pyodide/benchmark/benchmarks/lstsqr.py

20 lines
635 B
Python

# setup: import numpy as np ; N = 500000 ; X, Y = np.random.rand(N), np.random.rand(N) # noqa
# run: lstsqr(X, Y)
# from:
# http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day10_fortran_lstsqr.ipynb
# pythran export lstsqr(float[], float[])
import numpy as np
def lstsqr(x, y):
""" Computes the least-squares solution to a linear matrix equation. """
x_avg = np.average(x)
y_avg = np.average(y)
dx = x - x_avg
var_x = np.sum(dx ** 2)
cov_xy = np.sum(dx * (y - y_avg))
slope = cov_xy / var_x
y_interc = y_avg - slope * x_avg
return (slope, y_interc)