NeoVertex1

import numpy as np
import matplotlib.pyplot as plt
import pygame
from scipy.io.wavfile import write
import time
import threading
import sys
import cmd
import os

secemp9

class Agent:
    def __init__(self, name, ports=None):
        self.name = name
        self.ports = ports if ports else []
        # Each element of ports is either None or a tuple (other_agent, other_port_index)

    def __repr__(self):
        return f"{self.name}"

def connect(a, pa, b, pb):

rtkclouds

class Rezero(layers.Layer):
    def __init__(self):
        super().__init__()
        self.alpha1 = tf.Variable(0.0, trainable=True)

    def call(self, inputs, training):
        return self.alpha1*inputs
class CustomRezero(tf.keras.layers.Layer):

qpwo

function import-ts-repl() { (
    set -e
    file=$1
    rm -f ${file}.js ${file}.js.map
    esbuild ${file} --target=node14 --format=cjs --sourcemap --outfile=${file}.js --platform=node --bundle
    node -r ${file}.js
    rm -f ${file}.js ${file}.js.map
); }

qpwo

// January 2022, Luke Harold Miles, public domain obviously
// Adapted from: https://github1s.com/expressjs/express/blob/master/examples/auth/index.js
// You can run with `ts-node express-typescript-auth.ts`
// Source for this file to get updates: https://gist.github.com/qpwo/4b2ae3aaaf222d099b34898152118c43

import { pbkdf2Sync, randomBytes, timingSafeEqual } from 'crypto'
import type { NextFunction, Request, Response } from 'express'
import express from 'express'
import type { Session } from 'express-session'
import session from 'express-session'

qpwo

"""
A simple predator-prey multi-agent reinforcement learning environment in
python without dependencies, and an implementation of an example q-learning agent.
Python 3.9
https://gist.github.com/qpwo/a19f43368afd77288bf3b7db81fdc18b/
February, 2021 -- Public domain dedication
"""

from dataclasses import dataclass, field
import random

robertmaxwilliams

// Compile with gcc -std=c99 mersenne_twister.c -o /dev/random
// ps the -o /dev/random part is a joke, it's not actually meant to be a random device.
// Absolutely no warranty, Kopyleft I guess who gives a shit
#include <stdint.h>
#include <stdio.h>
uint32_t x[624];
uint32_t index = 0;
void twist() {
    for (int k = 0; k < 624; k++) {
        x[k] = x[(k+397)%624] 

g-leech

import math
import scipy.stats as st

# assumes bivariate normal, dichotomised groups
def dichotomy_r_to_d(r) :
    d = 2*r / (math.sqrt(1 - r**2))
    return d

# Equation 9
# https://sci-hub.tw/10.1037/1082-989X.11.4.386

Mr0grog

# Experiments in capturing combined output streams from subprocesses.
# 
# It turns out it's kind of hard to get the interleaved results of stdout and
# stderr in Python. However, in a lot of situations where Python is calling
# out to other processes, you probably want to swallow the child process's
# stderr when things to right and print it when things go wrong. Since some
# programs may be outputting results on stdout and warnings and errors on
# stderr, it makes sense that you'd want it all, and all in the order it was
# printed when things go wrong.
# 

minhoryang

READ pallets/flask#1938