Sorry for reviving an old thread.
I got interested in timings for vectorized vs. unvectorized versions of
functions in MATLAB. So I wrote MATLAB versions of the `trapz` functions
mentioned in this thread, and measured their running times by applying the
`timeit` macro several hundred times. The results did surprise me:
trapz1 (vectorized) : ~ 35 µs
trapz2 (unvectorized): ~ 12 µs -- per loop
for evaluating the trapezoidal rule on `x=linspace(0,pi); y=sin(x)`.
I know one should not draw conclusions from such simple benchmarks. Still,
does
this comparison imply the old myth to "
*vectorize MATLAB functions to make them faster*" is not true any more?
The JIT compiler that MATLAB is equipped with is not as powerful as Julia's
LLVM-based compiler, but it seems to be at work. Have others had similar
experiences, or am I on a wrong path here?
On Wednesday, April 23, 2014 11:20:50 PM UTC+2, Tomas Lycken wrote:
>
>
>
> On Wednesday, April 23, 2014 11:10:15 PM UTC+2, Cameron McBride wrote:
>>
>> Or you can use the non-vectorized version and save the overhead of the
>> temporary arrays being created by the addition and multiplication steps.
>>
>
> There's really no way I can hide that I learnt scientific computing in
> Matlab, is there? :P
>
>
>>
>> On Wed, Apr 23, 2014 at 7:52 AM, Tomas Lycken <tomas....@gmail.com>
>> wrote:
>>
>>> The trapezoidal rule (http://en.wikipedia.org/wiki/Trapezoidal_rule)
>>> would probably be almost trivial to implement.
>>>
>>> function trapz{T<:Real}(x::Vector{T}, y::Vector{T})
>>> if (length(y) != length(x))
>>> error("Vectors must be of same length")
>>> end
>>> sum( (x[2:end] .- x[1:end-1]).*(y[2:end].+y[1:end-1]) ) / 2
>>> end
>>>
>>> x = [0:0.01:pi]
>>> y = sin(x)
>>>
>>> trapz(x,y) # 1.9999820650436642
>>>
>>> This, of course, only currently works on vectors of real numbers, but
>>> it's easy to extend it if you want.
>>>
>>> And there might be more accurate methods as well, of course (see e.g.
>>> http://en.wikipedia.org/wiki/Simpson%27s_rule) but this one's often
>>> "good enough".
>>>
>>> // T
>>>
>>> On Wednesday, April 23, 2014 8:43:48 AM UTC+2, Evgeny Shevchenko wrote:
>>>
>>>> Hi, John.
>>>> No, I didn't. I didn't find it and it seems to be not what i need:
>>>>
>>>> "no method quadgk(Array{Float64,1}, Array{Float64,1})"
>>>>
>>>> quadgk(f,a,b,...) expects a function as its first argument but I mean
>>>> the case when y = f(x), but i don't have f, e.g. obtained experimental
>>>> data, so x and y are 1-D arrays of floats.
>>>>
>>>>
>>>> On Tue, Apr 22, 2014 at 7:49 PM, John Myles White <johnmyl...@gmail.com
>>>> > wrote:
>>>>
>>>>> Have you tried the quadgk function?
>>>>>
>>>>> -- John
>>>>>
>>>>> On Apr 22, 2014, at 7:32 AM, Evgeny Shevchenko <eu...@ya.ru> wrote:
>>>>>
>>>>> Hi
>>>>>
>>>>> Is there a package for numeric integration of obtained arrays, say x
>>>>> and y? Couldn't find one, the led me to use @pyimport and numpy.trapz.
>>>>>
>>>>> --
>>>>> pupoque@IRC
>>>>>
>>>>>
>>>>>
>>>>
>>
