def test_eval_bootstrap_rdm(self):
from pyrsa.inference import eval_bootstrap_rdm
from pyrsa.rdm import RDMs
from pyrsa.model import ModelFixed
rdms = RDMs(np.random.rand(11, 10)) # 11 5x5 rdms
m = ModelFixed('test', rdms.get_vectors()[0])
m2 = ModelFixed('test2', rdms.get_vectors()[1])
value = eval_bootstrap_rdm([m, m2], rdms, N=10)
def test_eval_bootstrap(self):
from pyrsa.inference import eval_bootstrap
from pyrsa.rdm import RDMs
from pyrsa.model import ModelFixed
rdms = RDMs(np.random.rand(11, 10)) # 11 5x5 rdms
m = ModelFixed('test', rdms.get_vectors()[0])
m2 = ModelFixed('test2', rdms.get_vectors()[1])
result = eval_bootstrap([m, m2], rdms, N=10)
assert result.evaluations.shape[1] == 2
assert result.evaluations.shape[0] == 10
def test_bootstrap_testset_rdm(self):
from pyrsa.inference import bootstrap_testset_rdm
from pyrsa.rdm import RDMs
from pyrsa.model import ModelFixed
rdms = RDMs(np.random.rand(11, 10)) # 11 5x5 rdms
m = ModelFixed('test', rdms.get_vectors()[0])
m2 = ModelFixed('test2', rdms.get_vectors()[1])
evaluations, n_rdms = bootstrap_testset_rdm([m, m2],
rdms,
method='cosine',
fitter=None,
N=100,
rdm_descriptor=None)
def test_eval_fixed(self):
from pyrsa.inference import eval_fixed
from pyrsa.rdm import RDMs
from pyrsa.model import ModelFixed
rdms = RDMs(np.random.rand(11, 10)) # 11 5x5 rdms
m = ModelFixed('test', rdms.get_vectors()[0])
value = eval_fixed(m, rdms)
def test_eval_bootstrap_rdm(self):
from pyrsa.inference import eval_bootstrap_rdm
from pyrsa.rdm import RDMs
from pyrsa.model import ModelFixed
rdms = RDMs(np.random.rand(11, 10)) # 11 5x5 rdms
m = ModelFixed('test', rdms.get_vectors()[0])
value = eval_bootstrap_rdm(m, rdms, N=10)
value = eval_bootstrap_rdm(m, rdms, N=10, boot_noise_ceil=True)
def test_bootstrap_testset_pattern(self):
from pyrsa.inference import bootstrap_testset_pattern
from pyrsa.rdm import RDMs
from pyrsa.model import ModelFixed
rdms = RDMs(np.random.rand(11, 10)) # 11 5x5 rdms
m = ModelFixed('test', rdms.get_vectors()[0])
evaluations, n_cond = bootstrap_testset_pattern(
m,
rdms,
method='cosine',
fitter=None,
N=100,
pattern_descriptor=None)
