Replace 'cat()' by 'message()'

emMixHMM.R, emMixHMMR.R, emMixRHLP.R: Replace 'cat()' by 'message()'. Thus messages can be suppressed if needed.

DESCRIPTION

@@ -16,7 +16,7 @@ Description: Provides a variety of original and flexible user-friendly
     segmentation of heterogeneous functional data (i.e time series, or more 
     generally longitudinal data, fitted by unsupervised algorithms, including 
     EM algorithms. Functional Latent Data Models for Clustering heterogeneous 
-    curves ('FLaMingos') are originally introduced and written in Matlab by
+    curves ('FLaMingos') are originally introduced and written in 'Matlab' by
     Faicel Chamroukhi 
     <https://github.com/fchamroukhi?utf8=?&tab=repositories&q=mix&type=public&language=matlab>. 
     The references are mainly the following ones.

R/cemMixRHLP.R

@@ -75,7 +75,7 @@ cemMixRHLP <- function(X, Y, K, R, p = 3, q = 1, variance_type = c("heteroskedas
   while (try_CEM < n_tries) {
     try_CEM <- try_CEM + 1
     if (n_tries > 1 && verbose) {
-      cat(paste0("EM try number: ", try_CEM, "\n\n"))
+      message("EM try number: ", try_CEM, "\n")
     }
 
     # Initialization
@@ -103,11 +103,11 @@ cemMixRHLP <- function(X, Y, K, R, p = 3, q = 1, variance_type = c("heteroskedas
 
       iter <- iter + 1
       if (verbose) {
-        cat(paste0("CEM - mixRHLP: Iteration: ", iter, " | Complete log-likelihood: "  , stat$com_loglik, "\n"))
+        message("CEM - mixRHLP: Iteration: ", iter, " | Complete log-likelihood: "  , stat$com_loglik)
       }
 
       if (prev_com_loglik - stat$com_loglik > 1e-5) {
-        warning(paste0("CEM complete log-likelihood is decreasing from ", prev_com_loglik, "to ", stat$com_loglik, " !"))
+        warning("CEM complete log-likelihood is decreasing from ", prev_com_loglik, "to ", stat$com_loglik, " !")
         top <- top + 1
         if (top > 20)
           break
@@ -129,12 +129,12 @@ cemMixRHLP <- function(X, Y, K, R, p = 3, q = 1, variance_type = c("heteroskedas
       best_com_loglik <- stat$com_loglik
     }
     if (n_tries > 1 && verbose) {
-      cat(paste0("Max value of the complete log-likelihood: ", stat$com_loglik, "\n\n"))
+      message("Max value of the complete log-likelihood: ", stat$com_loglik, "\n\n")
     }
   }
 
   if (n_tries > 1 && verbose) {
-    cat(paste0("Best value of the complete log-likelihood: ", statSolution$com_loglik, "\n"))
+    message("Best value of the complete log-likelihood: ", statSolution$com_loglik, "\n")
   }
 
   statSolution$computeStats(paramSolution)

R/emMixHMM.R

@@ -66,7 +66,7 @@ emMixHMM <- function(Y, K, R, variance_type = c("heteroskedastic", "homoskedasti
     try_EM <- try_EM + 1
 
     if (n_tries > 1 && verbose) {
-      cat(paste0("EM try number: ", try_EM, "\n\n"))
+      message("EM try number: ", try_EM, "\n")
     }
 
     # Initialization
@@ -93,11 +93,11 @@ emMixHMM <- function(Y, K, R, variance_type = c("heteroskedastic", "homoskedasti
       iter <- iter + 1
 
       if (verbose) {
-        cat(paste0("EM - mixHMMs: Iteration: ", iter, " | log-likelihood: "  , stat$loglik, "\n"))
+        message("EM - mixHMMs: Iteration: ", iter, " | log-likelihood: "  , stat$loglik)
       }
 
       if (prev_loglik - stat$loglik > 1e-4) {
-        warning(paste0("EM log-likelihood is decreasing from ", prev_loglik, "to ", stat$loglik, " !"))
+        warning("EM log-likelihood is decreasing from ", prev_loglik, "to ", stat$loglik, " !")
       }
 
       converged <- (abs((stat$loglik - prev_loglik) / prev_loglik) <= threshold)
@@ -118,13 +118,13 @@ emMixHMM <- function(Y, K, R, variance_type = c("heteroskedastic", "homoskedasti
     }
 
     if (n_tries > 1 && verbose) {
-      cat(paste0("Max value of the log-likelihood: ", stat$loglik, "\n\n"))
+      message("Max value of the log-likelihood: ", stat$loglik, "\n\n")
     }
 
   }
 
   if (n_tries > 1 && verbose) {
-    cat(paste0("Best value of the log-likelihood: ", statSolution$loglik, "\n\n"))
+    message("Best value of the log-likelihood: ", statSolution$loglik, "\n")
   }
 
   # Finding the curve partition by using the MAP rule

R/emMixHMMR.R

@@ -68,7 +68,7 @@ emMixHMMR <- function(X, Y, K, R, p = 3, variance_type = c("heteroskedastic", "h
   while (try_EM < n_tries) {
     try_EM <- try_EM + 1
     if (n_tries > 1 && verbose) {
-      cat(paste0("EM try number: ", try_EM, "\n\n"))
+      message("EM try number: ", try_EM, "\n")
     }
 
     # Initialization
@@ -95,11 +95,11 @@ emMixHMMR <- function(X, Y, K, R, p = 3, variance_type = c("heteroskedastic", "h
       iter <- iter + 1
 
       if (verbose) {
-        cat(paste0("EM - mixHMMR: Iteration: ", iter, " || log-likelihood: "  , stat$loglik, "\n"))
+        message("EM - mixHMMR: Iteration: ", iter, " || log-likelihood: "  , stat$loglik)
       }
 
       if (prev_loglik - stat$loglik > 1e-4) {
-        warning(paste0("EM log-likelihood is decreasing from ", prev_loglik, "to ", stat$loglik, " !"))
+        warning("EM log-likelihood is decreasing from ", prev_loglik, "to ", stat$loglik, " !")
       }
 
       converged <- (abs((stat$loglik - prev_loglik) / prev_loglik) < threshold)
@@ -120,13 +120,13 @@ emMixHMMR <- function(X, Y, K, R, p = 3, variance_type = c("heteroskedastic", "h
     }
 
     if (n_tries > 1 && verbose) {
-      cat(paste0("Max value of the log-likelihood: ", stat$loglik, "\n\n"))
+      message("Max value of the log-likelihood: ", stat$loglik, "\n\n")
     }
 
   }
 
   if (n_tries > 1 && verbose) {
-    cat(paste0("Best value of the log-likelihood: ", statSolution$loglik, "\n"))
+    message("Best value of the log-likelihood: ", statSolution$loglik, "\n")
   }
 
   # Finding the curve partition by using the MAP rule

R/emMixRHLP.R

@@ -73,7 +73,7 @@ emMixRHLP <- function(X, Y, K, R, p = 3, q = 1, variance_type = c("heteroskedast
   while (try_EM < n_tries) {
     try_EM <- try_EM + 1
     if (n_tries > 1 && verbose) {
-      cat(paste0("EM try number: ", try_EM, "\n\n"))
+      message("EM try number: ", try_EM, "\n")
     }
 
     # Initialization
@@ -94,11 +94,11 @@ emMixRHLP <- function(X, Y, K, R, p = 3, q = 1, variance_type = c("heteroskedast
 
       iter <- iter + 1
       if (verbose) {
-        cat(paste0("EM - mixRHLP: Iteration: ", iter, " | log-likelihood: "  , stat$loglik, "\n"))
+        message("EM - mixRHLP: Iteration: ", iter, " | log-likelihood: "  , stat$loglik)
       }
 
       if (prev_loglik - stat$loglik > 1e-5) {
-        warning(paste0("EM log-likelihood is decreasing from ", prev_loglik, "to ", stat$loglik, " !"))
+        warning("EM log-likelihood is decreasing from ", prev_loglik, "to ", stat$loglik, " !")
         top <- top + 1
         if (top > 20) {
           break
@@ -122,15 +122,15 @@ emMixRHLP <- function(X, Y, K, R, p = 3, q = 1, variance_type = c("heteroskedast
     }
 
     if (n_tries > 1 && verbose) {
-      cat(paste0("Max value of the log-likelihood: ", stat$loglik, "\n\n"))
+      message("Max value of the log-likelihood: ", stat$loglik, "\n\n")
     }
   }
 
   # Computation of c_ig the hard partition of the curves and the cluster labels klas
   statSolution$MAP()
 
   if (n_tries > 1 && verbose) {
-    cat(paste0("Best value of the log-likelihood: ", statSolution$loglik, "\n"))
+    message("Best value of the log-likelihood: ", statSolution$loglik, "\n")
   }
 
   statSolution$computeStats(paramSolution)

R/flamingos-package.R

@@ -1,9 +1,9 @@
 #' FLaMingos: Functional Latent datA Models for clusterING heterogeneOus curveS
 #'
-#' @description `flamingos` is an open-source toolbox for the simultaneous clustering (or
-#' classification) and segmentation of heterogeneous functional data (i.e
-#' time-series ore more generally longitudinal data), with original and
-#' flexible functional latent variable models, fitted by unsupervised
+#' @description `flamingos` is an open-source toolbox for the simultaneous
+#' clustering (or classification) and segmentation of heterogeneous functional
+#' data (i.e time-series ore more generally longitudinal data), with original
+#' and flexible functional latent variable models, fitted by unsupervised
 #' algorithms, including EM algorithms.
 #'
 #' `flamingos` contains the following time series clustering and segmentation models:

R/kmeans.R

@@ -28,7 +28,7 @@ kmeans = function(X, K, nbr_runs = 20, nbr_iter_max = 300, verbose = FALSE) {
     while (nb_run < nbr_runs) {
       nb_run = nb_run + 1
       if (nbr_runs > 0 && verbose) {
-        cat(sprintf("Kmeans : run n?? %1i", nb_run), "\n")
+        message(sprintf("Kmeans : run # %1i", nb_run), "\n")
       }
 
       iter = 0
@@ -91,7 +91,7 @@ kmeans = function(X, K, nbr_runs = 20, nbr_iter_max = 300, verbose = FALSE) {
         previous_err = current_err
 
         if (verbose) {
-          cat(sprintf("Kmeans : Iteration  %1.1i, Objective %2.2f", iter, current_err), "\n")
+          message(sprintf("Kmeans : Iteration  %1.1i, Objective %2.2f", iter, current_err), "\n")
         }
         solution$stored_err = cbind(solution$stored_err, current_err)
       }

README.Rmd

@@ -71,7 +71,7 @@ library(flamingos)
 <details>
   <summary>mixRHLP</summary>
 
-```{r, message = FALSE}
+```{r, echo=TRUE}
 data("toydataset")
 x <- toydataset$x
 Y <- t(toydataset[,2:ncol(toydataset)])
@@ -102,7 +102,7 @@ mixrhlp$plot()
 <details>
   <summary>mixHMM</summary>
 
-```{r, message = FALSE}
+```{r, echo=TRUE}
 data("toydataset")
 Y <- t(toydataset[,2:ncol(toydataset)])
 
@@ -130,7 +130,7 @@ mixhmm$plot()
 <details>
   <summary>mixHMMR</summary>
 
-```{r, message = FALSE}
+```{r, echo=TRUE}
 data("toydataset")
 x <- toydataset$x
 Y <- t(toydataset[,2:ncol(toydataset)])