LLM Alignment Primer: Implementing Linear Motion Models in Python

LLM Alignment Primer: Implementing Linear Motion Models in Python

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Linearly Modelled Motion (LLM) is a fundamental concept widely used in robotics, computer graphics, and animation. In this description, we outline the key principles of LLM and provide a beginner's guide to implementing LLM in Python.

Linear motion models represent the relationship between spatial position, velocity, and acceleration of an object in one, two or three dimensions. By expressing motion as mathematical functions of time, we can predict and manipulate the behavior of moving objects.

First, let's explore some fundamental concepts. Here are the primary components of an LLM:

Position: The location of a moving object in space
Velocity: The rate of change of position
Acceleration: The rate of change of velocity

Next, we'll implement an LLM in Python through the popular NumPy library. We'll cover the mathematics behind LLM equations and how to code functions for position, velocity, and acceleration. One common application of LLM is to model the motion of projectiles, which we'll cover as an example.


Additional Resources:
[Numpy Documentation](https://numpy.org/doc/stable/)
[Quaternions Explained](https://en.wikipedia.org/wiki/Quatern...)
[3Blue1Brown - Quaternions](   • Видео  )

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