def test__UE_surrogate(self):
mat = self.binary_sts
pattern_hash = np.array([4])
N = 3
_, rate_avg_surr, _, n_emp_surr,indices_surr =\
ue._UE(mat, N, pattern_hash, method='surrogate_TrialByTrial', n_surr=100)
_, rate_avg, _, n_emp,indices =\
ue._UE(mat, N, pattern_hash, method='analytic_TrialByTrial')
self.assertTrue(np.allclose(n_emp ,n_emp_surr))
self.assertTrue(np.allclose(rate_avg ,rate_avg_surr))
for item0_cnt,item0 in enumerate(indices):
for item1_cnt,item1 in enumerate(item0):
self.assertTrue(np.allclose(indices_surr[item0_cnt][item1_cnt],item1))
def test__UE_surrogate(self):
mat = self.binary_sts
pattern_hash = np.array([4])
N = 3
_, rate_avg_surr, _, n_emp_surr,indices_surr =\
ue._UE(mat, N, pattern_hash, method='surrogate_TrialByTrial', n_surr=100)
_, rate_avg, _, n_emp,indices =\
ue._UE(mat, N, pattern_hash, method='analytic_TrialByTrial')
self.assertTrue(np.allclose(n_emp ,n_emp_surr))
self.assertTrue(np.allclose(rate_avg ,rate_avg_surr))
for item0_cnt,item0 in enumerate(indices):
for item1_cnt,item1 in enumerate(item0):
self.assertTrue(np.allclose(indices_surr[item0_cnt][item1_cnt],item1))
def test__UE_default(self):
mat = self.binary_sts
pattern_hash = np.array([4, 6])
expected_S = np.array([-0.26226523, 0.04959301])
expected_idx = [[[0], [3]], [[], [2, 4]]]
expected_nemp = np.array([1., 3.])
expected_nexp = np.array([1.04, 2.56])
expected_rate = np.array([0.9, 0.7, 0.6])
S, rate_avg, n_exp, n_emp, indices = ue._UE(mat, pattern_hash)
self.assertTrue(np.allclose(S, expected_S))
self.assertTrue(np.allclose(n_exp, expected_nexp))
self.assertTrue(np.allclose(n_emp, expected_nemp))
self.assertTrue(np.allclose(expected_rate, rate_avg))
for item0_cnt, item0 in enumerate(indices):
for item1_cnt, item1 in enumerate(item0):
self.assertTrue(
np.allclose(expected_idx[item0_cnt][item1_cnt], item1))
def test__UE_default(self):
mat = self.binary_sts
pattern_hash = np.array([4,6])
N = 3
expected_S = np.array([-0.26226523, 0.04959301])
expected_idx = [[[0], [3]], [[], [2, 4]]]
expected_nemp = np.array([ 1., 3.])
expected_nexp = np.array([ 1.04, 2.56])
expected_rate = np.array([ 0.9, 0.7, 0.6])
S, rate_avg, n_exp, n_emp,indices = ue._UE(mat,N,pattern_hash)
self.assertTrue(np.allclose(S ,expected_S))
self.assertTrue(np.allclose(n_exp ,expected_nexp))
self.assertTrue(np.allclose(n_emp ,expected_nemp))
self.assertTrue(np.allclose(expected_rate ,rate_avg))
for item0_cnt,item0 in enumerate(indices):
for item1_cnt,item1 in enumerate(item0):
self.assertTrue(np.allclose(expected_idx[item0_cnt][item1_cnt],item1))