sql-derivative-sensitivity-analyser_demo
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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 15:46] alisa [Setting up tasks] |
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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 67: | Line 66: | ||
| ==== Setting up tasks ==== | ==== Setting up tasks ==== | ||
| - | Click on the task ''Estimate the first arrival''. We need to insert here a query. Let the query return the earliest time when some ship arrives at the port located at the point (0,0). We assume that each ship starts moving at its maximum speed. | + | Click on the task ''Estimate the first arrival''. We need to insert here a query, and a schema of the table that results from executing that query. Let the query return the earliest time when some ship arrives at the port located at the point (0,0). We assume that each ship starts moving at its maximum speed. |
| + | |||
| + | The output table schema defines a table that contains just a single column. | ||
| <code> | <code> | ||
| - | create or replace function min_time() returns TABLE ( | + | create table min_time(cnt INT8); |
| - | cnt INT8 | + | </code> |
| - | ) as $$ | + | |
| + | The output table query describes how the arrival time is computed from ship location and its speed. | ||
| + | |||
| + | <code> | ||
| SELECT | SELECT | ||
| - | MIN ((ship.latitude ^ 2 + ship.longitude ^ 2) ^ 0.5 / ship.max_speed) | + | MIN ((ship.latitude ^ 2 + ship.longitude ^ 2) ^ 0.5 / ship.max_speed) AS cnt |
| FROM | FROM | ||
| ship | ship | ||
| - | $$ language SQL; | + | ; |
| </code> | </code> | ||
| Line 85: | Line 88: | ||
| 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. | ||
sql-derivative-sensitivity-analyser_demo.txt · Last modified: 2021/06/14 11:22 by alisa
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