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Revert "Hash categories (which makes it slower :( )"

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This reverts commit 34f2c1aed8.
This commit is contained in:
spinerak
2024-06-11 21:10:59 +02:00
parent 34f2c1aed8
commit 12ecd9f26b
1 changed files with 5 additions and 9 deletions
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14
worlds/yachtdice/Rules.py
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@@ -58,10 +58,6 @@ class Category:
def __init__(self, name, quantity=1):
self.name = name
self.quantity = quantity # how many times you have the category
def __hash__(self):
return hash((self.name, self.quantity))
# return mean score of a category
def mean_score(self, num_dice, num_rolls):
@@ -104,17 +100,17 @@ def extract_progression(state, player, options):
number_of_fixed_mults = state.count("Fixed Score Multiplier", player)
number_of_step_mults = state.count("Step Score Multiplier", player)
categories = tuple(
categories = [
Category(category_value, state.count(category_name, player))
for category_name, category_value in category_mappings.items()
if state.count(category_name, player) # want all categories that have count >= 1
)
]
extra_points_in_logic = state.count("1 Point", player)
extra_points_in_logic += state.count("10 Points", player) * 10
extra_points_in_logic += state.count("100 Points", player) * 100
return (categories, number_of_dice, number_of_rerolls, number_of_fixed_mults * 0.1, number_of_step_mults * 0.01, extra_points_in_logic)
return categories, number_of_dice, number_of_rerolls, number_of_fixed_mults * 0.1, number_of_step_mults * 0.01, extra_points_in_logic,
@@ -129,7 +125,7 @@ def dice_simulation_strings(categories, num_dice, num_rolls, fixed_mult, step_mu
Function that returns the feasible score in logic based on items obtained.
"""
tup = (
categories,
tuple([c.name + str(c.quantity) for c in categories]),
num_dice,
num_rolls,
fixed_mult,
@@ -142,7 +138,7 @@ def dice_simulation_strings(categories, num_dice, num_rolls, fixed_mult, step_mu
return yachtdice_cache[tup]
# sort categories because for the step multiplier, you will want low-scoring categories first
categories = sorted(categories, key=lambda category: category.mean_score(num_dice, num_rolls))
categories.sort(key=lambda category: category.mean_score(num_dice, num_rolls))
# function to add two discrete distribution.
# defaultdict is a dict where you don't need to check if an id is present, you can just use += (lot faster)