In this second tutorial video on Octave,
I'd like to start to tell you how to move
data around in Octave. So, if you have
data from a sheet learning problem, how do
you load that data in Octave? How do you
put it into a matrix? How do you
manipulate these matrices? How do you save
the results? How do you move data around
and operate the data? Here's my active
window as before. Picking up from where we
left off in the last video. If I type A,
that's the matrix that we generated,
right? [inaudible] just command=1, two,
three, four, five, six. And, this is a
three by two matrix. The size command in
[inaudible] let's you, Tells you what is
the size of the matrix so size A returns
D2 it turns out that the size command
itself is actually returning a 1x2 matrix
so you can actually set as Z = size of A
and as Z is now a 1x2 matrix where the
first element of this is three and the
second element of this is two. So you can
type size of as Z as Z is a 1x2 matrix
who's two elements contain the dimensions
of the matrix A you can also type size A1.
To give you back the first dimension of A
the size of the first dimension of A so
that's the number of rows a size of two to
give you back a two which is the number of
columns in the matrix A. If you have a
better V. So let's say V=1, two, three,
four. And you type length V, what this
does is it gives you the size of the
longest dimenstion. So, you can also type
length. A and because A is a three by two
matrix the longer dimension is a size
three so this should entail three. But
usually we apply we apply length only to a
vector. So, you know, length one, two,
three, four, five rather than apply link
to matrixes because that's a, that's a
little more confusing. Now. Lets look at
how to load data and find data on a file
system. When we start on Octave we're
usually, we're often in a path that is you
know the location of where the octave
program is so the PWD command shows the
current directory or the current path that
Octave is in so right now we're in this
somewhat maybe obscure directory the CD
command that stands for change directory
so I can go to C color / user / ANG /
desktop. And, now I'm in your, my desktop.
And if I type LS. Ls, is, sort of comes
from a Unix or Linux command. But LS will
list the directories on my desktop and so,
you know, these are the files that are on
my desktop right now. [sound], [sound] In
fact on my desktop are two files features
X and price Y that maybe come from a
machine learning problem I want to solve
so here's my desktop, here's features X.
And features x is this window, excuse me,
is this file, with two columns of data.
This is actually my housing prices data.
So I think, I think I have 47 rows in this
data set, and so, the first house at size
204 square feet has three bedrooms, second
house has 1600 square feet, has three
bedrooms, and so on. And price y is this
file. That, has the prices of the data in
my training set. So, [inaudible] why I
just text files with my data. [inaudible]
load this data into [inaudible] load
features X.dat. And if I do that, I load
the features X, we can load price Y. Got
that. And by the way, there are multiple
ways to do this. This command, if you put
features X dot dat in strings and load it
like so, this is oops typo there this is
equivalent commands so you can but this
way I'm just putting the file name with
the string and the file name in the string
and with octave use single quotes to
represent strings like so, so that's a
string and we can load. The file who's
name is given by that string. Now the who
command now shows me what variables I have
in my active work space. So who shows me
whether the variables the octave has in
memory currently features x and price y
are among them as well as the variables
that we created in this session. So you
can take features. X to display features X
and there's my data and I can type size
features X and that's my 47x2 matrix and
sum of the size times Y that gives me my
47x1 vector for this is a 47 dimensional
vector this tall column vector that has
all the prices Y in my training set. Now
the who function. Show's you one of the
variables that in the current workspace
there's also the who S variable that gives
you the detail view and so this also with
an SIDN this also lists my variables
except that it now listed sizes as well so
A is a 3x2 matrix and features X is a 47x2
matrix by price Y is a 47x1 matrix meaning
this is just a vector and it shows you
know how many bytes of memory it's taking
up as well as what type of data this is
double means double position floating
points so that just means these are. Are
a, a real value so floating-point numbers.
Now if you want to get rid of, of the
variable, you can use the clear command.
So clear features X. And type who's again
you notice that the features X variable
has now disappeared, and how do you see
data let's see let's take a variable V and
set it to price Y one colon ten this sets
V to be the first ten elements of, of the
vector Y so let's type who. Or whose,
whereas Y was a 47 by one vector. V is now
ten by one, because V=price Y, one:10,
this sets into the, just the first ten
elements of Y. Let's say I wanna save this
to date, to just [inaudible] save,
hello.net and D. This will save the
variable V into a file called hello.net.
So let's do that. And now. A file has
appeared on my desktop called hello.net. I
happen to have had installed in this
windows which is why you know this, this
icon looks like this because windows has
recognized this as a.net file but don't
worry about it if this file looks like it
has a different icon on your machine and
let's say I clear all my variables, so if
I if you type clear without anything then
this actually deletes all the variables in
your workspace, so type who's there's now
nothing left in the workspace and if I
load hello.net. I can now load that, my
variable V which is my, That the data that
previously saved into the hello.net file.
So hello.net what we did just now the save
hello.net to [inaudible] this saved the
data in a binary format a somewhat more
compressed binary format so that the V is
a wall of data this you know will be
somewhat more compressed and take up less
the space if you want to save the data in
a human readable format you type save
hello.text the variable V and then dash
key so this will save it. As a text, or as
Ascii formatted text. And now, once I've
done that, I have this file, hello.texas
just appeared on my desktop. And, if I
open this up, you see that this is a text
file with my, with my data saved away. So
that's how you load and save data now
let's talk a bit about how to manipulate
it let's say A = to that matrix again
[sound] so is my 3x2 matrix let's
[inaudible] indexing so if I type A3,2
this indexes into the 3,2 element of the
matrix A so this is like the, this is, you
know and normally we would write this as A
subscript three two or A subscript
[sound]. The 3-2, and so that's the
element in the third row and second column
of A which is the element six. I can also
type a two comma colon to fetch everything
in the second row. So the colon Means,
every element. Along that row or column.
So AF2 colon, comma colon is this second
row of A, right? And similarly if I do A
colon comma two, then this means get
everything in the second column of A. So
this gives me two four six, right? This
means of A everything comma second column.
So this is my second column of A, which is
two four six. Now, you might say use
somewhat more sophisticated indexing
operations. So [inaudible] I'll just
quickly show you an example. You do this
maybe less often, but let me do A, one,
three, comma, colon. This means get all
the elements of a, who's first index is
103. This means I get everything from the
first and third rows of a and from all .
Columns, so this was the matrix A, and so
A, one, three, comma, colon, means get
everything from the first row and from the
second row and, and from the third row.
And colon means you know, I want both the
first and the second columns and so this
gives me this one, two, five, six.
Although you, you use these sorts of more
sophisticated indexing options maybe
somewhat less often. To show you what else
we're going to do here's the A matrix and
that this was A: ,two gave me the second
column you can also use this to do
assignments so I can take the second
column of A and assign that to ten eleven
twelve and if I do that. I'm now, you
know, taking the second column of A and
I'm assigning this column vector ten,
eleven, twelve to it. So now, A is this
matrix, that's one, three, five and the
second column has been replaced by ten,
eleven, twelve. And here's another
operation, it's A, let's, let's set A to
be equal to A comma, 100, 101, 102, like
so. And what this will do, is append
another. Call in vector to the rate. So
now. Oops, I think I made a mistake.
Should I put semicolons there. And now. A
is = to this, okay? I hope that makes
sense. So, this 100, 101, 102, this is a
column vector. And what we did was, we
said, A, take A, and set it to the
original definition. And then we put that
[inaudible] vector to the right. And so
ended up taking the matrix A, and which
was, this, these six elements on the left.
So we took the matrix A, and we appended
another column vector to the right, which
is now why, now A is A, three by three
matrix, it looks like that. And finally
when we [inaudible] I sometime use if we
do A and then just a colon like so.
[inaudible] special case [inaudible]. What
this means is that put all elements of A
into a single column vector and this gives
me a nine by one vector [inaudible] all
the elements of A concatenated together.
Just couple of more examples I can also,
let's see. Let's say I set A to be equal
to One two three four five six okay and
let's say I said B is = to eleven twelve,
thirteen fourteen fifteen sixteen. I can
create a new matrix C. As AB. This just
means. So here's my matrix A, here's my
matrix B and another set C to be equal to
AB. When I'm doing [inaudible]
concatenating them onto each other. So the
left, the matrix A on the left and I have
the matrix B on right and that's how I
form this, you know just make sure it's a
C by putting them together. I can also do
C equals a semicolon B. The semicolon
notation means that means I go put the
next thing at bottom. So [inaudible]
semicolon [inaudible]. It also puts
[inaudible] A and B together expect that
it now puts them on top of each other. So
now I have A on top. And B at the bottom
and, C here, is now a six by two matrix.
So, so, just saying, the semi-colon thing
usually means, you know, go to the next
line. So, C is comprised by A, and go to
the bottom of that and then put B at the
bottom. And, by the way, this AB is the
same as A, B and so, you know, either of
these [inaudible] use the same result. So
with that hopefully you now know how to
construct matrices and hopefully this
slide show, show you some of the commands
that you can use to quickly put together
matrices and take matrices and you know
slam them together and form bigger
matrices, and with just a few lines of
code, Octave is very convenient in terms
of how quickly we can assemble complex
matrices and move data around. So that's
it for moving data around in the next
video we'll start to talk about how to
actually do complex computations on this,
on our data. So hopefully that gets you a
sense of. How, with just a few commands,
you can very quickly move data around in
Octave. You know, those [inaudible]
vectors and matrices, [inaudible] say
data. Put together matrices to create
bigger matrices, index into or select
specific elements of the matrices. I know
I went through a lot of commands. So I
think the best thing for you to do is,
afterward, to look at the transcript of
the things I was typing. You know, look at
the, look at the [inaudible] website, and
download the transcript of the session
from there. And, look for the transcript,
and type some of those commands into
Octave yourself, so that you can start
[inaudible] command. And then again to
work. And obviously there's no point at
all to try to memorize all these commands.
It's just. But, what you should do, is,
hopefully from this video, you have gotten
a sense of the sorts of things you could
do. So that, when, later on, when you're
to program a learning algorithm yourself,
if you are trying to find a specific
command that maybe you think Octave can
do, because you think you might have seen
it here. Should refer to the, to the
transcript of this session, and look for
that, in order to find the commands you
wanna use. So, that's it for moving data
around. And in the next video, what I'd
like to do is [inaudible] to tell you how
to actually do complex computations on our
data, and. Of how to compute some of the
data and actually start to implement
learning algorithms.