roms feature added

src/feature.rs

@@ -53,6 +53,7 @@ where
     Periodogram(Periodogram<T, Self>),
     _PeriodogramPeaks,
     ReducedChi2,
+    Roms,
     Skew,
     StandardDeviation,
     StetsonK,

src/features/mod.rs

@@ -91,6 +91,9 @@ pub use periodogram::PeriodogramPeaks as _PeriodogramPeaks;
 mod reduced_chi2;
 pub use reduced_chi2::ReducedChi2;
 
+mod roms;
+pub use roms::Roms;
+
 mod skew;
 pub use skew::Skew;
 

src/features/roms.rs

@@ -0,0 +1,107 @@
+use crate::evaluator::*;
+
+macro_const! {
+    const DOC: &str = r"
+    Robust median statistic
+
+    $$
+    \frac1{N-1} \sum_{i=0}^{N-1} \frac{|m_i - \mathrm{median}(m_i)|}{\sigma_i}
+    $$
+    For non-variable data, it should be less than one.
+
+    - Depends on: **magnitude**, **errors**
+    - Minimum number of observations: **2**
+    - Number of features: **1**
+
+    Enoch, Brown, Burgasser 2003. [DOI:10.1086/376598](https://www.doi.org/10.1086/376598)
+";
+}
+
+#[doc = DOC!()]
+#[derive(Clone, Default, Debug, Serialize, Deserialize, JsonSchema)]
+
+pub struct Roms {}
+
+impl Roms {
+    pub fn new() -> Self {
+        Self {}
+    }
+
+    pub const fn doc() -> &'static str {
+        DOC
+    }
+}
+
+lazy_info!(
+    ROMS_INFO,
+    Roms,
+    size: 1,
+    min_ts_length: 2,
+    t_required: false,
+    m_required: true,
+    w_required: true,
+    sorting_required: false,
+);
+
+impl FeatureNamesDescriptionsTrait for Roms {
+    fn get_names(&self) -> Vec<&str> {
+        vec!["roms"]
+    }
+
+    fn get_descriptions(&self) -> Vec<&str> {
+        vec!["robust median statistic"]
+    }
+}
+
+impl<T> FeatureEvaluator<T> for Roms
+where
+    T: Float,
+{
+    fn eval(&self, ts: &mut TimeSeries<T>) -> Result<Vec<T>, EvaluatorError> {
+        self.check_ts_length(ts)?;
+        let n = ts.lenf();
+        let m_median = ts.m.get_median();
+        let tmp_sum =
+            ts.m.as_slice()
+                .iter()
+                .zip(ts.w.as_slice().iter())
+                .map(|(&m, &w)| ((m - m_median).abs() * w.sqrt()))
+                .filter(|&x| x.is_finite())
+                .sum::<T>();
+        let value = tmp_sum / (n - T::one());
+        Ok(vec![value])
+    }
+}
+
+#[cfg(test)]
+#[allow(clippy::unreadable_literal)]
+#[allow(clippy::excessive_precision)]
+
+mod tests {
+    use super::*;
+    use crate::extractor::FeatureExtractor;
+    use crate::tests::*;
+
+    check_feature!(Roms);
+
+    feature_test!(
+        roms,
+        [Roms::new()],
+        [2.6035533],
+        [1.0_f32, 2.0, 3.0, 4.0, 5.0],
+        [1.0_f32, 1.0, 2.0, 3.0, 5.0],
+        [1.0_f32, 4.0, 1.0, 2.0, 4.0],
+    );
+
+    #[test]
+    fn roms_const_data() {
+        let eval = Roms::default();
+        let x = linspace(0.0_f32, 10.0, 10);
+        let y = vec![1.0_f32, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0];
+        let z = vec![1.0_f32, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0];
+        let mut ts = TimeSeries::new(&x, &y, &z);
+        let actual = eval.eval(&mut ts).unwrap();
+        let desired = [0.0_f32];
+        all_close(&actual, &desired, 1e-10);
+    }
+}