Difference between revisions of "Modeling"
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* The state of the model is stored in a Pandas Series too. | * The state of the model is stored in a Pandas Series too. | ||
* Put other interesting metrics in the state object too. | * Put other interesting metrics in the state object too. | ||
| + | * If you use randomness in a function, get the mean values of several runs. | ||
* Check the effect of different parameter values using [[Numpy]] [[Numpy#linspace | linspace]]. | * Check the effect of different parameter values using [[Numpy]] [[Numpy#linspace | linspace]]. | ||
<syntaxhighlight lang=python> | <syntaxhighlight lang=python> | ||
Revision as of 23:54, 19 December 2018
Mostly based on this paper that comes with its own modsim library.
Eyeopeners
- Store results in a list
for a in range(100):
funcAresults[a] = functionAcall(bla,bla)
funcBresults[a] = functionBcall(bla,bla)
- ModSimPy is using Series from Pandas to store results. This adds handy functions like.
- The state of the model is stored in a Pandas Series too.
- Put other interesting metrics in the state object too.
- If you use randomness in a function, get the mean values of several runs.
- Check the effect of different parameter values using Numpy linspace.
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)