def job_qhsic_med(paired_source, tr, te, r):
"""
Quadratic-time HSIC using the permutation test.
- Gaussian kernels.
- No parameter selection procedure. Use the median heuristic for both
X and Y.
- Use full sample for testing.
"""
# use full sample for testing. Merge training and test sets
pdata = tr + te
n_permute = 500
if pdata.sample_size() >= 5000:
# give up. Too big.
k, l = kl_kgauss_median(pdata)
qhsic = it.QuadHSIC(k, l, n_permute, alpha=alpha, seed=r + 1)
fake_result = {
'alpha': alpha,
'pvalue': 1,
'test_stat': -1,
'h0_rejected': False,
'time_secs': 0,
'n_permute': n_permute
}
return {'indtest': qhsic, 'test_result': fake_result, 'time_secs': 0}
# Actually do the test
with util.ContextTimer() as t:
k, l = kl_kgauss_median(pdata)
qhsic = it.QuadHSIC(k, l, n_permute, alpha=alpha, seed=r + 1)
qhsic_result = qhsic.perform_test(pdata)
return {'indtest': qhsic, 'test_result': qhsic_result, 'time_secs': t.secs}
def setUp(self):
n = 50
dx = 2
pdata_mean = get_pdata_mean(n, dx)
k, l = kl_median(pdata_mean)
self.qhsic = it.QuadHSIC(k, l, n_permute=60, alpha=0.01)
self.pdata_mean = pdata_mean
def job_qhsic_med(paired_source, tr, te, r):
"""
Quadratic-time HSIC using the permutation test.
- Gaussian kernels.
- No parameter selection procedure. Use the median heuristic for both
X and Y.
- Use full sample for testing.
"""
# use full sample for testing. Merge training and test sets
pdata = tr + te
n_permute = 300
with util.ContextTimer() as t:
k, l = kl_kgauss_median(pdata)
qhsic = it.QuadHSIC(k, l, n_permute, alpha=alpha, seed=r + 1)
qhsic_result = qhsic.perform_test(pdata)
return {'indtest': qhsic, 'test_result': qhsic_result, 'time_secs': t.secs}