Thnaks, It is true, but when I apply @benchmark v3 is 6 times slower as v1, 
also has a large allocation and I do not want it. For me speed is important 
and v3 is not without visual noise, too. Any more thoughts?

ben1 = @benchmark mapeBase_v1(a,f)
BenchmarkTools.Trial: 
  samples:          848
  evals/sample:     1
  time tolerance:   5.00%
  memory tolerance: 1.00%
  memory estimate:  32.00 bytes
  allocs estimate:  1
  minimum time:     4.35 ms (0.00% GC)
  median time:      5.87 ms (0.00% GC)
  mean time:        5.89 ms (0.00% GC)
  maximum time:     7.57 ms (0.00% GC)

ben2 = @benchmark mapeBase_v3(a,f)
BenchmarkTools.Trial: 
  samples:          145
  evals/sample:     1
  time tolerance:   5.00%
  memory tolerance: 1.00%
  memory estimate:  977.03 kb
  allocs estimate:  14
  minimum time:     32.69 ms (0.00% GC)
  median time:      33.91 ms (0.00% GC)
  mean time:        34.55 ms (0.10% GC)
  maximum time:     49.03 ms (3.25% GC)




On Tuesday, 25 October 2016 09:43:20 UTC+2, Jeffrey Sarnoff wrote:
>
> This may do what you want.
>
> function mapeBase_v3(actuals::Vector{Float64}, forecasts::Vector{Float64})
> # actuals - actual target values
> # forecasts - forecasts (model estimations)
>
>   sum_reldiffs = sumabs((x - y) / x for (x, y) in zip(actuals, forecasts) if 
> x != 0.0)  # Generator
>
>   count_zeros = sum( map(x->(x==0.0), actuals) )
>   count_nonzeros = length(actuals) - count_zeros
>   sum_reldiffs, count_nonzeros
> end
>
>
>
>
> On Tuesday, October 25, 2016 at 3:15:54 AM UTC-4, Martin Florek wrote:
>>
>> Hi all,
>> I'm new in Julia and I'm doing refactoring. I have the following function:
>>
>> function mapeBase_v1(A::Vector{Float64}, F::Vector{Float64})
>>   s = 0.0
>>   count = 0
>>   for i in 1:length(A)
>>     if(A[i] != 0.0)
>>       s += abs( (A[i] - F[i]) / A[i])
>>       count += 1
>>     end
>>   end
>>
>>   s, count 
>>
>> end   
>>
>> I'm looking for a simpler variant which is as follows:
>>
>> function mapeBase_v2(A::Vector{Float64}, F::Vector{Float64})
>> # A - actual target values
>> # F - forecasts (model estimations)
>>
>>   s = sumabs((x - y) / x for (x, y) in zip(A, F) if x != 0) # Generator
>>
>>   count = length(A) # ???
>>   s, countend
>>
>>
>> However with this variant can not determine the number of non-zero elements. 
>> I found option with length(A[A .!= 0.0]), but it has a large allocation. 
>> Please, someone knows a solution with generator, or variant v1 is very good 
>> choice?
>>
>>
>> Thanks in advance,
>> Martin
>>
>>

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