Formula Student Autonomous Systems
The code for the main driverless system
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adapt.py
Go to the documentation of this file.
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from
skopt.space
import
Real
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# Define the parameter space as a list of `Real` objects, specifying the range for each parameter.
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param_space = [
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Real(0, 20, name=
"angle_weight"
),
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Real(0, 20, name=
"distance_weight"
),
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Real(0, 20, name=
"ncones_weight"
),
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Real(0, 10, name=
"distance_exponent"
),
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Real(0, 10, name=
"angle_exponent"
),
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Real(0, 80, name=
"max_cost"
),
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]
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params1 = [
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8.089748165412072,
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4.481112717588674,
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6.538940942092987,
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0.7929921809618147,
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2.9846881916431918,
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64.55265403601587,
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]
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params2 = [
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0.0,
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14.16236432745398,
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5.33282693020819,
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0.8069423579816568,
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4.31592836948307,
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80.0,
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]
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params3 = [
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7.999146341232352,
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10.867903810813948,
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7.59670853621077,
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0.7951361065701903,
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5.001991175710679,
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80.0,
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]
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params4 = [
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2.5036366399844723,
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9.230972680602905,
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6.93447117874158,
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0.7985945531629085,
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3.7688073553028274,
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80.0,
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]
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params5 = [
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3.033271334001328,
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8.561458806002841,
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6.713705336752474,
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0.7968794824887211,
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3.531145094725373,
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80.0,
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]
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params6 = [11, 8, 8.7, 0.698, 5.3, 40]
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params7 = [
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1.913196876350798,
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16.26522041019923,
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8.818786857572633,
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0.697207576173325,
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6.196503788316914,
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74.6033253491355,
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]
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# Some specific parameters you may want to test
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parameters_list = [params1, params2, params3, params4, params5, params6, params7]
src
bayesian-opt
cone_coloring
adapt.py
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