转载http://www.iplaypy.com/code/algorithm/a2065.html

users = {"Angelica": {"Blues Traveler": 3.5, "Broken Bells": 2.0, "Norah Jones": 4.5, "Phoenix": 5.0,
                      "Slightly Stoopid": 1.5, "The Strokes": 2.5, "Vampire Weekend": 2.0},
         "Bill": {"Blues Traveler": 2.0, "Broken Bells": 3.5, "Deadmau5": 4.0, "Phoenix": 2.0, "Slightly Stoopid": 3.5,
                  "Vampire Weekend": 3.0},
         "Chan": {"Blues Traveler": 5.0, "Broken Bells": 1.0, "Deadmau5": 1.0, "Norah Jones": 3.0, "Phoenix": 5,
                  "Slightly Stoopid": 1.0},
         "Dan": {"Blues Traveler": 3.0, "Broken Bells": 4.0, "Deadmau5": 4.5, "Phoenix": 3.0, "Slightly Stoopid": 4.5,
                 "The Strokes": 4.0, "Vampire Weekend": 2.0},
         "Hailey": {"Broken Bells": 4.0, "Deadmau5": 1.0, "Norah Jones": 4.0, "The Strokes": 4.0,
                    "Vampire Weekend": 1.0},
         "Jordyn": {"Broken Bells": 4.5, "Deadmau5": 4.0, "Norah Jones": 5.0, "Phoenix": 5.0, "Slightly Stoopid": 4.5,
                    "The Strokes": 4.0, "Vampire Weekend": 4.0},
         "Sam": {"Blues Traveler": 5.0, "Broken Bells": 2.0, "Norah Jones": 3.0, "Phoenix": 5.0,
                 "Slightly Stoopid": 4.0, "The Strokes": 5.0},
         "Veronica": {"Blues Traveler": 3.0, "Norah Jones": 5.0, "Phoenix": 4.0, "Slightly Stoopid": 2.5,
                      "The Strokes": 3.0}
         }


# Python计算曼哈顿距离 www.iplaypy.com
def manhattan(rate1, rate2):
    distance = 0
    commonRating = False
    for key in rate1:
        if key in rate2:
            distance += abs(rate1[key] - rate2[key])
            commonRating = True
    if commonRating:
        return distance
    else:
        return -1


# python返回最近距离用户
def computeNearestNeighbor(username, users):
    distances = []
    for key in users:
        if key != username:
            distance = manhattan(users[username], users[key])
            distances.append((distance, key))
    distances.sort(key=lambda x: x[0], reverse=False)
    return distances


# 推荐python实现
def recommend(username, users):
    # 获得最近用户的name
    nearest = computeNearestNeighbor(username, users)[0][1]
    recommendations = []
    # 得到最近用户的推荐列表
    neighborRatings = users[nearest]
    for key in neighborRatings:
        if not key in users[username]:
            recommendations.append((key, neighborRatings[key]))
    recommendations.sort(key=lambda rat: rat[1], reverse=True)
    return recommendations


if __name__ == '__main__':
    recommendation = recommend('Hailey', users)
    print('recommend to Hailey:', recommendation)

#recommend to Hailey: [('Phoenix', 4.0), ('Blues Traveler', 3.0), ('Slightly Stoopid', 2.5)]