diff --git a/README b/README
deleted file mode 100644
index 22646271..00000000
--- a/README
+++ /dev/null
@@ -1,771 +0,0 @@
-Libsvm is a simple, easy-to-use, and efficient software for SVM
-classification and regression. It solves C-SVM classification, nu-SVM
-classification, one-class-SVM, epsilon-SVM regression, and nu-SVM
-regression. It also provides an automatic model selection tool for
-C-SVM classification. This document explains the use of libsvm.
-
-Libsvm is available at 
-http://www.csie.ntu.edu.tw/~cjlin/libsvm
-Please read the COPYRIGHT file before using libsvm.
-
-Table of Contents
-=================
-
-- Quick Start
-- Installation and Data Format
-- `svm-train' Usage
-- `svm-predict' Usage
-- `svm-scale' Usage
-- Tips on Practical Use
-- Examples
-- Precomputed Kernels 
-- Library Usage
-- Java Version
-- Building Windows Binaries
-- Additional Tools: Sub-sampling, Parameter Selection, Format checking, etc.
-- MATLAB/OCTAVE Interface
-- Python Interface
-- Additional Information
-
-Quick Start
-===========
-
-If you are new to SVM and if the data is not large, please go to 
-`tools' directory and use easy.py after installation. It does 
-everything automatic -- from data scaling to parameter selection.
-
-Usage: easy.py training_file [testing_file]
-
-More information about parameter selection can be found in
-`tools/README.'
-
-Installation and Data Format
-============================
-
-On Unix systems, type `make' to build the `svm-train' and `svm-predict'
-programs. Run them without arguments to show the usages of them.
-
-On other systems, consult `Makefile' to build them (e.g., see
-'Building Windows binaries' in this file) or use the pre-built
-binaries (Windows binaries are in the directory `windows').
-
-The format of training and testing data file is:
-
-<label> <index1>:<value1> <index2>:<value2> ...
-.
-.
-.
-
-Each line contains an instance and is ended by a '\n' character.  For
-classification, <label> is an integer indicating the class label
-(multi-class is supported). For regression, <label> is the target
-value which can be any real number. For one-class SVM, it's not used
-so can be any number.  The pair <index>:<value> gives a feature
-(attribute) value: <index> is an integer starting from 1 and <value>
-is a real number. The only exception is the precomputed kernel, where
-<index> starts from 0; see the section of precomputed kernels. Indices
-must be in ASCENDING order. Labels in the testing file are only used
-to calculate accuracy or errors. If they are unknown, just fill the
-first column with any numbers.
-
-A sample classification data included in this package is
-`heart_scale'. To check if your data is in a correct form, use
-`tools/checkdata.py' (details in `tools/README').
-
-Type `svm-train heart_scale', and the program will read the training
-data and output the model file `heart_scale.model'. If you have a test
-set called heart_scale.t, then type `svm-predict heart_scale.t
-heart_scale.model output' to see the prediction accuracy. The `output'
-file contains the predicted class labels.
-
-For classification, if training data are in only one class (i.e., all
-labels are the same), then `svm-train' issues a warning message:
-`Warning: training data in only one class. See README for details,'
-which means the training data is very unbalanced. The label in the
-training data is directly returned when testing.
-
-There are some other useful programs in this package.
-
-svm-scale:
-
-	This is a tool for scaling input data file.
-
-svm-toy:
-
-	This is a simple graphical interface which shows how SVM
-	separate data in a plane. You can click in the window to 
-	draw data points. Use "change" button to choose class 
-	1, 2 or 3 (i.e., up to three classes are supported), "load"
-	button to load data from a file, "save" button to save data to
-	a file, "run" button to obtain an SVM model, and "clear"
-	button to clear the window.
-
-	You can enter options in the bottom of the window, the syntax of
-	options is the same as `svm-train'.
-
-	Note that "load" and "save" consider dense data format both in
-	classification and the regression cases. For classification,
-	each data point has one label (the color) that must be 1, 2,
-	or 3 and two attributes (x-axis and y-axis values) in
-	[0,1). For regression, each data point has one target value
-	(y-axis) and one attribute (x-axis values) in [0, 1).
-
-	Type `make' in respective directories to build them.
-
-	You need Qt library to build the Qt version.
-	(available from http://www.trolltech.com)
-
-	You need GTK+ library to build the GTK version.
-	(available from http://www.gtk.org)
-	
-	The pre-built Windows binaries are in the `windows'
-	directory. We use Visual C++ on a 32-bit machine, so the
-	maximal cache size is 2GB.
-
-`svm-train' Usage
-=================
-
-Usage: svm-train [options] training_set_file [model_file]
-options:
--s svm_type : set type of SVM (default 0)
-	0 -- C-SVC		(multi-class classification)
-	1 -- nu-SVC		(multi-class classification)
-	2 -- one-class SVM	
-	3 -- epsilon-SVR	(regression)
-	4 -- nu-SVR		(regression)
--t kernel_type : set type of kernel function (default 2)
-	0 -- linear: u'*v
-	1 -- polynomial: (gamma*u'*v + coef0)^degree
-	2 -- radial basis function: exp(-gamma*|u-v|^2)
-	3 -- sigmoid: tanh(gamma*u'*v + coef0)
-	4 -- precomputed kernel (kernel values in training_set_file)
--d degree : set degree in kernel function (default 3)
--g gamma : set gamma in kernel function (default 1/num_features)
--r coef0 : set coef0 in kernel function (default 0)
--c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
--n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
--p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
--m cachesize : set cache memory size in MB (default 100)
--e epsilon : set tolerance of termination criterion (default 0.001)
--h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)
--b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
--wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)
--v n: n-fold cross validation mode
--q : quiet mode (no outputs)
-
-
-The k in the -g option means the number of attributes in the input data.
-
-option -v randomly splits the data into n parts and calculates cross
-validation accuracy/mean squared error on them.
-
-See libsvm FAQ for the meaning of outputs.
-
-`svm-predict' Usage
-===================
-
-Usage: svm-predict [options] test_file model_file output_file
-options:
--b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); for one-class SVM only 0 is supported
-
-model_file is the model file generated by svm-train.
-test_file is the test data you want to predict.
-svm-predict will produce output in the output_file.
-
-`svm-scale' Usage
-=================
-
-Usage: svm-scale [options] data_filename
-options:
--l lower : x scaling lower limit (default -1)
--u upper : x scaling upper limit (default +1)
--y y_lower y_upper : y scaling limits (default: no y scaling)
--s save_filename : save scaling parameters to save_filename
--r restore_filename : restore scaling parameters from restore_filename
-
-See 'Examples' in this file for examples.
-
-Tips on Practical Use
-=====================
-
-* Scale your data. For example, scale each attribute to [0,1] or [-1,+1].
-* For C-SVC, consider using the model selection tool in the tools directory.
-* nu in nu-SVC/one-class-SVM/nu-SVR approximates the fraction of training
-  errors and support vectors.
-* If data for classification are unbalanced (e.g. many positive and
-  few negative), try different penalty parameters C by -wi (see
-  examples below).
-* Specify larger cache size (i.e., larger -m) for huge problems.
-
-Examples
-========
-
-> svm-scale -l -1 -u 1 -s range train > train.scale
-> svm-scale -r range test > test.scale
-
-Scale each feature of the training data to be in [-1,1]. Scaling
-factors are stored in the file range and then used for scaling the
-test data.
-
-> svm-train -s 0 -c 5 -t 2 -g 0.5 -e 0.1 data_file 
-
-Train a classifier with RBF kernel exp(-0.5|u-v|^2), C=10, and
-stopping tolerance 0.1.
-
-> svm-train -s 3 -p 0.1 -t 0 data_file
-
-Solve SVM regression with linear kernel u'v and epsilon=0.1
-in the loss function.
-
-> svm-train -c 10 -w1 1 -w-2 5 -w4 2 data_file
-
-Train a classifier with penalty 10 = 1 * 10 for class 1, penalty 50 =
-5 * 10 for class -2, and penalty 20 = 2 * 10 for class 4.
-
-> svm-train -s 0 -c 100 -g 0.1 -v 5 data_file
-
-Do five-fold cross validation for the classifier using
-the parameters C = 100 and gamma = 0.1
-
-> svm-train -s 0 -b 1 data_file
-> svm-predict -b 1 test_file data_file.model output_file
-
-Obtain a model with probability information and predict test data with
-probability estimates
-
-Precomputed Kernels 
-===================
-
-Users may precompute kernel values and input them as training and
-testing files.  Then libsvm does not need the original
-training/testing sets.
-
-Assume there are L training instances x1, ..., xL and. 
-Let K(x, y) be the kernel
-value of two instances x and y. The input formats
-are:
-
-New training instance for xi:
-
-<label> 0:i 1:K(xi,x1) ... L:K(xi,xL) 
-
-New testing instance for any x:
-
-<label> 0:? 1:K(x,x1) ... L:K(x,xL) 
-
-That is, in the training file the first column must be the "ID" of
-xi. In testing, ? can be any value.
-
-All kernel values including ZEROs must be explicitly provided.  Any
-permutation or random subsets of the training/testing files are also
-valid (see examples below).
-
-Note: the format is slightly different from the precomputed kernel
-package released in libsvmtools earlier.
-
-Examples:
-
-	Assume the original training data has three four-feature
-	instances and testing data has one instance:
-
-	15  1:1 2:1 3:1 4:1
-	45      2:3     4:3
-	25          3:1
-
-	15  1:1     3:1
-
-	If the linear kernel is used, we have the following new
-	training/testing sets:
-
-	15  0:1 1:4 2:6  3:1
-	45  0:2 1:6 2:18 3:0 
-	25  0:3 1:1 2:0  3:1
- 
-	15  0:? 1:2 2:0  3:1
-
-	? can be any value.
-
-	Any subset of the above training file is also valid. For example,
-
-	25  0:3 1:1 2:0  3:1
-	45  0:2 1:6 2:18 3:0 
-
-	implies that the kernel matrix is
-
-		[K(2,2) K(2,3)] = [18 0]
-		[K(3,2) K(3,3)] = [0  1]
-
-Library Usage
-=============
-
-These functions and structures are declared in the header file
-`svm.h'.  You need to #include "svm.h" in your C/C++ source files and
-link your program with `svm.cpp'. You can see `svm-train.c' and
-`svm-predict.c' for examples showing how to use them. We define
-LIBSVM_VERSION and declare `extern int libsvm_version; ' in svm.h, so
-you can check the version number.
-
-Before you classify test data, you need to construct an SVM model
-(`svm_model') using training data. A model can also be saved in
-a file for later use. Once an SVM model is available, you can use it
-to classify new data.
-
-- Function: struct svm_model *svm_train(const struct svm_problem *prob,
-					const struct svm_parameter *param);
-
-    This function constructs and returns an SVM model according to
-    the given training data and parameters.
-
-    struct svm_problem describes the problem:
-	
-	struct svm_problem
-	{
-		int l;
-		double *y;
-		struct svm_node **x;
-	};
- 
-    where `l' is the number of training data, and `y' is an array containing
-    their target values. (integers in classification, real numbers in
-    regression) `x' is an array of pointers, each of which points to a sparse
-    representation (array of svm_node) of one training vector. 
-
-    For example, if we have the following training data:
-
-    LABEL	ATTR1	ATTR2	ATTR3	ATTR4	ATTR5
-    -----	-----	-----	-----	-----	-----
-      1		  0	  0.1	  0.2	  0	  0
-      2		  0	  0.1	  0.3	 -1.2	  0
-      1		  0.4	  0	  0	  0	  0
-      2		  0	  0.1	  0	  1.4	  0.5
-      3		 -0.1	 -0.2	  0.1	  1.1	  0.1
-
-    then the components of svm_problem are:
-
-    l = 5
-
-    y -> 1 2 1 2 3
-
-    x -> [ ] -> (2,0.1) (3,0.2) (-1,?)
-	 [ ] -> (2,0.1) (3,0.3) (4,-1.2) (-1,?)
-	 [ ] -> (1,0.4) (-1,?)
-	 [ ] -> (2,0.1) (4,1.4) (5,0.5) (-1,?)
-	 [ ] -> (1,-0.1) (2,-0.2) (3,0.1) (4,1.1) (5,0.1) (-1,?)
-
-    where (index,value) is stored in the structure `svm_node':
-
-	struct svm_node
-	{
-		int index;
-		double value;
-	};
-
-    index = -1 indicates the end of one vector. Note that indices must
-    be in ASCENDING order.
- 
-    struct svm_parameter describes the parameters of an SVM model:
-
-	struct svm_parameter
-	{
-		int svm_type;
-		int kernel_type;
-		int degree;	/* for poly */
-		double gamma;	/* for poly/rbf/sigmoid */
-		double coef0;	/* for poly/sigmoid */
-
-		/* these are for training only */
-		double cache_size; /* in MB */
-		double eps;	/* stopping criteria */
-		double C;	/* for C_SVC, EPSILON_SVR, and NU_SVR */
-		int nr_weight;		/* for C_SVC */
-		int *weight_label;	/* for C_SVC */
-		double* weight;		/* for C_SVC */
-		double nu;	/* for NU_SVC, ONE_CLASS, and NU_SVR */
-		double p;	/* for EPSILON_SVR */
-		int shrinking;	/* use the shrinking heuristics */
-		int probability; /* do probability estimates */
-	};
-
-    svm_type can be one of C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR.
-
-    C_SVC:		C-SVM classification
-    NU_SVC:		nu-SVM classification
-    ONE_CLASS:		one-class-SVM
-    EPSILON_SVR:	epsilon-SVM regression
-    NU_SVR:		nu-SVM regression
-
-    kernel_type can be one of LINEAR, POLY, RBF, SIGMOID.
-
-    LINEAR:	u'*v
-    POLY:	(gamma*u'*v + coef0)^degree
-    RBF:	exp(-gamma*|u-v|^2)
-    SIGMOID:	tanh(gamma*u'*v + coef0)
-    PRECOMPUTED: kernel values in training_set_file
-
-    cache_size is the size of the kernel cache, specified in megabytes.
-    C is the cost of constraints violation. 
-    eps is the stopping criterion. (we usually use 0.00001 in nu-SVC,
-    0.001 in others). nu is the parameter in nu-SVM, nu-SVR, and
-    one-class-SVM. p is the epsilon in epsilon-insensitive loss function
-    of epsilon-SVM regression. shrinking = 1 means shrinking is conducted;
-    = 0 otherwise. probability = 1 means model with probability
-    information is obtained; = 0 otherwise.
-
-    nr_weight, weight_label, and weight are used to change the penalty
-    for some classes (If the weight for a class is not changed, it is
-    set to 1). This is useful for training classifier using unbalanced
-    input data or with asymmetric misclassification cost.
-
-    nr_weight is the number of elements in the array weight_label and
-    weight. Each weight[i] corresponds to weight_label[i], meaning that
-    the penalty of class weight_label[i] is scaled by a factor of weight[i].
-    
-    If you do not want to change penalty for any of the classes,
-    just set nr_weight to 0.
-
-    *NOTE* Because svm_model contains pointers to svm_problem, you can
-    not free the memory used by svm_problem if you are still using the
-    svm_model produced by svm_train(). 
-
-    *NOTE* To avoid wrong parameters, svm_check_parameter() should be
-    called before svm_train().
-
-    struct svm_model stores the model obtained from the training procedure.
-    It is not recommended to directly access entries in this structure.
-    Programmers should use the interface functions to get the values.
-
-	struct svm_model
-	{
-		struct svm_parameter param;	/* parameter */
-		int nr_class;		/* number of classes, = 2 in regression/one class svm */
-		int l;			/* total #SV */
-		struct svm_node **SV;		/* SVs (SV[l]) */
-		double **sv_coef;	/* coefficients for SVs in decision functions (sv_coef[k-1][l]) */
-		double *rho;		/* constants in decision functions (rho[k*(k-1)/2]) */
-		double *probA;		/* pairwise probability information */
-		double *probB;
-		int *sv_indices;        /* sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set */
-
-		/* for classification only */
-
-		int *label;		/* label of each class (label[k]) */
-		int *nSV;		/* number of SVs for each class (nSV[k]) */
-					/* nSV[0] + nSV[1] + ... + nSV[k-1] = l */
-		/* XXX */
-		int free_sv;		/* 1 if svm_model is created by svm_load_model*/
-					/* 0 if svm_model is created by svm_train */
-	};
-
-    param describes the parameters used to obtain the model.
-
-    nr_class is the number of classes. It is 2 for regression and one-class SVM.
-
-    l is the number of support vectors. SV and sv_coef are support
-    vectors and the corresponding coefficients, respectively. Assume there are
-    k classes. For data in class j, the corresponding sv_coef includes (k-1) y*alpha vectors,
-    where alpha's are solutions of the following two class problems:
-    1 vs j, 2 vs j, ..., j-1 vs j, j vs j+1, j vs j+2, ..., j vs k
-    and y=1 for the first j-1 vectors, while y=-1 for the remaining k-j 
-    vectors. For example, if there are 4 classes, sv_coef and SV are like:
-
-        +-+-+-+--------------------+
-        |1|1|1|                    |
-        |v|v|v|  SVs from class 1  |
-        |2|3|4|                    |
-        +-+-+-+--------------------+
-        |1|2|2|                    |
-        |v|v|v|  SVs from class 2  |
-        |2|3|4|                    |
-        +-+-+-+--------------------+
-        |1|2|3|                    |
-        |v|v|v|  SVs from class 3  |
-        |3|3|4|                    |
-        +-+-+-+--------------------+
-        |1|2|3|                    |
-        |v|v|v|  SVs from class 4  |
-        |4|4|4|                    |
-        +-+-+-+--------------------+
-
-    See svm_train() for an example of assigning values to sv_coef.
-
-    rho is the bias term (-b). probA and probB are parameters used in
-    probability outputs. If there are k classes, there are k*(k-1)/2
-    binary problems as well as rho, probA, and probB values. They are
-    aligned in the order of binary problems:
-    1 vs 2, 1 vs 3, ..., 1 vs k, 2 vs 3, ..., 2 vs k, ..., k-1 vs k.
-
-    sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to
-    indicate support vectors in the training set.
-
-    label contains labels in the training data.
-
-    nSV is the number of support vectors in each class.
-
-    free_sv is a flag used to determine whether the space of SV should 
-    be released in free_model_content(struct svm_model*) and 
-    free_and_destroy_model(struct svm_model**). If the model is
-    generated by svm_train(), then SV points to data in svm_problem
-    and should not be removed. For example, free_sv is 0 if svm_model
-    is created by svm_train, but is 1 if created by svm_load_model.
-
-- Function: double svm_predict(const struct svm_model *model,
-                               const struct svm_node *x);
-
-    This function does classification or regression on a test vector x
-    given a model.
-
-    For a classification model, the predicted class for x is returned.
-    For a regression model, the function value of x calculated using
-    the model is returned. For an one-class model, +1 or -1 is
-    returned.
-
-- Function: void svm_cross_validation(const struct svm_problem *prob,
-	const struct svm_parameter *param, int nr_fold, double *target);
-
-    This function conducts cross validation. Data are separated to
-    nr_fold folds. Under given parameters, sequentially each fold is
-    validated using the model from training the remaining. Predicted
-    labels (of all prob's instances) in the validation process are
-    stored in the array called target.
-
-    The format of svm_prob is same as that for svm_train(). 
-
-- Function: int svm_get_svm_type(const struct svm_model *model);
-
-    This function gives svm_type of the model. Possible values of
-    svm_type are defined in svm.h.
-
-- Function: int svm_get_nr_class(const svm_model *model);
-
-    For a classification model, this function gives the number of
-    classes. For a regression or an one-class model, 2 is returned.
-
-- Function: void svm_get_labels(const svm_model *model, int* label)
-    
-    For a classification model, this function outputs the name of
-    labels into an array called label. For regression and one-class
-    models, label is unchanged.
-
-- Function: void svm_get_sv_indices(const struct svm_model *model, int *sv_indices)
-
-    This function outputs indices of support vectors into an array called sv_indices. 
-    The size of sv_indices is the number of support vectors and can be obtained by calling svm_get_nr_sv. 
-    Each sv_indices[i] is in the range of [1, ..., num_traning_data].
-
-- Function: int svm_get_nr_sv(const struct svm_model *model) 
-
-    This function gives the number of total support vector.
-
-- Function: double svm_get_svr_probability(const struct svm_model *model);
-
-    For a regression model with probability information, this function
-    outputs a value sigma > 0. For test data, we consider the
-    probability model: target value = predicted value + z, z: Laplace
-    distribution e^(-|z|/sigma)/(2sigma)
-
-    If the model is not for svr or does not contain required
-    information, 0 is returned.
-
-- Function: double svm_predict_values(const svm_model *model, 
-				    const svm_node *x, double* dec_values)
-
-    This function gives decision values on a test vector x given a
-    model, and return the predicted label (classification) or
-    the function value (regression).
-
-    For a classification model with nr_class classes, this function
-    gives nr_class*(nr_class-1)/2 decision values in the array
-    dec_values, where nr_class can be obtained from the function
-    svm_get_nr_class. The order is label[0] vs. label[1], ...,
-    label[0] vs. label[nr_class-1], label[1] vs. label[2], ...,
-    label[nr_class-2] vs. label[nr_class-1], where label can be
-    obtained from the function svm_get_labels. The returned value is
-    the predicted class for x. Note that when nr_class = 1, this 
-    function does not give any decision value.
-
-    For a regression model, dec_values[0] and the returned value are
-    both the function value of x calculated using the model. For a
-    one-class model, dec_values[0] is the decision value of x, while
-    the returned value is +1/-1.
-
-- Function: double svm_predict_probability(const struct svm_model *model, 
-	    const struct svm_node *x, double* prob_estimates);
-    
-    This function does classification or regression on a test vector x
-    given a model with probability information.
-
-    For a classification model with probability information, this
-    function gives nr_class probability estimates in the array
-    prob_estimates. nr_class can be obtained from the function
-    svm_get_nr_class. The class with the highest probability is
-    returned. For regression/one-class SVM, the array prob_estimates
-    is unchanged and the returned value is the same as that of
-    svm_predict.
-
-- Function: const char *svm_check_parameter(const struct svm_problem *prob,
-                                            const struct svm_parameter *param);
-
-    This function checks whether the parameters are within the feasible
-    range of the problem. This function should be called before calling
-    svm_train() and svm_cross_validation(). It returns NULL if the
-    parameters are feasible, otherwise an error message is returned.
-
-- Function: int svm_check_probability_model(const struct svm_model *model);
-
-    This function checks whether the model contains required
-    information to do probability estimates. If so, it returns
-    +1. Otherwise, 0 is returned. This function should be called
-    before calling svm_get_svr_probability and
-    svm_predict_probability.
-
-- Function: int svm_save_model(const char *model_file_name,
-			       const struct svm_model *model);
-
-    This function saves a model to a file; returns 0 on success, or -1
-    if an error occurs.
-
-- Function: struct svm_model *svm_load_model(const char *model_file_name);
-
-    This function returns a pointer to the model read from the file,
-    or a null pointer if the model could not be loaded.
-
-- Function: void svm_free_model_content(struct svm_model *model_ptr);
-
-    This function frees the memory used by the entries in a model structure.
-
-- Function: void svm_free_and_destroy_model(struct svm_model **model_ptr_ptr);
-
-    This function frees the memory used by a model and destroys the model
-    structure. It is equivalent to svm_destroy_model, which
-    is deprecated after version 3.0.
-
-- Function: void svm_destroy_param(struct svm_parameter *param);
-
-    This function frees the memory used by a parameter set.
-
-- Function: void svm_set_print_string_function(void (*print_func)(const char *));
-
-    Users can specify their output format by a function. Use
-        svm_set_print_string_function(NULL); 
-    for default printing to stdout.
-
-Java Version
-============
-
-The pre-compiled java class archive `libsvm.jar' and its source files are
-in the java directory. To run the programs, use
-
-java -classpath libsvm.jar svm_train <arguments>
-java -classpath libsvm.jar svm_predict <arguments>
-java -classpath libsvm.jar svm_toy
-java -classpath libsvm.jar svm_scale <arguments>
-
-Note that you need Java 1.5 (5.0) or above to run it.
-
-You may need to add Java runtime library (like classes.zip) to the classpath.
-You may need to increase maximum Java heap size.
-
-Library usages are similar to the C version. These functions are available:
-
-public class svm {
-	public static final int LIBSVM_VERSION=320; 
-	public static svm_model svm_train(svm_problem prob, svm_parameter param);
-	public static void svm_cross_validation(svm_problem prob, svm_parameter param, int nr_fold, double[] target);
-	public static int svm_get_svm_type(svm_model model);
-	public static int svm_get_nr_class(svm_model model);
-	public static void svm_get_labels(svm_model model, int[] label);
-	public static void svm_get_sv_indices(svm_model model, int[] indices);
-	public static int svm_get_nr_sv(svm_model model);
-	public static double svm_get_svr_probability(svm_model model);
-	public static double svm_predict_values(svm_model model, svm_node[] x, double[] dec_values);
-	public static double svm_predict(svm_model model, svm_node[] x);
-	public static double svm_predict_probability(svm_model model, svm_node[] x, double[] prob_estimates);
-	public static void svm_save_model(String model_file_name, svm_model model) throws IOException
-	public static svm_model svm_load_model(String model_file_name) throws IOException
-	public static String svm_check_parameter(svm_problem prob, svm_parameter param);
-	public static int svm_check_probability_model(svm_model model);
-	public static void svm_set_print_string_function(svm_print_interface print_func);
-}
-
-The library is in the "libsvm" package.
-Note that in Java version, svm_node[] is not ended with a node whose index = -1.
-
-Users can specify their output format by
-
-	your_print_func = new svm_print_interface()
-	{ 
-		public void print(String s)
-		{
-			// your own format
-		}
-	};
-	svm.svm_set_print_string_function(your_print_func);
-
-Building Windows Binaries
-=========================
-
-Windows binaries are in the directory `windows'. To build them via
-Visual C++, use the following steps:
-
-1. Open a DOS command box (or Visual Studio Command Prompt) and change
-to libsvm directory. If environment variables of VC++ have not been
-set, type
-
-"C:\Program Files\Microsoft Visual Studio 10.0\VC\bin\vcvars32.bat"
-
-You may have to modify the above command according which version of
-VC++ or where it is installed.
-
-2. Type
-
-nmake -f Makefile.win clean all
-
-3. (optional) To build shared library libsvm.dll, type
-
-nmake -f Makefile.win lib
-
-4. (optional) To build 64-bit windows binaries, you must
-	(1) Run vcvars64.bat instead of vcvars32.bat. Note that 
-	vcvars64.bat is located at "C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\bin\amd64\"
-	(2) Change CFLAGS in Makefile.win: /D _WIN32 to /D _WIN64	
-	
-Another way is to build them from Visual C++ environment. See details
-in libsvm FAQ.
-
-- Additional Tools: Sub-sampling, Parameter Selection, Format checking, etc.
-============================================================================
-
-See the README file in the tools directory.
-
-MATLAB/OCTAVE Interface
-=======================
-
-Please check the file README in the directory `matlab'.
-
-Python Interface
-================
-
-See the README file in python directory.
-
-Additional Information
-======================
-
-If you find LIBSVM helpful, please cite it as
-
-Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support
-vector machines. ACM Transactions on Intelligent Systems and
-Technology, 2:27:1--27:27, 2011. Software available at
-http://www.csie.ntu.edu.tw/~cjlin/libsvm
-
-LIBSVM implementation document is available at
-http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf
-
-For any questions and comments, please email cjlin@csie.ntu.edu.tw
-
-Acknowledgments:
-This work was supported in part by the National Science 
-Council of Taiwan via the grant NSC 89-2213-E-002-013.
-The authors thank their group members and users
-for many helpful discussions and comments. They are listed in
-http://www.csie.ntu.edu.tw/~cjlin/libsvm/acknowledgements
-
diff --git a/README.md b/README.md
new file mode 100644
index 00000000..737375eb
--- /dev/null
+++ b/README.md
@@ -0,0 +1,705 @@
+# LIBSVM
+**Libsvm** is a simple, easy-to-use, and efficient software for SVM **classification** and **regression**. It solves *C-SVM classification*, *nu-SVM classification*, *one-class-SVM*, *epsilon-SVM regression*, and *nu-SVM regression*. It also provides an **automatic model selection tool** for C-SVM classification. This document explains the use of libsvm.
+
+Libsvm is available at http://www.csie.ntu.edu.tw/~cjlin/libsvm   
+Please read the COPYRIGHT file before using libsvm.
+
+## Table of Contents
+
+-   [Quick Start](#QuickStart)
+-   [Installation and Data Format](#InstallationAdDataFormat)
+-   [svm-train Usage](#SvmTrainUsage)
+-   [svm-predict Usage](#SvmPredictUsage)
+-   [svm-scale Usage](#SvmScaleUsage)
+-   [Tips on Practical Use](#Tips)
+-   [Examples](#Examples)
+-   [Precomputed Kernels ](#PrecomputedKernels)
+-   [Library Usage](#LibraryUsage)
+    -   [function svm_train](#svm_train)
+    -   [function svm_predict](#svm_predict)
+    -   [function svm_cross_validation](#svm_cross_validation)
+    -   [function svm_get_svm_type](#svm_get_svm_type)
+    -   [function svm_get_nr_class](#svm_get_nr_class)
+    -   [function svm_get_labels](#svm_get_labels)
+    -   [function svm_get_sv_indices](#svm_get_sv_indices)
+    -   [function svm_get_nr_sv](#svm_get_nr_sv)
+    -   [function svm_get_svr_probability](#svm_get_svr_probability)
+    -   [function svm_predict_values](#svm_predict_values)
+    -   [function svm_predict_probability](#svm_predict_probability)
+    -   [function svm_check_parameter](#svm_check_parameter)
+    -   [function svm_check_probability_model](#svm_check_probability_model)
+    -   [function svm_save_model](#svm_save_model)
+    -   [function svm_load_model](#svm_load_model)
+    -   [function svm_free_model_content](#svm_free_model_content)
+    -   [function svm_free_and_destroy_model](#svm_free_and_destroy_model)
+    -   [function svm_destroy_param](#svm_destroy_param)
+    -   [function svm_set_print_string_function](#svm_set_print_string_function)
+-   [Java Version](#JavaVersion)
+-   [Building Windows Binaries](#BuildingWindowsBinaries)
+-   [Additional Tools: Sub-sampling, Parameter Selection, Format checking, etc.](#AdditionalTools)
+-   [MATLAB/OCTAVE Interface](#MatlabInterface)
+-   [Python Interface](#PythonInterface)
+-   [Additional Information](#AdditionalInfo)
+
+<h2 id="QuickStart">Quick Start</h2>
+
+If you are new to SVM and if the data is not large, please go to `tools` directory and use easy.py after installation. It does everything automatic,from data scaling to parameter selection.  
+
+    Usage: easy.py training_file [testing_file]
+
+More information about parameter selection can be found in [tools/README.md](./tools/README.md)
+
+<h2 id="InstallationAdDataFormat">Installation and Data Format</h2>
+
+On Unix systems, type `make` to build the `svm-train` and `svm-predict`programs. Run them without arguments to show the usages of them.  
+
+On other systems, consult `Makefile` to build them (e.g., see [Building Windows Binaries](#BuildingWindowsBinaries)) or use the `pre-built` binaries (Windows binaries are in the directory [windows](./windows)).
+
+The **format** of training and testing data file is:
+```
+<label> <index1>:<value1> <index2>:<value2> ...
+.
+.
+.
+```
+Each line contains an instance and is ended by a **'\n'** character.  
+
+***`<label>:`***
+
+> 1. For classification, `<label>` is an integer indicating the class label (multi-class is supported). 
+> 2. For regression, `<label> ` is the target value which can be any real number. 
+> 3. For one-class SVM, it's not used so can be any number.
+>   
+
+***`<index>:<value>:`***  
+> The pair `<index>:<value>` gives a **feature(attribute) value**.  
+>
+> `<index>` is an integer starting from 1 and `<value>` is a real number. The only exception of `<index>` is the precomputed kernel, where `<index>` starts from 0 (see the section of [precomputed kernels](#PrecomputedKernels)).
+> Indices must be in **ASCENDING** order. Labels in the testing file are only used to calculate accuracy or errors. If they are unknown, just fill the first column with any numbers.
+
+A sample classification data included in this package is [`heart_scale`](./heart_scale). To check if your data is in a correct form, use [`tools/checkdata.py`](./tools/checkdata.py)(details in [`tools/README.md`](./tools/README.md)).
+
+Type
+
+    svm-train heart_scale
+
+and the program will read the training data and output the model file **heart_scale.model**. If you have a test set called **heart_scale.t**, then use **svm-predict** to see the prediction accuracy. 
+
+    svm-predict heart_scale.t heart_scale.model output
+
+The **output** file contains the predicted class labels.
+
+For classification, if training data are in **only one class** (i.e., all labels are the same), then `svm-train` issues a warning message:
+
+    Warning: training data in only one class. See README for details.
+    
+which means the training data is very **unbalanced**. The label in the training data is **directly returned** when testing.
+
+There are some other useful programs in this package.
+
+***svm-scale***:
+
+>This is a tool for scaling input data file.
+
+***svm-toy***:
+
+> This is a simple graphical interface which shows how SVM separate data in a plane. You can click in the window to draw data points. Use "change" button to choose class 1, 2 or 3 (i.e., up to three classes are supported), "load" button to load data from a file, "save" button to save data to a file, "run" button to obtain an SVM model, and "clear" button to clear the window.
+>
+> You can enter options in the bottom of the window, the syntax of options is the same as `svm-train`.
+>
+> Note that "load" and "save" consider dense data format both in classification and the regression cases. For classification,each data point has one label (the color) that must be 1, 2,or 3 and two attributes (x-axis and y-axis values) in [0,1). For regression, each data point has one target value (y-axis) and one attribute (x-axis values) in [0, 1).
+>
+> Type `make` in respective directories to build them.
+>
+> You need Qt library to build the Qt version.(available from http://www.trolltech.com)
+>
+> You need GTK+ library to build the GTK version. (available from http://www.gtk.org)
+>
+> The pre-built Windows binaries are in the `windows` directory. We use Visual C++ on a 32-bit machine, so the maximal cache size is 2GB.
+
+<h2 id="SvmTrainUsage">svm-train Usage</h2>
+
+    Usage: svm-train [options] training_set_file [model_file]
+    
+***options***:
+```
+-s svm_type : set type of SVM (default 0)
+	0 -- C-SVC		(multi-class classification)
+	1 -- nu-SVC		(multi-class classification)
+	2 -- one-class SVM	
+	3 -- epsilon-SVR	(regression)
+	4 -- nu-SVR		(regression)
+-t kernel_type : set type of kernel function (default 2)
+	0 -- linear: u'*v
+	1 -- polynomial: (gamma*u'*v + coef0)^degree
+	2 -- radial basis function: exp(-gamma*|u-v|^2)
+	3 -- sigmoid: tanh(gamma*u'*v + coef0)
+	4 -- precomputed kernel (kernel values in training_set_file)
+-d degree : set degree in kernel function (default 3)
+-g gamma : set gamma in kernel function (default 1/num_features)
+-r coef0 : set coef0 in kernel function (default 0)
+-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
+-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
+-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
+-m cachesize : set cache memory size in MB (default 100)
+-e epsilon : set tolerance of termination criterion (default 0.001)
+-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)
+-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
+-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)
+-v n: n-fold cross validation mode
+-q : quiet mode (no outputs)
+```
+
+The **k** in the **-g** option means the number of attributes in the input data.
+
+option **-v** randomly splits the data into n parts and calculates cross validation accuracy/mean squared error on them.
+
+See libsvm [FAQ](http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html) for the meaning of outputs.
+
+<h2 id="SvmPredictUsage">svm-predict Usage</h2>
+
+    Usage: svm-predict [options] test_file model_file output_file
+    
+***options***:
+```
+-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); for one-class SVM only 0 is supported
+-q : quiet mode (no outputs)
+```
+*model_file* is the model file generated by svm-train.
+*test_file* is the test data you want to predict.
+**svm-predict** will produce output in the output_file.
+
+<h2 id="SvmScaleUsage">svm-scale Usage</h2>
+
+    Usage: svm-scale [options] data_filename
+    
+***options***:
+```
+-l lower : x scaling lower limit (default -1)
+-u upper : x scaling upper limit (default +1)
+-y y_lower y_upper : y scaling limits (default: no y scaling)
+-s save_filename : save scaling parameters to save_filename
+-r restore_filename : restore scaling parameters from restore_filename
+```
+See [Examples](#Examples) in this file for examples.
+
+<h2 id="Tips">Tips on Practical Use</h2>
+
+1. Scale your data. For example, scale each attribute to `[0,1]` or `[-1,+1].`
+2. For C-SVC, consider using the model selection tool in the tools directory.
+3. nu in nu-SVC/one-class-SVM/nu-SVR approximates the fraction of training errors and support vectors.
+4. If data for classification are unbalanced (e.g. many positive and few negative), try different penalty parameters C by -wi (see examples below).
+5. Specify larger cache size (i.e., larger -m) for huge problems.
+
+<h2 id="Examples">Examples</h2>
+
+1. Scale each feature of the training data to be in [-1,1]. Scaling factors are stored in the file range and then used for scaling the test data.  
+
+        svm-scale -l -1 -u 1 -s range train > train.scale
+        svm-scale -r range test > test.scale
+
+2. Train a classifier with **RBF kernel** `exp(-0.5|u-v|^2)`, C=10, and stopping tolerance 0.1.    
+
+        svm-train -s 0 -c 5 -t 2 -g 0.5 -e 0.1 data_file 
+
+3. Solve SVM regression with linear kernel u'v and epsilon=0.1 in the loss function.
+
+        svm-train -s 3 -p 0.1 -t 0 data_file
+
+4. Train a classifier with penalty `10 = 1 * 10` for class 1, penalty `50 = 5 * 10` for class -2, and penalty `20 = 2 * 10` for class 4.
+
+        svm-train -c 10 -w1 1 -w-2 5 -w4 2 data_file
+
+5. Do five-fold cross validation for the classifier using the parameters C = 100 and gamma = 0.1
+
+        svm-train -s 0 -c 100 -g 0.1 -v 5 data_file
+
+6. Obtain a model with probability information and predict test data with probability estimates
+
+        svm-train -s 0 -b 1 data_file
+        svm-predict -b 1 test_file data_file.model output_file
+
+<h2 id="PrecomputedKernels">Precomputed Kernels</h2>
+
+Users may precompute kernel values and input them as training and testing files.  Then libsvm does not need the original training/testing sets.
+
+Assume there are L training instances `x1, ..., xL`. and Let `K(x, y)` be the kernel value of two instances `x` and `y`. The input formats are:
+
+New training instance for `xi`:
+
+    <label> 0:i 1:K(xi,x1) ... L:K(xi,xL) 
+
+New testing instance for any `x`:
+
+    <label> 0:? 1:K(x,x1) ... L:K(x,xL) 
+
+That is, in the training file the first column must be the "**ID**" of `xi`. In testing, `?` can be any value.
+
+All kernel values including ZEROs must be explicitly provided.  Any permutation or random subsets of the training/testing files are also valid (see examples below).
+
+**Note**: *the format is slightly different from the precomputed kernel package released in libsvmtools earlier.*
+
+***Examples***:
+
+	Assume the original training data has three four-feature
+	instances and testing data has one instance:
+
+	15  1:1 2:1 3:1 4:1
+	45      2:3     4:3
+	25          3:1
+
+	15  1:1     3:1
+
+	If the linear kernel is used, we have the following new
+	training/testing sets:
+
+	15  0:1 1:4 2:6  3:1
+	45  0:2 1:6 2:18 3:0 
+	25  0:3 1:1 2:0  3:1
+ 
+	15  0:? 1:2 2:0  3:1
+
+	? can be any value.
+
+	Any subset of the above training file is also valid. For example,
+
+	25  0:3 1:1 2:0  3:1
+	45  0:2 1:6 2:18 3:0 
+
+	implies that the kernel matrix is
+
+		[K(2,2) K(2,3)] = [18 0]
+		[K(3,2) K(3,3)] = [0  1]
+
+<h2 id="LibraryUsage">Library Usage</h2>
+
+These functions and structures are declared in the header file `svm.h`.  You need to `#include "svm.h"` in your C/C++ source files and link your program with `svm.cpp`. You can see `svm-train.c` and `svm-predict.c` for examples showing how to use them. We define **LIBSVM_VERSION** and declare `extern int libsvm_version; ` in svm.h, so you can check the version number.
+
+Before you classify test data, you need to construct an **SVM model** (`svm_model`) using training data. A model can also be saved in a file for later use. Once an SVM model is available, you can use it to classify new data.
+
+<h3 id="svm_train">function svm_train</h3>
+```c++
+struct svm_model *svm_train(const struct svm_problem *prob,
+					const struct svm_parameter *param);
+```
+This function constructs and returns an **SVM model** according to the given training data and parameters.
+
+struct `svm_problem` describes the problem:
+```c++
+struct svm_problem
+{
+	int l;
+	double *y;
+	struct svm_node **x;
+};
+```
+
+where  
+> `l` is the number of training data,  
+> `y` is an array containing their target values. (integers in classification, real numbers in regression)  
+> `x` is an array of pointers, each of which points to a sparse representation (array of svm_node) of one training vector.  
+
+For example, if we have the following training data:
+```
+LABEL    ATTR1    ATTR2    ATTR3    ATTR4    ATTR5
+-----    -----    -----    -----    -----    -----
+  1       0        0.1      0.2        0        0
+  2       0        0.1      0.3     -1.2        0
+  1       0.4        0      0          0        0
+  2       0        0.1      0        1.4      0.5
+  3      -0.1     -0.2      0.1      1.1      0.1
+```
+then the components of **svm_problem** are:
+```
+l = 5
+
+y -> 1 2 1 2 3
+
+x -> [ ] -> (2,0.1) (3,0.2) (-1,?)
+ [ ] -> (2,0.1) (3,0.3) (4,-1.2) (-1,?)
+ [ ] -> (1,0.4) (-1,?)
+ [ ] -> (2,0.1) (4,1.4) (5,0.5) (-1,?)
+ [ ] -> (1,-0.1) (2,-0.2) (3,0.1) (4,1.1) (5,0.1) (-1,?)
+```
+where `(index,value)` is stored in the structure `svm_node`:
+```C++
+struct svm_node
+{
+	int index;
+	double value;
+};
+```
+index = **-1** indicates the end of one vector. Note that indices must be in **ASCENDING** order.
+ 
+struct `svm_parameter` describes the parameters of an **SVM model**:
+```C++
+struct svm_parameter
+{
+	int svm_type;
+	int kernel_type;
+	int degree;	/* for poly */
+	double gamma;	/* for poly/rbf/sigmoid */
+	double coef0;	/* for poly/sigmoid */
+
+	/* these are for training only */
+	double cache_size; /* in MB */
+	double eps;	/* stopping criteria */
+	double C;	/* for C_SVC, EPSILON_SVR, and NU_SVR */
+	int nr_weight;		/* for C_SVC */
+	int *weight_label;	/* for C_SVC */
+	double* weight;		/* for C_SVC */
+	double nu;	/* for NU_SVC, ONE_CLASS, and NU_SVR */
+	double p;	/* for EPSILON_SVR */
+	int shrinking;	/* use the shrinking heuristics */
+	int probability; /* do probability estimates */
+};
+```
+`svm_type` can be one of `C_SVC`, `NU_SVC`, `ONE_CLASS`, `EPSILON_SVR`, `NU_SVR`.
+```
+C_SVC:		    C-SVM classification
+NU_SVC:		    nu-SVM classification
+ONE_CLASS:		one-class-SVM
+EPSILON_SVR:	epsilon-SVM regression
+NU_SVR:		    nu-SVM regression
+```
+`kernel_type` can be one of `LINEAR`, `POLY`, `RBF`, `SIGMOID`.
+```
+LINEAR:	        u'*v
+POLY:	        (gamma*u'*v + coef0)^degree
+RBF:	        exp(-gamma*|u-v|^2)
+SIGMOID:	    tanh(gamma*u'*v + coef0)
+PRECOMPUTED:    kernel values in training_set_file
+```
+`cache_size` is the size of the kernel cache, specified in megabytes.  
+`eps` is the stopping criterion. (we usually use 0.00001 in nu-SVC,0.001 in others). nu is the parameter in nu-SVM, nu-SVR, and one-class-SVM. p is the epsilon in epsilon-insensitive loss function of epsilon-SVM regression. shrinking = 1 means shrinking is conducted; = 0 otherwise. probability = 1 means model with probability information is obtained; = 0 otherwise.  
+`C` is the cost of constraints violation.   
+`nr_weight`, `weight_label`, and `weight` are used to change the penalty for some classes (If the weight for a class is not changed, it is set to 1). This is useful for training classifier using unbalanced input data or with asymmetric misclassification cost.  
+
+`nr_weight` is the number of elements in the array weight_label and weight. Each weight[i] corresponds to weight_label[i], meaning that the penalty of class weight_label[i] is scaled by a factor of weight[i].  
+    
+If you do not want to change penalty for any of the classes,just set nr_weight to 0.
+
+***NOTE*** Because `svm_model` contains pointers to `svm_problem`, you can not free the memory used by `svm_problem` if you are still using the `svm_model` produced by `svm_train()`. 
+
+***NOTE*** To avoid wrong parameters, `svm_check_parameter()` should be called before `svm_train()`.
+
+struct `svm_model` stores the model obtained from the training procedure. **It is not recommended to directly access entries in this structure**. Programmers should use the **interface functions** to get the values.
+```C++
+struct svm_model
+{
+	struct svm_parameter param;	/* parameter */
+	int nr_class;		/* number of classes, = 2 in regression/one class svm */
+	int l;			/* total #SV */
+	struct svm_node **SV;		/* SVs (SV[l]) */
+	double **sv_coef;	/* coefficients for SVs in decision functions (sv_coef[k-1][l]) */
+	double *rho;		/* constants in decision functions (rho[k*(k-1)/2]) */
+	double *probA;		/* pairwise probability information */
+	double *probB;
+	int *sv_indices;        /* sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set */
+
+	/* for classification only */
+
+	int *label;		/* label of each class (label[k]) */
+	int *nSV;		/* number of SVs for each class (nSV[k]) */
+				/* nSV[0] + nSV[1] + ... + nSV[k-1] = l */
+	/* XXX */
+	int free_sv;		/* 1 if svm_model is created by svm_load_model*/
+				/* 0 if svm_model is created by svm_train */
+};
+```
+
+`param` describes the parameters used to obtain the model.
+
+`nr_class` is the number of classes. It is 2 for regression and one-class SVM.
+
+`l` is the number of support vectors. SV and sv_coef are support vectors and the corresponding coefficients, respectively. Assume there are k classes. For data in class j, the corresponding sv_coef includes (k-1) y*alpha vectors, where alpha's are solutions of the following two class problems: 1 vs j, 2 vs j, ..., j-1 vs j, j vs j+1, j vs j+2, ..., j vs k and y=1 for the first j-1 vectors, while y=-1 for the remaining k-j vectors. For example, if there are 4 classes, sv_coef and SV are like:
+```
+        +-+-+-+--------------------+
+        |1|1|1|                    |
+        |v|v|v|  SVs from class 1  |
+        |2|3|4|                    |
+        +-+-+-+--------------------+
+        |1|2|2|                    |
+        |v|v|v|  SVs from class 2  |
+        |2|3|4|                    |
+        +-+-+-+--------------------+
+        |1|2|3|                    |
+        |v|v|v|  SVs from class 3  |
+        |3|3|4|                    |
+        +-+-+-+--------------------+
+        |1|2|3|                    |
+        |v|v|v|  SVs from class 4  |
+        |4|4|4|                    |
+        +-+-+-+--------------------+
+```
+See `svm_train()` for an example of assigning values to sv_coef.
+
+`rho` is the bias term (-b). probA and probB are parameters used in probability outputs. If there are k classes, there are k*(k-1)/2 binary problems as well as rho, probA, and probB values. They are aligned in the order of binary problems: 1 vs 2, 1 vs 3, ..., 1 vs k, 2 vs 3, ..., 2 vs k, ..., k-1 vs k.
+
+sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate support vectors in the training set.
+
+`label` contains labels in the training data.
+
+`SV` is the number of support vectors in each class.
+
+`ree_sv` is a flag used to determine whether the space of SV should be released in free_model_content(struct svm_model*) and free_and_destroy_model(struct svm_model**). If the model is generated by svm_train(), then SV points to data in svm_problem and should not be removed. For example, free_sv is 0 if svm_model is created by svm_train, but is 1 if created by svm_load_model.
+
+<h3 id="svm_predict">function svm_predict</h3>
+
+```C++
+double svm_predict(const struct svm_model *model, const struct svm_node *x);
+```
+
+This function does classification or regression on a test vector x given a model.
+
+For a classification model, **the predicted class for x is returned**. For a regression model, **the function value of x calculated using the model is returned**. For an one-class model, +1 or -1 is returned.
+
+<h3 id="svm_cross_validation">function svm_cross_validation</h3>
+
+```C++
+void svm_cross_validation(const struct svm_problem *prob,
+	const struct svm_parameter *param, int nr_fold, double *target);
+```
+
+This function conducts cross validation. Data are separated to nr_fold folds. Under given parameters, sequentially each fold is validated using the model from training the remaining. Predicted labels (of all prob's instances) in the validation process are stored in the array called target.
+
+The format of svm_prob is same as that for svm_train(). 
+
+<h3 id="svm_get_svm_type">function svm_get_svm_type</h3>
+
+```C++
+int svm_get_svm_type(const struct svm_model *model);
+```
+
+This function gives svm_type of the model. Possible values of svm_type are defined in svm.h.
+
+<h3 id="svm_get_nr_class">svm_get_nr_class</h3>
+
+```C++
+int svm_get_nr_class(const svm_model *model);
+```
+
+For a classification model, this function gives the number of classes. For a regression or an one-class model, 2 is returned.
+
+<h3 id="svm_get_labels">function svm_get_labels</h3>
+
+```C++
+void svm_get_labels(const svm_model *model, int* label)
+```
+    
+For a classification model, this function outputs the name of labels into an array called label. For regression and one-class models, label is unchanged.
+
+<h3 id="svm_get_sv_indices">function svm_get_sv_indices</h3>
+
+```C++
+void svm_get_sv_indices(const struct svm_model *model, int *sv_indices)
+```
+
+This function outputs indices of support vectors into an array called sv_indices. The size of sv_indices is the number of support vectors and can be obtained by calling svm_get_nr_sv. Each sv_indices[i] is in the range of [1, ..., num_traning_data].
+
+<h3 id="svm_get_nr_sv">function svm_get_nr_sv</h3>
+
+```C++
+int svm_get_nr_sv(const struct svm_model *model) 
+```
+This function gives the number of total support vector.
+
+<h3 id="svm_get_svr_probability">function svm_get_svr_probability</h3>
+
+```C++
+double svm_get_svr_probability(const struct svm_model *model);
+```
+For a regression model with probability information, this function outputs a value sigma > 0. For test data, we consider the probability model: target value = predicted value + z, z: Laplace distribution e^(-|z|/sigma)/(2sigma)
+
+If the model is not for svr or does not contain required information, 0 is returned.
+
+<h3 id="svm_predict_values">function svm_predict_values</h3>
+
+```C++
+double svm_predict_values(const svm_model *model, 
+				    const svm_node *x, double* dec_values)
+```
+This function gives decision values on a test vector x given a model, and return the predicted label (classification) or the function value (regression).
+
+For a classification model with nr_class classes, this function gives nr_class*(nr_class-1)/2 decision values in the array dec_values, where nr_class can be obtained from the function svm_get_nr_class. The order is label[0] vs. label[1], ..., label[0] vs. label[nr_class-1], label[1] vs. label[2], ..., label[nr_class-2] vs. label[nr_class-1], where label can be obtained from the function svm_get_labels. The returned value is the predicted class for x. Note that when nr_class = 1, this function does not give any decision value.
+
+For a regression model, dec_values[0] and the returned value are both the function value of x calculated using the model. For a one-class model, dec_values[0] is the decision value of x, while the returned value is +1/-1.
+
+<h3 id="svm_predict_probability">function svm_predict_probability</h3>
+
+```C++
+double svm_predict_probability(const struct svm_model *model, 
+	    const struct svm_node *x, double* prob_estimates);
+```
+This function does classification or regression on a test vector x given a model with probability information.
+
+For a classification model with probability information, this function gives nr_class probability estimates in the array prob_estimates. nr_class can be obtained from the function svm_get_nr_class. The class with the highest probability is returned. For regression/one-class SVM, the array prob_estimates is unchanged and the returned value is the same as that of svm_predict.
+
+<h3 id="svm_check_parameter">function svm_check_parameter</h3>
+
+```C++
+const char *svm_check_parameter(const struct svm_problem *prob,
+                                            const struct svm_parameter *param);
+```
+This function checks whether the parameters are within the feasible range of the problem. This function should be called before calling svm_train() and svm_cross_validation(). It returns NULL if the parameters are feasible, otherwise an error message is returned.
+
+<h3 id="svm_check_probability_model">function svm_check_probability_model</h3>
+
+```C++
+int svm_check_probability_model(const struct svm_model *model);
+```
+This function checks whether the model contains required information to do probability estimates. If so, it returns +1. Otherwise, 0 is returned. This function should be called before calling svm_get_svr_probability and svm_predict_probability.
+
+<h3 id="svm_save_model">function svm_save_model</h3>
+
+```C++
+int svm_save_model(const char *model_file_name,
+			       const struct svm_model *model);
+```
+This function saves a model to a file; returns 0 on success, or -1 if an error occurs.
+
+<h3 id="svm_load_model">function svm_load_model</h3>
+
+```C++
+struct svm_model *svm_load_model(const char *model_file_name);
+```
+
+This function returns a pointer to the model read from the file, or a null pointer if the model could not be loaded.
+
+<h3 id="svm_free_model_content">function svm_free_model_content</h3>
+
+```C++
+void svm_free_model_content(struct svm_model *model_ptr);
+```
+This function frees the memory used by the entries in a model structure.
+
+<h3 id="svm_free_and_destroy_model">function svm_free_and_destroy_model</h3>
+
+```C++
+void svm_free_and_destroy_model(struct svm_model **model_ptr_ptr);
+```
+This function frees the memory used by a model and destroys the model structure. It is equivalent to svm_destroy_model, which is deprecated after version 3.0.
+
+<h3 id="svm_destroy_param">function svm_destroy_param</h3>
+
+```C++
+void svm_destroy_param(struct svm_parameter *param);
+```
+This function frees the memory used by a parameter set.
+
+<h3 id="svm_set_print_string_function">function svm_set_print_string_function</h3>
+
+```C++
+void svm_set_print_string_function(void (*print_func)(const char *));
+```
+Users can specify their output format by a function. Use svm_set_print_string_function(NULL); for default printing to stdout.
+
+<h2 id="JavaVersion">Java Version</h2>
+
+The pre-compiled java class archive `libsvm.jar` and its source files are in the java directory. To run the programs, use
+```
+java -classpath libsvm.jar svm_train <arguments>
+java -classpath libsvm.jar svm_predict <arguments>
+java -classpath libsvm.jar svm_toy
+java -classpath libsvm.jar svm_scale <arguments>
+```
+***Note***
+- you need Java 1.5 (5.0) or above to run it.
+- You may need to add Java runtime library (like classes.zip) to the classpath.
+- You may need to increase maximum Java heap size.
+
+Library usages are similar to the C version. These functions are available:
+```java
+public class svm {
+	public static final int LIBSVM_VERSION=320; 
+	public static svm_model svm_train(svm_problem prob, svm_parameter param);
+	public static void svm_cross_validation(svm_problem prob, svm_parameter param, int nr_fold, double[] target);
+	public static int svm_get_svm_type(svm_model model);
+	public static int svm_get_nr_class(svm_model model);
+	public static void svm_get_labels(svm_model model, int[] label);
+	public static void svm_get_sv_indices(svm_model model, int[] indices);
+	public static int svm_get_nr_sv(svm_model model);
+	public static double svm_get_svr_probability(svm_model model);
+	public static double svm_predict_values(svm_model model, svm_node[] x, double[] dec_values);
+	public static double svm_predict(svm_model model, svm_node[] x);
+	public static double svm_predict_probability(svm_model model, svm_node[] x, double[] prob_estimates);
+	public static void svm_save_model(String model_file_name, svm_model model) throws IOException
+	public static svm_model svm_load_model(String model_file_name) throws IOException
+	public static String svm_check_parameter(svm_problem prob, svm_parameter param);
+	public static int svm_check_probability_model(svm_model model);
+	public static void svm_set_print_string_function(svm_print_interface print_func);
+}
+```
+The library is in the "**libsvm**" package.  
+Note that in Java version, svm_node[] is not ended with a node whose index = -1.
+
+Users can specify their output format by
+```java
+	your_print_func = new svm_print_interface()
+	{ 
+		public void print(String s)
+		{
+			// your own format
+		}
+	};
+	svm.svm_set_print_string_function(your_print_func);
+```
+<h2 id="BuildingWindowsBinaries">Building Windows Binaries</h2>
+
+Windows binaries are in the directory `windows`. To build them via Visual C++, use the following steps:
+
+1. Open a DOS command box (or Visual Studio Command Prompt) and change to libsvm directory. If environment variables of VC++ have not been
+set, type
+
+        C:\Program Files\Microsoft Visual Studio 10.0\VC\bin\vcvars32.bat
+
+    You may have to modify the above command according which version of
+VC++ or where it is installed.
+
+2. Type
+
+        nmake -f Makefile.win clean all
+
+3. (optional) To build shared library libsvm.dll, type
+
+        nmake -f Makefile.win lib
+
+4. (optional) To build 64-bit windows binaries, you must 
+    1. Run vcvars64.bat instead of vcvars32.bat. Note that
+	vcvars64.bat is located at
+
+            C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\bin\amd64\
+
+	2. Change CFLAGS in Makefile.win: `/D _WIN32` to `/D _WIN64`
+
+Another way is to build them from Visual C++ environment. See details in libsvm [FAQ](http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html).
+
+<h2 id="AdditionalTools">Additional Tools: Sub-sampling, Parameter Selection, Format checking, etc.</h2>
+
+See the [README.md](./tools/README.md) file in the tools directory.
+
+<h2 id="MatlabInterface">MATLAB/OCTAVE Interface</h2>
+
+Please check the file [README.md](./matlab/README.md) in the directory `matlab`.
+
+<h2 id="PythonInterface">Python Interface</h2>
+
+See the [README.md](./python/README.md) file in python directory.
+
+<h2 id="AdditionalInfo">Additional Information</h2>
+
+If you find LIBSVM helpful, please cite it as
+
+>Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
+
+LIBSVM implementation document is available at http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf
+
+For any questions and comments, please email cjlin@csie.ntu.edu.tw
+
+**Acknowledgments**:
+This work was supported in part by  
+> the National Science Council of Taiwan via the grant NSC 89-2213-E-002-013.  
+
+The authors thank their group members and users for many helpful discussions and comments.They are listed in  
+http://www.csie.ntu.edu.tw/~cjlin/libsvm/acknowledgements
+
diff --git a/matlab/README b/matlab/README
deleted file mode 100644
index ce1bcf83..00000000
--- a/matlab/README
+++ /dev/null
@@ -1,245 +0,0 @@
------------------------------------------
---- MATLAB/OCTAVE interface of LIBSVM ---
------------------------------------------
-
-Table of Contents
-=================
-
-- Introduction
-- Installation
-- Usage
-- Returned Model Structure
-- Other Utilities
-- Examples
-- Additional Information
-
-
-Introduction
-============
-
-This tool provides a simple interface to LIBSVM, a library for support vector
-machines (http://www.csie.ntu.edu.tw/~cjlin/libsvm). It is very easy to use as
-the usage and the way of specifying parameters are the same as that of LIBSVM.
-
-Installation
-============
-
-On Windows systems, pre-built binary files are already in the 
-directory '..\windows', so no need to conduct installation. Now we 
-provide binary files only for 64bit MATLAB on Windows. If you would 
-like to re-build the package, please rely on the following steps.
-
-We recommend using make.m on both MATLAB and OCTAVE. Just type 'make'
-to build 'libsvmread.mex', 'libsvmwrite.mex', 'svmtrain.mex', and
-'svmpredict.mex'.
-
-On MATLAB or Octave:
-
-        >> make
-
-If make.m does not work on MATLAB (especially for Windows), try 'mex
--setup' to choose a suitable compiler for mex. Make sure your compiler
-is accessible and workable. Then type 'make' to start the
-installation.
-
-Example:
-
-	matlab>> mex -setup
-	(ps: MATLAB will show the following messages to setup default compiler.)
-	Please choose your compiler for building external interface (MEX) files:
-	Would you like mex to locate installed compilers [y]/n? y
-	Select a compiler:
-	[1] Microsoft Visual C/C++ version 7.1 in C:\Program Files\Microsoft Visual Studio
-	[0] None
-	Compiler: 1
-	Please verify your choices:
-	Compiler: Microsoft Visual C/C++ 7.1
-	Location: C:\Program Files\Microsoft Visual Studio
-	Are these correct?([y]/n): y
-
-	matlab>> make
-
-On Unix systems, if neither make.m nor 'mex -setup' works, please use
-Makefile and type 'make' in a command window. Note that we assume 
-your MATLAB is installed in '/usr/local/matlab'. If not, please change 
-MATLABDIR in Makefile.
-
-Example:
-        linux> make
-
-To use octave, type 'make octave':
-
-Example:
-	linux> make octave
-
-For a list of supported/compatible compilers for MATLAB, please check
-the following page:
-
-http://www.mathworks.com/support/compilers/current_release/
-
-Usage
-=====
-
-matlab> model = svmtrain(training_label_vector, training_instance_matrix [, 'libsvm_options']);
-
-        -training_label_vector:
-            An m by 1 vector of training labels (type must be double).
-        -training_instance_matrix:
-            An m by n matrix of m training instances with n features.
-            It can be dense or sparse (type must be double).
-        -libsvm_options:
-            A string of training options in the same format as that of LIBSVM.
-
-matlab> [predicted_label, accuracy, decision_values/prob_estimates] = svmpredict(testing_label_vector, testing_instance_matrix, model [, 'libsvm_options']);
-matlab> [predicted_label] = svmpredict(testing_label_vector, testing_instance_matrix, model [, 'libsvm_options']);
-
-        -testing_label_vector:
-            An m by 1 vector of prediction labels. If labels of test
-            data are unknown, simply use any random values. (type must be double)
-        -testing_instance_matrix:
-            An m by n matrix of m testing instances with n features.
-            It can be dense or sparse. (type must be double)
-        -model:
-            The output of svmtrain.
-        -libsvm_options:
-            A string of testing options in the same format as that of LIBSVM.
-
-Returned Model Structure
-========================
-
-The 'svmtrain' function returns a model which can be used for future
-prediction.  It is a structure and is organized as [Parameters, nr_class,
-totalSV, rho, Label, ProbA, ProbB, nSV, sv_coef, SVs]:
-
-        -Parameters: parameters
-        -nr_class: number of classes; = 2 for regression/one-class svm
-        -totalSV: total #SV
-        -rho: -b of the decision function(s) wx+b
-        -Label: label of each class; empty for regression/one-class SVM
-        -sv_indices: values in [1,...,num_traning_data] to indicate SVs in the training set
-        -ProbA: pairwise probability information; empty if -b 0 or in one-class SVM
-        -ProbB: pairwise probability information; empty if -b 0 or in one-class SVM
-        -nSV: number of SVs for each class; empty for regression/one-class SVM
-        -sv_coef: coefficients for SVs in decision functions
-        -SVs: support vectors
-
-If you do not use the option '-b 1', ProbA and ProbB are empty
-matrices. If the '-v' option is specified, cross validation is
-conducted and the returned model is just a scalar: cross-validation
-accuracy for classification and mean-squared error for regression.
-
-More details about this model can be found in LIBSVM FAQ
-(http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html) and LIBSVM
-implementation document
-(http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf).
-
-Result of Prediction
-====================
-
-The function 'svmpredict' has three outputs. The first one,
-predictd_label, is a vector of predicted labels. The second output,
-accuracy, is a vector including accuracy (for classification), mean
-squared error, and squared correlation coefficient (for regression).
-The third is a matrix containing decision values or probability
-estimates (if '-b 1' is specified). If k is the number of classes
-in training data, for decision values, each row includes results of 
-predicting k(k-1)/2 binary-class SVMs. For classification, k = 1 is a
-special case. Decision value +1 is returned for each testing instance,
-instead of an empty vector. For probabilities, each row contains k values
-indicating the probability that the testing instance is in each class.
-Note that the order of classes here is the same as 'Label' field
-in the model structure.
-
-Other Utilities
-===============
-
-A matlab function libsvmread reads files in LIBSVM format: 
-
-[label_vector, instance_matrix] = libsvmread('data.txt'); 
-
-Two outputs are labels and instances, which can then be used as inputs
-of svmtrain or svmpredict. 
-
-A matlab function libsvmwrite writes Matlab matrix to a file in LIBSVM format:
-
-libsvmwrite('data.txt', label_vector, instance_matrix)
-
-The instance_matrix must be a sparse matrix. (type must be double)
-For 32bit and 64bit MATLAB on Windows, pre-built binary files are ready 
-in the directory `..\windows', but in future releases, we will only 
-include 64bit MATLAB binary files.
-
-These codes are prepared by Rong-En Fan and Kai-Wei Chang from National
-Taiwan University.
-
-Examples
-========
-
-Train and test on the provided data heart_scale:
-
-matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
-matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07');
-matlab> [predict_label, accuracy, dec_values] = svmpredict(heart_scale_label, heart_scale_inst, model); % test the training data
-
-For probability estimates, you need '-b 1' for training and testing:
-
-matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
-matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07 -b 1');
-matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
-matlab> [predict_label, accuracy, prob_estimates] = svmpredict(heart_scale_label, heart_scale_inst, model, '-b 1');
-
-To use precomputed kernel, you must include sample serial number as
-the first column of the training and testing data (assume your kernel
-matrix is K, # of instances is n):
-
-matlab> K1 = [(1:n)', K]; % include sample serial number as first column
-matlab> model = svmtrain(label_vector, K1, '-t 4');
-matlab> [predict_label, accuracy, dec_values] = svmpredict(label_vector, K1, model); % test the training data
-
-We give the following detailed example by splitting heart_scale into
-150 training and 120 testing data.  Constructing a linear kernel
-matrix and then using the precomputed kernel gives exactly the same
-testing error as using the LIBSVM built-in linear kernel.
-
-matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
-matlab>
-matlab> % Split Data
-matlab> train_data = heart_scale_inst(1:150,:);
-matlab> train_label = heart_scale_label(1:150,:);
-matlab> test_data = heart_scale_inst(151:270,:);
-matlab> test_label = heart_scale_label(151:270,:);
-matlab>
-matlab> % Linear Kernel
-matlab> model_linear = svmtrain(train_label, train_data, '-t 0');
-matlab> [predict_label_L, accuracy_L, dec_values_L] = svmpredict(test_label, test_data, model_linear);
-matlab>
-matlab> % Precomputed Kernel
-matlab> model_precomputed = svmtrain(train_label, [(1:150)', train_data*train_data'], '-t 4');
-matlab> [predict_label_P, accuracy_P, dec_values_P] = svmpredict(test_label, [(1:120)', test_data*train_data'], model_precomputed);
-matlab>
-matlab> accuracy_L % Display the accuracy using linear kernel
-matlab> accuracy_P % Display the accuracy using precomputed kernel
-
-Note that for testing, you can put anything in the
-testing_label_vector.  For more details of precomputed kernels, please
-read the section ``Precomputed Kernels'' in the README of the LIBSVM
-package.
-
-Additional Information
-======================
-
-This interface was initially written by Jun-Cheng Chen, Kuan-Jen Peng,
-Chih-Yuan Yang and Chih-Huai Cheng from Department of Computer
-Science, National Taiwan University. The current version was prepared
-by Rong-En Fan and Ting-Fan Wu. If you find this tool useful, please
-cite LIBSVM as follows
-
-Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support
-vector machines. ACM Transactions on Intelligent Systems and
-Technology, 2:27:1--27:27, 2011. Software available at
-http://www.csie.ntu.edu.tw/~cjlin/libsvm
-
-For any question, please contact Chih-Jen Lin <cjlin@csie.ntu.edu.tw>,
-or check the FAQ page:
-
-http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#/Q10:_MATLAB_interface
diff --git a/matlab/README.md b/matlab/README.md
new file mode 100644
index 00000000..322ef5d5
--- /dev/null
+++ b/matlab/README.md
@@ -0,0 +1,199 @@
+# MATLAB/OCTAVE interface of LIBSVM
+
+## Table of Contents
+
+- Introduction
+- Installation
+- Usage
+- Returned Model Structure
+- Other Utilities
+- Examples
+- Additional Information
+
+
+## Introduction
+
+This tool provides a simple interface to LIBSVM, a library for support vector machines (http://www.csie.ntu.edu.tw/~cjlin/libsvm). It is very easy to use as the usage and the way of specifying parameters are the same as that of LIBSVM.
+
+## Installation
+
+On Windows systems, pre-built binary files are already in the directory `..\windows`, so no need to conduct installation. Now we provide binary files only for **64bit MATLAB on Windows**. If you would like to re-build the package, please rely on the following steps.
+
+We recommend using `make.m` on both MATLAB and OCTAVE. Just type `make` to build `libsvmread.mex`, `libsvmwrite.mex`, `svmtrain.mex`, and `svmpredict.mex`.
+
+On MATLAB or Octave:
+
+    >> make
+
+If `make.m` does not work on MATLAB (especially for Windows), try `mex -setup` to choose a suitable compiler for mex. Make sure your compiler is accessible and workable. Then type 'make' to start the installation.
+
+Example:
+```matlab
+	matlab>> mex -setup
+	(ps: MATLAB will show the following messages to setup default compiler.)
+	Please choose your compiler for building external interface (MEX) files:
+	Would you like mex to locate installed compilers [y]/n? y
+	Select a compiler:
+	[1] Microsoft Visual C/C++ version 7.1 in C:\Program Files\Microsoft Visual Studio
+	[0] None
+	Compiler: 1
+	Please verify your choices:
+	Compiler: Microsoft Visual C/C++ 7.1
+	Location: C:\Program Files\Microsoft Visual Studio
+	Are these correct?([y]/n): y
+
+	matlab>> make
+```
+
+On Unix systems, if `neither` make.m nor `mex -setup` works, please use Makefile and type `make` in a command window. Note that we assume your MATLAB is installed in `/usr/local/matlab`. If not, please change MATLABDIR in Makefile.
+
+Example:
+
+    linux> make
+
+To use octave, type `make octave`:
+
+Example:
+
+	linux> make octave
+
+For a list of *supported/compatible* compilers for MATLAB, please check the following page:
+
+http://www.mathworks.com/support/compilers/current_release/
+
+## Usage
+```matlab
+matlab> model = svmtrain(training_label_vector, training_instance_matrix [, 'libsvm_options']);
+```
+
+`training_label_vector`:
+
+> An m by 1 vector of training labels (type must be double).
+
+`training_instance_matrix`:
+> An m by n matrix of m training instances with n features. It can be dense or sparse (type must be double).
+
+`libsvm_options`:
+> A string of training options in the same format as that of LIBSVM.
+
+```matlab
+matlab> [predicted_label, accuracy, decision_values/prob_estimates] = svmpredict(testing_label_vector, testing_instance_matrix, model [, 'libsvm_options']);
+matlab> [predicted_label] = svmpredict(testing_label_vector, testing_instance_matrix, model [, 'libsvm_options']);
+```
+
+`testing_label_vector`:
+> An m by 1 vector of prediction labels. If labels of test data are unknown, simply use any random values. (type must be double)
+
+`testing_instance_matrix`:
+> An m by n matrix of m testing instances with n features. It can be dense or sparse. (type must be double)
+
+`model`:
+> The output of svmtrain.
+
+`libsvm_options`:
+> A string of testing options in the same format as that of LIBSVM.
+
+## Returned Model Structure
+
+The `svmtrain` function returns a model which can be used for future prediction. It is a structure and is organized as
+```matlab
+[Parameters, nr_class, totalSV, rho, Label, ProbA, ProbB, nSV, sv_coef, SVs]:
+```
+
+> `Parameters`: parameters  
+> `nr_class`: number of classes; = 2 for regression/one-class svm  
+> `totalSV`: total #SV  
+> `rho`: -b of the decision function(s) wx+b  
+> `Label`: label of each class; empty for regression/one-class SVM  
+> `sv_indices`: values in [1,...,num_traning_data] to indicate SVs in the training set  
+> `ProbA`: pairwise probability information; empty if -b 0 or in one-class SVM  
+> `ProbB`: pairwise probability information; empty if -b 0 or in one-class SVM  
+> `nSV`: number of SVs for each class; empty for regression/one-class SVM  
+> `sv_coef`: coefficients for SVs in decision functions  
+> `SVs`: support vectors  
+
+If you do not use the option `-b 1`, ProbA and ProbB are empty matrices. If the `-v` option is specified, cross validation is conducted and the returned model is just a scalar: cross-validation accuracy for classification and mean-squared error for regression.
+
+More details about this model can be found in [LIBSVM FAQ](http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html) and [LIBSVM implementation document](http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf).
+
+## Result of Prediction
+
+The function `svmpredict` has three outputs.  
+> The first one, **predictd_label**, is a vector of predicted labels.  
+> The second output, **accuracy**, is a vector including accuracy (for classification), mean squared error, and squared correlation coefficient (for regression).   
+> The third is a **matrix** containing decision values or probability estimates (if `-b 1` is specified).  
+
+If `k` is the number of classes in training data, for decision values, each row includes results of predicting `k(k-1)/2` binary-class SVMs. For classification, `k = 1` is a special case. Decision value +1 is returned for each testing instance, instead of an empty vector. For probabilities, each row contains `k` values indicating the probability that the testing instance is in each class. Note that the order of classes here is the same as `Label` field in the model structure.
+
+## Other Utilities
+
+A matlab function libsvmread reads files in LIBSVM format: 
+```matlab
+[label_vector, instance_matrix] = libsvmread('data.txt'); 
+```
+Two outputs are labels and instances, which can then be used as inputs of **svmtrain** or **svmpredict**.
+
+A matlab function **libsvmwrite** writes Matlab matrix to a file in LIBSVM format:
+```matlab
+libsvmwrite('data.txt', label_vector, instance_matrix)
+```
+The `instance_matrix` must be a sparse matrix. (type must be double) For 32bit and 64bit MATLAB on Windows, pre-built binary files are ready in the directory `..\windows`, but in future releases, we will only include 64bit MATLAB binary files.
+
+These codes are prepared by Rong-En Fan and Kai-Wei Chang from National Taiwan University.
+
+## Examples
+
+Train and test on the provided data **heart_scale**:
+```matlab
+matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
+matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07');
+matlab> [predict_label, accuracy, dec_values] = svmpredict(heart_scale_label, heart_scale_inst, model); % test the training data
+```
+For probability estimates, you need `-b 1` for training and testing:
+```matlab
+matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
+matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07 -b 1');
+matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
+matlab> [predict_label, accuracy, prob_estimates] = svmpredict(heart_scale_label, heart_scale_inst, model, '-b 1');
+```
+To use precomputed kernel, you must include sample serial number as the first column of the training and testing data (assume your kernel matrix is K, # of instances is n):
+```matlab
+matlab> K1 = [(1:n)', K]; % include sample serial number as first column
+matlab> model = svmtrain(label_vector, K1, '-t 4');
+matlab> [predict_label, accuracy, dec_values] = svmpredict(label_vector, K1, model); % test the training data
+```
+We give the following detailed example by **splitting heart_scale into 150 training and 120 testing data**.  Constructing a linear kernel matrix and then using the precomputed kernel gives exactly the same testing error as using the LIBSVM built-in linear kernel.
+```matlab
+matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
+matlab>
+matlab> % Split Data
+matlab> train_data = heart_scale_inst(1:150,:);
+matlab> train_label = heart_scale_label(1:150,:);
+matlab> test_data = heart_scale_inst(151:270,:);
+matlab> test_label = heart_scale_label(151:270,:);
+matlab>
+matlab> % Linear Kernel
+matlab> model_linear = svmtrain(train_label, train_data, '-t 0');
+matlab> [predict_label_L, accuracy_L, dec_values_L] = svmpredict(test_label, test_data, model_linear);
+matlab>
+matlab> % Precomputed Kernel
+matlab> model_precomputed = svmtrain(train_label, [(1:150)', train_data*train_data'], '-t 4');
+matlab> [predict_label_P, accuracy_P, dec_values_P] = svmpredict(test_label, [(1:120)', test_data*train_data'], model_precomputed);
+matlab>
+matlab> accuracy_L % Display the accuracy using linear kernel
+matlab> accuracy_P % Display the accuracy using precomputed kernel
+```
+Note that for testing, you can put anything in the **testing_label_vector**.  For more details of precomputed kernels, please read the section **recomputed Kernels** in the README of the LIBSVM package.
+
+## Additional Information
+
+This interface was initially written by
+> Jun-Cheng Chen, Kuan-Jen Peng, Chih-Yuan Yang and Chih-Huai Cheng from Department of Computer Science, National Taiwan University.
+
+The current version was prepared by Rong-En Fan and Ting-Fan Wu. If you find this tool useful, please cite LIBSVM as follows
+
+> Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
+
+For any question, please contact Chih-Jen Lin <cjlin@csie.ntu.edu.tw>, or check the FAQ page:
+
+http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#/Q10:_MATLAB_interface
diff --git a/python/README b/python/README
deleted file mode 100644
index d7055943..00000000
--- a/python/README
+++ /dev/null
@@ -1,367 +0,0 @@
-----------------------------------
---- Python interface of LIBSVM ---
-----------------------------------
-
-Table of Contents
-=================
-
-- Introduction
-- Installation
-- Quick Start
-- Design Description
-- Data Structures
-- Utility Functions
-- Additional Information
-
-Introduction
-============
-
-Python (http://www.python.org/) is a programming language suitable for rapid
-development. This tool provides a simple Python interface to LIBSVM, a library
-for support vector machines (http://www.csie.ntu.edu.tw/~cjlin/libsvm). The
-interface is very easy to use as the usage is the same as that of LIBSVM. The
-interface is developed with the built-in Python library "ctypes."
-
-Installation
-============
-
-On Unix systems, type
-
-> make
-
-The interface needs only LIBSVM shared library, which is generated by
-the above command. We assume that the shared library is on the LIBSVM
-main directory or in the system path.
-
-For windows, the shared library libsvm.dll for 32-bit python is ready
-in the directory `..\windows'. You can also copy it to the system
-directory (e.g., `C:\WINDOWS\system32\' for Windows XP). To regenerate
-the shared library, please follow the instruction of building windows
-binaries in LIBSVM README.
-
-Quick Start
-===========
-
-There are two levels of usage. The high-level one uses utility functions
-in svmutil.py and the usage is the same as the LIBSVM MATLAB interface.
-
->>> from svmutil import *
-# Read data in LIBSVM format
->>> y, x = svm_read_problem('../heart_scale')
->>> m = svm_train(y[:200], x[:200], '-c 4')
->>> p_label, p_acc, p_val = svm_predict(y[200:], x[200:], m)
-
-# Construct problem in python format
-# Dense data
->>> y, x = [1,-1], [[1,0,1], [-1,0,-1]]
-# Sparse data
->>> y, x = [1,-1], [{1:1, 3:1}, {1:-1,3:-1}]
->>> prob  = svm_problem(y, x)
->>> param = svm_parameter('-t 0 -c 4 -b 1')
->>> m = svm_train(prob, param)
-
-# Precomputed kernel data (-t 4)
-# Dense data
->>> y, x = [1,-1], [[1, 2, -2], [2, -2, 2]]
-# Sparse data
->>> y, x = [1,-1], [{0:1, 1:2, 2:-2}, {0:2, 1:-2, 2:2}]
-# isKernel=True must be set for precomputer kernel
->>> prob  = svm_problem(y, x, isKernel=True)
->>> param = svm_parameter('-t 4 -c 4 -b 1')
->>> m = svm_train(prob, param)
-# For the format of precomputed kernel, please read LIBSVM README.
-
-
-# Other utility functions
->>> svm_save_model('heart_scale.model', m)
->>> m = svm_load_model('heart_scale.model')
->>> p_label, p_acc, p_val = svm_predict(y, x, m, '-b 1')
->>> ACC, MSE, SCC = evaluations(y, p_label)
-
-# Getting online help
->>> help(svm_train)
-
-The low-level use directly calls C interfaces imported by svm.py. Note that
-all arguments and return values are in ctypes format. You need to handle them
-carefully.
-
->>> from svm import *
->>> prob = svm_problem([1,-1], [{1:1, 3:1}, {1:-1,3:-1}])
->>> param = svm_parameter('-c 4')
->>> m = libsvm.svm_train(prob, param) # m is a ctype pointer to an svm_model
-# Convert a Python-format instance to svm_nodearray, a ctypes structure
->>> x0, max_idx = gen_svm_nodearray({1:1, 3:1})
->>> label = libsvm.svm_predict(m, x0)
-
-Design Description
-==================
-
-There are two files svm.py and svmutil.py, which respectively correspond to
-low-level and high-level use of the interface.
-
-In svm.py, we adopt the Python built-in library "ctypes," so that
-Python can directly access C structures and interface functions defined
-in svm.h.
-
-While advanced users can use structures/functions in svm.py, to
-avoid handling ctypes structures, in svmutil.py we provide some easy-to-use
-functions. The usage is similar to LIBSVM MATLAB interface.
-
-Data Structures
-===============
-
-Four data structures derived from svm.h are svm_node, svm_problem, svm_parameter, 
-and svm_model. They all contain fields with the same names in svm.h. Access 
-these fields carefully because you directly use a C structure instead of a 
-Python object. For svm_model, accessing the field directly is not recommanded. 
-Programmers should use the interface functions or methods of svm_model class
-in Python to get the values. The following description introduces additional
-fields and methods.
-
-Before using the data structures, execute the following command to load the
-LIBSVM shared library:
-
-    >>> from svm import *
-
-- class svm_node:
-
-    Construct an svm_node.
-
-    >>> node = svm_node(idx, val)
-
-    idx: an integer indicates the feature index.
-
-    val: a float indicates the feature value.
-
-    Show the index and the value of a node.
-
-    >>> print(node) 
-
-- Function: gen_svm_nodearray(xi [,feature_max=None [,isKernel=False]])
-
-    Generate a feature vector from a Python list/tuple or a dictionary:
-
-    >>> xi, max_idx = gen_svm_nodearray({1:1, 3:1, 5:-2})
-
-    xi: the returned svm_nodearray (a ctypes structure)
-
-    max_idx: the maximal feature index of xi
-
-    feature_max: if feature_max is assigned, features with indices larger than
-                 feature_max are removed.
-    
-    isKernel: if isKernel == True, the list index starts from 0 for precomputed
-              kernel. Otherwise, the list index starts from 1. The default 
-	      value is False.
-
-- class svm_problem:
-
-    Construct an svm_problem instance
-
-    >>> prob = svm_problem(y, x)
-
-    y: a Python list/tuple of l labels (type must be int/double).
-
-    x: a Python list/tuple of l data instances. Each element of x must be
-       an instance of list/tuple/dictionary type.
-
-    Note that if your x contains sparse data (i.e., dictionary), the internal
-    ctypes data format is still sparse.
-
-    For pre-computed kernel, the isKernel flag should be set to True:
-
-    >>> prob = svm_problem(y, x, isKernel=True)
-
-    Please read LIBSVM README for more details of pre-computed kernel.
-
-- class svm_parameter:
-
-    Construct an svm_parameter instance
-
-    >>> param = svm_parameter('training_options')
-
-    If 'training_options' is empty, LIBSVM default values are applied.
-
-    Set param to LIBSVM default values.
-
-    >>> param.set_to_default_values()
-
-    Parse a string of options.
-
-    >>> param.parse_options('training_options')
-
-    Show values of parameters.
-
-    >>> print(param)
-
-- class svm_model:
-
-    There are two ways to obtain an instance of svm_model:
-
-    >>> model = svm_train(y, x)
-    >>> model = svm_load_model('model_file_name')
-
-    Note that the returned structure of interface functions
-    libsvm.svm_train and libsvm.svm_load_model is a ctypes pointer of
-    svm_model, which is different from the svm_model object returned
-    by svm_train and svm_load_model in svmutil.py. We provide a
-    function toPyModel for the conversion:
-
-    >>> model_ptr = libsvm.svm_train(prob, param)
-    >>> model = toPyModel(model_ptr)
-
-    If you obtain a model in a way other than the above approaches,
-    handle it carefully to avoid memory leak or segmentation fault.
-
-    Some interface functions to access LIBSVM models are wrapped as
-    members of the class svm_model:
-
-    >>> svm_type = model.get_svm_type()
-    >>> nr_class = model.get_nr_class()
-    >>> svr_probability = model.get_svr_probability()
-    >>> class_labels = model.get_labels()
-    >>> sv_indices = model.get_sv_indices()
-    >>> nr_sv = model.get_nr_sv()
-    >>> is_prob_model = model.is_probability_model()
-    >>> support_vector_coefficients = model.get_sv_coef()
-    >>> support_vectors = model.get_SV() 
-
-Utility Functions
-=================
-
-To use utility functions, type
-
-    >>> from svmutil import *
-
-The above command loads
-    svm_train()        : train an SVM model
-    svm_predict()      : predict testing data
-    svm_read_problem() : read the data from a LIBSVM-format file.
-    svm_load_model()   : load a LIBSVM model.
-    svm_save_model()   : save model to a file.
-    evaluations()      : evaluate prediction results.
-
-- Function: svm_train
-
-    There are three ways to call svm_train()
-
-    >>> model = svm_train(y, x [, 'training_options'])
-    >>> model = svm_train(prob [, 'training_options'])
-    >>> model = svm_train(prob, param)
-
-    y: a list/tuple of l training labels (type must be int/double).
-
-    x: a list/tuple of l training instances. The feature vector of
-       each training instance is an instance of list/tuple or dictionary.
-
-    training_options: a string in the same form as that for LIBSVM command
-                      mode.
-
-    prob: an svm_problem instance generated by calling
-          svm_problem(y, x).
-	  For pre-computed kernel, you should use
-	  svm_problem(y, x, isKernel=True)
-
-    param: an svm_parameter instance generated by calling
-           svm_parameter('training_options')
-
-    model: the returned svm_model instance. See svm.h for details of this
-           structure. If '-v' is specified, cross validation is
-           conducted and the returned model is just a scalar: cross-validation
-           accuracy for classification and mean-squared error for regression.
-
-    To train the same data many times with different
-    parameters, the second and the third ways should be faster..
-
-    Examples:
-
-    >>> y, x = svm_read_problem('../heart_scale')
-    >>> prob = svm_problem(y, x)
-    >>> param = svm_parameter('-s 3 -c 5 -h 0')
-    >>> m = svm_train(y, x, '-c 5')
-    >>> m = svm_train(prob, '-t 2 -c 5')
-    >>> m = svm_train(prob, param)
-    >>> CV_ACC = svm_train(y, x, '-v 3')
-
-- Function: svm_predict
-
-    To predict testing data with a model, use
-
-    >>> p_labs, p_acc, p_vals = svm_predict(y, x, model [,'predicting_options'])
-
-    y: a list/tuple of l true labels (type must be int/double). It is used
-       for calculating the accuracy. Use [0]*len(x) if true labels are
-       unavailable.
-
-    x: a list/tuple of l predicting instances. The feature vector of
-       each predicting instance is an instance of list/tuple or dictionary.
-
-    predicting_options: a string of predicting options in the same format as
-                        that of LIBSVM.
-
-    model: an svm_model instance.
-
-    p_labels: a list of predicted labels
-
-    p_acc: a tuple including accuracy (for classification), mean
-           squared error, and squared correlation coefficient (for
-           regression).
-
-    p_vals: a list of decision values or probability estimates (if '-b 1'
-            is specified). If k is the number of classes in training data, 
-	    for decision values, each element includes results of predicting 
-	    k(k-1)/2 binary-class SVMs. For classification, k = 1 is a 
-	    special case. Decision value [+1] is returned for each testing
-	    instance, instead of an empty list.
-	    For probabilities, each element contains k values indicating
-            the probability that the testing instance is in each class.
-            Note that the order of classes is the same as the 'model.label'
-            field in the model structure.
-
-    Example:
-
-    >>> m = svm_train(y, x, '-c 5')
-    >>> p_labels, p_acc, p_vals = svm_predict(y, x, m)
-
-- Functions:  svm_read_problem/svm_load_model/svm_save_model
-
-    See the usage by examples:
-
-    >>> y, x = svm_read_problem('data.txt')
-    >>> m = svm_load_model('model_file')
-    >>> svm_save_model('model_file', m)
-
-- Function: evaluations
-
-    Calculate some evaluations using the true values (ty) and predicted
-    values (pv):
-
-    >>> (ACC, MSE, SCC) = evaluations(ty, pv)
-
-    ty: a list of true values.
-
-    pv: a list of predict values.
-
-    ACC: accuracy.
-
-    MSE: mean squared error.
-
-    SCC: squared correlation coefficient.
-
-
-Additional Information
-======================
-
-This interface was written by Hsiang-Fu Yu from Department of Computer
-Science, National Taiwan University. If you find this tool useful, please
-cite LIBSVM as follows
-
-Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support
-vector machines. ACM Transactions on Intelligent Systems and
-Technology, 2:27:1--27:27, 2011. Software available at
-http://www.csie.ntu.edu.tw/~cjlin/libsvm
-
-For any question, please contact Chih-Jen Lin <cjlin@csie.ntu.edu.tw>,
-or check the FAQ page:
-
-http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html
diff --git a/python/README.md b/python/README.md
new file mode 100644
index 00000000..8cea471b
--- /dev/null
+++ b/python/README.md
@@ -0,0 +1,295 @@
+# Python interface of LIBSVM
+
+## Table of Contents
+
+- Introduction
+- Installation
+- Quick Start
+- Design Description
+- Data Structures
+- Utility Functions
+- Additional Information
+
+## Introduction
+
+Python (http://www.python.org/) is a programming language suitable for rapid development. This tool provides a simple Python interface to LIBSVM, a library for support vector machines (http://www.csie.ntu.edu.tw/~cjlin/libsvm). The interface is very easy to use as the usage is the same as that of LIBSVM. The interface is developed with the built-in Python library "ctypes."
+
+## Installation
+
+On Unix systems, type
+
+    make
+
+The interface needs only LIBSVM shared library, which is generated by the above command. We assume that the shared library is on the LIBSVM main directory or in the system path.
+
+For windows, the shared library libsvm.dll for 32-bit python is ready in the directory `..\windows'. You can also copy it to the system directory (e.g., `C:\WINDOWS\system32\' for Windows XP). To regenerate the shared library, please follow the instruction of building windows binaries in LIBSVM README.
+
+## Quick Start
+
+There are two levels of usage. The high-level one uses utility functions in svmutil.py and the usage is the same as the LIBSVM MATLAB interface.
+```python
+>>> from svmutil import *
+# Read data in LIBSVM format
+>>> y, x = svm_read_problem('../heart_scale')
+>>> m = svm_train(y[:200], x[:200], '-c 4')
+>>> p_label, p_acc, p_val = svm_predict(y[200:], x[200:], m)
+
+# Construct problem in python format
+# Dense data
+>>> y, x = [1,-1], [[1,0,1], [-1,0,-1]]
+# Sparse data
+>>> y, x = [1,-1], [{1:1, 3:1}, {1:-1,3:-1}]
+>>> prob  = svm_problem(y, x)
+>>> param = svm_parameter('-t 0 -c 4 -b 1')
+>>> m = svm_train(prob, param)
+
+# Precomputed kernel data (-t 4)
+# Dense data
+>>> y, x = [1,-1], [[1, 2, -2], [2, -2, 2]]
+# Sparse data
+>>> y, x = [1,-1], [{0:1, 1:2, 2:-2}, {0:2, 1:-2, 2:2}]
+# isKernel=True must be set for precomputer kernel
+>>> prob  = svm_problem(y, x, isKernel=True)
+>>> param = svm_parameter('-t 4 -c 4 -b 1')
+>>> m = svm_train(prob, param)
+# For the format of precomputed kernel, please read LIBSVM README.
+
+# Other utility functions
+>>> svm_save_model('heart_scale.model', m)
+>>> m = svm_load_model('heart_scale.model')
+>>> p_label, p_acc, p_val = svm_predict(y, x, m, '-b 1')
+>>> ACC, MSE, SCC = evaluations(y, p_label)
+
+# Getting online help
+>>> help(svm_train)
+```
+The low-level use directly calls C interfaces imported by svm.py. Note that all arguments and return values are in ctypes format. You need to handle them carefully.
+```python
+>>> from svm import *
+>>> prob = svm_problem([1,-1], [{1:1, 3:1}, {1:-1,3:-1}])
+>>> param = svm_parameter('-c 4')
+>>> m = libsvm.svm_train(prob, param) # m is a ctype pointer to an svm_model
+# Convert a Python-format instance to svm_nodearray, a ctypes structure
+>>> x0, max_idx = gen_svm_nodearray({1:1, 3:1})
+>>> label = libsvm.svm_predict(m, x0)
+```
+## Design Description
+
+There are two files svm.py and svmutil.py, which respectively correspond to low-level and high-level use of the interface.
+
+In svm.py, we adopt the Python built-in library "ctypes," so that Python can directly access C structures and interface functions defined in svm.h.
+
+While advanced users can use structures/functions in svm.py, to avoid handling ctypes structures, in svmutil.py we provide some easy-to-use functions. The usage is similar to LIBSVM MATLAB interface.
+
+## Data Structures
+
+Four data structures derived from svm.h are svm_node, svm_problem, svm_parameter, and svm_model. They all contain fields with the same names in svm.h. Access these fields carefully because you directly use a C structure instead of a Python object. For svm_model, accessing the field directly is not recommanded. Programmers should use the interface functions or methods of svm_model class in Python to get the values. The following description introduces additional fields and methods.
+
+Before using the data structures, execute the following command to load the LIBSVM shared library:
+```python
+>>> from svm import *
+```
+
+### class svm_node:
+
+Construct an svm_node.
+```python
+>>> node = svm_node(idx, val)
+```
+
+`idx`: an integer indicates the feature index.
+
+`val`: a float indicates the feature value.
+
+Show the index and the value of a node.
+```python
+>>> print(node) 
+```
+### Function: gen_svm_nodearray(xi [,feature_max=None [,isKernel=False]])
+
+Generate a feature vector from a Python list/tuple or a dictionary:
+```python
+>>> xi, max_idx = gen_svm_nodearray({1:1, 3:1, 5:-2})
+```
+`xi`: the returned svm_nodearray (a ctypes structure)
+
+`max_idx`: the maximal feature index of xi
+
+`feature_max`: if feature_max is assigned, features with indices larger than feature_max are removed.
+    
+`isKernel`: if isKernel == True, the list index starts from 0 for precomputed kernel. Otherwise, the list index starts from 1. The default value is False.
+
+### class svm_problem
+
+Construct an svm_problem instance
+```python
+>>> prob = svm_problem(y, x)
+```
+`y`: a Python list/tuple of l labels (type must be int/double).
+
+`x`: a Python list/tuple of l data instances. Each element of x must be an instance of list/tuple/dictionary type.
+
+Note that if your x contains sparse data (i.e., dictionary), the internal ctypes data format is still sparse.
+
+For pre-computed kernel, the isKernel flag should be set to True:
+```python
+>>> prob = svm_problem(y, x, isKernel=True)
+```
+Please read LIBSVM README for more details of pre-computed kernel.
+
+### class svm_parameter
+
+Construct an svm_parameter instance
+```python
+>>> param = svm_parameter('training_options')
+```
+If 'training_options' is empty, LIBSVM default values are applied.
+
+Set param to LIBSVM default values.
+```python
+>>> param.set_to_default_values()
+```
+Parse a string of options.
+```python
+>>> param.parse_options('training_options')
+```
+
+Show values of parameters.
+```python
+>>> print(param)
+```
+### class svm_model
+
+There are two ways to obtain an instance of svm_model:
+```python
+>>> model = svm_train(y, x)
+>>> model = svm_load_model('model_file_name')
+```
+Note that the returned structure of interface functions libsvm.svm_train and libsvm.svm_load_model is a ctypes pointer of svm_model, which is different from the svm_model object returned by svm_train and svm_load_model in svmutil.py. We provide a function toPyModel for the conversion:
+```python
+    >>> model_ptr = libsvm.svm_train(prob, param)
+    >>> model = toPyModel(model_ptr)
+```
+
+If you obtain a model in a way other than the above approaches, handle it carefully to avoid memory leak or segmentation fault.
+
+Some interface functions to access LIBSVM models are wrapped as members of the class svm_model:
+```python
+>>> svm_type = model.get_svm_type()
+>>> nr_class = model.get_nr_class()
+>>> svr_probability = model.get_svr_probability()
+>>> class_labels = model.get_labels()
+>>> sv_indices = model.get_sv_indices()
+>>> nr_sv = model.get_nr_sv()
+>>> is_prob_model = model.is_probability_model()
+>>> support_vector_coefficients = model.get_sv_coef()
+>>> support_vectors = model.get_SV() 
+```
+### Utility Functions
+
+To use utility functions, type
+```python
+>>> from svmutil import *
+```
+The above command loads
+
+>    `svm_train()`        : train an SVM model
+>    `svm_predict()`      : predict testing data
+>    `svm_read_problem()` : read the data from a LIBSVM-format file.
+>    `svm_load_model()`   : load a LIBSVM model.
+>    `svm_save_model()`   : save model to a file.
+>    `evaluations()`      : evaluate prediction results.
+
+### Function: svm_train
+
+There are three ways to call `svm_train()`
+```python
+>>> model = svm_train(y, x [, 'training_options'])
+>>> model = svm_train(prob [, 'training_options'])
+>>> model = svm_train(prob, param)
+```
+
+`y`: a list/tuple of l training labels (type must be int/double).
+
+`x`: a list/tuple of l training instances. The feature vector of each training instance is an instance of list/tuple or dictionary.
+
+`training_options`: a string in the same form as that for LIBSVM command mode.
+
+`prob`: an svm_problem instance generated by calling svm_problem(y, x).  
+	  For pre-computed kernel, you should use svm_problem(y, x, isKernel=True)
+
+`param`: an svm_parameter instance generated by calling  
+
+    svm_parameter('training_options')
+
+`model`: the returned svm_model instance. See svm.h for details of this structure. If '-v' is specified, cross validation is conducted and the returned model is just a scalar: cross-validation accuracy for classification and mean-squared error for regression.
+
+To train the same data many times with different parameters, the second and the third ways should be faster..
+
+Examples:
+```python
+>>> y, x = svm_read_problem('../heart_scale')
+>>> prob = svm_problem(y, x)
+>>> param = svm_parameter('-s 3 -c 5 -h 0')
+>>> m = svm_train(y, x, '-c 5')
+>>> m = svm_train(prob, '-t 2 -c 5')
+>>> m = svm_train(prob, param)
+>>> CV_ACC = svm_train(y, x, '-v 3')
+```
+
+### Function: svm_predict
+
+To predict testing data with a model, use
+```python
+>>> p_labs, p_acc, p_vals = svm_predict(y, x, model [,'predicting_options'])
+```
+`y`: a list/tuple of l true labels (type must be int/double). It is used for calculating the accuracy. Use [0]*len(x) if true labels are unavailable.
+
+`x`: a list/tuple of l predicting instances. The feature vector of each predicting instance is an instance of list/tuple or dictionary.
+
+`predicting_options`: a string of predicting options in the same format as that of LIBSVM.
+
+`model`: an svm_model instance.
+
+`p_labels`: a list of predicted labels
+
+`p_acc`: a tuple including accuracy (for classification), mean squared error, and squared correlation coefficient (for regression).
+
+`p_vals`: a list of decision values or probability estimates (if '-b 1'is specified). If k is the number of classes in training data, for decision values, each element includes results of predicting k(k-1)/2 binary-class SVMs. For classification, k = 1 is a special case. Decision value [+1] is returned for each testing instance, instead of an empty list. For probabilities, each element contains k values indicating the probability that the testing instance is in each class. Note that the order of classes is the same as the 'model.label'field in the model structure.
+
+Example:
+```python
+>>> m = svm_train(y, x, '-c 5')
+>>> p_labels, p_acc, p_vals = svm_predict(y, x, m)
+```
+
+### Functions:  svm_read_problem/svm_load_model/svm_save_model
+
+See the usage by examples:
+```python
+>>> y, x = svm_read_problem('data.txt')
+>>> m = svm_load_model('model_file')
+>>> svm_save_model('model_file', m)
+```
+### Function: evaluations
+
+Calculate some evaluations using the true values (ty) and predicted values (pv):
+```python
+>>> (ACC, MSE, SCC) = evaluations(ty, pv)
+```
+`ty`: a list of true values.
+`pv`: a list of predict values.
+`ACC`: accuracy.
+`MSE`: mean squared error.
+`SCC`: squared correlation coefficient.
+
+
+## Additional Information
+
+This interface was written by Hsiang-Fu Yu from Department of Computer Science, National Taiwan University. If you find this tool useful, please cite LIBSVM as follows
+
+Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
+
+For any question, please contact Chih-Jen Lin <cjlin@csie.ntu.edu.tw>, or check the FAQ page:
+
+http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html
diff --git a/tools/README b/tools/README
deleted file mode 100644
index 172f4afc..00000000
--- a/tools/README
+++ /dev/null
@@ -1,210 +0,0 @@
-This directory includes some useful codes:
-
-1. subset selection tools.
-2. parameter selection tools.
-3. LIBSVM format checking tools
-
-Part I: Subset selection tools
-
-Introduction
-============
-
-Training large data is time consuming. Sometimes one should work on a
-smaller subset first. The python script subset.py randomly selects a
-specified number of samples. For classification data, we provide a
-stratified selection to ensure the same class distribution in the
-subset.
-
-Usage: subset.py [options] dataset number [output1] [output2]
-
-This script selects a subset of the given data set.
-
-options:
--s method : method of selection (default 0)
-     0 -- stratified selection (classification only)
-     1 -- random selection
-
-output1 : the subset (optional)
-output2 : the rest of data (optional)
-
-If output1 is omitted, the subset will be printed on the screen.
-
-Example
-=======
-
-> python subset.py heart_scale 100 file1 file2
-
-From heart_scale 100 samples are randomly selected and stored in
-file1. All remaining instances are stored in file2.
-
-
-Part II: Parameter Selection Tools
-
-Introduction
-============
-
-grid.py is a parameter selection tool for C-SVM classification using
-the RBF (radial basis function) kernel. It uses cross validation (CV)
-technique to estimate the accuracy of each parameter combination in
-the specified range and helps you to decide the best parameters for
-your problem.
-
-grid.py directly executes libsvm binaries (so no python binding is needed)
-for cross validation and then draw contour of CV accuracy using gnuplot.
-You must have libsvm and gnuplot installed before using it. The package
-gnuplot is available at http://www.gnuplot.info/
-
-On Mac OSX, the precompiled gnuplot file needs the library Aquarterm,
-which thus must be installed as well. In addition, this version of
-gnuplot does not support png, so you need to change "set term png
-transparent small" and use other image formats. For example, you may
-have "set term pbm small color".
-
-Usage: grid.py [grid_options] [svm_options] dataset
-
-grid_options :
--log2c {begin,end,step | "null"} : set the range of c (default -5,15,2)
-    begin,end,step -- c_range = 2^{begin,...,begin+k*step,...,end}
-    "null"         -- do not grid with c
--log2g {begin,end,step | "null"} : set the range of g (default 3,-15,-2)
-    begin,end,step -- g_range = 2^{begin,...,begin+k*step,...,end}
-    "null"         -- do not grid with g
--v n : n-fold cross validation (default 5)
--svmtrain pathname : set svm executable path and name
--gnuplot {pathname | "null"} :
-    pathname -- set gnuplot executable path and name
-    "null"   -- do not plot 
--out {pathname | "null"} : (default dataset.out)
-    pathname -- set output file path and name
-    "null"   -- do not output file
--png pathname : set graphic output file path and name (default dataset.png)
--resume [pathname] : resume the grid task using an existing output file (default pathname is dataset.out)
-    Use this option only if some parameters have been checked for the SAME data.
-
-svm_options : additional options for svm-train
-
-The program conducts v-fold cross validation using parameter C (and gamma)
-= 2^begin, 2^(begin+step), ..., 2^end.
-
-You can specify where the libsvm executable and gnuplot are using the
--svmtrain and -gnuplot parameters.
-
-For windows users, please use pgnuplot.exe. If you are using gnuplot
-3.7.1, please upgrade to version 3.7.3 or higher. The version 3.7.1
-has a bug. If you use cygwin on windows, please use gunplot-x11.
-
-If the task is terminated accidentally or you would like to change the
-range of parameters, you can apply '-resume' to save time by re-using
-previous results.  You may specify the output file of a previous run
-or use the default (i.e., dataset.out) without giving a name. Please
-note that the same condition must be used in two runs. For example,
-you cannot use '-v 10' earlier and resume the task with '-v 5'.
-
-The value of some options can be "null." For example, `-log2c -1,0,1
--log2 "null"' means that C=2^-1,2^0,2^1 and g=LIBSVM's default gamma
-value. That is, you do not conduct parameter selection on gamma.
-
-Example
-=======
-
-> python grid.py -log2c -5,5,1 -log2g -4,0,1 -v 5 -m 300 heart_scale
-
-Users (in particular MS Windows users) may need to specify the path of
-executable files. You can either change paths in the beginning of
-grid.py or specify them in the command line. For example,
-
-> grid.py -log2c -5,5,1 -svmtrain "c:\Program Files\libsvm\windows\svm-train.exe" -gnuplot c:\tmp\gnuplot\binary\pgnuplot.exe -v 10 heart_scale
-
-Output: two files
-dataset.png: the CV accuracy contour plot generated by gnuplot
-dataset.out: the CV accuracy at each (log2(C),log2(gamma))
-
-The following example saves running time by loading the output file of a previous run.
-
-> python grid.py -log2c -7,7,1 -log2g -5,2,1 -v 5 -resume heart_scale.out heart_scale
-
-Parallel grid search
-====================
-
-You can conduct a parallel grid search by dispatching jobs to a
-cluster of computers which share the same file system. First, you add
-machine names in grid.py:
-
-ssh_workers = ["linux1", "linux5", "linux5"]
-
-and then setup your ssh so that the authentication works without
-asking a password.
-
-The same machine (e.g., linux5 here) can be listed more than once if
-it has multiple CPUs or has more RAM. If the local machine is the
-best, you can also enlarge the nr_local_worker. For example:
-
-nr_local_worker = 2
-
-Example:
-
-> python grid.py heart_scale
-[local] -1 -1 78.8889  (best c=0.5, g=0.5, rate=78.8889)
-[linux5] -1 -7 83.3333  (best c=0.5, g=0.0078125, rate=83.3333)
-[linux5] 5 -1 77.037  (best c=0.5, g=0.0078125, rate=83.3333)
-[linux1] 5 -7 83.3333  (best c=0.5, g=0.0078125, rate=83.3333)
-.
-.
-.
-
-If -log2c, -log2g, or -v is not specified, default values are used.
-
-If your system uses telnet instead of ssh, you list the computer names
-in telnet_workers.
-
-Calling grid in Python
-======================
-
-In addition to using grid.py as a command-line tool, you can use it as a
-Python module. 
-
->>> rate, param = find_parameters(dataset, options)
-
-You need to specify `dataset' and `options' (default ''). See the following example.
-
-> python
-
->>> from grid import *
->>> rate, param = find_parameters('../heart_scale', '-log2c -1,1,1 -log2g -1,1,1')
-[local] 0.0 0.0 rate=74.8148 (best c=1.0, g=1.0, rate=74.8148)
-[local] 0.0 -1.0 rate=77.037 (best c=1.0, g=0.5, rate=77.037)
-.
-.
-[local] -1.0 -1.0 rate=78.8889 (best c=0.5, g=0.5, rate=78.8889)
-.
-.
->>> rate
-78.8889
->>> param
-{'c': 0.5, 'g': 0.5}
-
-
-Part III: LIBSVM format checking tools
-
-Introduction
-============
-
-`svm-train' conducts only a simple check of the input data. To do a
-detailed check, we provide a python script `checkdata.py.'
-
-Usage: checkdata.py dataset
-
-Exit status (returned value): 1 if there are errors, 0 otherwise.
-
-This tool is written by Rong-En Fan at National Taiwan University.
-
-Example
-=======
-
-> cat bad_data
-1 3:1 2:4
-> python checkdata.py bad_data
-line 1: feature indices must be in an ascending order, previous/current features 3:1 2:4
-Found 1 lines with error.
-
-
diff --git a/tools/README.md b/tools/README.md
new file mode 100644
index 00000000..65482235
--- /dev/null
+++ b/tools/README.md
@@ -0,0 +1,171 @@
+This directory includes some useful codes:
+
+1. subset selection tools.
+2. parameter selection tools.
+3. LIBSVM format checking tools
+
+# Part I: Subset selection tools
+
+## Introduction
+
+Training large data is time consuming. Sometimes one should work on a smaller subset first. The python script subset.py randomly selects a specified number of samples. For classification data, we provide a stratified selection to ensure the same class distribution in the subset.
+
+    Usage: subset.py [options] dataset number [output1] [output2]
+
+This script selects a subset of the given data set.
+
+***options***:
+```
+-s method : method of selection (default 0)
+     0 -- stratified selection (classification only)
+     1 -- random selection
+
+output1 : the subset (optional)
+output2 : the rest of data (optional)
+```
+If output1 is omitted, the subset will be printed on the screen.
+
+## Example
+```python
+python subset.py heart_scale 100 file1 file2
+```
+
+From heart_scale 100 samples are randomly selected and stored in file1. All remaining instances are stored in file2.
+
+# Part II: Parameter Selection Tools
+
+## Introduction
+
+grid.py is a parameter selection tool for **C-SVM classification** using the **RBF (radial basis function) kernel**. It uses cross validation (CV) technique to estimate the accuracy of each parameter combination in the specified range and helps you to decide the best parameters for your problem.
+
+**grid.py** directly executes libsvm binaries (so no python binding is needed) for cross validation and then draw contour of CV accuracy using **gnuplot**. You must have libsvm and gnuplot installed before using it. The package gnuplot is available at http://www.gnuplot.info/
+
+On Mac OSX, the precompiled gnuplot file needs the library Aquarterm, which thus must be installed as well. In addition, this version of gnuplot does not support png, so you need to change "set term png transparent small" and use other image formats. For example, you may have "set term pbm small color".
+
+    Usage: grid.py [grid_options] [svm_options] dataset
+
+grid_options :
+```
+-log2c {begin,end,step | "null"} : set the range of c (default -5,15,2)
+    begin,end,step -- c_range = 2^{begin,...,begin+k*step,...,end}
+    "null"         -- do not grid with c
+-log2g {begin,end,step | "null"} : set the range of g (default 3,-15,-2)
+    begin,end,step -- g_range = 2^{begin,...,begin+k*step,...,end}
+    "null"         -- do not grid with g
+-v n : n-fold cross validation (default 5)
+-svmtrain pathname : set svm executable path and name
+-gnuplot {pathname | "null"} :
+    pathname -- set gnuplot executable path and name
+    "null"   -- do not plot 
+-out {pathname | "null"} : (default dataset.out)
+    pathname -- set output file path and name
+    "null"   -- do not output file
+-png pathname : set graphic output file path and name (default dataset.png)
+-resume [pathname] : resume the grid task using an existing output file (default pathname is dataset.out)
+    Use this option only if some parameters have been checked for the SAME data.
+```
+
+    svm_options : additional options for svm-train
+
+The program conducts v-fold cross validation using parameter `C (and gamma) = 2^begin, 2^(begin+step), ..., 2^end`.
+
+You can specify where the libsvm executable and gnuplot are using the `-svmtrain` and `-gnuplot` parameters.
+
+For windows users, please use **pgnuplot.exe**. If you are using gnuplot 3.7.1, please upgrade to version **3.7.3** or higher. The version 3.7.1 has a bug. If you use cygwin on windows, please use gunplot-x11.
+
+If the task is terminated accidentally or you would like to change the range of parameters, you can apply `-resume` to save time by re-using previous results.  You may specify the output file of a previous run or use the default (i.e., dataset.out) without giving a name. Please note that the same condition must be used in two runs. For example, you cannot use `-v 10` earlier and resume the task with `-v 5`.
+
+The value of some options can be "null." For example, `-log2c -1,0,1 -log2 "null"` means that `C=2^-1,2^0,2^1` and `g=LIBSVM's default gamma value`. That is, you do not conduct parameter selection on gamma.
+
+## Example
+```
+python grid.py -log2c -5,5,1 -log2g -4,0,1 -v 5 -m 300 heart_scale
+```
+Users (in particular MS Windows users) may need to specify the path of executable files. You can either change paths in the beginning of grid.py or specify them in the command line. For example,
+```
+grid.py -log2c -5,5,1 -svmtrain "c:\Program Files\libsvm\windows\svm-train.exe" -gnuplot c:\tmp\gnuplot\binary\pgnuplot.exe -v 10 heart_scale
+```
+
+Output: two files
+dataset.png: the CV accuracy contour plot generated by gnuplot
+dataset.out: the CV accuracy at each `(log2(C),log2(gamma))`
+
+The following example saves running time by loading the output file of a previous run.
+```
+python grid.py -log2c -7,7,1 -log2g -5,2,1 -v 5 -resume heart_scale.out heart_scale
+```
+
+## Parallel grid search
+
+You can conduct a parallel grid search by dispatching jobs to a cluster of computers which share the same file system. First, you add machine names in grid.py:
+```python
+ssh_workers = ["linux1", "linux5", "linux5"]
+```
+
+and then setup your ssh so that the authentication works without asking a password.
+
+The same machine (e.g., linux5 here) can be listed more than once if it has multiple CPUs or has more RAM. If the local machine is the best, you can also enlarge the `nr_local_worker`. For example:
+
+    nr_local_worker = 2
+
+Example:
+```
+> python grid.py heart_scale
+[local] -1 -1 78.8889  (best c=0.5, g=0.5, rate=78.8889)
+[linux5] -1 -7 83.3333  (best c=0.5, g=0.0078125, rate=83.3333)
+[linux5] 5 -1 77.037  (best c=0.5, g=0.0078125, rate=83.3333)
+[linux1] 5 -7 83.3333  (best c=0.5, g=0.0078125, rate=83.3333)
+.
+.
+.
+```
+If `-log2c`, `-log2g`, or `-v` is not specified, default values are used.
+
+If your system uses telnet instead of ssh, you list the computer names in telnet_workers.
+
+## Calling grid in Python
+
+In addition to using grid.py as a command-line tool, you can use it as a Python module.
+```python
+>>> rate, param = find_parameters(dataset, options)
+```
+You need to specify `dataset` and `options` (default ''). See the following example.
+```python
+> python
+
+>>> from grid import *
+>>> rate, param = find_parameters('../heart_scale', '-log2c -1,1,1 -log2g -1,1,1')
+[local] 0.0 0.0 rate=74.8148 (best c=1.0, g=1.0, rate=74.8148)
+[local] 0.0 -1.0 rate=77.037 (best c=1.0, g=0.5, rate=77.037)
+.
+.
+[local] -1.0 -1.0 rate=78.8889 (best c=0.5, g=0.5, rate=78.8889)
+.
+.
+>>> rate
+78.8889
+>>> param
+{'c': 0.5, 'g': 0.5}
+
+```
+# Part III: LIBSVM format checking tools
+
+## Introduction
+
+`svm-train` conducts only a simple check of the input data. To do a detailed check, we provide a python script `checkdata.py.`
+
+    Usage: checkdata.py dataset
+
+Exit status (returned value): **1** if there are errors, **0** otherwise.
+
+This tool is written by Rong-En Fan at National Taiwan University.
+
+## Example
+```
+> cat bad_data
+1 3:1 2:4
+> python checkdata.py bad_data
+line 1: feature indices must be in an ascending order, previous/current features 3:1 2:4
+Found 1 lines with error.
+```
+