I would like to estimate Webull parameters using Grey Wolf Optimizer in matlab. I have written the code and it's working well with trial function. However my problem is just one, when define the objective function (which is Weibull pdf), do we minimize the function or maximize it?
I tested my code with a test function with a predetermined constraints and the question was clearly defined to be minimization problem
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I don't understand this issue:
Issue: HIS Metriken - Cyclomatic (CR-MET4): [function_name] 13>10
It appears in Klocwork analysis while checking the issues of Code: METRICS.E.HIS_Metriken___Cyclomatic__CR_MET4_
Can anyone support?
Thanks
Do you see all those ifs, elses, loops in that function?
Those are the problem, you need to either design this function's logic more elegantly or split it into more functions with well-defined purposes.
By the way, I can only see that problematic function of yours because I am especially clairvoyant. For this kind of question you should normally show your code, just to be fair towards all those other users which cannot read your mind like I did.
Naaa, not really. The cyclomatic complexity is a measure for number of potential paths through your function. And that you have crossed the treshold of 10 by 3 means your function must be full of control structs, which create many paths.
I need to implement an intelligent agent to play Abalone game, for this kind of game the best way to proceed seems a min-max strategy with alpha beta pruning.
I have already implemented a naive search algorithm that use min-max with pruning,
my problem is...
How to generate the nodes of the tree where perform the search?
I have no idea of the right way to do this, and how assign the weigh to each node.
For generating the tree nodes: You need to implement a method that returns a collection of all possible legal moves given the current board position and the player whose turn it is. All these moves will become children of the node representing the current board position. Repeat until memory is exhausted to generate the game tree ;) or rather until you reach a reasonable tree depth.
For alpha-beta search you also need an evaluation function which calculates the weight for each board position/node. You can do some research or think about such a function yourself, maybe considering the number of stones still on the board. However a bad evaluation function can seriously screw up your results, so take care and run a lot of tests.
If you have trouble coming up with a reasonable evaluation function, I recommend you take a look into Monte-Carlo techniques such as UCT.
http://en.wikipedia.org/wiki/Monte_Carlo_tree_search
These tackle the problem using a probabilistic approach and have some nice advantages over alpha-beta. Also they don't require an evaluation function so you could skip this step.
Good luck!
I have published two libraries for move generation in Abalone. You didn't mention the programming language used for your search implementation, but you can easily port the functions.
For C++, https://sourceforge.net/projects/abnet/
For Python, https://gitlab.com/peer.sommerlund/haliotis
For an evaluation function, distance between all your marbles, or distance to their gravity center (same thing), works nicely. Tino Werner used this with a twist for his program that won ICGA 2003.
For understanding distance when using hex coordinates, I can recommend Amit Patel's page: https://www.redblobgames.com/grids/hexagons/
I am currently trying to write a code to solve a non linear system of equations. I am using the functions of the gsl library, more specifically the multiroot_fdf_solver.
My problem is that it currently doesn't want to converge. More specifically, I have the following behavior:
-if my initial conditions are close to the result, the gsl_multiroot_fdf_solver_iterate does not update the parameters at all. I tried to display the results on the different steps, and I have for all the parameters dx = NaN (I think this quite srange), the status of gsl_multiroot_fdf_solver_iterate is "success" and the status of gsl_multiroot_test_residual is "the iteration has not converged yet"
-the parameters are only updated if my initial conditions are really far from the expected result. Obvisously in this case it does not converge to the right values.
I have already checked multiple times the expression of my function and my Jacobian, and they seem good.
I have to precise that my Jacobian (and my system as well) are quite complicated expression with many trigonometric function.
Would you have any idea of what it could be? Is it possible that if the expression of the Jacobian is too complicated, it has troubles to compute it?
Thank you in advance for your answers, I am really stucked at this point.
i have a noisy set of data and want to find the peaks in it. There is a matlab function for this exact task which includes smoothing of the data. I is called findpeaks.m
Now as im working in C i would either would have to code this by myself or use some functions which im not aware of. I hope you can tell me if they exist and where i can find them, as this is a very common problem.
To be clear what im searching of: a function to first smooth my data and then calculate the peaks, both preferably with some parameters for smoothing method, peak width etc.
Thanks!
I need to implement Minesweeper solver. I have started to implement rule based agent.
I have implemented certain rules. I have a heuristic function for choosing best matching rule for current cell (with info about surrounding cells) being treated. So for each chosen cell it can decide for 8 surroundings cells to open them, to mark them or to do nothing. I mean. at the moment, the agent gets as an input some revealed cell and decides what to do with surrounding cells (at the moment, the agent do not know, how to decide which cell to treat).
My question is, what algorithm to implement for deciding which cell to treat?
Suppose, for, the first move, the agent will reveal a corner cell (or some other, according to some rule for the first move). What to do after that?
I understand that I need to implement some kind of search. I know many search algorithms (BFS, DFS, A-STAR and others), that is not the problem, I just do not understand how can I use here these searches.
I need to implement it in a principles of Artificial Intelligence: A modern approach.
BFS, DFS, and A* are probably not appropriate here. Those algorithms are good if you are trying to plan out a course of action when you have complete knowledge of the world. In Minesweeper, you don't have such knowledge.
Instead, I would suggest trying to use some of the logical inference techniques from Section III of the book, particularly using SAT or the techniques from Chapter 10. This will let you draw conclusions about where the mines are using facts like "one of the following eight squares is a mine, and exactly two of the following eight squares is a mine." Doing this at each step will help you identify where the mines are, or realize that you must guess before continuing.
Hope this helps!
I ported this (with a bit of help). Here is the link to it working: http://robertleeplummerjr.github.io/smartSweepers.js/ . Here is the project: https://github.com/robertleeplummerjr/smartSweepers.js
Have fun!