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@So-Cool
So-Cool / ProbLog.ipynb
Created September 28, 2015 15:18
`ProbLog2` **pseudo** Jupyter (iPython notebook) interface
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@So-Cool
So-Cool / MarkdownExample.md
Created October 13, 2015 15:45
Markdown Example

Welcome to Marxico

@(Sample notebook)[Marxico|Manual|Markdown]

Marxico is a delicate Markdown editor for Evernote. With reliable storage and sync powered by Evernote, Marxico offers greate writing experience.

  • Versatile - supporting code highlight, LaTex & flow charts, inserting images by all means.
  • Exquisite - neat but powerful editor, featuring offline docs, shortcuts, live preview.
  • Sophisticated - deeply integrated with Evernote, supporting notebook & tags, two-way bind editing.
@So-Cool
So-Cool / extractor.py
Last active January 23, 2024 19:53
Prettify ugly MS Word HTML
import os
import sys
from bs4 import BeautifulSoup
if len(sys.argv) != 3:
sys.exit("First argument is HTML second is an output directory.")
with open(sys.argv[1], "r") as html:
soup = BeautifulSoup(html.read(), "html.parser")
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@So-Cool
So-Cool / xkcd.ipynb
Created March 24, 2016 22:35
XKCD in Python
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@So-Cool
So-Cool / slider_ex.ipynb
Created April 5, 2016 10:46
Jupyter notebook with sliders
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@So-Cool
So-Cool / cuckooml.md
Last active August 31, 2016 20:18
CuckooML datasets
@So-Cool
So-Cool / find10.md
Last active December 29, 2016 15:53
How to find a 'perfect 10'

Perfect10

How it works?

How to find a 'perfect 10'?

This simple algorithm applies Optional Stopping Time Theorem used for calculating optimal exercising time for American Options. Simply, it tells you whether your current partner is optimal.
Optimality is calculated in a sens of score represented by integer: 1--10, that you assign to your current partner. You also need to specify a few more parameters like:

  • score(1--10) of you current partner,
@So-Cool
So-Cool / Readme.md
Last active May 27, 2017 16:23
AISB hackathon -- story comprehension

Automated story comprehension

This notebook shows how to extract background knowledge and relations from a story text. To this end, we use Stanford's NLP parser available to download here.

We parsed the following story (story.txt file):

Mary was sleeping.
Her phone rang.
She was annoyed.
Mary answered the phone.
Ann told the good news to Mary.