Merge pull request #139 from vks/fix-doctests

Fix doctests

Author: troublescooter

CHANGELOG.md

@@ -1,3 +1,16 @@
+v0.14.0
+- upgrade `rand` dependency to `0.8`
+- fix inaccurate sampling of `Gamma`
+- Implemented Empirical distribution
+- Implemented Laplace distribution
+- Removed Checked* traits
+- Almost clippy-clean
+- Almost fully enabled rustfmt
+- Begin applying consistent numeric relative-accuracy targets with the approx crate
+- Introduce macro to generate testing boilerplate, yet not all tests use this yet
+- Moved to dynamic vectors in the MultivariateNormal distribution
+- Reduced a number of distribution-specific traits into the Distribution and DiscreteDistribution traits
+
 v0.13.0
 - Implemented `MultivariateNormal` distribution (depends on `nalgebra 0.19`)
 - Implemented `Dirac` distribution

src/distribution/beta.rs

@@ -291,7 +291,7 @@ impl Continuous<f64, f64> for Beta {
     ///
     /// where `α` is shapeA, `β` is shapeB, and `Γ` is the gamma function
     fn pdf(&self, x: f64) -> f64 {
-        if x < 0.0 || x > 1.0 {
+        if !(0.0..=1.0).contains(&x) {
             0.0
         } else if self.shape_a.is_infinite() {
             if ulps_eq!(x, 1.0) {
@@ -329,7 +329,7 @@ impl Continuous<f64, f64> for Beta {
     ///
     /// where `α` is shapeA, `β` is shapeB, and `Γ` is the gamma function
     fn ln_pdf(&self, x: f64) -> f64 {
-        if x < 0.0 || x > 1.0 {
+        if !(0.0..=1.0).contains(&x) {
             -INF
         } else if self.shape_a.is_infinite() {
             if ulps_eq!(x, 1.0) {

src/distribution/categorical.rs

@@ -282,7 +282,7 @@ pub fn prob_mass_to_cdf(prob_mass: &[f64]) -> Vec<f64> {
 fn binary_index(search: &[f64], val: f64) -> usize {
     use std::cmp;
 
-    let mut low = 0 as isize;
+    let mut low = 0_isize;
     let mut high = search.len() as isize - 1;
     while low <= high {
         let mid = low + ((high - low) / 2);

src/distribution/chi.rs

@@ -145,7 +145,7 @@ impl Distribution<f64> for Chi {
     /// where `k` is degrees of freedom and `Γ` is the gamma function
     fn mean(&self) -> Option<f64> {
         if self.freedom.is_infinite() {
-            return None;
+            None
         } else if self.freedom > 300.0 {
             // Large n approximation based on the Stirling series approximation to the Gamma function
             // This avoids call the Gamma function with large arguments and returning NaN

src/distribution/negative_binomial.rs

@@ -51,7 +51,7 @@ impl NegativeBinomial {
         if p.is_nan() || p < 0.0 || p > 1.0 || r.is_nan() || r < 0.0 {
             Err(StatsError::BadParams)
         } else {
-            Ok(NegativeBinomial { p, r })
+            Ok(NegativeBinomial { r, p })
         }
     }
 

src/distribution/normal.rs

@@ -90,7 +90,7 @@ impl ContinuousCDF<f64, f64> for Normal {
     /// where `μ` is the mean, `σ` is the standard deviation and `erfc_inv` is
     /// the inverse of the complementary error function
     fn inverse_cdf(&self, x: f64) -> f64 {
-        if x < 0.0 || x > 1.0 {
+        if !(0.0..=1.0).contains(&x) {
             panic!("x must be in [0, 1]");
         } else {
             self.mean - (self.std_dev * f64::consts::SQRT_2 * erf::erfc_inv(2.0 * x))

src/distribution/students_t.rs

@@ -155,7 +155,7 @@ impl ContinuousCDF<f64, f64> for StudentsT {
     /// Student's T-distribution at `x`
     fn inverse_cdf(&self, x: f64) -> f64 {
         // first calculate inverse_cdf for normal Student's T
-        assert!(x >= 0.0 && x <= 1.0);
+        assert!((0.0..=1.0).contains(&x));
         let x = 2. * x.min(1. - x);
         let a = 0.5 * self.freedom;
         let b = 0.5;

src/function/beta.rs

@@ -115,7 +115,7 @@ pub fn checked_beta_reg(a: f64, b: f64, x: f64) -> Result<f64> {
         Err(StatsError::ArgMustBePositive("a"))
     } else if b <= 0.0 {
         Err(StatsError::ArgMustBePositive("b"))
-    } else if x < 0.0 || x > 1.0 {
+    } else if !(0.0..=1.0).contains(&x) {
         Err(StatsError::ArgIntervalIncl("x", 0.0, 1.0))
     } else {
         let bt = if is_zero(x) || ulps_eq!(x, 1.0) {
@@ -275,7 +275,7 @@ pub fn inv_beta_reg(mut a: f64, mut b: f64, mut x: f64) -> f64 {
     const SAE: i32 = -30;
     const FPU: f64 = 1e-30; // 10^SAE
 
-    debug_assert!(x >= 0.0 && x <= 1.0 && a > 0.0 && b > 0.0);
+    debug_assert!((0.0..=1.0).contains(&x) && a > 0.0 && b > 0.0);
 
     if x == 0.0 {
         return 0.0;

src/function/logistic.rs

@@ -24,7 +24,7 @@ pub fn logit(p: f64) -> f64 {
 ///
 /// If `p < 0.0` or `p > 1.0`
 pub fn checked_logit(p: f64) -> Result<f64> {
-    if p < 0.0 || p > 1.0 {
+    if !(0.0..=1.0).contains(&p) {
         Err(StatsError::ArgIntervalIncl("p", 0.0, 1.0))
     } else {
         Ok((p / (1.0 - p)).ln())

src/statistics/slice_statistics.rs

@@ -129,7 +129,7 @@ impl<D: AsMut<[f64]> + AsRef<[f64]>> OrderStatistics<f64> for Data<D> {
     }
 
     fn quantile(&mut self, tau: f64) -> f64 {
-        if tau < 0.0 || tau > 1.0 || self.is_empty() {
+        if !(0.0..=1.0).contains(&tau) || self.is_empty() {
             return f64::NAN;
         }