[−][src]Trait ndarray_stats::SummaryStatisticsExt
Extension trait for ArrayBase
providing methods
to compute several summary statistics (e.g. mean, variance, etc.).
Required methods
fn mean(&self) -> Result<A, EmptyInput> where
A: Clone + FromPrimitive + Add<Output = A> + Div<Output = A> + Zero,
A: Clone + FromPrimitive + Add<Output = A> + Div<Output = A> + Zero,
Returns the arithmetic mean
x̅ of all elements in the array:
1 n
x̅ = ― ∑ xᵢ
n i=1
If the array is empty, Err(EmptyInput)
is returned.
Panics if A::from_usize()
fails to convert the number of elements in the array.
fn harmonic_mean(&self) -> Result<A, EmptyInput> where
A: Float + FromPrimitive,
A: Float + FromPrimitive,
Returns the harmonic mean
HM(X)
of all elements in the array:
⎛ n ⎞⁻¹
HM(X) = n ⎜ ∑ xᵢ⁻¹⎟
⎝i=1 ⎠
If the array is empty, Err(EmptyInput)
is returned.
Panics if A::from_usize()
fails to convert the number of elements in the array.
fn geometric_mean(&self) -> Result<A, EmptyInput> where
A: Float + FromPrimitive,
A: Float + FromPrimitive,
Returns the geometric mean
GM(X)
of all elements in the array:
⎛ n ⎞¹⁄ₙ
GM(X) = ⎜ ∏ xᵢ⎟
⎝i=1 ⎠
If the array is empty, Err(EmptyInput)
is returned.
Panics if A::from_usize()
fails to convert the number of elements in the array.
fn kurtosis(&self) -> Result<A, EmptyInput> where
A: Float + FromPrimitive,
A: Float + FromPrimitive,
Returns the kurtosis Kurt[X]
of all elements in the array:
Kurt[X] = μ₄ / σ⁴
where μ₄ is the fourth central moment and σ is the standard deviation of the elements in the array.
This is sometimes referred to as Pearson's kurtosis. Fisher's kurtosis can be computed by subtracting 3 from Pearson's kurtosis.
If the array is empty, Err(EmptyInput)
is returned.
Panics if A::from_usize()
fails to convert the number of elements in the array.
fn skewness(&self) -> Result<A, EmptyInput> where
A: Float + FromPrimitive,
A: Float + FromPrimitive,
Returns the Pearson's moment coefficient of skewness γ₁ of all elements in the array:
γ₁ = μ₃ / σ³
where μ₃ is the third central moment and σ is the standard deviation of the elements in the array.
If the array is empty, Err(EmptyInput)
is returned.
Panics if A::from_usize()
fails to convert the number of elements in the array.
fn central_moment(&self, order: u16) -> Result<A, EmptyInput> where
A: Float + FromPrimitive,
A: Float + FromPrimitive,
Returns the p-th central moment of all elements in the array, μₚ:
1 n
μₚ = ― ∑ (xᵢ-x̅)ᵖ
n i=1
If the array is empty, Err(EmptyInput)
is returned.
The p-th central moment is computed using a corrected two-pass algorithm (see Section 3.5 in Pébay et al., 2016). Complexity is O(np) when n >> p, p > 1.
Panics if A::from_usize()
fails to convert the number of elements
in the array or if order
overflows i32
.
fn central_moments(&self, order: u16) -> Result<Vec<A>, EmptyInput> where
A: Float + FromPrimitive,
A: Float + FromPrimitive,
Returns the first p central moments of all elements in the array, see central moment for more details.
If the array is empty, Err(EmptyInput)
is returned.
This method reuses the intermediate steps for the k-th moment to compute the (k+1)-th, being thus more efficient than repeated calls to central moment if the computation of central moments of multiple orders is required.
Panics if A::from_usize()
fails to convert the number of elements
in the array or if order
overflows i32
.
fn __private__(&self, _: PrivateMarker)
This method makes this trait impossible to implement outside of
ndarray-stats
so that we can freely add new methods, etc., to
this trait without breaking changes.
We don't anticipate any other crates needing to implement this trait, but if you do have such a use-case, please let us know.
Warning This method is not considered part of the public API, and client code should not rely on it being present. It may be removed in a non-breaking release.
Implementations on Foreign Types
impl<A, S, D> SummaryStatisticsExt<A, S, D> for ArrayBase<S, D> where
S: Data<Elem = A>,
D: Dimension,
[src]
S: Data<Elem = A>,
D: Dimension,
fn mean(&self) -> Result<A, EmptyInput> where
A: Clone + FromPrimitive + Add<Output = A> + Div<Output = A> + Zero,
[src]
A: Clone + FromPrimitive + Add<Output = A> + Div<Output = A> + Zero,
fn harmonic_mean(&self) -> Result<A, EmptyInput> where
A: Float + FromPrimitive,
[src]
A: Float + FromPrimitive,
fn geometric_mean(&self) -> Result<A, EmptyInput> where
A: Float + FromPrimitive,
[src]
A: Float + FromPrimitive,
fn kurtosis(&self) -> Result<A, EmptyInput> where
A: Float + FromPrimitive,
[src]
A: Float + FromPrimitive,
fn skewness(&self) -> Result<A, EmptyInput> where
A: Float + FromPrimitive,
[src]
A: Float + FromPrimitive,
fn central_moment(&self, order: u16) -> Result<A, EmptyInput> where
A: Float + FromPrimitive,
[src]
A: Float + FromPrimitive,
fn central_moments(&self, order: u16) -> Result<Vec<A>, EmptyInput> where
A: Float + FromPrimitive,
[src]
A: Float + FromPrimitive,