Make rustfmt happy

rustfmt.toml

@@ -12,9 +12,6 @@ use_small_heuristics = "Default"
 # TODO: single line functions only where short, please?
 # https://github.com/rust-lang/rustfmt/issues/3358
 fn_single_line = false
-fn_params_layout = "Compressed"
-overflow_delimited_expr = true
-where_single_line = true
 
 # enum_discrim_align_threshold = 20
 # struct_field_align_threshold = 20
@@ -23,7 +20,6 @@ where_single_line = true
 edition = "2021"
 
 # Misc:
-inline_attribute_width = 80
 blank_lines_upper_bound = 2
 reorder_impl_items = true
 # report_todo = "Unnumbered"

src/distribution/bernoulli.rs

@@ -101,7 +101,7 @@ impl DiscreteCDF<u64, f64> for Bernoulli {
         self.b.cdf(x)
     }
 
-    /// Calculates the survival function for the 
+    /// Calculates the survival function for the
     /// bernoulli distribution at `x`.
     ///
     /// # Formula
@@ -158,6 +158,7 @@ impl Distribution<f64> for Bernoulli {
     fn mean(&self) -> Option<f64> {
         self.b.mean()
     }
+
     /// Returns the variance of the bernoulli
     /// distribution
     ///
@@ -169,6 +170,7 @@ impl Distribution<f64> for Bernoulli {
     fn variance(&self) -> Option<f64> {
         self.b.variance()
     }
+
     /// Returns the entropy of the bernoulli
     /// distribution
     ///
@@ -181,6 +183,7 @@ impl Distribution<f64> for Bernoulli {
     fn entropy(&self) -> Option<f64> {
         self.b.entropy()
     }
+
     /// Returns the skewness of the bernoulli
     /// distribution
     ///

src/distribution/beta.rs

@@ -155,7 +155,7 @@ impl ContinuousCDF<f64, f64> for Beta {
         } else if ulps_eq!(self.shape_a, 1.0) && ulps_eq!(self.shape_b, 1.0) {
             1. - x
         } else {
-            beta::beta_reg(self.shape_b, self.shape_a, 1.0 - x) 
+            beta::beta_reg(self.shape_b, self.shape_a, 1.0 - x)
         }
     }
 }
@@ -208,6 +208,7 @@ impl Distribution<f64> for Beta {
         };
         Some(mean)
     }
+
     /// Returns the variance of the beta distribution
     ///
     /// # Remarks
@@ -230,6 +231,7 @@ impl Distribution<f64> for Beta {
         };
         Some(var)
     }
+
     /// Returns the entropy of the beta distribution
     ///
     /// # Formula
@@ -251,6 +253,7 @@ impl Distribution<f64> for Beta {
         };
         Some(entr)
     }
+
     /// Returns the skewness of the Beta distribution
     ///
     /// # Formula

src/distribution/binomial.rs

@@ -181,6 +181,7 @@ impl Distribution<f64> for Binomial {
     fn mean(&self) -> Option<f64> {
         Some(self.p * self.n as f64)
     }
+
     /// Returns the variance of the binomial distribution
     ///
     /// # Formula
@@ -191,6 +192,7 @@ impl Distribution<f64> for Binomial {
     fn variance(&self) -> Option<f64> {
         Some(self.p * (1.0 - self.p) * self.n as f64)
     }
+
     /// Returns the entropy of the binomial distribution
     ///
     /// # Formula
@@ -209,6 +211,7 @@ impl Distribution<f64> for Binomial {
         };
         Some(entr)
     }
+
     /// Returns the skewness of the binomial distribution
     ///
     /// # Formula

src/distribution/categorical.rs

@@ -12,7 +12,7 @@ use std::f64;
 /// # Examples
 ///
 /// ```
-///
+/// 
 /// use statrs::distribution::{Categorical, Discrete};
 /// use statrs::statistics::Distribution;
 /// use statrs::prec;
@@ -25,7 +25,7 @@ use std::f64;
 pub struct Categorical {
     norm_pmf: Vec<f64>,
     cdf: Vec<f64>,
-    sf: Vec<f64>
+    sf: Vec<f64>,
 }
 
 impl Categorical {
@@ -194,6 +194,7 @@ impl Distribution<f64> for Categorical {
                 .fold(0.0, |acc, (idx, &val)| acc + idx as f64 * val),
         )
     }
+
     /// Returns the variance of the categorical distribution
     ///
     /// # Formula
@@ -217,6 +218,7 @@ impl Distribution<f64> for Categorical {
             });
         Some(var)
     }
+
     /// Returns the entropy of the categorical distribution
     ///
     /// # Formula
@@ -294,7 +296,7 @@ pub fn prob_mass_to_cdf(prob_mass: &[f64]) -> Vec<f64> {
     cdf
 }
 
-/// Computes the sf from the given cumulative densities. 
+/// Computes the sf from the given cumulative densities.
 /// Performs no parameter or bounds checking.
 pub fn cdf_to_sf(cdf: &[f64]) -> Vec<f64> {
     let max = *cdf.last().unwrap();

src/distribution/chi.rs

@@ -185,6 +185,7 @@ impl Distribution<f64> for Chi {
             Some(mean)
         }
     }
+
     /// Returns the variance of the chi distribution
     ///
     /// # Remarks
@@ -203,6 +204,7 @@ impl Distribution<f64> for Chi {
         let mean = self.mean()?;
         Some(self.freedom - mean * mean)
     }
+
     /// Returns the entropy of the chi distribution
     ///
     /// # Remarks
@@ -228,6 +230,7 @@ impl Distribution<f64> for Chi {
                 / 2.0;
         Some(entr)
     }
+
     /// Returns the skewness of the chi distribution
     ///
     /// # Remarks

src/distribution/chi_squared.rs

@@ -177,6 +177,7 @@ impl Distribution<f64> for ChiSquared {
     fn mean(&self) -> Option<f64> {
         self.g.mean()
     }
+
     /// Returns the variance of the chi-squared distribution
     ///
     /// # Formula
@@ -189,6 +190,7 @@ impl Distribution<f64> for ChiSquared {
     fn variance(&self) -> Option<f64> {
         self.g.variance()
     }
+
     /// Returns the entropy of the chi-squared distribution
     ///
     /// # Formula
@@ -202,6 +204,7 @@ impl Distribution<f64> for ChiSquared {
     fn entropy(&self) -> Option<f64> {
         self.g.entropy()
     }
+
     /// Returns the skewness of the chi-squared distribution
     ///
     /// # Formula

src/distribution/dirac.rs

@@ -56,7 +56,6 @@ impl ContinuousCDF<f64, f64> for Dirac {
     /// dirac distribution at `x`
     ///
     /// Where the value is 1 if x > `v`, 0 otherwise.
-    ///
     fn cdf(&self, x: f64) -> f64 {
         if x < self.0 {
             0.0
@@ -69,7 +68,6 @@ impl ContinuousCDF<f64, f64> for Dirac {
     /// dirac distribution at `x`
     ///
     /// Where the value is 0 if x > `v`, 1 otherwise.
-    ///
     fn sf(&self, x: f64) -> f64 {
         if x < self.0 {
             1.0
@@ -117,6 +115,7 @@ impl Distribution<f64> for Dirac {
     fn mean(&self) -> Option<f64> {
         Some(self.0)
     }
+
     /// Returns the variance of the dirac distribution
     ///
     /// # Formula
@@ -129,6 +128,7 @@ impl Distribution<f64> for Dirac {
     fn variance(&self) -> Option<f64> {
         Some(0.0)
     }
+
     /// Returns the entropy of the dirac distribution
     ///
     /// # Formula
@@ -141,6 +141,7 @@ impl Distribution<f64> for Dirac {
     fn entropy(&self) -> Option<f64> {
         Some(0.0)
     }
+
     /// Returns the skewness of the dirac distribution
     ///
     /// # Formula

src/distribution/dirichlet.rs

@@ -107,6 +107,7 @@ impl Dirichlet {
     fn alpha_sum(&self) -> f64 {
         self.alpha.fold(0.0, |acc, x| acc + x)
     }
+
     /// Returns the entropy of the dirichlet distribution
     ///
     /// # Formula

src/distribution/discrete_uniform.rs

@@ -84,7 +84,7 @@ impl DiscreteCDF<i64, f64> for DiscreteUniform {
     }
 
     fn sf(&self, x: i64) -> f64 {
-        //1. - self.cdf(x)
+        // 1. - self.cdf(x)
         if x < self.min {
             1.0
         } else if x >= self.max {
@@ -137,6 +137,7 @@ impl Distribution<f64> for DiscreteUniform {
     fn mean(&self) -> Option<f64> {
         Some((self.min + self.max) as f64 / 2.0)
     }
+
     /// Returns the variance of the discrete uniform distribution
     ///
     /// # Formula
@@ -148,6 +149,7 @@ impl Distribution<f64> for DiscreteUniform {
         let diff = (self.max - self.min) as f64;
         Some(((diff + 1.0) * (diff + 1.0) - 1.0) / 12.0)
     }
+
     /// Returns the entropy of the discrete uniform distribution
     ///
     /// # Formula
@@ -159,6 +161,7 @@ impl Distribution<f64> for DiscreteUniform {
         let diff = (self.max - self.min) as f64;
         Some((diff + 1.0).ln())
     }
+
     /// Returns the skewness of the discrete uniform distribution
     ///
     /// # Formula

src/distribution/empirical.rs

@@ -56,7 +56,6 @@ impl Empirical {
     ///
     /// let mut result = Empirical::new();
     /// assert!(result.is_ok());
-    ///
     /// ```
     pub fn new() -> Result<Empirical> {
         Ok(Empirical {
@@ -65,13 +64,15 @@ impl Empirical {
             data: BTreeMap::new(),
         })
     }
+
     pub fn from_vec(src: Vec<f64>) -> Empirical {
         let mut empirical = Empirical::new().unwrap();
         for elt in src.into_iter() {
             empirical.add(elt);
         }
         empirical
     }
+
     pub fn add(&mut self, data_point: f64) {
         if !data_point.is_nan() {
             self.sum += 1.;
@@ -89,6 +90,7 @@ impl Empirical {
             *self.data.entry(NonNan(data_point)).or_insert(0) += 1;
         }
     }
+
     pub fn remove(&mut self, data_point: f64) {
         if !data_point.is_nan() {
             if let (Some(val), Some((mean, var))) =
@@ -111,6 +113,7 @@ impl Empirical {
             }
         }
     }
+
     // Due to issues with rounding and floating-point accuracy the default
     // implementation may be ill-behaved.
     // Specialized inverse cdfs should be used whenever possible.
@@ -158,7 +161,7 @@ impl ::rand::distributions::Distribution<f64> for Empirical {
 /// Panics if number of samples is zero
 impl Max<f64> for Empirical {
     fn max(&self) -> f64 {
-        self.data.keys().rev().map(|key| key.0) .next().unwrap()
+        self.data.keys().rev().map(|key| key.0).next().unwrap()
     }
 }
 
@@ -173,6 +176,7 @@ impl Distribution<f64> for Empirical {
     fn mean(&self) -> Option<f64> {
         self.mean_and_var.map(|(mean, _)| mean)
     }
+
     fn variance(&self) -> Option<f64> {
         self.mean_and_var.map(|(_, var)| var / (self.sum - 1.))
     }
@@ -256,8 +260,8 @@ mod tests {
         let unchanged = empirical.clone();
         empirical.add(2.0);
         empirical.remove(2.0);
-         //because of rounding errors, this doesn't hold in general
-         //due to the mean and variance being calculated in a streaming way
+        // because of rounding errors, this doesn't hold in general
+        // due to the mean and variance being calculated in a streaming way
         assert_eq!(unchanged, empirical);
     }
 }

src/distribution/erlang.rs

@@ -166,6 +166,7 @@ impl Distribution<f64> for Erlang {
     fn mean(&self) -> Option<f64> {
         self.g.mean()
     }
+
     /// Returns the variance of the erlang distribution
     ///
     /// # Formula
@@ -178,6 +179,7 @@ impl Distribution<f64> for Erlang {
     fn variance(&self) -> Option<f64> {
         self.g.variance()
     }
+
     /// Returns the entropy of the erlang distribution
     ///
     /// # Formula
@@ -191,6 +193,7 @@ impl Distribution<f64> for Erlang {
     fn entropy(&self) -> Option<f64> {
         self.g.entropy()
     }
+
     /// Returns the skewness of the erlang distribution
     ///
     /// # Formula

src/distribution/exponential.rs

@@ -152,6 +152,7 @@ impl Distribution<f64> for Exp {
     fn mean(&self) -> Option<f64> {
         Some(1.0 / self.rate)
     }
+
     /// Returns the variance of the exponential distribution
     ///
     /// # Formula
@@ -164,6 +165,7 @@ impl Distribution<f64> for Exp {
     fn variance(&self) -> Option<f64> {
         Some(1.0 / (self.rate * self.rate))
     }
+
     /// Returns the entropy of the exponential distribution
     ///
     /// # Formula
@@ -176,6 +178,7 @@ impl Distribution<f64> for Exp {
     fn entropy(&self) -> Option<f64> {
         Some(1.0 - self.rate.ln())
     }
+
     /// Returns the skewness of the exponential distribution
     ///
     /// # Formula