基于Qt实现的物流管理系统

本程序在matlab中实现了5种电压扰动，电压闪边，电压暂降，电压暂态脉冲等波形。

某知名电商拥有二十万条关于热水器的客户评价数据 ，希望能够从数据中，分析某一品牌的用户感情倾向，并详细分析该品牌产品的优缺点，进而提炼所有其他品牌热水器的卖点01项目背景02.产品销量统计03.海尔热水器情感分析04.其他品牌热水器卖点分析05小结:卖点综合分析06.数据来源及指标说明01项目背景★某知名电商拥有二十万条关于热水器的客户评价数据,希望能够从数据中,分析某一品牌的用户感情倾向,并详细分析该品牌产品的优缺点,进而提炼所有其他品牌热水器的卖点★网购大家电的行为日益成熟,及网店评价系统的日益完善,为我们提供了很好的数据积累★用前沿科技手段挖掘用户感情倾向,对生产和销售具有重要的指导意义01.项目背景02产品销量统计03.海尔热水器情感分析04.其他品牌热水器卖点分析05小结:卖点综合分析06.数据来源及指标说明02销量统计年至年总体销量如图,海尔销量约占总体的占了将近大半壁江山年销售汇总图表标题海尔·美的·万和c格兰仕··万家乐海尔美的万和格兰仕万家乐02销量统计年至年各年各品牌销售情况如图可以看出,海尔热水器于年早先于其他品牌进入网店销售,并且销量一直稳居前列年热水器各年销售统计■格兰仕■海尔■美的■万和■万家乐01.项目背景02.产品销量统计03海尔热水器情感分析04.其他品牌热水器卖点分析05小结:卖点综合分析06.数据来源及指标说明03海尔热水器客户情感分析★总体满意度指枋海尔品牌客户评价满意度星级海尔品牌客户评价比例差评差评★较差★★一般★★★很满意满意★★★★很满意★★★★★较差一般满意从图表中可以看出,客户对海尔热水器的综合评价较高,很满意的占45%,差评比率为差评·较差■一般■满意■很满意占24%,其中差评主要的原因为安装配件的费用03海尔热水器客户评价词频质量费用服务品牌加热安装费安装品牌从图表中可以看出,客户对于海保温便宜送货名牌外观实惠师傅大牌尔品牌的服务提及的频率较高配件性价比服务销售时可以着重强调服务及售后质量价格售后效果材料费发货水阀收费包装容量价钱保修温度价位质保出水量特价换货使用经济功品率质口口恒温性能效率插头耗电管道功能质量价格服务品牌

Maxwell仿真实例 重点讲述瞬态场，比如电感，变压器等等

微机课程设计 基于8086 的proteus仿真的4路竞赛抢答器 基本实现了，抢答，选手号码显示，计时显示的功能，运用8259a，8255，8253等芯片。

完整版，带目录，机器学习必备经典；大部头要用力啃。Machine learning A Probabilistic PerspectiveMachine LearningA Probabilistic PerspectiveKevin P. MurphyThe mit PressCambridge, MassachusettsLondon, Englando 2012 Massachusetts Institute of TechnologyAll rights reserved. No part of this book may be reproduced in any form by any electronic or mechanicalmeans(including photocopying, recording, or information storage and retrieval)without permission inwriting from the publisherFor information about special quantity discounts, please email special_sales@mitpress. mit. eduThis book was set in the HEx programming language by the author. Printed and bound in the UnitedStates of AmLibrary of Congress Cataloging-in-Publication InformationMurphy, Kevin Png:a piobabilistctive/Kevin P. Murphyp. cm. -(Adaptive computation and machine learning series)Includes bibliographical references and indexisBn 978-0-262-01802-9 (hardcover: alk. paper1. Machine learning. 2. Probabilities. I. TitleQ325.5M872012006.31-dc232012004558109876This book is dedicated to alessandro, Michael and stefanoand to the memory of gerard Joseph murphyContentsPreactXXVII1 IntroductionMachine learning: what and why?1..1Types of machine learning1.2 Supervised learning1.2.1Classification 31.2.2 Regression 83 Unsupervised learning 91.3.11.3.2Discovering latent factors 111.3.3 Discovering graph structure 131.3.4 Matrix completion 141.4 Some basic concepts in machine learning 161.4.1Parametric vs non-parametric models 161.4.2 A simple non-parametric classifier: K-nearest neighbors 161.4.3 The curse of dimensionality 181.4.4 Parametric models for classification and regression 191.4.5Linear regression 191.4.6Logistic regression1.4.7 Overfitting 221.4.8Model selection1.4.9No free lunch theorem242 Probability2.1 Introduction 272.2 A brief review of probability theory 282. 2. 1 Discrete random variables 282. 2.2 Fundamental rules 282.2.3B292. 2. 4 Independence and conditional independence 302. 2. 5 Continuous random variable32CONTENTS2.2.6 Quantiles 332.2.7 Mean and variance 332.3 Some common discrete distributions 342.3.1The binomial and bernoulli distributions 342.3.2 The multinomial and multinoulli distributions 352. 3.3 The Poisson distribution 372.3.4 The empirical distribution 372.4 Some common continuous distributions 382.4.1 Gaussian (normal) distribution 382.4.2Dte pdf 392.4.3 The Laplace distribution 412.4.4 The gamma distribution 412.4.5 The beta distribution 422.4.6 Pareto distribution2.5 Joint probability distributions 442.5.1Covariance and correlation442.5.2 The multivariate gaussian2.5.3 Multivariate Student t distribution 462.5.4 Dirichlet distribution 472.6 Transformations of random variables 492. 6. 1 Linear transformations 492.6.2 General transformations 502.6.3 Central limit theorem 512.7 Monte Carlo approximation 522.7.1 Example: change of variables, the MC way 532.7.2 Example: estimating T by Monte Carlo integration2.7.3 Accuracy of Monte Carlo approximation 542.8 Information theory562.8.1Entropy2.8.2 KL dive572.8.3 Mutual information 593 Generative models for discrete data 653.1 Introducti653.2 Bayesian concept learning 653.2.1Likelihood673.2.2 Prior 673.2.3P683.2.4Postedictive distribution3.2.5 A more complex prior 723.3 The beta-binomial model 723.3.1 Likelihood 733.3.2Prior743.3.3 Poster3.3.4Posterior predictive distributionCONTENTS3.4 The Dirichlet-multinomial model 783. 4. 1 Likelihood 793.4.2 Prior 793.4.3 Posterior 793.4.4Posterior predictive813.5 Naive Bayes classifiers 823.5.1 Model fitting 833.5.2 Using the model for prediction 853.5.3 The log-sum-exp trick 803.5.4 Feature selection using mutual information 863.5.5 Classifying documents using bag of words 84 Gaussian models4.1 Introduction974.1.1Notation974. 1.2 Basics 974. 1.3 MlE for an mvn 994.1.4 Maximum entropy derivation of the gaussian 1014.2 Gaussian discriminant analysis 1014.2.1 Quadratic discriminant analysis(QDA) 1024.2.2 Linear discriminant analysis (LDA) 1034.2.3 Two-claSs LDA 1044.2.4 MLE for discriminant analysis 1064.2.5 Strategies for preventing overfitting 1064.2.6 Regularized LDA* 104.2.7 Diagonal LDA4.2.8 Nearest shrunken centroids classifier1094.3 Inference in jointly Gaussian distributions 1104.3.1Statement of the result 1114.3.2 Examples4.3.3 Information form 1154.3.4 Proof of the result 1164.4 Linear Gaussian systems 1194.4.1Statement of the result 1194.4.2 Examples 1204.4.3 Proof of the result1244.5 Digression: The Wishart distribution4.5. 1 Inverse Wishart distribution 1264.5.2 Visualizing the wishart distribution* 1274.6 Inferring the parameters of an MVn 1274.6.1 Posterior distribution of u 1284.6.2 Posterior distribution of e1284.6.3 Posterior distribution of u and 2* 1324.6.4 Sensor fusion with unknown precisions 138

社区团购1.1.0源码,火热社区团购来了,带优惠券带扫码提货

processing做的可视化例子。适合初学者。

压缩文件里共有8篇pdf文档，分别是：CST微波工作室用户全书（卷一，卷二）---张敏博士所著，初学者必备CST工作室套装-高级概念 ---张敏博士所著，涉及到求解器，网格，VBA宏编译，后 处理等高级概念88-基本概念、仿真技巧0389-基本概念、仿真技巧04 ----这两篇文档主要讲了网格的设置CST网格设置技巧（与2016版对比）电磁场与波，微波技术、计算电磁学基本概念CST_Meshing in Time Domain_2011CST_VBA+external codes_2010

大气参数matlab计算代码，只需输入高度参数即可，计算可得到某一高度下的大气参数

Helix MP3 解码，全定点运算，可广泛移植多种嵌入式平台，包含中文移植手册，可快速掌握移植接口，可选择编译 ARM 平台汇编优化，仅支持MPEG-1 Audio Layer III。