| library(HMM) |
| library(readr) |
| ############## Data Parsing Phase ############## |
| cert_r4_2_dataset <- read_csv("~/cert_r4.2_dataset.csv") #Load the dataset. |
| Remember to change the path to file location on own machine. |
| username = "MCF0600" |
| allWeeks <- split(cert_r4_2_dataset[cert_r4_2_datasetuser %in% |
| username,]activity, cert_r4_2_dataset[cert_r4_2_datasetuser |
| %in% username,]week) #Filter dataset to only include data relevent to chosen user. |
| indx <- sapply(allWeeks, length) #Convert the allWeeks variable into DataFrame. |
| res <- as.data.frame(do.call(cbind,lapply(allWeeks, ＇length<-＇,max(indx)))) |
| ################################################# |
| ############## HMM Phase ######################## |
| hmm = initHMM(c(1,2,3,4,5,6,7,8,9,10), c(1,2,3,4,5,6,7)) #Initiate a 10 |
| state HMM with 7 labels (which represent activities of user.) |
| model = baumWelch(hmm, na.omit(unlist(res[1:5])), maxIterations=20, |
| pseudoCount =0.1, delta = 0.01) #Train our model with the first 5 weeks of user activity. |
| vector = c() |
| for (i in 6:length(res)) #For the remaining weeks of activity... |
|
| #Calculate probability of a given observed sequence with respect to our model |
| logForwardProbabilities = forward(modelhmm, na.omit(unlist(res[i]))) # |
| ... calculate the probability of week i occurring against model... |
| like <- ((logForwardProbabilities)) |
| lenthOfLike <- (length(like)/10) |
| answer <- sum(like[,lenthOfLike]) |
| vector[i - 5] <- answer #... store result of probability in vector... |
| model = baumWelch(modelhmm, na.omit(unlist(res[1:i])), maxIterations=20, |
| pseudoCount =0.1, delta=0.01) #... and update model with week i. |
|
| ######### Plot Result ######### |
| plot(6:(length(res)), vector[1:(length(vector))] -1, type="l", |
| xlab="Week", ylab="-Log Probability", main=paste("HMM for", username, sep=" ")) |
