def test_dishes(): food_kb = pl.KnowledgeBase("food") food_kb([ "likes(noor, sausage)", "likes(melissa, pasta)", "likes(dmitry, cookie)", "likes(nikita, sausage)", "likes(assel, limonade)", "food_type(gouda, cheese)", "food_type(ritz, cracker)", "food_type(steak, meat)", "food_type(sausage, meat)", "food_type(limonade, juice)", "food_type(cookie, dessert)", "flavor(sweet, dessert)", "flavor(savory, meat)", "flavor(savory, cheese)", "flavor(sweet, juice)", "food_flavor(X, Y) :- food_type(X, Z), flavor(Y, Z)", "dish_to_like(X, Y) :- likes(X, L), food_type(L, T), flavor(F, T), food_flavor(Y, F)" ]) assert food_kb.query(pl.Expr("likes(noor, sausage)")) == ["Yes"] answer = {"What": "cookie"} query = food_kb.query(pl.Expr("food_flavor(What, sweet)")) assert answer in query
def test_graph(): graph = pl.KnowledgeBase("graph") graph([ "edge(a, b, 6)", "edge(a, c, 1)", "edge(b, e, 4)", "edge(b, f, 3)", "edge(c, d, 3)", "edge(d, e, 8)", "edge(e, f, 2)", "path(X, Y, W) :- edge(X , Y, W)", "path(X, Y, W) :- edge(X, Z, W1), path(Z, Y, W2), W is W1 + W2" ]) query = graph.query(pl.Expr("path(a, e, W)"), cut=True) assert [d.get("W") for d in query][0] == 10
def test_iris(): iris_kb = pl.KnowledgeBase("iris") iris_kb([ "species(setosa, Truth) :- petal_width(W), Truth is W <= 0.80", "species(versicolor, Truth) :- petal_width(W), petal_length(L), Truth is W > 0.80 and L <= 4.95", "species(virginica, Truth) :- petal_width(W), petal_length(L), Truth is W > 0.80 and L > 4.95", "petal_length(5.1)", "petal_width(2.4)" ]) query = iris_kb.query(pl.Expr("species(Class, Truth)")) answer = list(filter(lambda d: d['Truth'] == "Yes", query))[0] correct_answer = {'Class': 'virginica', 'Truth': 'Yes'} assert answer == correct_answer
def ask_KB(new_kb, x): results = set() print("Query: " + str(x)) y = new_kb.query(pl.Expr(x)) print("Risultati\n", y) # Elimina i duplicati dai risultati s = "" for e in y: s = str(e) results.add(s) return results
def test_friends(): friends_kb = pl.KnowledgeBase("friends") friends_kb([ "stress(X, P) :- has_lot_work(X, P2), P is P2 * 0.2", "to_smoke(X, Prob) :- stress(X, P1), friends(Y, X), influences(Y, X, P2), smokes(Y), Prob is P1 * P2", "to_have_asthma(X, 0.4) :- smokes(X)", "to_have_asthma(X, Prob) :- to_smoke(X, P2), Prob is P2 * 0.25", "friends(X, Y) :- friend(X, Y)", "friends(X, Y) :- friend(Y, X)", "influences(X, Y, 0.6) :- friends(X, Y)", "friend(peter, david)", "friend(peter, rebecca)", "friend(daniel, rebecca)", "friend(david, david)", "smokes(peter)", "smokes(rebecca)", "has_lot_work(daniel, 0.8)", "has_lot_work(david, 0.3)" ]) david = {'Who': 'david', 'P': 0.036} assert david in friends_kb.query(pl.Expr("to_smoke(Who, P)")) dan_reb = [{ 'Who': 'rebecca', 'P': '0.4' }, { 'Who': 'daniel', 'P': 0.024000000000000004 }] assert all(i in friends_kb.query(pl.Expr("to_have_asthma(Who, P)")) for i in dan_reb)
print(film.loc[film.film_id == 1, "title"]) print(actor.head()) import pytholog as pl dvd = pl.KnowledgeBase("dvd_rental") for i in range(film.shape[0]): dvd([f"film({film.film_id[i]}, {film.title[i]}, {film.language_id[i]})"]) for i in range(language.shape[0]): dvd([f"language({language.language_id[i]}, {language.name[i]})"]) dvd(["film_language(F, L) :- film(_, F, LID), language(LID, L)"]) print(dvd.query(pl.Expr("film_language(young_language, L)"))) for i in range(category.shape[0]): dvd([f"category({category.category_id[i]}, {category.name[i]})"]) for i in range(film_category.shape[0]): dvd([ f"filmcategory({film_category.film_id[i]}, {film_category.category_id[i]})" ]) dvd([ "film_category(F, C) :- film(FID, F, _), filmcategory(FID, CID), category(CID, C)" ]) print(dvd.query(pl.Expr("film_category(F, sci-fi)"))) for i in range(actor.shape[0]):
def _query(kb, inpt): inpt = re.sub("\?", "", inpt) cut = (inpt[-1] == "!") return kb.query(pl.Expr(inpt[:-1]), cut=cut)