generalize twdtwCrossValidate for any DTW approach

r-spatial · Mar 17, 2017 · 3d5f37d · 3d5f37d
1 parent da00c14
commit 3d5f37d
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Showing 2 changed files with 7 additions and 2 deletions.
diff --git a/R/twdtwApply.R b/R/twdtwApply.R
@@ -111,7 +111,7 @@
 setGeneric(name = "twdtwApply", 
           def = function(x, y, resample=TRUE, length=NULL, weight.fun=NULL, 
                 dist.method="Euclidean", step.matrix = symmetric1, n=NULL, 
-                span=NULL, min.length=0.5, theta = 0.5, ...) standardGeneric("twdtwApply"))
+                span=NULL, min.length=0, theta = 0.5, ...) standardGeneric("twdtwApply"))
 
 
 #' @rdname twdtwApply 

diff --git a/R/twdtwCrossValidate.R b/R/twdtwCrossValidate.R
@@ -85,7 +85,12 @@ twdtwCrossValidate.twdtwTimeSeries = function(object, times, p, ...){
     validation_ts = subset(object, -I)
     patt = createPatterns(training_ts, ...)
     twdtw_res = twdtwApply(x = validation_ts, y = patt, n=1, ...)
-    df = do.call("rbind", lapply(twdtw_res[], function(xx) xx[which.min(xx$distance),]) )
+    df = do.call("rbind", lapply(twdtw_res[], function(xx) {
+      i = which.min(xx$distance)
+      if(length(i)<1)
+        return(data.frame(Alig.N=NA, from=NA, to=NA, distance=NA, label = "Unclassified"))
+      xx[i,]
+    }))
     ref = labels(twdtw_res)$timeseries
     pred = df$label
     data = data.frame(.adjustFactores(ref, pred, levels=NULL, labels=NULL), df[,!names(df)%in%"labels"])