skaslev

module NatInd

import Language.Reflection.Elab
import Language.Reflection.Utils

%default total

trivial : Elab ()
trivial = do compute
             g <- snd <$> getGoal

skaslev

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

import numpy as np
import matplotlib.pyplot as plt
import scipy.integrate as integrate

# Dimension of image in pixels
N = 256
# Number of samples to use for integration
M = 32

skaslev

-- Matthew Brecknell @mbrcknl
-- BFPG.org, March-April 2015

-- With thanks to Conor McBride (@pigworker) for his lecture series:
-- https://www.youtube.com/playlist?list=PL44F162A8B8CB7C87
-- from which I learned most of what I know about Agda, and
-- from which I stole several ideas for this talk.

open import Agda.Primitive using (_⊔_)

skaslev

""" 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

skaslev

import sys, marshal, functools, subprocess

child_script = """
import marshal, sys, types;
fn, args, kwargs = marshal.load(sys.stdin)
marshal.dump(
    types.FunctionType(fn, globals())(*args, **kwargs),
    sys.stdout)
"""

skaslev

# -*- coding: utf-8 -*-
import bohrium as np
from math import pi
import matplotlib.pyplot as plt
import matplotlib.colors as colors

fig = plt.figure()
plt.xticks([])
plt.yticks([])

skaslev

using UnityEngine;
using System.Linq;

[RequireComponent(typeof(Rigidbody))]
public class RigidbodyMassCalculator : MonoBehaviour {

	public float density = 1f;
	public bool recalculateOnAwake = true;
	
	Rigidbody rb;