temp = validParameter.validFile(parameters, "cutoff", false);
if (temp == "not found") { temp = "10"; }
convert(temp, cutoff);
- if (!hard) { cutoff += (5 / (precision * 10.0)); }
+ cutoff += (5 / (precision * 10.0));
method = validParameter.validFile(parameters, "method", false);
if (method == "not found") { method = "furthest"; }
- if ((method == "furthest") || (method == "nearest") || (method == "average")) { }
- else { m->mothurOut("Not a valid clustering method. Valid clustering algorithms are furthest, nearest or average."); m->mothurOutEndLine(); abort = true; }
+ if ((method == "furthest") || (method == "nearest") || (method == "average") || (method == "weighted")) { }
+ else { m->mothurOut("Not a valid clustering method. Valid clustering algorithms are furthest, nearest, average, and weighted."); m->mothurOutEndLine(); abort = true; }
showabund = validParameter.validFile(parameters, "showabund", false);
if (showabund == "not found") { showabund = "T"; }
if (method == "furthest") { cluster = new CompleteLinkage(rabund, list, matrix, cutoff, method); }
else if(method == "nearest"){ cluster = new SingleLinkage(rabund, list, matrix, cutoff, method); }
else if(method == "average"){ cluster = new AverageLinkage(rabund, list, matrix, cutoff, method); }
+ else if(method == "weighted"){ cluster = new WeightedLinkage(rabund, list, matrix, cutoff, method); }
tag = cluster->getTag();
if (outputDir == "") { outputDir += hasPath(globaldata->inputFileName); }
cluster->update(cutoff);
float dist = matrix->getSmallDist();
- float rndDist = roundDist(dist, precision);
+ float rndDist;
+ if (hard) {
+ rndDist = ceilDist(dist, precision);
+ }else{
+ rndDist = roundDist(dist, precision);
+ }
if(previousDist <= 0.0000 && dist != previousDist){
printData("unique");