There are two ways to deal with matrices in numpy. The standard numpy array in it 2D form can do all kinds of matrixy stuff, like dot products, transposes, inverses, or factorisations, though the syntax can be a little clumsy. For those who just can’t let go of matlab, there’s a matrix object which prettifies the syntax somewhat. For example, to do
import numpy as np #using arrays B = np.random.randn(2,2) # random array C = np.random.randn(2,2) A = np.dot(B.T, np.linalg.inv(C)) #using matrices B = np.mat(B) #cast as matrix C = np.mat(C) A = B.T*C.I
Despite the nicer syntax in the matrix version, I prefer to use arrays. This is in part because they play nicer with the rest of numpy, but mostly because they behave better in higher dimensions. This is really useful when you have to deal with lots of matrices, which seems to occur all the time in my work:
A = np.random.randn(100,2,2) # a hundred 2x2 arrays A2 = [np.mat(np.random.randn(2,2)) for i in range(100)] # a hundered 2x2 matrices
Transposing the list of matrices is easy: just use the .T operator. This doesn’t work for the 100x2x2 array though, since it switches the axis in a way we don;t want. The solution is to manually specify which axes to switch.
A2T = [a.T for a in A2] # matrix version AT = np.transpose(A,(0,2,1)) #array version 1 #or AT = np.rollaxis(A,-2,-1)# array version 2
Suppose you have a series of matrices which you want to (right) multiply by another matrix. This is messy with the matrix object, where you need to do list comprehension, but nice as pie with the array object.
#matrix version A = [np.mat(np.random.randn(2,2)) for i in range(100)] B = np.mat(np.random.randn(2,2)) AB = [a*B for a in A] #array version A = np.random.randn(100,2,2) B = np.random.randn(2,2) AB = np.dot(A,B)
Left-multiplication is a little harder, but possible using a transpose trick:
#matrix version BA = [Ba for a in A] #array version BA = np.transpose(np.dot(np.transpose(A,(0,2,1)),B.T),(0,2,1))
Okay, the syntax is getting ugly there, I’ll admit. Suppose now that you had two sets of matrices, and wanted the product of each element, as in
#matrix version A = [np.mat(np.random.randn(2,2)) for i in range(100)] B = [np.mat(np.random.randn(2,2)) for i in range(100)] AB = [a*b for a,b in zip(A,B)] #array version A = np.random.randn(100,2,2) B = np.random.randn(100,2,2) np.sum(np.transpose(A,(0,2,1)).reshape(100,2,2,1)*B.reshape(100,2,1,2),-3)
I’ll admit that the syntax of this is very weird. To see how it works, consider taking the (matrix) product of two arrays in a similar manner:
A = np.random.randn(2,2) B = np.random.randn(2,2) np.sum(A.T.reshape(2,2,1)*B.reshape(2,1,2),0)
The reshaping persuades numpy to broadcast the multiplication, which results in a 2x2x2 cube of numbers. Summing the numbers along the first dimension of the cube results in matrix multiplication. I have some scribbles which illustrate this, which I’ll post if anyone wants.
The main motivation for using arrays in this manner is speed. I find for loops in python to be rather slow (including within list comps), so I prefer to use numpy array methods whenever possible. The following runs a quick test, multiplying 1000 3×3 matrices together. It’s a little crude, but it shows the numpy.array method to be 10 times faster than the list comp of np.matrix.
import numpy as np import timeit #compare multiple matrix multiplication using list coms of matrices and deep arrays #1) the matrix method setup1 = """ import numpy as np A = [np.mat(np.random.randn(3,3)) for i in range(1000)] B = [np.mat(np.random.randn(3,3)) for i in range(1000)] """ test1 = """ AB = [a*b for a,b in zip(A,B)] """ timer1 = timeit.Timer(test1,setup1) print timer1.timeit(100) #2) the array method setup2 = """ import numpy as np A = np.random.randn(1000,3,3) B = np.random.randn(1000,3,3) """ test2 = """ AB = np.sum(np.transpose(A,(0,2,1)).reshape(1000,3,3,1)*B.reshape(1000,3,1,3),0) """ timer2 = timeit.Timer(test2,setup2) print timer2.timeit(100)
In the likely event that there’s a better way to do this, or I’ve screwed up some code somewhere, please leave me a comment 🙂