Linear algebra

NumPy includes linear algebra routines that can be quite handy.

For example, NumPy can calculate matrix and vector products efficiently (dot,
vdot), solve eigenproblems (linalg.eig, linalg.eigvals), solve linear
systems (linalg.solve), and do matrix inversion (linalg.inv).

A = numpy.array(((2, 1), (1, 3)))
B = numpy.array(((-2, 4.2), (4.2, 6)))

C = numpy.dot(A, B)
b = numpy.array((1, 2))

print(C)
# output:
# [[ 0.2 14.4]
# [ 10.6 22.2]]

print(b)
# output: [1 2]

# solve C x = b
x = numpy.linalg.solve(C, b)

print(x)
# output: [ 0.04453441 0.06882591]

Normally, NumPy utilises high performance numerical libraries in linear
algebra operations. This means that the performance of NumPy is actually quite
good and not far e.g. from the performance of a pure-C implementations using
the same libraries.

Polynomials

NumPy has also support for polynomials. One can for example do least square
fitting, find the roots of a polynomial, and evaluate a polynomial.

A polynomial f(x) is defined by an 1D array of coefficients (p) with
length N, such that (f(x) = p[0] x^{N-1} + p[1] x^{N-2} + … + p[N-1]).

# f(x) = x^2 + random noise (between 0,1)
x = numpy.linspace(-4, 4, 7)
f = x**2 + numpy.random.random(x.shape)

p = numpy.polyfit(x, f, 2)

print(p)
# output: [ 0.96869003 -0.01157275 0.69352514]
# f(x) = p[0] * x^2 + p[1] * x + p[2]