def apply(self):
learner = mean.MeanFitter()
learner.name = self.learner_name
if self.data is not None:
predictor = learner(self.data)
predictor.name = learner.name
else:
predictor = None
self.send("Learner", learner)
self.send("Predictor", predictor)
def test_mean(self):
nrows = 1000
ncols = 10
x = np.random.random_integers(1, 3, (nrows, ncols))
y = np.random.random_integers(0, 4, (nrows, 1)) / 3.0
t = data.Table(x, y)
learn = mean_.MeanFitter()
clf = learn(t)
true_mean = np.average(y)
x2 = np.random.random_integers(1, 3, (nrows, ncols))
y2 = clf(x2)
self.assertTrue(np.allclose(y2, true_mean))
def test_weights(self):
nrows = 100
ncols = 10
x = np.random.random_integers(1, 3, (nrows, ncols))
y = np.random.random_integers(0, 4, (nrows, 1)) / 3.0
heavy = 1
w = ((y == heavy) * 123 + 1.0) / 124.0
t = data.Table(x, y, W=w)
learn = mean_.MeanFitter()
clf = learn(t)
expected_mean = np.average(y, weights=w)
x2 = np.random.random_integers(1, 3, (nrows, ncols))
y2 = clf(x2)
self.assertTrue(np.allclose(y2, expected_mean))
def test_discrete(self):
iris = data.Table('iris')
learn = mean_.MeanFitter()
self.assertRaises(ValueError, learn, iris)
def test_empty(self):
autompg = data.Table('auto-mpg')
learn = mean_.MeanFitter()
clf = learn(autompg[:0])
y = clf(autompg[0])
self.assertTrue(y == 0)