smrmkt

import math
import csv
import random

random.seed(10)

n_numbers = 3 # how many numbers do we want to operate on

largest = 10 #largest INT

smrmkt

手順

• あらかじめ aws cli の設定をすませておいてください
• あとは以下のスクリプトを順番に実行してください

./emr_create_spark_cluster.sh`
./emr_pyspark_wc.sh`

smrmkt

from concurrent.futures import ThreadPoolExecutor, as_completed
from pyspark.sql import SparkSession
import sys

if __name__ == "__main__":
    hive_table_name = sys.argv[1]
    out_path = sys.argv[2]

    # setup and surpress create _$folder$ object to S3
    ss = SparkSession.builder \

smrmkt

install.packages("fitbitScraper")
devtools::install_github("trinker/plotflow")
library(fitbitScraper)
library(ggplot2)
library(plotflow)
library(dplyr)

# Fitbitからのデータ取得
cookie = login(email="www.fitbit.com ログイン用メールアドレス", password="www.fitbit.com ログイン用パスワード")
hr_data <- get_intraday_data(cookie, what="heart-rate", date="2016-10-06")

smrmkt

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

import numpy as np


class Person():
    def __init__(self, n):
        self.preference = np.random.permutation(range(n))
        self.preferred_queue = []
        self.position = None

smrmkt

library(dplyr)

# 選択肢1, 2のデータを作成して結合
# 項目は以下の通り
#   クラス番号
#   候補者1支持ダミー
#   候補者2支持ダミー
#   価値観1
#   価値観2
location1 <- matrix(c(rep(1, 100),

smrmkt

library(glmnet)
library(caret)
library(psych)

# load data
data(cars)
pairs.panels(cars)

# lm
fit.lm <- glm(Price ~ ., data = cars)

smrmkt

library(xgboost)
library(Matrix)

# load data
data = read.delim("data/sample.tsv", sep="\t")
data$v6 = NULL

# create data for k-fold cross validation
cv = function(d, k) {
  n = sample(nrow(d), nrow(d))

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library(randomForest)

# load data
data = read.delim("data/sample.tsv", sep="\t")

# create data for k-fold cross validation
cv = function(d, k) {
  n = sample(nrow(d), nrow(d))
  d.randomized = data[n,] # randomize data
  n.residual = k-nrow(d)%%k

smrmkt

data(JohnsonJohnson)
p=40

# y = α + βx + γ(x > t)(x - t + 1) + ε
t = c(1:84)
JJ2 = as.data.frame(cbind(JohnsonJohnson, t, (t>p)*(t-p)))
colnames(JJ2) = c('earnings', 't', 't2')
JJ2.lm <- lm(earnings~t+t2, data=JJ2)
summary(JJ2.lm)