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"""
# Grid Dist
what's a loss function that incorporates spatial distance?

e.g. i want...
loss(y..., y...) == 0
0 < loss(yt, y1) < loss(yt, y2)
loss(yt, y2) == loss(yt, y2.T)
Tweet: https://twitter.com/TheShubhanshu/status/1482132766607265796
"""

napsternxg

Wordle One Shot Solution (Hack)

NOTE: This is a hack to get the wordle solution

NOTE: Use it only when you are frustrated and just want to see the solution. It will ruin the fun of the game for you.

I am not doing anything fancy. It is just that the solution is always available on the page and I am reading it and firing the keypresses to get answer.

Be reminded that you should not copy JS from strangers on your pages, it can be used to steal your passwords or other sensitive information. You can read the full code used here and more details at: https://gist.github.com/napsternxg/a80021d6aec9d362db8c736bc2f9a888 Learn about internet security from A PROJECT OF THE ELECTRONIC FRONTIER FOUNDATION: SURVEILLANCE SELF-DEFENSE: TIPS, TOOLS AND HOW-TOS FOR SAFER ONLINE COMMUNICATIONS course from https://ssd.eff.org/en

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# Using python difflib

from difflib import Differ, ndiff, SequenceMatcher, HtmlDiff
from pprint import pprint
from IPython.display import display_html, HTML, display

css_style = """
<style>
del {
    text-decoration: line-through;

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import tensorflow as tf
from tensorboard.plugins.hparams import api as hp
import numpy as np

N, d = 100, 5

x_train = np.random.randn(N, d)
y_train = np.random.randint(0, 2, size=[N, 1])

HP_NUM_UNITS = hp.HParam('num_units', hp.Discrete([16, 32]))

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{
    "data": [
        {
            "id": "1",
            "name": "Tiger Nixon",
            "position": "System Architect",
            "salary": "$320,800",
            "start_date": "2011\/04\/25",
            "office": "Edinburgh",
            "extn": "5421"

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napsternxg

#defaultView:ImageGrid
SELECT ?human
#(SAMPLE(?humanLabel) AS ?humanLabel) 
(SAMPLE(?image) AS ?image) 
(SAMPLE(?sex_or_gender) AS ?sex_or_gender) 
#(SAMPLE(?sex_or_genderLabel) AS ?sex_or_genderLabel) 
(SAMPLE(?ethnic_group) AS ?ethnic_group) 
#(SAMPLE(?ethnic_groupLabel) AS ?ethnic_groupLabel) 
(SAMPLE(?date_of_birth) AS ?date_of_birth) 
(SAMPLE(?occupation) AS ?occupation) 

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function getCitationInfo() {
  t = document.querySelector("table#gsc_rsb_st")
  cite_hist = document.querySelector("div.gsc_md_hist_b")
  overall = Array.from(t.querySelectorAll("tr")).map((tr, i) => {
    selector = i == 0 ? "th" : "td";
    row = Array.from(tr.querySelectorAll(selector))
    return row.map(td => td.innerText)
  })
  temporal = {
    years: Array.from(cite_hist.querySelectorAll("span.gsc_g_t")).map(s => +s.innerText),

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import tensorflow as tf

base_path = "gs://bucket/some_folder"
target_folder = Path("./localfolder").resolve()
for folder, subfolders, files in tf.io.gfile.walk(base_path):
#   print(folder, subfolders, files)
  local_folder = target_folder.joinpath(*Path(folder).parts[4:])
#   print(folder, local_folder)
  local_folder.mkdir(parents=True, exist_ok=True)
  for from_file in files: