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let segment = (text) => {
    let it = Intl.v8BreakIterator('ja');
    it.adoptText(text);
    let result = [];
    let curr, next;
    while (true) {
        curr = it.current();
        next = it.next();
        if (next == -1) break;
        result.push(text.slice(curr, next));

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# coding: utf-8
import random

stroke_width = 4
radius = 10

glyphs = {
        'd': f'<circle cx="{radius}" cy="{radius}" r="{radius}" /><line x1="{radius*2}" y1="{radius}" x2="{radius*2}" y2="{-radius * 0.6}" />',
        'o': f'<circle cx="{radius}" cy="{radius}" r="{radius}" />',
        'g': f'<circle cx="{radius}" cy="{radius}" r="{radius}" /><path d="M {radius*2} {radius} C {radius*2} {radius*2}, {radius*2} {radius*2.8}, {radius} {radius*2.8}" />',

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# coding: utf-8
#
# Simulation program for unplugged neural network model.
# Reference:
# https://www.1101.com/morikawa/2001-03-12.html
# https://www.1101.com/morikawa/2001-04-02.html

import itertools
import random
import numpy as np

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# coding: utf-8
# Dynamic programing: knapsack problem.
# Rewrote the program in the webpage below in Python.
# 動的計画法（ナップサック問題）
# 下記のサイトのコードを Python にて書き直した。
# reference: http://dai1741.github.io/maximum-algo-2012/docs/dynamic-programming/

VALUES = [15, 3, 1, 8, 2, 4]
WEIGHTS = [11, 2, 3, 5, 1, 4]
MAX_WEIGHT = 15

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const CDP = require('chrome-remote-interface');
const chromeLauncher = require('lighthouse/chrome-launcher/chrome-launcher');

function onPageLoad(Runtime) {
  const js = "document.querySelector('title').textContent";
  return Runtime.evaluate({expression: js}).then(result => {
    console.log('Title of page: ' + result.result.value);
  });
}

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