void TestObjectiveFunctional::test_calculate_independent_parameters_gradient_central_differences(void)
{
message += "test_calculate_independent_parameters_gradient_central_differences\n";
MultilayerPerceptron mlp;
MockObjectiveFunctional mof(&mlp);
int independent_parameters_number;
Vector<double> independent_parameters_gradient;
// Test
mlp.set();
independent_parameters_number = mlp.get_independent_parameters_number();
independent_parameters_gradient = mof.calculate_independent_parameters_gradient_central_differences();
assert_true(independent_parameters_gradient.get_size() == independent_parameters_number, LOG);
// Test
mlp.set(1,1,1);
independent_parameters_number = mlp.get_independent_parameters_number();
independent_parameters_gradient = mof.calculate_independent_parameters_gradient_central_differences();
assert_true(independent_parameters_gradient.get_size() == independent_parameters_number, LOG);
// Test
mlp.set(1);
independent_parameters_number = mlp.get_independent_parameters_number();
independent_parameters_gradient = mof.calculate_independent_parameters_gradient_central_differences();
assert_true(independent_parameters_gradient.get_size() == independent_parameters_number, LOG);
}
void TestEvolutionaryAlgorithm::test_set_default(void)
{
message += "test_set_default\n";
MultilayerPerceptron mlp;
MockObjectiveFunctional mof(&mlp);
EvolutionaryAlgorithm ea(&mof);
// Test
ea.set_default();
assert_true(ea.get_population_size() == 0, LOG);
// Test
mlp.set(1);
ea.set_default();
assert_true(ea.get_population_size() == 10, LOG);
}
