Added Dirac Distribution [Feature Request]

src/distribution/dirac.rs

@@ -0,0 +1,324 @@
+use crate::distribution::{Continuous, Univariate};
+use crate::statistics::*;
+use crate::{Result, StatsError};
+use rand::distributions::Distribution;
+use rand::Rng;
+use std::f64;
+
+/// Implements the [Dirac Delta](https://en.wikipedia.org/wiki/Dirac_delta_function#As_a_distribution)
+/// distribution
+///
+/// # Examples
+///
+/// ```
+/// use statrs::distribution::{Dirac};
+///
+/// let n = Dirac::new(3.0).unwrap();
+/// assert_eq!(n.mean(), 3.0);
+/// assert_eq!(n.pdf(1.0), 0.0);
+/// ```
+#[derive(Debug, Copy, Clone, PartialEq)]
+pub struct Dirac(f64);
+
+impl Dirac {
+    ///  Constructs a new dirac distribution function at value `v`.
+    ///
+    /// # Errors
+    ///
+    /// Returns an error if `v` is not-a-number.
+    ///
+    /// # Examples
+    ///
+    /// ```
+    /// use statrs::distribution::Dirac;
+    ///
+    /// let mut result = Dirac::new(0.0);
+    /// assert!(result.is_ok());
+    ///
+    /// result = Dirac::new(f64::NAN);
+    /// assert!(result.is_err());
+    /// ```
+    pub fn new(v: f64) -> Result<Self> {
+        if v.is_nan() {
+            Err(StatsError::BadParams)
+        } else {
+            Ok(Dirac(v))
+        }
+    }
+}
+
+impl Distribution<f64> for Dirac {
+    fn sample<R: Rng + ?Sized>(&self, _: &mut R) -> f64 {
+        self.0
+    }
+}
+
+impl Univariate<f64, f64> for Dirac {
+    /// Calculates the cumulative distribution function for the
+    /// 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 } else { 1.0 }
+    }
+}
+
+impl Min<f64> for Dirac {
+    /// Returns the minimum value in the domain of the
+    /// dirac distribution representable by a double precision float
+    ///
+    /// # Formula
+    ///
+    /// ```ignore
+    /// v
+    /// ```
+    fn min(&self) -> f64 { self.0 }
+}
+
+impl Max<f64> for Dirac {
+    /// Returns the maximum value in the domain of the
+    /// dirac distribution representable by a double precision float
+    ///
+    /// # Formula
+    ///
+    /// ```ignore
+    /// v
+    /// ```
+    fn max(&self) -> f64 { self.0 }
+}
+
+impl Mean<f64> for Dirac {
+    /// Returns the mean of the dirac distribution
+    ///
+    /// # Remarks
+    ///
+    /// Since the only value that can be produced by this distribution is `v` with probability
+    /// 1, it is just `v`.
+    fn mean(&self) -> f64 { self.0 }
+}
+
+impl Variance<f64> for Dirac {
+    /// Returns the variance of the normal distribution
+    ///
+    /// # Formula
+    ///
+    /// ```ignore
+    /// 0
+    /// ```
+    ///
+    /// Since only one value can be produced there is no variance.
+    fn variance(&self) -> f64 { 0.0 }
+
+    /// Returns the standard deviation of the normal distribution
+    ///
+    /// # Remarks
+    ///
+    /// Since there is no variance in draws from this distribution the standard deviation is
+    /// also 0.
+    fn std_dev(&self) -> f64 { 0.0 }
+}
+
+impl Entropy<f64> for Dirac {
+    /// Returns the entropy of the dirac distribution
+    ///
+    /// # Formula
+    ///
+    /// ```ignore
+    /// 0
+    /// ```
+    ///
+    /// Since this distribution has full certainty, it encodes no information
+    fn entropy(&self) -> f64 { 0.0 }
+}
+
+impl Skewness<f64> for Dirac {
+    /// Returns the skewness of the dirac distribution
+    ///
+    /// # Formula
+    ///
+    /// ```ignore
+    /// 0
+    /// ```
+    fn skewness(&self) -> f64 { 0.0 }
+}
+
+impl Median<f64> for Dirac {
+    /// Returns the median of the dirac distribution
+    ///
+    /// # Formula
+    ///
+    /// ```ignore
+    /// v
+    /// ```
+    ///
+    /// where `v` is the point of the dirac distribution
+    fn median(&self) -> f64 {
+        self.0
+    }
+}
+
+impl Mode<f64> for Dirac {
+    /// Returns the mode of the normal distribution
+    ///
+    /// # Formula
+    ///
+    /// ```ignore
+    /// v
+    /// ```
+    ///
+    /// where `v` is the point of the dirac distribution
+    fn mode(&self) -> f64 {
+        self.0
+    }
+}
+
+impl Continuous<f64, f64> for Dirac {
+    /// Calculates the probability density function for the dirac distribution
+    /// at `x`
+    ///
+    /// # Formula
+    ///
+    /// ```ignore
+    /// 1 if x = v, 0 otherwise
+    /// ```
+    ///
+    /// where `v` is point of this dirac distribution
+    fn pdf(&self, x: f64) -> f64 {
+      if x == self.0 { 1.0 } else { 0.0 }
+    }
+
+    /// Calculates the log probability density function for the normal
+    /// distribution
+    /// at `x`
+    ///
+    /// # Formula
+    ///
+    /// ```ignore
+    /// ln(1 if x = v, 0 otherwise)
+    /// ```
+    ///
+    /// where `v` is the point of this dirac distribution
+    ///
+    /// # Remarks
+    /// This distribution is usually negative infinity everywhere except at `v`.
+    fn ln_pdf(&self, x: f64) -> f64 {
+      if self.0 == x { 0.0 } else { f64::NEG_INFINITY }
+    }
+}
+
+#[cfg_attr(rustfmt, rustfmt_skip)]
+#[cfg(test)]
+mod test {
+    use std::f64;
+    use crate::statistics::*;
+    use crate::distribution::{Univariate, Continuous, Dirac};
+    use crate::distribution::internal::*;
+
+    fn try_create(v: f64) -> Dirac {
+        let d = Dirac::new(v);
+        assert!(d.is_ok());
+        d.unwrap()
+    }
+
+    fn create_case(v: f64) {
+        let d = try_create(v);
+        assert_eq!(v, d.mean());
+    }
+
+    fn bad_create_case(v: f64) {
+        let d = Dirac::new(v);
+        assert!(d.is_err());
+    }
+
+    fn test_case<F>(v: f64, expected: f64, eval: F)
+        where F: Fn(Dirac) -> f64
+    {
+        let x = eval(try_create(v));
+        assert_eq!(expected, x);
+    }
+
+    #[test]
+    fn test_create() {
+        create_case(10.0);
+        create_case(-5.0);
+        create_case(10.0);
+        create_case(100.0);
+        create_case(f64::INFINITY);
+    }
+
+    #[test]
+    fn test_bad_create() {
+        bad_create_case(f64::NAN);
+    }
+
+    #[test]
+    fn test_variance() {
+        test_case(0.0, 0.0, |x| x.variance());
+        test_case(-5.0, 0.0, |x| x.variance());
+        test_case(f64::INFINITY, 0.0, |x| x.variance());
+    }
+
+    #[test]
+    fn test_entropy() {
+        test_case(0.0, 0.0, |x| x.entropy());
+        test_case(f64::INFINITY, 0.0, |x| x.entropy());
+    }
+
+    #[test]
+    fn test_skewness() {
+        test_case(0.0, 0.0, |x| x.skewness());
+        test_case(4.0, 0.0, |x| x.skewness());
+        test_case(0.3, 0.0, |x| x.skewness());
+        test_case(f64::INFINITY, 0.0, |x| x.skewness());
+    }
+
+    #[test]
+    fn test_mode() {
+        test_case(0.0, 0.0, |x| x.mode());
+        test_case(3.0, 3.0, |x| x.mode());
+        test_case(f64::INFINITY, f64::INFINITY, |x| x.mode());
+    }
+
+    #[test]
+    fn test_median() {
+        test_case(0.0, 0.0, |x| x.median());
+        test_case(3.0, 3.0, |x| x.median());
+        test_case(f64::INFINITY, f64::INFINITY, |x| x.median());
+    }
+
+    #[test]
+    fn test_min_max() {
+        test_case(0.0, 0.0, |x| x.min());
+        test_case(3.0, 3.0, |x| x.min());
+        test_case(f64::INFINITY, f64::INFINITY, |x| x.min());
+
+        test_case(0.0, 0.0, |x| x.max());
+        test_case(3.0, 3.0, |x| x.max());
+        test_case(f64::NEG_INFINITY, f64::NEG_INFINITY, |x| x.max());
+    }
+
+    #[test]
+    fn test_pdf() {
+        test_case(0.0, 0.0, |x| x.pdf(1.0));
+        test_case(3.0, 1.0, |x| x.pdf(3.0));
+        test_case(f64::NEG_INFINITY, 0.0, |x| x.pdf(1.0));
+        test_case(f64::NEG_INFINITY, 1.0, |x| x.pdf(f64::NEG_INFINITY));
+    }
+
+    #[test]
+    fn test_ln_pdf() {
+        test_case(0.0, 0.0, |x| x.ln_pdf(0.0));
+        test_case(3.0, 0.0, |x| x.ln_pdf(3.0));
+        test_case(f64::INFINITY, f64::NEG_INFINITY, |x| x.ln_pdf(1.0));
+        test_case(f64::INFINITY, 0.0, |x| x.ln_pdf(f64::INFINITY));
+    }
+
+    #[test]
+    fn test_cdf() {
+        test_case(0.0, 1.0, |x| x.cdf(0.0));
+        test_case(3.0, 1.0, |x| x.cdf(3.0));
+        test_case(f64::INFINITY, 0.0, |x| x.cdf(1.0));
+        test_case(f64::INFINITY, 1.0, |x| x.cdf(f64::INFINITY));
+    }
+}

src/distribution/mod.rs

@@ -9,6 +9,7 @@ pub use self::categorical::Categorical;
 pub use self::cauchy::Cauchy;
 pub use self::chi::Chi;
 pub use self::chi_squared::ChiSquared;
+pub use self::dirac::Dirac;
 pub use self::dirichlet::Dirichlet;
 pub use self::discrete_uniform::DiscreteUniform;
 pub use self::erlang::Erlang;
@@ -37,6 +38,7 @@ mod categorical;
 mod cauchy;
 mod chi;
 mod chi_squared;
+mod dirac;
 mod dirichlet;
 mod discrete_uniform;
 mod erlang;