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)