libtest: DSTify Stats

src/libtest/stats.rs

@@ -38,7 +38,7 @@ fn local_sort<T: Float>(v: &mut [T]) {
 }
 
 /// Trait that provides simple descriptive statistics on a univariate set of numeric samples.
-pub trait Stats <T: FloatMath + FromPrimitive>{
+pub trait Stats <T: FloatMath + FromPrimitive> for Sized? {
 
     /// Sum of the samples.
     ///
@@ -47,24 +47,24 @@ pub trait Stats <T: FloatMath + FromPrimitive>{
     /// ["Adaptive Precision Floating-Point Arithmetic and Fast Robust Geometric Predicates"]
     /// (http://www.cs.cmu.edu/~quake-papers/robust-arithmetic.ps)
     /// *Discrete & Computational Geometry 18*, 3 (Oct 1997), 305-363, Shewchuk J.R.
-    fn sum(self) -> T;
+    fn sum(&self) -> T;
 
     /// Minimum value of the samples.
-    fn min(self) -> T;
+    fn min(&self) -> T;
 
     /// Maximum value of the samples.
-    fn max(self) -> T;
+    fn max(&self) -> T;
 
     /// Arithmetic mean (average) of the samples: sum divided by sample-count.
     ///
     /// See: https://en.wikipedia.org/wiki/Arithmetic_mean
-    fn mean(self) -> T;
+    fn mean(&self) -> T;
 
     /// Median of the samples: value separating the lower half of the samples from the higher half.
     /// Equal to `self.percentile(50.0)`.
     ///
     /// See: https://en.wikipedia.org/wiki/Median
-    fn median(self) -> T;
+    fn median(&self) -> T;
 
     /// Variance of the samples: bias-corrected mean of the squares of the differences of each
     /// sample from the sample mean. Note that this calculates the _sample variance_ rather than the
@@ -73,7 +73,7 @@ pub trait Stats <T: FloatMath + FromPrimitive>{
     /// than `n`.
     ///
     /// See: https://en.wikipedia.org/wiki/Variance
-    fn var(self) -> T;
+    fn var(&self) -> T;
 
     /// Standard deviation: the square root of the sample variance.
     ///
@@ -81,13 +81,13 @@ pub trait Stats <T: FloatMath + FromPrimitive>{
     /// `median_abs_dev` for unknown distributions.
     ///
     /// See: https://en.wikipedia.org/wiki/Standard_deviation
-    fn std_dev(self) -> T;
+    fn std_dev(&self) -> T;
 
     /// Standard deviation as a percent of the mean value. See `std_dev` and `mean`.
     ///
     /// Note: this is not a robust statistic for non-normal distributions. Prefer the
     /// `median_abs_dev_pct` for unknown distributions.
-    fn std_dev_pct(self) -> T;
+    fn std_dev_pct(&self) -> T;
 
     /// Scaled median of the absolute deviations of each sample from the sample median. This is a
     /// robust (distribution-agnostic) estimator of sample variability. Use this in preference to
@@ -96,10 +96,10 @@ pub trait Stats <T: FloatMath + FromPrimitive>{
     /// deviation.
     ///
     /// See: http://en.wikipedia.org/wiki/Median_absolute_deviation
-    fn median_abs_dev(self) -> T;
+    fn median_abs_dev(&self) -> T;
 
     /// Median absolute deviation as a percent of the median. See `median_abs_dev` and `median`.
-    fn median_abs_dev_pct(self) -> T;
+    fn median_abs_dev_pct(&self) -> T;
 
     /// Percentile: the value below which `pct` percent of the values in `self` fall. For example,
     /// percentile(95.0) will return the value `v` such that 95% of the samples `s` in `self`
@@ -108,7 +108,7 @@ pub trait Stats <T: FloatMath + FromPrimitive>{
     /// Calculated by linear interpolation between closest ranks.
     ///
     /// See: http://en.wikipedia.org/wiki/Percentile
-    fn percentile(self, pct: T) -> T;
+    fn percentile(&self, pct: T) -> T;
 
     /// Quartiles of the sample: three values that divide the sample into four equal groups, each
     /// with 1/4 of the data. The middle value is the median. See `median` and `percentile`. This
@@ -116,13 +116,13 @@ pub trait Stats <T: FloatMath + FromPrimitive>{
     /// is otherwise equivalent.
     ///
     /// See also: https://en.wikipedia.org/wiki/Quartile
-    fn quartiles(self) -> (T,T,T);
+    fn quartiles(&self) -> (T,T,T);
 
     /// Inter-quartile range: the difference between the 25th percentile (1st quartile) and the 75th
     /// percentile (3rd quartile). See `quartiles`.
     ///
     /// See also: https://en.wikipedia.org/wiki/Interquartile_range
-    fn iqr(self) -> T;
+    fn iqr(&self) -> T;
 }
 
 /// Extracted collection of all the summary statistics of a sample set.
@@ -163,9 +163,9 @@ impl<T: FloatMath + FromPrimitive> Summary<T> {
     }
 }
 
-impl<'a, T: FloatMath + FromPrimitive> Stats<T> for &'a [T] {
+impl<T: FloatMath + FromPrimitive> Stats<T> for [T] {
     // FIXME #11059 handle NaN, inf and overflow
-    fn sum(self) -> T {
+    fn sum(&self) -> T {
         let mut partials = vec![];
 
         for &mut x in self.iter() {
@@ -198,26 +198,26 @@ impl<'a, T: FloatMath + FromPrimitive> Stats<T> for &'a [T] {
         partials.iter().fold(zero, |p, q| p + *q)
     }
 
-    fn min(self) -> T {
+    fn min(&self) -> T {
         assert!(self.len() != 0);
         self.iter().fold(self[0], |p, q| p.min(*q))
     }
 
-    fn max(self) -> T {
+    fn max(&self) -> T {
         assert!(self.len() != 0);
         self.iter().fold(self[0], |p, q| p.max(*q))
     }
 
-    fn mean(self) -> T {
+    fn mean(&self) -> T {
         assert!(self.len() != 0);
         self.sum() / FromPrimitive::from_uint(self.len()).unwrap()
     }
 
-    fn median(self) -> T {
+    fn median(&self) -> T {
         self.percentile(FromPrimitive::from_uint(50).unwrap())
     }
 
-    fn var(self) -> T {
+    fn var(&self) -> T {
         if self.len() < 2 {
             Float::zero()
         } else {
@@ -235,16 +235,16 @@ impl<'a, T: FloatMath + FromPrimitive> Stats<T> for &'a [T] {
         }
     }
 
-    fn std_dev(self) -> T {
+    fn std_dev(&self) -> T {
         self.var().sqrt()
     }
 
-    fn std_dev_pct(self) -> T {
+    fn std_dev_pct(&self) -> T {
         let hundred = FromPrimitive::from_uint(100).unwrap();
         (self.std_dev() / self.mean()) * hundred
     }
 
-    fn median_abs_dev(self) -> T {
+    fn median_abs_dev(&self) -> T {
         let med = self.median();
         let abs_devs: Vec<T> = self.iter().map(|&v| (med - v).abs()).collect();
         // This constant is derived by smarter statistics brains than me, but it is
@@ -253,18 +253,18 @@ impl<'a, T: FloatMath + FromPrimitive> Stats<T> for &'a [T] {
         abs_devs.as_slice().median() * number
     }
 
-    fn median_abs_dev_pct(self) -> T {
+    fn median_abs_dev_pct(&self) -> T {
         let hundred = FromPrimitive::from_uint(100).unwrap();
         (self.median_abs_dev() / self.median()) * hundred
     }
 
-    fn percentile(self, pct: T) -> T {
+    fn percentile(&self, pct: T) -> T {
         let mut tmp = self.to_vec();
         local_sort(tmp.as_mut_slice());
         percentile_of_sorted(tmp.as_slice(), pct)
     }
 
-    fn quartiles(self) -> (T,T,T) {
+    fn quartiles(&self) -> (T,T,T) {
         let mut tmp = self.to_vec();
         local_sort(tmp.as_mut_slice());
         let first = FromPrimitive::from_uint(25).unwrap();
@@ -276,7 +276,7 @@ impl<'a, T: FloatMath + FromPrimitive> Stats<T> for &'a [T] {
         (a,b,c)
     }
 
-    fn iqr(self) -> T {
+    fn iqr(&self) -> T {
         let (a,_,c) = self.quartiles();
         c - a
     }