pub fn plot(
name: &str,
x: Vec<f64>,
ys: Vec<Vec<f64>>,
label: Vec<String>,
path: Option<&str>,
) -> Result<(), Box<dyn Error>>Expand description
Plot the graph of the given data. ‘name’ is the name of the file. ‘x’ is the x-axis data. ‘ys’ is the y-axis data. ‘label’ is the label of the data. ‘path’ is the path to save the graph.
Examples found in repository?
examples/xor.rs (lines 59-65)
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}