Comments (1)
Unfortunately we don't currently have any built-in methods with the functionality you're suggesting. I'm also unaware of any functions for grouping sensors. I think your best bet is probably to implement a custom method. You could write a function that maps from the expanded sensor index (0 to 1999) to the original sensor index (0 to 19). Then you could apply this function to selected_sensors
. A few ideas for how to proceed from there:
- Pick the first k unique sensors to appear
- Of the top n features, choose the k which appear most often
- Use the individual sensor "saliences" (from the
sensor_coef_
attribute) to help decide which sensors to use, e.g. sum up the saliences across features for each sensor and select the k sensors with the highest scores. See theupdate_sensors
source code for examples of how to work withsensor_coef_
.
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Related Issues (8)
- Code example in README.rst is outdated HOT 4
- Oversampling HOT 1
- Grid search with SSPOC HOT 6
- [Defect] In the case of cost_constrained_qr, the algorithm should error out if the number of sensors is larger than the number of samples HOT 2
- Package Update HOT 1
- Multi-dimensional SSPOR HOT 1
- Documentation: Vandermonde Example
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