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

inkydragon

# test driven development
# TestSet for `@when` + `@otherwise`
using MLStyle
using Test
using Random

#=
    Test help macro/functions
=#

johncrossland

Sphinx with Markdown walkthrough for HTML and PDF output

This walkthrough installs Sphinx and configures it to output HTML and PDF from .md. If you install it on a VM, allocate over 25GB storage and multiple processors. You'll need Ubuntu 16.04 LTS, an internet connection, and sudo rights.

depth

2

Z-Shang

from collections import namedtuple

TailCall = namedtuple("TailCall", ['fn', 'args', 'kargs'])

class tco:
    def __init__(self, f):
        self.fn = f

    def __call__(self, *a, **k):
        retval = self.fn(*a, **k)

mewmew

// ### [ Lexical part ] ########################################################

_ascii_letter_upper
	: 'A' - 'Z'
;

_ascii_letter_lower
	: 'a' - 'z'
;

yasirkula

using System.Collections.Generic;
using System.Reflection;
using UnityEditor;
using UnityEditor.IMGUI.Controls;
using UnityEditorInternal;
using UnityEngine.SceneManagement;

public static class EditorCollapseAll
{
	private const BindingFlags INSTANCE_FLAGS = BindingFlags.Public | BindingFlags.NonPublic | BindingFlags.Instance;

svaksha

A quick and dirty syntax translation / conversion reference guide to ease the transition between Python and Julia. This is not meant as a reference to the language. For that you should read the manual.

Some important differences

• Arrays in Julia are indexed starting from 1.
• In Julia classes (i.e. types) don't own methods. Methods are implementations of generic functions and are invoked in a "static style", i.e. instead of Python's str1.rstrip(), we will have rstrip( str1 ), instead of file1.close(), close( file1 ).

Some important similarities.

Icelandjack

Reddit discussion.

Disclaimer 1: Type classes are great but they are not the right tool for every job. Enjoy some balance and balance to your balance.

Disclaimer 2: I should tidy this up but probably won’t.

Disclaimer 3: Yeah called it, better to be realistic.

Type classes are a language of their own, this is an attempt to document features and give a name to them.

eevee

"""Perlin noise implementation."""
# Licensed under ISC
from itertools import product
import math
import random


def smoothstep(t):
    """Smooth curve with a zero derivative at 0 and 1, making it useful for
    interpolating.

JamesPHoughton

def make_python_identifier(string, namespace=None, reserved_words=None,
                           convert='drop', handle='force'):
    """
    Takes an arbitrary string and creates a valid Python identifier.
    If the python identifier created is already in the namespace,
    or if the identifier is a reserved word in the reserved_words
    list, or is a python default reserved word,
    adds _1, or if _1 is in the namespace, _2, etc.

    Parameters