作为中级 R 用户,我知道 for 循环通常可以通过使用类似apply
或其他的函数来优化。但是,我不知道可以优化我当前代码以生成马尔可夫链矩阵的函数,该矩阵运行速度非常慢。我是否已经最大限度地提高了速度,或者是否有一些我忽略的东西?我试图通过在给定警报之前计算 24 小时时间段内的出现次数来找到马尔可夫链的转换矩阵。该向量ids
包含所有可能的 id(大约 1700)。
原始矩阵如下所示,例如:
>matrix
id time
1 1376084071
1 1376084937
1 1376023439
2 1376084320
2 1372983476
3 1374789234
3 1370234809
这是我尝试处理此问题的代码:
matrixtimesort <- matrix[order(-matrix$time),]
frequency = 86400 #number of seconds in 1 day
# Initialize matrix that will contain probabilities
transprobs <- matrix(data=0, nrow=length(ids), ncol=length(ids))
# Loop through each type of event
for (i in 1:length(ids)){
localmatrix <- matrix[matrix$id==ids[i],]
# Loop through each row of the event
for(j in 1:nrow(localmatrix)) {
localtime <- localmatrix[j,]$time
# Find top and bottom row number defining the 1-day window
indices <- which(matrixtimesort$time < localtime & matrixtimesort$time >= (localtime - frequency))
# Find IDs that occur within the 1-day window
positiveids <- unique(matrixtimesort[c(min(indices):max(indices)),]$id)
# Add one to each cell in the matrix that corresponds to the occurrence of an event
for (l in 1:length(positiveids)){
k <- which(ids==positiveids[l])
transprobs[i,k] <- transprobs[i,k] + 1
}
}
# Divide each row by total number of occurrences to determine probabilities
transprobs[i,] <- transprobs[i,]/nrow(localmatrix)
}
# Normalize rows so that row sums are equal to 1
normalized <- transprobs/rowSums(transprobs)
任何人都可以提出任何建议来优化速度吗?