def test_all_equal(self):
eq = Equilateral(10, -1, 1)
compare_dist = -1
for x in range(0, 10):
base_class = eq.encode(x)
for y in range(0, 10):
if x != y:
otherClass = eq.encode(y)
dist = distance.euclidean(base_class, otherClass)
if compare_dist < 0:
compare_dist = dist
else:
self.assertAlmostEqual(dist, compare_dist, 7)
def norm_col_equilateral(self, data_set, col, classes, normalized_low, normalized_high):
""" Normalize a column using equilateral. The classes parameter contains a map of the unique items in the
specified column. Typically this value is obtained by calling build_class_map.
"""
col = self.resolve_column(col)
eq = Equilateral(len(classes), normalized_low, normalized_high)
for row in data_set:
key = row[col]
value = classes[key]
row.pop(col)
vec = eq.encode(value)
for i in range(0, len(vec)):
row.insert(col + i, vec[i])
def norm_col_equilateral(self, data_set, col, classes, normalized_low, normalized_high):
""" Normalize a column using equilateral. The classes parameter contains a map of the unique items in the
specified column. Typically this value is obtained by calling build_class_map.
"""
col = self.resolve_column(col)
eq = Equilateral(len(classes), normalized_low, normalized_high)
for row in data_set:
key = row[col]
value = classes[key]
row.pop(col)
vec = eq.encode(value)
for i in range(0, len(vec)):
row.insert(col + i, vec[i])
def test_equilateral(self):
eq = Equilateral(3, -1, 1)
d = eq.encode(1);
self.assertAlmostEqual(0.8660254037844386, d[0], 7)
self.assertAlmostEqual(-0.5, d[1], 7)