Zae Myung Kim zaemyung

iCloud symlink
- ln -s ~/Library/Mobile\ Documents/com\~apple\~CloudDocs ~/iCloud
Map keys: right-option to f18, capslock to control
- /Users/zaemyung/Library/LaunchAgents/com.local.setting.key.remapping.plist

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
 Label

Docker Cheat Sheet

(Adopted from Docker Cheat Sheet)

You can also take a look at this one.
Here is also a good Docker tutorial on YouTube.

Build

Build an image from the Dockerfile in the current directory and tag the image

docker build -f Dockerfile -t {your_name/repo_name:tag} .

For building zaemyung/ml_non_root image, modify the UNAME in the dockerfile accordingly, and build as:

	import math

	import matplotlib.pyplot as plt
	import networkx as nx
	import regex as re
	from common_utils import draw_graph, load_pickle_file
	from matplotlib.gridspec import GridSpec


	def visualize_graph_motifs(motifs, motif_size, save_path, selected_motif_indices=None, show_edge_label=True):

	def compute_mfidf_distribution(machines, humans, motif_size):
	# document here is either "machine" or "human"
	machine_mf = np.array([sample[f'motif_m{motif_size}_hist_norm_by_edges'] for sample in machines])
	machine_mf = np.mean(machine_mf, axis=0)
	human_mf = np.array([sample[f'motif_m{motif_size}_hist_norm_by_edges'] for sample in humans])
	human_mf = np.mean(human_mf, axis=0)

	machine_df = np.array([sample[f'motif_m{motif_size}_hist_raw'] for sample in machines])
	machine_df = (machine_df > 0).astype(int)
	machine_df = np.mean(machine_df, axis=0)

	from sklearn.metrics import confusion_matrix

	class CustomTrainer(Trainer):


	def _inner_training_loop(
	self, batch_size=None, args=None, resume_from_checkpoint=None, trial=None, \
	ignore_keys_for_eval=None):

	import os
	from datetime import datetime
	from pathlib import Path

	# pip install boto3 python-dotenv beautifulsoup4
	import boto3
	import dotenv
	from bs4 import BeautifulSoup

	dotenv.load_dotenv()

	import math
	import textwrap

	import matplotlib.pyplot as plt
	import numpy as np
	import pandas as pd


	class ComplexRadar():
	"""

	function colorSelectedRangeGreen() {
	var sel = SpreadsheetApp.getActive().getSelection().getActiveRangeList().getRanges();
	for(var i = 0; i < sel.length; i++){
	var cell = sel[i].setBackground("#93c47d");
	}
	}

	function colorSelectedRangePink() {
	var sel = SpreadsheetApp.getActive().getSelection().getActiveRangeList().getRanges();
	for(var i = 0; i < sel.length; i++){

	The regex patterns in this gist are intended to match any URLs,
	including "mailto:[email protected]", "x-whatever://foo", etc. For a
	pattern that attempts only to match web URLs (http, https), see:
	https://gist.github.com/gruber/8891611


	# Single-line version of pattern:

	(?i)\b((?:[a-z][\w-]+:(?:/{1,3}\|[a-z0-9%])\|www\d{0,3}[.]\|[a-z0-9.\-]+[.][a-z]{2,4}/)(?:[^\s()<>]+\|\(([^\s()<>]+\|(\([^\s()<>]+\)))\))+(?:\(([^\s()<>]+\|(\([^\s()<>]+\)))\)\|[^\s`!()\[\]{};:'".,<>?«»“”‘’]))

	#!/usr/bin/env python
	# Ander Martinez Sanchez

	from __future__ import division, print_function
	from math import exp, log
	from collections import Counter


	def ngram_count(words, n):
	if n <= len(words):