How to use svmtrain for data classification and prediction
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After installing the libsvm toolkit, use svmtrain for data classification and prediction
% clear environment variables
Close all
Clear
Clc
Format compact
%% data extraction
% load test data wine
% contains data as classnumber = 3
Matrix of% wine:178*13
Column vector of% wine_labes:178*1
Load wine.mat
%% draw box visualization diagram of test data
Figure
Boxplot (wine,'orientation','horizontal','labels',categories)
Title ('box visualization of wine data', 'FontSize',12)
Xlabel ('attribute value', 'FontSize',12)
Grid on
%% draw the fractal dimension visualization diagram of the test data
Figure
Subplot (3, 5, 1)
Hold on
For run = 1PUR 178
Plot (run,wine_labels (run),'*')
End
Xlabel ('sample', 'FontSize',10)
Ylabel ('category tag', 'FontSize',10)
Title ('class','FontSize',10)
For run = 2:14
Subplot (3, 5, run)
Hold on
Str = ['attrib', num2str (run-1)]
For I = 1PUR 178
Plot (iGraine wine),'*')
End
Xlabel ('sample', 'FontSize',10)
Ylabel ('attribute value', 'FontSize',10)
Title (str,'FontSize',10)
End
Selected training set and test set
1-30 of the first category, 60-95 of the second category, 131-153 of the third category as the training set
Train_wine = [wine (1 wine 30:); wine (60 15); wine (131 15)]
The tags of the corresponding training sets should also be separated.
Train_wine_labels = [wine_labels (1:30); wine_labels (60:95); wine_labels (131 wine_labels 153)]
% take 31-59 of the first category, 96-130 of the second category, and 154-178 of the third category as the test set
Test_wine = [wine (31 wine 59:); wine (96 14 130:); wine (154 14: 178)]
The tags of the corresponding test sets should also be separated.
Test_wine_labels = [wine_labels (31:59); wine_labels (9614); wine_labels (154)]
%% data preprocessing
% data preprocessing to normalize the training set and test set to the [0d1] interval
[mtrain,ntrain] = size (train_wine)
[mtest,ntest] = size (test_wine)
Dataset = [train_wine;test_wine]
% mapminmax is the normalized function that comes with MATLAB
[dataset_scale,ps] = mapminmax (dataset',0,1)
Dataset_scale = dataset_scale'
Train_wine = dataset_scale (1)
Test_wine = dataset_scale (mtrain+1): (mtrain+mtest),:)
%% SVM network training
Model = svmtrain (train_wine_labels, train_wine,'- c 2-g 1')
% SVM Network Forecast
[predict_label, accuracy] = svmpredict (test_wine_labels, test_wine, model)
%% result analysis
Actual classification and prediction classification diagram of% test set
% from the chart, we can see that only one test sample was misclassified.
Figure
Hold on
Plot (test_wine_labels,'o')
Plot (predict_label,'r*')
Xlabel ('test set sample', 'FontSize',12)
Ylabel ('category tag', 'FontSize',12)
Legend ('actual test set classification', 'predictive test set classification')
Title ('actual and predictive classification diagrams of test sets', 'FontSize',12)
Grid on
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