Hi,
I understand but what I meant is that to train a logistic regression  
you need data that is made of a X matrix which represents the input  
data. Lets say we have 'm' examples. We also need a Y vector which  
represent the expected outputs for each of the 'm' training example.  
The training process is simply fitting the weights of the regression  
so the output of the regression is almost always outputs the right Y  
when you give an input X. I wanted to know how you are able to find  
the Y vector which is the expected output of the regression for the  
input data? Am I understanding correctly?

Zahra Khatami <z.khatam...@gmail.com> a écrit :

> Gabriel,
>
> I am not sure if I understand your concern correctly. The optimal
> parameters ( chunk size, preferching distance or policies) shouldn’t be
> found before training data. They are found for each of the hpx loops at
> runtime based on the loop static and dynamic parameters. That’s a main goal
> of this research. The candidates of these optimal parameters are chosen
> when training model. Then the optimal one will be selected between them at
> runtime, which may be different for each loop with different parameters.
>
> Thanks,
> Zahra,
>
> On Tue, Feb 20, 2018 at 7:51 AM Gabriel Laberge <gabriel.labe...@polymtl.ca>
> wrote:
>
>> Hi,
>> I had a questions on the way data was generated in order to train the
>> logistics regressions models talked about in [0]
>> https://arxiv.org/pdf/1711.01519.pdf
>> For each of the training examples, the optimal execution
>> policies,chunk sizes and prefetching distance had to be found before
>> the training process in order to have good data. I wonder if the
>> optimal parameters for the training examples were found by trial and
>> error or if there is another technique.
>> Thank you..
>>
>>
>>
>> _______________________________________________
>> hpx-users mailing list
>> hpx-users@stellar.cct.lsu.edu
>> https://mail.cct.lsu.edu/mailman/listinfo/hpx-users
>>
> --
> Best Regards, Zahra Khatami | PhD Student Center for Computation &
> Technology (CCT) School of Electrical Engineering & Computer Science
> Louisiana State University 2027 Digital Media Center (DMC) Baton Rouge, LA
> 70803



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