def discover_Equation(tokens, evolutionary_operator, basic_terms, **kwargs):
t1 = datetime.datetime.now()
iter_number = set_argument('iter_number', kwargs, 100)
pop_size = set_argument('pop_size', kwargs, 8)
eq_len = set_argument('eq_len', kwargs, 6)
max_factors = set_argument('max_factors', kwargs, 2)
test_output = set_argument('test_output', kwargs, True)
population = Population(evolutionary_operator, tokens, pop_size = pop_size, basic_terms = basic_terms,
eq_len = eq_len, max_factors_in_terms = max_factors)
best_fitnesses = population.Initiate_Evolution(iter_number = iter_number,
log_file = None, test_indicators = test_output)
# filename = 'Fitness_alpha_' + str(alpha) + '.npy'
# save_fitness(filename, best_fitnesses)
print('Achieved best fitness:', best_fitnesses[-1])#, 'with alpha =', alpha)
population.Calculate_True_Weights()
t2 = datetime.datetime.now()
eq_text_form = population.text_form()
print(eq_text_form)
def Discover_Equtaion(tokens, token_params, basic_terms, **kwargs):
'''
'''
t1 = datetime.datetime.now()
assert 'evaluator' in kwargs.keys() and 'eval_params' in kwargs.keys()
alpha = Set_argument('alpha', kwargs, 1)
a_proc = Set_argument('a_proc', kwargs, 0.2)
r_crossover = Set_argument('r_crossover', kwargs, 0.3)
r_param_mutation = Set_argument('r_param_mutation', kwargs, 0.7)
r_mutation = Set_argument('r_mutation', kwargs, 0.3)
mut_chance = Set_argument('mut_chance', kwargs, 0.6)
iter_number = Set_argument('iter_number', kwargs, 100)
pop_size = Set_argument('pop_size', kwargs, 8)
eq_len = Set_argument('eq_len', kwargs, 6)
max_factors = Set_argument('max_factors', kwargs, 2)
test_output = Set_argument('test_output', kwargs, False)
population = Population(kwargs['evaluator'],
kwargs['eval_params'],
tokens,
token_params,
pop_size=pop_size,
basic_terms=basic_terms,
a_proc=a_proc,
r_crossover=r_crossover,
r_param_mutation=r_param_mutation,
r_mutation=r_mutation,
mut_chance=mut_chance,
alpha=alpha,
eq_len=eq_len,
max_factors_in_terms=max_factors)
best_fitnesses = population.Initiate_Evolution(iter_number=iter_number,
estimator_type='Lasso',
log_file=None,
test_indicators=test_output)
print('Achieved best fitness:', best_fitnesses[-1], 'with alpha =', alpha)
population.Calculate_True_Weights(kwargs['evaluator'],
kwargs['eval_params'])
t2 = datetime.datetime.now()
res = ((t1, t2), (population.target_term, population.zipped_list),
best_fitnesses)
print('Discovered equation:')
print('- {', end='')
for key, value in population.target_term.items():
if value['power'] != 0 and key != '1':
print(' ', key, ':', value, end='')
print('} + ', end='')
for term_idx in range(len(population.zipped_list)):
print(population.zipped_list[term_idx][1], '* {', end='')
for key, value in population.zipped_list[term_idx][0].items():
if value['power'] != 0 and key != '1':
print(' ', key, ':', value, end='')
if term_idx != len(population.zipped_list) - 1:
print('} + ', end='')
else:
print('} = 0')