There many GUI tools that help with version control, but I prefer terminal commands for most git-related things.
The changes you make typically go through the following steps:
git add and now have changes staged. Stage is also referred to as "Index" or "cache")
| # Python 3.7 was used for this gist - it should work on many different versions (not going to enumerate them here) | |
| # I don't really like the docs for defaultdict. maybe they're more rigorous than this definition | |
| # i think this micro-example captures the essense of when/why to use defaultdict. | |
| import collections | |
| # create a normal dictionary and a defaultdict so we can compare functionality | |
| d = {} |
TCL-Expect scripts are an amazingly easy way to script out laborious tasks in the shell when you need to be interactive with the console. Think of them as a "macro" or way to programmaticly step through a process you would run by hand. They are similar to shell scripts but utilize the .tcl extension and a different #! call.
The first step, similar to writing a bash script, is to tell the script what it's executing under. For expect we use the following:
#!/usr/bin/expect
| library(lubridate) # <3 tidyverse | |
| # this is likely not the most efficient way to generate unix time stamps, but it is intuitive to me | |
| # I like to see how it is done step by step | |
| # to make this more efficient, you could store the "epoch" value outside the function so it | |
| # doesn't have to be calculated every time you call the function | |
| time_since_epoch <- function() { |
| #========================================================================= | |
| #STAT W4400 | |
| #Homework 03 | |
| # yl3394, Yanjin Li | |
| #Problem 1 AdaBoost | |
| #========================================================================= | |
| # In this problem I will implement AdaBoost algorithm in R. The algorithm | |
| # requires two auxiliary functions, to train and to evaluate the weak leaner. | |
| # And, then we will have the third function for implementing the resulting | |
| # boosting classifier. Here, we will use the decision stumps as our weak |
| #' Target Shuffling | |
| #' Author: Taylor Van Anne | |
| #' | |
| #' Note: this is just my interpretation of what target shuffling means | |
| #' to me. I think there are a few different ways to actually conduct | |
| #' the shuffling, but this is a single approach. | |
| #' | |
| #' A different approach than what I did here would be to shuffle the |
| ''' | |
| Simply display the contents of the webcam with optional mirroring using OpenCV | |
| via the new Pythonic cv2 interface. Press <esc> to quit. | |
| ''' | |
| import cv2 | |
| def show_webcam(mirror=False): | |
| cam = cv2.VideoCapture(0) | |
| while True: |
| # set up a stepsize | |
| alpha = 0.003 | |
| # set up a number of iteration | |
| iter = 500 | |
| # define the gradient of f(x) = x^4 - 3*x^3 + 2 |
| #' Most basic form of a TryCatch function in R | |
| #' tryCatch() takes two arguments: "expr" and "finally" | |
| #' | |
| #' "expr" is the expression you want to try. It will run as much | |
| #' as it can until coming across an error. When it hits an error, it | |
| #' will stop processing the code in the expression brackets and move | |
| #' to whatever is inside of the "finally" brackets. | |
| #' |
| # xgboost feature importance ---------------------------------------------------------------- | |
| #' Author: Taylor Van Anne | |
| #' 7/25/2017 | |
| #' | |
| #' This script is a simple demonstration of: | |
| #' 1) using cross validation to determine the optimal number of iterations for your xgboost model | |
| #' 2) runs xgboost with that number of iterations |