Hi Jimmie,

effectively I did not subsribe...

Having efficient methods for sliding window average is possible, here is how I would do it:

"Create a vector with 100,000 rows filles with random values (uniform distrubution in [0,1]"
v := LapackDGEMatrix randUniform: #(100000 1).

"extract values from rank 10001 to 20000"
w1 := v atIntervalFrom: 10001 to: 20000 by: 1.

"create a left multiplier matrix for performing average of w1"
a := LapackDGEMatrix nrow: 1 ncol: w1 nrow withAll: 1.0 / w1 size.

"get the average (this is a 1x1 matrix from which we take first element)"
avg1 := (a * w1) at: 1.

[ "select another slice of same size"
w2 := v atIntervalFrom: 15001 to: 25000 by: 1.

"get the average (we can recycle a)"
avg2 := (a * w2) at: 1 ] bench.

This gives:
��'16,500 per second. 60.7 microseconds per run.'

versus:
[w2 sum / w2 size] bench.
��'1,100 per second. 908 microseconds per run.'

For max and min, it's harder. Lapack/Blas only provide max of absolute value as primitive:
[w2 absMax] bench.
��'19,400 per second. 51.5 microseconds per run.'

Everything else will be slower, unless we write new primitives in C and connect them...

[w2 maxOf: [:each | each]] bench.
��'984 per second. 1.02 milliseconds per run.'

Le��dim. 19 mai 2019 ��14:58, Jimmie <jlhouchin@gmail.com> a ��crit��:

On 5/16/19 1:26 PM, Nicolas Cellier wrote:> Any feedback on this?
��> Did someone tried to use Smallapack in Pharo?
��> Jimmie?
��>

I am going to guess that you are not on pharo-users. My bad.
I posted this in pharo-users as I it wasn't Pharo development question.

I probably should have posted here or emailed you directly.

All I really need is good performance with a simple array of floats. No
matrix math. Nothing complicated. Moving Averages over a slice of the
array. A variety of different averages, weighted, etc. Max/min of the
array. But just a single simple array.

Any help greatly appreciated.

Thanks.

On 4/28/19 8:32 PM, Jimmie Houchin wrote:
Hello,

I have installed Smallapack into Pharo 7.0.3. Thanks Nicholas.

I am very unsure on my use of Smallapack. I am not a mathematician or
scientist. However the only part of Smallapack I am trying to use at the
moment is something that would�� be 64bit and compare to FloatArray so
that I can do some simple accessing, slicing, sum, and average on the array.

Here is some sample code I wrote just to play in a playground.

I have an ExternalDoubleArray, LapackDGEMatrix, and a FloatArray
samples. The ones not in use are commented out for any run.

fp is a download from
http://ratedata.gaincapital.com/2018/12%20December/EUR_USD_Week1.zip
and unzipped to a directory.

fp := '/home/jimmie/data/EUR_USD_Week1.csv'
index := 0.
pricesSum := 0.
asum := 0.
ttr := [
�� lines := fp asFileReference contents lines allButFirst.
�� a := ExternalDoubleArray new: lines size.
�� "la := LapackDGEMatrix allocateNrow: lines size ncol: 1.
�� a := la columnAt: 1."
�� "a := FloatArray new: lines size."
�� lines do: [ :line || parts price |
�� parts := ',' split: line.
�� index := index + 1.
�� price := Float readFrom: (parts last).
�� a at: index put: price.
�� pricesSum := pricesSum + price.
�� (index rem: 100) = 0 ifTrue: [
�� asum := a sum.
�� ]]] timeToRun.
{ index. pricesSum. asum. ttr }.
�� "ExternalDoubleArray an Array(337588 383662.5627699992
383562.2956199993 0:00:01:59.885)"
�� "FloatArray�� an Array(337588 383662.5627699992 383562.2954441309
0:00:00:06.555)"

FloatArray is not the precision I need. But it is over 18x faster.

I am afraid I must be doing something badly wrong. Python/Numpy is over
4x faster than FloatArray for the above.

If I am using Smallapack incorrectly please help.

Any help greatly appreciated.

Thanks.