Luke Miles qpwo

Real brains do not organize neurons into layers. It's a messy process where one neuron could connect directly to the input and the output, while another neuron is in a long chain, while a third neuron is connected to twenty different nodes at different depths. Who decided that artificial neural networks should be very constrained in terms of shape? Is it computational convenience, or does it actually work better?

	from collections import defaultdict
	def simple_cycles(G):

	def _unblock(thisnode,blocked,B):
	stack = set([thisnode])
	while stack:
	node = stack.pop()
	if node in blocked:
	blocked.remove(node)
	stack.update(B[node])

	# A 'map' also known as a 'dictionary' also known as a 'hashmap':
	# https://en.wikipedia.org/wiki/Hash_table
	# It maps 'keys' to 'values'. Keys and values can be anything: strings, tuples, integers, floats...
	# Here, the keys are integers and the values are lists of integers.
	d = {
	1: [1,2,3,4,5,6,7,8,9,0],
	2: [2,3,5,6,8,9,0],
	3: [3,6,9],
	4: [4,5,6,7,8,9,0],
	5: [5,6,8,9,0],

	#! /usr/bin/python
	# -- coding: utf8 --

	""" GAN-CLS """
	import tensorflow as tf
	import tensorlayer as tl
	from tensorlayer.layers import *
	from tensorlayer.prepro import *
	from tensorlayer.cost import *
	import numpy as np

	var lukemath = new (function() {
	var privatePi = 3.14;
	this.tau = 2 * privatePi;
	var privateSquare = function(n) {
	return n * n;
	}
	this.fourth = function(n) {
	return privateSquare(privateSquare(n));
	}
	})();

	# You run this with `$ python mastml.py settings.conf data.csv -o results/`
	# Second example: `$ python mastml.py input.conf compositions.csv -o Desktop/model-results/`

	# Or you open the website, upload a csv, upload a conf file, and download the resulting zip file

	# Sections and subsections are in CamelCase; parameters are in snake_case


	[GeneralSetup]
	input_features = Auto # Defaults to all but last column (specifying Auto is same as omiting this option)

	# Proof of undecidability of halting problem:

	from Turing import will_halt
	# will_halt(program, input) -> does_halt

	def loop_forever(garbage_input):
	return loop_forever(garbage_input)

	def square_root(n):
	return n ** 0.5

	"""
	A minimal implementation of Monte Carlo tree search (MCTS) in Python 3
	Luke Harold Miles, July 2019, Public Domain Dedication
	See also https://en.wikipedia.org/wiki/Monte_Carlo_tree_search
	https://gist.github.com/qpwo/c538c6f73727e254fdc7fab81024f6e1
	"""
	from abc import ABC, abstractmethod
	from collections import defaultdict
	import math