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Selecting coefficient ranges from coarse
calibration
This is an example of one decision algorithm for narrowing
coefficient ranges from a coarse calibration of Demo_city using the demo50
input images.
Using file control_stats.log:
- Sort file in descending order
using the Lee Sallee metric
Download entire control_stats.log file
used for this sort here
- For the top three rankings: take
high and low values of each of the coefficients
It is possible for more than one run to have the same score, creating
a tie
- For each coefficient: in the
scenario file to be used for fine calibration, low values are set to
_START
- For each coefficient: in the scenario
file to be used for fine calibration, high values are set to _STOP
- A _STEP
value is selected that will increment between the _START
and _STOP values 4-6 times
If only one coefficient value sorts into the top 10 (e.g.; "1"
for dispersion and spread) select _START,
_STEP, and _STOP
values that will explore a finer coefficient space around the value
Top 3 scores from demo50 coarse calibration, sorting
only on the Lee Sallee metric:
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sort
value
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initial
coefficient values
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| Run |
Product |
Compare |
Pop |
Edges |
Clusters |
Cluster
Size |
Leesalee |
Slope |
%Urban |
Xmean |
Ymean |
Rad |
Fmatch |
Diff |
Brd |
Sprd |
Slp |
RG |
| 15 |
0.08234 |
0.64331 |
0.92749 |
0.92189 |
0.99241 |
0.86224 |
0.34437 |
0.99988 |
0.88769 |
0.91861 |
0.94748 |
0.94694 |
0.6944 |
1 |
1 |
1 |
75 |
1 |
| 16 |
0.08234 |
0.64331 |
0.92749 |
0.92189 |
0.99241 |
0.86224 |
0.34437 |
0.99988 |
0.88769 |
0.91861 |
0.94748 |
0.94694 |
0.6944 |
1 |
1 |
1 |
75 |
25 |
| 17 |
0.08234 |
0.64331 |
0.92749 |
0.92189 |
0.99241 |
0.86224 |
0.34437 |
0.99988 |
0.88769 |
0.91861 |
0.94748 |
0.94694 |
0.6944 |
1 |
1 |
1 |
75 |
50 |
| 20 |
0.00213 |
0.61402 |
0.98361 |
0.9887 |
0.99985 |
0.89286 |
0.33744 |
0.85379 |
0.91667 |
0.97736 |
0.0221 |
0.99266 |
0.7044 |
1 |
1 |
1 |
100 |
1 |
| 21 |
0.00213 |
0.61402 |
0.98361 |
0.9887 |
0.99985 |
0.89286 |
0.33744 |
0.85379 |
0.91667 |
0.97736 |
0.0221 |
0.99266 |
0.7044 |
1 |
1 |
1 |
100 |
25 |
| 22 |
0.00213 |
0.61402 |
0.98361 |
0.9887 |
0.99985 |
0.89286 |
0.33744 |
0.85379 |
0.91667 |
0.97736 |
0.0221 |
0.99266 |
0.7044 |
1 |
1 |
1 |
100 |
50 |
| 145 |
0.02022 |
0.64854 |
0.98469 |
0.98736 |
0.97959 |
0.7033 |
0.31584 |
0.29837 |
0.91965 |
1 |
0.80372 |
0.99308 |
0.6728 |
1 |
25 |
1 |
100 |
1 |
| 146 |
0.02022 |
0.64854 |
0.98469 |
0.98736 |
0.97959 |
0.7033 |
0.31584 |
0.29837 |
0.91965 |
1 |
0.80372 |
0.99308 |
0.6728 |
1 |
25 |
1 |
100 |
25 |
| 147 |
0.02022 |
0.64854 |
0.98469 |
0.98736 |
0.97959 |
0.7033 |
0.31584 |
0.29837 |
0.91965 |
1 |
0.80372 |
0.99308 |
0.6728 |
1 |
25 |
1 |
100 |
50 |
Coefficient range for fine calibration:
| coefficient type |
{_START - _STOP, _STEP} |
| dispersion |
{0 - 20, 5} |
| breed |
{0 - 25, 5} |
| spread |
{0 - 20, 5} |
| slope |
{75 - 100, 5} |
| road gravity |
{0 - 50, 10} |
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