A classification procedure, no matter how sophisticated it is, generally consists of two steps: (1) assign a score/distance to each class, and (2) choose the class that yields the highest score/lowest distance.

This package provides a function classify and its friends to accomplish the second step, that is, to predict labels based on scores.

classify(x[, ord])

Classify based on scores given in x and the order of scores specified in ord.

Generally, ord can be any instance of type Ordering. However, it usually enough to use either Forward or Reverse:

  • ord = Forward: higher value indicates better match (e.g., similarity)
  • ord = Reverse: lower value indicates better match (e.g., distances)

When ord is omitted, it is defaulted to Forward.

When x is a vector, it produces an integer label. When x is a matrix, it produces a vector of integers, each for a column of x.

classify([0.2, 0.5, 0.3])  # --> 2
classify([0.2, 0.5, 0.3], Forward)  # --> 2
classify([0.2, 0.5, 0.3], Reverse)  # --> 1

classify([0.2 0.5 0.3; 0.7 0.6 0.2]') # --> [2, 1]
classify([0.2 0.5 0.3; 0.7 0.6 0.2]', Forward) # --> [2, 1]
classify([0.2 0.5 0.3; 0.7 0.6 0.2]', Reverse) # --> [1, 3]
classify!(r, x[, ord])

Write predicted labels to r.

classify_withscore(x[, ord])

Return a pair as (label, score), where score is the input score corresponding to the predicted label.

classify_withscores(x[, ord])

This function applies to a matrix x comprised of multiple samples (each being a column). It returns a pair (labels, scores).

classify_withscores!(r, s, x[, ord])

Write predicted labels to r and corresponding scores to s.