qpwo

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])

qpwo

# 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],

qpwo

#! /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

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?

qpwo

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));
  }
})();

qpwo

# 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)

qpwo

qpwo

# 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

qpwo

qpwo

"""
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