def confidence_for_intro_to_adv(simple_students, intro_grade, adv_grade): intro_with_grade = lambda student: in_sets_test(student, [intro_prog], [intro_grade]) adv_with_grade = lambda student: in_sets_test(student, [adv_prog], [adv_grade]) return alg.confidence(simple_students, intro_with_grade, adv_with_grade)
def data_asked(): results = [] threshold_for_rule = 0.1 last_n = 30 the_courses = unique_courses_from_codes(adv_g4_stripped) large_freq_itemsets = alg.apriori(threshold_for_rule, the_courses, adv_g4_stripped)[-last_n:] premises = [] for itemsetsup in large_freq_itemsets: premises.append(itemsetsup[0]) the_base_set = setify(strip_all_but_codes (take_out_course_not_grade(adv_prog, [4]))) for fr_itemset in premises: premise = lambda tr: fr_itemset <= tr consequent = lambda tr: adv_prog in tr print(the_base_set, "\n", fr_itemset, "\n", adv_prog, "\n") conf = alg.confidence(the_base_set, premise, consequent) results.append({'premise': fr_itemset, 'conf': conf}) return results