I'm having trouble interpreting the clingo v 5.2.2 output below:
Why did clingo print answer1 twice? I've checked that the two answer1s are exactly the same.
I'm using optimization in my code. Could that be the problem?
I've set up a test repo. The above output can be generated by running the following line in the test repo.
clingo --opt-mode=OptN examples/swap-shift.lp
Here's what I understand now: with the --opt-mode=OptN option, clingo will try multiple models until it finds all the optimal models, and it will print all the models it has witnessed followed by the optimal models. I think that could explain why answer1 was printed twice in my original question.
In the output, I think Models is the number of total witnesses (including the first optimal model that got printed twice). Optimal is the number of optimal models found.
I made my observation by observing the output of this example
clingo --opt-mode=OptN asp/_all.lp asp/examples/scatter.lp
See how Optimization decreases and the two optimum models are printed in the end.
While looking for an optimal answer set, clingo prints each answer set that is better than previously found answer sets.
If no better answer set can be found, this means that the optimality of the last answer set has been proved.
If you have use the optN mode, clingo then enumerates all answer sets with that optimal cost. This will again find the first optimal answer set that was found.
I personally do not know how to print only the optimal answer sets and each only one from the command line. It would be possible to implement such a behavior using the Python API.
As per official guide, you can use the flags --opt-mode=optN --quiet=1 to find and print all optimal solutions.
To compute all optimal answer sets, we can change clasp’s
optimization mode using option ‘--opt-mode=optN’. In this mode, clasp
first prints the tenta- tive answer sets where optimality is not yet
proven and afterwards prints the optimal answer sets. Note that the
first optimal answer set is printed twice in this mode. To omit
tentative answer sets in the output and only print optimal answer
sets, we can add option ‘--quiet=1’
From the Potassco guide version 2.2.0.
Related
I am new to Dynare++ and I have really quick question I cannot seem to find the answer too.
What is the difference between these two commands and why is the output different?
!dynare++ --per 50 --sim 3 file_name.mod
dynare file_name.mod
In the first command its unable to find steady state values based on my initial values and in the second it can. Why?
The first command calls the standalone Dynare++ instead of Matab-based Dynare. The latter uses Matlab's solvers for numerically finding the steady state. Note that there is a dedicated forum for Dynare at https://forum.dynare.org
It seems Matlab is giving incorrect results for multinomial logistic regression.
In their example documentation using Fisher's Iris dataset [link], they give coefficients for the model which can be used on the same data set itself to get the modeled probabilities.
load fisheriris
sp = categorical(species);
[B,dev,stats] = mnrfit(meas,sp);
PHAT=mnrval(B,meas);
However, none of the expected value aggregates match the population aggregates which is a requirement for a MaxEnt classifer (See slide 35 [here], or Eq 14 [here], or Agresti "Categorical Data Analysis" pg 298, etc.)
For example
>> sum(PHAT)
>> 49.9828 49.8715 50.1456
should all equal 50 (population values), likewise for other aggregations
If the parameters
B=[36.9450 42.6378
12.2641 2.4653
14.4401 6.6809
-30.5885 -9.4294
-39.3232 -18.2862]
were used instead then all aggregated sufficient statistics match.
Additionally it seems odd that Matlab is solving it with likelihoods, which can produce an error,
Warning: Maximum likelihood estimation did not converge. Iteration
limit exceeded. You may need to merge categories to increase observed
counts
where the only requirement, proved by MLE consideration, is that the expected values match and no likelihood evaluation is needed.
It would be a nice feature that if instead of true classes are given we can give an option for including just the aggregate information.
Submitted a technical error review within Mathworks website. Their reply:
Hello [----],
I am writing in reference to your Technical Support Case #01820504
regarding 'mnrfit'.
Thanks a lot for your patience and reporting this issue. This appears
to be unexpected behavior. It appears to be related to an existing
issue we have in our records, that "mnrfit" does not give correct
maximum likelihood estimates in certain cases. Since the "mnrfit"
function is not finding the maximum likelihood estimates for the
coefficients, we calculated the actual MLEs. When we use these
estimates, we get the desired result of all 50s in this case.
The issue is that, for this particular dataset in our example, the
classes can be separated perfectly. This means that the logistic
function, in order to get exact zero or one probabilities, needs to
have infinite coefficients. The "mnrfit" function carries out an
iterative procedure with the coefficients getting larger, but it stops
at a point where the results have the issue that you have found. We
certainly agree that "mnrfit" could be made to do better. Our
development team is working on it.
At this stage, I am not able to suggest a workaround other than to
write a custom implementation as my colleague and I had tried. For
now, I will be closing this request as I have already forwarded it to
our records. However, if you have any additional questions related to
this case, please do not hesitate to reach me.
Sincerely,
[----]
MathWorks Technical Support Department
The Matlab documentation seems unclear about how to ignore missing data when using kruskalwallis, the Kruskal-Wallis (or any other related) test. The same goes for unequal group size.
Very late answer, but I ran into the same problem myself today, might as well help some future searcher.
The solution is pretty straightforward. kruskalwallis is primarily used on matrices and by default compares equal-sized columns, but it does allow you to instead assign groups manually, with the optional variable "group". I was attempting to check whether a single value was unlikely to belong to a distribution from a different set, so this was straightforward. I just added the value I wanted to test on to the end of the set I was testing against, then made "group" a vector of ones the same size as the set, with a "2" added to the end for the new value. Looks like it worked quite nicely.
For numeric data, the the standard missing data value in Matlab is NaN. See ismissing. See also this article from The MathWorks. For tables, you might find standardizeMissing helpful as well as replaceWithMissing for dataset objects. I can't say anything about group size.
I am solving a stiff PDE in MATLAB using ode15, and it often freezes depending on the initial conditions. I never actually get an error, it just won't finish even after 10 hours when it should take around 30 seconds to run. I am experimenting with different spatial and time node intervals, but it is hard, because I don't get feedback.
Is there some sort of equivalent to diagnostic for fsolve? stats is not useful because it only displays an output after fsolve is finished.
Check out the documentation on odeset, and specifically the stats option. I think you basically just want to set stats to on and you will get some feedback.
Also, depending on your ODE, you may need a different solver. About half way down the page on this page there is a list of most of the solvers available in MATLAB. Depending on whether your function is stiff or non-stiff, and how accurate you need to get, one of those might work better for you. Sometimes I just code them all in and comment out all but one until I find the one that runs the best for me, but check out the documentation on each if you want to find the "right" one for your application.
Your question is confusing because you refer to both ode15s and fsolve locking up. These are two completely different functions. One does numerical integration and the other solves for roots. Also, fsolve has no option called 'Stats' (see doc fsolve). If you want continuous output from fsolve use:
options = optimist('Display','iter');
[x,fval,exitflag] = fsolve(myfun,x0,options)
This will display the iteration count, number of function evaluations, the function value, and other stuff depending on what algorithm you use (the alorithm can be adjusted via the 'Algorithm' option). Again see doc fsolve for full details.
As far as the 'Stats' option with ode15s goes, it's not going to give you very much information. I doubt that it will you figure out why your system is halting (if it even is ode15s that you have a problem with). What you can try is using an output function via the 'OutputFcn' option of odeset. You can try the simple odeprint first:
options = odeset('OutputFcn',#odeprint)
which will print your state after each integration step. Type edit odeprint to see the code and how you might write your own output function if you need to do more.
I have written matlab codes for two different block matching algorithms, extensive search and three step search, but i am not sure how i can check whether i am getting the correct results. Is there any standard way to check these or any standard code which i can run and compare my result with.I read somewhere that JM software can be used but i didnt find any way to use it.
You can always use the results produced by your algorithms to create the next frame of video and then analyze its quality by either visually inspecting it (which is rather subjective, and we like to deal in numbers) or calculating the mean square error between the produced image and the one you're trying to estimate. Mean square error of the exhaustive (extensive) search should be lower than the one three-step gives you.
Well, did you try to plot it? I mean,after the block-matching you have a new image, right?.
A way to know if you result if true or not is to check the sum of the difference of 2 frames.
A - pre_frame
B - post_frame
C - Compensated frame
If abs(abs(A-B)) is lower than abs(abs(A-C))) that mean it could be true.
Next time, try to specify your algoritm. Also, put your code here to help you more.