Eta-expand mrandomR to keep GHC 6.6.1 on birdy happy.

[match/match.git] / program / PMDefaults.hs

module PMDefaults where
import PMInstance
import PMConfig

import qualified NaiveMinCostFlow
import qualified CS2MinCostFlow

pmDefaults = PMConfig {

-- Choose a min-cost flow implementation (timings on mattlaptop2):

-- A naive implementation that is slow for all but the smallest instances
-- (30s on a 20x50 example).
minCostFlow = NaiveMinCostFlow.minCostFlow,

-- Uses CS2 (http://www.igsystems.com/cs2/), which requires a license for
-- non-research use but is faster (<1s on a 20x50 example, 64s on a 60x500
-- example).  Configure the path to cs2.exe in CS2MinCostFlow.hs.  Remember to
-- compile CS2 with -DPRINT_ANS, or this won't work!
--minCostFlow = CS2MinCostFlow.minCostFlow,

reviewsEachProposal = 3,

prefIsExpert = \p -> p <= 10,
prefIsKnowledgeable = \p -> p <= 20,

prefIsBoring = \p -> p > 15,
prefIsVeryBoring = \p -> p > 25,

prefIsConflict = \p -> p >= 40,

-- For now this is absolute.  Later it might be proportional to a reviewer's
-- target load.
loadTolerance = 1,

-- Cost to overload by one review.
-- tx = 0 at target load, 1 at end of tolerance.
marginalLoadCost = \tx -> 1000 + tx*1000,

-- Cost to review a boring (or very boring) proposal.
-- lx = 0 at no load, 1 at target load.
marginalBoringCost = \lx -> 1000 + lx*1000,
-- Additional cost to review a very boring proposal.
marginalVeryBoringCost = \lx -> 1000 + lx*1000,

-- Cost to make a review.
-- I'm using quadratic cost functions as a first attempt.
assignmentCost = \pref -> (numAsWt 10 + pref) ^ 2,

-- Bonus for a first knowledgeable or expert review.
knowledgeableBonus = 1000,

-- Bonus for an additional expert review.
expertBonus = 1000

}