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Fix class order of class definitions.
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@@ -53,10 +53,11 @@ Maintainer: Victor Maus <[email protected]> | |
| LazyData: true | ||
| RoxygenNote: 5.0.1 | ||
| Collate: | ||
| 'class-twdtwTimeSeries.R' | ||
| 'class-twdtwMatches.R' | ||
| 'class-twdtwRaster.R' | ||
| 'class-twdtwTimeSeries.R' | ||
| 'createPatterns.R' | ||
| 'crossValidation.R' | ||
| 'data.R' | ||
| 'dtw.R' | ||
| 'dwtSat.R' | ||
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| @@ -0,0 +1,110 @@ | ||
| #' @title Cross-validation | ||
| #' @name Cross-validation | ||
| #' | ||
| #' @author Victor Maus, \email{vwmaus1@@gmail.com} | ||
| #' | ||
| #' @description This functions create data partitions and compute Cross-validation metrics. | ||
| #' | ||
| #' @param object an object of class \code{\link[dtwSat]{twdtwTimeSeries}} or | ||
| #' \code{\link[dtwSat]{twdtwMatches}}. | ||
| #' | ||
| #' @param times Number of partitions to create. | ||
| #' | ||
| #' @param p the percentage of data that goes to training. | ||
| #' See \code{\link[caret]{createDataPartition}} for details. | ||
| #' | ||
| #' @param ... Other arguments to be passed to \code{\link[dtwSat]{createPatterns}}. | ||
| #' | ||
| #' @param matrix logical. If TRUE retrieves the confusion matrix. | ||
| #' FALSE retrieves User's Accuracy (UA) and Producer's Accuracy (PA). | ||
| #' Dafault is FALSE. | ||
| #' | ||
| #' @details | ||
| #' \describe{ | ||
| #' \item{\code{splitDataset}:}{This function splits the a set of time | ||
| #' series into training and validation. The function uses stratified | ||
| #' sampling and a simple random sampling for each stratum. Each data partition | ||
| #' returned by this function has the temporal patterns and a set of time series for | ||
| #' validation.} | ||
| #' \item{\code{twdtwCrossValidation}:}{The function \code{splitDataset} performs the Cross-validation of | ||
| #' the classification based on the labels of the classified time series | ||
| #' (Reference) and the labels of the classification (Predicted). This function | ||
| #' returns a data.frame with User's and Produce's Accuracy or a list for confusion | ||
| #' matrices.} | ||
| #' } | ||
| #' | ||
| #' @seealso | ||
| #' \code{\link[dtwSat]{twdtwMatches-class}}, | ||
| #' \code{\link[dtwSat]{twdtwApply}}, and | ||
| #' \code{\link[dtwSat]{twdtwClassify}}. | ||
| #' | ||
| #' @examples | ||
| #' \dontrun{ | ||
| #' load(system.file("lucc_MT/field_samples_ts.RData", package="dtwSat")) | ||
| #' set.seed(1) | ||
| #' partitions = splitDataset(field_samples_ts, p=0.1, times=5, | ||
| #' freq = 8, formula = y ~ s(x, bs="cc")) | ||
| #' log_fun = logisticWeight(alpha=-0.1, beta=50) | ||
| #' twdtw_res = lapply(partitions, function(x){ | ||
| #' res = twdtwApply(x = x$ts, y = x$patterns, weight.fun = log_fun, n=1) | ||
| #' twdtwClassify(x = res) | ||
| #' }) | ||
| #' cross_validation = twdtwCrossValidation(twdtw_res) | ||
| # head(cross_validation, 5) | ||
| #' } | ||
| NULL | ||
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| setGeneric("splitDataset", function(object, times, p, ...) standardGeneric("splitDataset")) | ||
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| #' @rdname Cross-validation | ||
| #' @aliases splitDataset | ||
| #' @export | ||
| setMethod("splitDataset", "twdtwTimeSeries", | ||
| function(object, times=1, p=0.1, ...) splitDataset.twdtwTimeSeries(object, times=times, p=p, ...)) | ||
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| splitDataset.twdtwTimeSeries = function(object, times, p, ...){ | ||
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| partitions = createDataPartition(y = labels(object), times, p, list = TRUE) | ||
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| res = lapply(partitions, function(I){ | ||
| training_ts = subset(object, I) | ||
| validation_ts = subset(object, -I) | ||
| patt = createPatterns(training_ts, ...) | ||
| list(patterns=patt, ts=validation_ts) | ||
| }) | ||
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| res | ||
| } | ||
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| setGeneric("twdtwCrossValidation", function(object, matrix=FALSE) standardGeneric("twdtwCrossValidation")) | ||
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| #' @rdname Cross-validation | ||
| #' @aliases twdtwCrossValidation | ||
| #' @export | ||
| setMethod("twdtwCrossValidation", "list", | ||
| function(object, matrix) twdtwCrossValidation.twdtwTimeSeries(object, matrix=matrix)) | ||
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| twdtwCrossValidation.twdtwTimeSeries = function(object, matrix){ | ||
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| res = lapply(object, function(x){ | ||
| ref = labels(x)$timeseries | ||
| levels = sort(as.character(unique(ref))) | ||
| labels = levels | ||
| # pred = factor(do.call("rbind", x[])$label, levels, labels) | ||
| pred = do.call("rbind", lapply(x[], function(xx) as.character(xx$label[which.min(xx$distance)])) ) | ||
| ref = factor(ref, levels, labels) | ||
| table(Reference=ref, Predicted=pred) | ||
| }) | ||
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| if(!matrix){ | ||
| res = do.call("rbind", lapply(seq_along(res), function(i){ | ||
| x = res[[i]] | ||
| Users = diag(x) / rowSums(x) | ||
| Producers = diag(x) / colSums(x) | ||
| data.frame(resample=i,label=names(Users), UA = Users, PA = Producers, row.names=NULL) | ||
| })) | ||
| } | ||
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| res | ||
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| } |
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