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我也要
目的，快速读写20G数据，，
方法：mmap＋memcpy
状态：暂时放弃治疗
bug: mmap＋memcpy, char 转int 又出问题了
研：mmap + memcpy 与传统 ofream读写对比
乱到我都不想看，，，毕竟草稿本。
各位大佬， 我想用mmap+memcpy将A文件（超过6gb,都是字符） 每个字符变成一个int数据 在写进B文件，但老是core 掉，我的做法如下: 1.mmap好A,关闭文件 2.mmap好B，长度4096， 3.循环读A的1024 个字符，转int,放进一个int 数组， 4.mmcpy 这个数组就B 5.改变offset,再映射，继续2-5的循环 请各位大佬指导下呗，跪谢
还有个问题啊，就是我A是6gb,那么我B直接指定大小4*6gb是否刚好？
刚开始应该用truncate()把文件B的长度设置为文件A的长度。应该是你没扩大文件B的长度，mmap()的参数offset超过文件B的长度，mmap()失败。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. 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