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## Managed Open Source blog code # http://blog.managedopensource.com # http://blog.managedopensource.com/2007/10/31/reversal-of-code-performance-optimizations-from-ruby-1-8-to-1-9 ## R analysis code for benchmarking results > real_totals.for <- c(4.227468, 4.210407, 4.163847, 4.224626) > real_totals.each <- c(3.314974, 3.363656, 3.321745, 3.379422) > real_totals.list <- c(4.384898, 4.361846, 4.355446, 4.401145) > real_totals.collect <- c(3.701988, 3.668885, 3.668928, 3.676394) > ratios.for_to_each <- real_totals.for / real_totals.each > ratios.each_to_for <- real_totals.each / real_totals.for > ratios.list_to_collect <- real_totals.list / real_totals.collect > ratios.collect_to_list <- real_totals.collect / real_totals.list > ratios.for_to_each [1] 1.275264 1.251735 1.253512 1.250103 > ratios.each_to_for [1] 0.7841512 0.7988909 0.7977587 0.7999340 > ratios.list_to_collect [1] 1.184471 1.188875 1.187117 1.197136 > ratios.collect_to_list [1] 0.8442586 0.8411313 0.8423771 0.8353267 > stdev.for <- sd(real_totals.for) > stdev.each <- sd(real_totals.each) > stdev.list <- sd(real_totals.list) > stdev.collect <- sd(real_totals.collect) > stdev.for [1] 0.02945461 > stdev.each [1] 0.03149215 > stdev.list [1] 0.02108821 > stdev.collect [1] 0.01569489 > avg.for <- mean(real_totals.for) > avg.each <- mean(real_totals.each) > avg.list <- mean(real_totals.list) > avg.collect <- mean(real_totals.collect) > avg.for [1] 4.206587 > avg.each [1] 3.344949 > avg.list [1] 4.375834 > avg.collect [1] 3.679049 > avg.for / avg.each [1] 1.257594 > avg.each / avg.for [1] 0.7951694 > avg.list / avg.collect [1] 1.189393 > avg.collect / avg.list [1] 0.8407652

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