sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 17k | content stringlengths 1 200k |
|---|---|---|---|---|
bbf9159467ae1fd644c24ce9fbb474caf007152f83c375284a4689d0e583bcf7 | R | 1,243 | 48 | ### FOR RUNNING ON BEOCAT
setwd("/homes/amkusmec/pc_comparison")
library(tidyverse)
library(keras)
library(argparse)
source("11c.weather_build_model.R")
parser <- ArgumentParser()
parser$add_argument("--seed", type = "integer")
args <- parser$parse_args()
### Load the tuned hyperparameters ----------------------... |
e258d8dc378e503bf0e0c4295c22a5b196526a4d0570ec94be34ff3e307aaf0b | R | 1,253 | 50 | ---
title: "Plot Age Distribution"
output: html_notebook
---
Version 1.0, July 2025, SA
This script plots the distribution of ages examined.
Input: babyhippos_demos.csv
Output: Fig. S14
# Packages and functions
```{r}
library(ggplot2)
```
# Read in the data
```{r}
dir<-"/your/path/here" ## CHANGE TO YOUR DIRE... |
aca6495bec008bb59ff9852d1f6751a0b73cd847f1057039d2d2b0007962839b | R | 1,259 | 33 | # code for extended figure 6 panel a
# source the functions from FLXenium folder, which loads useful libraries and has
# a bunch of functions useful for general analysis of spatial data
miceadds::source.all('~/Multidimensional_STEPN/4.Xenium/resources/FLXenium/')
# source the ependymoma project specific functions
so... |
f1ce238e0e4434dfb97370152f80b552417a5720b46c98c98e7d0bc4c74b94fb | R | 1,282 | 56 | ### FOR RUNNING ON BEOCAT
setwd("/homes/amkusmec/pc_comparison")
library(tidyverse)
library(keras)
library(argparse)
source("14b.tanh_hPC_build_model.R")
parser <- ArgumentParser()
parser$add_argument("--job", type = "integer")
args <- parser$parse_args()
### For testing only
# args <- parser$parse_args(c("--job... |
609fca06822417ecf5f6008e77b7a2a5432e92c1b4c75200c454dc6ff0d54363 | R | 1,291 | 56 | ### FOR RUNNING ON BEOCAT
setwd("/homes/amkusmec/pc_comparison")
library(tidyverse)
library(keras)
library(argparse)
source("13b.sigmoid_hPC_build_model.R")
parser <- ArgumentParser()
parser$add_argument("--job", type = "integer")
args <- parser$parse_args()
### For testing only
# args <- parser$parse_args(c("--... |
0eaa08da7bf4166f79f05dfc3d3c63bd243c8f06cc0334df65d83fd4f8407cde | R | 1,295 | 45 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
source("src/BGGE2.R")
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
ug <- unique(ref$Pedigree)
ue1 <- unique(ref$Environment1)
ue2 <- unique(ref$Environment2)
ref <- ref %>%
... |
30c0dcef605fac1bc2250f5cfef451ff884782b97eef84c9042419196d367928 | R | 1,296 | 29 | # UI-elements for News tab
tabPanel(title = "News", icon = icon("newspaper"),
div(id = "home",
h4(class = "outer", "v1.0 (12-12-2025)"),
h5(class = "outer", "Home:"),
tags$ul(
tags$li('Description of the App and information related to citation... |
03dd3f6730eb39035776235bdc50e63a4a5d29620ab9b6b8c89d91afce9cfd92 | R | 1,297 | 45 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
source("src/BGGE2.R")
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
ug <- unique(ref$Pedigree)
ue1 <- unique(ref$Environment1)
ue2 <- unique(ref$Environment2)
ref <- ref %>%
... |
0b64d7f7d11a0c30ab3b11267e1b93fc3cefb6a789a5c9da970f99c76a600973 | R | 1,297 | 45 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
source("src/BGGE2.R")
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
ug <- unique(ref$Pedigree)
ue1 <- unique(ref$Environment1)
ue2 <- unique(ref$Environment2)
ref <- ref %>%
... |
12b96e3d48733403feb66565c615dbd30ec54543ac758862c0b833215587105f | R | 1,297 | 37 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
source("src/int_tuner_classes.R")
### Unlike the sub-model tuners, data loading and processing occurs inside
### the run_trial() function of the tuner. With 10 CV-folds and 10 weight
### initializations, we... |
0edaf63e08bcc63c3f1fe4704826421e42de960383a6d5dca01e02d675230bc2 | R | 1,299 | 39 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
source("src/gPCs_tuner_classes.R")
cv_y <- read_rds("results/DNNs/cv_data/cv_y.rds")
cv_gpcs <- read_rds("results/DNNs/cv_data/cv_gpcs.rds")
training_data <- zip_lists("X" = lapply(cv_gpcs, `[[`, 1L),
... |
1b1e0567e1e224f0e840102a66fef617b0bf427fe65bf0f477a934b34dbc4bca | R | 1,300 | 37 | #' Whole blood DNA methylation profiles.
#'
#' A sample dataset of whole blood DNA methylation profiles from 10 individuals
#'
#' @format A matrix with 599 rows and 10 variables
"bulkdata"
#' Cortical DNA methylation profiles.
#'
#' A sample dataset of cortical DNA methylation profiles from 10 individuals
#'... |
45820cfebef14baeedd8e69be6c31acff4edca56812529c8dbb5dfa1723cd4ae | R | 1,305 | 56 | ### FOR RUNNING ON BEOCAT
setwd("/homes/amkusmec/pc_comparison")
library(tidyverse)
library(keras)
library(argparse)
source("11c.weather_build_model.R")
parser <- ArgumentParser()
parser$add_argument("--job", type = "integer")
args <- parser$parse_args()
### For testing only
# args <- parser$parse_args(c("--job"... |
cf97277158894229366dbdc811ace1bb02dc73876f35f69b490bf2b71acec17f | R | 1,312 | 45 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
source("src/BGGE2.R")
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
ug <- unique(ref$Pedigree)
ue1 <- unique(ref$Environment1)
ue2 <- unique(ref$Environment2)
ref <- ref %>%
... |
b31dd7c0d15a89ed248bcd452495faab84b52952e76b8abc1a70cd2d4cbcb3ff | R | 1,314 | 41 | # Run 'pre' & 'peri'
# Generates KeepMeansIndi - they are stacks x cells
# For memory lesion data, only 3 stacks: 1=sac, 2=together, 3=apart
PRINT_ALL = F
Per = KeepMeansIndi.per
Pre = KeepMeansIndi.pre
cat(sprintf("Does 'saccade' change? %6.3f -> %6.3f p=%.4f\n",
mean(Pre["62",]),
mean(Per["62",]),... |
da304ffd0f90e1c483d5a2d0cffd033cdc2abfc7626e555b050a6ca49579dac8 | R | 1,314 | 45 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
source("src/BGGE2.R")
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
ug <- unique(ref$Pedigree)
ue1 <- unique(ref$Environment1)
ue2 <- unique(ref$Environment2)
ref <- ref %>%
... |
e83fd5c44690b16980e55d8b4d0f08707ec6087e3fee7c8954e7b3609915a50b | R | 1,335 | 38 | setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
source("src/kernel_functions.R")
### The reference table has the pedigree and environment of each data record --
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE) %>%
mutate(yNA = if_else(Set == "Test", NA_real_, GrainYield),
... |
14f8f6843026eb2bc4ce226dc6649a43805635a1d4d19b074afc1ebfba3e92b4 | R | 1,340 | 46 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
source("src/BGGE2.R")
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
ug <- unique(ref$Pedigree)
ue1 <- unique(ref$Environment1)
ue2 <- unique(ref$Environment2)
ref <- ref %>%
... |
79d0b41de619831840822b9e1592a5690f7619c744ed192202a70e9d55da5628 | R | 1,351 | 44 | #' This function is used to find the optimal threshold for each cell type
#'
#' @param model_data The output of the ds_split_data_dnn function.
#' @param prob The predicted probability matrix
#'
#' @import pROC
#'
#' @return List containing the classification output and the probability matrix of the model.
#' @export
#... |
5e712c82865ff393fc7c3f09505139cdcbeb0dd1941612e6669557b2aab4532c | R | 1,363 | 44 | ##' Tranform the vectors of weights to matrix structure
##' @param object an nlpcaNet
##' @return weights in matrix structure
##' @author Henning Redestig
##' @aliases vector2matrices,nlpcaNet-method
setMethod("vector2matrices", "nlpcaNet", function(object) {
netDim <- dim(object@net)
posBegin <- 1
posEnd <- 0
... |
5a6e14b33f59433686ae5f7dca6a0fb1284cda049d490015a73ebf50c072e7c4 | R | 1,366 | 49 |
rm(list = ls(all.names = TRUE)) # clear everything from memory
cat("\014") # clear terminal (send the control+L character)
library(msdap) # load MS-DAP R package
dataset = import_dataset_maxquant_evidencetxt(path = "E:/DATA/PXD007683/txt_mbr")
dataset = import_fasta(
dataset,
files = c(
"E:/DATA/PXD007683/fa... |
086bfae4970618631f75b8980219f6ffdcfabc6a03241f3f2fea071f3284ac2c | R | 1,367 | 58 | #!/usr/bin/env Rscript
# Check required packages
if (!requireNamespace('tools', quietly = TRUE)) {
stop("Cannot find the tools package, exiting", call. = FALSE)
}
# Get the script name
ProgramName <- function() {
prefix <- '--file='
name <- sub(
pattern = prefix,
replacement = '',
x = grep... |
9ad10f2a8b2b49f08bca74e8023376d8d6358982d592d5b32d4b6bf6c66344c3 | R | 1,373 | 40 | #' Heatmaps of class probabilities
#'
#' This function identifies true label groups between reference groups and clusters.
#'
#' @param out output of the function `ms_identity_map`. # TODOELI: here as well, some param name more informative of what needs to be provided?
#'
#' @return A ggplot object, with the heatmap di... |
5e1712d08bebce4c10377855edf442d20a888562a59608956b27bfcc8674a23d | R | 1,384 | 37 | # code for extended figure 5
# source the functions from FLXenium folder, which loads useful libraries and has
# a bunch of functions useful for general analysis of spatial data
miceadds::source.all('~/FLXenium/functions/')
# source the ependymoma project specific functions
source('~/ependymoma/xenium/scripts_revisi... |
82bc672aa6d024757bbce08c176f84b794435dd65c67005ea66f3660a7e3b72e | R | 1,397 | 44 | if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")
setTimeLimit(100000); setSessionTimeLimit(10000)
library(ggplot2)
library("cowplot")
library("gridExtra")
library(ggpubr)
library(plyr)
# load data
data <- read.csv(file="//lexport/iss01.charpier/analyses/stephen.whitma... |
44821c1490180c25512e4d71e4b96f7d27dd34219a99077f0fd8926f5f23cfea | R | 1,399 | 27 | # source the functions from FLXenium folder, which loads useful libraries and has
# a bunch of functions useful for general analysis of spatial data
miceadds::source.all('~/FLXenium/functions/')
# source the ependymoma project specific functions
source('~/ependymoma/xenium/scripts_revisions/resources/epn_functions.R'... |
4d325099c2ccd0f74a27b8c408c69c42ec5d47edcb30db0c1e31900c23190537 | R | 1,404 | 54 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(tensorflow)
tf$config$list_logical_devices()
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/11a.weather_tuner.R")[7:103]))
tuner <- CVTuner(
oracle = BayesianOptimization(
objective = Object... |
e7287c9b99cd178f2e1c630db6c4e5c7e80a7c152498d5572cbf58360192e1c8 | R | 1,411 | 39 | #' Plot performance as radarchart/spider plot
#'
#' This function plots the performance calculated by `ds_performance()`
#'
#' @param data Output of `ds_performance()`
#' @param parameters The parameters that will be plotted
#' @param legend Plot with legend (Default=TRUE)
#' @param colors Vectors of colors for the plo... |
a81d6a936d068b07dc08f2268114841a53ab9c07820aaa71e1627ed2e3949ff7 | R | 1,430 | 46 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
# Daniel's index file
didx <- read_csv("processed/ForRIdx.csv", show_col_types = FALSE) %>%
rename(X = `...1`) %>%
mutate(X = X + 1)
# Sorting by didx$PythonIndex + 1 puts this file in the correct order as
# Daniel's phenotype file.
ref ... |
7fc2e2b570ccf1fdeeaacdb651a18c4b5068af7ec39a29bf5c141b7c1abf471f | R | 1,437 | 34 | testthat::context("DEA reproducibility")
msdap::enable_log(FALSE)
datasets = prepare_test_datasets(1000)
cl <<- msdap::initialize_multiprocessing()
for(lbl in names(datasets$as_tibble)) {
eset_peptides = datasets$as_eset[[lbl]]
test_func = list(ebayes = function() msdap::de_interface_ebayes(eset_proteins = msdap:... |
6cde4152561e11bea7af6846c9eca5274c78af76d043b4c4e35e1664537ffead | R | 1,469 | 49 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
rmse <- function(y, yhat) sqrt(mean((y - yhat)^2))
ref2 <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
y <- scale(ref2$GrainYield[ref2$Set == "Train"])
yc <- attr(y, "scaled:center")
ys <- attr(y, "scaled:scale")
rm(ref2... |
da9ebb808eaf41e85e1df3061c1ff149655316a4c0a20e685e7590b6e0c5ba13 | R | 1,471 | 49 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
rmse <- function(y, yhat) sqrt(mean((y - yhat)^2))
ref2 <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
y <- scale(ref2$GrainYield[ref2$Set == "Train"])
yc <- attr(y, "scaled:center")
ys <- attr(y, "scaled:scale")
rm(ref2... |
710ee74b8126bc742222b169e05ed444d2f4b2c227aab2294300ee2123e1c942 | R | 1,478 | 47 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
source("src/BGGE2.R")
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
ug <- unique(ref$Pedigree)
ue1 <- unique(ref$Environment1)
ue2 <- unique(ref$Environment2)
ref <- ref %>%
... |
9d631233a0e00f9a8cf11b26ef1ad55f05926f6713f5bcaa62450e04999e6ab2 | R | 1,479 | 49 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
rmse <- function(y, yhat) sqrt(mean((y - yhat)^2))
ref2 <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
y <- scale(ref2$GrainYield[ref2$Set == "Train"])
yc <- attr(y, "scaled:center")
ys <- attr(y, "scaled:scale")
rm(ref2... |
4e4c5d9beff9568149e4d61fe0a9afa38836c6f81e21520cac12209d7ed1f5dc | R | 1,486 | 47 | #' Cumulative barplot of cell mapped compositions
#'
#' This function identifies true label groups between reference groups and clusters.
#'
#' @param class.fac A named vector of classified cells.
#' @param obs.fac A named vector of clusters.
#'
#' @return A cumulative barplot describing the cell identity compositions ... |
4517f226714469db67bd10cc10ee6d565c4c19e37f3c21cc85eb696575b4abf8 | R | 1,494 | 31 | ##' This function can be used to conveniently replace the expression
##' matrix in an \code{ExpressionSet} with the completed data from a
##' \code{pcaRes} object.
##'
##' This is not a standard \code{as} function as \code{pcaRes}
##' object alone not can be converted to an \code{ExpressionSet} (the
##' \code{pcaRes} o... |
e633c7717e471264572081f96185a3596c580da79ed80ea0980ee924c55a6744 | R | 1,524 | 48 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/04a.L2_tuner.R")[7:96]))
###Load the tuned hyperparameters
# `epochs` and `batch_size` have to be explicitly converted to R objects for
# the keras fit funct... |
3c881fc7c2f33009fb49b3ea970779a6d282f2698571e46af6d5f0082b91fea9 | R | 1,526 | 55 | setwd("/homes/amkusmec")
library(tidyverse)
library(keras)
library(argparse)
source("tuner_src/weather_build_model.R")
parser <- ArgumentParser()
parser$add_argument("--model", type = "integer")
parser$add_argument("--fold", type = "integer")
args <- parser$parse_args()
### For testing only
# args <- parser$parse... |
cfaefe1448490ba3fc54d06b86f010ef4996092732f53c3b951cdceb66f5f207 | R | 1,535 | 47 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
library(argparse)
source("src/BGGE2.R")
parser <- ArgumentParser()
parser$add_argument("--seed", "-s", type = "integer")
args <- parser$parse_args()
# The phenotypes have already been scaled and center... |
ec814f0feecda18d4d3a0bfd5857b8fa1c4d01afed258a77d6265f255b2fb261 | R | 1,535 | 48 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/07a.tanh_tuner.R")[7:84]))
###Load the tuned hyperparameters
# `epochs` and `batch_size` have to be explicitly converted to R objects for
# the keras fit fun... |
f9c95e8cedc74200a07fd1381a8ddd617215bea86d804ee936536941e4dc9499 | R | 1,537 | 48 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/03a.dense_tuner.R")[7:83]))
###Load the tuned hyperparameters
# `epochs` and `batch_size` have to be explicitly converted to R objects for
# the keras fit fu... |
9436f91099b05ecc63c187e2d0bf8dd1fd0159e2cc7dc4dcf9096b20777836bb | R | 1,540 | 50 | #' Comparing test information curves of parallel tests
#'
#' This function takes two fitted mirt-model and visualizes test their test information curves on top of each other. This can be helpful for finding parallel tests.
#'
#'
#' @param model1 an object of class `SingleGroupClass` returned by the function `mirt()`.... |
660b716012fb4b3ef835a66783fef59c58d0b2f0aae88899bfea31e1573ee491 | R | 1,546 | 48 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/05a.sigmoid_tuner.R")[7:84]))
###Load the tuned hyperparameters
# `epochs` and `batch_size` have to be explicitly converted to R objects for
# the keras fit ... |
4b9d450563c9936fe5a19cc6602e6e6b2d5ce9a6d4af19f97ec347d32f1b4d67 | R | 1,549 | 47 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
library(argparse)
source("src/BGGE2.R")
parser <- ArgumentParser()
parser$add_argument("--seed", "-s", type = "integer")
args <- parser$parse_args()
# The phenotypes have already been scaled and center... |
ad6938dec7e0132f6cb191970fe0fa90972766c2326a58aa4587a046674db818 | R | 1,557 | 47 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
library(argparse)
source("src/BGGE2.R")
parser <- ArgumentParser()
parser$add_argument("--seed", "-s", type = "integer")
args <- parser$parse_args()
# The phenotypes have already been scaled and center... |
966cf7caff764cc76c644a264c5d5183945b3ffd4048778700268162085b665d | R | 1,568 | 47 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
library(argparse)
source("src/BGGE2.R")
parser <- ArgumentParser()
parser$add_argument("--seed", "-s", type = "integer")
args <- parser$parse_args()
# The phenotypes have already been scaled and center... |
8ee13f62d97939ef8cbf54cde0af7d082d8f3f8068cc38ac0860454b3fcda3c9 | R | 1,570 | 47 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
library(argparse)
source("src/BGGE2.R")
parser <- ArgumentParser()
parser$add_argument("--seed", "-s", type = "integer")
args <- parser$parse_args()
# The phenotypes have already been scaled and center... |
cfbc5074150cdc4534c7a4b237ba7ea1ac94fa9e773a1c4ac32b5d799e9e0836 | R | 1,570 | 37 | #' sim_nbglmm
#'
#' Simulated counts data from a NB-GLMM for a single trait
#'
#' Data are simulated counts from 50 samples in a single data frame, from which the
#' X and Z design matrices, can be constructed (see examples). There are 2 random effects and 2 fixed
#' effect variables used to simulate the count trait.
#... |
6b5edae81409120cfc6a483fc13891bb85da6d456eafcd352db69b77043d13d7 | R | 1,577 | 35 | # this file takes a single input, the sample_name (such as 'STEPN47_Region_2'). One can simply create a variable
# by running something like: "sample_name <- 'STEPN47_Region_2'", and run this script line by line, skipping the
# first part where the sample_name is read from supplied arguments, or can execute this scri... |
c62142d4d3d45dce2e12e4411a033a2db40e2c8a44fd4fbc59c8c791c223be84 | R | 1,578 | 48 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/10a.soil_tuner.R")[7:83]))
###Load the tuned hyperparameters
# `epochs` and `batch_size` have to be explicitly converted to R objects for
# the keras fit fun... |
8715e5bde6c9a27db43a6ddc3afbbafb1bb7fce359411a48f05c512b77113f3f | R | 1,583 | 45 | #' Scale Characteristic Curve
#'
#' Once model-based theta score estimates are computed, it often is of interest to transform those estimates into the original scale metric. A scale characteristic function provides a means of transforming estimated theta scores to expected true scores in the original scale metric. This... |
53244eb89a7b5c93b9d35a4663ad342cdb20325f5ed9b66434d9307853fefb80 | R | 1,585 | 53 | ### Neighbourhood abstracted graph ###
#' Build an abstracted graph of neighbourhoods for visualization
#'
#'
#' @param x A \code{\linkS4class{Milo}} object with a non-empty \code{nhoods}
#' slot.
#' @param overlap A numeric scalar that thresholds graph edges based on the number
#' of overlapping cells between neighb... |
4424054568e10fc5e133b6b51e9c25598d68362cdf1e557b9580265d903b7239 | R | 1,587 | 38 | #' Calculates module eigengenes, average expression, membership and p.values from different sc data.
#'
#' This function calculates module eigengenes, average expression, membership and p.values, from a Seurat object, using as reference a set of gene modules.
#' @param modules scWGCNA.data or named vector. An scWGCNA.... |
c62436ece2e0c1a31508b73c9f6314dc4c7000d4222c5f52ed7db7e489ab4bb1 | R | 1,588 | 47 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
library(argparse)
source("src/BGGE2.R")
parser <- ArgumentParser()
parser$add_argument("--seed", "-s", type = "integer")
args <- parser$parse_args()
# The phenotypes have already been scaled and center... |
09c75072a89e2958be108b0dbdbd158913d7d460802ae41bef486a17c02e70d4 | R | 1,590 | 58 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
dir_int <- "results/DNNs/intermediate_results"
if (!dir.exists(dir_int)) dir.create(dir_int)
extract_layer <- function(model, df_in) {
last_layer <- length(model$layers) - 1
tf_in <- model$layers[[1]]$input
tf_out <- mo... |
962d06d5e16ce72478f9b11aef467a3d1491fc5d8fa3ecbff39bc054c4acae7f | R | 1,593 | 49 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
source("src/BGGE2.R")
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
ug <- unique(ref$Pedigree)
ue1 <- unique(ref$Environment1)
ue2 <- unique(ref$Environment2)
ref <- ref %>%
... |
db7d92733d1e1fc0f7d2e7802b18dbec6baec86264407de5a19bc5db3cc6fb04 | R | 1,595 | 61 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
# Function for rmse
rmse <- function(y, yhat) sqrt(mean((yhat - y)^2))
# Phenotypic data
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
y_train <- ref %>%
filter(Set == "Train") %>%
pull(GrainYi... |
05d4b0b9480fac221b152891d544bf2b729bed1b12a0e1f6b719986dd23a7004 | R | 1,610 | 53 | #' Plotting test information curve
#'
#' This function takes a fitted mirt-model and visualizes test information curve.
#'
#'
#' @param model an object of class `SingleGroupClass` returned by the function `mirt()`.
#' @param theta_range range to be shown on the x-axis
#' @param adj_factor adjustment factor for prope... |
1a63913882f09dff176c311d3e7892e1c174b1cc89bf5629dd77d124dcf3e71b | R | 1,614 | 59 |
#' @export
setGeneric("graph", function(x) standardGeneric("graph"))
#' @export
setGeneric("graph<-", function(x, value) standardGeneric("graph<-"))
#' @export
setGeneric("nhoodDistances", function(x) standardGeneric("nhoodDistances"))
#' @export
setGeneric("nhoodDistances<-", function(x, value) standardGeneric("n... |
d7d76f6c3789d0c2eadb5ad0284f1e07c669aab61ce22ecba099f666474fb064 | R | 1,620 | 24 | #!/usr/bin/env Rscript
library(optparse)
# library(UpSetR)
library(ComplexUpset)
library(ggplot2)
option_list=list(
make_option(c('-i', '--input'), help='input binary matrix with experiments as columns'),
make_option(c('-o', '--outfile'), help='path to output file'),
make_option(c('-n', '--numSamples'), help='numb... |
5270c564504a91b804b0a322b947a826e8678a1f24007af153940ddc210200d9 | R | 1,621 | 45 | # in this file, I load the required libraries, and have the functions to set up the global variables
# Load necessary libraries. User should have them installed
library(tidyverse)
library(glue)
library(qs)
library(Seurat)
library(readxl)
library(patchwork) # keeping as required because it is used quite widely in the ... |
f71012d2e2a5203f7c23b799158f1d8a6eb51b24195d00834f2b4028a20eb69c | R | 1,639 | 34 | # # Frank Koopmans (frank.koopmans@vu.nl)
# library(msEmpiRe)
# library(MSnbase)
#
# # load ExpressionSet we prepared earlier; LFQbench dataset processed by Spectronaut. we selected only peptides with good q-value and applied vsn normalization
# load("C:/temp/ExpressionSet_contrast A vs B.RData")
#
# # we stored our in... |
33ee29f4345050e5a0112fdab6a8a8aab5655689af64dedf60805971509e13c1 | R | 1,643 | 59 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
source("~/BGGE/R/BGGE2.R")
ref <- read_csv("ref_fixed.csv", show_col_types = FALSE) %>%
mutate(yNA = if_else(Set == "Train", GrainYield, as.numeric(NA)),
yS = scale(yNA, center = TRUE, scale = TRUE) %>% drop())
yc <- attr(ref$yS, "... |
440e85920331765e6dff4f121fffe3c44b027cdf9484513fa6e230c5b5c5d6df | R | 1,643 | 59 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
source("~/BGGE/R/BGGE2.R")
ref <- read_csv("ref_fixed.csv", show_col_types = FALSE) %>%
mutate(yNA = if_else(Set == "Train", GrainYield, as.numeric(NA)),
yS = scale(yNA, center = TRUE, scale = TRUE) %>% drop())
yc <- attr(ref$yS, "... |
0f958c6dbd6a932a10d5f2ce4155109405434c6b72f9f61b920c37745f68b3b5 | R | 1,644 | 39 | # this file takes a single input, the sample_name (such as 'STEPN47_Region_2'). One can simply create a variable
# by running something like: "sample_name <- 'STEPN47_Region_2'", and run this script line by line, skipping the
# first part where the sample_name is read from supplied arguments, or can execute this scri... |
0b2fbe0d7c6385d6b8ebdd7d6f6d62757c161f43c2168390f6a2bcf1ee90a940 | R | 1,674 | 55 | # main.r Plot power spectra for 5 stacks, ONE class (Larry)
# And scatter plot
MONK = "both" # tyr, zen, both
AREA = "PRR" # PRR, LIP, cross (same hemisphere), crossX2 (diff hemis)
NEAR = 2 # [1.25 or 2] distance (mm) (0 == off -- too noisy to use!)
ALIGN = "GoCue" # "" (Target), "end" (cue), "GoCue", "go" or... |
1eb211ddcad9ac323e61e03b8ceaefc8a0323ac58eddd05ce58711db26f29484 | R | 1,688 | 51 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(tensorflow)
tf$config$list_logical_devices()
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/14a.tanh_hPC_tuner.R")[10:87]))
###Load the tuned hyperparameters
# `epochs` and `batch_size` have to be... |
68cf55e2807f7451179e5332962f9d49e524c2a9bfda98feda1bf35a20cb3e28 | R | 1,692 | 51 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(tensorflow)
tf$config$list_logical_devices()
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/12a.ReLU_hPC_tuner.R")[10:86]))
###Load the tuned hyperparameters
# `epochs` and `batch_size` have to be... |
6f09520a28c78089142c196be681d2515ca758234c76e4b28486f173c71fb5f6 | R | 1,700 | 51 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(tensorflow)
tf$config$list_logical_devices()
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/13a.sigmoid_hPC_tuner.R")[10:87]))
###Load the tuned hyperparameters
# `epochs` and `batch_size` have to... |
e6f05eb3796beb577962d0779a5452ecfa67b2e1a7958169b2982f95e9d2086a | R | 1,717 | 53 | library(here)
library(tidyverse)
library(cowplot)
library(ggplot2)
### Establish location ---
here::i_am("src/ASO/ASO_Correlate_DAT_with_Rotarod.R")
### Read in input files ---
ASO_rotarod <- read.csv(here("data/ASO/ASO Rotarod - Rotarod.csv"))
ASO_rotarod <- ASO_rotarod %>% filter(Day=="One") %>%
filter(ASO_Tg==... |
92551e80995ba2d536fa169717c6560bb0736cf6ecf5795803a6a15ea4b27158 | R | 1,723 | 43 | #install.packages("pROC")
#ÒýÓðü
library(pROC)
rsFile="model.riskMatrix.txt" #·çÏÕ¾ØÕóÎļþ
method="NaiveBayes" #Ñ¡Ôñ»úÆ÷ѧϰµÄ·½·¨(ÐèÒª¸ù¾ÝÈÈͼ½øÐÐÐÞ¸Ä)
setwd("C:\\Users\\wx197\\Desktop\\×ÔѧÍø113»úÆ÷ѧϰ\\205.geoML×ÊÁÏ\\205.geoML×ÊÁÏ\\16.ROC") #ÉèÖù¤×÷Ŀ¼
#¶ÁÈ¡·çÏÕÎļþ
riskRT=r... |
725f6b3c80755682aa3f61464cbc7302d141381b9a0288ba13196ea5ab3d5fc4 | R | 1,734 | 80 | library(sjPlot)
# library(broom.mixed) # for tidy()
library(dplyr)
library(emmeans)
library(lme4)
library(lmerTest)
library(ggplot2)
library(effects)
# library(moments)
# library(ggeffects)
# library(robustlmm)
# library(glmmTMB)
# library(patchwork)
# library(forcats)
# library(scales)
df <- read.csv("E:\\work\... |
8f3f3c3798be21051f20d25e80a45c887eb7992cc1e713cc7faa384b0820e217 | R | 1,734 | 54 | #install.packages("glmnet")
#install.packages("pROC")
#???ð?
library(glmnet)
library(pROC)
expFile="merge.normalize.txt" #?????????ļ?
geneFile="modelGene.list.txt" #?????б??ļ?
setwd("C:\\Users\\wx197\\Desktop\\自å¦ç½‘113机器å¦ä¹ \\205.geoML资料\\205.geoML资料\\20.geneROC") #???ù?... |
b6ae1fdc3b21e7633ff1a218f078dc4275f46363b4baee7e43e9f26e91792b8c | R | 1,752 | 50 | library(knitr)
round_p <- function(x) {
return(ifelse(x < 0.001, "<.001", as.character(x)))
}
# function to round saved values
table_rounding <- function(tbl) {
for(nm in names(tbl)) {
ifelse(nm %in% c("p_raw", "p_adj_fdr", "p", "p[GG]", "p-value"), digits <- 3, digits <-2)
if(class(tbl[[nm]]) != "cha... |
7602c1e56ae3f7a557acf89a4e4e0e2ffde952e660ae463b82ab523f66683b3c | R | 1,753 | 64 | # ------------- Figure S2 --------------
#----Figure S2A----
library(clusterProfiler)
library(org.Hs.eg.db)
library(dplyr)
library(ggplot2)
#Extract the top 200 upregulated genes for each module
modules <- paste0("module", 1:5)
subsce.corr.markers.up <- read.csv("./NSCLC/Figure/figure2/subsce.corr.markers.up.csv")
get_... |
001aad1b7db16732e888bd81668201f2f7ad5ffa8affae85181c2d1b1ac99b38 | R | 1,759 | 65 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
### Data preparation ----------------------------------------------------------
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
y_train <- ref %>%
filter(Set == "Train") %>%
pull(GrainYield) %>%
... |
571bf3124b505ad7f03fb6a339fd10a93a4e5af2930ddda642e91505d83001a2 | R | 1,771 | 65 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
### Data preparation ----------------------------------------------------------
d <- read_rds("09.pc_comparison/input_data.rds")
x_test <- d$Test$X
y_test <- d$Test$y
rm(d); gc()
# Function for rmse
rmse <- function(y, yhat) ... |
f24cbe3170353838c4be8eb0d424ece36dabd24db00d12ce1d525fcb1f2a5657 | R | 1,772 | 62 | ### FOR RUNNING ON BEOCAT
setwd("/homes/amkusmec/interactions")
library(tidyverse)
library(keras)
library(abind)
library(argparse)
source("06b.tanh_SNPs_build_model.R")
parser <- ArgumentParser()
parser$add_argument("--job", type = "integer")
args <- parser$parse_args()
### For testing only
# args <- parser$pars... |
14b6c57f516a5ca00c962f1070ad928b2633ddf0f184d2400e4e8cf3cd3a8cff | R | 1,774 | 65 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
### Data preparation ----------------------------------------------------------
d <- read_rds("09.pc_comparison/input_data.rds")
x_test <- d$Test$X
y_test <- d$Test$y
rm(d); gc()
# Function for rmse
rmse <- function(y, yhat) ... |
78161663c05c224556cbeea09b9115babc5a628c6193cda0828c87b42773fa76 | R | 1,775 | 62 | ### FOR RUNNING ON BEOCAT
setwd("/homes/amkusmec/interactions")
library(tidyverse)
library(keras)
library(abind)
library(argparse)
source("04b.relu_SNPs_build_model.R")
parser <- ArgumentParser()
parser$add_argument("--job", type = "integer")
args <- parser$parse_args()
### For testing only
# args <- parser$pars... |
1cb1a0605b82f44bc1c30672ca9a5fc9218b5819485b13e46c97c08eb7674ecc | R | 1,783 | 62 | ### FOR RUNNING ON BEOCAT
setwd("/homes/amkusmec/interactions")
library(tidyverse)
library(keras)
library(abind)
library(argparse)
source("09b.tanh_hPC_build_model.R")
parser <- ArgumentParser()
parser$add_argument("--job", type = "integer")
args <- parser$parse_args()
### For testing only
# args <- parser$parse... |
96c39bfd8021f64b507c74f88da7c97fd410f691e729c91dc24cbd776ca4ad38 | R | 1,784 | 62 | ### FOR RUNNING ON BEOCAT
setwd("/homes/amkusmec/interactions")
library(tidyverse)
library(keras)
library(abind)
library(argparse)
source("05b.sigmoid_SNPs_build_model.R")
parser <- ArgumentParser()
parser$add_argument("--job", type = "integer")
args <- parser$parse_args()
### For testing only
# args <- parser$p... |
69a0e29330413269ca5243f5333fbf2d12cdfcb2fea11d87b6babf8d6f436402 | R | 1,785 | 45 | #install.packages("ggplot2")
#install.packages("ggpubr")
#ÒýÓðü
library(ggplot2)
library(ggpubr)
setwd("C:\\Users\\wx197\\Desktop\\×ÔѧÍø113»úÆ÷ѧϰ\\205.geoML×ÊÁÏ\\205.geoML×ÊÁÏ\\08.PCA") #ÉèÖù¤×÷Ŀ¼
#¶¨ÒåPCA·ÖÎöµÄº¯Êý
bioPCA=function(inputFile=null, outFile=null, titleName=null){
#¶ÁÈ¡ÊäÈëÎļþ,Ìá... |
52d4fb296bcf0bf731717a345877638107b0b20fdd51df5a2c3bd1d93e7be060 | R | 1,786 | 62 | ### FOR RUNNING ON BEOCAT
setwd("/homes/amkusmec/interactions")
library(tidyverse)
library(keras)
library(abind)
library(argparse)
source("07b.relu_hPC_build_model.R")
parser <- ArgumentParser()
parser$add_argument("--job", type = "integer")
args <- parser$parse_args()
### For testing only
# args <- parser$parse... |
d2e683107a19809321d8aa8915b50de6cd091e1c7d43c4cd875e6a2ee9b05dca | R | 1,786 | 65 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
### Data preparation ----------------------------------------------------------
d <- read_rds("09.pc_comparison/input_data.rds")
x_test <- d$Test$X
y_test <- d$Test$y
rm(d); gc()
# Function for rmse
rmse <- function(y, yhat) ... |
9286608ca41e4a2303fa19662927d60367f33abe2eed8d8bc9c41c9f106088dd | R | 1,788 | 29 | # code for extended figure 8 panel a
# source the functions from FLXenium folder, which loads useful libraries and has
# a bunch of functions useful for general analysis of spatial data
miceadds::source.all('~/Multidimensional_STEPN/4.Xenium/resources/FLXenium/')
# source the ependymoma project specific functions
so... |
a694b41b4c1c530387a20364ff0055140fd9f942c51389f66857c0c39feb2d42 | R | 1,790 | 50 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
library(argparse)
source("src/BGGE2.R")
parser <- ArgumentParser()
parser$add_argument("--seed", "-s", type = "integer")
args <- parser$parse_args()
# The phenotypes have already been scaled and center... |
7ea84e4b674e8b8e248cccd16b82371998666e3c7939dad89f66416adb9af8cd | R | 1,795 | 62 | ### FOR RUNNING ON BEOCAT
setwd("/homes/amkusmec/interactions")
library(tidyverse)
library(keras)
library(abind)
library(argparse)
source("08b.sigmoid_hPC_build_model.R")
parser <- ArgumentParser()
parser$add_argument("--job", type = "integer")
args <- parser$parse_args()
### For testing only
# args <- parser$pa... |
d39972b9a7e0c6951e2ca63268a0aca30898643fa2e23d56b0a555aa65a361b8 | R | 1,798 | 45 |
# validate the normalization function, optimized for speed and making some assumptions while doing so, maintains data integrity
# # very naive log2 data generator
# set.seed(1)
# x = rnorm(100, mean = 5, sd = 1); x[x<1] = 1 # some random data
# m = NULL
# nsamples = 6
# m_groups = letters[1 + (1:nsamples > nsamples/... |
4fd85f1d30cada9ae6384b34fbbbe2814bbd4e902a557e874e075eaea68c1efd | R | 1,799 | 56 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/04a.L2_tuner.R")[7:96]))
# Creates a directory for intermediate caching of history objects
cv_hp_dir <- "09.pc_comparison/opt_L2_cvFit"
if (!dir.exists(cv_hp_d... |
5f61df2b5e9efaaeb24ebb4d0c0326fb080a52db96e6d69d0f9d94f495b54974 | R | 1,803 | 50 | ### Function for plotting alpha diversity ---
generate_adiv_plots <- function(input_data, X, Y, min, max){
#read in files
data <- as.data.frame(input_data)
#declare order of variables
data$Genotype <- factor(data$Genotype, levels=c("WT", "HET","MUT"))
#graph plot
ggplot(data=data,aes(x={{X}},y={{Y}}, fil... |
6a67ad0840c01d05aaa01491551c6e9354dfb69f1d5ca2a1b1d3fbf68fb88d3e | R | 1,805 | 56 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/07a.tanh_tuner.R")[7:84]))
# Creates a directory for intermediate caching of history objects
cv_hp_dir <- "09.pc_comparison/opt_tanh_cvFit"
if (!dir.exists(cv_... |
bc010a29d01a5363b44d2599b78de106b8b0878247446bf32ef39d78fd373420 | R | 1,805 | 56 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/10a.soil_tuner.R")[7:83]))
# Creates a directory for intermediate caching of history objects
cv_hp_dir <- "09.pc_comparison/opt_soil_cvFit"
if (!dir.exists(cv_... |
db9f9055fe734d0d6a6f27b8695183f671a5d7d69a89b149005e20403ea050d5 | R | 1,806 | 71 | library(sjPlot)
library(broom.mixed) # for tidy()
library(dplyr)
library(emmeans)
library(lme4)
library(lmerTest)
library(ggplot2)
library(moments)
library(ggeffects)
library(robustlmm)
library(glmmTMB)
library(patchwork)
library(forcats)
library(scales)
df <- read.csv("E:\\work\\q_backup_06_03_2025\\projects\\is... |
73ecde68ba515e0ac67d15447c06049b36d35d8bffe6b55e52433562c2e8da7c | R | 1,808 | 56 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/03a.dense_tuner.R")[7:83]))
# Creates a directory for intermediate caching of history objects
cv_hp_dir <- "09.pc_comparison/opt_dense_cvFit"
if (!dir.exists(c... |
2465ee9eb89e7ff47ca25e5df3141a1833602a2652989d86398b78c8df22074e | R | 1,809 | 64 | #' This function plots the correlation matrix between two datasets
#'
#' This function generates a correlation plot
#'
#' @param x.data The Seurat Object that contains the first data
#' @param x.assay The assay in the Seurat Object that contains the first data
#' @param y.data The Seurat Object that contains the second... |
71921c2cbfd3ae1897166ceb813053844efb5c9fc425f84eef26196191ac9474 | R | 1,809 | 50 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
library(argparse)
source("src/BGGE2.R")
parser <- ArgumentParser()
parser$add_argument("--seed", "-s", type = "integer")
args <- parser$parse_args()
# The phenotypes have already been scaled and center... |
108fda399fda2c64a062112f38a890f4d514838b114dd5f00ce803312ca65e99 | R | 1,814 | 56 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/05a.sigmoid_tuner.R")[7:84]))
# Creates a directory for intermediate caching of history objects
cv_hp_dir <- "09.pc_comparison/opt_sigmoid_cvFit"
if (!dir.exis... |
2805b47e16c54ea711ab5632e8d3b7506ce0e6ddb25665eeb59ce418947b0186 | R | 1,821 | 55 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/08a.tanh_PC_tuner.R")[7:84]))
# Creates a directory for intermediate caching of history objects
cv_hp_dir <- "09.pc_comparison/opt_tanh_PC_cvFit"
if (!dir.exis... |
12bd40e83b800abf6a7c0f66a565b3a6dc000a6873935a4bc5990c4d21c67e11 | R | 1,830 | 55 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/06a.sigmoid_PC_tuner.R")[7:84]))
# Creates a directory for intermediate caching of history objects
cv_hp_dir <- "09.pc_comparison/opt_sigmoid_PC_cvFit"
if (!di... |
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