| 200 | } |
| 201 | |
| 202 | fn cov2corr(cov: &DMatrix<f64>) -> Result<DMatrix<f64>, HrpError> { |
| 203 | let n = cov.nrows(); |
| 204 | if n == 0 || cov.ncols() != n { |
| 205 | return Err(HrpError::DimensionMismatch("covariance must be square")); |
| 206 | } |
| 207 | let mut std = vec![0.0; n]; |
| 208 | for i in 0..n { |
| 209 | let v = cov[(i, i)]; |
| 210 | if v <= 0.0 { |
| 211 | return Err(HrpError::NoData); |
| 212 | } |
| 213 | std[i] = v.sqrt(); |
| 214 | } |
| 215 | let mut corr = DMatrix::zeros(n, n); |
| 216 | for i in 0..n { |
| 217 | for j in 0..n { |
| 218 | corr[(i, j)] = cov[(i, j)] / (std[i] * std[j]); |
| 219 | } |
| 220 | } |
| 221 | Ok(corr) |
| 222 | } |
| 223 | |
| 224 | fn corr_to_distances(corr: &DMatrix<f64>) -> DMatrix<f64> { |
| 225 | let n = corr.nrows(); |