Inertial model #40
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Inertial model #40
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The InertialAgent has a velocity also. Co-authored-by: Moritz Sallermann <[email protected]>
We need to implement a velocity Verlet algorithm for iteration(), in the inertial model. However, many functions are not needed in the ActivityDrivenModel. Therefore, we are considering inheritance of the abstract class instead Co-authored-by: Moritz Sallermann <[email protected]>
We have not yet implemented the InertialModel specific behaviour, but now InertialModel inherits from ActivityDrivenModelAbstract base class. Co-authored-by: Moritz Sallermann <[email protected]>
We changed the definition of get_euler_slopes. Now the timestep is multiplied in the update rule. This enables us to re-use get_euler_slopes in the InertialModel. Co-authored-by: Moritz Sallermann <[email protected]>
Implemented a Velocity Verlet algorithm for the Inertial model. Co-authored-by: Moritz Sallermann <[email protected]>
Co-authored-by: Moritz Sallermann <[email protected]>
We have implemented the Inertial model (more specifically the activity driven inertial model). The example added runs and produces output. TODO: unit test the shit of this! Co-authored-by: Moritz Sallermann <[email protected]>
We have implemented a unit test with 1 agent and 1 bot for the activity driven inertial model Co-authored-by: Moritz Sallermann <[email protected]>
We removed a superfluous `using WeightT = typename Network::WeightT;`. These are present at the top of the class Co-authored-by: Moritz Sallermann <[email protected]>
Activity Driven Inertial model
Co-authored-by: Amrita Goswami <[email protected]>
Test is a generous word, as we actually just write out some results and see if it crashes. But we plotted the histograms vs the expected pdf in python and they match. It's hard to quantify this in a unit test.
Added the probability density functions to the truncated gaussian and the power law distribution. Also renamed the cumulative distribution functions to cdf. Co-authored-by: Amrita Goswami <[email protected]>
Fixed issues with bivariate_gaussian_copula, due to previous renaming of cdf. Also put it into the same unit test and plotted the histograms. Co-authored-by: Amrita Goswami <[email protected]>
Use the copula to generate correlated activities and reluctances. Co-authored-by: Amrita Goswami <[email protected]>
Bivariate gaussian copula for reluctances and activities
Co-authored-by: Moritz Sallermann <[email protected]>
The multi-dimensional DeffuantVector model has a different AgentT from the regular Deffuant model. Therefore, the model and simulation have to be built with the DeffuantVector agent. This requires some repititive logic in main.cpp. Also fixed a small typo in the TOML input file for the DeffuantVector model example.
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An inertial version of the activity driven model.