lawkit_core/generate/
normal.rs1use super::{DataGenerator, GenerateConfig};
2use crate::error::Result;
3use rand::prelude::*;
4use rand_distr::{Distribution, Normal};
5
6#[derive(Debug, Clone)]
7pub struct NormalGenerator {
8 pub mean: f64,
9 pub stddev: f64,
10}
11
12impl NormalGenerator {
13 pub fn new(mean: f64, stddev: f64) -> Self {
14 Self { mean, stddev }
15 }
16}
17
18impl DataGenerator for NormalGenerator {
19 type Output = Vec<f64>;
20
21 fn generate(&self, config: &GenerateConfig) -> Result<Self::Output> {
22 let mut rng = config.create_rng();
23 let mut numbers = Vec::with_capacity(config.samples);
24
25 let normal = Normal::new(self.mean, self.stddev).map_err(|e| {
26 crate::error::BenfError::ParseError(format!("Invalid normal parameters: {e}"))
27 })?;
28
29 for _ in 0..config.samples {
30 let value = normal.sample(&mut rng);
31 numbers.push(value);
32 }
33
34 if config.fraud_rate > 0.0 {
36 inject_normal_fraud(&mut numbers, config.fraud_rate, &mut rng);
37 }
38
39 Ok(numbers)
40 }
41}
42
43fn inject_normal_fraud(numbers: &mut [f64], fraud_rate: f64, rng: &mut impl Rng) {
44 let fraud_count = (numbers.len() as f64 * fraud_rate) as usize;
45 let mean = numbers.iter().sum::<f64>() / numbers.len() as f64;
46 let stddev =
47 (numbers.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / numbers.len() as f64).sqrt();
48
49 for _ in 0..fraud_count {
51 let index = rng.gen_range(0..numbers.len());
52 let outlier_multiplier = rng.gen_range(3.5..6.0);
53 let sign = if rng.gen_bool(0.5) { 1.0 } else { -1.0 };
54 numbers[index] = mean + sign * outlier_multiplier * stddev;
55 }
56}
57
58#[cfg(test)]
59mod tests {
60 use super::*;
61
62 #[test]
63 fn test_normal_generator() {
64 let generator = NormalGenerator::new(100.0, 15.0);
65 let config = GenerateConfig::new(1000).with_seed(42);
66
67 let result = generator.generate(&config).unwrap();
68 assert_eq!(result.len(), 1000);
69
70 let mean = result.iter().sum::<f64>() / result.len() as f64;
72 let variance = result.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / result.len() as f64;
73 let stddev = variance.sqrt();
74
75 assert!((mean - 100.0).abs() < 5.0); assert!((stddev - 15.0).abs() < 3.0); }
78}