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You may be able to do this with an iteration callback (to get the median) and using LossFunctionWrapper, wrapping ScaledLoss which you adjust/replace as needed.
On Mon, Aug 15, 2016 at 8:15 AM, Anton Zhilin <antony...@gmail.com> wrote:
How about this: before each step I manually compute all residuals, take the median and send it to LossFunctions. Can I somehow do this? If so, how?--
понедельник, 15 августа 2016 г., 18:05:11 UTC+3 пользователь Sameer Agarwal написал:no, because in calculating the median, you are making all the residuals depend on each other.On Mon, Aug 15, 2016 at 8:02 AM Anton Zhilin <antony...@gmail.com> wrote:So there is no way do do this with sparse problems? Do LossFunctions take residuals from a single CostFunction only?I don't mind losing a little bit of performance on this normalization.
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do what alex says. except that you will not get any trust region sizing out of this.so run the solver for a few iterations instead of 1.also you do not need to deal with ownership issues, as long as you have the ptr to the cost function and can call a method on it to update the scaling.Sameer
On Mon, Aug 15, 2016 at 10:03 AM Alex Stewart <alex...@gmail.com> wrote:
You might be able to do this via something like this:- Setup Problem with CostFunctions using Problem::Options::cost_function_ownership = DO_NOT_TAKE_OWNERSHIP, and retain externally the CostFunction object pointers (so that you can set the current scale factor on each) - given that you want to scale them all by the same value, the ordering isn’t important.— Your other options would be either: rebuilding the Problem at each iteration of the loop below (safe, but probably slow / a bit unwieldy) - or (bad) using GetCostFunctionForResidualBlock() & cost_cast().for (i=0; i<num_iterations; ++i) {- Solve() with max_num_iterations = 1.- Evaluate residuals and compute median.- Update the CostFunctions with the new scale factor via the externally retained pointers.}-Alex
On Aug 15, 2016, at 5:50 PM, Anton Zhilin <antony...@gmail.com> wrote:
Ok, I'll look into these, thanks!
понедельник, 15 августа 2016 г., 18:30:29 UTC+3 пользователь wjr написал:You may be able to do this with an iteration callback (to get the median) and using LossFunctionWrapper, wrapping ScaledLoss which you adjust/replace as needed.--
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