Gedevan-Aleksizde

---
title: "benchmark"
author: "ill-identified"
output:
  pdf_document: default
  html_document: default
---

## JIT 有効になってるとこれの結果が変わるかを検証

Gedevan-Aleksizde

require(tidyverse) # ver. 1.2.1
require(CARBayes) # ver. 5.0
require(sf) # ver. 0.6-3
require(ggplot2)  # ver. 3.0.0
require(ggthemes) # ver. 4.0.0
require(ggmcmc) # ver. 1.1

rmse <- function(y, pred){
  return(sqrt(mean((y - pred)^2)))
}

Gedevan-Aleksizde

require(tidyverse)
require(ggthemes)
require(rjson)
require(jsonlite)

# 参考
# https://www.data.jma.go.jp/gmd/risk/obsdl/index.php
# https://www.data.jma.go.jp/gmd/risk/obsdl/top/help3.html#hukajoho
# https://twitter.com/mehori/status/1020644999703089152

Gedevan-Aleksizde

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 31 22:57:58 2018
https://www.quora.com/How-can-I-extract-only-text-data-from-HTML-pages
https://qiita.com/matsu0228/items/edf7dbba9b0b0246ef8f
@author: ks
"""

import sys, re, datetime

Gedevan-Aleksizde

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import sys
import argparse
import random
from pathlib import Path
from itertools import chain
# fname = Path('~/Downloads/vpylm/out_2018-04-01T040258.txt')

Gedevan-Aleksizde

require(ggplot2)
require(purrr)
require(stringr)
require(glue)
require(qrencoder)
require(png)
require(gridExtra)

# given_name 必須. 名
# family_name 任意. 姓

Gedevan-Aleksizde

# --------------------------------
# calculate DIC* from stanfit
#
# * Spiegelhalter et al. (2002)
# --------------------------------

DIC <- function(stanfit, df.input, dev ){
  # stanfit: stanfit object
  # df.input: input data.frame
  # dev: function that calculate the dev. of post.mean; 

Gedevan-Aleksizde

require(KFAS) # 1.2.9
require(dplyr)
require(tidyr)
require(ggplot2)

# --- ARIMA(2, 1) with linear trend ---
# generate a dataset
set.seed(42)
t <- 100
y <- arima.sim(n = t, model = list(ar=c(.3, -0.1),  ma=.2), sd=.1) + seq(from=1, to=10, length.out = t)

Gedevan-Aleksizde

require(bsts) # 0.7.1

data(iclaims)     # bring the initial.claims data into scope

# --- model 1 ----
ss <- AddLocalLinearTrend(list(), initial.claims$iclaimsNSA)
ss <- AddSeasonal(ss, initial.claims$iclaimsNSA, nseasons = 52)
model1 <- bsts(initial.claims$iclaimsNSA,
               state.specification = ss,
               niter = 1000)

Gedevan-Aleksizde

# ------ common part ----

require(ggplot2)
require(dplyr)
require(tidyr)
require(dlm) # 1.1-4
require(KFAS) # 1.2.9
require(bsts) # 0.7.1

# calculate the reference price