def test_ridge_reg_state(self):
data = datasets['dumb']
clf = RidgeReg()
clf.train(data)
clf.ca.enable('predictions')
p = clf.predict(data.samples)
self.failUnless((p == clf.ca.predictions).all())
def testRidgeRegState(self):
data = datasets['dumb']
clf = RidgeReg()
clf.train(data)
clf.states.enable('predictions')
p = clf.predict(data.samples)
self.failUnless((p == clf.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.failUnless(cor[0] > .8)
def testRidgeReg(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.labels)
self.failUnless(cor[0] > .8)
