Let's learn from each other!

最新推荐文章于 2023-10-11 01:37:10 发布
最新推荐文章于 2023-10-11 01:37:10 发布 · 229 阅读 · 0

博主尝试将记事本中的文字剪切并粘贴到博客中保存，但在操作过程中不慎用其他内容覆盖了剪切板里的原始信息。本文探讨了如何找回被覆盖的剪切板内容。
　　本来从记事本中剪切了一段文字，想放到博客中保存下来，这是上周六写的一点心得体会。
　　登录到csdn博客，发现有四位网友申请加我为好友。有朋自网络来，不亦乐乎！当然是要通过申请了。回复一句什么话好呢，首先想到的是：互相学习，以前都是这句，但中文似乎太没有新意，至少得用英文吧。可是，我的英文比较烂，当初就是因为这才没有考上清华北大，以至于流落到IT业混饭，这行的就业门槛比较低啊！还好有互联网，很容易就找到了这句：Let's learn from each other。急不可耐地复制下来，也好显摆一下。一般没文化的人都喜欢说自己又读了什么书，不会英文的人总爱摆弄几句单词。
　　可是，马上我就后悔了。那我刚才剪切（非复制）的那段文字跑到哪儿去了呢？那可是我的灵光一现，丢了可就没有了。
　　我欲哭无泪，只好在这里求助：如何找到剪切板中被覆盖的信息呢？
　　Let's learn from each other！
iteye_5985
关注
实例讲解Pandas和Scikit-learn的用法
AI天才研究院
08-03 1794
数据预处理(Data Preprocessing)是数据科学的一个重要环节，数据预处理将源数据转换成训练模型所需的结构化、易于分析的数据形式，并对缺失值、异常值进行处理，使得数据具有更好的质量、有效性和可预测性。数据预处理也是许多机器学习算法的前置条件。Pandas和Scikit-learn都是Python中的两个最流行的数据处理库。在本文中，我们将介绍Pandas和Scikit-learn工具包，以及它们如何处理数据的预处理任务。
Learning to Learn: A Deep Dive into DeepMind‘s Discovered Update Rules
步子哥的博客
11-07 31
In the world of deep learning and reinforcement learning (RL), we often take our optimizers for granted. We import or , tune a learning rate, and trust these hand-designed algorithms to descend the loss landscape. But what if the learning algorithm itself
[置顶] Hamming Problem（打表，需技巧，不错的题）
weixin_33709364的博客
08-09 185
Hamming Problem Time Limit:1000MS     Memory Limit:32768KB     64bit IO Format:%I64d & %I64u Submit Status Practice FZU 1045 Description For each three prime ...
java英语句子_经典英语句子 (转） - HelloWorld 善战者,求之于势,不责于人;故能择人而任势。 - BlogJava...
weixin_33896830的博客
02-27 3531
一、30句经典英语句子1． Pain past is pleasure.(过去的痛苦就是快乐。)[无论多么艰难一定要咬牙冲过去，将来回忆起来一定甜蜜无比。]2． While there is life, there is hope.(有生命就有希望/留得青山在，不怕没柴烧。)3． Wisdom in the mind is better than money in the hand.(脑中有知识，...
Let'sEncrypt免费域名申请一键式脚本-目前最简单的脚本
热门推荐
xiaoguaihu12的博客
08-30 1万+
注: 该脚本为开源组织提供 里面的所有东西都不要改, 复制下来,放到文本里, 放到linux下, 执行就好了. 网络上很多各种各样的教程形式, 我也在网络上查询了很长时间, 发现公司线上服务器上有个脚本,,不知道是不是公司人写的, 拿第一句话,到百度上一搜索, 发现第一个链接点击去, 就是脚本开源组织提供的地址. 步骤: 1. 桌面创建文本:certbot-au...
cp01 Giving Computers the Ability to Learn from Data_Python 3.5.2_Anaconda_scikit-learn_roadmap
Linli522362242的专栏
05-23 2241
In my opinion, machine learning, the application and science of algorithms that make sense of data, is the most exciting field of all the computer sciences! We are living in an age where data comes in abundance大量; using self-learning algorithms from t...
Let 's Study C++ Here ,hello kitty!
shangxinlei88的博客
05-20 1252
 Welcome! If you're new to C++, learn C++ with our C++ tutorial, starting at C++ Made Easy, Lesson 1 (all lessons) If you want to learn C instead, check out our C tutorial C Made Easy, Lesson 1 
welearn考试切屏会有显示吗_welearn随行课堂班级测试答案
weixin_39789370的博客
01-14 4496
welearn随行课堂班级测试答案更多相关问题短文改错。 此题要求改正所给短文中的错误.对标有题号的每一行作出判断： 如有错误(每行只有一个错误)，则按下—What _____ great fun it would be if we could light candles to celebrate his birthday!— Forget it! T_____, the young girl l...
ChatGPT Prompt Engineering for Developers from DeepLearning.AI
Michael’s Blog
06-11 2853
【代码】ChatGPT Prompt Engineering for Developers from DeepLearning.AI。
From Prediction to Prevention: Improving Transportation
AI天才研究院
09-22 2037
作者：禅与计算机程序设计艺术Traffic management systems (TMS) play a crucial role in improving transportation security by preventing collisions and traffic congestion. TMS solutions use various techniques such as route planning, real-time traffic information monitoring,
The AI Journey: A Beginner’s Guide to Artificial Intelligence
AI天才研究院
10-11 903
作者：禅与计算机程序设计艺术 Artificial Intelligence (AI) is the field of computer science that aims to create intelligent machines that can learn, think like humans, make decisions, and interact with other systems. It has applications in fields such as robotics, image
A Beginner’s Guide to Backpropagation in Neural Networks
AI天才研究院
08-25 372
作者：禅与计算机程序设计艺术Backpropagation is one of the most popular algorithms used for training neural networks. It belongs to a family of gradient-based optimization methods that use calculus to approximate the gradients of loss functions with respect to network pa
【嵌入式AIoT】系统架构与轻量化模型部署：面向智能家居与工业监测的边缘智能终端实战设计
12-21
内容概要：本文系统介绍了嵌入式AIoT应用场景的实战方法与实践路径，涵盖从场景选择、系统架构设计到AI模型部署、通信与数据管理的全流程。重点阐述了嵌入式系统与人工智能、物联网技术的融合，强调在资源受限环境下实现感知、分析与决策一体化的智能终端系统。文章详细解析了感知层、边缘计算层和云端服务层的协同机制，突出边缘侧数据处理与轻量化AI模型优化的重要性，并倡导以实际需求为导向，避免“为AI而AI”的设计误区。同时，强调系统稳定性、远程维护和长期运行能力的建设。; 适合人群：具备嵌入式系统基础、物联网或AI相关背景，有一定开发经验的工程师或工作1-3年的技术研发人员；适用于希望深入理解AIoT系统集成与实战落地的学习者。; 使用场景及目标：①智能家居、工业监测、智慧农业等分布式智能系统开发；②在算力、功耗受限的嵌入式设备上实现AI推理与边缘智能；③构建高效、可靠、可维护的端云协同AIoT系统。; 阅读建议：建议结合实际硬件平台进行项目化学习，边学边练，重点关注系统架构设计、模型优化与通信协议选型，注重整体系统思维的培养，而非仅关注单一技术点。
基于Python 的UART 文件传输工具
最新发布
12-21
基于Python 的UART 文件传输工具，基于Pyside6的GUI界面
C# 基于 Onnx 与 P2PNet 的人群检测与计数系统源码实现
12-21
基于C#编程语言与Onnx运行时环境，本文档详细阐述了一种利用P2PNet架构实现人群密度估计与个体计数的技术方案。该方案通过解析预训练模型，实现了对图像或视频流中人群分布的精准检测，并提供了可靠的计数功能。核心内容包括模型加载与推理流程的完整实现、数据处理管道的构建以及性能优化策略的探讨。本文旨在为相关领域的开发人员提供一个清晰、可复现的参考实现，着重于工程实践的严谨性与代码模块的可用性。所有实现代码均经过结构化组织与详细注释，以确保其易于理解与集成。 资源来源于网络分享，仅用于学习交流使用，请勿用于商业，如有侵权请联系我删除！
Theoretical Machine Learning Notes (Princeton COS511)
12-21
根据原作 https://pan.quark.cn/s/459657bcfd45 的源码改编 Classic-ML-Methods-Algo 引言 建立这个项目,是为了梳理和总结传统机器学习(Machine Learning)方法(methods)或者算法(algo),和各位同仁相互学习交流. 现在的深度学习本质上来自于传统的神经网络模型,很大程度上是传统机器学习的延续,同时也在不少时候需要结合传统方法来实现. 任何机器学习方法基本的流程结构都是通用的;使用的评价方法也基本通用;使用的一些数学知识也是通用的. 本文在梳理传统机器学习方法算法的同时也会顺便补充这些流程,数学上的知识以供参考. 机器学习 机器学习是人工智能(Artificial Intelligence)的一个分支,也是实现人工智能最重要的手段.区别于传统的基于规则(rule-based)的算法,机器学习可以从数据中获取知识,从而实现规定的任务[Ian Goodfellow and Yoshua Bengio and Aaron Courville的Deep Learning].这些知识可以分为四种: 总结（summarization） 预测(prediction) 估计(estimation) 假想验证(hypothesis testing) 机器学习主要关心的是预测[Varian在Big Data : New Tricks for Econometrics],预测的可以是连续性的输出变量,分类,聚类或者物品之间的有趣关联. 机器学习分类 根据数据配置(setting,是否有标签，可以是连续的也可以是离散的)和任务目标,我们可以将机器学习方法分为四种: 无监督(unsupervised) 训练数据没有给定...
食堂线上预约点餐系统_一个基于现代Web技术构建的面向学校企业及园区食堂的综合性数字化餐饮服务平台_该系统旨在通过线上化流程彻底革新传统食堂就餐模式_核心功能模块包括用户端小程序.zip
12-21
食堂线上预约点餐系统_一个基于现代Web技术构建的面向学校企业及园区食堂的综合性数字化餐饮服务平台_该系统旨在通过线上化流程彻底革新传统食堂就餐模式_核心功能模块包括用户端小程序.zip
Unity STL文件读取插件：pb_Stl-master
12-21
Unity STL文件导入工具 该工具为Unity引擎提供标准STL格式三维模型文件的导入功能，支持二进制与ASCII两种编码格式的解析。通过集成此插件，开发者可直接在Unity编辑器中加载由各类CAD软件或三维扫描设备生成的STL模型文件，无需借助第三方转换软件进行格式预处理。 核心特性包括： 1. 完整几何数据解析：精确读取顶点坐标、法线向量及三角面片拓扑关系 2. 自适应材质分配：根据模型几何特征自动生成并配置基础材质球 3. 内存优化机制：采用流式加载技术处理大型STL文件，避免编辑器卡顿 4. 坐标系统转换：自动校正不同三维软件坐标系差异，确保模型方向一致性 技术实现层面，插件通过托管DLL封装了经过优化的STL解析算法，在保证读取精度的同时维持了较高执行效率。导入后的模型将自动生成标准网格组件，支持Unity光照系统、碰撞体生成及导航网格烘焙等后续处理流程。 该工具适用于机械设计展示、三维打印预览、工业仿真等需要频繁交换CAD数据的开发场景，显著简化了从工程设计到实时渲染的工作流程。 资源来源于网络分享，仅用于学习交流使用，请勿用于商业，如有侵权请联系我删除！
Task 3: Simulate Election (4 marks) To select the winner of the election, instant-runoff voting (IRV) is used. This method selects a winner over the course of a number of rounds. The following procedure is repeated until there is only one candidate left: Assign each ballot to the candidate who is ranked highest on that ballot and who has not yet been eliminated. Eliminate the candidate(s) with the lowest number of assigned ballots in that round, so they are not counted in future rounds. For this task, you must run a simulated IRV for an election given a list of valid positional ballots. Write a function simulate_election(ballots, parties) You need to write a function that returns the full history of elimination rounds for the given election. Arguments: ballots: a list of valid positional ballots. parties: the ordered list of parties contesting the election. Returns: a list of elimination round history, where each round consists of a tuple with: A dictionary mapping each remaining candidate to their vote total A set of candidates to be eliminated after that round. Details: If two candidates are tied for the lowest vote total in a round, they are both eliminated simultaneously If there is no unique winner, your program should raise a TiedElectionError as defined at the top of the provided code. As a reminder, you can raise an error using the built in Python statement raise TiedElectionError. This is a custom Exception type that we have defined for you. You can use it like any other Exception , and there is no need to understand how we have defined it. Interested students can learn more about custom object and classes in this optional worksheet. Example Calls:
10-17
```python def simulate_election(ballots, parties): """ Simulates an Instant-Runoff Voting (IRV) election and returns the history of elimination rounds. Arguments: ballots: List of valid positional ballots. Each ballot is a list of party indices representing the voter's ranking (from highest to lowest preference). parties: Ordered list of parties (candidates) in the election. Used to map indices to names. Returns: A list of tuples. Each tuple contains: - A dictionary mapping each remaining candidate (by name) to their vote total - A set of candidates (by name) to be eliminated in that round Raises: TiedElectionError: If at the end of the process there is more than one candidate remaining and they are tied (i.e., no unique winner). """ # Make a copy of remaining candidates (by name), so we can eliminate them over time remaining = set(parties) # This will store the full history of rounds history = [] # Keep going until one or zero candidates remain while len(remaining) > 1: # Dictionary to count votes for each remaining candidate vote_count = {candidate: 0 for candidate in remaining} # Assign each ballot to the highest-ranked candidate still in the race for ballot in ballots: for rank in ballot: candidate = parties[rank] # Map index back to party name if candidate in remaining: vote_count[candidate] += 1 break # Found top-preferred active candidate; stop checking this ballot # Find the minimum number of votes received min_votes = min(vote_count.values()) # Identify all candidates with the minimum vote count (to be eliminated) to_eliminate = {cand for cand, votes in vote_count.items() if votes == min_votes} # Record this round's state before elimination history.append((dict(vote_count), set(to_eliminate))) # Eliminate the bottom candidates remaining -= to_eliminate # After loop: either 0 or 1 candidate remains if len(remaining) == 0: # No candidate left → tie among last ones eliminated raise TiedElectionError("Election resulted in a tie.") # Now only one candidate should remain — but we must check final condition # According to problem: if no *unique* winner exists, raise error # The only way this happens here is if the last two were tied and both eliminated, # which leaves `remaining` empty. But what if two remain and have equal votes? # Actually, our loop stops when len(remaining) <= 1. # So suppose two candidates are tied for last, both get eliminated — fine. # But if two candidates are left and both have same votes? They would NOT be eliminated # unless they are the lowest — but they're the only ones! So both would be eliminated # **only** if both have the same (lowest) value — which they do. # Therefore, if two final candidates are tied, they are BOTH eliminated → remaining becomes empty # So we already catch that via the above check. # However, let’s ensure correctness: if the last round eliminates all remaining candidates # because they’re tied for lowest, then we end up with no winner → must raise error. # Our current logic already does that: after the loop, if `remaining` is empty → raise error. return history ``` ### Explanation: The function simulates **Instant-Runoff Voting (IRV)** where voters rank candidates by preference (via indices). In each round: 1. **Votes are reassigned**: Every ballot goes to the highest-ranked candidate on that ballot who hasn't been eliminated yet. 2. **Count votes**: For all currently remaining candidates, tally how many ballots assign them as top choice. 3. **Eliminate weakest**: All candidates with the fewest votes (minimum count) are marked for elimination — even if multiple tie for lowest, they are all removed **in the same round**. 4. **Record round**: Append a tuple containing: - A dictionary of `{candidate: vote_total}` - A set of candidates to eliminate 5. **Repeat** until one or zero candidates remain. If **zero** candidates remain at the end (e.g., final two were tied and both eliminated), it means there’s no unique winner → raise `TiedElectionError`. > Note: The key insight is that ties in the **lowest** vote totals cause simultaneous eliminations. Only when exactly **one** candidate remains do we accept them as the unique winner. --- ### Example Usage: ```python # Suppose: parties = ["Liberal", "Labor", "Greens", "National"] ballots = [ [0, 1, 2, 3], # prefers Liberal > Labor > Greens > National [1, 2, 0, 3], # prefers Labor > Greens > Liberal > National [2, 1, 0, 3], # prefers Greens > Labor > Liberal > National ] # Call: history = simulate_election(ballots, parties) for i, (votes, elim) in enumerate(history): print(f"Round {i+1}: Votes={votes}, Eliminated={elim}") ``` This would output something like: ``` Round 1: Votes={'Liberal': 1, 'Labor': 1, 'Greens': 1, 'National': 0}, Eliminated={'National'} Round 2: Votes={'Liberal': 1, 'Labor': 2, 'Greens': 1}, Eliminated={'Liberal', 'Greens'} ... ``` And if only `Labor` remains → success. If next round tries to eliminate both final two due to tie → raises `TiedElectionError`. ---
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
红包 添加红包
表情包 插入表情
表情包 代码片
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付：10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明：

1.余额是钱包充值的虚拟货币，按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载，可以购买VIP、付费专栏及课程。

余额充值