heat.spatial.distance

Module for (pairwise) distance functions

Module Contents

cdist(X: heat.core.dndarray.DNDarray, Y: heat.core.dndarray.DNDarray = None, quadratic_expansion: bool = False) → heat.core.dndarray.DNDarray

Calculate Euclidian distance between two DNDarrays:

\[d(x,y) = \sqrt{(|x-y|^2)}\]

Returns 2D DNDarray of size :math: m times n

Parameters:

• X (DNDarray) – 2D array of size :math: m times f

• Y (DNDarray) – 2D array of size :math: n times f

• quadratic_expansion (bool) – Whether to use quadratic expansion for \(\sqrt{(|x-y|^2)}\) (Might yield speed-up)

rbf(X: heat.core.dndarray.DNDarray, Y: heat.core.dndarray.DNDarray = None, sigma: float = 1.0, quadratic_expansion: bool = False) → heat.core.dndarray.DNDarray

Calculate Gaussian distance between two DNDarrays:

\[d(x,y) = exp(-(|x-y|^2/2\sigma^2)\]

Returns 2D DNDarray of size :math: m times n

Parameters:

• X (DNDarray) – 2D array of size :math: m times f

• Y (DNDarray) – 2D array of size n times f

• sigma (float) – Scaling factor for gaussian kernel

• quadratic_expansion (bool) – Whether to use quadratic expansion for \(\sqrt{(|x-y|^2)}\) (Might yield speed-up)

manhattan(X: heat.core.dndarray.DNDarray, Y: heat.core.dndarray.DNDarray = None, expand: bool = False)

Calculate Manhattan distance between two DNDarrays:

\[d(x,y) = \sum{|x_i-y_i|}\]

Returns 2D DNDarray of size :math: m times n

Parameters:

• X (DNDarray) – 2D array of size :math: m times f

• Y (DNDarray) – 2D array of size :math: n times f

• expand (bool) – Whether to use dimension expansion (Might yield speed-up)