Eclipse + libsvm-3.12 implement simple linear classification with SVM

0. Download The libsvm compressed package and decompress it to the local directory (from: http://www.csie.ntu.edu.tw /~ Cjlin/libsvm/index.html)
1. Create a JAVA project and import the libsvm package and its source code.
2. Write the test code and use the libsvm function for classification prediction.
3. Reference

1. Create a JAVA project and import the libsvm package and its source code.

1. After creating a JAVA project and main function, right-click Project => build path => configure build path, Java Build Path> Libraries> Add External JARs, and import libsvm. jar.

2. Associate libsvm Source code (for more information, see the Source code): Open libsvm. jar => Source attachment :( None) => Edit, External location => External Folder

Select the java directory under the libsvm-3.12, all the way OK.

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2. Compile the test code and use the libsvm function for classification prediction.

The test code is as follows:

1 import libsvm. svm; 2 import libsvm. svm_model; 3 import libsvm. svm_node; 4 import libsvm. svm_parameter; 5 import libsvm. svm_problem; 6 7 public class jmain {8 9/** 10 * @ param args11 */12 public static void main (String [] args) {13 // defines training set points a {10.0, 10.0} and point B {-10.0,-10.0}, corresponding to lable {1.0, -1.0} 14 svm_node pa0 = new svm_node (); 15 pa0.index = 0; 16 pa0.value = 10.0; 17 svm_node pa1 = new svm_node (); 18 pa 1. index =-1; 19 pa1.value = 10.0; 20 svm_node pb0 = new svm_node (); 21 pb0.index = 0; 22 pb0.value =-10.0; 23 svm_node pb1 = new svm_node (); 24 pb1.index = 0; 25 pb1.value =-10.0; 26 svm_node [] pa = {pa0, pa1}; // point a27 svm_node [] pb = {pb0, pb1 }; // point b28 svm_node [] [] datas = {pa, pb}; // vector table 29 double [] lables = {1.0,-1.0} of the training set }; // lable30 31 for a and B // defines the svm_problem object 32 svm_problem problem = new svm_problem (); 33 problem. l = 2; // number of vectors 34 problem. x = datas; // training set vector table 35 problem. y = lables; // the corresponding lable array 36 37 // defines svm_parameter object 38 svm_parameter param = new svm_parameter (); 39 param. svm_type = svm_parameter.C_SVC; 40 param. kernel_type = svm_parameter.LINEAR; 41 param. cache_size = 100; 42 param. eps = 0.00001; 43 param. C = 1; 44 45 // train SVM classification model 46 System. out. println (svm. svm_check_parameter (problem, param); // If the parameter is correct The svm. svm_check_parameter () function returns null; otherwise, the error description is returned. 47 svm_model model = svm. svm_train (problem, param); // svm. svm_train () Train the SVM classification model 48 49 // define the test data point c50 svm_node pc0 = new svm_node (); 51 pc0.index = 0; 52 pc0.value =-0.1; 53 svm_node pc1 = new svm_node (); 54 pc1.index =-1; 55 pc1.value = 0.0; 56 svm_node [] pc = {pc0, pc1 }; 57 58 // lable59 System of prediction test data. out. println (svm. svm_predict (model, pc); 60} 61}

The running result is:

null*optimization finished, #iter = 1nu = 0.0033333333333333335obj = -0.0033333333333333335, rho = 0.0nSV = 2, nBSV = 0Total nSV = 2-1.0

The first row of null is the output of svm. svm_check_parameter (problem, param), indicating that the parameter settings are correct. The-1.0 in the last row indicates that the predicted lable for the cpoint is-1.0.

Note the following:
1. svm. svm_train () is used for training, and svm. svm_predict () is used for prediction. svm_problem, svm_parameter, svm_model, and svm_node are used for "struct" objects.
2. svm_node indicates {vector component serial number, vector component value}. Many sparse matrices use this method to store data, which saves space. svm_node [] indicates a vector, svm_node.index of the last component of a vector is represented by-1. svm_node [] [] indicates a set of vectors, that is, the training set.

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Reference:

1. Video: one instance handles libsvm Classification "Learn SVM Step by Step" by faruto
2. How to Use libsvm for classification
3. Use of the function library libsvm2.88

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