def transform_discrete(var):
if (len(var.values) < 2 or treat == Continuize.Remove
or treat == Continuize.RemoveMultinomial
and len(var.values) > 2):
return []
if treat == Continuize.AsOrdinal:
new_var = ContinuousVariable(var.name,
compute_value=Identity(var),
sparse=var.sparse)
return [new_var]
if treat == Continuize.AsNormalizedOrdinal:
n_values = max(1, len(var.values))
if self.zero_based:
return [
ContinuousVariable(var.name,
compute_value=Normalizer(
var, 0, 1 / (n_values - 1)),
sparse=var.sparse)
]
else:
return [
ContinuousVariable(var.name,
compute_value=Normalizer(
var, (n_values - 1) / 2,
2 / (n_values - 1)),
sparse=var.sparse)
]
new_vars = []
if treat == Continuize.Indicators:
base = -1
elif treat in (Continuize.FirstAsBase,
Continuize.RemoveMultinomial):
base = 0
else:
base = dists[var_ptr].modus()
ind_class = [Indicator1, Indicator][self.zero_based]
for i, val in enumerate(var.values):
if i == base:
continue
new_var = ContinuousVariable("{}={}".format(var.name, val),
compute_value=ind_class(var, i),
sparse=var.sparse)
new_vars.append(new_var)
return new_vars
def test_normalizer(self):
t1 = Normalizer(self.disc1, 0, 1)
t1a = Normalizer(self.disc1a, 0, 1)
t2 = Normalizer(self.disc2, 0, 1)
self.assertEqual(t1, t1)
self.assertEqual(t1, t1a)
self.assertNotEqual(t1, t2)
self.assertEqual(hash(t1), hash(t1a))
self.assertNotEqual(hash(t1), hash(t2))
t1 = Normalizer(self.disc1, 0, 1)
t1a = Normalizer(self.disc1a, 1, 1)
self.assertNotEqual(t1, t1a)
self.assertNotEqual(hash(t1), hash(t1a))
t1 = Normalizer(self.disc1, 0, 1)
t1a = Normalizer(self.disc1a, 0, 2)
self.assertNotEqual(t1, t1a)
self.assertNotEqual(hash(t1), hash(t1a))
def normalized_var(var, translate, scale):
return Orange.data.ContinuousVariable(var.name,
compute_value=Normalizer(
var, translate, scale))
def normalized_var(var, translate, scale):
new_var = Orange.data.ContinuousVariable(var.name)
norm = Normalizer(var, translate, scale)
new_var.compute_value = norm
return new_var