def perpot_example(pp_args):
weeks = 12
goal = 0.3
max_load = 1.0
cycle_days = [
u.WeekDays.monday, u.WeekDays.tuesday, u.WeekDays.wednesday,
u.WeekDays.friday, u.WeekDays.saturday
]
training_days = u.microcycle_days(cycle_days, weeks)
training_days = u.filter_weeks(training_days, [3, 7, 11])
# pp_args = params.pp_parms8
print(pp_args)
solution = differential_evolution(
weeks,
goal,
training_days,
POP_SIZE,
max_load,
pp_model.after_plan,
# u.sort_loads,
**pp_args)
solution_fitness = u.fitness(solution, goal, GOOD_ENOUGH_THRES,
pp_model.after_plan, **pp_args)
perf_after_plan = pp_model.after_plan(solution, **pp_args)
u.print_ea_result(solution, solution_fitness, perf_after_plan, goal)
'''
def fitnessfatigue_example(ff_args):
weeks = 12
goal = 1.1 * ff_args["initial_p"]
max_load = 150.0
min_load = 0.0
cycle_days = [u.WeekDays.monday, u.WeekDays.tuesday, u.WeekDays.wednesday, u.WeekDays.friday, u.WeekDays.saturday]
training_days = u.microcycle_days(cycle_days, weeks)
# training_days = u.filter_weeks(training_days, [3, 7, 11])
# print(ff_args)
solution = differential_evolution(
weeks,
goal,
training_days,
POP_SIZE,
max_load,
min_load,
ff_model.after_plan,
prequel_plan=[],
pp_func=u.sort_loads,
recomb_weight=0.7,
scale_factor=0.8,
pop_init_divisor=1,
**ff_args
)
solution_fitness = u.fitness(solution, goal, GOOD_ENOUGH_THRES, ff_model.after_plan, **ff_args)
perf_after_plan = ff_model.after_plan(solution, **ff_args)
u.print_ea_result(solution, solution_fitness, perf_after_plan, goal)
def fitnessfatigue_example(ff_args):
weeks = 12
goal = 1.1 * ff_args['initial_p']
max_load = 150.0
min_load = 0.0
cycle_days = [
u.WeekDays.monday, u.WeekDays.tuesday, u.WeekDays.wednesday,
u.WeekDays.friday, u.WeekDays.saturday
]
training_days = u.microcycle_days(cycle_days, weeks)
# training_days = u.filter_weeks(training_days, [3, 7, 11])
# print(ff_args)
solution = differential_evolution(weeks,
goal,
training_days,
POP_SIZE,
max_load,
min_load,
ff_model.after_plan,
prequel_plan=[],
pp_func=u.sort_loads,
recomb_weight=0.7,
scale_factor=0.8,
pop_init_divisor=1,
**ff_args)
solution_fitness = u.fitness(solution, goal, GOOD_ENOUGH_THRES,
ff_model.after_plan, **ff_args)
perf_after_plan = ff_model.after_plan(solution, **ff_args)
u.print_ea_result(solution, solution_fitness, perf_after_plan, goal)
def perpot_example(pp_args):
weeks = 12
goal = 0.3
max_load = 1.0
cycle_days = [u.WeekDays.monday, u.WeekDays.tuesday, u.WeekDays.wednesday, u.WeekDays.friday, u.WeekDays.saturday]
training_days = u.microcycle_days(cycle_days, weeks)
training_days = u.filter_weeks(training_days, [3, 7, 11])
# pp_args = params.pp_parms8
print(pp_args)
solution = differential_evolution(
weeks,
goal,
training_days,
POP_SIZE,
max_load,
pp_model.after_plan,
# u.sort_loads,
**pp_args
)
solution_fitness = u.fitness(solution, goal, GOOD_ENOUGH_THRES, pp_model.after_plan, **pp_args)
perf_after_plan = pp_model.after_plan(solution, **pp_args)
u.print_ea_result(solution, solution_fitness, perf_after_plan, goal)
"""