How can I fix the seed in NetLogo for random-normal function?
Explanation: I use the random-normal in setup to initialize Nturtles their positions.
Next time also when run setup I wish the random-normal to assign these turtles the same positions.
random-seed is the command that you are looking for.
If you want to play around with random seeds, I would suggest that you read the Random numbers section of the programming guide as well. I think you'll find some other useful information there.
Related
I have to test different algorithms with netlogo. I have a different Netlogo model to simulate each algorithm.
I would like to use Behavior Space tool to run a series of simulations with every model, and use random-seed to replicate the events that occur at random with all the models (algorithms).
Therefore, I will set a Behavior Space Experiment for each method. And in the Experiment settings I will set a number of Repetitions to compute different samples of the results.
The problem is that setting a random-seed, for example in the setup procedure of the models will produce that repetition of events for each Experiment of the Behavior Space, however, it will produce the same results in all the repetitions of the experiment.
What I would need is to run the series of simulations of the model setting the random-seed only in the first simulation, so that the results obtained repeating the simulations of the model with Netlogo (the samples obtained) will be different, and all the Experiments will use the same sequence of random events that I would need to compare the different algorithms.
Is there any way of setting a Behavior Space Experiment with a number of repetitions, and generate the same random sequence in another experiment with the same number of repetitions?
Regards
Use behaviorspace-run-number. E.g., as the simplest example, in the model setup include the line
random-seed behaviorspace-run-number
After reading a few papers on Neuro Evolution, more specifically NEAT, I realised that there was very little information regarding how you should weight each synapse at the start of the Neural Network. I understand that at the start, using NEAT, all the input neurons are connected to the output neuron, and then evolution takes place from there. However, should you weight each synapse randomly at the start, or simply set each one to 1?
It doesn't really matter a lot - it matters most how you mutate the weights of the connections in a genome.
However, setting the weights of each genome's connections to a random value is best: it acts like a small random search in the 'right' direction. If you'd set all the weights the same in for each genome, then weights in genomes will be extremely similar: keep in mind that a genome has a lot of connections, and with a mutation rate of 0.3 and two mutation options for example, only 15% of the population will have at least óne different weight after just 1 generation.
So make it something random, like random() * .2 - .1 (distribute between [-0.1, 0.1]). Just figure out what values work best for you.
I am messing about with GIS and what not. So I want to have as many patches as I can alas when I go too large 3600 x 1800 I end up with a crash and a verbose error starting with A InvocationTargetException.
Is it system based or in the network code?
How big can I go?
This is answered in the NetLogo FAQ at http://ccl.northwestern.edu/netlogo/docs/faq.html#howbig :
The NetLogo engine has no fixed limits on size [...]
In practice, the main limit is memory. Are you sure you need that high of patch resolution?
In cases where your model has turtles sampling raster data, you can use high resolution GIS raster datasets while keeping patch resolution low and then have turtles sample the dataset on the fly instead of importing into patch variables. Look closely at how the GIS Gradient Example is written in ModelLibrary/ CodeExamples/ GIS.
I need to create a very large grid of patches to have GIS information of a very large network (such as a city-wide network). My question is how to get NetLogo to model such a world? When I set the max-pxcor and max-pycor to large numbers, it stop working. I need a world of for example size 50000 * 50000.
Thanks for your help.
See http://ccl.northwestern.edu/netlogo/docs/faq.html#howbig , which says in part: “The NetLogo engine has no fixed limits on size...”
It's highly unlikely that you'll be able to fit a 50,000 x 50,000 world, in your computer though — that's 2.5 billion patches. Memory usage in NetLogo is proportional to the number of agents, and patches are agents too.
You might take Stephin Guerin's advice at http://netlogo-users.18673.x6.nabble.com/Re-Rumors-of-Relogo-tp4869241p4869247.html on how to avoid needing an enormous patch grid when modeling transportation networks.
In my engineering class we are programming a "non-trivial" predator/prey pursuit problem.
Here's the gist of the situation:
There is a prey that is trying to escape a predator. Each can be modeled as a particle that can be animated in MATLAB (we have to use this coding language).
The prey:
can maneuver (turn) easier than the predator can
The predator:
can move faster than the prey
I have to create code for both the predator and the prey, which will be used in a class competition.
This is basically what the final product will look like:
http://www.brown.edu/Departments/Engineering/Courses/En4/Projects/pred_prey.gif
The goal is to catch the other team's prey in the shortest amount of time, and for my prey to become un-catchable for the other team's predator (or at least escape for a long period of time).
Here are the specific design constraints:
3. Design Constraints:
Predator and prey can only move in the x-y plane
Simulations will run for a time period of 250 seconds.
Both predator and prey will be subjected to three forces: (a) The propulsive force; (b) a viscous drag
force; and (c) a random time-varying force. (all equations given)
1. The propulsive forces will be determined by functions provided by the two competing groups
The predator is assumed to catch the prey if the distance between predator and prey drops below 1m.
You may not use the rand() function in computing your predator/prey forces – the only random forces
should be those generated by the script provided. (EOM with random forces are impossible for the
ODE solver to integrate, and it ends up in an infinite loop).
For the competition, we will provide the MATLAB code that will compute and animate the trajectories of
the competitors, and will determine the winner of each contest. The test code will be working in SI units.
I am looking for any resources that may be able to help me with some strategy. I have looked at basic pursuit curves, but I would love to look at some examples where the prey is not moving in a straight line. Any other coding advice or strategies would be greatly appreciated!
It's a good idea to start with the fundamentals in any field, and you can't go past the work of Issacs (Differential Games: A mathematical theory with applications to warfare and pursuit, control and optimization). This will almost certainly end up being a reference in any academic research project you may end up writing up.
Steven Lavalle's excellent book Motion Planning has a number of aspects that may be of interest including a section on visibility based pursuit evasion.
As for many mathematical topics, Wolfram Mathworld has some good diagrams and links that might get you thinking in the right direction (eg Pursuit Curves).
If you want to have a look at a curious problem in the area that is well understood try the Homicidal chauffeur problem - this will at least give you some grounds for comparing complexity / efficiency of different techniques. In particular, this is probably a good way to get a feel for level set methods (the paper Homicidal Chau eur Game. Computation of Level Sets of the Value Function by Patsko and Turova appears to have a number of images that might be helpful)