also fixes hostpython paths so that pyodide can be built everywhere a clang based hostpython is available
Use PYMAJOR/PYMINOR according to Python devguide. also fixes hostpython paths so that pyodide can be built everywhere a clang based hostpython is available
# 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)

# 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)
