This repository contains the code from an Active Analytics blog entry for calculating the Chain Ladder function in less than 2 microseconds.
The files contained in the repostory are:
- speed-cl.c contains the functions for calculating the age-to-age factors and the chain ladder
- traingle.jl function contains the functions for calling the C chain ladder function and carrying out the bechmark
- Compile the c-code:
$ gcc speed-cl.c -shared -o speed-cl.so -fPIC
- In the chain ladder function, the code:
ccall((:get_factor, "speed-cl"), Void, (Ptr{Float64}, Ptr{Float64}, Int32), pointer(tri), pointer(fact), p)
ccall((:cl, "speed-cl"), Void, (Ptr{Float64}, Ptr{Float64}, Int32), pointer(tri), pointer(fact), p)
the "speed-cl" needs to be the path to the linked library.
# The auto paid data from the ChainLadder package
julia> x = [101125. 209921 266618 305107 327850 340669 348430 351193 353353 353584
102541 203213 260677 303182 328932 340948 347333 349813 350523 NaN
114932 227704 298120 345542 367760 377999 383611 385224 NaN NaN
114452 227761 301072 340669 359979 369248 373325 NaN NaN NaN
115597 243611 315215 354490 372376 382738 NaN NaN NaN NaN
127760 259416 326975 365780 386725 NaN NaN NaN NaN NaN
135616 262294 327086 367357 NaN NaN NaN NaN NaN NaN
127177 244249 317972 NaN NaN NaN NaN NaN NaN NaN
128631 246803 NaN NaN NaN NaN NaN NaN NaN NaN
126288 NaN NaN NaN NaN NaN NaN NaN NaN NaN];
julia> chain_ladder(x)
10x10 Array{Float64,2}:
101125.0 209921.0 266618.0 305107.0 327850.0 340669.0 348430.0 351193.0 353353.0 353584.0
102541.0 203213.0 260677.0 303182.0 328932.0 340948.0 347333.0 349813.0 350523.0 350752.0
114932.0 227704.0 298120.0 345542.0 367760.0 377999.0 383611.0 385224.0 386801.0 387054.0
114452.0 227761.0 301072.0 340669.0 359979.0 369248.0 373325.0 375696.0 377234.0 377481.0
115597.0 243611.0 315215.0 354490.0 372376.0 382738.0 389122.0 391594.0 393197.0 393454.0
127760.0 259416.0 326975.0 365780.0 386725.0 398767.0 405418.0 407994.0 409664.0 409932.0
135616.0 262294.0 327086.0 367357.0 390851.0 403021.0 409744.0 412346.0 414035.0 414305.0
127177.0 244249.0 317972.0 361420.0 384534.0 396507.0 403121.0 405682.0 407343.0 407609.0
128631.0 246803.0 317180.0 360519.0 383575.0 395519.0 402117.0 404671.0 406328.0 406593.0
126288.0 251312.0 322974.0 367105.0 390583.0 402745.0 409463.0 412064.0 413751.0 414021.0
julia> bench(1000)
1x6 DataFrame:
min lq median uq max neval
[1,] 1.586 1.604 1.614 1.68475 8.172 1000
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