def test_plr(self):
data = datasets['dumb2']
clf = PLR()
clf.train(data)
# prediction has to be perfect
self.failUnless((clf.predict(data.samples) == data.targets).all())
def test_plr(self):
data = datasets['dumb2']
clf = PLR()
clf.train(data)
# prediction has to be perfect
self.assertTrue((clf.predict(data.samples) == data.targets).all())
def test_plr_state(self):
data = datasets['dumb2']
clf = PLR()
clf.train(data)
clf.ca.enable('estimates')
clf.ca.enable('predictions')
p = clf.predict(data.samples)
self.failUnless((p == clf.ca.predictions).all())
self.failUnless(np.array(clf.ca.estimates).shape == np.array(p).shape)
def test_plr_state(self):
data = datasets['dumb2']
clf = PLR()
clf.train(data)
# Also get "sensitivity". Was introduced to check a bug with
# processing dataset with numeric labels
sa = clf.get_sensitivity_analyzer()
sens = sa(data)
clf.ca.enable('estimates')
clf.ca.enable('predictions')
p = clf.predict(data.samples)
self.assertTrue((p == clf.ca.predictions).all())
self.assertTrue(np.array(clf.ca.estimates).shape == np.array(p).shape)
def test_plr_state(self):
data = datasets['dumb2']
clf = PLR()
clf.train(data)
# Also get "sensitivity". Was introduced to check a bug with
# processing dataset with numeric labels
sa = clf.get_sensitivity_analyzer()
sens = sa(data)
clf.ca.enable('estimates')
clf.ca.enable('predictions')
p = clf.predict(data.samples)
self.assertTrue((p == clf.ca.predictions).all())
self.assertTrue(np.array(clf.ca.estimates).shape == np.array(p).shape)