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question:import numpy as np from sklearn.metrics.pairwise import cosine_similarity from sklearn.preprocessing import StandardScaler class RecommendationSystem: def __init__(self): self.user_ratings = None self.product_features = None self.user_profiles = None def preprocess_data(self, user_ratings, product_features): Preprocesses the raw user and product data. def build_user_profiles(self): Builds user profiles based on their ratings and browsing history. def recommend_products(self, user_id, top_n=5): Recommends top_n products for a given user based on their profile. def evaluate_recommendations(self, actual_ratings, predicted_ratings): Evaluates the recommendation system using Mean Squared Error. def handle_cold_start(self, new_user_ratings, new_product_features): Handles cold-start problems for new users or products. def ensure_user_privacy(self): Ensures user privacy by anonymizing user data. from solution import RecommendationSystem import numpy as np def test_preprocess_data(): recommender = RecommendationSystem() user_ratings = np.array([[4, 5, 2], [3, 2, 4], [5, 4, 3]]) product_features = np.array([[0.5, 1], [0.2, 0.8], [0.7, 0.9]]) recommender.preprocess_data(user_ratings, product_features) assert recommender.user_ratings.shape == user_ratings.shape assert recommender.product_features.shape == product_features.shape def test_build_user_profiles(): recommender = RecommendationSystem() user_ratings = np.array([[4, 5, 2], [3, 2, 4], [5, 4, 3]]) product_features = np.array([[0.5, 1], [0.2, 0.8], [0.7, 0.9]]) recommender.preprocess_data(user_ratings, product_features) recommender.build_user_profiles() assert recommender.user_profiles.shape == (3, 2) def test_recommend_products(): recommender = RecommendationSystem() user_ratings = np.array([[4, 5, 2], [3, 2, 4], [5, 4, 3]]) product_features = np.array([[0.5, 1], [0.2, 0.8], [0.7, 0.9]]) recommender.preprocess_data(user_ratings, product_features) recommender.build_user_profiles() recommendations = recommender.recommend_products(0, top_n=2) assert len(recommendations) == 2 def test_evaluate_recommendations(): recommender = RecommendationSystem() actual_ratings = np.array([5, 3, 4]) predicted_ratings = np.array([4.5, 3.2, 3.9]) mse = recommender.evaluate_recommendations(actual_ratings, predicted_ratings) assert mse < 0.5 def test_handle_cold_start(): recommender = RecommendationSystem() user_ratings = np.array([[4, 5, 2], [3, 2, 4], [5, 4, 3]]) product_features = np.array([[0.5, 1], [0.2, 0.8], [0.7, 0.9]]) recommender.preprocess_data(user_ratings, product_features) new_user_ratings = np.array([[2, 3, 4]]) new_user_profiles = recommender.handle_cold_start(new_user_ratings, None) assert new_user_profiles.shape == (1, recommender.product_features.shape[1]) def test_ensure_user_privacy(): recommender = RecommendationSystem() user_ratings = np.array([[4, 5, 2], [3, 2, 4], [5, 4, 3]]) recommender.preprocess_data(user_ratings, np.array([[0.5, 1], [0.2, 0.8], [0.7, 0.9]])) anonymized_data = recommender.ensure_user_privacy() assert anonymized_data.shape == user_ratings.shape assert not np.array_equal(anonymized_data, user_ratings)
answer:import numpy as np from sklearn.metrics.pairwise import cosine_similarity from sklearn.preprocessing import StandardScaler class RecommendationSystem: def __init__(self): self.user_ratings = None self.product_features = None self.user_profiles = None def preprocess_data(self, user_ratings, product_features): Preprocesses the raw user and product data. self.user_ratings = StandardScaler().fit_transform(user_ratings) self.product_features = StandardScaler().fit_transform(product_features) def build_user_profiles(self): Builds user profiles based on their ratings and browsing history. # Here we assume that user ratings matrix is user x products self.user_profiles = np.dot(self.user_ratings, self.product_features) def recommend_products(self, user_id, top_n=5): Recommends top_n products for a given user based on their profile. user_profile = self.user_profiles[user_id, :] product_scores = cosine_similarity(user_profile.reshape(1, -1), self.product_features)[0] recommended_product_indices = np.argsort(-product_scores)[:top_n] return recommended_product_indices def evaluate_recommendations(self, actual_ratings, predicted_ratings): Evaluates the recommendation system using Mean Squared Error. mse = np.mean((actual_ratings - predicted_ratings) ** 2) return mse def handle_cold_start(self, new_user_ratings, new_product_features): Handles cold-start problems for new users or products. if new_user_ratings is not None: new_user_ratings = StandardScaler().fit_transform(new_user_ratings) new_user_profiles = np.dot(new_user_ratings, self.product_features) return new_user_profiles if new_product_features is not None: new_product_features = StandardScaler().fit_transform(new_product_features) new_recommendations = cosine_similarity(self.user_profiles, new_product_features) return new_recommendations def ensure_user_privacy(self): Ensures user privacy by anonymizing user data. anonymized_data = np.random.permutation(self.user_ratings) return anonymized_data
question:class TreeNode: def __init__(self, val=0, left=None, right=None): self.val = val self.left = left self.right = right def diameter_of_binary_tree(root: TreeNode) -> int: Returns the diameter of the binary tree. >>> root = TreeNode(1) >>> root.left = TreeNode(2) >>> root.right = TreeNode(3) >>> root.left.left = TreeNode(4) >>> root.left.right = TreeNode(5) >>> diameter_of_binary_tree(root) 3 >>> root = TreeNode(1) >>> root.left = TreeNode(2) >>> root.right = TreeNode(3) >>> diameter_of_binary_tree(root) 2 >>> root = TreeNode(1) >>> root.right = TreeNode(2) >>> root.right.right = TreeNode(3) >>> diameter_of_binary_tree(root) 2 >>> diameter_of_binary_tree(None) 0
answer:class TreeNode: def __init__(self, val=0, left=None, right=None): self.val = val self.left = left self.right = right def diameter_of_binary_tree(root: TreeNode) -> int: Returns the diameter of the binary tree. def depth(node: TreeNode) -> int: nonlocal max_diameter if not node: return 0 left_depth = depth(node.left) right_depth = depth(node.right) max_diameter = max(max_diameter, left_depth + right_depth) return max(left_depth, right_depth) + 1 max_diameter = 0 depth(root) return max_diameter
question:def all_palindromes_and_length(lst: list, min_length: int) -> bool: Returns True if all strings in the list lst are palindromes and the length of the list exceeds min_length. >>> all_palindromes_and_length(["madam", "racecar", "level"], 2) True >>> all_palindromes_and_length(["hello", "world"], 1) False >>> all_palindromes_and_length(["deified", "civic", "radar"], 3) False
answer:def all_palindromes_and_length(lst: list, min_length: int) -> bool: Returns True if all strings in the list lst are palindromes and the length of the list exceeds min_length. return all(s == s[::-1] for s in lst) and len(lst) > min_length
question:def is_palindrome(s: str) -> bool: Checks if the given string is a palindrome, considering only alphanumeric characters and ignoring cases. >>> is_palindrome("A man, a plan, a canal: Panama") True >>> is_palindrome("racecar") True >>> is_palindrome("hello") False >>> is_palindrome("") True >>> is_palindrome("a") True >>> is_palindrome("A") True >>> is_palindrome("No lemon, no melon") True >>> is_palindrome("12321") True >>> is_palindrome("123456") False
answer:def is_palindrome(s: str) -> bool: Checks if the given string is a palindrome, considering only alphanumeric characters and ignoring cases. # Filter only alphanumeric characters and transform to lowercase filtered_chars = [char.lower() for char in s if char.isalnum()] # Check if the filtered characters form a palindrome return filtered_chars == filtered_chars[::-1]