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  1. step :: Set set => State label set -> [State label set]

    set-cover Math.SetCover.Exact

    This is the key of the search algorithm. The search algorithm tries to build partitions by adding sets to a partition list successively. A step starts on a partial partition and looks for new sets that could be added. The goal is to avoid to check a set again down in a search branch and to quickly determine search directions that lead to a dead end. To this end a search step selects a certain set element and tries all sets that contain that element and that do not overlap with the partial partition. Practically, step selects an element with the minimal number of non-overlapping sets it is contained in. If this number is zero, then the search can be aborted in this branch. Most oftenly the power of the algorithm originates from the formulation of a problem as a set-cover problem and from the equal treatment of all elements. E.g. in the Soma cube example the algorithm chooses whether to do a case analysis on all bricks that cover a certain position, or to do a case analysis on all positions that are possible for a certain brick. The algorithm might not be extraordinarily fast, but in all cases it consumes only little memory since it only has to maintain the current state of search. Precondition: freeElements of the input state must not be empty.

  2. step :: Methods queue set -> State queue label set -> [State queue label set]

    set-cover Math.SetCover.Exact.Priority

    No documentation available.

  3. step :: State label -> [State label]

    set-cover Math.SetCover.Exact.UArray

    No documentation available.

  4. step :: k -> v -> v -> SF k v

    step-function Data.Function.Step

    Step function. step k v1 v2 = \ x -> if x < k then v1 else v2. 

    >>> putSF $ step 1 2 3
\x -> if
| x < 1     -> 2
| otherwise -> 3

  5. step :: k -> v -> v -> SF k v

    step-function Data.Function.Step.Discrete.Closed

    Step function. step k v1 v2 = \ x -> if x < k then v1 else v2. 

    >>> putSF $ step 1 2 3
\x -> if
| x <= 1    -> 2
| otherwise -> 3

  6. step :: k -> v -> v -> SF k v

    step-function Data.Function.Step.Discrete.Open

    Step function. step k v1 v2 = \ x -> if x < k then v1 else v2. 

    >>> putSF $ step 1 2 3
\x -> if
| x < 1     -> 2
| otherwise -> 3

  7. step :: Benchmark -> IO Benchmark

    chronos-bench Chronos.Bench

    Run the benchmark once and update its analysis. For functions with very low runtimes multiple runs will be executed.

  8. step :: Num a => [a]

    dsp DSP.Source.Basic

    unit step

  9. step :: (Ord e, Container c e) => c e -> c e

    qchas QC

    A more efficient implementation of cmap (\x -> if x>0 then 1 else 0) 

    >>> step $ linspace 5 (-1,1::Double)
5 |> [0.0,0.0,0.0,1.0,1.0]

  10. step :: Statement -> IO StepResult

    squeather Squeather

    Evaluate a prepared statement. Returns Row if the Statement has returned a row of data. In that case, use column or columns to get individual columns or all columns, respectively. Returns Done if there is no data to retrieve. In that case, step should not be called again without first calling reset.

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