I have the following problem:
I have over 20 different models which I want to simulate one after another but I want to change the simulation directory each time.
Right now I'm manually changing directory after each simulation (from ./ModelOne to ./ModelTwo) and I'd like to know if there's a way to change it automatically when I initialize or translate the new model.
Regards
Nev
the best way is to write a script I think:
pathOfSave = {"E:\\work\\modelica\\SimulationResult\\Model1\\","E:\\work\\modelica\\SimulationResult\\Model2\\"};
nbSim = 2;
pathOfMod = { "MyModel.",
"MyModel.};
modelsToSimulate = { ""Model1" ,
"Model2"};
//If equdistant=true: ensure that the same number of data points is written in all result files
//store variables at events is disabled.
experimentSetupOutput(equdistant=false, events=false);
//Keep in the plot memory the last nbSim results
experimentSetupOutput(equdistant=false, events=false);
for i in 1:nbSim loop
//delete the result file if it already exists
Modelica.Utilities.Files.removeFile(pathOfSave + modelsToSimulate[i]);
//translate models
translateModel(pathOfMod[i]+modelsToSimulate[i]);
// simulate
simulateModel(
pathOfMod[i]+modelsToSimulate[i],
method="dassl",
stopTime=186350,
numberOfIntervals=nbOfPoi,
resultFile=pathOfSave + modelsToSimulate[i]);
end for;
You can also put the command cd("mynewpath") in the initial algorithm section, if you want it tobe attached to the model.
model example
Real variable;
protected
parameter String currDir = Modelica.Utilities.System.getWorkDirectory();
initial algorithm
cd("C:\\Users\\xxx\\Documents\\Dymola\\MyModelFolder");
equation
variable = time;
when terminal() then
cd(currDir);
end when;
end example;
In any case you can find all commands of dymola in the manual one under the section "builtin commands".
I hope this helps,
Marco
Related
I am new to Julia and trying to use the Julia package DifferentialEquations to simultaneously solve for several conditions of the same set of coupled ODEs. My system is a model of an experiment and in one of the conditions, I increase the amount of one of the dependent variables at mid-way through the process.
I would like to be able to adjust the condition of this single trajectory, however so far I am only able to adjust all the trajectories at once. Is it possible to access a single one using callbacks? If not, is there a better way to do this?
Here is a simplified example using the lorentz equations for what I want to be doing:
#Differential Equations setup
function lorentz!(du,u,p,t)
a,r,b=p
du[1]= a*(u[2]-u[1])
du[2]=u[1]*(r-u[3])-u[2]
du[3]=u[1]*u[2]-b*u[3];
end
#function to cycle through inital conditions
function prob_func(prob,i,repeat)
remake(prob; u0 = u0_arr[i]);
end
#inputs
t_span=[(0.0,100.0),(0.0,100.0)];
u01=[0.0;1.0;0.0];
u02=[0.0;1.0;0.0];
u0_arr = [u01,u02];
p=[10.,28.,8/3];
#initialising the Ensemble Problem
prob = ODEProblem(lorentz!,u0_arr[1],t_span[1],p);
CombinedProblem = EnsembleProblem(prob,
prob_func = prob_func, #-> (prob),#repeat is a count for how many times the trajectories had been repeated
safetycopy = true # determines whether a safetly deepcopy is called on the prob before the prob_func (sounds best to leave as true for user-given prob_func)
);
#introducing callback
function condition(u,t,repeat)
return 50 .-t
end
function affect!(repeat)
repeat.u[1]=repeat.u[1] +50
end
callback = DifferentialEquations.ContinuousCallback(condition, affect!)
#solving
sim=solve(CombinedProblem,Rosenbrock23(),EnsembleSerial(),trajectories=2,callback=callback);
# Plotting for ease of understanding example
plot(sim[1].t,sim[1][1,:])
plot!(sim[2].t,sim[2][1,:])
I want to produce something like this:
Example_desired_outcome
But this code produces:
Example_current_outcome
Thank you for your help!
You can make that callback dependent on a parameter and make the parameter different between problems. For example:
function f(du,u,p,t)
if p == 0
du[1] = 2u[1]
else
du[1] = -2u[1]
end
du[2] = -u[2]
end
condition(t,u,integrator) = u[2] - 0.5
affect!(integrator) = integrator.prob.p = 1
For more information, check out the FAQ on this topic: https://diffeq.sciml.ai/stable/basics/faq/#Switching-ODE-functions-in-the-middle-of-integration
I am working on agent-based modelling where I have created a function that generates a random point inside a region and then converts that point to agent. However, when I use this function later, the points don't give me a proper location on the map for further computation. I want to know what I am doing wrong? Or how can this be improved.
The return type for the function is ArrayList
ArrayList<DeliveryHouse> House = new ArrayList<DeliveryHouse>();
int i;
Point p;
DeliveryHouse h;
for(i=0;i<packagesC2C;i++)
{
p = main.Region2.randomPointInside();
h = main.deliveryHouse.setLocation(p);
House.add(i, h);
};
return House;
The major problem I see with your code is that you are not initializing the agent "h" but pointing it to one agent you have at main which means you keep changing its location in the loop and it will only reflect the last location. The array list you created will have multiple references to same agent instead of one agent per location.
If you want to create an agent per location you need to create an agent population called "deliveryHouses" of the agent type "DeliveryHouse" and change your code to the following. Note that the population will have all the agents so you do not need any output from the function.
int i;
Point p;
for(i=0;i<packagesC2C;i++)
{
DeliveryHouse h = main.add_deliveryHouses();
p = main.Region2.randomPointInside();
h.setLocation(p);
}
I like h2o.ai for machine learning using R.
https://cran.r-project.org/web/packages/h2o/h2o.pdf
I like random forests, but I'm making a few thousand predictions in a loop.
It is spamming up my memory with things like this:
I can't afford to keep them all in memory. I'm making my very nice computer work very hard. That means it doesn't have the capacity to hold all the balls in the air at once.
If I could assign a destination frame name to the prediction then each new one would overwrite the old ones.
How do I assign a destination frame name when I am performing "h2o.predict" on an object?
Things that I have tried that did not work:
h2o.predict(object = rf.hex, newdata = test.hex, predictions_frame = "predict.hex")
h2o.predict(object = rf.hex, newdata = test.hex, destination_frame = "predict.hex")
h2o.predict(object = rf.hex, newdata = test.hex, model_id = "predict.hex")
There is no way that I am aware of.
But as an alternative, inside your loop, you could call h2o.rm() on the return value from h2o.predict(). It is worth calling h2o.gc() as well. Something like:
for(data in alldata){
# ... prepare newdata
p = h2o.predict(model, newdata)
# ... do something with p here
h2o.rm(p)
h2o.rm(newdata) # If also not needed any more
h2o.gc()
}
Aside: you said "I'm making a few thousand predictions in a loop". Assuming they were all against the same model, remember you can batch them up, and give all thousand predictions in a single newdata dataframe. One call to h2o.predict() with 1000 entries is much more efficient than making 1000 h2o.predict() calls, for one newdata entry at a time.
When I simulate the model below, I get additional variables labelled $STATESET1, which are obviously auto-generated.
What is the purpose of these variables from the perspective of the user? Generally I am only interested in the solution, not in the specific strategies a specific solver achieved it with, right? So isn't this more like something that should be output only if one turns on model debugging of some kind rather than being something the average OpenModelica user can take advantage of? What if there is more than one "state set" (say $STATESET1 and $STATESET2): how am I supposed to know how these variables relate to my model, given their generic names? More specifically, what is $STATESET1.x[:]? Nothing in the original or flattened model gives a hint on this...
model StateSetTest
import SI = Modelica.SIunits;
Real[3] q(start = zeros(3), each fixed = true);
Real q4(start = 1);
Real[3] w(start = zeros(3), each fixed = true);
SI.Torque[3] TResult;
equation
q * q + q4 * q4 = 1;
w = 2.0 * (q4 * der(q) - der(q4) * q - cross(der(q), q));
der(w) = TResult;
TResult = zeros(3);
end StateSetTest;
They are used for dynamic state selection, i.e. changing the state during the simulation. And yes, they are not really needed for the user. I guess we could filter them out from OMEdit. I'll open a ticket about this.
I'm trying to cluster image data (stored in 100 separate csv files) with ELKI's XMeans algorithm. It works well for the first two files, but then the algorithm hangs on forever while processing the third file. It looks like the problem occurs at every 3rd file or so, because when I start the loop, that goes over all files at the fourth file, it works for the fourth and the fifth file, but not for the sixth file. Same goes for the 9th and 11th file... but maybe that's coincidence.
My XMeans call looks like this:
DatabaseConnection dbc = new ArrayAdapterDatabaseConnection(data);
Database db = new StaticArrayDatabase(dbc, null);
db.initialize();
Relation<NumberVector> rel = db.getRelation(TypeUtil.NUMBER_VECTOR_FIELD);
DBIDRange ids = (DBIDRange) rel.getDBIDs();
SquaredEuclideanDistanceFunction dist = SquaredEuclideanDistanceFunction.STATIC;
RandomlyGeneratedInitialMeans init = new RandomlyGeneratedInitialMeans(RandomFactory.DEFAULT);
KMeansInitialization initializer = new FirstKInitialMeans();
PredefinedInitialMeans splitInitializer = new PredefinedInitialMeans(data);
KMeansQualityMeasure informationCriterion = new WithinClusterMeanDistanceQualityMeasure();
RandomFactory random = new RandomFactory(123);
KMeans<NumberVector, KMeansModel> innerKMeans = new KMeansHamerly<>(dist, 50, 1, init, true);
XMeans<NumberVector, KMeansModel> xm = new XMeans<>(dist, 5, 50, 1, innerKMeans, initializer, splitInitializer, informationCriterion, random);
Clustering<KMeansModel> c = xm.run(db, rel);
I'm not too sure about these four lines, so maybe that's why it works for some files and for others it doesn't:
KMeansInitialization initializer = new FirstKInitialMeans();
PredefinedInitialMeans splitInitializer = new PredefinedInitialMeans(data);
KMeansQualityMeasure informationCriterion = new WithinClusterMeanDistanceQualityMeasure();
RandomFactory random = new RandomFactory(123);
data is just a double[][] which contains the data from the input files.
Any help would be very appreciated!
Please, use the Parameterization API to configure X-means.
Because of the nested k-means, it is very easy to configure things badly.
The initializer of the inner k-means class must be set to this:
PredefinedInitialMeans splitInitializer = new PredefinedInitialMeans((double[][]) null);
KMeans<NumberVector, KMeansModel> innerKMeans = new KMeansHamerly<>(dist, 50, 1, splitInitializer, true);
because otherwise X-means currently cannot control the initialization of the inner algorithm. I will remove this parameter, and have XMeans set the initializer of the inner algorithm.
Without a stack trace (as mentioned by #Anony-Mousse) it is hard to say what is happening. My best guess is that this meta-algorithm (an algorithm that runs another algorithm!) is not correctly configured and maybe chooses bad initialial values?