## MATLAB EXPO 2018 MATLAB与Spark/Hadoop相集成：实现大数据的处理和价值挖 马文辉  ## 内容 ## 大数据及其带来的挑战 ## ■ MATLAB大数据处理 tall数组 并行与分布式计算 ## ■ MATLAB与Spark/Hadoop集成 MATLAB访问HDFS(Hadoop分布式文件系统) 在Spark/Hadoop集群上运行MATLAB代码 ## 应用演示－汽车传感器数据分析 ## 大数据概述 大数据的"4V"特征： - Volumes - 数据规模，数据规模巨大 互联网、社交网络的普及，全社会的数字化转型，数据规模向PB级发展 Variety 数据规模增大、数据复杂度增加，增加处理难度和所需时间；  ## MATLAB的大数据处理 ## 内存与数据访问 64-bit processors ■ Memory Mapped Variables ■ Disk Variables Databases - Datastore

regression. These exercises have been extensively tested with Matlab, but they should also work in Octave, which has been called a “free version of Matlab”. If you are using Octave, be sure to install the Image gradient descent algorithm, based on which, we can predict the height given a new age value. In Matlab/Octave, you can load the training set using the commands x = load('ex1x.dat'); y = load('ex1y 1 features (in addition to the usual $ x_{0} = 1 $ , so $ x \in R^{2} $ ). If you're using Matlab/Octave, run the following commands to plot your training set (and label the axes): figure % open

Questions 405 37.1 General 405 Is Julia named after someone or something? 405 Why don't you compile Matlab/Python/R/... code to Julia? 405 37.2 Sessions and the REPL 406 How do I delete an object in memory ease and expressiveness for high-level numerical computing, in the same way as languages such as R, MATLAB, and Python, but also supports general programming. To achieve this, Julia builds upon the lineage following languages, then you should start by reading the section on noteworthy differences from MATLAB, R, Python, C/C++ or Common Lisp. This will help you avoid some common pitfalls since Julia differs

Releases 481 38 Noteworthy Differences from other Languages 483 38.1 Noteworthy differences from MATLAB 483 38.2 Noteworthy differences from R 485 38.3 Noteworthy differences from Python 488 38.4 Noteworthy ease and expressiveness for high-level numerical computing, in the same way as languages such as R, MATLAB, and Python, but also supports general programming. To achieve this, Julia builds upon the lineage following languages, then you should start by reading the section on noteworthy differences from MATLAB, R, Python, C/C++ or Common Lisp. This will help you avoid some common pitfalls since Julia differs

Releases 453 38 Noteworthy Differences from other Languages 455 38.1 Noteworthy differences from MATLAB 455 38.2 Noteworthy differences from R 457 38.3 Noteworthy differences from Python 460 38.4 Noteworthy ease and expressiveness for high-level numerical computing, in the same way as languages such as R, MATLAB, and Python, but also supports general programming. To achieve this, Julia builds upon the lineage following languages, then you should start by reading the section on noteworthy differences from MATLAB, R, Python, C/C++ or Common Lisp. This will help you avoid some common pitfalls since Julia differs

Releases 475 ## 38 Noteworthy Differences from other Languages 477 38.1 Noteworthy differences from MATLAB 477 38.2 Noteworthy differences from R 479 38.3 Noteworthy differences from Python 482 38.4 Noteworthy ease and expressiveness for high-level numerical computing, in the same way as languages such as R, MATLAB, and Python, but also supports general programming. To achieve this, Julia builds upon the lineage following languages, then you should start by reading the section on noteworthy differences from MATLAB, R, Python, C/C++ or Common Lisp. This will help you avoid some common pitfalls since Julia differs

Releases 475 ## 38 Noteworthy Differences from other Languages 477 38.1 Noteworthy differences from MATLAB 477 38.2 Noteworthy differences from R 479 38.3 Noteworthy differences from Python 482 38.4 Noteworthy ease and expressiveness for high-level numerical computing, in the same way as languages such as R, MATLAB, and Python, but also supports general programming. To achieve this, Julia builds upon the lineage following languages, then you should start by reading the section on noteworthy differences from MATLAB, R, Python, C/C++ or Common Lisp. This will help you avoid some common pitfalls since Julia differs

Releases 475 ## 38 Noteworthy Differences from other Languages 477 38.1 Noteworthy differences from MATLAB 477 38.2 Noteworthy differences from R 479 38.3 Noteworthy differences from Python 482 38.4 Noteworthy ease and expressiveness for high-level numerical computing, in the same way as languages such as R, MATLAB, and Python, but also supports general programming. To achieve this, Julia builds upon the lineage following languages, then you should start by reading the section on noteworthy differences from MATLAB, R, Python, C/C++ or Common Lisp. This will help you avoid some common pitfalls since Julia differs

ease and expressiveness for high-level numerical computing, in the same way as languages such as R, MATLAB, and Python, but also supports general programming. To achieve this, Julia builds upon the lineage following languages, then you should start by reading the section on noteworthy differences from MATLAB, R, Python, C/C++ or Common Lisp. This will help you avoid some common pitfalls since Julia differs frequently expressed using Short-Circuit Evaluation in Julia, as outlined in the next section. Unlike C, MATLAB, Perl, Python, and Ruby - but like Java, and a few other stricter, typed languages - it is an error

Releases 414 38 Noteworthy Differences from other Languages 416 38.1 Noteworthy differences from MATLAB 416 38.2 Noteworthy differences from R 418 38.3 Noteworthy differences from Python 420 38.4 Noteworthy ease and expressiveness for high-level numerical computing, in the same way as languages such as R, MATLAB, and Python, but also supports general programming. To achieve this, Julia builds upon the lineage following languages, then you should start by reading the section on noteworthy differences from MATLAB, R, Python, C/C++ or Common Lisp. This will help you avoid some common pitfalls since Julia differs