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Valkyrie Profile Max Stats Leveling in the Second Room of Seraphic Gate
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defmodule ValkyrieProfile.Leveling do | |
@enemies %{ | |
left: 7000, | |
right: 10500, | |
up: 35000 | |
} | |
@equipment_boosts %{ | |
[] => 1.0, | |
[:ring] => 1.3, | |
[:coin] => 1.6, | |
[:ring, :coin] => 1.9 | |
} | |
@crystal_boosts %{ | |
0 => 1.0, | |
40 => 3.0 | |
} | |
@possible_battles for {enemy, exp} <- @enemies, | |
{equipments, boost1} <- @equipment_boosts, | |
{crystals, boost2} <- @crystal_boosts, | |
do: {round(exp * boost1 * boost2), equipments, enemy, crystals} | |
@type exp() :: non_neg_integer() | |
@type equipment() :: :ring | :coin | |
@type enemy() :: :left | :right | :up | |
@type crystals() :: 0 | 40 | |
@type battle() :: {exp(), [equipment()], enemy(), crystals()} | |
@doc """ | |
找到获取的总经验值为小于 exp 的最大值的战斗组合。 | |
如果有多种方案可以获得相同的总经验值, | |
则挑选其中战斗次数最少的。 | |
如果仍有多种方案战斗次数也相同, | |
则挑选其中需要打出的魔晶石总量最少的。 | |
""" | |
@spec optimal_strategy(exp()) :: [battle()] | |
def optimal_strategy(exp_needed) do | |
Task.async(fn -> | |
find_optimal(exp_needed) | |
end) | |
|> Task.await() | |
|> Enum.group_by(&elem(&1, 1)) | |
|> Map.values() | |
|> List.flatten() | |
end | |
@spec find_optimal(exp()) :: [battle()] | |
defp find_optimal(exp_needed) do | |
memoized(exp_needed, fn -> | |
@possible_battles | |
|> Enum.filter(&(elem(&1, 0) < exp_needed)) | |
|> Enum.map(&{&1, find_optimal(exp_needed - elem(&1, 0))}) | |
|> Enum.max_by( | |
fn {{exp, _, _, crystals}, sub_battles} -> | |
{ | |
exp + Enum.sum(Enum.map(sub_battles, &elem(&1, 0))), | |
-length(sub_battles), | |
-crystals - Enum.sum(Enum.map(sub_battles, &elem(&1, 3))) | |
} | |
end, | |
fn -> nil end | |
) | |
|> then(fn | |
nil -> [] | |
{battle, sub_battles} -> [battle | sub_battles] | |
end) | |
end) | |
end | |
@spec memoized(key :: term(), (() -> result :: term())) :: (result :: term()) | |
defp memoized(key, fun) do | |
with nil <- Process.get(key) do | |
tap(fun.(), &Process.put(key, &1)) | |
end | |
end | |
end |
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