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光线追踪程序代码
完整实现了OpenGL中的光线追踪算法,具有很好的参考意义.
- 2020-12-09下载
- 积分：1
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s7-200控制步进电机.mwp
西门子S7-200控制步进电机程序.mwp
- 2021-05-06下载
- 积分：1
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matlab MMN排队源文件
M/M/N 排队系统（多服务员排队系统）的仿真（难度系数：）多服务员排队系统在仿真上较单服务员排队系统要复杂的多，在此先对该排队系统作一些必要的假设：（1）顾客源是无穷的；（2）排队长度没有限制；（3）到达系统的顾客按先到先服务原则依次进入服务；（4）服务员在仿真过程中没有休假；（5）顾客到达时排成一队，当有服务台空闲时进入服务状态；按照顾客到达的时间概率分布为泊松分布，顾客服务时间的长短服从负指数分布，试完成M/M/1排队系统的仿真。系统输入为：泊松分布和负指数分布中的参数，服务台个数，系统输出是：平均等待时间、平均队长、服务利用率。要求有输入、输出界面、顾客到达和离
- 2020-12-06下载
- 积分：1
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【PDF】《Machine learning A Probabilistic Perspective》 MLAPP；by Kevin Murphy
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
- 2020-12-10下载
- 积分：1
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线阵天线方向图 Matlab源文件
运行Ftheta.m文件可以看到一个GUI界面，只需要在界面里输入线阵天线的参数就可以得到线阵天线的方向图（当d>lamda时会有栅瓣产生，原理可以参考《相控阵雷达技术》）。Fuction.m文件是画方向图的函数。
- 2021-05-06下载
- 积分：1
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基于51单片机和PROTEUS的非常形象的交通灯控制设计
基于51单片机和PROTEUS的非常形象的交通灯控制设计，基于51单片机和PROTEUS的非常形象的交通灯控制设计，基于51单片机和PROTEUS的非常形象的交通灯控制设计。
- 2020-12-10下载
- 积分：1
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Labview中使用Access和SQL数据库的方法
Labview中使用Access和SQL数据库的方法。总结的文档和实例代码。
- 2020-12-09下载
- 积分：1
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MATLAB 动态规划——源代码（+详细注释）
MATLAB 动态规划——源代码（+详细注释）， 对于动态规划入门学习以及数学建模很有帮助！
- 2020-12-06下载
- 积分：1
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Fluent-UDF常见问题解析
搜集了FLUENT-UDF使用过程中常见的一些问题，对初学者有一定帮助
- 2020-12-09下载
- 积分：1
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Canny边缘检测Matlab代码
自编的Canny边缘检测算子，通过与maltab自带的边缘检测算子进行对比发现我们编写的canny算子的性能几乎和matlab自带的性能一致。该程序注重减少for循环，在速度上具有一定的优势，且附带该论文的pdf。希望能够对大家有所帮助。
- 2020-12-06下载
- 积分：1
