llimllib

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 = []

sklam

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, ):

jvns

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?".

whoisjake

$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

Zulko

#
# 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.
#

jkeirstead

gabrieleangeletti

import tensorflow as tf
import numpy as np
import os

import zconfig
import utils


class RBM(object):

EderSantana

Code for Keras plays catch blog post

Train

python qlearn.py

Test

1. Generate figures

8bit-pixies

Smoothies

• Frozen Berries
• Thickened Cream

Vegetables

• Any frozen variety (check carbs)

Eggs (Any)

hanxiao

// 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;