def test_ridge_reg_state(self):
data = datasets['dumb']
clf = RidgeReg()
clf.train(data)
clf.ca.enable('predictions')
p = clf.predict(data.samples)
self.assertTrue((p == clf.ca.predictions).all())
def test_ridge_reg_state(self):
data = datasets['dumb']
clf = RidgeReg()
clf.train(data)
clf.ca.enable('predictions')
p = clf.predict(data.samples)
self.assertTrue((p == clf.ca.predictions).all())
def test_ridge_reg(self):
# not the perfect dataset with which to test, but
# it will do for now.
data = datasets['dumb']
clf = RidgeReg()
clf.train(data)
# prediction has to be almost perfect
# test with a correlation
pre = clf.predict(data.samples)
cor = pearsonr(pre,data.targets)
self.assertTrue(cor[0] > .8)
def test_ridge_reg(self):
# not the perfect dataset with which to test, but
# it will do for now.
data = datasets['dumb']
clf = RidgeReg()
clf.train(data)
# prediction has to be almost perfect
# test with a correlation
pre = clf.predict(data.samples)
cor = pearsonr(pre, data.targets)
self.assertTrue(cor[0] > .8)
ds.sa['subject'] = np.repeat(index, len(ds))
#ds.fa['parcel'] = msk_data
ds_all.append(ds)
verbose(2, "subject %i of %i loaded" % (index, nsubs))
fds = vstack(ds_all) #stack datasets
#classifier algorithm
if clf_type is 'SVM':
clf = LinearCSVMC(tube_epsilon=0.01)
elif clf_type is 'SVM-rbf':
clf = RbfCSVMC(tube_epsilon=0.01)
elif clf_type is 'ridgeReg':
clf = RidgeReg()
elif clf_type is 'gpr':
clf = GPR()
# #feature selection
# fsel = SensitivityBasedFeatureSelection(
# OneWayAnova(),
# FractionTailSelector(0.05, mode='select', tail='upper'))
# fclf = FeatureSelectionClassifier(clf, fsel)
#cross-validation algorithm
if cv_type is 'split_half':
cv = CrossValidation(clf,
HalfPartitioner(count=2,
selection_strategy='random', attr='subject'),
errorfx=mean_match_accuracy)
