Benchmarks
The benchmarks below were run on Julia version 1.11.5.
Default example
X = [40 30 20 10; 35 50 100 75; 30 80 70 120; 20 30 40 50]
u = [150, 300, 400, 150]
v = [200, 300, 400, 100]
@benchmark ipf(X, [u, v])BenchmarkTools.Trial: 10000 samples with 1 evaluation per sample.
Range (min … max): 54.121 μs … 12.064 ms ┊ GC (min … max): 0.00% … 99.01%
Time (median): 57.718 μs ┊ GC (median): 0.00%
Time (mean ± σ): 62.745 μs ± 153.441 μs ┊ GC (mean ± σ): 3.99% ± 1.70%
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54.1 μs Histogram: log(frequency) by time 92.7 μs <
Memory estimate: 38.00 KiB, allocs estimate: 813.Large contingency table
X = reshape(repeat(1:16, 625), 100, 100)
Y = reshape(repeat(1:5, 2000), 100, 100) + X
m = ArrayMargins(Y)
@benchmark ipf(X, m)BenchmarkTools.Trial: 10000 samples with 1 evaluation per sample.
Range (min … max): 142.347 μs … 5.731 ms ┊ GC (min … max): 0.00% … 14.17%
Time (median): 152.916 μs ┊ GC (median): 0.00%
Time (mean ± σ): 168.698 μs ± 120.020 μs ┊ GC (mean ± σ): 5.53% ± 9.00%
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142 μs Histogram: log(frequency) by time 697 μs <
Memory estimate: 274.31 KiB, allocs estimate: 888.Six-dimensional contingency table
X = reshape(repeat(1:12, 100), 6, 4, 2, 5, 5)
Y = reshape(repeat(1:5, 240), 6, 4, 2, 5, 5) + X
m = ArrayMargins(Y)
@benchmark ipf(X, m)BenchmarkTools.Trial: 10000 samples with 1 evaluation per sample.
Range (min … max): 425.546 μs … 9.064 ms ┊ GC (min … max): 0.00% … 94.05%
Time (median): 442.598 μs ┊ GC (median): 0.00%
Time (mean ± σ): 454.199 μs ± 194.932 μs ┊ GC (mean ± σ): 1.78% ± 3.93%
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426 μs Histogram: frequency by time 514 μs <
Memory estimate: 108.06 KiB, allocs estimate: 1726.