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sql-derivative-sensitivity-analyser_demo [2018/11/27 17:01] alisa [Running sensitivity analysis] |
sql-derivative-sensitivity-analyser_demo [2019/06/01 15:43] alisa [Setting up data objects] |
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<code> | <code> | ||
- | // a longer description of the norm | ||
u1 = lp 2.0 latitude longitude; | u1 = lp 2.0 latitude longitude; | ||
u2 = scaleNorm 0.2 u1; | u2 = scaleNorm 0.2 u1; | ||
Line 85: | Line 84: | ||
We can now play around with the model and see how the error can be reduced. | We can now play around with the model and see how the error can be reduced. | ||
- | * Try to reduce β, e.g. try = 0.1. This does not affect security in any way, but may give smaller noise level. | + | * Try to reduce β, e.g. try β = 0.01. This does not affect security in any way, but may give smaller noise level. |
* Try to reset scalings of //Table norm// to ''1.0'', or even try larger values. The error descreases, as we now consider smaller changes in the input (which means that we lose in security). | * Try to reset scalings of //Table norm// to ''1.0'', or even try larger values. The error descreases, as we now consider smaller changes in the input (which means that we lose in security). | ||
* Try out different row sensitivity. Instead of ''rows: all ;'', try some particular row, ''rows: 0 ;'' or ''rows: 1 ;''. It can be seen that ships with higher speed have larger sensitivity and hence add more noise, since changing their locations even a little may affect the arrival time more significantly. | * Try out different row sensitivity. Instead of ''rows: all ;'', try some particular row, ''rows: 0 ;'' or ''rows: 1 ;''. It can be seen that ships with higher speed have larger sensitivity and hence add more noise, since changing their locations even a little may affect the arrival time more significantly. |