From 0d2c1ccee7ec921281c325a84a143182a5d83560 Mon Sep 17 00:00:00 2001
From: Andrea Cavalli <andrea@warp.ovh>
Date: Mon, 2 Dec 2019 13:03:20 +0100
Subject: [PATCH] ddof

---
 src/moments/variance0.rs | 10 +++++-----
 src/moments/variance1.rs | 10 +++++-----
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/moments/variance0.rs b/src/moments/variance0.rs
index 5913bad..0ea2ab7 100644
--- a/src/moments/variance0.rs
+++ b/src/moments/variance0.rs
@@ -1,4 +1,4 @@
-const ddof: f64 = 0.;
+const ddof0: f64 = 0.;
 
 /// Estimate the arithmetic mean and the variance of a sequence of numbers
 /// ("population").
@@ -51,7 +51,7 @@ impl Variance0 {
         // See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance.
         let n = f64::approx_from(self.avg.len()).unwrap();
         self.avg.add_inner(delta_n);
-        self.sum_2 += delta_n * delta_n * n * (n - ddof);
+        self.sum_2 += delta_n * delta_n * n * (n - ddof0);
     }
 
     /// Determine whether the sample is empty.
@@ -79,10 +79,10 @@ impl Variance0 {
     /// This is an unbiased estimator of the variance of the population.
     #[inline]
     pub fn sample_variance(&self) -> f64 {
-        if self.avg.len() < 1 + ddof as u64 {
+        if self.avg.len() < 1 + ddof0 as u64 {
             return 0.;
         }
-        self.sum_2 / f64::approx_from(self.avg.len() - ddof as u64).unwrap()
+        self.sum_2 / f64::approx_from(self.avg.len() - ddof0 as u64).unwrap()
     }
 
     /// Calculate the population variance of the sample.
@@ -91,7 +91,7 @@ impl Variance0 {
     #[inline]
     pub fn population_variance(&self) -> f64 {
         let n = self.avg.len();
-        if n < 1 + ddof as u64 {
+        if n < 1 + ddof0 as u64 {
             return 0.;
         }
         self.sum_2 / f64::approx_from(n).unwrap()
diff --git a/src/moments/variance1.rs b/src/moments/variance1.rs
index e401c30..67efdfb 100644
--- a/src/moments/variance1.rs
+++ b/src/moments/variance1.rs
@@ -1,4 +1,4 @@
-const ddof: f64 = 1.;
+const ddof1: f64 = 1.;
 
 /// Estimate the arithmetic mean and the variance of a sequence of numbers
 /// ("population").
@@ -51,7 +51,7 @@ impl Variance1 {
         // See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance.
         let n = f64::approx_from(self.avg.len()).unwrap();
         self.avg.add_inner(delta_n);
-        self.sum_2 += delta_n * delta_n * n * (n - ddof);
+        self.sum_2 += delta_n * delta_n * n * (n - ddof1);
     }
 
     /// Determine whether the sample is empty.
@@ -79,10 +79,10 @@ impl Variance1 {
     /// This is an unbiased estimator of the variance of the population.
     #[inline]
     pub fn sample_variance(&self) -> f64 {
-        if self.avg.len() < 1 + ddof as u64 {
+        if self.avg.len() < 1 + ddof1 as u64 {
             return 0.;
         }
-        self.sum_2 / f64::approx_from(self.avg.len() - ddof as u64).unwrap()
+        self.sum_2 / f64::approx_from(self.avg.len() - ddof1 as u64).unwrap()
     }
 
     /// Calculate the population variance of the sample.
@@ -91,7 +91,7 @@ impl Variance1 {
     #[inline]
     pub fn population_variance(&self) -> f64 {
         let n = self.avg.len();
-        if n < 1 + ddof as u64 {
+        if n < 1 + ddof1 as u64 {
             return 0.;
         }
         self.sum_2 / f64::approx_from(n).unwrap()