D

dynamic programming

This project adopts Reinforcement Learning to solve a variant of the stochastic VRP, where customers are highly variable, demands are stochastic, and vehicles have a duration limit.

The project finds the best spots for restaurants, avoiding placing restaurants too close together, and maximizing profit while keeping them a minimum distance apart. It uses dynamic programming to compute revenue and backtracking to select sites.

Study of execution time of differents algorithms to solve the knapsack problem, in differents instances of the problem created with a generator of knapsack problem.

This package is an attempt to make it easy to render paragraphs to a PIL Image in Python, with excellent line-breaking via the Knuth-Plass algorithm (commonly known as the TeX/LaTeX line-breaking algorithm). A simple greedy wrapping algorithm is available as a fallback. Console/text output is supported as well as PIL output.

Python dice probability package.

Gutenberg is a pipeline for training a neural network in segmenting and recognising frequent words in early printed books, in particular we focus on Gutenberg’s Bible.