Hello Marcus,

Thank you for your guidance so far. The pull request I started working on last 
month now provides an interface for handling unconstrained problems 
(https://github.com/mlpack/mlpack/pull/1225). Please let me know if it requires 
further improvements.

I have started working on my application and will share it as soon as I have a 
reviewable draft. Currently, I'm brain-storming some ideas related with API 
design for handling constrained problems.

We previously conversed on using lambda functions for defining the constraints. 
This seems alright, but we should also have a mechanism for detecting whether 
the user wants to add constraints at all. For this, I think we can make use for 
traits. I imagine something like this would work:


template<typename ConstraintType>
struct ConstraintTraits
{
  // If true, the constraints are enforced after each update.
  // This defaults to false.
  static const bool HasConstraints = false;
};

The user can then make a template specialization to set the flag:

template<>
struct ConstraintTraits<bool>
{
  static const bool HasConstraints = true;
}

Which can then be used to enforce constraints:

template<typename ConstraintType>
if (BoundTraits<ConstraintType>::HasConstraints)
{
  // Enforce constraints.
}

Something similar is already being done in bound_traits.hpp 
(https://github.com/mlpack/mlpack/blob/3d3d733ba3c41c4f51764f44185767384ab6d9c7/src/mlpack/core/tree/bound_traits.hpp#L31).
 Additionally, I think constraints can also be provided as part of the 
ConstraintTraits structure using a lambda function as the structure member.

Another approach would be to provide constraints as part of the evaluation 
function class. In the implementation I proposed, the template class passed to 
the Optimize class method would contain the constraint definition.

Could you please let me know which of these techniques would be more desirable? 
Or, should I mention both in my proposal and try to come up with pros and cons 
for each?

Thank you,
Adeel

________________________________
From: Marcus Edel <marcus.e...@fu-berlin.de>
Sent: Tuesday, January 30, 2018 6:05 PM
To: Adeel Ahmad
Cc: mlpack@lists.mlpack.org
Subject: Re: [mlpack] Query regarding constrained and unconstrained methods

Hello Adeel,

I have created a minimal layout for the PSO (unconstrained problems). Please see
this commit (https://github.com/adl1995/mlpack/commit/493c87968a9291582bb663d537
22818295e5cd47). Could you please provide some feedback on the current progress?
Maybe it would be easier to use GitHub to provide the feedback?

Not sure what your plans are, if you like to work on the standard PSO just for
fun and to jump into the codebase, my recommendation is to open a PR, but don't
feel obligated we don't require a code submission.

The overall structure looks good, it's clear and I think once you go for the
actual implementation this should be helpful since you can just modify one of
the existing tests to get the first results. One comment about the test (file
pso_test.cpp): writing tests is done with the Boost Unit Test Framework:
http://www.boost.org/doc/libs/1_60_0/libs/test/doc/html/index.html Realistically
Boost.Test - 
1.60.0<http://www.boost.org/doc/libs/1_60_0/libs/test/doc/html/index.html>
www.boost.org
Distributed under the Boost Software License, Version 1.0. (See accompanying 
file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)

you can look at some of the other tests in src/mlpack/tests/ for examples;
BOOST_REQUIRE_CLOSE() and BOOST_REQUIRE_EQUAL() are the most useful macros.  If
you make a test suite called "TestSuite" (BOOST_AUTO_TEST_SUITE(TestSuite)), and
then build 'mlpack_test' ('make mlpack_test'), you can run only the tests in
that test suite with 'bin/mlpack_test -t TestSuite'.  A specific test case
called 'TestCase' (BOOST_AUTO_TEST_CASE(TestCase)) could be run with
'bin/mlpack_test -t TestSuite/TestCase'.  So the main you implemented is good
for debugging but at the end, we should write one that fits in the existing
infrastructure.

Is it advisable to follow the test driven development workflow i.e. write test
cases (failing) before the actual implementation?

That is a good idea, realistically you can take a look at the CNE or CMA-ES test
cases and adapt the code accordingly, we can also add more tests along the way.

Also, if we want to implement further variants of PSO later on, what would be
the best way to do this? For CMA-ES, this is achieved using a policy based
design where each variant has a unique class and it implements the Select()
method. I'm not sure, but I think all PSO variants mostly differ in how the
velocity and position is updated. So, we can use the same Optimize() method and
maybe use a updateParameters() method, which updates the particles' velocity and
position based on the PSO variant, all using a policy based design. However, I'm
not sure how the user would provide variant specific parameters e.g. the
constriction factor k, since this is absent in vanilla PSO.

An easy solution would be to use a dummy parameter for the variants that don't
provide a specific parameter. Here is an example: https://github.com/mlpack/mlpa
ck/blob/master/src/mlpack/core/optimizers/sgdr/cyclical_decay.hpp, https://githu
b.com/mlpack/mlpack/blob/master/src/mlpack/core/optimizers/sgdr/snapshot_ensembl<http://b.com/mlpack/mlpack/blob/master/src/mlpack/core/optimizers/sgdr/snapshot_ensembl>
es.hpp in this case the iterate is only used in the second decay policy. We
could do the same for the PSO implementation, another idea would be to use
SFINAE and select the methods according to the given parameters.

Thanks,
Marcus


On 29. Jan 2018, at 19:10, Adeel Ahmad 
<adeelahma...@hotmail.com<mailto:adeelahma...@hotmail.com>> wrote:

Hello Marcus,

I have created a minimal layout for the PSO (unconstrained problems). Please 
see this commit 
(https://github.com/adl1995/mlpack/commit/493c87968a9291582bb663d53722818295e5cd47).
 Could you please provide some feedback on the current progress? Maybe it would 
be easier to use GitHub to provide the feedback?

Is it advisable to follow the test driven development workflow i.e. write test 
cases (failing) before the actual implementation?

Also, if we want to implement further variants of PSO later on, what would be 
the best way to do this? For CMA-ES, this is achieved using a policy based 
design where each variant has a unique class and it implements the Select() 
method. I'm not sure, but I think all PSO variants mostly differ in how the 
velocity and position is updated. So, we can use the same Optimize() method and 
maybe use a updateParameters() method, which updates the particles' velocity 
and position based on the PSO variant, all using a policy based design. 
However, I'm not sure how the user would provide variant specific parameters 
e.g. the constriction factor k, since this is absent in vanilla PSO.

Thank you,
Adeel
________________________________
From: Marcus Edel <marcus.e...@fu-berlin.de<mailto:marcus.e...@fu-berlin.de>>
Sent: Wednesday, January 24, 2018 7:55 PM
To: Adeel Ahmad
Subject: Re: [mlpack] Query regarding constrained and unconstrained methods

Hello Adeel,

I'm not sure how we can provide multiple constraints in
(https://stackoverflow.com/a/28747100). Would we have to use pack expansion like
in the code block above?

Also, wouldn't it be easier if the user could provide multiple constraints in a
single lambda function, rather than creating a separate lambda function for each
constraint? For example:

void optimize(std::function<bool(double, double)> constraint)
{
  ...
  while(!constraint(x, y))
    // do something
}

int main() {
  auto constraint = [](double x, double y) { return (x < 3 && y > 4);};
  optimize(constraint);
}

Maybe I'm missing out on a crucial point which makes the above solution
unusable. Please let me know if I am.

Agreed, that would make everything a lot easier, will have to think about a
situation where that might be contra productive. I guess if someone likes to add
another constrained this approach would mean to redefine everything, but I don't
think this is a huge burden. Anyway, I'll think about the approach with the
single constrained and multiple constrained and let you know once I figured
something out.

> I'd like to start with the unconstrained PSO method first, but if you like to
> start with a proof of concept for the constrained method feel free to do so.
> Either way, really like all the thoughts you already put into the idea.

I have no issue starting out with the unconstrained PSO method first. However,
could you please provide some guidelines on how to start with the
implementation? I was thinking of creating a fork of the mlpack repository and
including the optimizer in src/mlpack/core/optimizers/pso. Would that be
alright?

Yes, that's the recommended way to do it, I would also open another branch in 
the
fork (git checkout -b pso) makes the workflow easier if you work on more than
one feature at the same time. Also, you can take a look at the existing
optimizer classes to get some more details especially
https://github.com/mlpack/mlpack/tree/master/src/mlpack/core/optimizers/cmaes
[https://avatars3.githubusercontent.com/u/10216045?s=400&v=4]<https://github.com/mlpack/mlpack/tree/master/src/mlpack/core/optimizers/cmaes>

mlpack/mlpack<https://github.com/mlpack/mlpack/tree/master/src/mlpack/core/optimizers/cmaes>
github.com<http://github.com/>
[https://assets-cdn.github.com/images/modules/open_graph/github-logo.png]<http://github.com/>

Build software better, together<http://github.com/>
github.com
GitHub is where people build software. More than 27 million people use GitHub 
to discover, fork, and contribute to over 80 million projects.

mlpack: a scalable C++ machine learning library --

should be helpful here. Another reference is: https://arxiv.org/abs/1711.06581
[1711.06581] A generic and fast C++ optimization 
framework<https://arxiv.org/abs/1711.06581>
arxiv.org<http://arxiv.org/>
arXiv.org e-Print archive<http://arxiv.org/>
arxiv.org
arXiv is an e-print service in the fields of physics, mathematics, computer 
science, quantitative biology, quantitative finance, statistics, electrical 
engineering ...

Abstract: The development of the mlpack C++ machine learning library (this http 
URL) has required the design and implementation of a flexible, robust 
optimization ...

which provides some more details about the Optimizer API in general.

Thanks,
Marcus


On 24. Jan 2018, at 09:54, Adeel Ahmad 
<adeelahma...@hotmail.com<mailto:adeelahma...@hotmail.com>> wrote:

Hello Marcus,

I was able to create a variadic template that takes an arbitrary number of 
constraints following this blog post 
(https://eli.thegreenplace.net/2014/variadic-templates-in-c/). The code block 
below returns True only when all the constraints are satisfied:

template<typename T>
bool constraintCheck(T v) {
  return v(3);
}

template<typename T, typename... Args>
bool constraintCheck(T constraint, Args... args) {
  return constraint(3) && constraintCheck(args...);
}

int main() {
  auto constraint1=[](int x){ return x < 3; };
  auto constraint2=[](int x){ return x > 3; };
  auto constraint3=[](int x){ return x % 3 == 0; };
  auto constraint4=[](int x){ return x == 3; };

  constraintCheck(constraint1, constraint2, constraint3, constraint4); // 
evaluates to 0
}

I'm not sure how we can provide multiple constraints in 
(https://stackoverflow.com/a/28747100). Would we have to use pack expansion 
like in the code block above?

Also, wouldn't it be easier if the user could provide multiple constraints in a 
single lambda function, rather than creating a separate lambda function for 
each constraint? For example:

void optimize(std::function<bool(double, double)> constraint)
{
  ...
  while(!constraint(x, y))
    // do something
}

int main() {
  auto constraint = [](double x, double y) { return (x < 3 && y > 4);};
  optimize(constraint);
}


Maybe I'm missing out on a crucial point which makes the above solution 
unusable. Please let me know if I am.


> I'd like to start with the unconstrained PSO method first, but if you like to
> start with a proof of concept for the constrained method feel free to do so.
> Either way, really like all the thoughts you already put into the idea.

I have no issue starting out with the unconstrained PSO method first. However, 
could you please provide some guidelines on how to start with the 
implementation? I was thinking of creating a fork of the mlpack repository and 
including the optimizer in src/mlpack/core/optimizers/pso. Would that be 
alright?

Thank you,
Adeel

________________________________
From: Marcus Edel <marcus.e...@fu-berlin.de<mailto:marcus.e...@fu-berlin.de>>
Sent: Tuesday, January 23, 2018 5:02 AM
To: Adeel Ahmad
Subject: Re: [mlpack] Query regarding constrained and unconstrained methods

Hello Adeel,

What do you think about https://stackoverflow.com/a/28747100, I think in
combination with a variadic template we could even pass an arbitrary number of
constraints. What I don't like about the solution is that this contains a
virtual function, maybe we can avoid that somehow, have to think about that.

Should I start working on a minimal script that implements the PSO using lambda
functions?

I'd like to start with the unconstrained PSO method first, but if you like to
start with a proof of concept for the constrained method feel free to do so.
Either way, really like all the thoughts you already put into the idea.

Thanks,
Marcus

On 22. Jan 2018, at 13:37, Adeel Ahmad 
<adeelahma...@hotmail.com<mailto:adeelahma...@hotmail.com>> wrote:

Hello Marcus,

Thank you for the clarification on the usage of C++11 lambda functions. This 
seems a more intuitive approach, rather than using a vector representation. I 
can think of two ways by which the user can apply constraints. One is to define 
the constraint as a lambda function and pass it as a function pointer. However, 
this way the lambda function should not capture anything 
(https://stackoverflow.com/a/28746827), so we have to rely on std::function 
instead. This could be implemented like this:

void optimize(std::function<bool(double)> constraint)
{
  ...
  while(!constraint(x))
    // do something
}

auto constraint = [](double x) { return x < 3; };
optimize(constraint, /* other parameters */)

Because we are relying on capture, we cannot pass constraint as a function 
pointer.

Another way is to define the constraint as a class member and initialize it 
with a lambda function with the user input, like this:

class PSO
{
public:
  PSO(double x)
  {
    constraint = [](double x) { return x < 3; };
  }
private:
  std::function<bool(double)> constraint;
}

The former technique has the advantage that the user can define any sort of 
constraint they want, while in the latter technique only a handful of 
constraints could be offered (maybe this limitation could be eliminated using 
additional parameters).

Should I start working on a minimal script that implements the PSO using lambda 
functions?


> I think it's just fine, to let the user select the value, however, we should
> note that there are some good initial values in the documentation and 
> examples.
> Does this sound reasonable?


Yes, it sounds fine if the user can initialize the value. Maybe we can point 
out in the optimizer documentation on the recommended initial values from the 
paper.

Thank you,
Adeel


________________________________
From: Marcus Edel <marcus.e...@fu-berlin.de<mailto:marcus.e...@fu-berlin.de>>
Sent: Sunday, January 21, 2018 7:53 PM
To: Adeel Ahmad
Cc: mlpack@lists.mlpack.org<mailto:mlpack@lists.mlpack.org>
Subject: Re: [mlpack] Query regarding constrained and unconstrained methods

Hello Adeel,

I have done some research on C++ lambda functions. Did you mean to use these
instead of the standard accessors and mutators? From what I have found, lambda
functions are used for writing an anonymous inline functor right into the spot
where it is called, like in this example below (source):

std::for_each(v.begin(), v.end(), [](int) { /* do something here */ });

Although they can be used to modify the parameters (passed in a capture list) by
using the mutable keyword, I don't know what advantage this would have over the
standard accessors and mutators. If you had a different use in mind, please let
me know.

I was thinking to use the C++11 lambda functions to define the constraints
instead of using a matrix representation, I had something like this in mind:

auto constraint = [](double x) { return x < 3; };
std::cout << "constraint: " << constraint(6) << std::endl;

I think it might be a good idea to work on a proof of concept before deciding on
the design, what do you think?

I have read some sections from the Velocity Adaptation in Particle Swarm
Optimization paper. The PSO variant presented there is somewhat similar to PSO
with inertia weight in Looking Inside Particle Swarm Optimization in Constrained
Search Spaces paper. The algorithm presented in section 4 for PSO with Velocity
Adaptation uses Velocity Length l for scaling the particle velocity based on its
current behavior. There are various initialization methods for setting the
initial value of velocity length, such as l = r, l = r / sqrt(n). If I opt to
implement this PSO variant in my GSoC application, would I leave it to the user
for specifying the value of l, or set it by default following a heuristic, or
maybe a combination of both?

I think it's just fine, to let the user select the value, however, we should
note that there are some good initial values in the documentation and examples.
Does this sound reasonable?

Thanks,
Marcus


On 20. Jan 2018, at 11:19, Adeel Ahmad 
<adeelahma...@hotmail.com<mailto:adeelahma...@hotmail.com>> wrote:

Hello Marcus,

I have done some research on C++ lambda functions. Did you mean to use these 
instead of the standardaccessors and mutators? From what I have found, lambda 
functions are used for writing an anonymous inline functor right into the spot 
where it is called, like in this example below 
(source<https://stackoverflow.com/a/7627218>):

std::for_each(v.begin(), v.end(), [](int) { /* do something here */ });

Although they can be used to modify the parameters (passed in a capture list) 
by using the mutable keyword, I don't know what advantage this would have over 
the standard accessors and mutators. If you had a different use in mind, please 
let me know.

Yes, a policy based design seems like a much better option for implementing the 
optimizer. We could create a base class named PSO and use its methods in 
another class, for instance, LBPSO; using the former class' object. This would 
be more intuitive if other variants of PSO are to be implemented in the future.

I have read some sections from the Velocity Adaptation in Particle Swarm 
Optimization paper. The PSO variant presented there is somewhat similar to PSO 
with inertia weight in Looking Inside Particle Swarm Optimization in 
Constrained Search Spaces paper. The algorithm presented in section 4 for PSO 
with Velocity Adaptation uses Velocity Length l for scaling the particle 
velocity based on its current behavior. There are various initialization 
methods for setting the initial value of velocity length, such as l = r, l = r 
/ sqrt(n). If I opt to implement this PSO variant in my GSoC application, would 
I leave it to the user for specifying the value of l, or set it by default 
following a heuristic, or maybe a combination of both?

Thank you,
Adeel

________________________________
From: Marcus Edel <marcus.e...@fu-berlin.de<mailto:marcus.e...@fu-berlin.de>>
Sent: Thursday, January 18, 2018 6:51 PM
To: Adeel Ahmad
Cc: mlpack@lists.mlpack.org<mailto:mlpack@lists.mlpack.org>
Subject: Re: [mlpack] Query regarding constrained and unconstrained methods

Hello Adeel,

I have read the research paper you linked. In the paper, two variants of PSO are
mentioned -- inertia weight and constriction factor based. It is stated that the
local-best particle swarm optimizer (LBPSO) with constriction k produces the
best results. I assume all variants must be implemented for GSoC, however, in
the paper a modified version of PSO is presented (MPSO), which dynamically
updates two hyper-parameters, k and c2 (acceleration constant for social
elements in the swarm), should this be implemented as well? I suppose this won't
be time consuming if vanilla PSO is already in place.

I'm not sure it would be reasonable to implement every variant mentioned in the
paper over the summer, keep in mind that each method has to be tested (writing
good tests is time-consuming). So my recommendation is, focus on a single
variant, in your proposal you can point out that if there is time left you aim
for another variant. But at the end it's up to you, choose the methods you think
are interesting. Also, there is another interesting paper that might be
interesting as well: "Particle Swarm Optimization with Velocity Adaptation" by 
S.
Helwig et al. (let me know if you can't access the paper).

Regarding the design of the optimizer itself, it was pointed out earlier by Ryan
that the SDP (semidefinite program) optimizer supports constraints. In there,
the constraints are specified as Armadillo matrices, and set using setters. I
think the same methodology could be applied for PSO.

Right, as pointed out on the ideas page a matrix representation is definitely
one option another would be to use C++11 lambda functions:
https://en.wikipedia.org/wiki/C%2B%2B11#Lambda_functions_and_expressions which I
C++11 - 
Wikipedia<https://en.wikipedia.org/wiki/C%2B%2B11#Lambda_functions_and_expressions>
en.wikipedia.org<http://en.wikipedia.org/>
[https://upload.wikimedia.org/wikipedia/commons/3/3c/Macrosiphum_rosae_auf_Rosenknospe.jpg]<http://en.wikipedia.org/>

Wikipedia, the free encyclopedia<http://en.wikipedia.org/>
en.wikipedia.org

C++11 is a version of the standard for the programming language C++. It was 
approved by International Organization for Standardization (ISO) on 12 August 
2011 ...

think would be easier to use as someone could naturally define the constraints.
Let me know what you think, coming up with a good structure is part of the
project.

For specifying whether the
PSO is local or global, a boolean could be used. However, the constriction
factor k should only be created in case of constriction based PSO, I'm not sure
what would be the best design for this.

Another option would be to use a policy based design, provide a separate class
for each method and reuse as much code as possible internally. We do something
similar for Adam, RmsProp, etc. each optimizer basically uses the SGD class and
all we do is to provide a wrapper class to set optimizer specific parameter. Let
me know what you think.

Would it be possible for us to discuss the optimizer architecture in more detail
on the mailing list?

Absolutely, we are here to help.

Thanks,
Marcus


On 18. Jan 2018, at 08:54, Adeel Ahmad 
<adeelahma...@hotmail.com<mailto:adeelahma...@hotmail.com>> wrote:

Hello Marcus,

I have read the research paper you linked. In the paper, two variants of PSO 
are mentioned -- inertia weight and constriction factor based. It is stated 
that the local-best particle swarm optimizer (LBPSO) with constriction k 
produces the best results. I assume all variants must be implemented for GSoC, 
however, in the paper a modified version of PSO is presented (MPSO), which 
dynamically updates two hyper-parameters, k and c2 (acceleration constant for 
social elements in the swarm), should this be implemented as well? I suppose 
this won't be time consuming if vanilla PSO is already in place.

Regarding the design of the optimizer itself, it was pointed out earlier by 
Ryan that the SDP (semidefinite program) optimizer supports constraints. In 
there, the constraints are specified as Armadillo matrices, and set using 
setters. I think the same methodology could be applied for PSO. For specifying 
whether the PSO is local or global, a boolean could be used. However, the 
constriction factor k should only be created in case of constriction based PSO, 
I'm not sure what would be the best design for this.

Would it be possible for us to discuss the optimizer architecture in more 
detail on the mailing list?

Thank you,
Adeel




________________________________
From: Marcus Edel <marcus.e...@fu-berlin.de<mailto:marcus.e...@fu-berlin.de>>
Sent: Wednesday, January 17, 2018 5:39 PM
To: Adeel Ahmad
Cc: mlpack@lists.mlpack.org<mailto:mlpack@lists.mlpack.org>
Subject: Re: [mlpack] Query regarding constrained and unconstrained methods

Hello Adeel,

sorry for the slow reponse on this one. There are various approaches to solve
constrained problems; one is the use of a penalty function. The constrained
problem is transformed to an unconstrained one, by penalizing the constraints so
that it can be solved using an unconstrained optimization method. You might take
a look at: "Looking Inside Particle Swarm Optimization in Constrained Search
Spaces" by Jorge Isacc Flores-Mendoza and Efrén Mezura-Montes they describe
various PSO method to solve constrained problems.

I apologize if I misunderstood what constrained problems are, but can't we apply
constraints to the methods already present in "src/mlpack/methods/*" directory?
Or, are these unrelated? In the latter case, are there some specialized methods
for constrained problems that need to be implemented for this project?

Currently, mlpack does not implement an optimizer that can handle constrained
problems. So for example, if you like to solve the constrained (cube, line)
Rosenbrock function:

f(x, y) = (1 - x)^2 + 100(y - x^2)^2

with constraints (x - 1)^3 - y +1 < 0 and x + y - 2 < 0

Currently, there is no structure to represent the problem and there is no
optimizer that can solve the constrained problem. Comming up with a structure is
one part of the project implementing an optimizer (PSO) that can handle
constrained problems is the other part. But as pointed out in the project idea,
it's recommended to start with a PSO implementation for unconstrained problems
and to extend the work later on.

Regarding the test cases structuring, I've found that in some cases a
test_function.cpp or <method_name>_test_function.cpp file is present in the main
method directory, such as here (https://github.com/mlpack/mlpack/blob/master/src
[https://avatars3.githubusercontent.com/u/10216045?s=400&v=4]<https://github.com/mlpack/mlpack/blob/master/src>

mlpack/mlpack<https://github.com/mlpack/mlpack/blob/master/src>
github.com<http://github.com/>
[https://assets-cdn.github.com/images/modules/open_graph/github-logo.png]<http://github.com/>

Build software better, together<http://github.com/>
github.com
GitHub is where people build software. More than 27 million people use GitHub 
to discover, fork, and contribute to over 80 million projects.

mlpack: a scalable C++ machine learning library --

/mlpack/core/optimizers/gradient_descent/test_function.cpp). Later, an object of
this class is created in the main tests directory ("src/mlpack/tests/*"), in
this case, here (https://github.com/mlpack/mlpack/blob/master/src/mlpack/tests/g
radient_descent_test.cpp). So, my question is this, what is the preferred
structure for writing test cases? In this case, I think this could have been
directly tested without the need of a separate GDTestFunction class, however,
this might not have been a neat alternative.

There is an open PR which consolidates different problems into one folder
(https://github.com/mlpack/mlpack/pull/1151); the benefit for not implementing
[https://avatars0.githubusercontent.com/u/4209744?s=400&v=4]<https://github.com/mlpack/mlpack/pull/1151>

Optimization Test Problems by zoq · Pull Request #1151 · 
mlpack/mlpack<https://github.com/mlpack/mlpack/pull/1151>
github.com<http://github.com/>
[https://assets-cdn.github.com/images/modules/open_graph/github-logo.png]<http://github.com/>

Build software better, together<http://github.com/>
github.com
GitHub is where people build software. More than 27 million people use GitHub 
to discover, fork, and contribute to over 80 million projects.

Common functions used for testing optimization algorithms, will add more 
functions and test integrations once we agree on this.

the test function inside the test itself, is that someone could reuse the
functionality for other methods/tests. One example is the SGDTestFunction which
is used to test Adam, SGD, RMSProp, etc.

I hope this is helpful, let us know if we should clarify anything.

Thanks,
Marcus


On 16. Jan 2018, at 19:58, Adeel Ahmad 
<adeelahma...@hotmail.com<mailto:adeelahma...@hotmail.com>> wrote:

Greetings,

I'm following a potential idea for GSoC 2018 titled "Particle swarm 
optimization". I have read a few documents and familiarized myself with the 
algorithm. It's listed in the idea description: "So this project is divided 
into two parts: First implement one or two unconstrained methods and afterwards 
takes a look at one --contained-- (constrained [?]) method". I apologize if I 
misunderstood what constrained problems are, but can't we apply constraints to 
the methods already present in "src/mlpack/methods/*" directory? Or, are these 
unrelated? In the latter case, are there some specialized methods for 
constrained problems that need to be implemented for this project?

Regarding the test cases structuring, I've found that in some cases a 
test_function.cpp or <method_name>_test_function.cpp file is present in the 
main method directory, such as here 
(<https://github.com/mlpack/mlpack/blob/3d3d733ba3c41c4f51764f44185767384ab6d9c7/src/mlpack/core/optimizers/gradient_descent/test_function.cpp>https://github.com/mlpack/mlpack/blob/master/src/mlpack/core/optimizers/gradient_descent/test_function.cpp).
 Later, an object of this class is created in the main tests directory 
("src/mlpack/tests/*"), in this case, here 
(<https://github.com/mlpack/mlpack/blob/97fd47de5e0a51adf3c01957f6646eb5cc3651d5/src/mlpack/tests/gradient_descent_test.cpp>https://github.com/mlpack/mlpack/blob/master/src/mlpack/tests/gradient_descent_test.cpp).
 So, my question is this, what is the preferred structure for writing test 
cases? In this case, I think this could have been directly tested without the 
need of a separate GDTestFunction class, however, this might not have been a 
neat alternative.

Thank you,
Adeel
_______________________________________________
mlpack mailing list
mlpack@lists.mlpack.org<mailto:mlpack@lists.mlpack.org>
http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
mlpack Info Page - 
knife.lugatgt.org<http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack>
knife.lugatgt.org
Discussion of mlpack, a scalable C++ machine learning library. To see the 
collection of prior postings to the list, visit the mlpack Archives.




_______________________________________________
mlpack mailing list
mlpack@lists.mlpack.org
http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack

Reply via email to