Fixed little issues pre-release

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: tsmp
 Type: Package
 Title: Time Series with Matrix Profile
-Version: 0.2.13.9053
+Version: 0.2.14.9009
 Authors@R: c(
     person("Francisco", "Bischoff", email = "fbischoff@med.up.pt", role = c("aut", "cre"), comment = c(ORCID = "https://orcid.org/0000-0002-5301-8672")),
     person("Michael", "Yeh", email = "myeh003@ucr.edu", role = c("res", "ccp", "ctb"), comment = c(ORCID = "https://orcid.org/0000-0002-9807-2963")),
@@ -15,6 +15,7 @@ URL: https://github.com/franzbischoff/tsmp
 BugReports: https://github.com/franzbischoff/tsmp/issues
 Depends: R (>= 2.10), audio, doSNOW, parallel, foreach
 Encoding: UTF-8
+Language: en-US
 LazyData: true
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 6.1.0

NAMESPACE

@@ -20,7 +20,6 @@ export(stamp)
 export(stamp.par)
 export(unconstrain.search)
 import(audio)
-import(beepr)
 import(doSNOW)
 import(foreach)
 import(parallel)

R/fluss.R

@@ -125,7 +125,7 @@ fluss.extract <- function(arc.counts, num.segments, window.size, exclusion.zone
 #' Computes the arc count with edge correction (CAC).
 #'
 #' Original paper suggest using the classic statistical-process-control heuristic to set a threshold
-#' where a semantic change may occur in CAC. This may be useful in realtime implementation as we don't
+#' where a semantic change may occur in CAC. This may be useful in real-time implementation as we don't
 #' know in advance the number of domain changes to look for. Please check original paper (1).
 #'
 #' @param profile.index the profile index for arc counting.

R/m_guide_search.R

@@ -4,7 +4,7 @@
 #'
 #' Although this functions handles Multivariate Time Series, it can also be used to handle Univariate Time Series.
 #'
-#' @param data a `matrix` of `numeric`, where each colums is a time series. Accepts `vector` (see details), `list` and `data.frame` too.
+#' @param data a `matrix` of `numeric`, where each column is a time series. Accepts `vector` (see details), `list` and `data.frame` too.
 #' @param window.size an `int` with the size of the sliding window.
 #' @param matrix.profile multidimensional matrix profile (matrix)
 #' @param profile.index multidimensional profile index (from [mstomp()] or [mstomp.par()]).

R/m_unconstrain_search.R

@@ -4,7 +4,7 @@
 #'
 #' Although this functions handles Multivariate Time Series, it can also be used to handle Univariate Time Series.
 #'
-#' @param data a `matrix` of `numeric`, where each colums is a time series. Accepts `vector` (see details), `list` and `data.frame` too.
+#' @param data a `matrix` of `numeric`, where each column is a time series. Accepts `vector` (see details), `list` and `data.frame` too.
 #' @param window.size an `int` with the size of the sliding window.
 #' @param matrix.profile multidimensional matrix profile (from [mstomp()] or [mstomp.par()]).
 #' @param profile.index multidimensional profile index (from [mstomp()] or [mstomp.par()]).

R/misc.R

@@ -70,9 +70,35 @@ fast.movavg <- function(data, n) {
 std <- function(x) {
   sdx <- stats::sd(x)
 
-  if (sdx == 0)
+  if (sdx == 0) {
     return(sdx)
+  }
 
   return(sqrt((length(x) - 1) / length(x)) * sdx)
 }
 
+#' Play sound with `audio`
+#'
+#' @param data sound data provided by this package
+#'
+#' @keywords internal
+#' @import audio
+beep <- function(data) {
+  if (!(is.null(audio::audio.drivers()) || nrow(audio::audio.drivers()) == 0)) {
+    tryCatch({
+      audio::play(data)
+    },
+    error = function(cond) {
+      message("Failed to play audio alert")
+      message(cond)
+      invisible()
+    },
+    warning = function(cond) {
+      message("Something went wrong playing audio alert")
+      message(cond)
+      invisible()
+    }
+    )
+  }
+  invisible()
+}

R/mstomp.R

@@ -8,7 +8,7 @@
 #' Although this functions handles Multivariate Time Series, it can also be used to handle Univariate Time Series.
 #' `verbose` changes how much information is printed by this function; `0` means nothing, `1` means text, `2` means text and sound.
 #'
-#' @param data a `matrix` of `numeric`, where each colums is a time series. Accepts `vector` (see details), `list` and `data.frame` too.
+#' @param data a `matrix` of `numeric`, where each column is a time series. Accepts `vector` (see details), `list` and `data.frame` too.
 #' @param window.size an `int` with the size of the sliding window.
 #' @param must.dim an `int` or `vector` of which dimensions to forcibly include (default is `NULL`).
 #' @param exc.dim an `int` or `vector` of which dimensions to exclude (default is `NULL`).
@@ -114,7 +114,7 @@ mstomp <- function(data, window.size, must.dim = NULL, exc.dim = NULL, exclusion
     on.exit(close(pb))
   }
   if (verbose > 1) {
-    on.exit(audio::play(sounds[[1]]), TRUE)
+    on.exit(beep(sounds[[1]]), TRUE)
   }
 
   ## initialization
@@ -198,7 +198,7 @@ mstomp <- function(data, window.size, must.dim = NULL, exc.dim = NULL, exclusion
 
     if (n.dim > 1) {
       dist.pro.sort <- t(apply(distance.profile, 1, sort))
-    } # sort by row, put all -Inf to the first columns
+    } # sort by row, put all -Inf to the first column
     else {
       dist.pro.sort <- distance.profile
     }

R/mstomp_par.R

@@ -8,7 +8,7 @@
 #' Although this functions handles Multivariate Time Series, it can also be used to handle Univariate Time Series.
 #' `verbose` changes how much information is printed by this function; `0` means nothing, `1` means text, `2` means text and sound.
 #'
-#' @param data a `matrix` of `numeric`, where each colums is a time series. Accepts `vector` (see details), `list` and `data.frame` too.
+#' @param data a `matrix` of `numeric`, where each column is a time series. Accepts `vector` (see details), `list` and `data.frame` too.
 #' @param window.size an `int`. Size of the sliding window.
 #' @param must.dim an `int` or `vector` of which dimensions to forcibly include (default is `NULL`).
 #' @param exc.dim an `int` or `vector` of which dimensions to exclude (default is `NULL`).
@@ -29,9 +29,8 @@
 #'
 #' @examples
 #' # using all dimensions
-#' Sys.sleep(1) # sometimes sleep is needed if you run parallel multiple times in a row
 #' mp <- mstomp.par(toy_data$data[1:100,], 30, verbose = 0)
-#' @import audio doSNOW foreach parallel
+#' @import doSNOW foreach parallel
 
 mstomp.par <- function(data, window.size, must.dim = NULL, exc.dim = NULL, exclusion.zone = 1 / 2, verbose = 2, n.workers = 2) {
   eps <- .Machine$double.eps^0.5
@@ -140,7 +139,7 @@ mstomp.par <- function(data, window.size, must.dim = NULL, exc.dim = NULL, exclu
     on.exit(close(pb), TRUE)
   }
   if (verbose > 1) {
-    on.exit(audio::play(sounds[[1]]), TRUE)
+    on.exit(beep(sounds[[1]]), TRUE)
   }
 
   ## initialize variable

R/sdts_predict.R

@@ -87,7 +87,7 @@ sdts.predict <- function(model, data, window.size) {
 #' `beta` is used to balance F-score towards recall (`>1`) or precision (`<1`).
 #'
 #' @param gtruth a `vector` of `logical`. Ground truth annotation.
-#' @param pred a `vector` of `logical`. Predictied annotation from [sdts.predict()]
+#' @param pred a `vector` of `logical`. Predicted annotation from [sdts.predict()]
 #' @param beta a `numeric`. See details. (default is `1`).
 #'
 #' @return Returns a `list` with `f.score`, `precision` and `recall`.

R/sdts_train.R

@@ -148,7 +148,7 @@ sdts.train <- function(data, label, window.size, beta = 1, pat.max = Inf, parall
     on.exit(close(pb))
   }
   if (verbose > 1) {
-    on.exit(audio::play(sounds[[1]]), TRUE)
+    on.exit(beep(sounds[[1]]), TRUE)
   }
 
   for (i in 1:n.window.size) {
@@ -342,7 +342,7 @@ sdts.train <- function(data, label, window.size, beta = 1, pat.max = Inf, parall
 #' @param beta a number that balance the F-Score. Beta > 1 towards recall, < towards precision
 #' @param window.size an integer with the sliding window size
 #'
-#' @return Returns the best threashold and its F-Score
+#' @return Returns the best threshold and its F-Score
 #'
 #' @keywords internal
 #'
@@ -384,7 +384,7 @@ golden.section <- function(dist.pro, label, pos.st, pos.ed, beta, window.size) {
 #' @param window.size an integer with the sliding window size
 #' @param fit.idx an integer with the index of the current threshold
 #'
-#' @return Returns the best threashold and its F-Score
+#' @return Returns the best threshold and its F-Score
 #'
 #' @keywords internal
 

R/simple.R

@@ -4,7 +4,7 @@
 #'
 #' `verbose` changes how much information is printed by this function; `0` means nothing, `1` means text, `2` means text and sound.
 #'
-#' @param data a `matrix` of `numeric`, where each colums is a time series. Accepts `list` and `data.frame` too.
+#' @param data a `matrix` of `numeric`, where each column is a time series. Accepts `list` and `data.frame` too.
 #' @param window.size an `int` with the size of the sliding window.
 #' @param exclusion.zone a `numeric`. Size of the exclusion zone, based on query size (default is `1/2`).
 #' @param verbose an `int`. See details. (Default is `2`).
@@ -76,7 +76,7 @@ simple.fast <- function(data, window.size, exclusion.zone = 1 / 2, verbose = 2)
     on.exit(close(pb))
   }
   if (verbose > 1) {
-    on.exit(audio::play(sounds[[1]]), TRUE)
+    on.exit(beep(sounds[[1]]), TRUE)
   }
 
   ## compute necessary values

R/stamp.R

@@ -10,7 +10,7 @@
 #' @param ... a `matrix` or a `vector`. If a second time series is supplied it will be a join matrix profile.
 #' @param window.size an `int`. Size of the sliding window.
 #' @param exclusion.zone a `numeric`. Size of the exclusion zone, based on query size (default is `1/2`). See details.
-#' @param s.size a `numeric`. for anytime algorithm, represents the size (in observations) the random calculation will occour (default is `Inf`).
+#' @param s.size a `numeric`. for anytime algorithm, represents the size (in observations) the random calculation will occur (default is `Inf`).
 #' @param verbose an `int`. See details. (Default is `2`).
 #'
 #' @return Returns the matrix profile `mp` and profile index `pi`.
@@ -98,7 +98,7 @@ stamp <- function(..., window.size, exclusion.zone = 1 / 2, s.size = Inf, verbos
     on.exit(close(pb))
   }
   if (verbose > 1) {
-    on.exit(audio::play(sounds[[1]]), TRUE)
+    on.exit(beep(sounds[[1]]), TRUE)
   }
   # anytime must return the result always
   on.exit(return(list(

R/stamp_par.R

@@ -10,7 +10,7 @@
 #' @param ... a `matrix` or a `vector`. If a second time series is supplied it will be a join matrix profile.
 #' @param window.size an `int`. Size of the sliding window.
 #' @param exclusion.zone a `numeric`. Size of the exclusion zone, based on query size (default is `1/2`). See details.
-#' @param s.size a `numeric`. for anytime algorithm, represents the size (in observations) the random calculation will occour (default is `Inf`).
+#' @param s.size a `numeric`. for anytime algorithm, represents the size (in observations) the random calculation will occur (default is `Inf`).
 #' @param n.workers an `int`. Number of workers for parallel. (Default is `2`).
 #' @param verbose an `int`. See details. (Default is `2`).
 #'
@@ -25,7 +25,6 @@
 #' @references Website: <http://www.cs.ucr.edu/~eamonn/MatrixProfile.html>
 #'
 #' @examples
-#' Sys.sleep(1) # sometimes sleep is needed if you run parallel multiple times in a row
 #' mp <- stamp.par(toy_data$data[1:200,1], window.size = 30, verbose = 0)
 #' \dontrun{
 #' ref.data <- toy_data$data[,1]
@@ -36,7 +35,7 @@
 #' mp <- stamp.par(ref.data, query.data, window.size = 30, s.size = round(nrows(query.data) * 0.1))
 #' }
 #'
-#' @import beepr doSNOW foreach parallel
+#' @import doSNOW foreach parallel
 stamp.par <- function(..., window.size, exclusion.zone = 1 / 2, s.size = Inf, n.workers = 2, verbose = 2) {
   args <- list(...)
   data <- args[[1]]
@@ -107,7 +106,7 @@ stamp.par <- function(..., window.size, exclusion.zone = 1 / 2, s.size = Inf, n.
     on.exit(close(pb), TRUE)
   }
   if (verbose > 1) {
-    on.exit(audio::play(sounds[[1]]), TRUE)
+    on.exit(beep(sounds[[1]]), TRUE)
   }
   # anytime must return the result always
   on.exit(return(list(

R/toy_data.R

@@ -1,11 +1,11 @@
 #' Original data used in the mSTAMP demo
 #'
-#' A synthetic dataset with embeded MOTIFs for multidimensional discovery
+#' A synthetic dataset with embedded MOTIFs for multidimensional discovery
 #'
 #' @docType data
 #' @format A `list` with a `matrix` with 550 rows and 3 dimensions and an `int`:
 #' \describe{
-#'   \item{data}{data with embeded MOTIFs}
+#'   \item{data}{data with embedded MOTIFs}
 #'   \item{sub.len}{size of sliding window}
 #' }
 #' @source \url{https://sites.google.com/view/mstamp/}

README.Rmd

@@ -20,7 +20,7 @@ knitr::opts_chunk$set(
 ```{r include=FALSE}
 library(git2r)
 # current.branch <- repository_head()$name
-current.branch <- "develop"
+current.branch <- "master"
 lifecycle <- "maturing" # maturing-blue # stable-brightgreen
 lifecycle.color <- "blue"
 ```

README.md

@@ -10,8 +10,8 @@ Francisco Bischoff
 [![Packagist](https://img.shields.io/packagist/l/doctrine/orm.svg)](https://choosealicense.com/licenses/mit)
 [![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing)
 [![Build
-Status](https://travis-ci.com/franzbischoff/tsmp.svg?branch=develop)](https://travis-ci.com/franzbischoff/tsmp)
-[![codecov](https://codecov.io/gh/franzbischoff/tsmp/branch/develop/graph/badge.svg)](https://codecov.io/gh/franzbischoff/tsmp)
+Status](https://travis-ci.com/franzbischoff/tsmp.svg?branch=master)](https://travis-ci.com/franzbischoff/tsmp)
+[![codecov](https://codecov.io/gh/franzbischoff/tsmp/branch/master/graph/badge.svg)](https://codecov.io/gh/franzbischoff/tsmp)
 [![CRAN
 version](http://www.r-pkg.org/badges/version/tsmp)](https://cran.r-project.org/package=tsmp)