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@hanxiao
hanxiao / testRegex.js
Last active November 18, 2024 06:50
Regex for chunking by using all semantic cues
// Updated: Aug. 20, 2024
// Run: node testRegex.js whatever.txt
// Live demo: https://jina.ai/tokenizer
// LICENSE: Apache-2.0 (https://www.apache.org/licenses/LICENSE-2.0)
// COPYRIGHT: Jina AI
const fs = require('fs');
const util = require('util');
// Define variables for magic numbers
const MAX_HEADING_LENGTH = 7;

Smoothies

  • Frozen Berries
  • Thickened Cream

Vegetables

  • Any frozen variety (check carbs)

Eggs (Any)

@EderSantana
EderSantana / CATCH_Keras_RL.md
Last active June 22, 2024 17:07
Keras plays catch - a single file Reinforcement Learning example
@gabrieleangeletti
gabrieleangeletti / rbm_after_refactor.py
Last active July 27, 2021 14:32
Restricted Boltzmann Machine implementation in TensorFlow, before and after code refactoring. Blog post: http://blackecho.github.io/blog/programming/2016/02/21/refactoring-rbm-tensor-flow-implementation.html
import tensorflow as tf
import numpy as np
import os
import zconfig
import utils
class RBM(object):
category value sector
UK production emissions 632 UK
Carbon flows from EU 88 EU
Carbon flows to EU -61 EU
Carbon flows from other Annex 1 82 Annex 1
Carbon flows to other Annex 1 -39 Annex 1
Carbon flows from non-Annex 1 104 Other non-Annex 1
Carbon flows from non-Annex 1 64 China
Carbon flows to non-Annex 1 -25 Non-Annex 1
UK consumption emissions 845 UK
@Zulko
Zulko / soccer_cuts.py
Last active March 31, 2024 12:03
A python script to automatically summarize soccer videos based on the crowd's reactions
#
# This Python script makes a summary of a football game by cutting
# the video around the 10 % loudest moments, which generally
# include the goals and other important events.
# For more details, see this blog post:
# http://zulko.github.io/blog/2014/07/04/automatic-soccer-highlights-compilations-with-python/
#
# LICENCE: Creative Commons 0 - Public Domain
# I, the author of this script, wave any rights and place this work in the public domain.
#
@whoisjake
whoisjake / Vagrantfile.spark
Created March 26, 2014 16:14
Vagrant file that will load an entire Spark development hacking environment. Download this raw and rename it to Vagrantfile
$script = <<SCRIPT
apt-get update --fix-missing
apt-get install -y python-software-properties
add-apt-repository ppa:terry.guo/gcc-arm-embedded
apt-get update
apt-get install -y build-essential
apt-get install -y gcc-arm-none-eabi
apt-get install -y dfu-util
apt-get install -y git-core
@jvns
jvns / interview-questions.md
Last active November 3, 2024 03:54
A list of questions you could ask while interviewing

A lot of these are outright stolen from Edward O'Campo-Gooding's list of questions. I really like his list.

I'm having some trouble paring this down to a manageable list of questions -- I realistically want to know all of these things before starting to work at a company, but it's a lot to ask all at once. My current game plan is to pick 6 before an interview and ask those.

I'd love comments and suggestions about any of these.

I've found questions like "do you have smart people? Can I learn a lot at your company?" to be basically totally useless -- everybody will say "yeah, definitely!" and it's hard to learn anything from them. So I'm trying to make all of these questions pretty concrete -- if a team doesn't have an issue tracker, they don't have an issue tracker.

I'm also mostly not asking about principles, but the way things are -- not "do you think code review is important?", but "Does all code get reviewed?".

import json
from pprint import pprint as pp
import numpy as np
from numba import autojit, typeof, int32
INF = float('inf')
@autojit
def jenks_matrics_init(data, n_classes, ):
@llimllib
llimllib / jenks2.py
Last active April 3, 2023 19:26
The jenks algorithm in python. Since replaced with: https://github.com/llimllib/jenks-python which has tests and stuff
import json
from pprint import pprint as pp
def jenks_matrices_init(data, n_classes):
#fill the matrices with data+1 arrays of n_classes 0s
lower_class_limits = []
variance_combinations = []
for i in xrange(0, len(data)+1):
temp1 = []
temp2 = []