def test_smlr_sensitivities(self):
data = normal_feature_dataset(perlabel=10, nlabels=2, nfeatures=4)
# use SMLR on binary problem, but not fitting all weights
clf = SMLR(fit_all_weights=False)
clf.train(data)
# now ask for the sensitivities WITHOUT having to pass the dataset
# again
sens = clf.get_sensitivity_analyzer(force_train=False)(None)
self.assertTrue(sens.shape == (len(data.UT) - 1, data.nfeatures))
def test_smlr_sensitivities(self):
data = normal_feature_dataset(perlabel=10, nlabels=2, nfeatures=4)
# use SMLR on binary problem, but not fitting all weights
clf = SMLR(fit_all_weights=False)
clf.train(data)
# now ask for the sensitivities WITHOUT having to pass the dataset
# again
sens = clf.get_sensitivity_analyzer(force_train=False)(None)
self.assertTrue(sens.shape == (len(data.UT) - 1, data.nfeatures))
def test_smlr_state(self):
data = datasets["dumb"]
clf = SMLR()
clf.train(data)
clf.ca.enable("estimates")
clf.ca.enable("predictions")
p = np.asarray(clf.predict(data.samples))
self.assertTrue((p == clf.ca.predictions).all())
self.assertTrue(np.array(clf.ca.estimates).shape[0] == np.array(p).shape[0])
def test_smlr_state():
data = datasets['dumb']
clf = SMLR()
clf.train(data)
clf.ca.enable('estimates')
clf.ca.enable('predictions')
p = np.asarray(clf.predict(data.samples))
assert_array_equal(p, clf.ca.predictions)
assert_equal(np.array(clf.ca.estimates).shape[0], np.array(p).shape[0])
def test_smlr_state(self):
data = datasets['dumb']
clf = SMLR()
clf.train(data)
clf.ca.enable('estimates')
clf.ca.enable('predictions')
p = np.asarray(clf.predict(data.samples))
self.failUnless((p == clf.ca.predictions).all())
self.failUnless(np.array(clf.ca.estimates).shape[0] == np.array(p).shape[0])
def test_smlr_state(self):
data = datasets['dumb']
clf = SMLR()
clf.train(data)
clf.ca.enable('estimates')
clf.ca.enable('predictions')
p = np.asarray(clf.predict(data.samples))
self.assertTrue((p == clf.ca.predictions).all())
self.assertTrue(
np.array(clf.ca.estimates).shape[0] == np.array(p).shape[0])
def train_readout_mnlogit(stimset, samples):
(ds_train, ds_valid) = to_mvpa_dataset(stimset, samples)
clf = SMLR()
clf.train(ds_train)
preds = clf.predict(ds_valid)
actual = ds_valid.sa['targets']
zeq = np.array([a == p for (a,p) in zip(actual, preds)])
nc = float(len((zeq == True).nonzero()[0]))
#print '%d correct out of %d' % (nc, len(preds))
percent_correct = nc / float(len(preds))
#print 'SMLogit Percent Correct: %0.3f' % percent_correct
return percent_correct
def test_smlr(self):
data = datasets["dumb"]
clf = SMLR()
clf.train(data)
# prediction has to be perfect
#
# XXX yoh: whos said that?? ;-)
#
# There is always a tradeoff between learning and
# generalization errors so... but in this case the problem is
# more interesting: absent bias disallows to learn data you
# have here -- there is no solution which would pass through
# (0,0)
predictions = clf.predict(data.samples)
self.assertTrue((predictions == data.targets).all())
def test_smlr(self):
data = datasets['dumb']
clf = SMLR()
clf.train(data)
# prediction has to be perfect
#
# XXX yoh: whos said that?? ;-)
#
# There is always a tradeoff between learning and
# generalization errors so... but in this case the problem is
# more interesting: absent bias disallows to learn data you
# have here -- there is no solution which would pass through
# (0,0)
predictions = clf.predict(data.samples)
self.assertTrue((predictions == data.targets).all())
