def test_glmnet_c_sensitivities():
data = normal_feature_dataset(perlabel=10, nlabels=2, nfeatures=4)
# use GLMNET on binary problem
clf = GLMNET_C()
clf.train(data)
# now ask for the sensitivities WITHOUT having to pass the dataset
# again
sens = clf.get_sensitivity_analyzer(force_train=False)(None)
#failUnless(sens.shape == (data.nfeatures,))
assert_equal(sens.shape, (len(data.UT), data.nfeatures))
def test_glmnet_c_sensitivities():
data = normal_feature_dataset(perlabel=10, nlabels=2, nfeatures=4)
# use GLMNET on binary problem
clf = GLMNET_C()
clf.train(data)
# now ask for the sensitivities WITHOUT having to pass the dataset
# again
sens = clf.get_sensitivity_analyzer(force_train=False)(None)
#failUnless(sens.shape == (data.nfeatures,))
assert_equal(sens.shape, (len(data.UT), data.nfeatures))
def test_glmnet_c():
# define binary prob
data = datasets['dumb2']
# use GLMNET on binary problem
clf = GLMNET_C()
clf.ca.enable('estimates')
clf.train(data)
# test predictions
pre = clf.predict(data.samples)
assert_array_equal(pre, data.targets)
def test_glmnet_c():
# define binary prob
data = datasets['dumb2']
# use GLMNET on binary problem
clf = GLMNET_C()
clf.ca.enable('estimates')
clf.train(data)
# test predictions
pre = clf.predict(data.samples)
assert_array_equal(pre, data.targets)
