Work on Son's Notebook

Example of axis in numpy

Tue, 27 Jun 2023 00:00:00 +0000

While working on the Kohonen Self Organizing Map, I encountered difficulties in understanding how the axis parameter in NumPy works. Initially, in a 2-dimensional scenario, we have coordinates (x, y), where x represents the width or number of rows, and y represents the height or number of columns in the matrix. However, things become more complex when we introduce another dimension.

For instance, let’s consider an array of pixels with a size of 10x10 and three color channels: Red, Green, and Blue. This array would be represented as (10, 10, 3), indicating the dimensions of width, height, and depth respectively. Here’s my understanding of it:

Racket for Beginners

Tue, 09 May 2023 00:00:00 +0000

Racket, why racket? I don’t know the main reason, but it is interesting and importantly my professor requires it for our Psychology course. I have never learned Racket before but I have a bit of experience in Erlang. In Erlang we cannot assign a value to a variable like Racket because = operator is a pattern matching that is similar to car and cdr. The game is totally changed in Racket. Now we can just define whatever value we want.

R Notebook - dplyr

Mon, 25 Oct 2021 00:00:00 +0000

We are gonna go through filter(), arrange(), select(), mutate(), summarise(), group_by(), slice(), rename(), transmute(), sample_n(), sample_frac()

Pipe#

%>% is pronounced THEN

All-in-one Example#

manager <- c(1, 2, 3, 4, 5) 
date <- c("10/24/08", "10/28/08", "10/1/08", "10/12/08", "5/1/09") 
country <- c("US", "US", "UK", "UK", "UK") 
gender <- c("M", "F", "F", "M", "F") 
age <- c(32, 45, 25, 39, 99) 
q1 <- c(5, 3, 3, 0, 2) 
q2 <- c(4, 5, 0, 3, 2) 
q3 <- c(5, 2, 5, 4, 1) 
q4 <- c(5, 0, 5, NA, 2) 
q5 <- c(5, 5, 2, NA, 1) 
leadership <- data.frame(manager, date, country, gender, age, 
 q1, q2, q3, q4, q5, stringsAsFactors=FALSE)
> leadership
leadership <- leadership %>%
 mutate()

 manager date country gender age q1 q2 q3 q4 q5
1 1 10/24/08 US M 32 5 4 5 5 5
2 2 10/28/08 US F 45 3 5 2 5 5
3 3 10/1/08 UK F 25 3 5 5 5 2
4 4 10/12/08 UK M 39 3 3 4 NA NA
5 5 5/1/09 UK F 99 2 2 1 2 1
library(dplyr) #A 
# add total_score and mean_score columns
leadership <- mutate(leadership, #B 
 total_score = q1 + q2 + q3 + q4 + q5, #B 
 mean_score = total_score / 5) #B 
> leadership
 manager date country gender age q1 q2 q3 q4 q5 total_score mean_score
1 1 10/24/08 US M 32 5 4 5 5 5 24 4.8
2 2 10/28/08 US F 45 3 5 2 5 5 20 4.0
3 3 10/1/08 UK F 25 3 5 5 5 2 20 4.0
4 4 10/12/08 UK M 39 3 3 4 NA NA NA NA
5 5 5/1/09 UK F 99 2 2 1 2 1 8 1.6
# recode or change item in variable gender to M::male, F::female
# this is similar to search and replace
leadership$gender <- recode(leadership$gender, #C 
 "M" = "male", "F" = "female") #C 
> leadership
 manager date country gender age q1 q2 q3 q4 q5 total_score mean_score
1 1 10/24/08 US male 32 5 4 5 5 5 24 4.8
2 2 10/28/08 US female 45 3 5 2 5 5 20 4.0
3 3 10/1/08 UK female 25 3 5 5 5 2 20 4.0
4 4 10/12/08 UK male 39 3 3 4 NA NA NA NA
5 5 5/1/09 UK female 99 2 2 1 2 1 8 1.6 
# recode is similar to renaming the row, and rename is for columns
# right to left
leadership <- rename(leadership, ID = "manager", sex = "gender")#D
> leadership
 ID date country sex age q1 q2 q3 q4 q5 total_score mean_score
1 1 10/24/08 US male 32 5 4 5 5 5 24 4.8
2 2 10/28/08 US female 45 3 5 2 5 5 20 4.0
3 3 10/1/08 UK female 25 3 5 5 5 2 20 4.0
4 4 10/12/08 UK male 39 3 3 4 NA NA NA NA
5 5 5/1/09 UK female 99 2 2 1 2 1 8 1.6

leadership <- arrange(leadership, sex, total_score) #E 
> leadership
 ID date country sex age q1 q2 q3 q4 q5 total_score mean_score
1 5 5/1/09 UK female 99 2 2 1 2 1 8 1.6
2 2 10/28/08 US female 45 3 5 2 5 5 20 4.0
3 3 10/1/08 UK female 25 3 5 5 5 2 20 4.0
4 1 10/24/08 US male 32 5 4 5 5 5 24 4.8
5 4 10/12/08 UK male 39 3 3 4 NA NA NA NA
> leadership <- arrange(leadership, sex, desc(total_score)) #E
> leadership
 ID date country sex age q1 q2 q3 q4 q5 total_score mean_score
1 2 10/28/08 US female 45 3 5 2 5 5 20 4.0
2 3 10/1/08 UK female 25 3 5 5 5 2 20 4.0
3 5 5/1/09 UK female 99 2 2 1 2 1 8 1.6
4 1 10/24/08 US male 32 5 4 5 5 5 24 4.8
5 4 10/12/08 UK male 39 3 3 4 NA NA NA NA

> leadership_ratings <- select(leadership, ID, mean_score) #F
> leadership_ratings
 ID mean_score
1 2 4.0
2 3 4.0
3 5 1.6
4 1 4.8
5 4 NA
leadership_men_high <- filter(leadership, #G
 sex == "male" & total_score > 10) #G 
> leadership_men_high <- filter(leadership, #G
+ sex == "male" & total_score > 10) #G
> leadership_men_high
 ID date country sex age q1 q2 q3 q4 q5 total_score mean_score
1 1 10/24/08 US male 32 5 4 5 5 5 24 4.8

filter()#

library('nycflights13')
library('dplyr')
filter(flights, month == 1, day == 1, dep_delay >= 15)

flights[flights$month == 1 & flights$day == 1 & flights$dep_delay >= 15, ]

# this is similar 
subset(flights, month == 1 & day == 1 & dep_delay >= 15)
# better

filter(), with %in%, select specific values in a col

Basic Git commands

Mon, 01 Jan 0001 00:00:00 +0000

Creating and Managing Git Repositories#

If you’re thinking of creating a project for your class, you’ll need to create a Git repository. Here are the steps:

Basic JavaScript Part 1

Mon, 01 Jan 0001 00:00:00 +0000

In this tutorial, I will introduce a few steps to access and edit elements in JavaScript. Mastering and understanding these steps can help you make a simple form, handling submission, and change the outlook of your website.

Table of Contents:

Placeholder for Table of Content (Must not be removed) {:toc}

Edit a text or HTML via jQuery#

This example implement jQuery.

jQuery is a popular JavaScript library that simplifies DOM manipulation and event handling in web development. One of its key strengths is its ability to quickly and easily edit the content of text or HTML elements on a web page.

Using Ajax, Xhttp, Fetch, and Axios to get page content

Mon, 01 Jan 0001 00:00:00 +0000

Axios is a popular JavaScript library used for making HTTP requests from a browser or Node.js. It provides a simple and elegant way to make asynchronous HTTP requests to a server, and handle responses in a clean and efficient manner.

Fetch is a built-in web API in modern browsers that allows you to make HTTP requests to a server and retrieve data in a simple and straightforward way. It provides an alternative to the traditional XMLHttpRequest (XHR) API, and is designed to be easy to use and flexible.