(TL;DR: matrices are rectangular arrays of numbers.)
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Last time, we learnt about dot products. We will now finally start talking aobut matrices.
Today’s topic: Matrices – a primer
We know what row vectors and column vectors are from our previous posts.
What would it look like if we were to stack a bunch of row vectors on top of each other? Similarly, what would it look like if we were to put a bunch of column vectors next to each other? Let’s experiment!
Let’s define some row vectors:
Let’s stack them on top of each other:
This rectangular array of numbers is an example of a matrix!
Let’s repeat the exercise with the same vectors represented as column vectors:
Putting these column vectors next to each other gives us this:
Looks like another matrix to me!
In fact, our row vectors and column vectors we saw in previous posts live double lives. A row vector goes by another name: “row matrix”. A column vector goes by another name, too: “column matrix”.
A little bit of notation
As before, we will be following the notation used in Goodfellow, Ian, et al.. We will depict matrices using uppercase, bold font:
Describing the size of a matrix
From the above, we can guess that matrices can be described in terms of rows and columns. How can we describe the number of rows and the number of columns we have in our matrices?
Let’s go back to our previous examples. They both happen to have three rows and three columns. So we have ourselves two 3 x 3 (“three by three”) matrices. The number of rows and the number of columns describe the “size” of our matrix.
Let’s generalise this. By convention, we have rows and columns in our matrix. Let’s use our matrix as an example. We haven’t defined what elements it contains. In fact, all we have said is that is a matrix. Whatever the contents of , it will will have rows and columns. So will be of size .
Keep it real
Let’s take this a step further. From previous posts, we are working with real numbers so let’s assume that our matrix of size will contain real numbers. That is, it is a real matrix. How can we depict this using some compact notation?
Remember when we touched on our vectors belonging to some abstract set of all real-valued vectors of similar dimensions when describing the dimensions of a vector? We can say something similar for our real-valued matrix of size . We can describe this set of all real-valued matrices like this:
So to show that our matrix belongs to the set of all matrices that are made up of real numbers, we simply say:
Going back to our examples above:
- Our real-valued row vectors (a.k.a. row matrices) are members of the set . That is, they are members of the set of all possible matrices made up of one row and columns of real numbers.
- Our real-valued column vectors (a.k.a. column matrices) are members of the set . That is, they are members of the set of all possible matrices made up of rows and one column of real numbers.
- Our real-valued square matrices are members of the set where . That is, they are members of the set of all possible real matrices that have the same number of rows as they have columns.
Not too bad, right?
How can we create matrices in R?
Stack those vectors
One way is to simply
cbind vectors. Let’s define 3 vectors
rbind them (i.e. stack them row-wise):
a <- c(1, 2, 3) b <- c(4, 5, 6) c <- c(7, 8, 9) A <- rbind(a, b, c) print(A) ## [,1] [,2] [,3] ## a 1 2 3 ## b 4 5 6 ## c 7 8 9
We have ourselves a matrix!
print(class(A)) ##  "matrix"
Let’s now stack them column-wise by using
cbind on our vectors:
A <- cbind(a, b, c) print(A) ## a b c ## [1,] 1 4 7 ## [2,] 2 5 8 ## [3,] 3 6 9
It’s a matrix!
print(class(A)) ##  "matrix"
The “usual” way to create a matrix would be to pass some vector or list
matrix function and specify its dimensions. For example, if we
want to use the values passed into the
matrix function to create a 3x3 matrix by using the values in a column-wise manner, we can do this:
matrix(1:9, nrow=3, ncol=3) ## [,1] [,2] [,3] ## [1,] 1 4 7 ## [2,] 2 5 8 ## [3,] 3 6 9
If instead we want to use the numbers in a row-wise manner, then we
can use the
byrow argument and set it to
matrix(1:9, nrow=3, ncol=3, byrow=TRUE) ## [,1] [,2] [,3] ## [1,] 1 2 3 ## [2,] 4 5 6 ## [3,] 7 8 9
The data types of the elements in the vector or list you pass into the
matrix function will be coerced to a single data type following the
usual coercion rules. For example, if we use a vector containing a
numeric type and a
integer type, let’s see what happens:
our_vec <- c(1.0, 1L) A <- matrix(our_vec, nrow=2, ncol=1) print(A) ## [,1] ## [1,] 1 ## [2,] 1
From previous posts, we know that the values in our vector have already
been coerced by
c before the
matrix function sees them! So,
naturally, we have a matrix of
sapply(A, class) ##  "numeric" "numeric"
We can even have a character matrix. Not sure why we’d want to create one…but we can!
x <- c("hello", "goodbye") matrix(x) ## [,1] ## [1,] "hello" ## [2,] "goodbye"
Elements are recycled
Recycling occurs like in vector addition posts. For example, if we try to create a matrix with four elements (2 rows times 2 columns) from a vector with only two elements, this is what we get:
matrix(c(1, 2), nrow=2, ncol=2) ## [,1] [,2] ## [1,] 1 1 ## [2,] 2 2
If the number of elements in our vector is not a multiple or submultiple of the number of rows or columns, we get warned of this fact:
matrix(c(1, 2, 3), nrow=2, ncol=1) ## Warning in matrix(c(1, 2, 3), nrow = 2, ncol = 1): data length  is not a ## sub-multiple or multiple of the number of rows  ## [,1] ## [1,] 1 ## [2,] 2
Next time, we’ll talk about matrix addition and introduce some notation we can use to access the individual elements contained within our matrices!