two_step_search: Two step search optimisation algorithm
In MalteLond/rfcd: Random Forest Change Point Detection
Description
Usage
Arguments
Value
Description
Find an (approximate) maximum of gain_function by repeatedly fitting and evaluating.
Usage
1
two_step_search(gain_function, split_candidates, control)
Arguments
gain_function
A function with formal arguments x, start, end and lambda that returns
a closure with argument split_point, that returns the gain after splitting the segment (start, end]
at split_point given data x and tuning parameter lambda.
split_candidates
An array of indices where gain function is evaluated.
control
An object of class hdcd_control as generated by hdcd_control.
Value
A list with a list of approximate gains after each split and the split points used to estimate the gains.
MalteLond/rfcd documentation built on June 19, 2019, 2:52 p.m.
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Description Usage Arguments Value
Description
Find an (approximate) maximum of gain_function by repeatedly fitting and evaluating.
Usage
1 | two_step_search(gain_function, split_candidates, control)
|
Arguments
gain_function |
A function with formal arguments |
split_candidates |
An array of indices where gain function is evaluated. |
control |
An object of class |
Value
A list with a list of approximate gains after each split and the split points used to estimate the gains.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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