I'm trying to solve a non-deterministic problem with DEAP. the problem is that the module only evaluate new chromosome and uses the score kept in memory for old ones.
How can i set the module, so at each generation the ENTIRE population will be evaluated, and not just the new ones?
Thx
I don't think DEAP package can do that.
You can simply implement the algorithm on your own or find the new packages.
Anyway, check out my library contains most of the state-of-the-art meta-heuristic algorithms. It also evaluates the entire population in each generation.
https://github.com/thieunguyen5991/mealpy
You can modify 2-3 lines in your current chosen algorithm like below to force evaluation on all items. This can be done via copying from the source, to your local script, and then editing the invalid_individual flagged item check pre evaluation. Make sure in main you call local easimple and not algorithms.easimple to make the switch to local code.
If you are using easimple or eaMuPlusLambda, for example, you can find that function here in this file:
https://github.com/DEAP/deap/blob/master/deap/algorithms.py#L85
The 0th gen case here may not change(but can change anyway, unless your individuals come with a fitness already and you want to skip evaluation):
#(line 149 in above URL)
invalid_ind = [ind for ind in population if not ind.fitness.valid]
And then inside the generational process loop:
#(line 171 in url above):
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
Removing the invalid check will result in all items being passed to evaluation:
invalid_ind = [ind for ind in population] #149
...
invalid_ind = [ind for ind in offspring] #171
But keep the algorithms import! note you also need to change varAnd(case of easimple) to algorithms.varAnd to prevent a break.
offspring = algorithms.varAnd(offspring, toolbox, cxpb, mutpb)
Related
Our use case could be described as a variant of 1D bin packing or sheet cutting.
Imagine a drywall with a beam framing.
We want to optimize the number and size of gypsum boards that would be needed to cover the wall.
Boards must start and end on a beam.
Boards must not overlap (hard constraint).
Less (i.e. bigger) boards, the better (soft constraint).
What we currently do:
Pre-generate all possible boards and pass them as problem facts.
Let the solver pick the best subset of those (nullable planning variable).
First Fit Decreasing + Simulated Annealing
Even relatively small walls (~6m, less than 20 possible boards to pick from) take sometimes minutes and while we mostly get a feasible solution, it's rarely optimal.
Is there a better way to model that?
EDIT
Our current domain model looks like the following. Please note that the planning entity only holds the selected/picked material but nothing else. I.e. currently our planning entities are all equal, which kind of prevents any optimization that depends on planning entity difficulty.
data class Assignment(
#PlanningId
private val id: Long? = null,
#PlanningVariable(
valueRangeProviderRefs = ["materials"],
strengthComparatorClass = MaterialStrengthComparator::class,
nullable = true
)
var material: Material? = null
)
data class Material(
val start: Double,
val stop: Double,
)
Active (sub)pillar change and swap move selectors. See optaplanner docs section about move selectors (move neighorhoods). The default moves (single swap and single change) are probably getting stuck in local optima (and even though SA helps them escape those, those escapes are probably not efficient enough).
That should help, but a custom move to swap two subpillars of the almost the same size, might improve efficiency further.
Also, as you're using SA (Simulated Annealing), know that SA is parameter sensitive. Use optaplanner-benchmark to try multiple SA starting temp parameters with different dataset set sizes. Also compare it to a plain LA (Late Acceptance) in benchmarks too. LA isn't fickle like SA can be. (With fickle I don't mean unstable. I mean potential dataset size sensitive parameter tweaking.)
I iterate over finie_vertieces, finite_edges and finite_faces after generating constrained delauny triangulation with Loyd optimization. I am on VS2012 using CGAL 4.12 under release mode. I see for a given case finite_verices list is repeatable (so is the vertex list under finite_faces), however, the ordering of the edges in finite_edges seems to change from run to run
for(auto eit = cdtp.finite_edges_begin(); eit != cdtp.finite_edges_end(); ++eit)
{
const auto isConstrainedEdge = cdtp.is_constrained(*eit);
auto & cFace = *(eit->first);
auto cwVert = cFace.vertex(cFace.cw(eit->second));
auto ccwVert = cFace.vertex(cFace.ccw(eit->second));
I use the above code snippet to extract vertex list, and vertex list with a given edge changes from run to run.
Any help is appreciated resolving this, as I am looking for consistent behavior in the code. My triangulation involves many line constraints on a two dimensional domain.
I was told it's likely dependable behaviour, but there is no guarantee of order. IIRC the documentation says the traversal order is not guaranteed. I think it's best to assume the iterators' transversal is not deterministic and could change.
You could use any of the _info extensions to embed information into the face, edge, etc (a hash perhaps?) which you could then check against to detect a change.
In my use case, I wanted to traverse the mesh in parallel and OpenMP didn't support the iterators. So I hold a vector of the Face_handles in memory which I can then easily index over. In conjunction with the _info data, you could use this to build a vector of edges,faces, etc with a guaranteed order using unique information in the ->info() field.
Another _info example.
I am running a wavelet transform (cmor) to estimate damping and frequencies that exists in a signal.cmor has 2 parameters that I can change them to get more accurate results. center frequency(Fc) and bandwidth frequency(Fb). If I construct a signal with few freqs and damping then I can measure the error of my estimation(fig 2). but in actual case I have a signal and I don't know its freqs and dampings so I can't measure the error.so a friend in here suggested me to reconstruct the signal and find error by measuring the difference between the original and reconstructed signal e(t)=|x(t)−x^(t)|.
so my question is:
Does anyone know a better function to find the error between reconstructed and original signal,rather than e(t)=|x(t)−x^(t)|.
can I use GA to search for Fb and Fc? or do you know a better search method?
Hope this picture shows what I mean, the actual case is last one. others are for explanations
Thanks in advance
You say you don't know the error until after running the wavelet transform, but that's fine. You just run a wavelet transform for every individual the GA produces. Those individuals with lower errors are considered fitter and survive with greater probability. This may be very slow, but conceptually at least, that's the idea.
Let's define a Chromosome datatype containing an encoded pair of values, one for the frequency and another for the damping parameter. Don't worry too much about how their encoded for now, just assume it's an array of two doubles if you like. All that's important is that you have a way to get the values out of the chromosome. For now, I'll just refer to them by name, but you could represent them in binary, as an array of doubles, etc. The other member of the Chromosome type is a double storing its fitness.
We can obviously generate random frequency and damping values, so let's create say 100 random Chromosomes. We don't know how to set their fitness yet, but that's fine. Just set it to zero at first. To set the real fitness value, we're going to have to run the wavelet transform once for each of our 100 parameter settings.
for Chromosome chr in population
chr.fitness = run_wavelet_transform(chr.frequency, chr.damping)
end
Now we have 100 possible wavelet transforms, each with a computed error, stored in our set called population. What's left is to select fitter members of the population, breed them, and allow the fitter members of the population and offspring to survive into the next generation.
while not done
offspring = new_population()
while count(offspring) < N
parent1, parent2 = select_parents(population)
child1, child2 = do_crossover(parent1, parent2)
mutate(child1)
mutate(child2)
child1.fitness = run_wavelet_transform(child1.frequency, child1.damping)
child2.fitness = run_wavelet_transform(child2.frequency, child2.damping)
offspring.add(child1)
offspring.add(child2)
end while
population = merge(population, offspring)
end while
There are a bunch of different ways to do the individual steps like select_parents, do_crossover, mutate, and merge here, but the basic structure of the GA stays pretty much the same. You just have to run a brand new wavelet decomposition for every new offspring.
I am working on fairly large Mathematica projects and the problem arises that I have to intermittently check numerical results but want to easily revert to having all my constructs in analytical form.
The code is fairly fluid I don't want to use scoping constructs everywhere as they add work overhead. Is there an easy way for identifying and clearing all assignments that are numerical?
EDIT: I really do know that scoping is the way to do this correctly ;-). However, for my workflow I am really just looking for a dirty trick to nix all numerical assignments after the fact instead of having the foresight to put down a Block.
If your assignments are on the top level, you can use something like this:
a = 1;
b = c;
d = 3;
e = d + b;
Cases[DownValues[In],
HoldPattern[lhs_ = rhs_?NumericQ] |
HoldPattern[(lhs_ = rhs_?NumericQ;)] :> Unset[lhs],
3]
This will work if you have a sufficient history length $HistoryLength (defaults to infinity). Note however that, in the above example, e was assigned 3+c, and 3 here was not undone. So, the problem is really ambiguous in formulation, because some numbers could make it into definitions. One way to avoid this is to use SetDelayed for assignments, rather than Set.
Another alternative would be to analyze the names in say Global' context (if that is the context where your symbols live), and then say OwnValues and DownValues of the symbols, in a fashion similar to the above, and remove definitions with purely numerical r.h.s.
But IMO neither of these approaches are robust. I'd still use scoping constructs and try to isolate numerics. One possibility is to wrap you final code in Block, and assign numerical values inside this Block. This seems a much cleaner approach. The work overhead is minimal - you just have to remember which symbols you want to assign the values to. Block will automatically ensure that outside it, the symbols will have no definitions.
EDIT
Yet another possibility is to use local rules. For example, one could define rule[a] = a->1; rule[d]=d->3 instead of the assignments above. You could then apply these rules, extracting them as say
DownValues[rule][[All, 2]], whenever you want to test with some numerical arguments.
Building on Andrew Moylan's solution, one can construct a Block like function that would takes rules:
SetAttributes[BlockRules, HoldRest]
BlockRules[rules_, expr_] :=
Block ## Append[Apply[Set, Hold#rules, {2}], Unevaluated[expr]]
You can then save your numeric rules in a variable, and use BlockRules[ savedrules, code ], or even define a function that would apply a fixed set of rules, kind of like so:
In[76]:= NumericCheck =
Function[body, BlockRules[{a -> 3, b -> 2`}, body], HoldAll];
In[78]:= a + b // NumericCheck
Out[78]= 5.
EDIT In response to Timo's comment, it might be possible to use NotebookEvaluate (new in 8) to achieve the requested effect.
SetAttributes[BlockRules, HoldRest]
BlockRules[rules_, expr_] :=
Block ## Append[Apply[Set, Hold#rules, {2}], Unevaluated[expr]]
nb = CreateDocument[{ExpressionCell[
Defer[Plot[Sin[a x], {x, 0, 2 Pi}]], "Input"],
ExpressionCell[Defer[Integrate[Sin[a x^2], {x, 0, 2 Pi}]],
"Input"]}];
BlockRules[{a -> 4}, NotebookEvaluate[nb, InsertResults -> "True"];]
As the result of this evaluation you get a notebook with your commands evaluated when a was locally set to 4. In order to take it further, you would have to take the notebook
with your code, open a new notebook, evaluate Notebooks[] to identify the notebook of interest and then do :
BlockRules[variablerules,
NotebookEvaluate[NotebookPut[NotebookGet[nbobj]],
InsertResults -> "True"]]
I hope you can make this idea work.
The only effect AudioUnit on iOS is the "iTunes EQ", which only lets you use EQ pre-sets. I would like to use a customized eq in my audio graph
I came across this question on the subject and saw an answer suggesting using this DSP code in the render callback. This looks promising and people seem to be using this effectively on various platforms. However, my implementation has a ton of noise even with a flat eq.
Here's my 20 line integration into the "MixerHostAudio" class of Apple's "MixerHost" example application (all in one commit):
https://github.com/tassock/mixerhost/commit/4b8b87028bfffe352ed67609f747858059a3e89b
Any ideas on how I could get this working? Any other strategies for integrating an EQ?
Edit: Here's an example of the distortion I'm experiencing (with the eq flat):
http://www.youtube.com/watch?v=W_6JaNUvUjA
In the code in EQ3Band.c, the filter coefficients are used without being initialized. The init_3band_state method initialize just the gains and frequencies, but the coefficients themselves - es->f1p0 etc. are not initialized, and therefore contain some garbage values. That might be the reason for the bad output.
This code seems wrong in more then one way.
A digital filter is normally represented by the filter coefficients, which are constant, the filter inner state history (since in most cases the output depends on history) and the filter topology, which is the arithmetic used to calculate the output given the input and the filter (coeffs + state history). In most cases, and of course when filtering audio data, you expect to get 0's at the output if you feed 0's to the input.
The problems in the code you linked to:
The filter coefficients are changed in each call to the processing method:
es->f1p0 += (es->lf * (sample - es->f1p0)) + vsa;
The input sample is usually multiplied by the filter coefficients, not added to them. It doesn't make any physical sense - the sample and the filter coeffs don't even have the same physical units.
If you feed in 0's, you do not get 0's at the output, just some values which do not make any sense.
I suggest you look for another code - the other option is debugging it, and it would be harder.
In addition, you'd benefit from reading about digital filters:
http://en.wikipedia.org/wiki/Digital_filter
https://ccrma.stanford.edu/~jos/filters/