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Merge pull request #456 from RishabhSaini/issue/4012
Improving the encapsulation (chunking) algorithm
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,109 @@ | ||
| //! This module holds implementations of some basic statistical properties, such as mean and standard deviation. | ||
|
|
||
| pub(crate) fn mean(data: &[u64]) -> Option<f64> { | ||
| if data.is_empty() { | ||
| None | ||
| } else { | ||
| Some(data.iter().sum::<u64>() as f64 / data.len() as f64) | ||
| } | ||
| } | ||
|
|
||
| pub(crate) fn std_deviation(data: &[u64]) -> Option<f64> { | ||
| match (mean(data), data.len()) { | ||
| (Some(data_mean), count) if count > 0 => { | ||
| let variance = data | ||
| .iter() | ||
| .map(|value| { | ||
| let diff = data_mean - (*value as f64); | ||
| diff * diff | ||
| }) | ||
| .sum::<f64>() | ||
| / count as f64; | ||
| Some(variance.sqrt()) | ||
| } | ||
| _ => None, | ||
| } | ||
| } | ||
|
|
||
| //Assumed sorted | ||
| pub(crate) fn median_absolute_deviation(data: &mut [u64]) -> Option<(f64, f64)> { | ||
| if data.is_empty() { | ||
| None | ||
| } else { | ||
| //Sort data | ||
| //data.sort_by(|a, b| a.partial_cmp(b).unwrap()); | ||
|
|
||
| //Find median of data | ||
| let median_data: f64 = match data.len() % 2 { | ||
| 1 => data[data.len() / 2] as f64, | ||
| _ => 0.5 * (data[data.len() / 2 - 1] + data[data.len() / 2]) as f64, | ||
| }; | ||
|
|
||
| //Absolute deviations | ||
| let mut absolute_deviations = Vec::new(); | ||
| for size in data { | ||
| absolute_deviations.push(f64::abs(*size as f64 - median_data)) | ||
| } | ||
|
|
||
| absolute_deviations.sort_by(|a, b| a.partial_cmp(b).unwrap()); | ||
| let l = absolute_deviations.len(); | ||
| let mad: f64 = match l % 2 { | ||
| 1 => absolute_deviations[l / 2], | ||
| _ => 0.5 * (absolute_deviations[l / 2 - 1] + absolute_deviations[l / 2]), | ||
| }; | ||
|
|
||
| Some((median_data, mad)) | ||
| } | ||
| } | ||
|
|
||
| #[test] | ||
| fn test_mean() { | ||
| assert_eq!(mean(&[]), None); | ||
| for v in [0u64, 1, 5, 100] { | ||
| assert_eq!(mean(&[v]), Some(v as f64)); | ||
| } | ||
| assert_eq!(mean(&[0, 1]), Some(0.5)); | ||
| assert_eq!(mean(&[0, 5, 100]), Some(35.0)); | ||
| assert_eq!(mean(&[7, 4, 30, 14]), Some(13.75)); | ||
| } | ||
|
|
||
| #[test] | ||
| fn test_std_deviation() { | ||
| assert_eq!(std_deviation(&[]), None); | ||
| for v in [0u64, 1, 5, 100] { | ||
| assert_eq!(std_deviation(&[v]), Some(0 as f64)); | ||
| } | ||
| assert_eq!(std_deviation(&[1, 4]), Some(1.5)); | ||
| assert_eq!(std_deviation(&[2, 2, 2, 2]), Some(0.0)); | ||
| assert_eq!( | ||
| std_deviation(&[1, 20, 300, 4000, 50000, 600000, 7000000, 80000000]), | ||
| Some(26193874.56387471) | ||
| ); | ||
| } | ||
|
|
||
| #[test] | ||
| fn test_median_absolute_deviation() { | ||
| //Assumes sorted | ||
| assert_eq!(median_absolute_deviation(&mut []), None); | ||
| for v in [0u64, 1, 5, 100] { | ||
| assert_eq!(median_absolute_deviation(&mut [v]), Some((v as f64, 0.0))); | ||
| } | ||
| assert_eq!(median_absolute_deviation(&mut [1, 4]), Some((2.5, 1.5))); | ||
| assert_eq!( | ||
| median_absolute_deviation(&mut [2, 2, 2, 2]), | ||
| Some((2.0, 0.0)) | ||
| ); | ||
| assert_eq!( | ||
| median_absolute_deviation(&mut [ | ||
| 1, 2, 3, 3, 4, 4, 4, 5, 5, 6, 6, 6, 7, 7, 7, 8, 9, 12, 52, 90 | ||
| ]), | ||
| Some((6.0, 2.0)) | ||
| ); | ||
|
|
||
| //if more than half of the data has the same value, MAD = 0, thus any | ||
| //value different from the residual median is classified as an outlier | ||
| assert_eq!( | ||
| median_absolute_deviation(&mut [0, 1, 1, 1, 1, 1, 1, 1, 0]), | ||
| Some((1.0, 0.0)) | ||
| ); | ||
| } |
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