Trait ndarray_stats::DeviationExt

source · [−]
pub trait DeviationExt<A, S, D> where
    S: Data<Elem = A>,
    D: Dimension, {
    fn count_eq<T>(
        &self,
        other: &ArrayBase<T, D>
    ) -> Result<usize, MultiInputError>
    where
        A: PartialEq,
        T: Data<Elem = A>;
    fn count_neq<T>(
        &self,
        other: &ArrayBase<T, D>
    ) -> Result<usize, MultiInputError>
    where
        A: PartialEq,
        T: Data<Elem = A>;
    fn sq_l2_dist<T>(
        &self,
        other: &ArrayBase<T, D>
    ) -> Result<A, MultiInputError>
    where
        A: AddAssign + Clone + Signed,
        T: Data<Elem = A>;
    fn l2_dist<T>(&self, other: &ArrayBase<T, D>) -> Result<f64, MultiInputError>
    where
        A: AddAssign + Clone + Signed + ToPrimitive,
        T: Data<Elem = A>;
    fn l1_dist<T>(&self, other: &ArrayBase<T, D>) -> Result<A, MultiInputError>
    where
        A: AddAssign + Clone + Signed,
        T: Data<Elem = A>;
    fn linf_dist<T>(&self, other: &ArrayBase<T, D>) -> Result<A, MultiInputError>
    where
        A: Clone + PartialOrd + Signed,
        T: Data<Elem = A>;
    fn mean_abs_err<T>(
        &self,
        other: &ArrayBase<T, D>
    ) -> Result<f64, MultiInputError>
    where
        A: AddAssign + Clone + Signed + ToPrimitive,
        T: Data<Elem = A>;
    fn mean_sq_err<T>(
        &self,
        other: &ArrayBase<T, D>
    ) -> Result<f64, MultiInputError>
    where
        A: AddAssign + Clone + Signed + ToPrimitive,
        T: Data<Elem = A>;
    fn root_mean_sq_err<T>(
        &self,
        other: &ArrayBase<T, D>
    ) -> Result<f64, MultiInputError>
    where
        A: AddAssign + Clone + Signed + ToPrimitive,
        T: Data<Elem = A>;
    fn peak_signal_to_noise_ratio<T>(
        &self,
        other: &ArrayBase<T, D>,
        maxv: A
    ) -> Result<f64, MultiInputError>
    where
        A: AddAssign + Clone + Signed + ToPrimitive,
        T: Data<Elem = A>;
    fn __private__(&self, _: PrivateMarker);
}
Expand description

An extension trait for ArrayBase providing functions to compute different deviation measures.

Required Methods

Counts the number of indices at which the elements of the arrays self and other are equal.

The following errors may be returned:

  • MultiInputError::EmptyInput if self is empty
  • MultiInputError::ShapeMismatch if self and other don’t have the same shape

Counts the number of indices at which the elements of the arrays self and other are not equal.

The following errors may be returned:

  • MultiInputError::EmptyInput if self is empty
  • MultiInputError::ShapeMismatch if self and other don’t have the same shape

Computes the squared L2 distance between self and other.

 n
 ∑  |aᵢ - bᵢ|²
i=1

where self is a and other is b.

The following errors may be returned:

  • MultiInputError::EmptyInput if self is empty
  • MultiInputError::ShapeMismatch if self and other don’t have the same shape

Computes the L2 distance between self and other.

     n
√ (  ∑  |aᵢ - bᵢ|² )
    i=1

where self is a and other is b.

The following errors may be returned:

  • MultiInputError::EmptyInput if self is empty
  • MultiInputError::ShapeMismatch if self and other don’t have the same shape

Panics if the type cast from A to f64 fails.

Computes the L1 distance between self and other.

 n
 ∑  |aᵢ - bᵢ|
i=1

where self is a and other is b.

The following errors may be returned:

  • MultiInputError::EmptyInput if self is empty
  • MultiInputError::ShapeMismatch if self and other don’t have the same shape

Computes the L∞ distance between self and other.

max(|aᵢ - bᵢ|)
 ᵢ

where self is a and other is b.

The following errors may be returned:

  • MultiInputError::EmptyInput if self is empty
  • MultiInputError::ShapeMismatch if self and other don’t have the same shape

Computes the mean absolute error between self and other.

       n
1/n *  ∑  |aᵢ - bᵢ|
      i=1

where self is a and other is b.

The following errors may be returned:

  • MultiInputError::EmptyInput if self is empty
  • MultiInputError::ShapeMismatch if self and other don’t have the same shape

Panics if the type cast from A to f64 fails.

Computes the mean squared error between self and other.

       n
1/n *  ∑  |aᵢ - bᵢ|²
      i=1

where self is a and other is b.

The following errors may be returned:

  • MultiInputError::EmptyInput if self is empty
  • MultiInputError::ShapeMismatch if self and other don’t have the same shape

Panics if the type cast from A to f64 fails.

Computes the unnormalized root-mean-square error between self and other.

√ mse(a, b)

where self is a, other is b and mse is the mean-squared-error.

The following errors may be returned:

  • MultiInputError::EmptyInput if self is empty
  • MultiInputError::ShapeMismatch if self and other don’t have the same shape

Panics if the type cast from A to f64 fails.

Computes the peak signal-to-noise ratio between self and other.

10 * log10(maxv^2 / mse(a, b))

where self is a, other is b, mse is the mean-squared-error and maxv is the maximum possible value either array can take.

The following errors may be returned:

  • MultiInputError::EmptyInput if self is empty
  • MultiInputError::ShapeMismatch if self and other don’t have the same shape

Panics if the type cast from A to f64 fails.

source

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

source

fn __private__(&self, _: PrivateMarker)

Implementors