def test_lars_state(self):
#data = datasets['dumb2']
# for some reason the R code fails with the dumb data
data = datasets['chirp_linear']
clf = LARS()
clf.train(data)
clf.ca.enable('predictions')
p = clf.predict(data.samples)
self.assertTrue((p == clf.ca.predictions).all())
def test_lars_state(self):
#data = datasets['dumb2']
# for some reason the R code fails with the dumb data
data = datasets['chirp_linear']
clf = LARS()
clf.train(data)
clf.ca.enable('predictions')
p = clf.predict(data.samples)
self.failUnless((p == clf.ca.predictions).all())
def test_lars(self):
# not the perfect dataset with which to test, but
# it will do for now.
#data = datasets['dumb2']
# for some reason the R code fails with the dumb data
data = datasets['chirp_linear']
clf = LARS()
clf.train(data)
# prediction has to be almost perfect
# test with a correlation
pre = clf.predict(data.samples)
cor = pearsonr(pre, data.targets)
if cfg.getboolean('tests', 'labile', default='yes'):
self.assertTrue(cor[0] > .8)
def test_lars(self):
# not the perfect dataset with which to test, but
# it will do for now.
#data = datasets['dumb2']
# for some reason the R code fails with the dumb data
data = datasets['chirp_linear']
clf = LARS()
clf.train(data)
# prediction has to be almost perfect
# test with a correlation
pre = clf.predict(data.samples)
cor = pearsonr(pre, data.targets)
if cfg.getboolean('tests', 'labile', default='yes'):
self.failUnless(cor[0] > .8)
