int test_main(int, char* [])
{
expected_results();
#ifdef TEST_GSL
gsl_set_error_handler_off();
#endif
#ifdef TEST_FLOAT
test_beta(0.1F, "float");
#endif
#ifdef TEST_DOUBLE
test_beta(0.1, "double");
#endif
#ifndef BOOST_MATH_NO_LONG_DOUBLE_MATH_FUNCTIONS
#ifdef TEST_LDOUBLE
test_beta(0.1L, "long double");
#endif
#ifndef BOOST_MATH_NO_REAL_CONCEPT_TESTS
#ifdef TEST_REAL_CONCEPT
test_beta(boost::math::concepts::real_concept(0.1), "real_concept");
#endif
#endif
#else
std::cout << "<note>The long double tests have been disabled on this platform "
"either because the long double overloads of the usual math functions are "
"not available at all, or because they are too inaccurate for these tests "
"to pass.</note>" << std::cout;
#endif
return 0;
}
int test_main(int, char* [])
{
expected_results();
BOOST_MATH_CONTROL_FP;
test_spots(0.0F);
test_spots(0.0);
#ifndef BOOST_MATH_NO_LONG_DOUBLE_MATH_FUNCTIONS
test_spots(0.0L);
#if !BOOST_WORKAROUND(__BORLANDC__, BOOST_TESTED_AT(0x582))
test_spots(boost::math::concepts::real_concept(0.1));
#endif
#else
std::cout << "<note>The long double tests have been disabled on this platform "
"either because the long double overloads of the usual math functions are "
"not available at all, or because they are too inaccurate for these tests "
"to pass.</note>" << std::cout;
#endif
test_beta(0.1F, "float");
test_beta(0.1, "double");
#ifndef BOOST_MATH_NO_LONG_DOUBLE_MATH_FUNCTIONS
test_beta(0.1L, "long double");
#ifndef BOOST_MATH_NO_REAL_CONCEPT_TESTS
#if !BOOST_WORKAROUND(__BORLANDC__, BOOST_TESTED_AT(0x582))
test_beta(boost::math::concepts::real_concept(0.1), "real_concept");
#endif
#endif
#else
std::cout << "<note>The long double tests have been disabled on this platform "
"either because the long double overloads of the usual math functions are "
"not available at all, or because they are too inaccurate for these tests "
"to pass.</note>" << std::cout;
#endif
return 0;
}
// Checks various martingality conditions and calls the rate model (Hull and White) to fill the scenario array.
bool Variance_Reduction::test_rate(Scenario *s,Basic_Information &BI,int position_index,int position_step,double* tmp, double e){
bool control=1;
// Needs to call antithetic now because the variables in scenario[3+position_index+BI.nb_rate()] will be
// replaced one by one during the calculation of short_rate(...) and aren't also given as input in random.
ANTITHETIC(s->scenario[3+position_index+BI.nb_rate()],BI,position_step+1);
// Calls the Hull and White model.
Rate_Model::Hull_White(s->scenario[2],s->scenario[3+BI.nb_rate()],s->random[0][position_step],position_step,BI.a(),BI.sigma(),(1.0/BI.delta_t()),BI);
// s->scenario[3+BI.nb_rate()] contains the second gaussian variable when flag==1.
// Notice that the call is made with 1/BI.delta_t() because BI.delta_t is a frequency, not a duration. Ex : BI.delta_t = 12 for monthly simulation.
if(Beta==1||Beta==3){
control=test_beta(s->scenario[3+BI.nb_rate()][position_step+1],tmp,BI,e);
}
return control;
}
void test(T t, const char* p)
{
test_beta(t, p);
}
int
main(int argc, char *argv[])
{
gen_workspace *gen_workspace_p;
lapack_workspace *lapack_workspace_p;
size_t N;
int c;
int lower;
int upper;
int incremental;
size_t nmat;
gsl_matrix *A, *B;
gsl_rng *r;
int s;
int compute_schur;
size_t i;
gsl_ieee_env_setup();
gsl_rng_env_setup();
N = 30;
lower = -10;
upper = 10;
incremental = 0;
nmat = 0;
compute_schur = 0;
while ((c = getopt(argc, argv, "ic:n:l:u:z")) != (-1))
{
switch (c)
{
case 'i':
incremental = 1;
break;
case 'n':
N = strtol(optarg, NULL, 0);
break;
case 'l':
lower = strtol(optarg, NULL, 0);
break;
case 'u':
upper = strtol(optarg, NULL, 0);
break;
case 'c':
nmat = strtoul(optarg, NULL, 0);
break;
case 'z':
compute_schur = 1;
break;
case '?':
default:
printf("usage: %s [-i] [-z] [-n size] [-l lower-bound] [-u upper-bound] [-c num]\n", argv[0]);
exit(1);
break;
} /* switch (c) */
}
A = gsl_matrix_alloc(N, N);
B = gsl_matrix_alloc(N, N);
gen_workspace_p = gen_alloc(N, compute_schur);
lapack_workspace_p = lapack_alloc(N);
r = gsl_rng_alloc(gsl_rng_default);
if (incremental)
{
make_start_matrix(A, lower);
/* we need B to be non-singular */
make_random_integer_matrix(B, r, lower, upper);
}
fprintf(stderr, "testing N = %d", N);
if (incremental)
fprintf(stderr, " incrementally");
else
fprintf(stderr, " randomly");
fprintf(stderr, " on element range [%d, %d]", lower, upper);
if (compute_schur)
fprintf(stderr, ", with Schur vectors");
fprintf(stderr, "\n");
while (1)
{
if (nmat && (count >= nmat))
break;
++count;
if (!incremental)
{
make_random_matrix(A, r, lower, upper);
make_random_matrix(B, r, lower, upper);
}
else
{
s = inc_matrix(A, lower, upper);
if (s)
break; /* all done */
make_random_integer_matrix(B, r, lower, upper);
}
/*if (count != 89120)
continue;*/
/* make copies of matrices */
gsl_matrix_memcpy(gen_workspace_p->A, A);
gsl_matrix_memcpy(gen_workspace_p->B, B);
gsl_matrix_transpose_memcpy(lapack_workspace_p->A, A);
gsl_matrix_transpose_memcpy(lapack_workspace_p->B, B);
/* compute eigenvalues with LAPACK */
s = lapack_proc(lapack_workspace_p);
if (s != GSL_SUCCESS)
{
printf("LAPACK failed, case %lu\n", count);
exit(1);
}
#if 0
print_matrix(A, "A");
print_matrix(B, "B");
gsl_matrix_transpose(lapack_workspace_p->A);
gsl_matrix_transpose(lapack_workspace_p->B);
print_matrix(lapack_workspace_p->A, "S_lapack");
print_matrix(lapack_workspace_p->B, "T_lapack");
#endif
/* compute eigenvalues with GSL */
s = gen_proc(gen_workspace_p);
if (s != GSL_SUCCESS)
{
printf("=========== CASE %lu ============\n", count);
printf("Failed to converge: found %u eigenvalues\n",
gen_workspace_p->n_evals);
print_matrix(A, "A");
print_matrix(B, "B");
print_matrix(gen_workspace_p->A, "Af");
print_matrix(gen_workspace_p->B, "Bf");
print_matrix(lapack_workspace_p->A, "Ae");
print_matrix(lapack_workspace_p->B, "Be");
exit(1);
}
#if 0
print_matrix(gen_workspace_p->A, "S_gsl");
print_matrix(gen_workspace_p->B, "T_gsl");
#endif
/* compute alpha / beta vectors */
for (i = 0; i < N; ++i)
{
double beta;
gsl_complex alpha, z;
beta = gsl_vector_get(gen_workspace_p->beta, i);
if (beta == 0.0)
GSL_SET_COMPLEX(&z, GSL_POSINF, GSL_POSINF);
else
{
alpha = gsl_vector_complex_get(gen_workspace_p->alpha, i);
z = gsl_complex_div_real(alpha, beta);
}
gsl_vector_complex_set(gen_workspace_p->evals, i, z);
beta = gsl_vector_get(lapack_workspace_p->beta, i);
GSL_SET_COMPLEX(&alpha,
lapack_workspace_p->alphar[i],
lapack_workspace_p->alphai[i]);
if (beta == 0.0)
GSL_SET_COMPLEX(&z, GSL_POSINF, GSL_POSINF);
else
z = gsl_complex_div_real(alpha, beta);
gsl_vector_complex_set(lapack_workspace_p->evals, i, z);
gsl_vector_complex_set(lapack_workspace_p->alpha, i, alpha);
}
#if 0
gsl_sort_vector(gen_workspace_p->beta);
gsl_sort_vector(lapack_workspace_p->beta);
sort_complex_vector(gen_workspace_p->alpha);
sort_complex_vector(lapack_workspace_p->alpha);
s = test_alpha(gen_workspace_p->alpha,
lapack_workspace_p->alpha,
A,
B,
"gen",
"lapack");
s = test_beta(gen_workspace_p->beta,
lapack_workspace_p->beta,
A,
B,
"gen",
"lapack");
#endif
#if 1
sort_complex_vector(gen_workspace_p->evals);
sort_complex_vector(lapack_workspace_p->evals);
s = test_evals(gen_workspace_p->evals,
lapack_workspace_p->evals,
A,
B,
"gen",
"lapack");
#endif
if (compute_schur)
{
test_schur(A,
gen_workspace_p->A,
gen_workspace_p->Q,
gen_workspace_p->Z);
test_schur(B,
gen_workspace_p->B,
gen_workspace_p->Q,
gen_workspace_p->Z);
}
}
gsl_matrix_free(A);
gsl_matrix_free(B);
gen_free(gen_workspace_p);
lapack_free(lapack_workspace_p);
if (r)
gsl_rng_free(r);
return 0;
} /* main() */
