Struct Ridge

Source

pub struct Ridge { /* private fields */ }

Expand description

Ridge regression model.

Implementations§

Source §

impl Ridge

Source

pub fn new(n_x: u64, n_y: u64, beta: f64) -> Self

Create a new Ridge regression model. ‘n_x’ is the number of input variables and ‘n_y’ is the number of output variables. ‘beta’ is the regularization parameter.

Examples found in repository ?

examples/ridge.rs (line 9)

5fn main() {
6    let x = na::DVector::from_vec(vec![1.0, 2.0, 3.0, 4.0]);
7    let d = na::DVector::from_vec(vec![5.0, 6.0]);
8
9    let mut ridge = Ridge::new(4, 2, 0.1);
10    ridge.set_data(&x, &d);
11    let weight = ridge.fit();
12
13    println!("{}", ridge);
14    println!("Weight:\n{}", weight);
15}

More examples

Hide additional examples

examples/xor.rs (line 37)

12fn main() {
13    let (train_input, train_expected_output) = data_gen(TRAIN_STEP, RANDOM_SEED);
14    let (test_input, test_expected_output) = data_gen(TEST_STEP, TEST_RANDOM_SEED);
15
16    let path = format!("{}/examples/graph", env!("CARGO_MANIFEST_DIR"));
17
18    let n_u = train_input.first().unwrap().len() as u64;
19    let n_y = train_expected_output.first().unwrap().len() as u64;
20
21    let mut model = EchoStateNetwork::new(
22        n_u,
23        n_y,
24        N_X,
25        0.1,
26        1.0,
27        0.9,
28        |x| x.tanh(),
29        None,
30        None,
31        1.0,
32        |x| x.clone_owned(),
33        |x| x.clone_owned(),
34        false,
35    );
36
37    let mut optimizer = Ridge::new(N_X, n_y, BETA);
38
39    model.train(&train_input, &train_expected_output, &mut optimizer);
40
41    let estimated_output = model.estimate(&test_input);
42
43    let (bits_l2_error, bits_l1_error) =
44        get_bits_error_rate(estimated_output.clone(), test_expected_output.clone());
45    let (l2_error, l1_error) =
46        get_error_rate(estimated_output.clone(), test_expected_output.clone());
47    println!("Bits Mean Squared Error: {}", bits_l2_error);
48    println!("Bits Mean Absolute Error: {}", bits_l1_error);
49    println!("Mean Squared Error: {}", l2_error);
50    println!("Mean Absolute Error: {}", l1_error);
51
52    let y_estimated = estimated_output.iter().map(|x| x[0]).collect::<Vec<f64>>();
53    let y_expected = test_expected_output
54        .clone()
55        .into_iter()
56        .flatten()
57        .collect::<Vec<f64>>();
58
59    plotter::plot(
60        "XOR",
61        (0..TEST_STEP).map(|v| v as f64).collect::<Vec<f64>>(),
62        vec![y_expected, y_estimated],
63        vec!["Expected".to_string(), "Output".to_string()],
64        Some(&path),
65    )
66    .unwrap();
67
68    write_as_serde(
69        model,
70        optimizer,
71        &train_input,
72        &train_expected_output,
73        &test_input,
74        &test_expected_output,
75        estimated_output,
76        None,
77    );
78}

Source

pub fn set_data(&mut self, x: &DVector<f64>, d: &DVector<f64>)

Update the internal state of the Ridge regression model. ‘x’ is the input vector (explanatory variable) and ‘d’ is the output vector (response variable).

Examples found in repository ?

examples/ridge.rs (line 10)

5fn main() {
6    let x = na::DVector::from_vec(vec![1.0, 2.0, 3.0, 4.0]);
7    let d = na::DVector::from_vec(vec![5.0, 6.0]);
8
9    let mut ridge = Ridge::new(4, 2, 0.1);
10    ridge.set_data(&x, &d);
11    let weight = ridge.fit();
12
13    println!("{}", ridge);
14    println!("Weight:\n{}", weight);
15}

Source

pub fn fit(&self) -> DMatrix<f64>

Fit the Ridge regression model and return the weight matrix.

Examples found in repository ?

examples/ridge.rs (line 11)

5fn main() {
6    let x = na::DVector::from_vec(vec![1.0, 2.0, 3.0, 4.0]);
7    let d = na::DVector::from_vec(vec![5.0, 6.0]);
8
9    let mut ridge = Ridge::new(4, 2, 0.1);
10    ridge.set_data(&x, &d);
11    let weight = ridge.fit();
12
13    println!("{}", ridge);
14    println!("Weight:\n{}", weight);
15}