atremblay

from pylab import grid, show, savefig, subplots, tight_layout, style
import numpy as np

style.use('ggplot')
def phase_plane(A, x_min=-3, x_max=3, y_min=-3, y_max=3):
    fig, ax = subplots(1,1, figsize=(5,5), dpi=500)
    state_vec = np.array(
        np.meshgrid(
            np.linspace(x_min, x_max, 1000), 
            np.linspace(y_min, y_max, 1000)

atremblay

#######################################################################
#     copied from https://github.com/gillescastel/latex-snippets      #
#######################################################################
  
global !p
texMathZones = ['texMathZone'+x for x in ['A', 'AS', 'B', 'BS', 'C', 'CS', 'D', 'DS', 'E', 'ES', 'F', 'FS', 'G', 'GS', 'H', 'HS', 'I', 'IS', 'J', 'JS', 'K', 'KS', 'L', 'LS', 'DS', 'V', 'W', 'X', 'Y', 'Z']]

texIgnoreMathZones = ['texMathText']

texMathZoneIds = vim.eval('map('+str(texMathZones)+", 'hlID(v:val)')")

atremblay

import logging
import datetime
import tensorflow as tf


class BatchProfileCallback(tf.keras.callbacks.Callback):
    """
    BatchProfileCallback

    Log how long a batch takes to run. Monitor potential slow downs.

atremblay

platform=$(uname -s)
arch=$(uname -m)
PYTHON_VERSION=3.10

if [[ "$platform" == 'Linux' ]]; then

   sudo apt install stow tldr
   curl -sS https://starship.rs/install.sh | sh
   
   git clone --depth 1 https://github.com/wbthomason/packer.nvim\ ~/.local/share/nvim/site/pack/packer/start/packer.nvim

atremblay

import os
import tensorflow as tf
from twilio.rest import Client


class SMSCallback(tf.keras.callbacks.Callback):
    """
    SMSCallback
    
    Because you are not always standing in front of your computer waiting for

atremblay

# This is part of the implementation of this paper
# https://www.aaai.org/Papers/FLAIRS/1999/FLAIRS99-042.pdf
# This was written for MLDB (www.mldb.ai). Not tested for other databases
# Conditional entropy $H(Y|X) = -\sum_x{P(x)\sum_y{P(y|x)\log(P(y|x))}}$

from pandas import DataFrame
def conditional_entropy(Y, X, table):
    df = mldb.query("""
    SELECT -sum(PX.p_x*A.right)
    FROM (

atremblay

import random
import numpy as np
# http://erikerlandson.github.io/blog/2015/11/20/very-fast-reservoir-sampling/
def fast_reservoir_sampling(file_handle, N=1000000, callable=None):
    sample = []

    if callable is None:
        callable = lambda x: x

    j = N