I currently have agents of type patient seizing an exam room when it becomes available, then, to mimic a first come first serve system where different healthcare practitioners see the patient, I have utilized a SelectOutputIn and 4 SelectOutputOut blocks corresponding to the 4 different practitioners that can see the patient. Each SelectOutputOut block has the same probability corresponding to their resource type. The problem arises when all practitioners are busy, it seems to send patients only to the Physio path and overload it. Is this because it is physically the last block in the order? How can I make the distribution of patients random even if all practitioners are busy?
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It is correct. If all probabilities are zero, AnyLogic seems to pick the last option. To address your issue, we need to add a condition for when all practitioners are busy. So you will have 3 possible outcomes as follows:
Surgeons.idle() + Fellows.idle() + Residents.idle() + Physios.idle() == 0 ? 0.25 : Surgeons.idle() > 0 ? 0.25 : 0
The difference between the first and second 0.25 is that in the first case, all ports will have a 0.25 probability. Whereas in the second case, 3 of 4, or 2 of 4 or even 1 of 4 will have a 0.25 probability depending on how many resources are available. AnyLogic normalizes the probabilities. So if two ports have 0.25 probability, then it's like saying it's a 50/50 chance.
Finally, if the code seems too long, you can replace the first part by a function to have a cleaner/shorter code.
The function body could be:
return Surgeons.idle() + Fellows.idle() + Residents.idle() + Physios.idle();
Assume you named the function function, you can simplify your code in the probability field as follows:
function() == 0 ? 0.25 : Surgeons.idle() > 0 ? 0.25 : 0
Related
I am modelling a network of agents who interact with each other. Each agent is randomly connected to 8 others in the network. Each agent has an initial float-value between 0 and 1. If two agents' values are close enough (determined by the threshold "x" e.g. x = 0.4, v1 = 0.1, v2 = 0.3, so distance of 0.2 < x), one agent influences the other so that the values go even closer together.
However, if the values are greater than the other threshold "y", e.g. y = 0.8, one agent influences the other contrarily, so that the values drift further away from each other. If the difference is between x and y, no influence is taken place.
Now I wanted to create a stopping rule for my network, where an equilibrium is found.
Thank you very much!!!
I started with: if the total number of links (connections between agents) equals to the sum of 1.) number of links where the value difference is below 0.001 (so the two agents have almost the same value) and 2.) links with difference > 0.9, the iterations shall stop. But it is quasi never the case. Is there a possibility where it stops not too late and where it works for whatever values x and y have?
here is my initial code:
if count links = (count links with [value_difference < 0.001] + count links with [value_difference > 0.9])
[print number-of-clusters-conti
stop]
I modelled the following logic in stella:
(IF "cause" > 0 THEN MONTECARLO("probabilityofconsequence") ELSE 0
But Im not getting the correct syntax on AnyLogic:
(cause > 0) ? (uniform() < probabilityofconsequence) ? 1 : 0 : 0
Any ideas?
Disclaimer:
What stella does is with the Montecarlo function a series of zeros and ones from a Bernoulli distribution based on the probability provided. The probability is the percentage probability of an event happening per DT divided by DT (it is similar too, but not the same as, the percent probability of an event per unit time). The probability value can be either a variable or a constant, but should evaluate to a number between 0 and 100/DT (numbers outside the range will be set to 0 or 100/DT). The expected value of the stream of numbers generated summed over a unit time is equation to probability/100.
MONTECARLO is equivalent to the following logic:
IF (UNIFORM(0,100,<seed>) < probability*DT THEN 1 ELSE 0
the equivalent in anylogic should be:
cause>0 && uniform(0,100) < probability*DT ? 1 : 0
you need to create a variable called DT that is the equal to either the fixed time step that you have chosen in your model configuration, or the value you consider that should be adequate.
Since anylogic depending on how you are running the model, doesn't consider the fixed time step as fixed, you need to define the DT yourself.
No matter what, you are going to get results not exactly equal to stella probably since the time steps are not necessarily the same... but maybe similar enough should satisfy you
I'm attempting to model fMRI data so I can check the efficacy of an experimental design. I have been following a couple of tutorials and have a question.
I first need to model the BOLD response by convolving a stimulus input time series with a canonical haemodynamic response function (HRF). The first tutorial I checked said that one can make an HRF that is of any amplitude as long as the 'shape' of the HRF is correct so they created the following HRF in matlab:
hrf = [ 0 0 1 5 8 9.2 9 7 4 2 0 -1 -1 -0.8 -0.7 -0.5 -0.3 -0.1 0 ]
And then convolved the HRF with the stimulus by just using 'conv' so:
hrf_convolved_with_stim_time_series = conv(input,hrf);
This is very straight forward but I want my model to eventually be as accurate as possible so I checked a more advanced tutorial and they did the following. First they created a vector of 20 timepoints then used the 'gampdf' function to create the HRF.
t = 1:1:20; % MEASUREMENTS
h = gampdf(t,6) + -.5*gampdf(t,10); % HRF MODEL
h = h/max(h); % SCALE HRF TO HAVE MAX AMPLITUDE OF 1
Is there a benefit to doing it this way over the simpler one? I suppose I have 3 specific questions.
The 'gampdf' help page is super short and only says the '6' and '10' in each function call represents 'A' which is a 'shape' parameter. What does this mean? It gives no other information. Why is it 6 in the first call and 10 in the second?
This question is directly related to the above one. This code is written for a situation where there is a TR = 1 and the stimulus is very short (like 1s). In my situation my TR = 2 and my stimulus is quite long (12s). I tried to adapt the above code to make a working HRF for my situation by doing the following:
t = 1:2:40; % 2s timestep with the 40 to try to equate total time to above
h = gampdf(t,6) + -.5*gampdf(t,10); % HRF MODEL
h = h/max(h); % SCALE HRF TO HAVE MAX AMPLITUDE OF 1
Because I have no idea what the 'gampdf' parameters mean (or what that line does, in all actuality) I'm not sure this gives me what I'm looking for. I essentially get out 20 values where 1-14 have SOME numeric value in them but 15-20 are all 0. I'm assuming there will be a response during the entire 12s stimulus period (first 6 TRs so values 1-6) with the appropriate rectification which could be the rest of the values but I'm not sure.
Final question. The other code does not 'scale' the HRF to have an amplitude of 1. Will that matter, ultimately?
The canonical HRF you choose is dependent upon where in the brain the BOLD signal is coming from. It would be inappropriate to choose just any HRF. Your best source of a model is going to come from a lit review. I've linked a paper discussing the merits of multiple HRF models. The methods section brings up some salient points.
I am currently trying to understand the ANN that I created for an assignment that essentially takes gray scale (0-150)images (120x128) and determines whether the person is Male or Female. It works for the most part. I am treating this like a boolean problem where the output(Male = 1, Female = 0). I am able to get the ANN to correctly identify Male or Female. However the outputs I am getting for the Males are (0.3-0.6) depending on the run. Should I be getting the value ~1 out?
I am using a sigmoid unit 1/(1+e^-y) and have tried to take the inverse. I have tried this using 5 - 60 hidden units on 1 layer and tried 2 outputs with flip flop results. I want to understand this so that I can apply this to a non-boolean problem. ie If I want a numerical output how would I got about doing that or am I using the wrong machine learning technique?
You can use binary function at the output with some threshold. Assuming, you have assigned 0 for female and 1 for male in training, while testing you will get values in between 0 and 1 and also some times below 0 and above 1......So to make a decision at the output value just add threshold of 0.5 and check output value, if it is less than 0.5 then estimated class is female and if it is equal to or greater than 0.5 then estimated class is male.
I need to implement a Robot Brain, I used feedforward neural network as a Controller. The robot has 24 sonar sonsor, and only one ouput which is R=Right, L=Left, F=Forward, B=Back. I also have a large dataset which contain sonar data and the desired output. The FNN is trained using backpropagation algorithm.
I used neuroph Studio to construct the FNN and to do the trainnig. Here the network params:
Input layer: 24
Hidden Layer: 10
Output Layer: 1
LearnningRate: 0.5
Momentum: 0.7
GlobalError: 0.1
My problem is that during iteration the error drop slightly and seems to be static. I tried to change the parameter but I'm not getting any useful result!!
Thanks for your help
Use 1 of n encoding for the output. Use 4 output neurons, and set up your target (output) data like this:
1 0 0 0 = right
0 1 0 0 = left
0 0 1 0 = forward
0 0 0 1 = back
Reduce the number of input sensors (and corresponding input neurons) to begin with, down to 3 or 5. This will simplify things so you can understand what's going on. Later you can build back up to 24 inputs.
Neural networks often get stuck in local minima during training, that could be why your error is static. Increasing the momentum can help avoid this.
Your learning rate looks quite high. Try 0.1, but play around with these values. Every problem is different and there are no values guaranteed to work.