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
Related
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
How can I generate a simulink model to tell me if a certain output x signal has reached a threshold level after n seconds during the system run time? I would like to consider the last value of x and if so, enable an alarm value of -1.
I assume you want to do the following comparison:
x(n) >= threshold
In Simulink there is a block called "weighted sample time" that you can use to know the sample time. Then you can use a comparator to check if sample time is equal to n and another comparator to make sure x is greater than n
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.
Im learning(started today) neural networks and could finish a 2x2x1 network(forward data feeding and backward error propagated) that can learn AND operation for one set of inputs. It also dodges any local minimums using randomized parameters. My first source for this is: http://www.codeproject.com/Articles/14342/Designing-And-Implementing-A-Neural-Network-Librar
The problem is: it learns 0 AND 0 using inputs (0,0) but when I give (0,1) it forgets 0 AND 0 then learns 0 AND 1. Is this a general newbie bug?
What I tried:
loop for 10000 times
learn 0 and 0
end loop
loop for 10000 times
learn 0 and 1 (forgets 0 and 0)
end loop
loop for 10000 times
learn 1 and 0 (forgets 0 and 1)
end loop
loop for 10000 times
learn 1 and 1 (forgets 1 and 0)
end loop
only one set is learned
fail
Trial 2:
loop for 10000 times
learn 0 and 0
learn 0 and 1
learn 1 and 0
learn 1 and 1
end loop
gives same result for all input combinations.
fail.
Activation function for each neuron: hyperbolic tangent
2x2 structure: all-pairs
2x1 structure: all-pairs
Randomized learning rate: yes, small enough to keep far from explosive iteration (per iteration)
Randomized bias per neuron: yes, between -0.5 and +0.5 (just at start)
Randomized weighting: yes, between -0.5 and +0.5 (just at start)
Edit: Bias and weight updates are done for all-pairs of hidden and output layers.
Edit: All neurons(hidden+output) use same activation function.
Without specific code it is hard to say for sure, but I think the issue is that you are only giving it one case to learn at a time. You should give it a matrix of your different learning examples, with an expected result vector. Then, when you update your weights and biases, you are finding the values that minimize the error between your network output for all cases, and the expected output for all cases.
For an AND gate, your input would be (in MATLAB code, not sure what language you are using but that syntax is easy to understand):
input = [0, 0;
0, 1;
1, 0;
1, 1];
And your expected output would be:
output = [0;
0;
0;
1];
I think what you are doing now is basically finding the weights and biases that minimize the error between the network output and the expected output for just one input case, and then re-training those weights and biases to minimize the error for the second case, then the third, then the fourth. If you put them in arrays like this it should minimize the overall error for all cases. This is just my best guess though without any code to go on.
I have three values X,Y and Z. These values have a range of values between 0 and 1 (0 and 1 included).
When I call a function f(X,Y,Z) it returns a value V (value between 0 and 1). My Goal is to choose X,Y,Z so that the returned value V is as close as possible to 1.
The selection Process should be automated and the right values for X,Y,Z are unknown.
Due to my Use Case it is possible to set Y and Z to 1 (the value 1 hasn't any influence on the output) and search for the best value of X.
After that I can replace X by that value and do the same for Y. Same procedure for Z.
How can I find the "maximum of the function"? Is there somekind of "gradient descend" or hill climbing algorithm or something like that?
The whole modul is written in perl so maybe there is an package for perl that can solve that problem?
You can use Simulated Annealing. Its a multi-variable optimization technique. It is also used to get a partial solution for the Travelling Salesperson problem. Its one of the search algorithms mentioned in Peter Norvig's Intro to AI book as well.
Its a hill climbing algorithm which depends on random variables. Also it won't necessarily give you the 'optimal' answer. You can also vary the iterations required by it as per your computational/time needs.
http://en.wikipedia.org/wiki/Simulated_annealing
http://www1bpt.bridgeport.edu/sed/projects/449/Fall_2000/fangmin/chapter2.htm
I suggest you take a look at Math::Amoeba which implements the Nelder–Mead method for finding stationary points on functions.