-#' This function is not meant to be called directly by the user.
-#'
-#' @param data A data frame containing the data to be segmented.
-#' @param theta Estimated theta from the previous iteration.
-#' @param family Family of the model.
-#' @keywords internal
-#'
-#' @noRd
-#' @return Gradient at the current data.
-cost_update_gradient <- function(data, theta, family) {
-    .Call(`_fastcpd_cost_update_gradient`, data, theta, family)
-}
-
-#' Function to calculate the Hessian matrix at the current data.
+#' Solve logistic/poisson regression using Gradient Descent Extension to the
+#' multivariate case
 #' This function is not meant to be called directly by the user.
 #'
 #' @param data A data frame containing the data to be segmented.
-#' @param theta Estimated theta from the previous iteration.
+#' @param theta Estimate of the parameters. If null, the function will estimate
+#'   the parameters.
 #' @param family Family of the model.
-#' @param min_prob Minimum probability to avoid numerical issues.
+#' @param lambda Lambda for L1 regularization. Only used for lasso.
+#' @param cv Whether to perform cross-validation to find the best lambda.
+#' @param start Starting point for the optimization for warm start.
 #' @keywords internal
+#' @importFrom glmnet glmnet cv.glmnet predict.glmnet
+#' @importFrom fastglm fastglm
 #'
 #' @noRd
-#' @return Hessian at the current data.
-cost_update_hessian <- function(data, theta, family, min_prob) {
-    .Call(`_fastcpd_cost_update_hessian`, data, theta, family, min_prob)
+#' @return Negative log likelihood of the corresponding data with the given
+#'   family.
+negative_log_likelihood <- function(data, theta, family, lambda, cv = FALSE, start = NULL) {
+    .Call(`_fastcpd_negative_log_likelihood`, data, theta, family, lambda, cv, start)
 }
 
 #' Vanilla PELT implementation.
@@ -98,25 +120,3 @@ fastcpd_vanilla <- function(data, beta, segment_count, trim, momentum_coef, k, f
     .Call(`_fastcpd_fastcpd_vanilla`, data, beta, segment_count, trim, momentum_coef, k, family, epsilon, min_prob, winsorise_minval, winsorise_maxval, p, cost, cp_only)
 }
 
-#' Solve logistic/poisson regression using Gradient Descent Extension to the
-#' multivariate case
-#' This function is not meant to be called directly by the user.
-#'
-#' @param data A data frame containing the data to be segmented.
-#' @param theta Estimate of the parameters. If null, the function will estimate
-#'   the parameters.
-#' @param family Family of the model.
-#' @param lambda Lambda for L1 regularization. Only used for lasso.
-#' @param cv Whether to perform cross-validation to find the best lambda.
-#' @param start Starting point for the optimization for warm start.
-#' @keywords internal
-#' @importFrom glmnet glmnet cv.glmnet predict.glmnet
-#' @importFrom fastglm fastglm
-#'
-#' @noRd
-#' @return Negative log likelihood of the corresponding data with the given
-#'   family.
-negative_log_likelihood <- function(data, theta, family, lambda, cv = FALSE, start = NULL) {
-    .Call(`_fastcpd_negative_log_likelihood`, data, theta, family, lambda, cv, start)
-}
-

src/RcppExports.cpp

@@ -11,6 +11,33 @@ Rcpp::Rostream<true>&  Rcpp::Rcout = Rcpp::Rcpp_cout_get();
 Rcpp::Rostream<false>& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get();
 #endif
 
+// cost_update_gradient
+arma::colvec cost_update_gradient(arma::mat data, arma::colvec theta, std::string family);
+RcppExport SEXP _fastcpd_cost_update_gradient(SEXP dataSEXP, SEXP thetaSEXP, SEXP familySEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< arma::mat >::type data(dataSEXP);
+    Rcpp::traits::input_parameter< arma::colvec >::type theta(thetaSEXP);
+    Rcpp::traits::input_parameter< std::string >::type family(familySEXP);
+    rcpp_result_gen = Rcpp::wrap(cost_update_gradient(data, theta, family));
+    return rcpp_result_gen;
+END_RCPP
+}
+// cost_update_hessian
+arma::mat cost_update_hessian(arma::mat data, arma::colvec theta, std::string family, double min_prob);
+RcppExport SEXP _fastcpd_cost_update_hessian(SEXP dataSEXP, SEXP thetaSEXP, SEXP familySEXP, SEXP min_probSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< arma::mat >::type data(dataSEXP);
+    Rcpp::traits::input_parameter< arma::colvec >::type theta(thetaSEXP);
+    Rcpp::traits::input_parameter< std::string >::type family(familySEXP);
+    Rcpp::traits::input_parameter< double >::type min_prob(min_probSEXP);
+    rcpp_result_gen = Rcpp::wrap(cost_update_hessian(data, theta, family, min_prob));
+    return rcpp_result_gen;
+END_RCPP
+}
 // cost_update
 Rcpp::List cost_update(const arma::mat data, arma::mat theta_hat, arma::mat theta_sum, arma::cube hessian, const int tau, const int i, Rcpp::Function k, const std::string family, arma::colvec momentum, const double momentum_coef, const double epsilon, const double min_prob, const double winsorise_minval, const double winsorise_maxval, const double lambda, Rcpp::Function cost_gradient, Rcpp::Function cost_hessian);
 RcppExport SEXP _fastcpd_cost_update(SEXP dataSEXP, SEXP theta_hatSEXP, SEXP theta_sumSEXP, SEXP hessianSEXP, SEXP tauSEXP, SEXP iSEXP, SEXP kSEXP, SEXP familySEXP, SEXP momentumSEXP, SEXP momentum_coefSEXP, SEXP epsilonSEXP, SEXP min_probSEXP, SEXP winsorise_minvalSEXP, SEXP winsorise_maxvalSEXP, SEXP lambdaSEXP, SEXP cost_gradientSEXP, SEXP cost_hessianSEXP) {
@@ -38,30 +65,19 @@ BEGIN_RCPP
     return rcpp_result_gen;
 END_RCPP
 }
-// cost_update_gradient
-arma::colvec cost_update_gradient(arma::mat data, arma::colvec theta, std::string family);
-RcppExport SEXP _fastcpd_cost_update_gradient(SEXP dataSEXP, SEXP thetaSEXP, SEXP familySEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< arma::mat >::type data(dataSEXP);
-    Rcpp::traits::input_parameter< arma::colvec >::type theta(thetaSEXP);
-    Rcpp::traits::input_parameter< std::string >::type family(familySEXP);
-    rcpp_result_gen = Rcpp::wrap(cost_update_gradient(data, theta, family));
-    return rcpp_result_gen;
-END_RCPP
-}
-// cost_update_hessian
-arma::mat cost_update_hessian(arma::mat data, arma::colvec theta, std::string family, double min_prob);
-RcppExport SEXP _fastcpd_cost_update_hessian(SEXP dataSEXP, SEXP thetaSEXP, SEXP familySEXP, SEXP min_probSEXP) {
+// negative_log_likelihood
+Rcpp::List negative_log_likelihood(arma::mat data, Rcpp::Nullable<arma::colvec> theta, std::string family, double lambda, bool cv, Rcpp::Nullable<arma::colvec> start);
+RcppExport SEXP _fastcpd_negative_log_likelihood(SEXP dataSEXP, SEXP thetaSEXP, SEXP familySEXP, SEXP lambdaSEXP, SEXP cvSEXP, SEXP startSEXP) {
 BEGIN_RCPP
     Rcpp::RObject rcpp_result_gen;
     Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< arma::mat >::type data(dataSEXP);
-    Rcpp::traits::input_parameter< arma::colvec >::type theta(thetaSEXP);
+    Rcpp::traits::input_parameter< Rcpp::Nullable<arma::colvec> >::type theta(thetaSEXP);
     Rcpp::traits::input_parameter< std::string >::type family(familySEXP);
-    Rcpp::traits::input_parameter< double >::type min_prob(min_probSEXP);
-    rcpp_result_gen = Rcpp::wrap(cost_update_hessian(data, theta, family, min_prob));
+    Rcpp::traits::input_parameter< double >::type lambda(lambdaSEXP);
+    Rcpp::traits::input_parameter< bool >::type cv(cvSEXP);
+    Rcpp::traits::input_parameter< Rcpp::Nullable<arma::colvec> >::type start(startSEXP);
+    rcpp_result_gen = Rcpp::wrap(negative_log_likelihood(data, theta, family, lambda, cv, start));
     return rcpp_result_gen;
 END_RCPP
 }
@@ -89,31 +105,15 @@ BEGIN_RCPP
     return rcpp_result_gen;
 END_RCPP
 }
-// negative_log_likelihood
-Rcpp::List negative_log_likelihood(arma::mat data, Rcpp::Nullable<arma::colvec> theta, std::string family, double lambda, bool cv, Rcpp::Nullable<arma::colvec> start);
-RcppExport SEXP _fastcpd_negative_log_likelihood(SEXP dataSEXP, SEXP thetaSEXP, SEXP familySEXP, SEXP lambdaSEXP, SEXP cvSEXP, SEXP startSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< arma::mat >::type data(dataSEXP);
-    Rcpp::traits::input_parameter< Rcpp::Nullable<arma::colvec> >::type theta(thetaSEXP);
-    Rcpp::traits::input_parameter< std::string >::type family(familySEXP);
-    Rcpp::traits::input_parameter< double >::type lambda(lambdaSEXP);
-    Rcpp::traits::input_parameter< bool >::type cv(cvSEXP);
-    Rcpp::traits::input_parameter< Rcpp::Nullable<arma::colvec> >::type start(startSEXP);
-    rcpp_result_gen = Rcpp::wrap(negative_log_likelihood(data, theta, family, lambda, cv, start));
-    return rcpp_result_gen;
-END_RCPP
-}
 
 RcppExport SEXP run_testthat_tests(void *);
 
 static const R_CallMethodDef CallEntries[] = {
-    {"_fastcpd_cost_update", (DL_FUNC) &_fastcpd_cost_update, 17},
     {"_fastcpd_cost_update_gradient", (DL_FUNC) &_fastcpd_cost_update_gradient, 3},
     {"_fastcpd_cost_update_hessian", (DL_FUNC) &_fastcpd_cost_update_hessian, 4},
-    {"_fastcpd_fastcpd_vanilla", (DL_FUNC) &_fastcpd_fastcpd_vanilla, 14},
+    {"_fastcpd_cost_update", (DL_FUNC) &_fastcpd_cost_update, 17},
     {"_fastcpd_negative_log_likelihood", (DL_FUNC) &_fastcpd_negative_log_likelihood, 6},
+    {"_fastcpd_fastcpd_vanilla", (DL_FUNC) &_fastcpd_fastcpd_vanilla, 14},
     {"run_testthat_tests", (DL_FUNC) &run_testthat_tests, 1},
     {NULL, NULL, 0}
 };