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Babysitter interpolation-approximation

Function interpolation and approximation methods

install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/science/mathematics/skills/interpolation-approximation" ~/.claude/skills/a5c-ai-babysitter-interpolation-approximation && rm -rf "$T"
manifest: library/specializations/domains/science/mathematics/skills/interpolation-approximation/SKILL.md
tags
#mathematics#numerical-analysis#function-approximation#data-fitting#scipy#chebfun
source content

Interpolation and Approximation

Purpose

Provides function interpolation and approximation methods for data fitting and function representation.

Capabilities

  • Polynomial interpolation (Lagrange, Newton, Chebyshev)
  • Spline interpolation (cubic, B-spline)
  • Rational approximation (Pade)
  • Least squares fitting
  • Minimax approximation (Remez algorithm)
  • Approximation error bounds

Usage Guidelines

  1. Method Selection: Choose based on smoothness and accuracy needs
  2. Node Placement: Use Chebyshev nodes to minimize Runge phenomenon
  3. Spline Order: Select spline degree based on continuity requirements
  4. Error Analysis: Bound approximation errors rigorously

Tools/Libraries

  • Chebfun
  • scipy.interpolate