Jump VS Energy train 2.0
Code used to perform the simulations.
unknown
r
3 years ago
15 kB
72
Indexable
library(data.table)
library(foreach)
library(ggplot2)
# General setup:
# - Subscriber status (150 base e, 720 natural daily e)
# - Living in PI without yacht (4525 base happy, 2% gains bonus)
# - Sleep is for the weak, can train 24/7
# - No additional Job, Faction, Education or Book bonus
# - Using refills daily (+150 e daily)
# - All trains are done on Dex (just because) in George's gym (10 e per train)
# - Training is done for 60 days (as many trains as cooldowns allow)
# - Starting stat value is the same for all trains
# Stat offsets for the formula
# Strength Speed Dexterity Defense
# 1600 1600 1800 2100
# 1700 2000 1500 -600
# 700 1350 1000 1500
# Gym dots can be exctraced from the API or from https://algisk.shinyapps.io/Torn-UI
# Define the updated gym formula (reduced version)
gymGains <- function(S=10000, dot=7.3, A=1800, B=1500, C=1000, E=10, H=4525, usedE=150, perkFaction=0,
perkPI=2, perkEdu=0, perkGeneral=0, perkJob=0, perkBook=0) {
# Check if stat over 50mil
# 50,000,000 + ( STAT - 50,000,000 ) / ( 8.77635 * LOG(STAT) ) UPDATED
Sreal <- S
S <- ifelse(S<50e6, S,(S-50e6)/(8.77635*log10(S))+50e6)
# Combine all multipliers
multi <- (1+perkFaction/100)*(1+perkPI/100)*(1+perkEdu/100)*(1+perkGeneral/100)*(1+perkJob/100)*(1+perkBook/100)
# Plug in the formula for single train
dS <- (1/200000)*dot*E*multi*(S*round(1+0.07*round(log((1+H/250),base=exp(1)),4),4)+8*H^1.05+A*(1-(H/99999)^2)+B)
dSe <- C*(1/200000)*dot*E*multi
# Get prediction for total energy used
## number of trains
NT <- floor(usedE/E)
# Initial setup
lowH <- highH <- H
lowStat <- highStat <- Sreal
lowest <- (1/200000)*dot*E*multi*(S*round(1+0.07*round(log((1+H/250),base=exp(1)),4),4)+8*H^1.05+A*(1-(H/99999)^2)+B-C*(1/200000))
highest <- (1/200000)*dot*E*multi*(S*round(1+0.07*round(log((1+H/250),base=exp(1)),4),4)+8*H^1.05+A*(1-(H/99999)^2)+B+C*(1/200000))
firstTrain <- data.table(iteration=1, lowest_possible=lowest, highest_possible=highest, lowest_stat=lowStat, highest_stat=highStat, lowest_happy=lowH, highest_happy=highH)
# Loop through all iterations
allTrains <- foreach(I=1:(NT+1), .combine=rbind) %do% {
lowH <- ifelse(lowH-0.6*E<0,0,lowH-0.6*E)
highH <- ifelse(highH-0.4*E<0,0,highH-0.4*E)
lowStat <- lowStat + lowest
highStat <- lowStat + highest
newLow <- ifelse(lowStat < 50e6, lowStat, (lowStat-50e6)/(8.77635*log10(lowStat))+50e6)
newHigh <- ifelse(highStat < 50e6, highStat, (highStat-50e6)/(8.77635*log10(highStat))+50e6)
lowest <- (1/200000)*dot*E*multi*(newLow*round(1+0.07*round(log((1+lowH/250),base=exp(1)),4),4)+8*lowH^1.05+A*(1-(lowH/99999)^2)+B-C*(1/200000))
highest <- (1/200000)*dot*E*multi*(newHigh*round(1+0.07*round(log((1+highH/250),base=exp(1)),4),4)+8*highH^1.05+A*(1-(highH/99999)^2)+B+C*(1/200000))
data.table(iteration=I+1, lowest_possible=lowest, highest_possible=highest, lowest_stat=round(lowStat), highest_stat=round(highStat), lowest_happy=lowH, highest_happy=highH)
}
allTrains <- rbind(firstTrain, allTrains)
minTrain <- allTrains[NT+1]$lowest_stat-Sreal
maxTrain <- allTrains[NT+1]$highest_stat-Sreal
avgTrain <- mean(c(minTrain, maxTrain))
# Predicted total
dM <- ifelse(usedE/E==1, dS, avgTrain)
dMe <- (maxTrain-minTrain)/2
res <- data.table(
Train=c("Single", "Total"),
GainsN=c(round(dS,2),round(dM,2)),
ErrorN=c(round(dSe,1),round(dMe,1)),
Gains=c(round(dS,2),round(dM,2)),
Error=c(round(dSe,1),round(dMe,1))
)
return(list(res,NT))
}
# Define a wrapper for simulating different jumps and train numbers
jumpSim <- function(startStat=10000, jumpH=4525, jumpE=150, nJump=60,
jumpType=NA, Days=60, vStart=NA) {
out <- data.table(
Jump=0,
Stat=startStat,
Gain=0,
Error=0,
Type=jumpType,
Start=vStart
)
newStat <- startStat
jumps <- foreach(I=1:nJump, .combine=rbind) %do% {
totalGain <- gymGains(usedE=jumpE, H=jumpH, S=newStat)
newStat <- newStat+as.numeric(totalGain[[1]][2,2])
data.table(
Jump=I,
Stat=newStat,
Gain=round(((newStat)/startStat)*100-100),
Error=as.numeric(totalGain[[1]][2,3]),
Type=jumpType,
Start=vStart
)
}
res <- rbind(out, jumps)
res$Day <- seq(0,Days, length.out=nrow(res))
return(res)
}
##################
# Classic happy jump
# 4x Xanax + 5x EDVD + Ecstasy + Refill = 32h CD (one train every 1.5 days)
# - Energy: 1000
# - Happy: 34050
# - Cost per train: ~22mil
# - Number of trains: 40
# - Total cost: 880mil
HJ <- rbind(
jumpSim(startStat=10e3, jumpH=34050, jumpE=1150, nJump=40, jumpType="Happy", vStart="10k"),
jumpSim(startStat=300e3, jumpH=34050, jumpE=1150, nJump=40, jumpType="Happy", vStart="300k"),
jumpSim(startStat=5e6, jumpH=34050, jumpE=1150, nJump=40, jumpType="Happy", vStart="5m"),
jumpSim(startStat=100e6, jumpH=34050, jumpE=1150, nJump=40, jumpType="Happy", vStart="100m")
)
# Classic happy jump: Adult Novelties
# * Double happy from EDVDs
# 4x Xanax + 5x EDVD + Ecstasy + Refill = 32h CD (one train every 1.5 days)
# - Energy: 1150
# - Happy: 59050
# - Cost per train: ~22mil
# - Number of trains: 40
# - Total cost: 880mil
HJAN <- rbind(
jumpSim(startStat=10e3, jumpH=59050, jumpE=1150, nJump=40, jumpType="Happy: Adult Novelties", vStart="10k"),
jumpSim(startStat=300e3, jumpH=59050, jumpE=1150, nJump=40, jumpType="Happy: Adult Novelties", vStart="300k"),
jumpSim(startStat=5e6, jumpH=59050, jumpE=1150, nJump=40, jumpType="Happy: Adult Novelties", vStart="5m"),
jumpSim(startStat=100e6, jumpH=59050, jumpE=1150, nJump=40, jumpType="Happy: Adult Novelties", vStart="100m")
)
# Single choco jump
# 1x Xanax + 48x Big Boxes of Chocolate + Ecstasy + Refill = 24h CD (one train per day)
# - Energy: 550
# - Happy: 12410
# - Cost per train: ~2.4mil
# - Number of trains: 60
# - Total cost: 144mil
SCJ <- rbind(
jumpSim(startStat=10e3, jumpH=12410, jumpE=550, nJump=60, jumpType="Single Choco", vStart="10k"),
jumpSim(startStat=300e3, jumpH=12410, jumpE=550, nJump=60, jumpType="Single Choco", vStart="300k"),
jumpSim(startStat=5e6, jumpH=12410, jumpE=550, nJump=60, jumpType="Single Choco", vStart="5m"),
jumpSim(startStat=100e6, jumpH=12410, jumpE=550, nJump=60, jumpType="Single Choco", vStart="100m")
)
# Half choco jump
# 1x Xanax + 24x Big Boxes of Chocolate + Ecstasy + Refill(*) = 12h CD (two trains per day)
# * Since you can refill energy with points only once per day, i will split the energy gain over both jumps
# - Energy: 475
# - Happy: 10730
# - Cost per train: ~1mil + daily refill
# - Number of trains: 120
# - Total cost: 120mil + 60x Refill = 188mil
HCJ <- rbind(
jumpSim(startStat=10e3, jumpH=10730, jumpE=475, nJump=120, jumpType="Half Choco", vStart="10k"),
jumpSim(startStat=300e3, jumpH=10730, jumpE=475, nJump=120, jumpType="Half Choco", vStart="300k"),
jumpSim(startStat=5e6, jumpH=10730, jumpE=475, nJump=120, jumpType="Half Choco", vStart="5m"),
jumpSim(startStat=100e6, jumpH=10730, jumpE=475, nJump=120, jumpType="Half Choco", vStart="100m")
)
# Energy training
# Energy training is very hard to put into exact formulas, because there are many ways to go about it.
# You can waste a bit of E regen and time Xanax with max E for optimal Happy, you can ignore Happy and just spam E on cooldown,
# you can include FHCs in your ration to get even more E, etc.
# Energy training: 3x Xanax
# * For simplicity, let's say you train every 5 hours for natural regen + one refill and split the xanax gains evenly
# 3x Xanax + 750 natural regen + 150 refill = 6 trains per day
# - Energy: 270 (average)
# - Happy: 4525
# - Cost per train: ~600k
# - Number of trains: 360
# - Total cost: 216mil
ET <- rbind(
jumpSim(startStat=10e3, jumpH=4525, jumpE=270, nJump=360, jumpType="Energy training: 3 Xanax", vStart="10k"),
jumpSim(startStat=300e3, jumpH=4525, jumpE=270, nJump=360, jumpType="Energy training: 3 Xanax", vStart="300k"),
jumpSim(startStat=5e6, jumpH=4525, jumpE=270, nJump=360, jumpType="Energy training: 3 Xanax", vStart="5m"),
jumpSim(startStat=100e6, jumpH=4525, jumpE=270, nJump=360, jumpType="Energy training: 3 Xanax", vStart="100m")
)
# Energy training: 2x Xanax
# * For simplicity, let's say you train every 5 hours for natural regen + one refill and split the xanax gains evenly
# 2x Xanax + 750 natural regen + 150 refill = 6 trains per day
# - Energy: 230 (average)
# - Happy: 4525
# - Cost per train: ~470k
# - Number of trains: 360
# - Total cost: 170mil
ET2 <- rbind(
jumpSim(startStat=10e3, jumpH=4525, jumpE=230, nJump=360, jumpType="Energy training: 2 Xanax", vStart="10k"),
jumpSim(startStat=300e3, jumpH=4525, jumpE=230, nJump=360, jumpType="Energy training: 2 Xanax", vStart="300k"),
jumpSim(startStat=5e6, jumpH=4525, jumpE=230, nJump=360, jumpType="Energy training: 2 Xanax", vStart="5m"),
jumpSim(startStat=100e6, jumpH=4525, jumpE=230, nJump=360, jumpType="Energy training: 2 Xanax", vStart="100m")
)
# Energy training: FHC (extreme case)
# * In this case, we split the E gain from FHCs over all 6 trains, but ignore the happy gain
# This is far from a perfect way to account for the single big train you get from stacking FHCs, but i'm not going through that trouble!
# 3x Xanax + 720 natural regen + 4x FHC + 150 refill = 6 trains per day
# - Energy: 370 (average)
# - Happy: 4525
# - Cost per train: ~10.6mil
# - Number of trains: 360
# - Total cost: 3.8bil
FHC <- rbind(
jumpSim(startStat=10e3, jumpH=4525, jumpE=370, nJump=360, jumpType="Energy training: FHC", vStart="10k"),
jumpSim(startStat=300e3, jumpH=4525, jumpE=370, nJump=360, jumpType="Energy training: FHC", vStart="300k"),
jumpSim(startStat=5e6, jumpH=4525, jumpE=370, nJump=360, jumpType="Energy training: FHC", vStart="5m"),
jumpSim(startStat=100e6, jumpH=4525, jumpE=370, nJump=360, jumpType="Energy training: FHC", vStart="100m")
)
## Combine the data
toplot <- rbind(HJ,HJAN,SCJ,HCJ,ET,ET2,FHC)
toplot$Start <- factor(toplot$Start, levels=rev(c("100m","5m","300k","10k")))
toplot$Type <- factor(toplot$Type, levels=unique(toplot$Type))
## Plot results
ggplot(toplot, aes(x=Day, y=Stat, color=Type)) +
geom_line(lwd=2) + theme_bw(base_size = 18) +
scale_color_brewer(palette="Paired") + theme(legend.position = "top") +
facet_wrap(.~Start, scales="free")
## Without FHC
ggplot(toplot[Type!="Energy training: FHC"], aes(x=Day, y=Stat, color=Type)) +
geom_line(lwd=2) + theme_bw(base_size = 18) +
scale_color_brewer(palette="Paired") + theme(legend.position = "top") +
facet_wrap(.~Start, scales="free")
# If you are just starting out, Jumping is a great way to reach the level where pure energy training is better
# Value of happy is linear, while gains from additional energy spent are almost exponential
# If your stats are close to 200k, you should consider dropping jumps and focus on energy training instead
# To get even more gains, use the free booster cooldown for E items
# Or just drink beer and do crimes :)
## Calculate costs per %
costplot <- toplot[Day==60]
costplot$CostPerGain <- 0
costplot[Type=="Happy"]$CostPerGain <- costplot[Type=="Happy"]$Gain/880e6
costplot[Type=="Happy: Adult Novelties"]$CostPerGain <- costplot[Type=="Happy: Adult Novelties"]$Gain/880e6
costplot[Type=="Single Choco"]$CostPerGain <- costplot[Type=="Single Choco"]$Gain/144e6
costplot[Type=="Half Choco"]$CostPerGain <- costplot[Type=="Half Choco"]$Gain/188e6
costplot[Type=="Energy training: 3 Xanax"]$CostPerGain <- costplot[Type=="Energy training: 3 Xanax"]$Gain/216e6
costplot[Type=="Energy training: 2 Xanax"]$CostPerGain <- costplot[Type=="Energy training: 2 Xanax"]$Gain/170e6
costplot[Type=="Energy training: FHC"]$CostPerGain <- costplot[Type=="Energy training: FHC"]$Gain/3.8e9
out <- melt(costplot[,.(Type,Gain,CostPerGain*1e9,Start)], id.vars=c("Type","Start"))
ggplot(out, aes(y=Type, x=value, fill=variable)) +
geom_bar(stat="identity", position="dodge") +
theme_bw(base_size = 16) + theme(legend.position = "top") +
xlab("Gains / Value") +
scale_fill_brewer(palette="Set1", name="",
breaks=c("Gain","V3"), labels=c("Gain %","Value (scaled)")) +
facet_wrap(.~Start, scales="free")
# FHC energy training provides best gains, but is absurdly expensive
# Best value option is to do 3 or 2 Xanax per day training if your stat is at least 250k
# Jumping is a great way to grow early on, before you reach the critical threshold of 250k
# Happy jump is faster than Half Choco jumps, but not by that much when you factor in the cost
# TLDR: Do Half Choco jumps up to ~200k stat value, then switch to pure energy training (at least 2 Xanax/day)
## Energy vs Happy value
stats <- c(1e2,1e3,10e3,50e3,100e3,150e3,200e3,250e3,300e3,500e3,1e6,1.5e6)
# Using time period of 1 happy jump
tmp <- foreach(myStat=stats, .combine=rbind) %do% {
data.table(
Stat=myStat,
Gain=c(
max(jumpSim(startStat=myStat, jumpH=34050, jumpE=1150, nJump=1, jumpType="Happy", vStart="10k")$Stat)-myStat,
max(jumpSim(startStat=myStat, jumpH=59050, jumpE=1150, nJump=1, jumpType="Happy: Adult Novelties", vStart="10k")$Stat)-myStat,
# max(jumpSim(startStat=myStat, jumpH=12410, jumpE=550, nJump=3, jumpType="Single Choco", vStart="10k")$Stat),
max(jumpSim(startStat=myStat, jumpH=10730, jumpE=475, nJump=3, jumpType="Half Choco", vStart="10k")$Stat)-myStat,
max(jumpSim(startStat=myStat, jumpH=4525, jumpE=270, nJump=9, jumpType="Energy training: 3 Xanax", vStart="10k")$Stat)-myStat,
max(jumpSim(startStat=myStat, jumpH=4525, jumpE=230, nJump=9, jumpType="Energy training: 2 Xanax", vStart="10k")$Stat)-myStat
# max(jumpSim(startStat=myStat, jumpH=4525, jumpE=370, nJump=18, jumpType="Energy training: FHC", vStart="10k")$Stat)
),
Type=c("Happy","Happy: Adult Novelites","Half Choco","Energy training: 3 Xanax","Energy training: 2 Xanax")
)
}
# When to switch methods?
ggplot(tmp, aes(x=Stat, y=Gain, color=Type)) +
geom_line(lwd=1.5) + xlab("Starting stat") + ylab("Stat gain") +
geom_rect(aes(xmin=245e3,xmax=450e3,ymin=20e3,ymax=50e3), fill=NA, col="red") +
geom_point(aes(x=1.32e6,y=99e3), col="red", size=3) +
theme_bw(base_size = 18) +
scale_color_brewer(palette="Paired") +
scale_x_continuous(breaks=seq(0,1.5e6,300e3)) +
theme(legend.position = "top")
# Switching from Jump training to Energy based training is best around 250-400k stat
# If you insist on jumping, then Half-Choco jumps become more efficient at around 1.2mil stat
# Only exception is if you are working in an AN company - in that case, Happy jumping offers the best gains up to ~900k stat
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