`Hypot`#

Description#

Two-argument Euclidean distance: Hypot(x, y) = sqrt(x*x + y*y). Computes sqrt(x² + y²) in a numerically stable way that avoids overflow for very large |x| or |y| and underflow for very small ones.

This is a 2-input, 1-output function (FunctorBase<_, 2, 1>). Inputs are paired column-by-column for arrays.

Equation:

\[ y[t] = \sqrt{x_1[t]^2 + x_2[t]^2} \]

Parameters: Hypot takes no parameters.

NaN handling: A NaN in either input produces a NaN output.

NaN handling#

Policy: ignore. A NaN in any input at index t causes the function to skip that step: output at t is NaN and internal state is unchanged. Subsequent finite samples are processed as if step t had not occurred.

Examples#

Usage example#

import numpy as np
from screamer import Hypot

# Scalar pair
Hypot()(3.0, 4.0)              # 5.0

# Two parallel 1D arrays
ux = np.random.randn(100)
uy = np.random.randn(100)
speed = Hypot()(ux, uy)        # shape (100,)

# Two parallel 2D arrays (column-by-column pairing)
UX = np.random.randn(100, 4)
UY = np.random.randn(100, 4)
Hypot()(UX, UY).shape          # (100, 4)

Reference#

Equivalent to numpy.hypot. Also returned as the radial component of Cart2Polar.

Hypot#