kenzotakahashi

from sklearn import datasets

import csv, math, random
import pandas as pd
import numpy as np
from collections import defaultdict

kenzotakahashi

def customer_count(status, gender)
  Customer.where(status: status, gender: gender).size
end

kenzotakahashi

# Specify the provider and access details
provider "aws" {
    region = "ap-northeast-1"
}

resource "aws_instance" "web" {
    ami = "ami-374db956"
    instance_type = "t2.micro"
    tags {
        Name = "NomadAgent"

kenzotakahashi

require 'rails_helper'

RSpec.describe User, type: :model do

  describe ".is_gmail_user?" do
    context "given a gmail address" do
      it "returns true" do
        user1 = User.new({email: '[email protected]'})
        expect(user1.is_gmail_user?).to eql(true)
      end

kenzotakahashi

require 'rails_helper'

RSpec.describe "the signin process", :type => :feature do
  # before :each do
  #   User.new(:email => '[email protected]', :password => 'password')
  # end

  it "signs me in" do
    visit '/login'
    within(".row") do

kenzotakahashi

require "redis"

redis = Redis.new

redis.zadd "date:2016-8-5", 214, 1
redis.zadd "date:2016-8-5", 252, 2
redis.zadd "date:2016-8-5", 5222, 3
redis.zadd "date:2016-8-5", 2231, 4
redis.zadd "date:2016-8-5", 442, 5

kenzotakahashi

#week3/plactice/app/jobs/pokemon/pokemon_job.rb
module Pokemon
  class PokemonJob < ApplicationJob
    queue_as :default

    def perform(*args)
      puts "ポケモンGO!"
    end
  end
end

kenzotakahashi

yamanote = ["大崎","五反田","目黒","恵比寿","渋谷","原宿","代々木","新宿","新大久保","高田馬場","目白","池袋","大塚","巣鴨","駒込","田端","西日暮里","日暮里","鶯谷","上野","御徒町","秋葉原","神田","東京","有楽町","新橋","浜松町","田町","品川"]


class ArrayIterator
  def initialize(array)
    @array = array
    @index = 0
  end

  def item

kenzotakahashi

from urllib import request
from lxml import etree
import re

def get_index(e):
	tag = e.tag
	prev_list = [i for i in e.itersiblings(preceding=True) if i.tag == tag]
	next_list = [i for i in e.itersiblings() if i.tag == tag]
	if len(prev_list + next_list) == 0:
		return None

kenzotakahashi

import numpy as np
from sklearn.model_selection import train_test_split

import torch
import torch.nn as nn
from torch.autograd import Variable

np.random.seed(1337)

MAX_LEN = 30