regressor 1.0.0

Package for fast regression

regressor

This is a small library that tries to run simple linear regression quickly on modern x86 hardware. This uses vectorized operations to speed up calculating dot products and means.

As a result, this is ~10X faster than scipy.stats.linregress, but only runs on x86-64 hardware with AVX extensions (most desktops and servers as of 2020).

Install

pip install regressor

Usage

>>> import numpy as np
>>> from regressor import linregress
>>> x = np.array([1.0, 2.0, 3.0, 4.0, 5.0], dtype=np.float32)
>>> y = np.array([5.0, 4.0, 3.0, 2.0, 1.0], dtype=np.float32)
>>> linregress(x, y)
LinregressResult(slope=-1.0, intercept=6.0, rvalue=-1.0, pvalue=1.2e-30, stderr=0.0)

Performance

The graph below compares the times required for simple linear regressions between this package and scipy.stats.linregress, starting from arrays with 100 elements up to arrays with 100 million elements. In general, this package performs simple linear regression in about 1/15th of the time required by scipy.stats.linregress.