X-Git-Url: https://git.auder.net/pieces/Cwda/n_black_bishop.svg?a=blobdiff_plain;f=code%2Fdraft_R_pkg%2FR%2Fmain.R;h=695b9282945444db8dff267efc291edf2e95dc87;hb=7f0781b723158cf8e6b25dcab0bae18acae40be8;hp=3411720a01c234dce67a3f68203673be0b2fd543;hpb=3dcbfeef0dc92444287dd78a16c80e58a98a6ee7;p=epclust.git

diff --git a/code/draft_R_pkg/R/main.R b/code/draft_R_pkg/R/main.R
index 3411720..695b928 100644
--- a/code/draft_R_pkg/R/main.R
+++ b/code/draft_R_pkg/R/main.R
@@ -1,36 +1,100 @@
-#TODO: setRefClass... to avoid copy data !!
-#http://stackoverflow.com/questions/2603184/r-pass-by-reference
-
-#fields: data (can be NULL or provided by user), coeffs (will be computed
-#con can be a character string naming a file; see readLines()
-#data can be in DB format, on one column : TODO: guess (from header, or col. length...)
-epclust = function(data=NULL, con=NULL, raw=FALSE, K, nbPerChunk, ..., where_to_store_tmp_data, and how ?)
-#options for tmp files: in RAM, on disk, on DB (can be distributed)
-{
-
+#' @include defaults.R
 
-	#on input: can be data or con; data handled by writing it to file (ascii or bin ?!),
+#' @title Cluster power curves with PAM in parallel
+#'
+#' @description Groups electricity power curves (or any series of similar nature) by applying PAM
+#' algorithm in parallel to chunks of size \code{nbSeriesPerChunk}
+#'
+#' @param data Access to the data, which can be of one of the three following types:
+#' \itemize{
+#'   \item data.frame: each line contains its ID in the first cell, and all values after
+#'   \item connection: any R connection object (e.g. a file) providing lines as described above
+#'   \item function: a custom way to retrieve the curves; it has two arguments: the start index
+#'     (start) and number of curves (n); see example in package vignette.
+#' }
+#' @param K Number of clusters
+#' @param nbSeriesPerChunk Number of series in each group
+#' @param writeTmp Function to write temporary wavelets coefficients (+ identifiers);
+#'   see defaults in defaults.R
+#' @param readTmp Function to read temporary wavelets coefficients (see defaults.R)
+#' @param WER "end" to apply stage 2 after stage 1 has iterated and finished, or "mix"
+#'   to apply it after every stage 1
+#' @param ncores number of parallel processes; if NULL, use parallel::detectCores()
+#'
+#' @return A data.frame of the final medoids curves (identifiers + values)
+epclust = function(data, K, nbSeriesPerChunk, writeTmp=ref_writeTmp, readTmp=ref_readTmp,
+	WER="end", ncores=NULL)
+{
+	#TODO: setRefClass(...) to avoid copy data:
+	#http://stackoverflow.com/questions/2603184/r-pass-by-reference
 
+	#0) check arguments
+	if (!is.data.frame(data) && !is.function(data))
+		tryCatch({dataCon = open(data)},
+			error="data should be a data.frame, a function or a valid connection")
+	if (!is.integer(K) || K < 2)
+		stop("K should be an integer greater or equal to 2")
+	if (!is.integer(nbSeriesPerChunk) || nbSeriesPerChunk < K)
+		stop("nbSeriesPerChunk should be an integer greater or equal to K")
+	if (!is.function(writeTmp) || !is.function(readTmp))
+		stop("read/writeTmp should be functional (see defaults.R)")
+	if (WER!="end" && WER!="mix")
+		stop("WER takes values in {'end','mix'}")
+	#concerning ncores, any non-integer type will be treated as "use parallel:detectCores()"
 
-	if (!is.null(data))
+	#1) acquire data (process curves, get as coeffs)
+	index = 1
+	nbCurves = nrow(data)
+	while (index < nbCurves)
 	{
-		#full data matrix
-		index = 1
-		n = nrow(data)
-		while (index < n)
+		if (is.data.frame(data))
+		{
+			#full data matrix
+			writeTmp( getCoeffs( data[index:(min(index+nbSeriesPerChunk-1,nbCurves)),] ) )
+		} else if (is.function(data))
+		{
+			#custom user function to retrieve next n curves, probably to read from DB
+			writeTmp( getCoeffs( data(index, nbSeriesPerChunk) ) )
+		} else
 		{
-			getCoeffs(data
-			index = index + nbSeriesPerChunk
+			#incremental connection
+			#TODO: find a better way to parse than using a temp file
+			ascii_lines = readLines(dataCon, nbSeriesPerChunk)
+			seriesChunkFile = ".tmp/seriesChunk"
+			writeLines(ascii_lines, seriesChunkFile)
+			writeTmp( getCoeffs( read.csv(seriesChunkFile) ) )
 		}
-	} else if (!is.null(con))
+		index = index + nbSeriesPerChunk
+	}
+	if (exists(dataCon))
+		close(dataCon)
+
+	library(parallel)
+	ncores = ifelse(is.integer(ncores), ncores, parallel::detectCores())
+	cl = parallel::makeCluster(ncores)
+	parallel::clusterExport(cl=cl, varlist=c("X", "Y", "K", "p"), envir=environment())
+	li = parallel::parLapply(cl, 1:B, getParamsAtIndex)
+
+	#2) process coeffs (by nbSeriesPerChunk) and cluster in parallel (just launch async task, wait for them to complete, and re-do if necessary)
+	repeat
 	{
-		#incremental connection
-		#read it one by one and get coeffs until nbSeriesPerChunk
-		#then launch a clustering task............
-		readLines()
-	} else
-		stop("at least 'data' or 'con' argument must be present")
+		completed = rep(FALSE, ............)
+		#while there is jobs to do (i.e. size of tmp "file" is greater than nbSeriesPerChunk),
+		#A) determine which tasks which processor will do (OK)
+		#B) send each (sets of) tasks in parallel
+		#C) flush tmp file (current parallel processes will write in it)
+		#always check "complete" flag (array, as I did in MPI) to know if "slaves" finished
+	}
+
+	parallel::stopCluster(cl)
 
+	#3) readTmp last results, apply PAM on it, and return medoids + identifiers
+
+	#4) apply stage 2 (in parallel ? inside task 2) ?)
+	if (WER == "end")
+	{
+		#from center curves, apply stage 2...
+	}
 }
 
 getCoeffs = function(series)