Interpolating natural cubic splines. Includes batching, GPU support, support for missing values, evaluating derivatives of the spline, and backpropagation.
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Updated
Oct 4, 2022 - Python
Interpolating natural cubic splines. Includes batching, GPU support, support for missing values, evaluating derivatives of the spline, and backpropagation.
Implementation of particle swarm optimization (PSO) for path planning when the environment is known.
De Boor's Algorithm for NURBS Curve
Mobile Robot Path Planning and Obstacle Avoidance Using PSO in Python
Parametric differentiable curves with PyTorch for continuous embeddings, shape-restricted models, or KANs
High order and sparse layers in pytorch. Lagrange Polynomial, Piecewise Lagrange Polynomial, Piecewise Discontinuous Lagrange Polynomial (Chebyshev nodes) and Fourier Series layers of arbitrary order. Piecewise implementations could be thought of as a 1d grid (for each neuron) where each grid element is Lagrange polynomial. Both full connected a…
A proof of concept of how to integrate Spark Lineage in Azure Purview
Personalization of the image enhancement style from few samples.
🚀 InterpolatePy: A fast and precise Python library for production-ready trajectory planning, offering 20+ algorithms for C² continuous splines, jerk-limited S-curves, and quaternion interpolation for robotics, animation, and scientific computing.
Sliced Iterative Normalizing Flow with Minimal Dependencies
VolSplinesLib is a Python library for interpolating implied volatility surfaces using various volatility models. The library provides tools for fitting and interpolating models to market data, supporting popular methods like RFV, SLV, SABR, and SVI.
Automated line profiles along biological structures
PyTorch layers to parametrize functions through interpolation.
Lagrange Interpolation, Bezier Spline, Cardinal Spline for Computer Graphics Project
SplineKit is a Python-based open-source software library aimed at the manipulation of one-dimensional periodic splines.
Multivariate simplex b-spline than can be fitted to N dimensional data using least squares
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