文件列表:

PRML-PRMLT-2e96df7 (0, 2017-11-16)
PRML-PRMLT-2e96df7\chapter01 (0, 2017-11-16)
PRML-PRMLT-2e96df7\chapter01\condEntropy.m (696, 2017-11-16)
PRML-PRMLT-2e96df7\chapter01\entropy.m (294, 2017-11-16)
PRML-PRMLT-2e96df7\chapter01\jointEntropy.m (559, 2017-11-16)
PRML-PRMLT-2e96df7\chapter01\mutInfo.m (757, 2017-11-16)
PRML-PRMLT-2e96df7\chapter01\nmi.m (923, 2017-11-16)
PRML-PRMLT-2e96df7\chapter01\nvi.m (796, 2017-11-16)
PRML-PRMLT-2e96df7\chapter01\relatEntropy.m (658, 2017-11-16)
PRML-PRMLT-2e96df7\chapter02 (0, 2017-11-16)
PRML-PRMLT-2e96df7\chapter02\logDirichlet.m (522, 2017-11-16)
PRML-PRMLT-2e96df7\chapter02\logGauss.m (547, 2017-11-16)
PRML-PRMLT-2e96df7\chapter02\logKde.m (391, 2017-11-16)
PRML-PRMLT-2e96df7\chapter02\logMn.m (304, 2017-11-16)
PRML-PRMLT-2e96df7\chapter02\logMvGamma.m (544, 2017-11-16)
PRML-PRMLT-2e96df7\chapter02\logSt.m (1347, 2017-11-16)
PRML-PRMLT-2e96df7\chapter02\logVmf.m (416, 2017-11-16)
PRML-PRMLT-2e96df7\chapter02\logWishart.m (424, 2017-11-16)
PRML-PRMLT-2e96df7\chapter03 (0, 2017-11-16)
PRML-PRMLT-2e96df7\chapter03\linReg.m (818, 2017-11-16)
PRML-PRMLT-2e96df7\chapter03\linRegFp.m (1322, 2017-11-16)
PRML-PRMLT-2e96df7\chapter03\linRegPred.m (833, 2017-11-16)
PRML-PRMLT-2e96df7\chapter03\linRnd.m (512, 2017-11-16)
PRML-PRMLT-2e96df7\chapter04 (0, 2017-11-16)
PRML-PRMLT-2e96df7\chapter04\binPlot.m (586, 2017-11-16)
PRML-PRMLT-2e96df7\chapter04\fda.m (703, 2017-11-16)
PRML-PRMLT-2e96df7\chapter04\logitBin.m (967, 2017-11-16)
PRML-PRMLT-2e96df7\chapter04\logitBinPred.m (358, 2017-11-16)
PRML-PRMLT-2e96df7\chapter04\logitMn.m (2124, 2017-11-16)
PRML-PRMLT-2e96df7\chapter04\logitMnPred.m (391, 2017-11-16)
PRML-PRMLT-2e96df7\chapter04\sigmoid.m (110, 2017-11-16)
PRML-PRMLT-2e96df7\chapter04\softmax.m (257, 2017-11-16)
PRML-PRMLT-2e96df7\chapter05 (0, 2017-11-16)
PRML-PRMLT-2e96df7\chapter05\mlp.m (883, 2017-11-16)
PRML-PRMLT-2e96df7\chapter05\mlpPred.m (276, 2017-11-16)
PRML-PRMLT-2e96df7\chapter06 (0, 2017-11-16)
PRML-PRMLT-2e96df7\chapter06\kn2sd.m (271, 2017-11-16)
PRML-PRMLT-2e96df7\chapter06\knCenter.m (1048, 2017-11-16)
... ...

Introduction ------- This package is a Matlab implementation of the algorithms described in the classical machine learning textbook: Pattern Recognition and Machine Learning by C. Bishop ([PRML](http://research.microsoft.com/en-us/um/people/cmbishop/prml/)). Note: this package requires Matlab **R2016b** or latter, since it utilizes a new syntax of Matlab called [Implicit expansion](https://cn.mathworks.com/help/matlab/release-notes.html?rntext=implicit+expansion&startrelease=R2016b&endrelease=R2016b&groupby=release&sortby=descending) (a.k.a. broadcasting in Python). Description ------- While developing this package, I stick to following principles * Succinct: The code is extremely terse. Minimizing the number of lines is one of the primal goals. As a result, the core of the algorithms can be easily spot. * Efficient: Many tricks for making Matlab scripts fast were applied (eg. vectorization and matrix factorization). Many functions are even comparable with C implementations. Usually, functions in this package are orders faster than Matlab builtin ones which provide the same functionality (eg. kmeans). If anyone have found any Matlab implementation that is faster than mine, I am happy to further optimize. * Robust: Many tricks for numerical stability are applied, such as probability computation in log scale and square root matrix update to enforce matrix symmetry, etc. * Readable: The code is heavily commented. Reference formulas in PRML book are indicated for corresponding code lines. Symbols are in sync with the book. * Practical: The package is designed not only to be easily read, but also to be easily used to facilitate ML research. Many functions in this package are already widely used (see [Matlab file exchange](http://www.mathworks.com/matlabcentral/fileexchange/?term=authorid%3A49739)). Installation ------- 1. Download the package to your local path (e.g. PRMLT/) by running: `git clone https://github.com/PRML/PRMLT.git`. 2. Run Matlab and navigate to PRMLT/, then run the init.m script. 3. Try demos in PRMLT/demo directory to verify installation correctness. Enjoy! FeedBack ------- If you found any bug or have any suggestion, please do file issues. I am graceful for any feedback and will do my best to improve this package. License ------- Currently Released Under GPLv3 Contact ------- sth4nth at gmail dot com

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