How can I optimize a function with fixed steps? I have developed a function with five thresholds as entry that I want to optimize. I actually tried to optimize them with different solvers, but the steps that the solver takes are so tiny that the function never converge in a good solution.
Defined thresholds vary from 0 to 1, and I want them to take steps of 0.01. For example, in case of threshold_0, I want It to vary from initial guess 0.6 to 0.61 or 0.59, etc. depending on error result.
from scipy import optimize
initial_guess = [0.6,0.3,0.6,0.5,0.5]
def get_sobel3d_accuracy_from_thresholds(thresholds,array_dicts,ponderation_dict):
...
return error
result = optimize.minimize(
get_sobel3d_accuracy_from_thresholds, # function to optimize
initial_guess,
args=(array_dicts,ponderation_dict), # extra fixed args
method='nelder-mead',
options={'xatol': 1e-8, 'disp': True})
What I want to get is a solution that minimizes de error returned from the function get_sobel3d_accuracy_from_thresholds as follows:
optimized_thresholds = [0.61, 0.3, 0.81, 0.52, 0.44]
I would also like to fix boundaries for thresholds from 0 to 1, but I think that It can be done only with some solvers, right?
bounds = [(0, 1) for n in range(0,5)]
thank you all.
