As given above, the time is in sum_reldiffs, the space is in the
count_zeros.
On Tuesday, October 25, 2016 at 4:42:02 AM UTC-4, Jeffrey Sarnoff wrote:
>
> (you may like that visual noise if the code finds more than 1 year's use)
> Which matters more to you saving time or saving space?
>
> On Tuesday, October 25, 2016 at 4:39:33 AM UTC-4, Martin Florek wrote:
>>
>> 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
>>>>
>>>>
