__author__ = 'SejongPark' from chapter2 import recommendations movies = recommendations.transform_prefs(recommendations.critics) result1 = recommendations.top_matches(movies, 'Superman Returns') result2 = recommendations.top_matches(movies, 'Superman Returns', 5, recommendations.sim_distance) print(result1) print(result2)
# coding=utf-8 __author__ = "SejongPark" from chapter2 import recommendations # 유클리디안 계산 결과 계산하기 print(recommendations.sim_distance(recommendations.critics, "Lisa Rose", "Gene Seymour")) # 피어슨 결과... print(recommendations.sim_pearson(recommendations.critics, "Lisa Rose", "Gene Seymour")) # 평론가 순위 매기기 print(recommendations.top_matches(recommendations.critics, "Toby"))
# coding=utf-8 __author__ = 'SejongPark' from chapter2 import recommendations # 유클리디안 계산 결과 계산하기 print( recommendations.sim_distance(recommendations.critics, 'Lisa Rose', 'Gene Seymour')) # 피어슨 결과... print( recommendations.sim_pearson(recommendations.critics, 'Lisa Rose', 'Gene Seymour')) # 평론가 순위 매기기 print(recommendations.top_matches(recommendations.critics, 'Toby'))