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horse/horse/legacy.kk

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module horse/legacy
import std/num/decimal
import std/data/linearmap
import std/data/linearset
import horse/game-id
import horse/spark
import horse/prob/dist
// A legacy, or parent and grandparents.
pub struct legacy
uma: veteran
sub1: veteran
sub2: veteran
// A veteran, or the result of a completed career.
pub struct veteran
uma: uma-id
sparks: list<spark-id>
saddles: list<saddle-id>
// Get all saddles shared between two lists thereof.
pub fun shared-saddles(a: list<saddle-id>, b: list<saddle-id>): list<saddle-id>
val sa: linearSet<saddle-id> = a.foldl(linear-set(Nil)) fn(s, id) if id.is-valid then s.add(id) else s
val c: linearSet<saddle-id> = b.foldl(linear-set(Nil)) fn(s, id) if sa.member(id) then s.add(id) else s
c.list
// Get the individual affinity for a legacy.
// Any invalid ID is treated as giving 0.
pub fun parent-affinity(
trainee: uma-id,
legacy: legacy,
other-parent: uma-id,
?character-id: (uma-id) -> character-id,
?saddle-bonus: (list<saddle-id>) -> int,
?pair-affinity: (a: character-id, b: character-id) -> int,
?trio-affinity: (a: character-id, b: character-id, c: character-id) -> int
): int
val t = trainee.character-id
val p1 = legacy.uma.uma.character-id
val s1 = legacy.sub1.uma.character-id
val s2 = legacy.sub2.uma.character-id
val p2 = other-parent.character-id
pair-affinity(t, p1) + pair-affinity(p1, p2)
+ trio-affinity(t, p1, s1) + trio-affinity(t, p1, s2)
+ saddle-bonus(shared-saddles(legacy.uma.saddles, legacy.sub1.saddles)) + saddle-bonus(shared-saddles(legacy.uma.saddles, legacy.sub2.saddles))
// Get the individual affinities for a legacy's sub-legacies.
// The first value is the legacy for the `legacy.sub1` and the second is for
// `legacy.sub2`.
// Any invalid ID is treated as giving 0.
pub fun sub-affinity(
trainee: uma-id,
legacy: legacy,
?character-id: (uma-id) -> character-id,
?saddle-bonus: (list<saddle-id>) -> int,
?trio-affinity: (a: character-id, b: character-id, c: character-id) -> int
): (int, int)
val t = trainee.character-id
val p = legacy.uma.uma.character-id
val s1 = legacy.sub1.uma.character-id
val s2 = legacy.sub2.uma.character-id
val r1 = trio-affinity(t, p, s1) + saddle-bonus(shared-saddles(legacy.uma.saddles, legacy.sub1.saddles))
val r2 = trio-affinity(t, p, s2) + saddle-bonus(shared-saddles(legacy.uma.saddles, legacy.sub2.saddles))
(r1, r2)
// Associate each spark with its actual chance to activate given an individual
// affinity value and the possible effects when it does.
pub fun uma/inspiration(l: list<spark-id>, affinity: int, ?spark-type: (spark-id) -> spark-type, ?rarity: (spark-id) -> rarity, ?effects: (spark-id) -> list<list<spark-effect>>): list<(spark-id, decimal, list<list<spark-effect>>)>
val a = decimal(1 + affinity, -2)
l.map() fn(id) (id, min(id.base-proc * a, 1.decimal), id.effects)
// Get the complete list of effects that may occur in an inspiration event
// and the respective probability of activation.
// Duplicates, i.e. multiple veterans with the same spark, are preserved.
pub fun inspiration(
trainee: uma-id,
parent1: legacy,
parent2: legacy,
?character-id: (uma-id) -> character-id,
?saddle-bonus: (list<saddle-id>) -> int,
?pair-affinity: (a: character-id, b: character-id) -> int,
?trio-affinity: (a: character-id, b: character-id, c: character-id) -> int,
?spark-type: (spark-id) -> spark-type,
?rarity: (spark-id) -> rarity,
?effects: (spark-id) -> list<list<spark-effect>>
): list<(spark-id, decimal, list<list<spark-effect>>)>
val p1a = parent-affinity(trainee, parent1, parent2.uma.uma)
val p2a = parent-affinity(trainee, parent2, parent1.uma.uma)
val (s11a, s12a) = sub-affinity(trainee, parent1)
val (s21a, s22a) = sub-affinity(trainee, parent2)
[
inspiration(parent1.uma.sparks, p1a),
inspiration(parent1.sub1.sparks, s11a),
inspiration(parent1.sub2.sparks, s12a),
inspiration(parent2.uma.sparks, p2a),
inspiration(parent2.sub1.sparks, s21a),
inspiration(parent2.sub2.sparks, s22a),
].concat
// Reduce a spark effect list to the skill it is able to give.
pub fun skills(l: list<list<spark-effect>>): maybe<skill-id>
val r: linearSet<skill-id> = l.head(Nil).foldl(linear-set(Nil)) fn(s, eff)
match eff
Skill-Hint(id, _) -> s + id
_ -> s
r.list.head
// Reduce a spark effect list to the aptitude it is able to give.
pub fun aptitudes(l: list<list<spark-effect>>): maybe<aptitude>
val r: linearSet<aptitude> = l.head(Nil).foldl(linear-set(Nil)) fn(s, eff)
match eff
Aptitude-Up(apt) -> s + apt
_ -> s
r.list.head
// Get the overall chance of each count of sparks, including zero, providing a
// given type of effect activating in a single inspiration event.
pub fun inspiration-gives(l: list<(spark-id, decimal, list<list<spark-effect>>)>, f: (list<list<spark-effect>>) -> maybe<a>, ?a/(==): (a, a) -> bool): linearMap<a, list<decimal>>
val m: linearMap<_, list<decimal>> = l.foldl(LinearMap(Nil)) fn(m, (_, p, eff))
match f(eff)
Nothing -> m
Just(a) -> m.map/update(a, [p]) fn(cur, pp) pp.append(cur)
m.map() fn(_, v) poisson-binomial(v)