8
Nov

Lec 14 | MIT 6.00SC Introduction to Computer Science and Programming, Spring 2011


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MIT OpenCourseWare at ocw.mit.edu. PROFESSOR: All right. Today I want to spend a few more
minutes on plotting, and then return to a subject that
will occupy us for a couple of weeks, which is the use of randomness in solving problems. Don’t save. All right, so let’s
first look at one more example of plotting. It’s simple. It’s so simple you’ll find
it’s not in your handout. So here it is to start with. PROFESSOR: All I’m doing here is
I wrote a little program to show the effect of compound
interest, nothing very sophisticated. We start with some principal and
an interest rate, and then we just apply it over
and over again. And then we’re going to plot to
show what the principal has become if we just keep
compounding the interest. So it is kind of what
you’d expect. Compound interest is
a nice formula. You can actually get rich
applying it, and we see this nice little graph. On the other hand, we can’t
really tell what it is. And this is the sort of thing
that I see all too often, including my graduate
students produce it. They come to my office, they
show me a graph, and they start explaining it. And I usually refuse to look at
it if it looks like this. There is no point in ever, and I
mean ever, producing a graph that does not have a title
and labeled axes. And in particular, you have
to label the axes to say what they mean. Fortunately, it’s easy
enough to do. And here, I’ve just done that. So I’m going to run the same
code to compute the interest, but I’m going to put a
title on the graph. You’ve seen this before,
I just want to remind you how it works. PyLab.title And then I’m
going to label the x-axis and the y-axis. And that gives me a much
more useful graph. Nothing magical here, it’s
just a reminder that you really need to do
these things. You’ll notice here I’ve not only
told you that this is the years of compounding and that
this is the principal but I’ve measured it in dollars. Maybe I should have been even
more explicit and said, well, US dollars, whatever. One of the things I did want
to point out is you saw the two of these various icons that
will let you do things like zoom in on a graph
and save a graph. Here’s this icon that I think
Professor Grimson mentioned, in fact, I know he did. It’s a floppy disk, just in case
you’ve never seen one, I brought a floppy disk
to show you. This is one of the older
floppy disks. These were invented
in 1971 by IBM. They were originally 8 inches in
diameter and held all of 80 kilobytes of data. And as you can see, unlike
later floppy disks, they actually flopped. Eventually, Apple and others
pioneered a non-floppy floppy disk, that was in the ’80s. The interesting thing today is I
typically carry around a USB stick with me about that big
that holds roughly 400,000 times more data than
this floppy. And so it’s just quite
incredible how things have gone along. All right. I now want to return to what
will be the main theme for, as I said, a couple of weeks
which is randomness. And in order to talk about
randomness we have to talk about probability. And I know that Professor
Grimson started down that path just before spring break, but
if you’re anything like me your mind kind of deteriorated
a little bit over spring break, and your head isn’t
quite into things. And so, I’m just going to back
up a tiny bit and start over to get our heads into it, and
then fairly quickly move on to new things. So let’s start by asking
a simple question. You can tell my head isn’t
quite yet back to things because I forgot that I needed
to begin by gathering chalk. I’ve now got it. And we’ll come over here and
take a look at some examples. All right. So the first question I want
to ask is, suppose I take a 6-sided die, a fair one, and I
roll it 10 times, what’s the probability of not getting a
single 1, out of that die? Well, how do we go about
answering this? Well, there is a wrong way to
do it, which is sort of the obvious way, and many people
will start down this path. They’ll say, well the
probability of rolling a 1 on the– not rolling a 1 on the first
try is 1 over 6. Right? That’s true? That’s not true. What’s the probability of not
rolling a 1 the first time? 5 over 6. All right. What’s the probability of not
rolling a 1 on the second try? 5 over 6. Well, the wrong thing to do, of
course, would be to start adding them up. We say, well, OK, we’ll
just add these up. Well, one way we can tell that’s
wrong is if we add up 10 of these, we get
more than 1. Probabilities can never be
more than 1 as we’ll see. So let’s now try and think
of the right way to look at this problem. So you can think about it. If we roll these– a die 10 times, each time
I’ll get a number. So I might get a 3, and then
a 4, and then a 2. How many possible 10-digit
numbers are there? On a 6-sided die, if
I roll it 10 times? How many? AUDIENCE: 6 to the 10th? PROFESSOR: 6 to the 10th. Exactly Just when we look at
binary numbers, if I take a 10-digit binary number, and ask
how many different numbers can I represent in 10 binary
digits, it’s going to be 2 to the 10th. Here we’re base 6. So it’s going to be
6 to the 10th. Pretty big number. Now I can say, how many
of those numbers don’t contain a 1? All right. So that’s really the question
I’m now asking. How many of these don’t
contain a 1? So as we said, if I look at the
first roll the odds of not getting a one the first time
is 5 over 6 Now what’s the odds of not getting 1 the first
or the second time? It’s 5 over 6 times 5 over 6. That makes sense? Because these are independent
events. And that’s a key notion here. I’m assuming that whether I get
a 1 on the second roll is independent of whether I got
a 1 on the first roll. It should be true, assuming
my dice– die is fair. Similarly, I can do this for
the third roll et cetera. So the probability of not
getting a 1 in 10 rolls is going to be (5 over
6) to the 10th. That makes sense? If not, speak up, because things
are going to get more complicated quickly. All right. So that’s pretty simple. You– you all– are you all
with me on that? Now, suppose I ask you
the inverse question. What is the probability of
getting at least one 1 if I roll the die 10 times? So here I’ve given you how to
compute the probability of not getting any 1’s. Suppose I asked you the
probability of at least one 1? Yeah? AUDIENCE: [INAUDIBLE] 1 minus not having a 1? PROFESSOR: Exactly. Thank you. So that would be 1 minus because
we know that the probability, the sum of all the
possible things that we can do when we do a probability always has to be 1. It was a good effort. That’s it. If you take– if you want to get something
where everything is covered, the probabilities always
have to sum to 1. And so now, there are only
two possibilities here. One possibility is I
don’t get any 1’s. One possibility is I
get at least one 1. So if I take all of the
possibilities, and I subtract the possibilities of not getting
any 1’s, the result must be the probability of
getting at least one 1. This is a very common trick in
computing probabilities. Very often when I ask or
somebody says, what’s the probability of x? The simplest way to compute
it, is to compute the probability of not x and
subtract it from 1. OK. Again, heading down a wrong
track for this, one might have said, well all right, the
probability of getting a 1 on the first roll is 1 over 6. The probability of getting
a 1 on the second roll is 1 over 6. The probability of getting
a third roll is 1 over 6. I’ll just add them up, and
that will give me the probability of getting
at least one one. How do I– how can I be sure
that’s wrong? Well when I’m done, I would
claim the probability is something like that. And we know that
can’t be true. Because a probability always
has to be less than or equal to 1. So this is a good trick to keep
in mind, whenever you’re given a probability problem, try
and figure out whether you have a good way to compute it
directly, or whether it’s simpler to compute the not of
the probability, and then subtract it from 1. Probability is really
a fun field. It’s interesting, it’s history,
it’s intimately connected with the history
of gambling. And, in fact, almost all of
early probability theory owes its existence to gamblers. People like Cardano, Pascal,
Fermat, Bernoulli, de Moivre, Laplace, all famous names you’ve
heard, were motivated by desire to understand
games of chance. Mostly, it started with dice. I’ve been talking about dice. And in fact, dice are probably
the human race’s oldest gambling implement. They date at least,
archaeologically, to about 600 BC, where a pair of dice was
found in Egyptian tombs, actually longer than that. Two millennia before the birth
of Christ, people found dice in Egyptian tombs. Typically, they were made
from animal bones, but that doesn’t matter. Pascal’s interest in it, and
Pascal is really considered the founder of probability
theory, came when a friend asked him to solve the following
problem which I want to work out with you. Is it profitable to bet that
given 24 rolls of a pair of fair dice, you would
roll a double 6? He actually had a friend who
was in the business of gambling, making these bets. So he said, you’ve got a pair of
dice, you roll it 24 times and ask the question, what is
the probability of getting what we call today “box cars”,
in those days they just called two 6’s. This was considered a really
hard problem in the mid-17th century. And in fact, Pascal and Fermat,
two pretty smart guys as it happens, debated this. They exchanged letters with each
other trying to figure out how to solve this problem. It shows how math has advanced
because, in fact, today, it’s quite an easy problem. So let’s work it through
and think how would we answer this question. So what’s the probability of
rolling, of not rolling, a double 6 on the first try? Well, the probability of
not rolling a 6 on one die is a sixth — 1 over 6. The probability of not rolling
a one with the next die is also 1 over 6. So the probability of not
getting a die in the first roll, first double 6’s is– the probability of getting
a double 6 is 1/36. So the probability of not
getting a double 6 is 35/36. Right? So now we know that the
probability of not getting it is that. What’s the probability of not
getting it 24 times in a row? It’s that. Which is approximately
equal to 0.51. So you can see why the answer
was not obvious just by experience. But there is a slight edge in
betting that you will not get a double 6 in 24 times. Again, assuming you
have fair dice. As old as dice is, people have
built cheater’s dice. The excavation of Pompeii, for
example, they discovered a pair of loaded dice, dice with
a little weight in it so one number would come up more
often than it should. And in fact, if you look at the
internet today, you will find many sites where you can,
let’s see, the one I found this morning says quote, “Are
you on unusually unlucky when it comes to rolling dice? Investing in a pair of dice
that’s more reliable might be just what you need.” And then
it says, “Of course for amusement only.” Yeah,
we believe that. All right. As much as I trust probability
theory, I don’t trust my ability to use it. And so what I did is wrote a
little simulation to see if Pascal was right when
he did this. So I’ve got the first– just
this little test roll, which rolls a dice number of times,
gets the result. Now, then I decided
to check Pascal. So I was going to run 100,000
trials, and keep track of the number of times it worked. So what you’ll see I’m doing
here is for i in the range number of trials, and this is
the way we’ll do a lot of these simulations. And in fact, as we deal with
probability, we’ll be dealing a lot with the notion of
simulation, as you are doing in your current problem set. So for i in range number of
trials, for j in range 24, because that was Pascal’s
friend’s game, I’ll roll the first die, I’ll roll
the second die. If they’re both 6’s I’ll
say, yes equals 1. And I’ll break and then I’ll
compute the probability of winning or losing. OK? So let’s let it rip. So now let’s let it rip. There it is. And we can see that it actually
comes out pretty close to what Pascal
predicted. Should we be surprised that it
didn’t come out to exactly? Well let’s see, is it exactly? What is 35/36 to the 24th? So that’s the– well, to 17 digits of precision,
the exact answer. And you can see we came up with
something close to that. Not exactly that, and we
wouldn’t expect to. Now I only did 100,000 trials. If I did a million trials,
I’d probably come up with something even closer, but if I
did 2 trials, who knows what I get– come up with it, right? Could be– I could get 1, I could get lucky
both times, or unlucky. Later on, we’ll talk more about
the question, how do we know how many trials to run? Now, the interesting thing is
I’m sure it took me less time to write this program
than it took Pascal to solve the problem. Now the truth is, I had several
hundred years of other people’s work to build on. But in general, I think one of
the questions you’ll find is, is it easier sometimes to write
a simulation, than it is to do the probabilities? What I often do in
practice is both. I’ll scratch my head and figure
out how to figure out the answer analytically, and
then if it’s easy, I’ll write some code to simulate the
problem, and expect to get roughly the same answer, giving
me confidence I’ve done it correctly. On the other hand, if I’ve done
the simulation and it had come up with something totally
bogus, or totally different, then I would have had to work
hard to figure out which was right, the code or the math. Same sort of thing you saw when
you looked at the random walk, and the first time it was
done an answer showed up that was just wrong. But, you need to have some
intuition about a problem, so that you can look at
it and say, yeah, that’s in the ballpark. And if it’s not, it’s
time to worry. This kind of simulation that
I’ve just done for the dice game is what’s called a “Monte
Carlo simulation”. It is the most popular kind of
simulation named after a Casino on the Riviera, in the
small principality of Monaco. This was back in the time when
it was hard to find a place you could gamble, and this
happened to be one of the places you could. The term was coined in 1949 by
Stanislaw Ulam and Nicholas Metropolis, two very well-known
mathematicians. Ulam, who later became famous
for designing the hydrogen bomb with Teller, invented the
method in 1946, and I’m going to quote from his description
of how he invented it. “The first thoughts and attempts
I made to practice the Monte Carlo method, were
suggested by a question which occurred to me in 1946, as I
was convalescing from an illness and playing
solitaires. The question was, what are the
chances that a canfield solitaire laid out with
52 cards will come out successfully? After spending a lot of time
trying to estimate them by pure combinatorial calculations,
I wondered whether a more practical method
than quote ‘abstract thinking’ end quote, might not
be to lay it out, say, 100 times, and simply observe
and count the number of successful plays. This was already possible to
envision with the beginning of the new era of fast computers. And I immediately thought of
problems, as you would, I’m sure, immediately thought of
problems of neutron diffusion and other questions of
mathematical physics. And more generally, how to
change processes described by certain differential equations
into an equivalent form interpretable as a succession
of random operations. Later, I described the idea to
John von Neumann, and we began to plan actual calculations.” So as early as 1946, people
were thinking about the question of moving away from
solving systems of equations, to using randomized techniques
to simulate things and try to find out what the actual
answer was that way. Now of course “fast”
is a relative term. Ulam was probably referring to
the ENIAC computer, which could perform about 10 to the
3 additions a second. Not very many, 1,000 operations
a second, and weighed approximately 25 tons. Now today’s computers, by
comparison, perform 10 to the 9th additions and weigh about
10 to the minus 3 tons. All right. This technique was used during
the Manhattan Project to predict what would
happen doing– during nuclear fission
and worked. Monte Carlo simulations are an
example of what’s called “inferential statistics”. In brief, and I’m going to be
brief because this is not a statistics, course, inferential
statistics is based upon one guiding
principle. And that principle is that a
random sample tends to exhibit the same properties
as the population from which it is drawn. So if I try and sample people,
say, for predicting an election, the notion is if I go
and I asked a 1,000 people at random in Massachusetts who
they’re going to vote for, the average will be about the same
as if I looked at the whole population. So whenever we use a statistical
method like this, so for example, we assumed here,
is those 100,000 times I threw the pair of dice, that
that would be representative of all possible throws of the
dice, the infinite number of possible throws. One always has to ask the
question whether this is true, or whether one has a sampling
technique that is, for example, giving you
a biased sample. Little later in the term, we’ll
talk about many ways in which you can get fooled here
and think you’re doing a fair statistical analysis, and get
all the math right, and still come up with the wrong answer
because this assumption doesn’t actually hold. All right. Let’s think about it now in
terms of coins, a little simpler than dice, where you
can flip a coin and you get either a head or a tail. Suppose Harvey Dent, for
example, flipped a coin and it came up heads. Would you feel good inferring
from that that the next time he flipped a coin it would
also come up heads? I wouldn’t. Suppose he flipped it
heads and it came up heads twice, in a row. Would you feel comfortable
with the third flip would be a head? Probably not. But suppose he flipped it a 100
times in a row, and it was a head each time. What would you infer? I would infer that the coin
two-headed And, in fact, every time it was going to come up
heads, because it is so improbable that if it
was a fair coin– what’s the probability of having
a 100 heads in a row with a fair coin? 1 over what? AUDIENCE: 1 over 100– 1 over 2 to the 100th. Right? PROFESSOR: A half the
first time times a half times a half. A huge number, a very small
number rather, right? So the probability and a fair
coin of getting hundred heads in a row is so low with just 100
flips, that I would begin to think that the coin
was not fair. All right. Suppose, however, I flipped it
100 times and I got 52 heads and 48 tails. Well, I wouldn’t assume
anything from that. Would I assume that the next
time I flipped it a 100 times I’d get the same
52 to 48 ratio? Probably not, right? Your common sense tells
you you wouldn’t. All right. Probably, it tells you, you
wouldn’t even feel comfortable guessing that there would
be more heads than tails the next time. So when we think about these
things, we have to think about the number of tests and how
close the answer is to what you would get if you did
things at random. This is sort of comparing– this is technically called
comparing something to the null hypothesis. The null hypothesis is
what you would get with a random event. And when you do a simulation,
if you get something that is far from that, or when you
sample a population, you get something that’s distant from
the null hypothesis, you can assume that maybe you’re
seeing something real. All right. Let’s look at this in a little
less abstract way. So let’s go look at
some coin flips. So I wrote a simple
program, flip. Just flip the coin some number
of times and tells me what fraction came up heads. So we can run it, and
let’s look at a– suppose I flip a 100,
I get 0.55. If I flip 10, I get 0.4. If I flip 10 again, I get 0.5. Now look at that, the same
thing twice in a row but now I get 0.2. So obviously, I shouldn’t infer
too much from 10 flips and even from 100 where
I got 0.55. Let’s see what happens if I
flip 100 again, 0.41, big difference. So this is suggesting that we
can’t feel very good about what happens here. Now if I do the following, well
I’m feeling a little bit better about this, well
for one bad reason and one good reason. The bad reason is, I know the
answers 0.5, and these are both close to 0.5, so I
feel warm and fuzzy. But that’s cheating. I wouldn’t need to write
the simulation If I knew the answer. But mostly I feel good about it
because I’m getting kind of the same answer every time. OK, and that’s important. The more I do, the more stable
it gets with the larger the number of trials. This is an example of what’s
called “the law of large numbers”, also known as
Bernoulli’s Law, after one of the Bernoulli family of
mathematicians, and I can’t for the life of me remember
which Bernoulli. There are a whole
bunch of them. Anyway the law states, and it’s
important to understand this because again it underlies
the inferential statistics, that in repeated
independent tests, and it’s important to note the word
“independent”, each test has to be independent of
the earlier test. In this case, the tests are
flips of the coin with the same actual probability we’ll
call it p, often used to represent probability, of an
outcome for each test, the chance that the fraction of
times that outcome occurs the outcome that with probability,
p, converges to p as number of trials goes to infinity. All right. So if I did an infinite number
of trials, the fraction of heads I would get in this case
would be exactly 0.5. Of course I can’t do an infinite
number of trials. But that’s the law of large
numbers that says the– Now, it’s worth noting that this
law does not imply that if I start out with deviations
from the expected behavior, those deviations are likely to
be quote “evened out” by opposite deviations
in the future. So if I happen to start by
getting a whole bunch of heads in a row, it does not mean that
I’m more likely to get tails in a subsequent trial. All right. Because if I were– if that were true, then they
wouldn’t be independent. Independent means memoryless. So if I have an independent
process, what happens in the future cannot be affected
by the past. And therefore, I don’t
get this business of “they even out”. Now people refuse
to believe this. If you go to any gambling place,
you’ll discover that if people threw the roulette wheel,
if black comes up 20 times in a row, they’ll be
a rush to bet on red. Because everyone will say, red
is do, red is do, red is do. And every psychologist who has
ever done this experiment, finds that people don’t
believe it. That it’s not true. People just don’t get
probability, and it happens so often it’s got a name called
“the gambler’s fallacy”. And there’s been great examples
of people going broke doing this. Now notice that the law of large
numbers here is about the fraction of times
I get an outcome. It does not imply for example,
that the absolute difference between the number of heads and
the number of tails will get smaller as I run
more trials. Right? It doesn’t say anything
at all about that. It says the ratio of head to
tails will approach 1, but not that the difference
between them. All right, let’s look at an
example showing that off. So what I’ve got here is this
program called “flip plot”. This is on your hand out. This is just going to run this
business of flipping coins. I should point out just– I did it this way just
to show you. What I’m doing is each flip– if
random.random is less than 5, I’ll call it a head,
0.5, I’ll call it heads, otherwise a tails. You’ll notice that it appears
that maybe I’m biasing a little bit, because I’m
giving 0.5 a value. But there are so many floating
point numbers between 0 and 1, that the probability of getting
exactly 0.5 is so small that I can ignore it. It isn’t going to really make a
difference Random.random is the key issue, the key function
that’s used to implement all the other random
functions that we have in that package. All right. So I’m going to do it, and
I’m going to do it over a range of values. The minimum exponent to the
maximum exponent and for exponent in range min x to max
x plus 1, I’m going to choose an x value that is 2 to that. So this lets me go
over a big range. So I’ll see what happens if I
get 1 flip, and 2 flips, and 4 and 8 and 16 and 32 et cetera. And then I’m going to
just do some plots. I’m going to plot the absolute
difference between heads and tails and the ratio
of heads to tails. Let’s see what happens
when we run that. Actually, probably nothing
because I didn’t uncomment the run part of it. Let’s do that. So I’m going to call flip plot
with 4 and 20, running from four trials 2 to the
4 to 2 to the 20. Let’s see what we get. Now, you may get different
things when you run at different times. In fact, you will. So here we see something
kind of uninteresting. Let’s cheat and see what we got
the first time I ran it, which is on your hand out, and
I have a PowerPoint with it. I was– I knew this might happen. Doesn’t usually, but sometimes
when you run it you get surprising results. So here’s what happened
when I first ran it. Here was the difference between
heads and tails. And it seems that, OK, the
difference was low, it went up, it went down, it went
up, it went down. It seemed to go down
dramatically. If you remember what we just saw
when I ran it, we also saw something where it went up a
little bit then it went down and then shot up dramatically at
the end, which was why that scale is so funny. And if we look at the ratio,
what we see is it seems to start above 1, drop below
1, and then seems to converge towards 1. Now, I show this because I
want to make a couple of points of plotting. Let’s look at this out here. Looks like we have a pretty
dramatic trend of this linear drop. Do we? Do we actually have
a trend here? Well let’s think about it. The default behavior of the plot
command in PyLab is to connect points by lines. How many points do I actually
have out here? Well, you saw the code. You have the code
in the hand out. How many points do you think
there are out here? A 1,000? A 100? 3? 2? 2 to 3. Right? Depending on what I mean
by “out here”. So what we see here is something
that happens a lot. People plot a small number of
points, connect them by a line, and mislead the audience
into thinking there’s a trend when, in fact, maybe all
you have is an outlier. So it’s problematical here
to do it that way. So let’s see what happens
if we change the code. And what I’m going to do is
change it in two ways. Well, maybe I’ll change
it in one way first. Uncomment. Uncomment. So what I’m doing here is I am
plotting in a different way. This quote “BO” says, don’t
connect it by lines but just put a dot as an “O” and
B says, make it blue. I used blue because it’s
my favorite color. So now if we look at these
things, we’ll see something pretty different. So that’s the difference between
heads and tails, that’s the ratio. But now, if we look at the
difference between heads and tails here, what we see
is it’s pretty sparse. So yeah, maybe there’s a trend,
but maybe not, right? Because way out here I’m only
connecting two points, giving an illusion that there is a
trend but, in fact, no reason to believe it. So I always think if you’re
plotting a small number of points, you’re much better off
just plotting the points, than you are trying to
connect them. Now if we look at this one,
again, maybe we’d feel kind of comfortable if there is a trend
here, that there are several points on this line. We can’t see much of what’s
going on over here, which gets me to the next thing I want
to do is I’m going to use logarithmic axes here. So PyLab.semilogx says make
the x-axis logarithmic. PyLab.semilogy the y-axis. And so in the case of the
absolute difference, I’m going to make both logarithmic. Why am I doing that? Because both have a large
range, and by making it logarithmic, I can see what’s
happening at the left side in this case where things
are changing. When I look at the ratios, the
y-axis does not have a very large range, and so there’s no
need to make it logarithmic. We’ll run it. So here, we can see
the difference between heads and tails. And now we can see what’s going
on at the left as we can in Figure (4). And we can see things
much more clearly. So log scales can be
enormously useful. And in fact, I use them a lot,
everyone uses them a lot but, again, it’s very important to
observe the fact that it’s logarithmic and not
get fooled. All right. So I talked about linear
scaling, logarithmic scaling and we now have charts where I
can, perhaps, actually reach some conclusion about
what’s going on. The next question is, how
certain can I be? Can I really be certain that,
indeed, this should be converging to 1? Here, if I sort of look at it,
it does look like there’s kind of a linear trend of the
absolute difference growing as the number of trials grows. How certain can I be of that? You can never get absolute
certainty from sampling, because you could never be
sure if you haven’t been vastly lucky or unlucky. That’s not to say you
can’t get the absolute correct answer. Maybe I could get 0.5, which
is the correct answer. But I can’t know that that’s
the correct answer. So now the question I want to
pursue, and it’s what we’ll cover on Thursday, is what
techniques can I use to make a statement of the form, I’m
certain within the following range that I have the
right answer. That I know the right answer
is highly likely to be this close to the answer my
simulation is giving me. And we’ll look at how we can
make those statements and actually believe them. OK, see you on Thursday.

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8 Comments

  • Elimelech Braun says:

    Thank you!

  • EaglesQuestions says:

    For assignment ps6, what are we suppose to do if the graph window always freezes?

  • Nitish Rajoria says:

    The python version that mit opencourseware provided me doesn't have pylab module please any one suggest me what to do because this barrier is causing me to lag behind in this course..please do reply soon..thank you

  • Mustafa Adam says:

    When proffesor Guttag said that probability of not getting 1 on the first roll OR not getting a one on second roll 8:52 is not an accurate to describe what he meant.

    I think mathematically, he should have said AND instead of OR .. Am I correct ?

  • A C says:

    24:46 – Guttag's subtle sarcasm, I love it

  • ninja2koo says:

    If anyone is having issues installing numpy and pylab on Windows, this video tutorial was extremely helpful to me. It shows you how to install numpy and matplotlib (pylab) through the command prompt assuming you have at least Python 2.7.

    https://www.youtube.com/watch?v=-llHYUMH9Dg

    I hope this helps you as it was extremely helpful for me!

    Cheers!

  • Indranil Ghosh says:

    which version of mathplotlib should I use for Python 2.7.11?

  • 2yKlone Bee says:

    this one looks like it was filmed with a carrot.

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