View Rstan_VARMApq.R
library(tidyr) # ver. 0.3.1. | |
library(dplyr) # ver. 0.4.3 | |
library(rstan) # ver. 2.9.0 | |
library(forecast) # ver. 6.2 | |
# 並列化させる設定 | |
rstan_options(auto_write = TRUE) | |
options(mc.cores = parallel::detectCores()) | |
N <- 2 # num series |
View varma_pop.R
library(tidyr) # ver. 0.3.1. | |
library(dplyr) # ver. 0.4.3 | |
library(rstan) # ver. 2.9.0 | |
library(forecast) # ver. 6.2 | |
library(zoo) # ver. 1.7-11 | |
library(gdata) # ver. 2.13.3 | |
library(loo) # ver. 0.1.6 | |
View Rstan_VARMA11_2.R
library(tidyr) # ver. 0.3.1. | |
library(dplyr) # ver. 0.4.3 | |
library(rstan) # ver. 2.9.0 | |
library(forecast) # ver. 6.2 | |
rstan_options(auto_write = TRUE) | |
options(mc.cores = parallel::detectCores()) | |
# VARMA(1,1) N=2 | |
N <- 2 # num series | |
Time <- 400 # span |
View Gaussian_VARMA.stan
/* VARMA (p,q) */ | |
data { | |
int<lower=1> T ; // num observations | |
int<lower=1> N ; // num series | |
int<lower=0> p ; // AR(p) | |
int<lower=0> q ; // MA(q) | |
vector[N] y[T] ; // observed outputs | |
int<lower=0> T_forecast ; // forecasting span | |
} |
View Rstan_VARMA11.R
library(tidyr) # ver. 0.3.1. | |
library(dplyr) # ver. 0.4.3 | |
library(rstan) # ver. 2.9.0 | |
library(forecast) # ver. 6.2 | |
# 並列化させる設定 | |
rstan_options(auto_write = TRUE) | |
options(mc.cores = parallel::detectCores()) | |
setwd("~/Documents/blog/20160212_StanVARMA/") # 任意のフォルダに書き換える |
View VARMApq.stan
/* VARMA (p,q) */ | |
data { | |
int<lower=1> T ; // num observations | |
int<lower=1> N ; // num series | |
int<lower=0> p ; // AR(p) | |
int<lower=0> q ; // MA(q) | |
vector[N] y[T] ; // observed outputs | |
int<lower=0> T_forecast ; // forecasting span | |
} |
View VARMA11.stan
/* VARMA (1,1) */ | |
data { | |
int<lower=1> T ; // num observations | |
int<lower=1> N ; // num series | |
vector[N] y[T] ; // observed outputs | |
int<lower=0> T_forecast ; // forecasting span | |
} | |
parameters { |
View RFM_Bayes.stan
/* ----------------------------------------------------------------------------- | |
Estimate latent variable hierarchical Bayes RFM model | |
stan ver. 2.9 | |
CREATED BY: 09/APR/2015, ill-identified | |
------------------------------------------------------------------------------- */ | |
data { | |
int<lower=1> N ; // num of customers |
View RFM_stan_exec.R
library(dplyr) | |
library(tidyr) | |
library(ggplot2) | |
library(rstan) | |
library(loo) | |
# working directory | |
work.dir <- "HOGEHOGE/20160406_RFM" | |
df <- read.csv(paste(work.dir, "rfm.csv",sep="/"), stringsAsFactors = F) |
View rfm_handle.py
# !/usr/bin/env python3 | |
# -*- coding: utf-8 -*- | |
# coding: utf-8 | |
# In[1]: | |
get_ipython().magic('matplotlib inline' | |
import pystan # ver. 2.9.0.0 | |
import numpy as np # ver.1.11.0 | |
import pandas as pd # ver. 0.18.0 # xlrd, mumxpr module needed |
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