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%% options
clear_frames_after = 5;
segment = "c"; % can be 'a', 'b', or 'c'.

%% diff eqs
%because r=1
px = @(t,x) cos(t)-x;
py = @(t,y) sin(t)-y;

p = @(t,x,y) [px(t,x),py(t,y)];

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%% Constants
segments = ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"] % replace 'j' with the sub-assignment letter
                 % lowercase only
%% A
% sun
P = [0, 0];
M = 10;

% planet
p = [1, 0];

lunaluxie

{
	// Place your global snippets here. Each snippet is defined under a snippet name and has a scope, prefix, body and 
	// description. Add comma separated ids of the languages where the snippet is applicable in the scope field. If scope 
	// is left empty or omitted, the snippet gets applied to all languages. The prefix is what is 
	// used to trigger the snippet and the body will be expanded and inserted. Possible variables are: 
	// $1, $2 for tab stops, $0 for the final cursor position, and ${1:label}, ${2:another} for placeholders. 
	// Placeholders with the same ids are connected.
	// Example:
	// "Print to console": {
	// 	"scope": "javascript,typescript",

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import gym
import numpy as np
import matplotlib.pyplot as plt

env_type = "FrozenLake8x8-v0"
algorithm_type = "q_learning"
policy_type = "epsilon_greedy"
run_name = 'run-{0}-{1}-{2}'.format(env_type, algorithm_type, policy_type)

# Random seed

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"""Graph based autodiff
Supports two modes
    - Forward mode
    - Reverse mode (much more efficient)
        We use reverse mode
        Yet the graph method is still inefficient
"""
import numpy as np

class Expression():

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import hashlib 
import random 
import string

def hash_password(password, salt=None, iterations=100000):
	"""
	Hash a string using SHA3-512

	input:
		String: password

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lunaluxie

import tensorflow as tf
tf.reset_default_graph()

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

### model ###

# input
with tf.name_scope('input') as scope:

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def flatten(obj):
  flat = []
  if hasattr(obj, '__iter__'):
    for i in obj:
      flat.extend(flatten(i))
  else:
    flat.append(obj)
  return flat

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def flatten(obj):
    flat = []
    if hasattr(obj, '__iter__'):
      for i in obj:
        flat.extend(flatten(i))
    else:
      flat.append(obj)
    return flat

def merge_dicts(list_of_dicts, flat=True):