Yesterday Once More

本文回忆了通过老歌重温往昔美好时光的经历。每当熟悉的旋律响起,那些珍贵的记忆便如潮水般涌来,仿佛将人带回那个简单而快乐的时代。

Carpenter 

When I was young I'd listen to the radio
Waiting for my favorite songs
When they played I'd sing along,
It make me smile.

Those were such happy times and not so long ago
How I wondered where they'd gone.
But they're back again just like a long lost friend
All the songs I love so well.
Every shalala every wo'wo
still shines.

Every shing-a-ling-a-ling that they're starting to sing
so fine

When they get to the part
where he's breaking her heart
It can really make me cry
just like before.
It's yesterday once more.
(Shoobie do lang lang)
Looking bak on how it was in years gone by
And the good times that had
makes today seem rather sad,
So much has changed.

It was songs of love that I would sing to them
And I'd memorise each word.
Those old melodies still sound so good to me
As they melt the years away
Every shalala every wo'wo still shines

Every shing-a-ling-a-ling that they're startingTo sing
so fine
All my best memorise come back clearly to me
Some can even make me cry
just like before.
It's yesterday once more.
(Shoobie do lang lang)
Every shalala every wo'wo still shines.
Every shing-a-ling-a-ling that they're starting to sing
so fine
Every shalala every wo'wo still shines.
Every shing-a-ling-a-ling that they're starting to sing
so fine

import pandas as pd # 示例数据集结构 data = { 'user_id': ['U1', 'U1', 'U2', 'U3', 'U3'], 'item_id': ['S1', 'S3', 'S2', 'S1', 'S4'], 'rating': [5, 3, 4, 2, 5] } df = pd.DataFrame(data) from surprise import Dataset, Reader from surprise import SVD, KNNBaseline from surprise.model_selection import cross_validate # 1. 数据加载 reader = Reader(rating_scale=(1, 5)) data = Dataset.load_from_df(df[['user_id', 'item_id', 'rating']], reader) # ========== ID与名称映射 ========== SONG_MAPPING = { 'S4': 'Yesterday Once More', 'S7': 'My Heart Will Go On', 'S12': 'Unchained Melody', # 可扩展更多映射关系 } # ============================= # 2. 算法选择(矩阵分解示例) algo = SVD(n_factors=100, n_epochs=20, lr_all=0.005, reg_all=0.02) # 3. 交叉验证评估 cross_validate(algo, data, measures=['RMSE', 'MAE'], cv=3, verbose=True) # 4. 训练完整模型 trainset = data.build_full_trainset() algo.fit(trainset) # 5. 预测示例 uid = 'U2' # 用户ID iid = 'S3' # 歌曲ID pred = algo.predict(uid, iid) print(f'预测评分:{pred.est:.2f}') def get_top_recommendations(user_id, n=5): # 获取所有未评分歌曲 all_songs = df['item_id'].unique() rated_songs = df[df['user_id'] == user_id]['item_id'] unseen = [song for song in all_songs if song not in rated_songs.values] # 预测评分并排序 predictions = [algo.predict(user_id, song) for song in unseen] top = sorted(predictions, key=lambda x: x.est, reverse=True)[:n] return [(pred.iid, pred.est) for pred in top] def get_recommendations(user_id): # 原有预测逻辑(假设返回ID列表) recommended_ids = ['S4', 'S7', 'S12'] # 示例预测结果 # 转换ID为歌曲名称 return [SONG_MAPPING.get(sid, f'Unknown Song ({sid})') for sid in recommended_ids] 请根据这段代码,给出协同过滤算法的音乐推荐算法,最后输出的结果要求是音乐的名称。请给出完整的代码
03-14
内容概要:本文介绍了一个基于冠豪猪优化算法(CPO)的无人机三维路径规划项目,利用Python实现了在复杂三维环境中为无人机规划安全、高效、低能耗飞行路径的完整解决方案。项目涵盖空间环境建模、无人机动力学约束、路径编码、多目标代价函数设计以及CPO算法的核心实现。通过体素网格建模、动态障碍物处理、路径平滑技术和多约束融合机制,系统能够在高维、密集障碍环境下快速搜索出满足飞行可行性、安全性与能效最优的路径,并支持在线重规划以适应动态环境变化。文中还提供了关键模块的代码示例,包括环境建模、路径评估和CPO优化流程。; 适合人群:具备一定Python编程基础和优化算法基础知识,从事无人机、智能机器人、路径规划或智能优化算法研究的相关科研人员与工程技术人员,尤其适合研究生及有一定工作经验的研发工程师。; 使用场景及目标:①应用于复杂三维环境下的无人机自主导航与避障;②研究智能优化算法(如CPO)在路径规划中的实际部署与性能优化;③实现多目标(路径最短、能耗最低、安全性最高)耦合条件下的工程化路径求解;④构建可扩展的智能无人系统决策框架。; 阅读建议:建议结合文中模型架构与代码示例进行实践运行,重点关注目标函数设计、CPO算法改进策略与约束处理机制,宜在仿真环境中测试不同场景以深入理解算法行为与系统鲁棒性。
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