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113 lines
3.2 KiB
R
113 lines
3.2 KiB
R
library(foreach)
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library(iterators)
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library(ggplot2)
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library(dplyr)
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#------
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# messages <- read.csv2("chat/bus_time_data-1659191816218.csv", sep = ",")
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# messages <- read.csv2("chat/zeta_molly_data-1659195953292.csv", sep = ",")
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# messages <- messages %>% mutate(pub_time = as.POSIXct(strptime(pub_time, "%Y-%m-%d %T")))
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messages <- read.csv2("transcript/minecraft_data-1659808309634.csv", sep = ",")
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messages <- messages %>% mutate(
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start_time = as.POSIXct(strptime(start_time, "%Y-%m-%d %T")),
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end_time = as.POSIXct(strptime(end_time, "%Y-%m-%d %T"))
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)
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n_m <- nrow(messages)
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#--------
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ggplot(messages, aes(x=pub_time)) + theme_minimal() + geom_histogram(binwidth = 3600)
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#--------
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# Total length of messages
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len <- as.numeric(messages[n_m, 1] - messages[1,1]) * 24 * 3600
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# Poisson lambda for messages per second
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lambda <- nrow(messages)/len
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ggplot() + geom_histogram(mapping = aes(x=cumsum(rexp(n_m, lambda))), binwidth = 3600)
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# Plot with overlaid posterior
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ggplot(messages, aes(x=pub_time)) + theme_minimal() +
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geom_histogram(binwidth = 600) +
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geom_histogram(mapping = aes(x=cumsum(rexp(n_m, lambda)) + messages[1,1]), binwidth = 600, fill="red", alpha=0.3)
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#-------
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# Diffs and diff probabilities
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diffps <- messages %>% transmute(diff = as.numeric(pub_time - lag(pub_time)), p = pexp(as.numeric(pub_time - lag(pub_time)), lambda))
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ggplot(diffps, aes(x=diff)) + theme_minimal() +
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stat_ecdf()
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# Diff with 0.01 quantile
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qdiff <- qexp(0.01, lambda)
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fmess <- messages %>% filter(
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as.numeric(difftime(pub_time, lag(pub_time), units = "secs")) < qdiff |
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as.numeric(difftime(lead(pub_time), pub_time, units = "secs")) < qdiff
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)
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#-------
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# Print messages
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for(i in 1:nrow(fmess)) {
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line = fmess[i,]
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cat(sprintf("[%s] <%s> %s\n", line$pub_time, line$nick, line$message))
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if(difftime(fmess[i+1,"pub_time"], line$pub_time, units = "secs") > qdiff)
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cat("----------------------\n")
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}
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# Show where messages were selected
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ggplot(messages, aes(x=pub_time)) + theme_minimal() +
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geom_histogram(binwidth = 600) +
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geom_histogram(data=fmess, binwidth = 600, fill="green", alpha=0.7)
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#-------------
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# Bayesian
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# p(d < 3/60) = 0.01
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# d ~ exp(l)
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# l ~ gamma(a_p, b_p)
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# l = argmax_l dgamma(a_p, 60)
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a_p <- 13
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b_p <- 60
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# Total length of messages
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# len <- as.numeric(difftime(messages[n_m,]$pub_time, messages[1,]$pub_time, units = "secs"))
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len <- 749503.076
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# Poisson lambda for messages per second
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library(extraDistr)
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gen_diffs <- rlomax(
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n_m,
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1/(b_p + len),
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(a_p + n_m)
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)
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ggplot() + geom_histogram(mapping = aes(x=cumsum(gen_diffs)), binwidth = 3600)
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# Plot with overlaid posterior
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ggplot(messages, aes(x=pub_time)) + theme_minimal() +
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geom_histogram(binwidth = 600) +
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geom_histogram(mapping = aes(x=cumsum(rexp(n_m, lambda)) + messages[1,1]), binwidth = 600, fill="red", alpha=0.3)
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#---------------
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# Diff with 0.01 quantile
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qdiff <- qlomax(0.01, 1/(b_p + len), (a_p + n_m))
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fmess <- messages %>% filter(
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as.numeric(difftime(pub_time, lag(pub_time), units = "secs")) < qdiff |
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as.numeric(difftime(lead(pub_time), pub_time, units = "secs")) < qdiff
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)
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