Fixed mapping finding labels of classification dataset
(perform crisp classification)
LABELS = LABELD(A,W)
Returns the labels of the classification dataset Z=A*W. For each object in Z (i.e. each row) the feature label or class label (i.e. the column label) of the maximum column value is returned.
Effectively, this performs the classification. It can also be considered as a conversion from soft labels (posteriors) stored in Z to crisp labels.
Be aware that in case A is not a dataset but an array of doubles, A*W is also an array of doubles and the output of A*W*LABELD is a column vector pointing to the column with maximum classification confidence. If the real labels strored in W are needed, convert A to a dataset first.
When the parameter THRESH is supplied, then all objects which classifier output falls below this value are rejected. The returned label is then NaN or a string with spaces (depending if the labels are numeric or string). Because the output of the classifier is used, it is recommended to convert the output to a posterior prob. output using