kimlaberinto

## Source for math political compass klein bottle meme by @KimPLab on Twitter
## Tweet: https://twitter.com/KimPLab/status/1381621398636949511

## Inspired by the math political compass torus meme by @jessebett 
## https://twitter.com/jessebett/status/1379162611414138885
## @jessebett source code notes:
## upcycled from torus knot fibration visualization:
## http://www.jessebett.com/TorusKnotFibration/torusknot.html

## Note:

gizmaa

Plotting

Last Update: May 13, 2019
Offline Version

Contents

• Translating
• Important Note
Basic Plot

sanzoghenzo

"""
Compare two Excel sheets
Inspired by https://pbpython.com/excel-diff-pandas-update.html
For the documentation, download this file and type:
python compare.py --help
"""

import argparse

import pandas as pd

ChrisRackauckas

DiffEqFlux.jl (Julia) vs Jax on an Epidemic Model

The Jax developers optimized a differential equation benchmark in this issue which used DiffEqFlux.jl as a performance baseline. The Julia code from there was updated to include some standard performance tricks and is the benchmark code here. Thus both codes have been optimized by the library developers.

Results

Forward Pass

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

gene1wood

Python relative imports in AWS Lambda fail with attempted relative import with no known parent package

The Problem

In AWS Lambda if I attempt an explicit relative import like this

.
├── lambda_file.py
└── example.py

jpivarski

ChrisRackauckas

Torchdiffeq vs DifferentialEquations.jl (/ DiffEqFlux.jl) Neural ODE Compatible Solver Benchmarks

Only non-stiff ODE solvers are tested since torchdiffeq does not have methods for stiff ODEs. The ODEs are chosen to be representative of models seen in physics and model-informed drug development (MIDD) studies (quantiative systems pharmacology) in order to capture the performance on realistic scenarios.

Summary

Below are the timings relative to the fastest method (lower is better). For approximately 1 million ODEs and less, torchdiffeq was more than an order of magnitude slower than DifferentialEquations.jl

brendanvinson

/*
First run npm install topojson --save and then link "node_modules/topojson/build/topojson.min.js" 
above this snippet in your html.

Usage: http://leafletjs.com/reference.html#geojson
*/

L.TopoJSON = L.GeoJSON.extend({
    addData: function (data) {
        var geojson, key;

pat-alt

using ConformalPrediction
using Distributions
using MLJ
using Plots

# Inputs:
N = 600
xmax = 3.0
d = Uniform(-xmax, xmax)