mark-andrews

From ef640acaf0a62d332040f2dc7a6247839eb4f664 Mon Sep 17 00:00:00 2001
From: Mark Andrews <[email protected]>
Date: Mon, 25 Oct 2021 20:03:04 +0100
Subject: [PATCH] Updating dependencies in PKGBUILD

* Add the quarto dependencies
* Change _nodever to 14.17.5
---
 PKGBUILD | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

mark-andrews

set.seed(10101) # Omit or change this if you like

N <- 25

x_1 <- rnorm(N)
x_2 <- rnorm(N)

beta_0 <- 1.25
beta_1 <- 1.75
beta_2 <- 2.25

mark-andrews

Sleep,Stress
3.1,36
4.4,33
3.3,33
3.6,37
3,32
3.3,30
3.7,35
4.1,47
4.7,30

mark-andrews

mark-andrews

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):

mark-andrews

import torch
import torchvision

tt = torchvision.transforms.Compose([torchvision.transforms.ToTensor(),
                                     torchvision.transforms.Normalize((0.1307,), (0.3081,))])

mnist_train = torchvision.datasets.MNIST('./files/', train=True, download=True, transform=tt)
mnist_test = torchvision.datasets.MNIST('./files/', train=False, download=True, transform=tt)

mark-andrews

# View the dummy code of a categorical variable
# assumes package fastDummies is installed
# assumes tidyverse is installed 
# Usage: 
get_dummy_code <- function(Df, variable){
  tmp_df <- fastDummies::dummy_cols(Df, remove_first_dummy = TRUE)
  tmp_df <- dplyr::select(tmp_df, dplyr::starts_with(variable))
  dplyr::arrange(dplyr::distinct(tmp_df), !!variable)
}

mark-andrews

library(tidyverse)
theme_set(theme_classic())

plot_normal <- function(mean = 0, sd = 1, xmin = -3, xmax = 3) {
  tibble(x = seq(xmin, xmax, length.out = 1000),
         d = dnorm(x, mean = mean, sd = sd)) %>% 
    ggplot(aes(x = x, y = d)) + geom_line() + 
    geom_segment(x = mean, 
                 xend = mean, 
                 y = 0, 

mark-andrews

# This is a set of functions etc to illustrate the concept of a sampling
# distribution using the example of sampling gold and silver coins 
# from a box.

library(tidyverse)

# set random number generator seed
set.seed(10101)

make_box <- function(N = 100, g = 0.5){

mark-andrews

stimuli <- letters[1:9]

n = 2
K <- 36

stimuli_list <- vector(mode = "list", length = K)
response_list <- vector(mode = "list", length = K)

for (i in seq(K)){
  if (i <= n){