(x: &[f64], y: &[f64])
| 10 | pub type CodependenceResult<T> = Result<T, CodependenceError>; |
| 11 | |
| 12 | fn corrcoef(x: &[f64], y: &[f64]) -> CodependenceResult<f64> { |
| 13 | if x.len() != y.len() { |
| 14 | return Err(CodependenceError::InputLengthMismatch); |
| 15 | } |
| 16 | if x.len() < 2 { |
| 17 | return Err(CodependenceError::InputTooShort); |
| 18 | } |
| 19 | |
| 20 | let n = x.len() as f64; |
| 21 | let mean_x = x.iter().sum::<f64>() / n; |
| 22 | let mean_y = y.iter().sum::<f64>() / n; |
| 23 | |
| 24 | let mut cov = 0.0; |
| 25 | let mut var_x = 0.0; |
| 26 | let mut var_y = 0.0; |
| 27 | |
| 28 | for (xi, yi) in x.iter().zip(y.iter()) { |
| 29 | let dx = xi - mean_x; |
| 30 | let dy = yi - mean_y; |
| 31 | cov += dx * dy; |
| 32 | var_x += dx * dx; |
| 33 | var_y += dy * dy; |
| 34 | } |
| 35 | |
| 36 | if var_x == 0.0 || var_y == 0.0 { |
| 37 | return Err(CodependenceError::ZeroVariance); |
| 38 | } |
| 39 | |
| 40 | Ok(cov / (var_x * var_y).sqrt()) |
| 41 | } |
| 42 | |
| 43 | fn histogram(values: &[f64], n_bins: usize) -> CodependenceResult<Vec<usize>> { |
| 44 | if n_bins == 0 { |
no test coverage detected