eclipseで次のことをしようと思って以下のコードを書きました。  
・output.txtのlibSVM形式のデータを入力として、svm_trainを実行する。  
・パラメータに-v 3を追加して交差検証を行う。  
・libSVMからの出力をコンソールに表示する。

```

    import java.io.BufferedReader;
    import java.io.IOException;
    import java.io.InputStreamReader;
    
    public class libSVM {
        public static void main(String[] args) {
        try {
            ProcessBuilder pb = new ProcessBuilder(
                    "C:\\Users\\Name\\workspace\\libsvm-3.24\\windows\\svm-train.exe","-v 3",
                    "output.txt");
            pb. redirectErrorStream(true);
            Process p = pb.start();
            p.waitFor();
            System.out.println(pb.redirectInput());
            System.out.println(p.waitFor());
            System.out.println(p.exitValue());
                try (var reader = new BufferedReader(
                    new InputStreamReader(p.getInputStream()))) {
                    StringBuilder builder = new StringBuilder();
                    String line;
    
                    while ((line = reader.readLine()) != null) {
                        System.out.println(line);
                        builder.append(line);
                        builder.append(System.getProperty("line.separator"));
                    }
                    String result = builder.toString();
                    System.out.println(result);
                }
            } catch (IOException | InterruptedException e) {
                e.printStackTrace();
            }
        }
    }
    
```

また、libsvm.jarをビルドパスに追加しました。  
しかし、コンソールには以下のように表示されます。

```

    PIPE
    1
    1
    n-fold cross validation: n must >= 2
    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)
    n-fold cross validation: n must >= 2
    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)
    
    
```

戻り値も1なのでどこかおかしいようです。  
どのように直したらいいですか？
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2021-01-11T06:28:43.050
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2021-01-11T06:28:43.050
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question
java eclipse
libSVMの交差検証をJavaでeclipseを使ってやる方法