|
|
|
|
library(foreach)
|
|
|
|
|
library(iterators)
|
|
|
|
|
library(ggplot2)
|
|
|
|
|
library(dplyr)
|
|
|
|
|
|
|
|
|
|
#------
|
|
|
|
|
|
|
|
|
|
# messages <- read.csv2("chat/bus_time_data-1659191816218.csv", sep = ",")
|
|
|
|
|
# messages <- read.csv2("chat/zeta_molly_data-1659195953292.csv", sep = ",")
|
|
|
|
|
|
|
|
|
|
# messages <- messages %>% mutate(pub_time = as.POSIXct(strptime(pub_time, "%Y-%m-%d %T")))
|
|
|
|
|
|
|
|
|
|
n_m <- nrow(messages)
|
|
|
|
|
|
|
|
|
|
#--------
|
|
|
|
|
|
|
|
|
|
ggplot(messages, aes(x=pub_time)) + theme_minimal() + geom_histogram(binwidth = 3600)
|
|
|
|
|
|
|
|
|
|
#--------
|
|
|
|
|
|
|
|
|
|
# Total length of messages
|
|
|
|
|
len <- as.numeric(messages[n_m, 1] - messages[1,1]) * 24 * 3600
|
|
|
|
|
# Poisson lambda for messages per second
|
|
|
|
|
lambda <- nrow(messages)/len
|
|
|
|
|
|
|
|
|
|
ggplot() + geom_histogram(mapping = aes(x=cumsum(rexp(n_m, lambda))), binwidth = 3600)
|
|
|
|
|
|
|
|
|
|
# Plot with overlaid posterior
|
|
|
|
|
ggplot(messages, aes(x=pub_time)) + theme_minimal() +
|
|
|
|
|
geom_histogram(binwidth = 600) +
|
|
|
|
|
geom_histogram(mapping = aes(x=cumsum(rexp(n_m, lambda)) + messages[1,1]), binwidth = 600, fill="red", alpha=0.3)
|
|
|
|
|
|
|
|
|
|
#-------
|
|
|
|
|
|
|
|
|
|
# Diffs and diff probabilities
|
|
|
|
|
diffps <- messages %>% transmute(diff = as.numeric(pub_time - lag(pub_time)), p = pexp(as.numeric(pub_time - lag(pub_time)), lambda))
|
|
|
|
|
|
|
|
|
|
ggplot(diffps, aes(x=diff)) + theme_minimal() +
|
|
|
|
|
stat_ecdf()
|
|
|
|
|
|
|
|
|
|
# Diff with 0.01 quantile
|
|
|
|
|
qdiff <- qexp(0.01, lambda)
|
|
|
|
|
|
|
|
|
|
fmess <- messages %>% filter(
|
|
|
|
|
as.numeric(difftime(pub_time, lag(pub_time), units = "secs")) < qdiff |
|
|
|
|
|
as.numeric(difftime(lead(pub_time), pub_time, units = "secs")) < qdiff
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
#-------
|
|
|
|
|
|
|
|
|
|
# Print messages
|
|
|
|
|
|
|
|
|
|
for(i in 1:nrow(fmess)) {
|
|
|
|
|
line = fmess[i,]
|
|
|
|
|
|
|
|
|
|
cat(sprintf("[%s] <%s> %s\n", line$pub_time, line$nick, line$message))
|
|
|
|
|
|
|
|
|
|
if(difftime(fmess[i+1,"pub_time"], line$pub_time, units = "secs") > qdiff)
|
|
|
|
|
cat("----------------------\n")
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
# Show where messages were selected
|
|
|
|
|
ggplot(messages, aes(x=pub_time)) + theme_minimal() +
|
|
|
|
|
geom_histogram(binwidth = 600) +
|
|
|
|
|
geom_histogram(data=fmess, binwidth = 600, fill="green", alpha=0.7)
|
|
|
|
|
|
|
|
|
|
#-------------
|
|
|
|
|
|
|
|
|
|
# Bayesian
|
|
|
|
|
# p(d < 3/60) = 0.01
|
|
|
|
|
# d ~ exp(l)
|
|
|
|
|
# l ~ gamma(a_p, b_p)
|
|
|
|
|
# l = argmax_l dgamma(a_p, 60)
|
|
|
|
|
|
|
|
|
|
a_p <- 13
|
|
|
|
|
b_p <- 60
|
|
|
|
|
|
|
|
|
|
# Total length of messages
|
|
|
|
|
# len <- as.numeric(difftime(messages[n_m,]$pub_time, messages[1,]$pub_time, units = "secs"))
|
|
|
|
|
len <- 749503.076
|
|
|
|
|
# Poisson lambda for messages per second
|
|
|
|
|
|
|
|
|
|
library(extraDistr)
|
|
|
|
|
|
|
|
|
|
gen_diffs <- rlomax(
|
|
|
|
|
n_m,
|
|
|
|
|
1/(b_p + len),
|
|
|
|
|
(a_p + n_m)
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
ggplot() + geom_histogram(mapping = aes(x=cumsum(gen_diffs)), binwidth = 3600)
|
|
|
|
|
|
|
|
|
|
# Plot with overlaid posterior
|
|
|
|
|
ggplot(messages, aes(x=pub_time)) + theme_minimal() +
|
|
|
|
|
geom_histogram(binwidth = 600) +
|
|
|
|
|
geom_histogram(mapping = aes(x=cumsum(rexp(n_m, lambda)) + messages[1,1]), binwidth = 600, fill="red", alpha=0.3)
|
|
|
|
|
|
|
|
|
|
#---------------
|
|
|
|
|
|
|
|
|
|
# Diff with 0.01 quantile
|
|
|
|
|
qdiff <- qlomax(0.01, 1/(b_p + len), (a_p + n_m))
|
|
|
|
|
|
|
|
|
|
fmess <- messages %>% filter(
|
|
|
|
|
as.numeric(difftime(pub_time, lag(pub_time), units = "secs")) < qdiff |
|
|
|
|
|
as.numeric(difftime(lead(pub_time), pub_time, units = "secs")) < qdiff
|
|
|
|
|
)
|
