Issue #10298 has been updated by Takeshi Nishimatsu.


Thank you for the quick patch for enum.c.

Timing and test.
I see. performance is not improved.

```
$ uname -sprsv
Darwin 13.3.0 Darwin Kernel Version 13.3.0: Tue Jun  3 21:27:35 PDT 2014; root:xnu-2422.110.17~1/RELEASE_X86_64 i386
$ sysctl -n machdep.cpu.brand_string
Intel(R) Core(TM) i5-3210M CPU @ 2.50GHz
$ ./miniruby --version
ruby 2.2.0dev (2014-10-15 trunk 47735) [x86_64-darwin13]
$ /usr/bin/time ./miniruby -e 'p Array.new(10000000,0.1).reduce(:+)'
999999.9998389754
        0.69 real         0.65 user         0.03 sys
$ mv optimized/enum.c .
$ make -j2 miniruby
compiling enum.c
linking miniruby
$ /usr/bin/time ./miniruby -e 'p Array.new(10000000,0.1).reduce(:+)'
1000000.0
        0.55 real         0.51 user         0.04 sys
$ ./miniruby -e 'p Array.new(10000000,0.1).unshift(0.0).reduce(:-)'
-1000000.0
```

I understand that Array is not only for Float.

```
$ ./miniruby -e '[1,2,3].float_sum'
-e:1: [BUG] Segmentation fault at 0x00000000000013
```


----------------------------------------
Feature #10298: Array#float_sum (like math.fsum of Python)
https://bugs.ruby-lang.org/issues/10298#change-49459

* Author: Takeshi Nishimatsu
* Status: Feedback
* Priority: Low
* Assignee: 
* Category: math
* Target version: 
----------------------------------------
Here, I propose Array#float_sum in array.c (or math.c).
Array#float_sum returns an accurate total summation of Float
elements in an array using the Kahan summation algorithm
http://en.wikipedia.org/wiki/Kahan_summation_algorithm .
This algorithm can significantly reduce the numerical
error in the total obtained by adding a sequence of
finite precision floating point numbers, compared to the
obvious approach. Python already have math.fsum
https://docs.python.org/2/library/math.html#math.fsum .

```
[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1].float_sum   #=> 1.0
[].float_sum                                                   #=> 0.0
Array.new( 10, 0.1).float_sum    #=>  1.0
Array.new(100, 0.1).float_sum    #=> 10.0
# cf.
Array.new( 10, 0.1).reduce(:+)   #=>  0.9999999999999999
Array.new(100, 0.1).reduce(:+)   #=>  9.99999999999998
```

The name of method can be fsum, sum_float, etc., though
I propose float_sum.

This Array#float_sum is inspired by Feature #9834 Float#{next_float,prev_float}.


---Files--------------------------------
array.float_sum.patch (1.32 KB)


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