2018-04-05 22:07:33 +00:00
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#setup: import numpy as np ; N = 500000 ; X, Y = np.random.rand(N), np.random.rand(N)
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#run: lstsqr(X, Y)
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#from: http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day10_fortran_lstsqr.ipynb
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#pythran export lstsqr(float[], float[])
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import numpy as np
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2018-10-03 12:38:48 +00:00
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2018-04-05 22:07:33 +00:00
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def lstsqr(x, y):
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""" Computes the least-squares solution to a linear matrix equation. """
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x_avg = np.average(x)
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y_avg = np.average(y)
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dx = x - x_avg
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dy = y - y_avg
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var_x = np.sum(dx**2)
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cov_xy = np.sum(dx * (y - y_avg))
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slope = cov_xy / var_x
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y_interc = y_avg - slope*x_avg
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return (slope, y_interc)
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