Add mean method

Author: LukeMathWalker

Cargo.toml

@@ -47,6 +47,7 @@ serde = { version = "1.0", optional = true }
 defmac = "0.2"
 quickcheck = { version = "0.7.2", default-features = false }
 rawpointer = "0.1"
+approx = "0.3"
 
 [features]
 # Enable blas usage

src/numeric/impl_numeric.rs

@@ -46,6 +46,33 @@ impl<A, S, D> ArrayBase<S, D>
         sum
     }
 
+    /// Returns the [arithmetic mean] x̅ of all elements in the array:
+    ///
+    /// ```text
+    ///     1   n
+    /// x̅ = ―   ∑ xᵢ
+    ///     n  i=1
+    /// ```
+    ///
+    /// If the array is empty, `None` is returned.
+    ///
+    /// **Panics** if `A::from_usize()` fails to convert the number of elements in the array.
+    ///
+    /// [arithmetic mean]: https://en.wikipedia.org/wiki/Arithmetic_mean
+    pub fn mean(&self) -> Option<A>
+        where
+            A: Clone + FromPrimitive + Add<Output=A> + Div<Output=A> + Zero
+    {
+        let n_elements = self.len();
+        if n_elements == 0 {
+            None
+        } else {
+            let n_elements = A::from_usize(n_elements)
+                .expect("Converting number of elements to `A` must not fail.");
+            Some(self.sum() / n_elements)
+        }
+    }
+
     /// Return the sum of all elements in the array.
     ///
     /// *This method has been renamed to `.sum()` and will be deprecated in the

tests/numeric.rs

@@ -0,0 +1,35 @@
+extern crate approx;
+use std::f64;
+use ndarray::{Array1, array};
+use approx::abs_diff_eq;
+
+#[test]
+fn test_mean_with_nan_values() {
+    let a = array![f64::NAN, 1.];
+    assert!(a.mean().unwrap().is_nan());
+}
+
+#[test]
+fn test_mean_with_empty_array_of_floats() {
+    let a: Array1<f64> = array![];
+    assert!(a.mean().is_none());
+}
+
+#[test]
+fn test_mean_with_array_of_floats() {
+    let a: Array1<f64> = array![
+            0.99889651, 0.0150731 , 0.28492482, 0.83819218, 0.48413156,
+            0.80710412, 0.41762936, 0.22879429, 0.43997224, 0.23831807,
+            0.02416466, 0.6269962 , 0.47420614, 0.56275487, 0.78995021,
+            0.16060581, 0.64635041, 0.34876609, 0.78543249, 0.19938356,
+            0.34429457, 0.88072369, 0.17638164, 0.60819363, 0.250392  ,
+            0.69912532, 0.78855523, 0.79140914, 0.85084218, 0.31839879,
+            0.63381769, 0.22421048, 0.70760302, 0.99216018, 0.80199153,
+            0.19239188, 0.61356023, 0.31505352, 0.06120481, 0.66417377,
+            0.63608897, 0.84959691, 0.43599069, 0.77867775, 0.88267754,
+            0.83003623, 0.67016118, 0.67547638, 0.65220036, 0.68043427
+        ];
+    // Computed using NumPy
+    let expected_mean = 0.5475494059146699;
+    abs_diff_eq!(a.mean().unwrap(), expected_mean, epsilon = f64::EPSILON);
+}