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Run pip install libpymath
to dowload and install libpymath
for your system. There are also wheels provided for many of the latest Python versions on Windows, Mac OS* and Linux thanks to the cibuildwheel
project.
Easily create, manipulate and perform calculations with dense matrices. The matrices themselves are stored and manipulated with optimised C code, resulting in faster operations and more efficient calculations. To further increase the speed of the calculations, when libpymath imported into a project for the first time, it runs some tests on the CPU to find the optimal number of threads to use for the matrix calculations.
Wrap a progress bar around any Python iterator and have a progress bar generated automatically. The progress bar will adjust its width to the width of the console, shows the current percentage, time and time remaining, as well as the number of iterations per second the bar is running at. The rate at which the bar updates also adjusts dynamically, ensuring that it has a minimal impact on the speed of the loop, while updating frequently enough to provide relevant information.
Create, train and evaluate a neural network in only a few lines of code, customising the size of the network, its learning rate, it's activation functions (which can be customised on a per-layer basis) and the metrics which it logs. The network library also uses the efficient Matrix library, meaning it can train a simple network in under a second. You can also plot a graph of any metrics that are being logged, making it easy to evaluate the progress of the network.
FAQs
A general purpose Python math module
We found that libpymath demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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