===============================================================================
= JSONLab =
= An open-source MATLAB/Octave JSON encoder and decoder =
===============================================================================
*Copyright (C) 2011-2015 Qianqian Fang <fangq at nmr.mgh.harvard.edu>
*License: BSD License, see License_BSD.txt for details
*Version: 1.0 (Optimus - Final)
-------------------------------------------------------------------------------
Table of Content:
I. Introduction
II. Installation
III.Using JSONLab
IV. Known Issues and TODOs
V. Contribution and feedback
-------------------------------------------------------------------------------
I. Introduction
JSON ([http://www.json.org/ JavaScript Object Notation]) is a highly portable,
human-readable and "[http://en.wikipedia.org/wiki/JSON fat-free]" text format
to represent complex and hierarchical data. It is as powerful as
[http://en.wikipedia.org/wiki/XML XML], but less verbose. JSON format is widely
used for data-exchange in applications, and is essential for the wild success
of [http://en.wikipedia.org/wiki/Ajax_(programming) Ajax] and
[http://en.wikipedia.org/wiki/Web_2.0 Web2.0].
UBJSON (Universal Binary JSON) is a binary JSON format, specifically
optimized for compact file size and better performance while keeping
the semantics as simple as the text-based JSON format. Using the UBJSON
format allows to wrap complex binary data in a flexible and extensible
structure, making it possible to process complex and large dataset
without accuracy loss due to text conversions.
We envision that both JSON and its binary version will serve as part of
the mainstream data-exchange formats for scientific research in the future.
It will provide the flexibility and generality achieved by other popular
general-purpose file specifications, such as
[http://www.hdfgroup.org/HDF5/whatishdf5.html HDF5], with significantly
reduced complexity and enhanced performance.
JSONLab is a free and open-source implementation of a JSON/UBJSON encoder
and a decoder in the native MATLAB language. It can be used to convert a MATLAB
data structure (array, struct, cell, struct array and cell array) into
JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB
data structure. JSONLab supports both MATLAB and
[http://www.gnu.org/software/octave/ GNU Octave] (a free MATLAB clone).
-------------------------------------------------------------------------------
II. Installation
The installation of JSONLab is no different than any other simple
MATLAB toolbox. You only need to download/unzip the JSONLab package
to a folder, and add the folder's path to MATLAB/Octave's path list
by using the following command:
addpath('/path/to/jsonlab');
If you want to add this path permanently, you need to type "pathtool",
browse to the jsonlab root folder and add to the list, then click "Save".
Then, run "rehash" in MATLAB, and type "which loadjson", if you see an
output, that means JSONLab is installed for MATLAB/Octave.
-------------------------------------------------------------------------------
III.Using JSONLab
JSONLab provides two functions, loadjson.m -- a MATLAB->JSON decoder,
and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and
two equivallent functions -- loadubjson and saveubjson for the binary
JSON. The detailed help info for the four functions can be found below:
=== loadjson.m ===
<pre>
data=loadjson(fname,opt)
or
data=loadjson(fname,'param1',value1,'param2',value2,...)
parse a JSON (JavaScript Object Notation) file or string
authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2011/09/09, including previous works from
Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
created on 2009/11/02
Fran�ois Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
created on 2009/03/22
Joel Feenstra:
http://www.mathworks.com/matlabcentral/fileexchange/20565
created on 2008/07/03
$Id: loadjson.m 452 2014-11-22 16:43:33Z fangq $
input:
fname: input file name, if fname contains "{}" or "[]", fname
will be interpreted as a JSON string
opt: a struct to store parsing options, opt can be replaced by
a list of ('param',value) pairs - the param string is equivallent
to a field in opt. opt can have the following
fields (first in [.|.] is the default)
opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
for each element of the JSON data, and group
arrays based on the cell2mat rules.
opt.FastArrayParser [1|0 or integer]: if set to 1, use a
speed-optimized array parser when loading an
array object. The fast array parser may
collapse block arrays into a single large
array similar to rules defined in cell2mat; 0 to
use a legacy parser; if set to a larger-than-1
value, this option will specify the minimum
dimension to enable the fast array parser. For
example, if the input is a 3D array, setting
FastArrayParser to 1 will return a 3D array;
setting to 2 will return a cell array of 2D
arrays; setting to 3 will return to a 2D cell
array of 1D vectors; setting to 4 will return a
3D cell array.
opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
output:
dat: a cell array, where {...} blocks are converted into cell arrays,
and [...] are converted to arrays
examples:
dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
dat=loadjson(['examples' filesep 'example1.json'])
dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
</pre>
=== savejson.m ===
<pre>
json=savejson(rootname,obj,filename)
or
json=savejson(rootname,obj,opt)
json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
Object Notation) string
author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2011/09/09
$Id: savejson.m 458 2014-12-19 22:17:17Z fangq $
input:
rootname: the name of the root-object, when set to '', the root name
is ignored, however, when opt.ForceRootName is set to 1 (see below),
the MATLAB variable name will be used as the root name.
obj: a MATLAB object (array, cell, cell array, struct, struct array).
filename: a string for the file name to save the output JSON data.
opt: a struct for additional options, ignore to use default values.
opt can have the following fields (first in [.|.] is the default)
opt.FileName [''|string]: a file name to save the output JSON data
opt.FloatFormat ['%.10g'|string]: format to show each numeric element
of a 1D/2D array;
opt.ArrayIndent [1|0]: if 1, output explicit data array with
precedent indentation; if 0, no indentation
opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
array in JSON array format; if sets to 1, an
array will be shown as a struct with fields
"_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
sparse arrays, the non-zero elements will be
saved to _ArrayData_ field in triplet-format i.e.
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支持向量机的实现(包括线性核函数、高斯核函数等),以及基于SVM的垃圾邮件分类实现过程(MATLAB)
共40个文件
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txt:9个
mat:5个
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支持向量机(support vector machines,SVM)是一种二分类模型,它将实例的特征向量映射为空间中的一些点,SVM 的目的就是想要画出一条线,以 “最好地” 区分这两类点,以至如果以后有了新的点,这条线也能做出很好的分类。SVM 适合中小型数据样本、非线性、高维的分类问题。 SVM 最早是由 Vladimir N. Vapnik 和 Alexey Ya. Chervonenkis 在1963年提出,目前的版本(soft margin)是由 Corinna Cortes 和 Vapnik 在1993年提出,并在1995年发表。深度学习(2012)出现之前,SVM 被认为机器学习中近十几年来最成功,表现最好的算法。
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支持向量机.zip (40个子文件)
visualizeBoundary.m 729B
spamSample2.txt 245B
svmTrain.m 6KB
gaussianKernel.m 671B
emailFeatures.m 2KB
getVocabList.m 761B
ex6_spam.m 4KB
submit.m 1KB
ex6.pdf 328KB
readFile.m 396B
vocab.txt 20KB
ex6data1.mat 981B
ex6data2.mat 7KB
dataset3Params.m 1KB
emailSample2.txt 1KB
svmPredict.m 2KB
ex6.m 4KB
ex6data3.mat 6KB
linearKernel.m 323B
processEmail.m 4KB
plotData.m 569B
spamTest.mat 110KB
emailSample1.txt 393B
visualizeBoundaryLinear.m 410B
lib
submitWithConfiguration.m 6KB
makeValidFieldName.m 1KB
jsonlab
README.txt 19KB
saveubjson.m 16KB
loadjson.m 18KB
ChangeLog.txt 4KB
loadubjson.m 15KB
savejson.m 17KB
LICENSE_BSD.txt 2KB
varargin2struct.m 1KB
AUTHORS.txt 2KB
jsonopt.m 881B
mergestruct.m 771B
porterStemmer.m 10KB
spamTrain.mat 419KB
spamSample1.txt 655B
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