The Purpose of this vignette is to outline the creation of a
new cost function. In this example the cost function for a Normal
distribution with potential chnges in mean is presented; the result
being a simplified version of the gaussMean cost
function.
A cost function is an R6 class with the following public
methods
| Method | Inputs | Returns |
|---|---|---|
| initialize | Data series, default parameters for the background cost | Initialised R6 object |
| baseCost | start & end of the period, penalty | The backgorund cost of a data period |
| pointCost | time set, penalty | The cost of a time step if it was a point anomaly |
| collectiveCost | start & end of the period, penalty | The cost of the period if it was a collective anomaly |
| length | None | The length of the data series |
| param | start & end of the period | The estimated parameters if the period was anomalous |
The following commented code impliments these methods for the example distribution
library(R6)
newCost <- R6Class("newCost",
private = list(
x = NULL, ## storage for the input data
m = NULL, ## storage for the background mean
s = NULL ## storage for the background standard deviation
),
public=list(
## initialisation
initialize = function(x,m,s){
private$x <- as.numeric(x) ## convert and store data
private$m <- as.numeric(m) ## convert and store background mean
private$s <- as.numeric(s) ## convert and store background standard deviation
invisible(self) ## to allow chaining of methods
},
## length of the data
length = function(){ length(private$x) },
## compute the base cost
baseCost = function(a,b,pen=0){ ## start & end of period and penalty
-2*sum( dnorm(private$x[a:b],private$m,private$s,log=TRUE) ) + pen
},
## cost of a point anomaly - change in mean
pointCost = function(a,pen){
## change of mean in anomaly make new mean obseerved value
-2*dnorm(private$x[a],private$x[a],private$s,log=TRUE) + pen
},
## cost of a collective anomaly
collectiveCost = function(a,b,pen,len){
if( b-a+1 < len ){return(NA)} ## catch short a segments and return NA by convention
## change of mean in make new mean average of data
m_est <- mean(private$x[a:b])
-2*sum( dnorm(private$x[a:b],m_est,private$s,log=TRUE) ) + pen
},
## estimate the new parameters
param = function(a,b){
## the new parameter value is the change in mean
mean(x[a:b]) - private$m
}
)
)We can test this against the included implimentation
library(anomalous)
data("Lai2005fig4")
y <- Lai2005fig4[, 5]
## set the the new cost function and matching internal implimentation
nC <- newCost$new(y,m=median(y),s=mad(y))
fC <- gaussMean$new(y,m=median(y),s=mad(y), point_type="mean")
capa_nC <- capa(partition(2*log(length(y)),2*log(length(y)),2),nC)
summary(capa_nC)
#> start end type cost
#> 1 1 28 background 32.371108
#> 2 29 32 collective 14.346520
#> 3 33 53 background 29.309601
#> 4 54 54 point 11.010282
#> 5 55 81 background 35.087923
#> 6 82 85 collective 15.619015
#> 7 86 89 background 2.818136
#> 8 90 96 collective 22.069486
#> 9 97 123 background 36.743793
#> 10 124 124 point 11.010282
#> 11 125 125 background 9.874315
#> 12 126 133 collective 24.756257
#> 13 134 193 background 70.082216
capa_fC <- capa(partition(2*log(length(y)),2*log(length(y)),2),fC)
summary(capa_fC)
#> start end type cost
#> 1 1 28 background 32.371108
#> 2 29 32 collective 14.346520
#> 3 33 53 background 29.309601
#> 4 54 54 point 11.010282
#> 5 55 81 background 35.087923
#> 6 82 85 collective 15.619015
#> 7 86 89 background 2.818136
#> 8 90 96 collective 22.069486
#> 9 97 123 background 36.743793
#> 10 124 124 point 11.010282
#> 11 125 125 background 9.874315
#> 12 126 133 collective 24.756257
#> 13 134 193 background 70.082216