- make scaled least squares method
- Objective cp.Minimize(cp.diag(scale) @ cp.sum_square(ALPHA@W+A))
- Scale vector[0, 0.5, 1.5] cancel sensor 1 and gives sensor 3 high importance
- Make notebook for weighted least squares
- Test Huber robust optimization
- Add weight constrains
- Make ALPHA class
- Check if alpha passing to model is instance of ALpha class
- spelling check for notebook.
- doc strings for all functions check
- package test
- Make test release
- make 1.0.0 release
- Test least square with initial conditions
- Refactor Alpha check method
- splitting
- try to make alpha three dimensions with conditions as third dimension.
- Tried it but cvxpy does not support three dimensions
- Change W to be row instead of column vector(maybe keeping w as column as per Goodman)
- if W is None raise no solution
- replace custom error
- Make package dependent tests
- save and load model
- save and load alpha
- print alpha
- print model
- Test package on Windows
- ISO grade calculation
- Improve the readability of model summary
- make the model summary in Panadas table
- export to excel