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Genetic algorithm solving public transportation problem
This should cause more % of the graph to by covered by lines
Similary to pandas and scikit learn we may consider creating _latest_gen variable inside of SImulationEngine so we can access that generaion after simulation and play with it, dispaly lines separately etc.
When no edges are present in the graph this line throws:
ValueError: max() arg is an empty sequence
Genotype was 1 line with 1 stop.
implement some variations of survival function
ideas:
naprawić osmnx config option
tak żeby działały notebooki
dla random mapy
dla mapy krk
grid search
Determine type of best_paths that is used everywhere and type its usages. Ideally use type aliases to not repeat weird nested type in N places.
In Python 3.9+ you don't need to import List,Tuple etc. but you can use list, tuple etc. as type annotations.
implement some variations of the score function
add metrics to allow monitoring:
Create the graph of the real city. Remember about representation we agreed on (junctions and stops are vertices, junctions with value 0 and stops with preprocessed value, also stops have degree <= 2).
Or at least make current generated graph meet the aforementioned conditions.
when #17 and maybe others are done do grid search of the hiperparameters
Hiperparameters to optimize:
Do that for many fitness functions, but do not compare results numerically.
This somehow prints different results:
# calculating fitness for all organisms
population_with_fitness: List[Tuple[Genotype, float]] = [
(organism, self.fitness_function(organism, self.G))
for organism in population
]
print(population_with_fitness[0][1])
population_with_fitness: List[Tuple[Genotype, float]] = [
(organism, self.fitness_function(organism, self.G))
for organism in population
]
print(population_with_fitness[0][1])
This is related:
G.graph["points"][np.where(lines_stopping_count == 0)] = 0
lines_stopping_count[np.where(lines_stopping_count == 0)] = 1
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