Least-squares

numeric.linalg.lstsq(a, b)

Compute least-squares solution to equation Ax = b.

Compute a vector x such that the 2-norm |b - A x| is minimized.

Parameters

a(M, N) array

Left hand side matrix (2-D array).

b(M,) array

Right hand side vector.

Returns

x(N,) array

Least-squares solution. Return shape matches shape of b.

residues(0,) or () or (K,) ndarray

Sums of residues, squared 2-norm for each column in b - a x.

Examples:

x = array([1, 2.5, 3.5, 4, 5, 7, 8.5])
y = array([0.3, 1.1, 1.5, 2.0, 3.2, 6.6, 8.6])
M = ones((len(x),2))
M[:,1] = x**2
p, res = linalg.lstsq(M, y)
print p

#Plot
plot(x, y, 'bo', label='data')
xx = linspace(0, 9, 101)
yy = p[0] + p[1]*xx**2
plot(xx, yy, label='least squares fit, $y = a + bx^2$')
xlabel('x')
ylabel('y')
legend(loc='upper left')
grid(alpha=0.25)

Result:

>>> run script...
array([3.0, 2.23606797749979, 2.0, 0.0])