numpy.linalg¶

Functions in the linalg module can be called by prepending them by numpy.linalg.. The module defines the following seven functions:

numpy.linalg.cholesky
numpy.linalg.det
numpy.linalg.eig
numpy.linalg.inv
numpy.linalg.norm
numpy.linalg.qr

cholesky¶

numpy: https://docs.scipy.org/doc/numpy-1.17.0/reference/generated/numpy.linalg.cholesky.html

The function of the Cholesky decomposition takes a positive definite, symmetric square matrix as its single argument, and returns the square root matrix in the lower triangular form. If the input argument does not fulfill the positivity or symmetry condition, a ValueError is raised.

# code to be run in micropython

from ulab import numpy as np

a = np.array([[25, 15, -5], [15, 18,  0], [-5,  0, 11]])
print('a: ', a)
print('\n' + '='*20 + '\nCholesky decomposition\n', np.linalg.cholesky(a))

a:  array([[25.0, 15.0, -5.0],
     [15.0, 18.0, 0.0],
     [-5.0, 0.0, 11.0]], dtype=float)

====================
Cholesky decomposition
 array([[5.0, 0.0, 0.0],
     [3.0, 3.0, 0.0],
     [-1.0, 1.0, 3.0]], dtype=float)

det¶

numpy: https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.det.html

The det function takes a square matrix as its single argument, and calculates the determinant. The calculation is based on successive elimination of the matrix elements, and the return value is a float, even if the input array was of integer type.

# code to be run in micropython

from ulab import numpy as np

a = np.array([[1, 2], [3, 4]], dtype=np.uint8)
print(np.linalg.det(a))

-2.0

Benchmark¶

Since the routine for calculating the determinant is pretty much the same as for finding the inverse of a matrix, the execution times are similar:

# code to be run in micropython

from ulab import numpy as np

@timeit
def matrix_det(m):
    return np.linalg.inv(m)

m = np.array([[1, 2, 3, 4, 5, 6, 7, 8], [0, 5, 6, 4, 5, 6, 4, 5],
              [0, 0, 9, 7, 8, 9, 7, 8], [0, 0, 0, 10, 11, 12, 11, 12],
             [0, 0, 0, 0, 4, 6, 7, 8], [0, 0, 0, 0, 0, 5, 6, 7],
             [0, 0, 0, 0, 0, 0, 7, 6], [0, 0, 0, 0, 0, 0, 0, 2]])

matrix_det(m)

execution time:  294  us

eig¶

numpy: https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.eig.html

The eig function calculates the eigenvalues and the eigenvectors of a real, symmetric square matrix. If the matrix is not symmetric, a ValueError will be raised. The function takes a single argument, and returns a tuple with the eigenvalues, and eigenvectors. With the help of the eigenvectors, amongst other things, you can implement sophisticated stabilisation routines for robots.

# code to be run in micropython

from ulab import numpy as np

a = np.array([[1, 2, 1, 4], [2, 5, 3, 5], [1, 3, 6, 1], [4, 5, 1, 7]], dtype=np.uint8)
x, y = np.linalg.eig(a)
print('eigenvectors of a:\n', y)
print('\neigenvalues of a:\n', x)

eigenvectors of a:
 array([[0.8151560042509081, -0.4499411232970823, -0.1644660242574522, 0.3256141906686505],
       [0.2211334179893007, 0.7846992598235538, 0.08372081379922657, 0.5730077734355189],
       [-0.1340114162071679, -0.3100776411558949, 0.8742786816656, 0.3486109343758527],
       [-0.5183258053659028, -0.292663481927148, -0.4489749870391468, 0.6664142156731531]], dtype=float)

eigenvalues of a:
 array([-1.165288365404889, 0.8029365530314914, 5.585625756072663, 13.77672605630074], dtype=float)

The same matrix diagonalised with numpy yields:

# code to be run in CPython

a = array([[1, 2, 1, 4], [2, 5, 3, 5], [1, 3, 6, 1], [4, 5, 1, 7]], dtype=np.uint8)
x, y = eig(a)
print('eigenvectors of a:\n', y)
print('\neigenvalues of a:\n', x)

eigenvectors of a:
 [[ 0.32561419  0.815156    0.44994112 -0.16446602]
 [ 0.57300777  0.22113342 -0.78469926  0.08372081]
 [ 0.34861093 -0.13401142  0.31007764  0.87427868]
 [ 0.66641421 -0.51832581  0.29266348 -0.44897499]]

eigenvalues of a:
 [13.77672606 -1.16528837  0.80293655  5.58562576]

When comparing results, we should keep two things in mind:

the eigenvalues and eigenvectors are not necessarily sorted in the same way
an eigenvector can be multiplied by an arbitrary non-zero scalar, and it is still an eigenvector with the same eigenvalue. This is why all signs of the eigenvector belonging to 5.58, and 0.80 are flipped in ulab with respect to numpy. This difference, however, is of absolutely no consequence.

Computation expenses¶

Since the function is based on Givens rotations and runs till convergence is achieved, or till the maximum number of allowed rotations is exhausted, there is no universal estimate for the time required to find the eigenvalues. However, an order of magnitude can, at least, be guessed based on the measurement below:

# code to be run in micropython

from ulab import numpy as np

@timeit
def matrix_eig(a):
    return np.linalg.eig(a)

a = np.array([[1, 2, 1, 4], [2, 5, 3, 5], [1, 3, 6, 1], [4, 5, 1, 7]], dtype=np.uint8)

matrix_eig(a)

execution time:  111  us

inv¶

numpy: https://docs.scipy.org/doc/numpy-1.17.0/reference/generated/numpy.linalg.inv.html

A square matrix, provided that it is not singular, can be inverted by calling the inv function that takes a single argument. The inversion is based on successive elimination of elements in the lower left triangle, and raises a ValueError exception, if the matrix turns out to be singular (i.e., one of the diagonal entries is zero).

# code to be run in micropython

from ulab import numpy as np

m = np.array([[1, 2, 3, 4], [4, 5, 6, 4], [7, 8.6, 9, 4], [3, 4, 5, 6]])

print(np.linalg.inv(m))

array([[-2.166666666666667, 1.500000000000001, -0.8333333333333337, 1.0],
       [1.666666666666667, -3.333333333333335, 1.666666666666668, -0.0],
       [0.1666666666666666, 2.166666666666668, -0.8333333333333337, -1.0],
       [-0.1666666666666667, -0.3333333333333333, 0.0, 0.5]], dtype=float64)

Computation expenses¶

Note that the cost of inverting a matrix is approximately twice as many floats (RAM), as the number of entries in the original matrix, and approximately as many operations, as the number of entries. Here are a couple of numbers:

# code to be run in micropython

from ulab import numpy as np

@timeit
def invert_matrix(m):
    return np.linalg.inv(m)

m = np.array([[1, 2,], [4, 5]])
print('2 by 2 matrix:')
invert_matrix(m)

m = np.array([[1, 2, 3, 4], [4, 5, 6, 4], [7, 8.6, 9, 4], [3, 4, 5, 6]])
print('\n4 by 4 matrix:')
invert_matrix(m)

m = np.array([[1, 2, 3, 4, 5, 6, 7, 8], [0, 5, 6, 4, 5, 6, 4, 5],
              [0, 0, 9, 7, 8, 9, 7, 8], [0, 0, 0, 10, 11, 12, 11, 12],
             [0, 0, 0, 0, 4, 6, 7, 8], [0, 0, 0, 0, 0, 5, 6, 7],
             [0, 0, 0, 0, 0, 0, 7, 6], [0, 0, 0, 0, 0, 0, 0, 2]])
print('\n8 by 8 matrix:')
invert_matrix(m)

2 by 2 matrix:
execution time:  65  us

4 by 4 matrix:
execution time:  105  us

8 by 8 matrix:
execution time:  299  us

The above-mentioned scaling is not obeyed strictly. The reason for the discrepancy is that the function call is still the same for all three cases: the input must be inspected, the output array must be created, and so on.

norm¶

numpy: https://numpy.org/doc/stable/reference/generated/numpy.linalg.norm.html

The function takes a vector or matrix without options, and returns its 2-norm, i.e., the square root of the sum of the square of the elements.

# code to be run in micropython

from ulab import numpy as np

a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

print('norm of a:', np.linalg.norm(a))
print('norm of b:', np.linalg.norm(b))

norm of a: 7.416198487095663
norm of b: 16.88194301613414

qr¶

numpy: https://numpy.org/doc/stable/reference/generated/numpy.linalg.qr.html

The function computes the QR decomposition of a matrix m of dimensions (M, N), i.e., it returns two such matrices, q’, and r, that m = qr, where q is orthonormal, and r is upper triangular. In addition to the input matrix, which is the first positional argument, the function accepts the mode keyword argument with a default value of reduced. If mode is reduced, q, and r are returned in the reduced representation. Otherwise, the outputs will have dimensions (M, M), and (M, N), respectively.

# code to be run in micropython

from ulab import numpy as np

A = np.arange(6).reshape((3, 2))
print('A: \n', A)

print('complete decomposition')
q, r = np.linalg.qr(A, mode='complete')
print('q: \n', q)
print()
print('r: \n', r)

print('\n\nreduced decomposition')
q, r = np.linalg.qr(A, mode='reduced')
print('q: \n', q)
print()
print('r: \n', r)

A:
 array([[0, 1],
       [2, 3],
       [4, 5]], dtype=int16)
complete decomposition
q:
 array([[0.0, -0.9128709291752768, 0.408248290463863],
       [-0.447213595499958, -0.3651483716701107, -0.8164965809277261],
       [-0.8944271909999159, 0.1825741858350553, 0.408248290463863]], dtype=float64)

r:
 array([[-4.47213595499958, -5.813776741499454],
       [0.0, -1.095445115010332],
       [0.0, 0.0]], dtype=float64)


reduced decomposition
q:
 array([[0.0, -0.9128709291752768],
       [-0.447213595499958, -0.3651483716701107],
       [-0.8944271909999159, 0.1825741858350553]], dtype=float64)

r:
 array([[-4.47213595499958, -5.813776741499454],
       [0.0, -1.095445115010332]], dtype=float64)