-
Notifications
You must be signed in to change notification settings - Fork 0
/
global.R
101 lines (98 loc) · 3.1 KB
/
global.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
library(shiny)
library(shinydashboard)
library(tidyverse)
library(tidyr)
# library(R39Toolbox)
library(ggplot2)
library(mgcv)
library(qgam)
library(opera)
library(plotly)
library(xgboost)
library(magrittr)
library(forecast)
library(RColorBrewer)
library(yarrr)
library(dummies)
library(ranger)
library(shinyBS)
library(caret)
options(shiny.maxRequestSize = 90 * 1024 ^ 2)
options(digits = 4)
options(warn = -1)
options(shiny.sanitize.errors = TRUE)
# Functions ----------------------------------------
# for (i in list.files(path = "../../../R/", full.names = TRUE)) {
# if(!stringr::str_detect(i, 'demo')) source(file = i, encoding = "UTF-8")
# }
for (i in list.files(path = "./func/", full.names = TRUE)) {
source(file = i, encoding = "UTF-8")
}
# Modules ------------------------------------------
# ------- Fixed parameters --------------
tabs.content <- list(
# ------ Constant bias params ---------
list(Title = "Constant_bias", Content = tagList(
numericInput("n_expert_cb",
label = h5("Number of experts : "),
value = 10),
numericInput("amplitude_cb",
label = h5("Amplitude : "),
value = 2),
fileInput("pretrained_cb",
label = h5("Pretrained experts (.rds):"))
)),
# ------ Bagging params ---------
list(Title = "Bagging", Content = tagList(
numericInput("n_expert_bagg",
label = h5("Number of experts : "),
value = 10),
sliderInput("sr_bagg",
label = h5("Sampling rate :"),
min = 0,
max = 1,
value = 0.8),
fileInput("pretrained_bagg",
label = h5("Pretrained experts (.rds):"))
)),
# ------ Boosting params ---------
list(Title = "Boosting", Content = tagList(
numericInput("n_expert_boost",
label = h5("Number of experts : "),
value = 10),
sliderInput("lambda_boost",
label = h5("Lambda :"),
min = 0,
max = 1,
value = 0.1),
fileInput("pretrained_boost",
label = h5("Pretrained experts (.rds):"))
)),
# ------ Rw params ---------
list(Title = "Random walk", Content = tagList(
numericInput("n_expert_rw",
label = h5("Number of experts : "),
value = 10),
numericInput("var_fact_rw",
label = h5("Variance factor : "),
value = 4),
fileInput("pretrained_rw",
label = h5("Pretrained experts (.rds):"))
)),
# ------ Qgam params ---------
list(Title = "QGam", Content = tagList(
# numericInput("n_expert_qgam",
# label = h5("Number of experts : "),
# value = 10),
fileInput("pretrained_qgam",
label = h5("Pretrained experts (.rds):"))
)),
# ------ Gamlss params ---------
list(Title = "Gamlss", Content = tagList(
# numericInput("n_expert_gamlss",
# label = h5("Number of experts : "),
# value = 10),
fileInput("pretrained_gamlss",
label = h5("Pretrained experts (.rds):"))
))
)