You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: svm-sign_learner.rs
    path of file: ./repos/rusty-machine/examples
    
    the code of the file until where you have to start completion: extern crate rusty_machine;

use rusty_machine::learning::svm::SVM;
// Necessary for the training trait.
use rusty_machine::learning::SupModel;
use rusty_machine::learning::toolkit::kernel::HyperTan;

use rusty_machine::linalg::Matrix;
use rusty_machine::linalg::Vector;

// Sign learner:
//   * Model input a float number
//   * Model output: A float representing the input sign.
//       If the input is positive, the output is close to 1.0.
//       If the input is negative, the output is close to -1.0.
//   * Model generated with the SVM API.
fn main() {
    println!("Sign learner sample:");

    println!("Training...");
    // Training data
    let inputs = Matrix::new(11, 1, vec![
                             -0.1, -2., -9., -101., -666.7,
                             0., 0.1, 1., 11., 99., 456.7
                             ]);
    let targets = Vector::new(vec![
                              -1., -1., -1., -1., -1.,
                              1., 1., 1., 1., 1., 1.
                              ]);

    // Trainee
    let mut svm_mod = SVM::new(HyperTan::new(100., 0.), 0.3);
    // Our train function returns a Result<(), E>
    svm_mod.train(&inputs, &ta
    
fn main() {
    println!("Sign learner sample:");

    println!("Training...");
    // Training data
    let inputs = Matrix::new(11, 1, vec![
                             -0.1, -2., -9., -101., -666.7,
                             0., 0.1, 1., 11., 99., 456.7
                             ]);
    let targets = Vector::new(vec![
                              -1., -1., -1., -1., -1.,
                              1., 1., 1., 1., 1., 1.
                              ]);

    // Trainee
    let mut svm_mod = SVM::new(HyperTan::new(100., 0.), 0.3);
    // Our train function returns a Result<(), E>
    svm_mod.train(&inputs, &targets).unwrap();

    println!("Evaluation...");
    let mut hits = 0;
    let mut misses = 0;
    // Evaluation
    //   Note: We could pass all input values at once to the `predict` method!
    //         Here, we use a loop just to count and print logs.
    for n in (-1000..1000).filter(|&x| x % 100 == 0) {
        let nf = n as f64;
        let input = Matrix::new(1, 1, vec![nf]);
        let out = svm_mod.predict(&input).unwrap();
        let res = if out[0] * nf > 0. {
            hits += 1;
            true
        } else if nf == 0. {
            hits += 1;
            true
        } else {
            misses += 1;
            false
        };

        println!("{} -> {}: {}", Matrix::data(&input)[0], out[0], res);
    }

    println!("Performance report:");
    println!("Hits: {}, Misses: {}", hits, misses);
    let hits_f = hits as f64;
    let total = (hits + misses) as f64;
    println!("Accuracy: {}", (hits_f / total) * 100.);
}