def test_assert_exception_to_many_terms():
x = Symbol('x')
basis_functions = [x,exp,log,sin] #Pay attention as the order is indeed important, for testing we put it in alphabetical order (apart from x)
fun_generator = DatasetCreator(basis_functions,max_linear_terms=5, random_terms=False)
with pytest.raises(IndexError):
for i in range(10):
fun_generator.generate_fun()
def test_get_back(constats_enabled):
x = Symbol('x')
basis_functions = [
x, exp, log, sin
] #Pay attention as the order is indeed important, for testing we put it in alphabetical order (apart from x)
for i in range(10):
fun_generator = DatasetCreator(basis_functions,
max_linear_terms=4,
constants_enabled=constats_enabled)
simpy_output, dictionary, dictionary_clean = fun_generator.generate_fun(
)
separated_dict = tokenization.extract_terms(dictionary_clean)
numberized_dict, mapping = tokenization.numberize_terms(separated_dict)
final_seq = tokenization.flatten_seq(numberized_dict, mapping=mapping)
ori_fun = lambdify(x, simpy_output, 'numpy')
try:
get_back = lambdify(x, tokenization.get_string(final_seq), 'numpy')
except:
pdb.set_trace()
input_x = np.arange(-3, 3, 0.1)
if not constats_enabled:
ori_y = np.nan_to_num(
fun_generator.handling_nan_evaluation(input_x, ori_fun))
new_y = np.nan_to_num(
fun_generator.handling_nan_evaluation(input_x, get_back))
assert np.all(ori_y == new_y)
def test_generate_X_and_Y():
x = Symbol('x')
basis_functions = [x,exp,log,sin] #Pay attention as the order is indeed important, for testing we put it in alphabetical order (apart from x)
fun_generator = DatasetCreator(basis_functions,max_linear_terms=4)
string, _, _ = fun_generator.generate_fun()
support = np.arange(1,20)
y = fun_generator.evaluate_function(support,string)
def test_separator():
x = Symbol('x')
basis_functions = [
x, exp, log, sin
] #Pay attention as the order is indeed important, for testing we put it in alphabetical order (apart from x)
fun_generator = DatasetCreator(basis_functions, max_linear_terms=4)
string, dictionary, _ = fun_generator.generate_fun()
separated_dict = tokenization.extract_terms(dictionary)
print(separated_dict['Single'])
def test_no_terms_are_zoo():
for i in range(100):
x = Symbol('x')
basis_functions = [x,exp,log,sin] #Pay attention as the order is indeed important, for testing we put it in alphabetical order (apart from x)
fun_generator = DatasetCreator(basis_functions,max_linear_terms=4)
string, dictionary, _ = fun_generator.generate_fun()
for terms in dictionary.values():
for term in terms:
assert term != zoo
def test_final():
x = Symbol('x')
basis_functions = [
x, exp, log, sin
] #Pay attention as the order is indeed important, for testing we put it in alphabetical order (apart from x)
fun_generator = DatasetCreator(basis_functions, max_linear_terms=4)
string, dictionary, _ = fun_generator.generate_fun()
separated_dict = tokenization.extract_terms(dictionary)
numberized_dict, mapping = tokenization.numberize_terms(separated_dict)
final_seq = tokenization.flatten_seq(numberized_dict, mapping=mapping)
print(final_seq)
print("hello")
def test_forecast_len():
x = Symbol('x')
basis_functions = [x,exp,log,sin] #Pay attention as the order is indeed important, for testing we put it in alphabetical order (apart from x)
fun_generator = DatasetCreator(basis_functions,max_linear_terms=4)
string, dictionary = fun_generator.generate_fun()
utils.forecast_len(dictionary)
def test_domain_calculator():
x = Symbol('x')
basis_functions = [x,exp,log,sin] #Pay attention as the order is indeed important, for testing we put it in alphabetical order (apart from x)
fun_generator = DatasetCreator(basis_functions,max_linear_terms=4)
string, dictionary, _ = fun_generator.generate_fun()
fun_generator.compute_admissible_domain(string)
def test_single_function_generation(constats_enabled):
x = Symbol('x')
basis_functions = [x,exp,log,sin] #Pay attention as the order is indeed important, for testing we put it in alphabetical order (apart from x)
fun_generator = DatasetCreator(basis_functions,max_linear_terms=4, constants_enabled=constats_enabled,max_compositions=4)
string, dictionary, dictionary_cleaned = fun_generator.generate_fun()