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

require(KFAS) # 1.2.9
require(dplyr)
require(tidyr)
require(ggplot2)
require(data.table)
require(zoo)

##### read dataset #####
# https://catalog.data.gov/dataset/allegheny-county-crash-data
# data description

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

source("common.R", encoding = "utf-8")

df$RP <- calc_RP(df$RP095, df$AP, .95)
z <- calc_Z(RP = df$RP, p = df$AP)
df <- mutate(df, z1=z$z1, z2=z$z2)
df <- mutate(df, z1E=z1*end, z2E=z2*end)

ss <- AddLocalLevel(list(), y = df$logPI) # c
ss <- AddAr(ss, lags=2, y = df$logPI) # AR(2)
# time-varying regression

Gedevan-Aleksizde

source("common.R", encoding = "utf-8")

# ----- KFAS ------
df$RP <- calc_RP(df$RP095, df$AP, .95)
z <- calc_Z(RP = df$RP, p = df$AP)
df <- mutate(df, z1=z$z1, z2=z$z2)
df <- mutate(df, z1E=z1*end, z2E=z2*end)

# specify model
model3KFAS <- SSModel(logPI ~ SSMtrend(1, Q=NA) +

Gedevan-Aleksizde

source("common.R", encoding = "utf-8")

# ---- dlm ------

# model 3
res <- data.frame()
for( a in seq(from=.1, to=.95, by=.05) ){
  RP <- calc_RP(RP = df$RP095, p = df$AP, a = a)
  z <- calc_Z(RP = RP, p = df$AP)
  z1 <- z$z1

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