Difference between revisions of "Modeling"
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* The state of the system attributes is stored in a [[Pandas#Series]] | * The state of the system attributes is stored in a [[Pandas#Series]] | ||
* Results of how the system attributes change over time are stored in Pandas Series too (1 Series per attribute). | * Results of how the system attributes change over time are stored in Pandas Series too (1 Series per attribute). | ||
− | * Parameters that determine how the system attributes change are stored | + | * Parameters that determine how the system attributes change are also stored in a pandas series. |
* The functions that change the system attributes take the system parameters as parameter. This way you can play with the parameter values to get the best result. | * The functions that change the system attributes take the system parameters as parameter. This way you can play with the parameter values to get the best result. | ||
** [[Numpy#linspace]] provides an equally distributed range of values so you can play easily between the limits you set. | ** [[Numpy#linspace]] provides an equally distributed range of values so you can play easily between the limits you set. | ||
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funcBresults[a] = functionBcall(bla,bla) | funcBresults[a] = functionBcall(bla,bla) | ||
</syntaxhighlight> | </syntaxhighlight> | ||
+ | |||
+ | =Naming conventions= | ||
+ | |||
+ | ;system | ||
+ | :Pandas Series to store parameters of how the model behaves | ||
+ | ;alpha | ||
+ | :Variable (in system) to store the net change. |
Revision as of 22:14, 31 December 2018
Mostly based on this paper that comes with its own modsim library.
- A model depicts a system.
- The state of the system attributes is stored in a Pandas#Series
- Results of how the system attributes change over time are stored in Pandas Series too (1 Series per attribute).
- Parameters that determine how the system attributes change are also stored in a pandas series.
- The functions that change the system attributes take the system parameters as parameter. This way you can play with the parameter values to get the best result.
- Numpy#linspace provides an equally distributed range of values so you can play easily between the limits you set.
- The quality of the model can be judged by calculating the relative error of the Approximation
Other remarks
- If you use randomness in a function, get the mean values of several runs.
Example code
funcAresults = pd.Series([])
p1_array = np.linspace(0,1,12)
for p1 in p1_array:
for a in range(50):
funcAresults[a] = functionAcall(p1,p2)
print(funcAresults)
Store results in a Pandas Series
for a in range(100):
funcAresults[a] = functionAcall(bla,bla)
funcBresults[a] = functionBcall(bla,bla)
Naming conventions
- system
- Pandas Series to store parameters of how the model behaves
- alpha
- Variable (in system) to store the net change.