Predict a value using least squares method.

leastsquares.py

import numpy as np

def get_line(points):
    x = np.array([p[0] for p in points])
    y = np.array([p[1] for p in points])
    A = np.vstack([x, np.ones(len(x))]).T
    m, c = np.linalg.lstsq(A, y)[0]
    return m, c

def get_y(x, m, c):
    return m * x + c

tests.py

import unittest

class Tests(unittest.TestCase):
    @property
    def ls(self):
        import leastsquares
        return leastsquares
    def test_ls(self):
        m, c = self.ls.get_line([(0,0), (1,1), (2,2), (3,3)])
        self.assertAlmostEqual(m, 1.0)
        self.assertAlmostEqual(c, 0.0)
        self.assertAlmostEqual(m * 4.0 + c, 4.0)

        m, c = self.ls.get_line([(0,1), (1,2), (2,3), (3,4)])
        self.assertAlmostEqual(m, 1.0)
        self.assertAlmostEqual(c, 1.0)
        self.assertAlmostEqual(m * 4.0 + c, 5.0)

if __name__ == '__main__':
    unittest.main()