Fix code for resolver=1 and resolver=2

src/moments/mod.rs

@@ -135,21 +135,23 @@ macro_rules! define_moments_common {
  #[cfg_attr(doc_cfg, doc(cfg(any(feature = "std", feature = "libm"))))]
  #[inline]
  pub fn sample_skewness(&self) -> f64 {
+ use num_traits::Float;
+
  if self.n < 2 {
  return 0.;
  }
  let n = self.n.to_f64().unwrap();
  if self.n < 3 {
  // Method of moments
  return self.central_moment(3) /
- num_traits::Float::powf(
+ Float::powf(
  n * (self.central_moment(2) / (n - 1.)), 1.5
  )
  }
  // Adjusted Fisher-Pearson standardized moment coefficient
- num_traits::Float::sqrt(n * (n - 1.)) /
+ Float::sqrt(n * (n - 1.)) /
  (n * (n - 2.)) *
- num_traits::Float::powf(
+ Float::powf(
  self.central_moment(3) / (self.central_moment(2) / n),
  1.5
  )

src/moments/skewness.rs

@@ -1,3 +1,5 @@
+use num_traits::Float;
+
 /// Estimate the arithmetic mean, the variance and the skewness of a sequence of
 /// numbers ("population").
 ///
@@ -98,8 +100,7 @@ impl Skewness {
  let n = self.len().to_f64().unwrap();
  let sum_2 = self.avg.sum_2;
  debug_assert_ne!(sum_2, 0.);
- num_traits::Float::sqrt(n) * self.sum_3
- / num_traits::Float::sqrt(sum_2*sum_2*sum_2)
+ Float::sqrt(n) * self.sum_3 / Float::sqrt(sum_2*sum_2*sum_2)
  }
 }
 

src/quantile.rs

@@ -1,9 +1,7 @@
 use core::cmp::min;
 
 use easy_cast::{Conv, ConvFloat};
-use num_traits::ToPrimitive;
-#[cfg(not(feature = "std"))]
-use num_traits::Float;
+use num_traits::{ToPrimitive, Float};
 use float_ord::sort as sort_floats;
 #[cfg(feature = "serde1")] use serde::{Serialize, Deserialize};
 
@@ -182,7 +180,7 @@ impl Estimate for Quantile {
  let d = self.m[i] - self.n[i].to_f64().unwrap();
  if d >= 1. && self.n[i + 1] - self.n[i] > 1 ||
  d <= -1. && self.n[i - 1] - self.n[i] < -1 {
- let d = d.signum();
+ let d = Float::signum(d);
  let q_new = self.parabolic(i, d);
  if self.q[i - 1] < q_new && q_new < self.q[i + 1] {
  self.q[i] = q_new;

src/weighted_mean.rs

@@ -1,4 +1,5 @@
 #[cfg(feature = "serde1")] use serde::{Serialize, Deserialize};
+use num_traits::Float;
 use super::{MeanWithError, Estimate, Merge};
 
 /// Estimate the weighted and unweighted arithmetic mean of a sequence of
@@ -283,7 +284,7 @@ impl WeightedMeanWithError {
  #[cfg_attr(doc_cfg, doc(cfg(any(feature = "std", feature = "libm"))))]
  #[inline]
  pub fn error(&self) -> f64 {
- num_traits::Float::sqrt(self.variance_of_weighted_mean())
+ Float::sqrt(self.variance_of_weighted_mean())
  }
 }