Experience
Dribs
Cost function so we can compare algorithms:
def calculate_total_kwh_m(mst_table): return (mst_table['routes']*abs(mst_table['heat_total'])).sum()
6 months ago
Iterative optimization with random perturbation of one distance.Reduction in total cost sum(abs(heat_total)*routes):
6 months ago
requirements.txt content extracted by github copilot:
numpy pandas geopandas pickle-mixin sparse xarray scipy matplotlib contextily
6 months ago