R/computeSimilarity.R

In LPStimeSeries: Learned Pattern Similarity and Representation for Time Series

computeSimilarity <- function(object=NULL,testseries=NULL,refseries=NULL, maxdepth=NULL, which.tree=NULL,
							sim.type=0, terminal=TRUE,testrepresentation,refrepresentation) {

  if(!is.null(object)){
	  if (!inherits(object, "learnPattern"))
			stop("object not of class learnPattern")
	  if (is.null(object$forest)) stop("No forest component in the object")
	  if (is.null(refseries) && is.null(testseries)) stop("You need to provide two time series data sets for similarity computation")
	  terminal <- FALSE
  }
  
  if(terminal){ #if computation will be performed over representations
	  ntest <- nrow(testrepresentation)
	  ntrain <- nrow(refrepresentation)
	  nofterminal <- ncol(refrepresentation)
	  ans <- .C("compute_similarity", 
				as.integer(as.matrix(testrepresentation)), 
				as.integer(ntest), 
				as.integer(as.matrix(refrepresentation)),  
				as.integer(ntrain), 
				as.integer(nofterminal),
				as.integer(sim.type), 
				result=integer(ntest*ntrain))
	  return(matrix(ans$result,ntest,ntrain))
	  
  } else {	 #if computation will be performed over raw time series
  
      if(!is.matrix(testseries)){
		if(length(testseries)>0){ #single time series
			testseries <- t(as.matrix(testseries))
		}
		else{
			stop("data (testseries) has 0 rows")
		}   
    } 
      if(!is.matrix(refseries)){
		if(length(refseries)>0){ #single time series
			refseries<- t(as.matrix(refseries))
		}
		else{
			stop("data (refseries) has 0 rows")
		}   
    }     
    if(is.null(maxdepth)) maxdepth <- object$maxdepth
    if(maxdepth>object$maxdepth) {
		maxdepth <- object$maxdepth
		warning("invalid depth: reset to the maximum depth provided during training!")
    }
    if(!is.null(which.tree)){
		if(length(which.tree)==0) stop("No trees are selected!")
		usedtrees=array(0,object$ntree)
		usedtrees[which.tree]=1
	} else {
		usedtrees=array(1,object$ntree)
	}
	mdim <- ncol(refseries)
	ntree <- object$ntree
	ntrain <- nrow(refseries)
	ntest <- nrow(testseries)
	keepIndex <- c("similarity")
	x <- t(data.matrix(refseries))
	xtst <- t(data.matrix(testseries))
	ans <- .C("regForest_similarity",
			as.double(x),
			as.double(xtst),
		    as.integer(ntrain),
			as.integer(ntest),
			as.double(object$segment.length),
		    as.integer(mdim),
			as.integer(object$ntree),
			as.integer(usedtrees),
			object$forest$leftDaughter,
			object$forest$rightDaughter,
			object$forest$nodestatus,
			object$forest$nodedepth,
			object$forest$nrnodes,
			object$forest$xbestsplit,
			object$forest$bestvar,
			object$forest$splitType,
			object$forest$ndbigtree,
			as.integer(maxdepth),
			as.integer(sim.type),
			similarity = integer(ntest*ntrain),
			PACKAGE = "LPStimeSeries")[keepIndex]
	}
	return(matrix(ans$similarity,ntest,ntrain))
}