R：下标越界
R: subscript is out of bounds
我正在使用 R。我正在尝试按照此处的本教程进行函数优化：https://rpubs.com/Argaadya/bayesian-optimization
对于这个例子，我首先生成一些随机数据：
#load libraries
library(dplyr)
# create some data for this example
a1 = rnorm(1000,100,10)
b1 = rnorm(1000,100,5)
c1 = sample.int(1000, 1000, replace = TRUE)
train_data = data.frame(a1,b1,c1)
从这里开始，我定义了我想要优化的功能（“健身”）。此函数采用 7 个输入并计算“总”平均值（单个标量值）。此函数所需的输入是：
- "random_1"（80 到 120 之间）
- "random_2"（介于“random_1”和 120 之间）
- "random_3"（85 到 120 之间）
- "random_4"（介于 random_2 和 120 之间）
- "split_1"（介于 0 和 1 之间）
- "split_2"（介于 0 和 1 之间）
- "split_3"（介于 0 和 1 之间）
要优化的函数（“fitness”）定义如下：
#define fitness function : returns a single scalar value called "total"
fitness <- function(random_1, random_2, random_3, random_4, split_1, split_2, split_3) {
#bin data according to random criteria
train_data <- train_data %>% mutate(cat = ifelse(a1 <= random_1 & b1 <= random_3, "a", ifelse(a1 <= random_2 & b1 <= random_4, "b", "c")))
train_data$cat = as.factor(train_data$cat)
#new splits
a_table = train_data %>%
filter(cat == "a") %>%
select(a1, b1, c1, cat)
b_table = train_data %>%
filter(cat == "b") %>%
select(a1, b1, c1, cat)
c_table = train_data %>%
filter(cat == "c") %>%
select(a1, b1, c1, cat)
#calculate quantile ("quant") for each bin
table_a = data.frame(a_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_1)))
table_b = data.frame(b_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_2)))
table_c = data.frame(c_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_3)))
#create a new variable ("diff") that measures if the quantile is bigger than the value of "c1"
table_a$diff = ifelse(table_a$quant > table_a$c1,1,0)
table_b$diff = ifelse(table_b$quant > table_b$c1,1,0)
table_c$diff = ifelse(table_c$quant > table_c$c1,1,0)
#group all tables
final_table = rbind(table_a, table_b, table_c)
# calculate the total mean : this is what needs to be optimized
mean = mean(final_table$diff)
}
**目标：**我现在想使用本教程中的“贝叶斯优化”算法 (https://rpubs.com/Argaadya/bayesian-optimization)。 objective就是求出这7个数产生最大的“均值”的值：
首先，定义“搜索范围”：
#define search bound for the 7 inputs
library(rBayesianOptimization)
random_1 = NULL
random_2 = NULL
random_3 = NULL
random_4 = NULL
split_1 = NULL
split_2 = NULL
split_3 = NULL
search_bound <- list(random_1 = c(80,120), random_2 = c(random_1,120),
random_3 = c(85,120), random_4 = c(random_2, 120), split_1 = c(0,1), split_2 = c(0,1), split_3 = c(0,1))
二、设置初始样本：
#set initial sample:
set.seed(123)
search_grid <- data.frame(random_1 = runif(20,80,120),
random_2 = runif(20,random_1,120),
random_3 = runif(20,85,120),
random_4 = runif(20,random_2,120),
split_1= runif(20,0,1),
split_2 = runif(20,0,1),
split_3 = runif(20,0,1)
)
最后，运行贝叶斯优化算法：
#run the bayesian optimization algorithm:
set.seed(1)
bayes_finance_ei <- BayesianOptimization(FUN = fitness, bounds = search_bound,
init_grid_dt = search_grid, init_points = 0,
n_iter = 10, acq = "ei")
但这会产生以下错误：
Error in FUN(X[[i]], ...) : subscript out of bounds
有人可以告诉我我做错了什么吗？我以为我遵循了教程中的所有必要步骤？
谢谢
您的代码中似乎存在一些错误，例如我认为您的适应度函数没有以所需格式返回数据，并且您的一些向量在定义之前就已被使用。
我做了一些更改，使您的代码更符合教程，并且似乎没有错误地完成，但我不能说结果是否“正确”或是否适合您使用-案例：
#load libraries
library(tidyverse)
#install.packages("rBayesianOptimization")
library(rBayesianOptimization)
# create some data for this example
a1 = rnorm(1000,100,10)
b1 = rnorm(1000,100,5)
c1 = sample.int(1000, 1000, replace = TRUE)
train_data = data.frame(a1,b1,c1)
#define fitness function : returns a single scalar value called "total"
fitness <- function(random_1, random_2, random_3, random_4, split_1, split_2, split_3) {
#bin data according to random criteria
train_data <- train_data %>% mutate(cat = ifelse(a1 <= random_1 & b1 <= random_3, "a", ifelse(a1 <= random_2 & b1 <= random_4, "b", "c")))
train_data$cat = as.factor(train_data$cat)
#new splits
a_table = train_data %>%
filter(cat == "a") %>%
select(a1, b1, c1, cat)
b_table = train_data %>%
filter(cat == "b") %>%
select(a1, b1, c1, cat)
c_table = train_data %>%
filter(cat == "c") %>%
select(a1, b1, c1, cat)
#calculate quantile ("quant") for each bin
table_a = data.frame(a_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_1)))
table_b = data.frame(b_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_2)))
table_c = data.frame(c_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_3)))
#create a new variable ("diff") that measures if the quantile is bigger than the value of "c1"
table_a$diff = ifelse(table_a$quant > table_a$c1,1,0)
table_b$diff = ifelse(table_b$quant > table_b$c1,1,0)
table_c$diff = ifelse(table_c$quant > table_c$c1,1,0)
#group all tables
final_table = rbind(table_a, table_b, table_c)
# calculate the total mean : this is what needs to be optimized
mean = mean(final_table$diff)
# Based on the tutorial you linked, the fitness func
# needs to return a list (a Score and a Pred)
# I'm not sure if this is in line with your intended use-case
# but it seems to work
result <- list(Score = mean, Pred = 0)
return(result)
}
# There were some bugs in this section,
# e.g. you were trying to call vectors ("random_1")
# that hadn't been defined yet
set.seed(123)
random_1 = runif(20,80,120)
random_2 = runif(20, random_1, 120)
random_3 = runif(20,85,120)
random_4 = runif(20, random_2, 120)
split_1= runif(20,0,1)
split_2 = runif(20,0,1)
split_3 = runif(20,0,1)
search_bound <- list(random_1 = c(80,120),
random_2 = c(random_1,120),
random_3 = c(85,120),
random_4 = c(random_2, 120),
split_1 = c(0,1),
split_2 = c(0,1),split_3 = c(0,1))
search_grid <- data.frame(random_1, random_2, random_3, random_4, split_1, split_2, split_3)
set.seed(1)
bayes_finance_ei <- BayesianOptimization(FUN = fitness, bounds = search_bound,
init_grid_dt = search_grid, init_points = 0,
n_iter = 10, acq = "ei")
#> elapsed = 0.076 Round = 1 random_1 = 91.5031 random_2 = 116.8522 random_3 = 89.9980 random_4 = 118.9459 split_1 = 0.2436195 split_2 = 0.599989 split_3 = 0.6478935 Value = 0.6020
#> elapsed = 0.023 Round = 2 random_1 = 111.5322 random_2 = 117.3987 random_3 = 99.50912 random_4 = 117.6454 split_1 = 0.6680556 split_2 = 0.3328235 split_3 = 0.3198206 Value = 0.4650
#> elapsed = 0.026 Round = 3 random_1 = 96.35908 random_2 = 111.5012 random_3 = 99.48035 random_4 = 114.7645 split_1 = 0.4176468 split_2 = 0.488613 split_3 = 0.30772 Value = 0.4520
#> elapsed = 0.024 Round = 4 random_1 = 115.3207 random_2 = 119.9732 random_3 = 97.90959 random_4 = 119.9805 split_1 = 0.7881958 split_2 = 0.9544738 split_3 = 0.2197676 Value = 0.8840
#> elapsed = 0.024 Round = 5 random_1 = 117.6187 random_2 = 119.1801 random_3 = 90.33557 random_4 = 119.8480 split_1 = 0.1028646 split_2 = 0.4829024 split_3 = 0.3694889 Value = 0.4690
#> elapsed = 0.024 Round = 6 random_1 = 81.82226 random_2 = 108.8724 random_3 = 89.85821 random_4 = 113.8633 split_1 = 0.4348927 split_2 = 0.8903502 split_3 = 0.9842192 Value = 0.9090
#> elapsed = 0.025 Round = 7 random_1 = 101.1242 random_2 = 111.3939 random_3 = 93.15619 random_4 = 118.3654 split_1 = 0.984957 split_2 = 0.9144382 split_3 = 0.1542023 Value = 0.8130
#> elapsed = 0.023 Round = 8 random_1 = 115.6968 random_2 = 118.2535 random_3 = 101.3087 random_4 = 119.6723 split_1 = 0.8930511 split_2 = 0.608735 split_3 = 0.091044 Value = 0.7490
#> elapsed = 0.024 Round = 9 random_1 = 102.0574 random_2 = 107.2457 random_3 = 94.30904 random_4 = 117.3770 split_1 = 0.8864691 split_2 = 0.4106898 split_3 = 0.1419069 Value = 0.3830
#> elapsed = 0.022 Round = 10 random_1 = 98.26459 random_2 = 101.4622 random_3 = 115.0240 random_4 = 109.6157 split_1 = 0.1750527 split_2 = 0.1470947 split_3 = 0.6900071 Value = 0.4150
#> elapsed = 0.023 Round = 11 random_1 = 118.2733 random_2 = 119.9362 random_3 = 86.60409 random_4 = 119.9843 split_1 = 0.1306957 split_2 = 0.9352998 split_3 = 0.6192565 Value = 0.9250
#> elapsed = 0.023 Round = 12 random_1 = 98.13337 random_2 = 117.8636 random_3 = 100.4770 random_4 = 119.2079 split_1 = 0.6531019 split_2 = 0.3012289 split_3 = 0.8913941 Value = 0.4050
#> elapsed = 0.025 Round = 13 random_1 = 107.1028 random_2 = 116.0110 random_3 = 112.9624 random_4 = 118.8439 split_1 = 0.3435165 split_2 = 0.06072057 split_3 = 0.6729991 Value = 0.3040
#> elapsed = 0.023 Round = 14 random_1 = 102.9053 random_2 = 116.5036 random_3 = 89.26647 random_4 = 116.5058 split_1 = 0.6567581 split_2 = 0.9477269 split_3 = 0.7370777 Value = 0.9340
#> elapsed = 0.022 Round = 15 random_1 = 84.11699 random_2 = 85.0002 random_3 = 104.6332 random_4 = 101.6362 split_1 = 0.3203732 split_2 = 0.7205963 split_3 = 0.5211357 Value = 0.5130
#> elapsed = 0.023 Round = 16 random_1 = 115.9930 random_2 = 117.9075 random_3 = 92.2286 random_4 = 118.3681 split_1 = 0.1876911 split_2 = 0.1422943 split_3 = 0.6598384 Value = 0.1610
#> elapsed = 0.024 Round = 17 random_1 = 89.84351 random_2 = 112.7160 random_3 = 89.46361 random_4 = 115.4826 split_1 = 0.7822943 split_2 = 0.5492847 split_3 = 0.8218055 Value = 0.5790
#> elapsed = 0.025 Round = 18 random_1 = 81.68238 random_2 = 89.97462 random_3 = 111.3658 random_4 = 108.3733 split_1 = 0.09359499 split_2 = 0.9540912 split_3 = 0.7862816 Value = 0.7880
#> elapsed = 0.023 Round = 19 random_1 = 93.11683 random_2 = 101.6705 random_3 = 116.3266 random_4 = 108.1188 split_1 = 0.466779 split_2 = 0.5854834 split_3 = 0.9798219 Value = 0.7410
#> elapsed = 0.026 Round = 20 random_1 = 118.1801 random_2 = 118.6017 random_3 = 98.1062 random_4 = 118.7571 split_1 = 0.5115055 split_2 = 0.4045103 split_3 = 0.4394315 Value = 0.4420
#> elapsed = 0.023 Round = 21 random_1 = 96.45638 random_2 = 111.5322 random_3 = 87.30664 random_4 = 117.3987 split_1 = 0.1028404 split_2 = 1.0000 split_3 = 0.9511012 Value = 0.9910
#> elapsed = 0.022 Round = 22 random_1 = 117.5734 random_2 = 91.5031 random_3 = 120.0000 random_4 = 116.8522 split_1 = 2.220446e-16 split_2 = 1.0000 split_3 = 2.220446e-16 Value = 0.0020
#> elapsed = 0.023 Round = 23 random_1 = 111.5021 random_2 = 111.5322 random_3 = 120.0000 random_4 = 117.3987 split_1 = 0.1188052 split_2 = 1.0000 split_3 = 0.6455233 Value = 0.1910
#> elapsed = 0.028 Round = 24 random_1 = 80.0000 random_2 = 92.90239 random_3 = 86.18939 random_4 = 116.8635 split_1 = 0.2557032 split_2 = 1.0000 split_3 = 0.3517052 Value = 0.5170
#> elapsed = 0.026 Round = 25 random_1 = 90.64032 random_2 = 92.3588 random_3 = 112.3187 random_4 = 117.3987 split_1 = 1.0000 split_2 = 1.0000 split_3 = 1.0000 Value = 0.9970
#> elapsed = 0.022 Round = 26 random_1 = 100.4363 random_2 = 104.1665 random_3 = 106.2099 random_4 = 117.3987 split_1 = 1.0000 split_2 = 1.0000 split_3 = 1.0000 Value = 0.9970
#> elapsed = 0.022 Round = 27 random_1 = 119.0981 random_2 = 91.5031 random_3 = 120.0000 random_4 = 117.3987 split_1 = 1.0000 split_2 = 1.0000 split_3 = 1.0000 Value = 0.9980
#> elapsed = 0.023 Round = 28 random_1 = 89.95279 random_2 = 101.9462 random_3 = 85.0000 random_4 = 116.9137 split_1 = 2.220446e-16 split_2 = 1.0000 split_3 = 1.0000 Value = 0.9980
#> elapsed = 0.027 Round = 29 random_1 = 113.5928 random_2 = 91.5031 random_3 = 120.0000 random_4 = 117.3987 split_1 = 1.0000 split_2 = 2.220446e-16 split_3 = 1.0000 Value = 0.9980
#> elapsed = 0.027 Round = 30 random_1 = 116.9869 random_2 = 91.5031 random_3 = 120.0000 random_4 = 117.2949 split_1 = 1.0000 split_2 = 0.505048 split_3 = 1.0000 Value = 0.9980
#>
#> Best Parameters Found:
#> Round = 27 random_1 = 119.0981 random_2 = 91.5031 random_3 = 120.0000 random_4 = 117.3987 split_1 = 1.0000 split_2 = 1.0000 split_3 = 1.0000 Value = 0.9980
由 reprex package (v2.0.0)
于 2021-07-08 创建
我正在使用 R。我正在尝试按照此处的本教程进行函数优化：https://rpubs.com/Argaadya/bayesian-optimization
对于这个例子，我首先生成一些随机数据：
#load libraries
library(dplyr)
# create some data for this example
a1 = rnorm(1000,100,10)
b1 = rnorm(1000,100,5)
c1 = sample.int(1000, 1000, replace = TRUE)
train_data = data.frame(a1,b1,c1)
从这里开始，我定义了我想要优化的功能（“健身”）。此函数采用 7 个输入并计算“总”平均值（单个标量值）。此函数所需的输入是：
- "random_1"（80 到 120 之间）
- "random_2"（介于“random_1”和 120 之间）
- "random_3"（85 到 120 之间）
- "random_4"（介于 random_2 和 120 之间）
- "split_1"（介于 0 和 1 之间）
- "split_2"（介于 0 和 1 之间）
- "split_3"（介于 0 和 1 之间）
要优化的函数（“fitness”）定义如下：
#define fitness function : returns a single scalar value called "total"
fitness <- function(random_1, random_2, random_3, random_4, split_1, split_2, split_3) {
#bin data according to random criteria
train_data <- train_data %>% mutate(cat = ifelse(a1 <= random_1 & b1 <= random_3, "a", ifelse(a1 <= random_2 & b1 <= random_4, "b", "c")))
train_data$cat = as.factor(train_data$cat)
#new splits
a_table = train_data %>%
filter(cat == "a") %>%
select(a1, b1, c1, cat)
b_table = train_data %>%
filter(cat == "b") %>%
select(a1, b1, c1, cat)
c_table = train_data %>%
filter(cat == "c") %>%
select(a1, b1, c1, cat)
#calculate quantile ("quant") for each bin
table_a = data.frame(a_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_1)))
table_b = data.frame(b_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_2)))
table_c = data.frame(c_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_3)))
#create a new variable ("diff") that measures if the quantile is bigger than the value of "c1"
table_a$diff = ifelse(table_a$quant > table_a$c1,1,0)
table_b$diff = ifelse(table_b$quant > table_b$c1,1,0)
table_c$diff = ifelse(table_c$quant > table_c$c1,1,0)
#group all tables
final_table = rbind(table_a, table_b, table_c)
# calculate the total mean : this is what needs to be optimized
mean = mean(final_table$diff)
}
**目标：**我现在想使用本教程中的“贝叶斯优化”算法 (https://rpubs.com/Argaadya/bayesian-optimization)。 objective就是求出这7个数产生最大的“均值”的值：
首先，定义“搜索范围”：
#define search bound for the 7 inputs
library(rBayesianOptimization)
random_1 = NULL
random_2 = NULL
random_3 = NULL
random_4 = NULL
split_1 = NULL
split_2 = NULL
split_3 = NULL
search_bound <- list(random_1 = c(80,120), random_2 = c(random_1,120),
random_3 = c(85,120), random_4 = c(random_2, 120), split_1 = c(0,1), split_2 = c(0,1), split_3 = c(0,1))
二、设置初始样本：
#set initial sample:
set.seed(123)
search_grid <- data.frame(random_1 = runif(20,80,120),
random_2 = runif(20,random_1,120),
random_3 = runif(20,85,120),
random_4 = runif(20,random_2,120),
split_1= runif(20,0,1),
split_2 = runif(20,0,1),
split_3 = runif(20,0,1)
)
最后，运行贝叶斯优化算法：
#run the bayesian optimization algorithm:
set.seed(1)
bayes_finance_ei <- BayesianOptimization(FUN = fitness, bounds = search_bound,
init_grid_dt = search_grid, init_points = 0,
n_iter = 10, acq = "ei")
但这会产生以下错误：
Error in FUN(X[[i]], ...) : subscript out of bounds
有人可以告诉我我做错了什么吗？我以为我遵循了教程中的所有必要步骤？
谢谢
您的代码中似乎存在一些错误，例如我认为您的适应度函数没有以所需格式返回数据，并且您的一些向量在定义之前就已被使用。
我做了一些更改，使您的代码更符合教程，并且似乎没有错误地完成，但我不能说结果是否“正确”或是否适合您使用-案例：
#load libraries
library(tidyverse)
#install.packages("rBayesianOptimization")
library(rBayesianOptimization)
# create some data for this example
a1 = rnorm(1000,100,10)
b1 = rnorm(1000,100,5)
c1 = sample.int(1000, 1000, replace = TRUE)
train_data = data.frame(a1,b1,c1)
#define fitness function : returns a single scalar value called "total"
fitness <- function(random_1, random_2, random_3, random_4, split_1, split_2, split_3) {
#bin data according to random criteria
train_data <- train_data %>% mutate(cat = ifelse(a1 <= random_1 & b1 <= random_3, "a", ifelse(a1 <= random_2 & b1 <= random_4, "b", "c")))
train_data$cat = as.factor(train_data$cat)
#new splits
a_table = train_data %>%
filter(cat == "a") %>%
select(a1, b1, c1, cat)
b_table = train_data %>%
filter(cat == "b") %>%
select(a1, b1, c1, cat)
c_table = train_data %>%
filter(cat == "c") %>%
select(a1, b1, c1, cat)
#calculate quantile ("quant") for each bin
table_a = data.frame(a_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_1)))
table_b = data.frame(b_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_2)))
table_c = data.frame(c_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_3)))
#create a new variable ("diff") that measures if the quantile is bigger than the value of "c1"
table_a$diff = ifelse(table_a$quant > table_a$c1,1,0)
table_b$diff = ifelse(table_b$quant > table_b$c1,1,0)
table_c$diff = ifelse(table_c$quant > table_c$c1,1,0)
#group all tables
final_table = rbind(table_a, table_b, table_c)
# calculate the total mean : this is what needs to be optimized
mean = mean(final_table$diff)
# Based on the tutorial you linked, the fitness func
# needs to return a list (a Score and a Pred)
# I'm not sure if this is in line with your intended use-case
# but it seems to work
result <- list(Score = mean, Pred = 0)
return(result)
}
# There were some bugs in this section,
# e.g. you were trying to call vectors ("random_1")
# that hadn't been defined yet
set.seed(123)
random_1 = runif(20,80,120)
random_2 = runif(20, random_1, 120)
random_3 = runif(20,85,120)
random_4 = runif(20, random_2, 120)
split_1= runif(20,0,1)
split_2 = runif(20,0,1)
split_3 = runif(20,0,1)
search_bound <- list(random_1 = c(80,120),
random_2 = c(random_1,120),
random_3 = c(85,120),
random_4 = c(random_2, 120),
split_1 = c(0,1),
split_2 = c(0,1),split_3 = c(0,1))
search_grid <- data.frame(random_1, random_2, random_3, random_4, split_1, split_2, split_3)
set.seed(1)
bayes_finance_ei <- BayesianOptimization(FUN = fitness, bounds = search_bound,
init_grid_dt = search_grid, init_points = 0,
n_iter = 10, acq = "ei")
#> elapsed = 0.076 Round = 1 random_1 = 91.5031 random_2 = 116.8522 random_3 = 89.9980 random_4 = 118.9459 split_1 = 0.2436195 split_2 = 0.599989 split_3 = 0.6478935 Value = 0.6020
#> elapsed = 0.023 Round = 2 random_1 = 111.5322 random_2 = 117.3987 random_3 = 99.50912 random_4 = 117.6454 split_1 = 0.6680556 split_2 = 0.3328235 split_3 = 0.3198206 Value = 0.4650
#> elapsed = 0.026 Round = 3 random_1 = 96.35908 random_2 = 111.5012 random_3 = 99.48035 random_4 = 114.7645 split_1 = 0.4176468 split_2 = 0.488613 split_3 = 0.30772 Value = 0.4520
#> elapsed = 0.024 Round = 4 random_1 = 115.3207 random_2 = 119.9732 random_3 = 97.90959 random_4 = 119.9805 split_1 = 0.7881958 split_2 = 0.9544738 split_3 = 0.2197676 Value = 0.8840
#> elapsed = 0.024 Round = 5 random_1 = 117.6187 random_2 = 119.1801 random_3 = 90.33557 random_4 = 119.8480 split_1 = 0.1028646 split_2 = 0.4829024 split_3 = 0.3694889 Value = 0.4690
#> elapsed = 0.024 Round = 6 random_1 = 81.82226 random_2 = 108.8724 random_3 = 89.85821 random_4 = 113.8633 split_1 = 0.4348927 split_2 = 0.8903502 split_3 = 0.9842192 Value = 0.9090
#> elapsed = 0.025 Round = 7 random_1 = 101.1242 random_2 = 111.3939 random_3 = 93.15619 random_4 = 118.3654 split_1 = 0.984957 split_2 = 0.9144382 split_3 = 0.1542023 Value = 0.8130
#> elapsed = 0.023 Round = 8 random_1 = 115.6968 random_2 = 118.2535 random_3 = 101.3087 random_4 = 119.6723 split_1 = 0.8930511 split_2 = 0.608735 split_3 = 0.091044 Value = 0.7490
#> elapsed = 0.024 Round = 9 random_1 = 102.0574 random_2 = 107.2457 random_3 = 94.30904 random_4 = 117.3770 split_1 = 0.8864691 split_2 = 0.4106898 split_3 = 0.1419069 Value = 0.3830
#> elapsed = 0.022 Round = 10 random_1 = 98.26459 random_2 = 101.4622 random_3 = 115.0240 random_4 = 109.6157 split_1 = 0.1750527 split_2 = 0.1470947 split_3 = 0.6900071 Value = 0.4150
#> elapsed = 0.023 Round = 11 random_1 = 118.2733 random_2 = 119.9362 random_3 = 86.60409 random_4 = 119.9843 split_1 = 0.1306957 split_2 = 0.9352998 split_3 = 0.6192565 Value = 0.9250
#> elapsed = 0.023 Round = 12 random_1 = 98.13337 random_2 = 117.8636 random_3 = 100.4770 random_4 = 119.2079 split_1 = 0.6531019 split_2 = 0.3012289 split_3 = 0.8913941 Value = 0.4050
#> elapsed = 0.025 Round = 13 random_1 = 107.1028 random_2 = 116.0110 random_3 = 112.9624 random_4 = 118.8439 split_1 = 0.3435165 split_2 = 0.06072057 split_3 = 0.6729991 Value = 0.3040
#> elapsed = 0.023 Round = 14 random_1 = 102.9053 random_2 = 116.5036 random_3 = 89.26647 random_4 = 116.5058 split_1 = 0.6567581 split_2 = 0.9477269 split_3 = 0.7370777 Value = 0.9340
#> elapsed = 0.022 Round = 15 random_1 = 84.11699 random_2 = 85.0002 random_3 = 104.6332 random_4 = 101.6362 split_1 = 0.3203732 split_2 = 0.7205963 split_3 = 0.5211357 Value = 0.5130
#> elapsed = 0.023 Round = 16 random_1 = 115.9930 random_2 = 117.9075 random_3 = 92.2286 random_4 = 118.3681 split_1 = 0.1876911 split_2 = 0.1422943 split_3 = 0.6598384 Value = 0.1610
#> elapsed = 0.024 Round = 17 random_1 = 89.84351 random_2 = 112.7160 random_3 = 89.46361 random_4 = 115.4826 split_1 = 0.7822943 split_2 = 0.5492847 split_3 = 0.8218055 Value = 0.5790
#> elapsed = 0.025 Round = 18 random_1 = 81.68238 random_2 = 89.97462 random_3 = 111.3658 random_4 = 108.3733 split_1 = 0.09359499 split_2 = 0.9540912 split_3 = 0.7862816 Value = 0.7880
#> elapsed = 0.023 Round = 19 random_1 = 93.11683 random_2 = 101.6705 random_3 = 116.3266 random_4 = 108.1188 split_1 = 0.466779 split_2 = 0.5854834 split_3 = 0.9798219 Value = 0.7410
#> elapsed = 0.026 Round = 20 random_1 = 118.1801 random_2 = 118.6017 random_3 = 98.1062 random_4 = 118.7571 split_1 = 0.5115055 split_2 = 0.4045103 split_3 = 0.4394315 Value = 0.4420
#> elapsed = 0.023 Round = 21 random_1 = 96.45638 random_2 = 111.5322 random_3 = 87.30664 random_4 = 117.3987 split_1 = 0.1028404 split_2 = 1.0000 split_3 = 0.9511012 Value = 0.9910
#> elapsed = 0.022 Round = 22 random_1 = 117.5734 random_2 = 91.5031 random_3 = 120.0000 random_4 = 116.8522 split_1 = 2.220446e-16 split_2 = 1.0000 split_3 = 2.220446e-16 Value = 0.0020
#> elapsed = 0.023 Round = 23 random_1 = 111.5021 random_2 = 111.5322 random_3 = 120.0000 random_4 = 117.3987 split_1 = 0.1188052 split_2 = 1.0000 split_3 = 0.6455233 Value = 0.1910
#> elapsed = 0.028 Round = 24 random_1 = 80.0000 random_2 = 92.90239 random_3 = 86.18939 random_4 = 116.8635 split_1 = 0.2557032 split_2 = 1.0000 split_3 = 0.3517052 Value = 0.5170
#> elapsed = 0.026 Round = 25 random_1 = 90.64032 random_2 = 92.3588 random_3 = 112.3187 random_4 = 117.3987 split_1 = 1.0000 split_2 = 1.0000 split_3 = 1.0000 Value = 0.9970
#> elapsed = 0.022 Round = 26 random_1 = 100.4363 random_2 = 104.1665 random_3 = 106.2099 random_4 = 117.3987 split_1 = 1.0000 split_2 = 1.0000 split_3 = 1.0000 Value = 0.9970
#> elapsed = 0.022 Round = 27 random_1 = 119.0981 random_2 = 91.5031 random_3 = 120.0000 random_4 = 117.3987 split_1 = 1.0000 split_2 = 1.0000 split_3 = 1.0000 Value = 0.9980
#> elapsed = 0.023 Round = 28 random_1 = 89.95279 random_2 = 101.9462 random_3 = 85.0000 random_4 = 116.9137 split_1 = 2.220446e-16 split_2 = 1.0000 split_3 = 1.0000 Value = 0.9980
#> elapsed = 0.027 Round = 29 random_1 = 113.5928 random_2 = 91.5031 random_3 = 120.0000 random_4 = 117.3987 split_1 = 1.0000 split_2 = 2.220446e-16 split_3 = 1.0000 Value = 0.9980
#> elapsed = 0.027 Round = 30 random_1 = 116.9869 random_2 = 91.5031 random_3 = 120.0000 random_4 = 117.2949 split_1 = 1.0000 split_2 = 0.505048 split_3 = 1.0000 Value = 0.9980
#>
#> Best Parameters Found:
#> Round = 27 random_1 = 119.0981 random_2 = 91.5031 random_3 = 120.0000 random_4 = 117.3987 split_1 = 1.0000 split_2 = 1.0000 split_3 = 1.0000 Value = 0.9980
由 reprex package (v2.0.0)
于 2021-07-08 创建