naoyashiga

import React, {useState, useEffect} from "react";

export const ColorCodes = () => {
  const [colors, setColors] = useState([]);
  const [correctColor, setCorrectColor] = useState("");

  const [isCorrect, setIsCorrect] = useState(false);
  const [isShowResult, setIsShowResult] = useState(false);

naoyashiga

"""
雑です。日本語として成立していないものも出力されます。
5600個くらい出ます。
"""
excludes = 'ぁぃぅぇぉゔゕゖゐゑゎ'
hiragana = [chr(i) for i in range(ord("ぁ"), ord("ゖ")+1) if chr(i) not in excludes]
print("".join(hiragana))
count = 1

for first in hiragana:

naoyashiga

import Foundation

enum timingFunction{
    case Linear,EaseIn,EaseOut,EaseInOut,
    Spring,
    EaseInSine,EaseOutSine,EaseInOutSine,
    EaseInQuad,EaseOutQuad,EaseInOutQuad,
    EaseInCubic,EaseOutCubic,EaseInOutCubic,
    EaseInQuart,EaseOutQuart,EaseInOutQuart,
    EaseInQuint,EaseOutQuint,EaseInOutQuint,

naoyashiga

# 1. デスクトップフォルダに移動
cd ~/Desktop/
# 2. ファイル生成コマンドを実行
for i in {1..200}; do echo "yo" > hack$i.html; done
# 3. 一括ファイル削除
rm hack*.html

naoyashiga

extension UIButton {
    func playImplicitBounceAnimation() {
        
        let bounceAnimation = CAKeyframeAnimation(keyPath: "transform.scale")
        bounceAnimation.values = [1.0 ,1.4, 0.9, 1.15, 0.95, 1.02, 1.0]
        bounceAnimation.duration = NSTimeInterval(0.5)
        bounceAnimation.calculationMode = kCAAnimationCubic
        
        layer.addAnimation(bounceAnimation, forKey: "bounceAnimation")
    }

naoyashiga

# -*- coding: utf-8 -*-

u"""
マルコフ連鎖を用いて適当な文章を自動生成するファイル
"""

import os.path
import sqlite3
import random

naoyashiga

# -*- coding: utf-8 -*-

# Inspired
# [TensorFlow-Examples/autoencoder.py at master · aymericdamien/TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py)

import tensorflow as tf
import numpy as np

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

naoyashiga

const request = require('request');

const PAGE_ACCESS_TOKEN = "";

var options = {
    hostname: "graph.facebook.com",
    path:  "/v2.6/me/messages?access_token=" + PAGE_ACCESS_TOKEN,
    method: 'POST',
    headers: {
        'Content-Type': 'application/json'

naoyashiga

#探す対象リスト:my_arrayはnumpy
#例:上位3件
K = 3

# ソートはされていない上位k件のインデックス
unsorted_max_indices = np.argpartition(-my_array, K)[:K]

# 上位k件の値
y = my_array[unsorted_max_indices]

naoyashiga

import cv2
from os import path
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline

cascades_dir = path.normpath(path.join(cv2.__file__, '..', '..', '..', '..', 'share', 'OpenCV', 'haarcascades'))
cascade_f = cv2.CascadeClassifier(path.join(cascades_dir, 'haarcascade_frontalface_alt2.xml'))

def faceDetect(filePath):