Last active
February 6, 2022 01:39
-
-
Save Mijago/724b1a7a03be5cf752d82d8562ff390d to your computer and use it in GitHub Desktop.
Test the Possibility of certain Armor Stat Distribuion with Linear Programming
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import numpy as np | |
import pulp as pl | |
# %%% CONFIG | |
MINIMUM_TIERS = 28 | |
MAXIMUM_WASTE = 15 | |
AVAILABLE_MODS = 5 | |
AVAILABLE_BONUS = [ | |
20, # PF | |
20, # stasis | |
10, # stasis | |
0, | |
10, # stasis | |
20 + 10 # RL # stasis | |
] | |
# Four entries. Use them to test if you can use an item for a specific distribution | |
ITEMS = [ | |
#[2,7,24,2,10, 21], # helmet | |
#[2,19,12,2,10,21], # legz | |
#[2,24,7,2,26,6], # chest | |
#[2,15,14,], | |
# [2,6,25, 6,20,6], # coda chest | |
#[2,20,11,6,6,20] , # boots ascendan | |
#[2,13,16,2,14,16], #some helmet | |
# [6,27,2,2,26,2], # stronghold | |
#[2,6,25,6,20,6], | |
[9, 16, 10, 11, 17, 2], | |
None, # [2,2,30, 2,2,30], | |
None, | |
None, | |
None | |
] | |
# The stats you want | |
#DESIRED_STATS = [0, 100, 0, 0, 100, 100] | |
AVAILABLE_BONUS = [0, 0, 0, 0, 0, 0] | |
DESIRED_STATS = [0,90,100,100,0,0] | |
#AVAILABLE_MODS = 0 | |
MINIMUM_TIERS = 31 | |
# %%% SOLVER | |
solver = pl.CPLEX_CMD() | |
model = pl.LpProblem("Stats", pl.LpMaximize) | |
possibleBonusStats = [ | |
[0,0,0], [0,0,2],[0,1,1], [0,1,2], [0,2,0], [0,2,1], [1,0,1], [1,1,0], [1,1,1], [1,2,0], [2,0,0], [2,0,1], [2,1,0] | |
] | |
plugs = np.array([[1, 1, 10], [1, 1, 11], [1, 1, 12], [1, 1, 13], [1, 1, 14], [1, 1, 15], | |
[1, 5, 5], [1, 5, 6], [1, 5, 7], [1, 5, 8], [1, 5, 9], [1, 5, 10], | |
[1, 5, 11], [1, 6, 5], [1, 6, 6], [1, 6, 7], [1, 6, 8], [1, 6, 9], | |
[1, 7, 5], [1, 7, 6], [1, 7, 7], [1, 7, 8], [1, 8, 5], [1, 8, 6], | |
[1, 8, 7], [1, 9, 5], [1, 9, 6], [1, 10, 1], [1, 10, 5], [1, 11, 1], | |
[1, 11, 5], [1, 12, 1], [1, 13, 1], [1, 14, 1], [1, 15, 1], [5, 1, 5], | |
[5, 1, 6], [5, 1, 7], [5, 1, 8], [5, 1, 9], [5, 1, 10], [5, 1, 11], | |
[5, 5, 1], [5, 5, 5], [5, 6, 1], [5, 7, 1], [5, 8, 1], [5, 9, 1], | |
[5, 10, 1], [5, 11, 1], [6, 1, 5], [6, 1, 6], [6, 1, 7], [6, 1, 8], | |
[6, 1, 9], [6, 5, 1], [6, 6, 1], [6, 7, 1], [6, 8, 1], [6, 9, 1], | |
[7, 1, 5], [7, 1, 6], [7, 1, 7], [7, 1, 8], [7, 5, 1], [7, 6, 1], | |
[7, 7, 1], [7, 8, 1], [8, 1, 5], [8, 1, 6], [8, 1, 7], [8, 5, 1], | |
[8, 6, 1], [8, 7, 1], [9, 1, 5], [9, 1, 6], [9, 5, 1], [9, 6, 1], | |
[10, 1, 1], [10, 1, 5], [10, 5, 1], [11, 1, 1], [11, 1, 5], [11, 5, 1], | |
[12, 1, 1], [13, 1, 1], [14, 1, 1], [15, 1, 1]]) | |
v_total_stats = pl.LpVariable.dicts('stat', range(0, 6), lowBound=0, upBound=0, cat='Integer') | |
for n in range(0, 6): | |
v_total_stats[n] += 10 # Masterworks :) | |
for n in range(0, 6): | |
v_total_stats[n] += AVAILABLE_BONUS[n] # add bonus | |
#for n in range(0, 6): model += v_total_stats[n] >= 2 # minimum of 2 | |
NUM_ITEMS = 4 | |
items = dict() | |
for item in range(0, NUM_ITEMS): | |
if ITEMS[item] is not None: | |
for n in range(0, 6): | |
v_total_stats[n] += ITEMS[item][n] | |
continue | |
v_item_stats = pl.LpVariable.dicts('item_%d_stat' % item, range(0, 6), lowBound=0, upBound=0, cat='Integer') | |
v_item_plugs_g1 = pl.LpVariable.dicts('item_%d_plug1' % item, range(0, len(plugs)), lowBound=0, upBound=2, cat='Integer') | |
v_item_plugs_g2 = pl.LpVariable.dicts('item_%d_plug2' % item, range(0, len(plugs)), lowBound=0, upBound=2, cat='Integer') | |
model += pl.lpSum(v_item_plugs_g1) == 2 | |
model += pl.lpSum(v_item_plugs_g2) == 2 | |
for n in range(0, 3): | |
for k, v in enumerate(plugs): | |
v_item_stats[n] += v_item_plugs_g1[k] * plugs[k][n] | |
v_item_stats[3 + n] += v_item_plugs_g2[k] * plugs[k][n] | |
v_total_stats[n] += v_item_stats[n] | |
v_total_stats[3 + n] += v_item_stats[3 + n] | |
items[item] = v_item_stats | |
# MODS | |
v_mods = pl.LpVariable.dicts('mod', range(0, 6), cat='Integer', lowBound=0, upBound=5) | |
model += pl.lpSum(v_mods) <= AVAILABLE_MODS | |
for mod in v_mods: | |
v_total_stats[mod] += 10 * v_mods[mod] | |
# Bonus of an exotic | |
v_exotic_boost = pl.LpVariable.dicts('exotic_boost', range(0, len(possibleBonusStats)), lowBound=0, upBound=1, cat='Integer') | |
model += pl.lpSum(v_exotic_boost) <= 1 | |
for boost in v_exotic_boost: | |
entry = possibleBonusStats[boost] | |
for r in [0,1,2]: | |
if entry[r] > 0: | |
v_total_stats[r] += entry[r] * v_exotic_boost[boost] | |
# desired stats | |
for stat in v_total_stats: | |
model += v_total_stats[stat] >= DESIRED_STATS[stat] | |
model += v_total_stats[stat] <= 109 | |
v_stat_sum = pl.lpSum(v_total_stats) | |
###### Variables to calculate waste | |
tiers = pl.LpVariable.dicts('tier', range(0, 6), lowBound=0, cat='Integer') | |
for stat in range(0, 6): | |
model += tiers[stat] >= v_total_stats[stat] / 10 - 0.5 | |
model += tiers[stat] <= v_total_stats[stat] / 10 | |
v_tier_sum = pl.lpSum(tiers) | |
model += v_tier_sum >= 0 | |
model += v_tier_sum >= MINIMUM_TIERS | |
waste = pl.LpVariable('waste') | |
model += ( | |
waste == | |
pl.lpSum([pl.lpSum(v_total_stats[stat] - tiers[stat] * 10) for stat in range(0, 6)]) | |
# + pl.lpSum([pl.lpSum(v_over_100[stat] * 10) for stat in range(0, 6)]) | |
# pl.lpSum([v_over_100[stat] * (v_total_stats[stat] - 100) for stat in range(0, 6)]) | |
) | |
model += waste <= MAXIMUM_WASTE | |
model += ( | |
v_stat_sum | |
# - modcost | |
-5* waste | |
) | |
#model += v_tier_sum; | |
result = model.solve() | |
print("~~~ Input ~~~") | |
print("Desired stats:", DESIRED_STATS) | |
print("Available bonus:", AVAILABLE_BONUS) | |
print("Existing items", ITEMS, "('None' means it must be calculated)") | |
print("Available Mods", AVAILABLE_MODS) | |
print() | |
print("~~~ OUTPUT ~~~") | |
print("stat\tvalue\tmods") | |
for stat in range(0, 6): | |
print(stat, "\t", pl.value(v_total_stats[stat]), "\t", pl.value(v_mods[stat])) | |
print("Using these (masterworked) items:") | |
for i in items: | |
print([pl.value(items[i][m]) for m in items[i]]) | |
print("Wasted points:", pl.value(waste)) | |
print("Total Stat Points:", pl.value(v_stat_sum)) | |
print("Total Tiers:", pl.value(pl.lpSum(tiers))) | |
print("Exotic Bonus Stats:", [possibleBonusStats[f] for f in range(0, len(possibleBonusStats)) if pl.value(v_exotic_boost[f]) == 1]) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import numpy as np | |
import pulp as pl | |
solver = pl.CPLEX_CMD() | |
availableBonus = np.array([ | |
20, # PF | |
20, # stasis | |
10, # stasis | |
0, | |
10, # stasis | |
20 +10 # RL # stasis | |
]) | |
availableMods = 5 | |
#x/7/8/x/8/6 | |
desiredStats= np.array([0,100,100,100,0,100]) | |
#availableBonus = np.array([0,20,10,0,10,10]) | |
existing_items = np.array([ | |
None, | |
None, | |
None, | |
None | |
]) | |
model = pl.LpProblem("Stats", pl.LpMinimize) | |
names = ["mob", "res", "rec", "dis", "int", "str", ] | |
var = [pl.LpVariable('%s%d'%(names[n%6], n//6), cat='Integer', lowBound=2, upBound=30) for n in range(0,4*6)] | |
mod = [pl.LpVariable('mod_%s%d'%(names[n%6], n//6),cat='Integer', lowBound=0, upBound=5) for n in range(0,6)] | |
i1,i2,i3,i4 = 0,6,12,18 | |
for n in var: | |
model += var != 3 | |
model += var != 4 | |
model += var != 5 | |
for item in [i1,i2,i3,i4]: | |
model += var[item + 0] + var[item + 1] + var[item + 2] <= 34 | |
model += var[item + 3] + var[item + 4] + var[item + 5] <= 34 | |
model += var[item + 0] + var[item + 1] + var[item + 2] >= 22 | |
model += var[item + 3] + var[item + 4] + var[item + 5] >= 22 | |
#for n in range(0,6): | |
#model += var[item + n] >= 2 | |
#model += var[item + n] <= 30 | |
model += mod[0]+mod[1]+mod[2]+mod[3]+mod[4]+mod[5] <= availableMods | |
for n in range(0,6): | |
#model += mod[n] >=0 | |
#model += mod[n] <=5 | |
model += var[i1 + n] + var[i2 + n]+ var[i3+ n] + var[i4 + n] + 10*mod[n] >= max(0,(desiredStats-availableBonus - 10)[n]) | |
model += var[i1 + n] + var[i2 + n]+ var[i3+ n] + var[i4 + n] + 10*mod[n] + availableBonus[n] + 10 <= 109 | |
# add existing items | |
for k, x in enumerate(existing_items): | |
if x is None: continue | |
for n in range(0,6): | |
model += var[6*k + n] == x[n] | |
# Goal: minimize the total stats | |
model += pl.lpSum(var) + 10 * (mod[0]+mod[1]+mod[2]+mod[3]+mod[4]+mod[5]) - 20* (mod[0]+mod[1]+mod[2]+mod[3]+mod[4]+mod[5]) | |
tiers = [pl.LpVariable('tier_%s%d'%(names[n%6], n//6),cat='Integer') for n in range(0,6)] | |
for tier in range(0,6): | |
model += tiers[tier] >= pl.lpSum([var[x + tier] for x in [i1,i2,i3,i4]])/10 -0.5 | |
model += tiers[tier] <= pl.lpSum([var[x + tier] for x in [i1,i2,i3,i4]])/10 | |
# optimize by waste; i just introduce it as a variable for readability | |
waste = pl.LpVariable('waste') | |
model += waste == pl.lpSum([ | |
(pl.lpSum([var[x + tier] for x in [i1,i2,i3,i4]]) - tiers[tier]*10 ) for tier in range(0, 6) | |
]) | |
model += waste | |
model += -pl.lpSum(var) | |
result = model.solve() | |
stats = np.array([pl.value(x) for x in var]).reshape(4,6) | |
mods = np.array([pl.value(x) for x in mod]) | |
tiers = np.array([pl.value(x) for x in tiers]) + 1 + availableBonus/10 + mods | |
statstotal = stats.sum(axis=0) + 10 + availableBonus + 10 * mods | |
print() | |
print() | |
print("desired stats: ", desiredStats) | |
print("available bonus stats:",availableBonus) | |
print("existing items:",existing_items) | |
print() | |
print("stats per item:\n",stats) | |
print("mods:", mods) | |
print() | |
print("stats", statstotal) | |
print("tiers by stats:", tiers) | |
print("waste:",pl.value(waste)) | |
print() | |
#print(model) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import numpy as np | |
import pulp as pl | |
# %%% CONFIG | |
MAXIMUM_WASTE = 100 # don't be too restrictive when you use a broad search. Slow! | |
AVAILABLE_MODS = 5 | |
AVAILABLE_BONUS = [ | |
20, # PF | |
20, # stasis | |
10, # stasis | |
0, | |
10, # stasis | |
20 + 10 # RL # stasis | |
] | |
# Four entries. Use them to test if you can use an item for a specific distribution | |
ITEMS = [ | |
None, # [2,2,30, 2,2,30], | |
None, | |
None, | |
None | |
] | |
# The stats you want | |
DESIRED_STATS = [0, 100, 0, 0, 0, 100] | |
# DESIRED_STATS = [0, 0, 0, 0, 0, 0] | |
# %%% SOLVER | |
solver = pl.CPLEX_CMD() | |
model = pl.LpProblem("Stats", pl.LpMaximize) | |
plugs = np.array([ | |
[1, 5, 11], [8, 5, 1], [8, 5, 1], [5, 5, 1], [9, 1, 6], [11, 1, 1], [5, 6, 1], [9, 6, 1], [5, 5, 5], [6, 1, 9], [5, 1, 11], [1, 8, 5], | |
[7, 8, 1], [6, 8, 1], [8, 1, 6], [5, 1, 7], [7, 1, 6], [1, 10, 1], [7, 1, 6], [1, 11, 1], [1, 8, 7], [11, 1, 5], [7, 5, 1], [8, 7, 1], | |
[1, 6, 9], [1, 12, 1], [1, 1, 13], [14, 1, 1], [1, 1, 11], [8, 6, 1], [7, 8, 1], [5, 5, 1], [1, 5, 9], [1, 15, 1], [5, 1, 8], [1, 6, 6], | |
[7, 1, 7], [1, 9, 6], [7, 1, 5], [9, 6, 1], [1, 6, 9], [10, 1, 1], [5, 1, 7], [5, 8, 1], [1, 6, 7], [9, 5, 1], [1, 7, 5], [7, 7, 1], | |
[1, 5, 6], [5, 9, 1], [6, 1, 9], [6, 1, 6], [6, 6, 1], [1, 5, 5], [7, 1, 8], [5, 7, 1], [5, 1, 5], [8, 1, 7], [1, 7, 8], [11, 5, 1], | |
[13, 1, 1], [6, 1, 8], [1, 14, 1], [11, 1, 1], [6, 1, 7], [1, 7, 8], [1, 11, 5], [15, 1, 1], [5, 1, 10], [5, 5, 5], [6, 7, 1], [7, 6, 1], | |
[1, 6, 6], [13, 1, 1], [1, 5, 10], [6, 1, 7], [8, 7, 1], [5, 8, 1], [5, 1, 8], [5, 1, 6], [1, 8, 5], [7, 5, 1], [1, 1, 10], [7, 1, 5], | |
[7, 6, 1], [6, 9, 1], [1, 10, 5], [6, 5, 1], [9, 1, 6], [6, 7, 1], [1, 6, 7], [12, 1, 1], [1, 1, 11], [1, 15, 1], [7, 1, 8], [1, 1, 12], | |
[1, 6, 5], [1, 9, 5], [8, 1, 5], [1, 13, 1], [1, 12, 1], [1, 5, 5], [6, 6, 1], [1, 11, 1], [1, 7, 5], [5, 11, 1], [1, 13, 1], [5, 7, 1], | |
[1, 1, 14], [1, 5, 7], [1, 1, 13], [1, 5, 7], [1, 7, 7], [15, 1, 1], [1, 5, 8], [1, 1, 15], [6, 9, 1], [10, 1, 5], [9, 1, 5], [6, 1, 5], | |
[5, 1, 9], [6, 1, 6], [8, 1, 5], [1, 9, 6], [1, 7, 6], [1, 5, 8], [1, 7, 6], [10, 5, 1], [1, 1, 15], [1, 6, 8], [5, 10, 1], [1, 1, 12], | |
[1, 8, 6], [8, 1, 7], [12, 1, 1], [1, 8, 7], [5, 1, 5] | |
]) | |
v_total_stats = pl.LpVariable.dicts('stat', range(0, 6), lowBound=0, upBound=0, cat='Integer') | |
for n in range(0, 6): | |
v_total_stats[n] += 10 # Masterworks :) | |
for n in range(0, 6): | |
v_total_stats[n] += AVAILABLE_BONUS[n] # add bonus | |
NUM_ITEMS = 4 | |
items = dict() | |
for item in range(0, NUM_ITEMS): | |
if ITEMS[item] is not None: | |
for n in range(0, 6): | |
v_total_stats[n] += ITEMS[item][n] | |
continue | |
v_item_stats = pl.LpVariable.dicts('item_%d_stat' % item, range(0, 6), lowBound=0, upBound=0, cat='Integer') | |
v_item_plugs_g1 = pl.LpVariable.dicts('item_%d_plug1' % item, range(0, len(plugs)), lowBound=0, upBound=2, cat='Integer') | |
v_item_plugs_g2 = pl.LpVariable.dicts('item_%d_plug2' % item, range(0, len(plugs)), lowBound=0, upBound=2, cat='Integer') | |
model += pl.lpSum(v_item_plugs_g1) == 2 | |
model += pl.lpSum(v_item_plugs_g2) == 2 | |
for n in range(0, 3): | |
for k, v in enumerate(plugs): | |
v_item_stats[n] += v_item_plugs_g1[k] * plugs[k][n] | |
v_item_stats[3 + n] += v_item_plugs_g2[k] * plugs[k][n] | |
v_total_stats[n] += v_item_stats[n] | |
v_total_stats[3 + n] += v_item_stats[3 + n] | |
items[item] = v_item_stats | |
# MODS | |
v_mods = pl.LpVariable.dicts('mod', range(0, 6), cat='Integer', lowBound=0, upBound=5) | |
model += pl.lpSum(v_mods) <= 5 | |
for mod in v_mods: | |
v_total_stats[mod] += 10 * v_mods[mod] | |
# desired stats | |
for stat in v_total_stats: | |
model += v_total_stats[stat] >= DESIRED_STATS[stat] | |
model += v_total_stats[stat] <= 109 | |
v_stat_sum = pl.lpSum(v_total_stats) | |
###### Variables to calculate waste | |
tiers = pl.LpVariable.dicts('tier', range(0, 6), lowBound=0, cat='Integer') | |
for stat in range(0, 6): | |
model += tiers[stat] >= v_total_stats[stat] / 10 - 0.5 | |
model += tiers[stat] <= v_total_stats[stat] / 10 | |
waste = pl.LpVariable('waste') | |
model += ( | |
waste == | |
pl.lpSum([pl.lpSum(v_total_stats[stat] - tiers[stat] * 10) for stat in range(0, 6)]) | |
# + pl.lpSum([pl.lpSum(v_over_100[stat] * 10) for stat in range(0, 6)]) | |
# pl.lpSum([v_over_100[stat] * (v_total_stats[stat] - 100) for stat in range(0, 6)]) | |
) | |
model += waste <= MAXIMUM_WASTE | |
model += ( | |
v_stat_sum | |
# - modcost | |
- waste | |
) | |
result = model.solve() | |
print("~~~ Input ~~~") | |
print("Desired stats:", DESIRED_STATS) | |
print("Available bonus:", AVAILABLE_BONUS) | |
print("Existing items", ITEMS, "('None' means it must be calculated)") | |
print("Available Mods", AVAILABLE_MODS) | |
print() | |
print("~~~ OUTPUT ~~~") | |
print("stat\tvalue\tmods") | |
for stat in range(0, 6): | |
print(stat, "\t", pl.value(v_total_stats[stat]), "\t", pl.value(v_mods[stat])) | |
print("Using these (masterworked) items:") | |
for i in items: | |
print([pl.value(items[i][m]) for m in items[i]]) | |
print("Wasted points:", pl.value(waste)) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Note that both files do the same.
armorStatSolver.old.py
for simple checking like [0,10,0,0,10,0]armorStatSolver.plugs.py
is way slower for these checks, but more accurate for things like [0,10,10,0,10,0]