void TestObjectiveFunctional::test_calculate_potential_evaluation(void)
{
message += "test_calculate_potential_evaluation\n";
MultilayerPerceptron mlp;
Vector<double> parameters;
MockObjectiveFunctional mof(&mlp);
double evaluation;
double potential_evaluation;
// Only neural parameters
mlp.set_network_architecture(1,1,1);
mlp.set_independent_parameters_number(0);
evaluation = mof.calculate_evaluation();
parameters = mlp.get_parameters();
potential_evaluation = mof.calculate_potential_evaluation(parameters);
assert_true(evaluation == potential_evaluation, LOG);
// Only independent parameters
mlp.set_network_architecture(0,0,0);
mlp.set_independent_parameters_number(1);
evaluation = mof.calculate_evaluation();
parameters = mlp.get_parameters();
potential_evaluation = mof.calculate_potential_evaluation(parameters);
assert_true(evaluation == potential_evaluation, LOG);
// Both neural and independent parameters
mlp.set_network_architecture(1,1,1);
mlp.set_independent_parameters_number(1);
evaluation = mof.calculate_evaluation();
parameters = mlp.get_parameters();
potential_evaluation = mof.calculate_potential_evaluation(parameters);
assert_true(evaluation == potential_evaluation, LOG);
}
void TestObjectiveFunctional::test_calculate_potential_Hessian(void)
{
message += "test_calculate_potential_Hessian\n";
MultilayerPerceptron mlp;
int parameters_number;
Vector<double> parameters;
MockObjectiveFunctional mof(&mlp);
Matrix<double> potential_Hessian;
mlp.set_network_architecture(1, 1, 1);
mlp.set_independent_parameters_number(0);
mlp.initialize_parameters(0.0);
parameters_number = mlp.get_parameters_number();
parameters = mlp.get_parameters();
potential_Hessian = mof.calculate_potential_Hessian(parameters);
assert_true(potential_Hessian.get_rows_number() == parameters_number, LOG);
assert_true(potential_Hessian.get_columns_number() == parameters_number, LOG);
mlp.set_network_architecture(0, 0, 0);
mlp.set_independent_parameters_number(1);
mlp.initialize_parameters(0.0);
parameters_number = mlp.get_parameters_number();
parameters = mlp.get_parameters();
potential_Hessian = mof.calculate_potential_Hessian(parameters);
assert_true(potential_Hessian.get_rows_number() == parameters_number, LOG);
assert_true(potential_Hessian.get_columns_number() == parameters_number, LOG);
mlp.set_network_architecture(1, 1, 1);
mlp.set_independent_parameters_number(1);
mlp.initialize_parameters(0.0);
parameters_number = mlp.get_parameters_number();
parameters = mlp.get_parameters();
potential_Hessian = mof.calculate_potential_Hessian(parameters);
assert_true(potential_Hessian.get_rows_number() == parameters_number, LOG);
assert_true(potential_Hessian.get_columns_number() == parameters_number, LOG);
}
void TestObjectiveFunctional::test_calculate_potential_gradient(void)
{
message += "test_calculate_potential_gradient\n";
MultilayerPerceptron mlp;
MockObjectiveFunctional mof(&mlp);
Vector<double> parameters;
// Only neural parameters
mlp.set_network_architecture(1, 1, 1);
mlp.set_independent_parameters_number(0);
mlp.initialize_parameters(0.0);
parameters = mlp.get_parameters();
assert_true(mof.calculate_potential_gradient(parameters) == 0.0, LOG);
// Only independent parameters
mlp.set_network_architecture(0, 0, 0);
mlp.set_independent_parameters_number(1);
mlp.initialize_parameters(0.0);
parameters = mlp.get_parameters();
assert_true(mof.calculate_potential_gradient(parameters) == 0.0, LOG);
// Both neural and independent parameters
mlp.set_network_architecture(1, 1, 1);
mlp.set_independent_parameters_number(1);
mlp.initialize_parameters(0.0);
parameters = mlp.get_parameters();
assert_true(mof.calculate_potential_gradient(parameters) == 0.0, LOG);
}