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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])

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# 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],

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#! /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

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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?

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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));
  }
})();

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# 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)

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# Luke Miles
# February 2019
# Drifting arm distribution demo
# (Public domain dedication)

using PyPlot

const num_trials = 1000
const num_pulls = 10000
const ε = 0.1

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A
B
C
D
Exception in thread "DefaultDispatcher-worker-1" java.lang.ArithmeticException: / by zero
	at CoolKt$main$1$f$1$1.invokeSuspend(cool.kt:17)
	at kotlin.coroutines.jvm.internal.BaseContinuationImpl.resumeWith(ContinuationImpl.kt:33)
	at kotlinx.coroutines.DispatchedTask.run(Dispatched.kt:238)
	at kotlinx.coroutines.scheduling.CoroutineScheduler.runSafely(CoroutineScheduler.kt:594)
	at kotlinx.coroutines.scheduling.CoroutineScheduler.access$runSafely(CoroutineScheduler.kt:60)

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{
    "telemetry.enableTelemetry": false,
    "telemetry.enableCrashReporter": false,
    "editor.minimap.enabled": false,
    "editor.lineNumbers": "off",
    "workbench.activityBar.visible": false,
    "workbench.statusBar.visible": false,
    "workbench.editor.showTabs": false,
    "editor.glyphMargin": false,
    "editor.folding": false,