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Libsvm的一些说明帮助吧

發布時間：2025/3/15 编程问答 33 豆豆

生活随笔收集整理的這篇文章主要介紹了 Libsvm的一些说明帮助吧小編覺得挺不錯的,現在分享給大家,幫大家做個參考.

歡迎關注我的博客晨鳧追風，轉載請注明出處http://blog.csdn.net/hit2015spring
hit2015spring晨鳧追風

這是一篇翻譯文章吧，主要是對libsvm里面所帶的一些函數進行說明。
下載完libsvm的包，解壓縮之后，主要就是一個readme文件，這個文件說明的了這個包該如何使用，還有一個Makefile文件，這個文件說明了libsvm源碼里面各個程序之間的依賴關系，當然這里面需要的一些makefile文件的知識，見makefile文件的一些解讀當然了忽略文件里面的一些編譯選項，上面是授人漁的部分。接下來是授人魚的部分

它主要可以編譯出三個可執行文件，分別用于預測，訓練，歸一化，最主要的便是依賴一個文件svm.cpp這個文件，這個文件包含了svm里面訓練，預測的方法，如果要自己調用svm的話，只要搞清楚svm.cpp這個文件里頭的函數方法就ok了，svm-predict.c和svm-train.c這兩個文件主要是提供兩個例子供大家參考，是怎么調用svm.cpp的。當然svm.cpp這個文件的說明在readme里面已經說的比較明白了，下面就是readme的一些翻譯說明。所以一開頭就說了，我這篇文章主要是算一篇翻譯文章吧：

接下來有用的部分主要是在庫的調用的那一部分，其他的就是原文了

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.

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 thesvm-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 directorywindows’).

The format of training and testing data file is:

: : …
.
.
.

Each line contains an instance and is ended by a ‘\n’ character. For
classification, is an integer indicating the class label
(multi-class is supported). For regression, 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 : gives a feature
(attribute) value: is an integer starting from 1 and
is a real number. The only exception is the precomputed kernel, where
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 fileheart_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. Theoutput’
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 64-bit machine.這是一個簡單的圖形界面，顯示SVM如何在平面中分離數據。您可以在窗口中點擊繪制數據點。使用“更改”按鈕選擇1，2或3類（即最多支持三個類）， “加載”按鈕從文件加載數據，“保存”按鈕將數據保存到文件中，“運行”按鈕獲取SVM模型，并“清除”按鈕來清除窗口。您可以在窗口底部輸入選項，選項的語法與`svm-train'相同。請注意，“加載”和“保存”在分類和回歸情況下均考慮密集數據格式。對于分類，每個數據點都有一個標簽（顏色）必須為1,2或3，并且[0,1]中有兩個屬性（x軸和y軸值）。對于回歸，每個數據點在[0,1]中有一個目標值（y軸）和一個屬性（x軸值）。在各個目錄中鍵入`make'來構建它們。您需要Qt庫來構建Qt版本。（可從http://www.trolltech.com獲得）您需要GTK +庫來構建GTK版本。（可從http://www.gtk.org獲得）預制的Windows二進制文件位于“Windows”目錄中。我們在64位機器上使用Visual C ++。

`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.
對于C-SVC，請考慮在tools目錄中使用模型選擇工具。
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).

如果用于分類的數據不平衡（例如，許多正數和少量負數），請嘗試使用-wi的不同懲罰參數C（參見下面的示例）。
Specify larger cache size (i.e., larger -m) for huge problems.
為巨大的問題指定較大的緩存大小（即，較大的-m）

Examples

//歸一化

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

獲取具有概率信息的模型，并用概率估計來預測測試數據

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:

0:i 1:K(xi,x1) … L:K(xi,xL)

New testing instance for any x:

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 3:115 1:1 3:1If 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 3:115 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 withsvm.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.

這個函數用來構建一個訓練模型

struct svm_problem describes the problem:結構體svm_problem的定義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. 'l'是訓練數據的個數，'y'是一個數組，包含著目標值，就是標簽（分類的時候為整形，回歸的時候為實數） 'x'是一個結構體型的數組的指針，每一個是指向一個訓練向量的稀疏表示，這個結構體定義為svm_node,前面表示索引，后面表示該索引位置的值For example, if we have the following training data:LABEL ATTR1 ATTR2 ATTR3 ATTR4 ATTR5 ----- ----- ----- ----- ----- -----1 0 0.1 0.2 0 02 0 0.1 0.3 -1.2 01 0.4 0 0 0 02 0 0.1 0 1.4 0.53 -0.1 -0.2 0.1 1.1 0.1then the components of svm_problem are:l = 5y -> 1 2 1 2 3x -> [ ] -> (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; };

前面表示索引，后面表示索引的值，是一個數據集的特征值

index = -1 indicates the end of one vector. Note that indices must be in ASCENDING order.-1表示這個向量的結束，索引必須以增量形式struct svm_parameter describes the parameters of an SVM model:訓練參數的一個結構體定義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.svm_type表示用哪一種svmC_SVC: C-SVM classification NU_SVC: nu-SVM classification ONE_CLASS: one-class-SVM EPSILON_SVR: epsilon-SVM regression NU_SVR: nu-SVM regressionkernel_type can be one of LINEAR, POLY, RBF, SIGMOID.表示用哪一種核函數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_filecache_size is the size of the kernel cache, specified in megabytes.//指定的緩存的大小，以M為單位 C is the cost of constraints violation. //懲罰參數 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.eps是停止標準。（我們通常在nu-SVC中使用0.00001，其他人使用0.001）。 nu是nu-SVM，nu-SVR和one-class-SVM中的參數。p是ε-SVM回歸的ε不敏感損失函數中的ε。收縮= 1表示進行收縮;否則為0。概率= 1表示獲得具有概率信息的模型; = 0否則。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，weight_label和weight用于改變某些類的懲罰（如果一個類的權重沒有改變，它被設置為1）。這對于使用不平衡輸入數據或不對稱錯誤分類成本的訓練分類器很有用。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].nr_weight是數組中的元素數量weight_label和權重。每個權重[i]對應于weight_label [i]，這意味著類wight_label [i]的懲罰按比例[i]進行縮放。If you do not want to change penalty for any of the classes, just set nr_weight to 0.如果您不想更改任何類的懲罰，只需將nr_weight設置為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(). *注意*由于svm_model包含指向svm_problem的指針，如果仍使用svm_train（）生成的svm_model，則無法釋放svm_problem使用的內存。*NOTE* To avoid wrong parameters, svm_check_parameter() should be called before svm_train().注意*為避免錯誤的參數，svm_check_parameter（）應在svm_train（）之前調用。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存儲從訓練過程獲得的模型。不建議直接訪問此結構中的條目。程序員應該使用接口函數來獲取值。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.param描述了用于獲取模型的參數。上面訓練模型時的參數nr_class is the number of classes. It is 2 for regression and one-class SVM.類別數，對于回歸和one-class來說是2l 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:l是支持向量的數量。 SV和sv_coef分別是支持向量和相應的系數。假設有k類。對于類j中的數據，對應的sv_coef包括（k-1）y * alpha向量，其中α是以下兩個類問題的解：1對j，2對j，...，j-1對j，j對j + 1，j對j + 2，...，j對k對于第一個j-1向量，y = 1，而對于剩余的k-j，y = -1 向量。例如，如果有4個類，sv_coef和SV就像：+-+-+-+--------------------+|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.有關將值分配給sv_coef的示例，請參見svm_train（）。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.

rho是偏差項（-b）。 probA和probB是在概率輸出中使用的參數。如果存在k個類，則存在k *（k-1）/ 2個二進制問題以及rho，probA和probB值。它們以二進制問題的順序排列：
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.sv_indices [0，...，nSV-1]是[1，...，num_traning_data]中的值，用于指示訓練集中的支持向量。label contains labels in the training data.nSV is the number of support vectors in each class. 每一類支持向量的個數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.

free_sv是用于確定SV的空間是否應在free_model_content（struct svm_model ）和free_and_destroy_model（struct svm_model *）中釋放的標志。
如果模型由svm_train（）生成，則SV指向svm_problem中的數據，不應該被刪除。例如，如果svm_train創建svm_model，則free_sv為0，如果由svm_load_model創建，則為1。

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.

此函數對給定模型的測試向量x執行分類或回歸。
對于分類模型，返回x的預測類。
對于回歸模型，返回使用模型計算的x的函數值。對于一級模型，返回+1或-1。

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.

此功能進行交叉驗證。數據被分隔成nr_fold折疊。下給定的參數，依次各個折疊使用來自訓練的剩余的模型驗證。
驗證過程中的預測標簽（所有prob的實例）都存儲在名為target的數組中。

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.

該函數給出了模型的svm_type。 svm_h的值可以在svm.h中定義。
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.

對于分類模型，該函數給出了類的數量。對于回歸或一類模型，返回2。
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.

對于分類模型，該函數將標簽的名稱輸出到名為標簽的數組中。對于回歸和一類模型，標簽不變。
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].

該函數將支持向量的索引輸出到名為sv_indices的數組中。 sv_indices的大小是支持向量的數量，可以通過調用svm_get_nr_sv獲得。
每個sv_indices [i]在[1，…，num_traning_data]的范圍內。
Function: int svm_get_nr_sv(const struct svm_model *model)

This function gives the number of total support vector.
該函數給出了總支持向量的數量。
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.

對于具有概率信息的回歸模型，該函數輸出一個值σ> 0.對于測試數據，我們考慮概率模型：
目標值=預測值+ z，z：拉普拉斯分布e ^（ - | z | / sigma）/ （2sigma）如果模型不適用于svr或不包含所需信息，則返回0。

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).

該函數給出給定模型的測試向量x上的決策值，并轉動預測標簽（分類）或函數值（回歸）。

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.

對于具有r_class類的分類模型，該函數在數組dec_values中給出nr_class *（nr_class-1）/ 2個決策值，
其中可以從函數svm_get_nr_class獲取nr_class。
順序是標簽[0]與標簽[1]，…，標簽[0]對標簽[nr_class-1]，
標簽[1]對標簽[2]，…，標簽[nr_class- 2]對標簽[nr_class-1]，
其中可以從函數svm_get_labels獲取標簽。返回的值是x的預測類。
請注意，當nr_class = 1時，此函數不給出任何決策值。

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.對于回歸模型，dec_values [0]和返回值都是使用模型計算的x的函數值。對于一類模型，dec_values [0]是x的決策值，返回值為+ 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.

該函數對給定具有概率信息的模型的測試向量x進行分類或回歸。

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.對于具有概率信息的分類模型，該函數在陣列prob_estimates中給出nr_class概率估計。nr_class可以從函數svm_get_nr_class獲取。返回最高概率的類。對于回歸/一類SVM，數組prob_estimates不變，返回值與svm_predict相同。

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.

該功能檢查參數是否在問題的可行范圍內。在調用svm_train（）和svm_cross_validation（）之前，
應該調用此函數。如果參數可行，則返回NULL，否則返回錯誤消息。
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.

此函數檢查模型是否包含要進行概率估計的所需信息。如果是，則返回+1。否則返回0。
在調用svm_get_svr_probability和svm_predict_probability之前應該調用此函數。

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.

此功能將模型保存到文件中; 成功返回0，否則發生錯誤。

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.

此函數返回指向從文件讀取的模型的指針，如果模型無法加載，則返回空指針。

Function: void svm_free_model_content(struct svm_model *model_ptr);

This function frees the memory used by the entries in a model structure.

此函數釋放模型結構中條目使用的內存。
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.

此函數釋放模型使用的內存并銷毀模型結構。它相當于svm_destroy_model，它在版本3.0之后不推薦使用。
Function: void svm_destroy_param(struct svm_parameter *param);

This function frees the memory used by a parameter set.

此函數釋放參數集使用的內存。
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.

用戶可以通過功能指定其輸出格式。使用svm_set_print_string函數（NULL）; 默認打印到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
java -classpath libsvm.jar svm_predict
java -classpath libsvm.jar svm_toy
java -classpath libsvm.jar svm_scale

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=322;
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 available in the directory `windows’. To re-build
them via Visual C++, use the following steps:

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 (x86)\Microsoft Visual Studio 12.0\VC\bin\amd64\vcvars64.bat”“

You may have to modify the above command according which version of
VC++ or where it is installed.

Type

nmake -f Makefile.win clean all

(optional) To build shared library libsvm.dll, type

nmake -f Makefile.win lib

(optional) To build 32-bit windows binaries, you must
(1) Setup “C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin\vcvars32.bat” instead of vcvars64.bat
(2) Change CFLAGS in Makefile.win: /D _WIN64 to /D _WIN32

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

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