def test_class_segregated(self):
city = segregated_city()
exp = mb.exposure(city)
classes = mb.uncover_classes(city, exp)
assert_equal(len(classes), 3)
def test_class_uniform(self):
city = uniform_city()
exp = mb.exposure(city)
classes = mb.uncover_classes(city, exp)
assert_equal(len(classes), 2)
def test_class_segregated(self):
city = segregated_city()
exp = mb.exposure(city)
classes = mb.uncover_classes(city, exp)
assert_equal(len(classes),3)
#
# Concantenate exposure values and variance
#
categories = [int(k) for k in exposure_val.iterkeys()]
exp = {c0: {c1: (exposure_val[c0][c1],
exposure_var[c0][c1])
for c1 in categories}
for c0 in categories}
#
# Extract linkage matrix
#
classes = mb.uncover_classes(households_all, exp)
#
# Prompt for names
#
print "Classes have been found! We need you to name them..."
print "Classes are the following:"
for cl in classes:
print cl
print "\n"
names = []
for cl in classes:
names.append(raw_input("Give a name for the class containing %s\n"%cl))
print "Thanks!"
def test_class_uniform(self):
city = uniform_city()
exp = mb.exposure(city)
classes = mb.uncover_classes(city, exp)
assert_equal(len(classes),2)