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# Fomule du pret pour obtenir la mensualite
def pmt(C, n, t):
    if t:
        return (C * t/12)/(1-(1 + t/12)**-n)
    else:
        return C/n        

# Formule pour obtenir la part d'interets de la mensualite
def impt(period, C, n, t):
    t_per = t/12

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import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline

n = range(1, 100)
phi = (3 - np.sqrt(5)) * np.pi

for a in [0.98, 0.985, 0.99, 0.995, 1, 1.005, 1.01, 1.015, 1.02]:
    fig = plt.figure()
    r = np.sqrt(n)

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import pandas as pd

# in the end, I went for a recursive function because tags can be arbitrarily nested
def extract_from_dict(var, key):
    if isinstance(var, dict):
        for k, v in var.items():
            if k == key:
                yield v
            if isinstance(v, (dict, list)):
                yield from gen_dict_extract(v, key)

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# -*- coding: utf-8 -*-

from datetime import datetime, timedelta

from airflow import DAG
from airflow.operators import GoogleApiToS3Transfer

default_args = {
    'owner': 'airflow',
    'start_date': datetime(2019, 6, 1),

louisguitton

## install homebrew
echo "Installing Homebrew.."
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
echo "Homebrew successfully installed"

## install brew cask
echo "Installing brew cask.."
brew tap homebrew/cask
echo "Homebrew cask successfully installed"

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version: '3'

services:
  nginx:
    image: nginx:1.15-alpine
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - ./nginx:/etc/nginx/conf.d

louisguitton

## Hi, here are commands that will set you up for the git and github training.
## Open a terminal and copy paste those commands group by group
## Read the output to check if the command really worked.

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

brew install git

curl https://raw.githubusercontent.com/git/git/master/contrib/completion/git-completion.bash > ~/.git-completion.bash

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louisguitton

How to calculate the alignment between BERT and spaCy tokens effectively and robustly

site: https://tamuhey.github.io/tokenizations/

Natural Language Processing (NLP) has made great progress in recent years because of neural networks, which allows us to solve various tasks with end-to-end architecture. However, many NLP systems still requires language-specific pre- and post-processing, especially in tokenizations. In this article, I describe an algorithm which simplifies calculating of correspondence between tokens (e.g. BERT vs. spaCy), one such process. And I introduce Python and Rust libraries that implement this algorithm.

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