estimator.util.local_minimum#
- class estimator.util.local_minimum(start, stop, precision=1, smallerf=<function local_minimum.<lambda>>, suppress_bounds_warning=False, log_level=5)[source]#
An iterator context for finding a local minimum using binary search.
We use the neighborhood of a point to decide the next direction to go into (gradient descent style), so the algorithm is not plain binary search (see
update()
function.)We also zoom out by a factor
precision
, find an approximate local minimum and then search the neighbourhood for the smallest value.Note
We combine an iterator and a context to give the caller access to the result.
Create a fresh local minimum search context.
- Parameters:
start – starting point
stop – end point (exclusive)
precision – only consider every
precision
-th value in the main loopsmallerf – a function to decide if
lhs
is smaller thanrhs
.suppress_bounds_warning – do not warn if a boundary is picked as optimal
- __call__(**kwargs)#
Call self as a function.
Methods
__init__
(start, stop[, precision, smallerf, ...])Create a fresh local minimum search context.
update
(res)TESTS:
Attributes
An iterator over the neighborhood of the currently best value.