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That's what you expect. So we're not going to do just plausible moves, we're going to do all moves, so if it's 11 by 11, you have to examine positions. That's the answer. One idiot seems to do a lot better than the other idiot. That's the character of the hex game. White moves at random on the board. Because once somebody has made a path from their two sides, they've also created a block. We've seen us doing a money color trial on dice games, on poker. I think we had an early stage trying to predict what the odds are of a straight flush in poker for a five handed stud, five card stud. So it's a very useful technique. This should be a review. And then you can probably make an estimate that hopefully would be that very, very small likelihood that we're going to have that kind of catastrophic event. You're going to do this quite simply, your evaluation function is merely run your Monte Carlo as many times as you can. A small board would be much easier to debug, if you write the code, the board size should be a parameter. So what about Monte Carlo and hex? So you can use it heavily in investment. Who have sophisticated ways to seek out bridges, blocking strategies, checking strategies in whatever game or Go masters in the Go game, territorial special patterns. And we're discovering that these things are getting more likely because we're understanding more now about climate change. And the one that wins more often intrinsically is playing from a better position. Because that involves essentially a Dijkstra like algorithm, we've talked about that before. And indeed, when you go to write your code and hopefully I've said this already, don't use the bigger boards right off the bat. So we make all those moves and now, here's the unexpected finding by these people examining Go. And that's a sophisticated calculation to decide at each move who has won. Critically, Monte Carlo is a simulation where we make heavy use of the ability to do reasonable pseudo random number generations. Of course, you could look it up in the table and you could calculate, it's not that hard mathematically. The insight is you don't need two chess grandmasters or two hex grandmasters. So probabilistic trials can let us get at things and otherwise we don't have ordinary mathematics work. But for the moment, let's forget the optimization because that goes away pretty quickly when there's a position on the board. But it will be a lot easier to investigate the quality of the moves whether everything is working in their program. You're not going to have to know anything else. And you do it again. So here's a way to do it. So it's not truly random obviously to provide a large number of trials. So we make every possible move on that five by five board, so we have essentially 25 places to move. You could do a Monte Carlo to decide in the next years, is an asteroid going to collide with the Earth. So there's no way for the other player to somehow also make a path. No possible moves, no examination of alpha beta, no nothing. So it's not going to be hard to scale on it. So it's a very trivial calculation to fill out the board randomly. And in this case I use 1. We're going to make the next 24 moves by flipping a coin. Turns out you might as well fill out the board because once somebody has won, there is no way to change that result. And we want to examine what is a good move in the five by five board. And you're going to get some ratio, white wins over 5,, how many trials? And so there should be no advantage for a corner move over another corner move. Instead, the character of the position will be revealed by having two idiots play from that position. How can you turn this integer into a probability?

無料 のコースのお試し 字幕 So what click here Monte Carlo bring to the table?

Here's our hex board, we're showing a five by five, so it's a relatively small hex board.

Filling out the rest of the board doesn't matter. So for this position, let's say you do it 5, times. I've actually informally tried that, they have wildly different guesses.

You readily get abilities to estimate all sorts of things. So here's a five by five board. Given how efficient you write your algorithm and how fast your computer hardware is. And if you run enough poker star monte carlo 2019 on five card stud, you've discovered that a straight flush is roughly one in 70, And if you tried の ジャック トランプ ブラック ask most poker players what that number was, they would probably not poker star monte carlo 2019 familiar with.

And that's poker star monte carlo 2019 going to be some assessment of that decision.

I'll explain it now, it's worth explaining now and repeating later. I have to watch why do I have to be recall why I need to be in the double domain. Use a small board, make sure everything is working on a small board.

So if I left out this, probability would always return 0.

Once having a position on the board, all the squares end up being unique in relation to pieces being placed on the board. So you could restricted some that optimization maybe the value. It's not a trivial calculation to decide who has won. But with very little computational experience, you can readily, you don't need to know to know the probabilistic stuff. So it's really only in the first move that you could use some mathematical properties of symmetry to say that this move and that move are the same. So black moves next and black moves at random on the board. You can actually get probabilities out of the standard library as well. But I'm going to explain today why it's not worth bothering to stop an examine at each move whether somebody has won. This white path, white as one here. And these large number of trials are the basis for predicting a future event. You're not going to have to do a static evaluation on a leaf note where you can examine what the longest path is. Rand gives you an integer pseudo random number, that's what rand in the basic library does for you. Sometimes white's going to win, sometimes black's going to win. So you might as well go to the end of the board, figure out who won. So here is a wining path at the end of this game. And we'll assume that white is the player who goes first and we have those 25 positions to evaluate. Okay, take a second and let's think about using random numbers again. And then, if you get a relatively high number, you're basically saying, two idiots playing from this move. So here you have a very elementary, only a few operations to fill out the board. Indeed, people do risk management using Monte Carlo, management of what's the case of getting a year flood or a year hurricane. And we fill out the rest of the board. So we could stop earlier whenever this would, here you show that there's still some moves to be made, there's still some empty places. And at the end of filling out the rest of the board, we know who's won the game. The rest of the moves should be generated on the board are going to be random. And there should be no advantage of making a move on the upper north side versus the lower south side. It's int divide. And then by examining Dijkstra's once and only once, the big calculation, you get the result. That's going to be how you evaluate that board. And that's the insight. All right, I have to be in the double domain because I want this to be double divide. Maybe that means implicitly this is a preferrable move. Why is that not a trivial calculation? You'd have to know some probabilities. You'd have to know some facts and figures about the solar system. We manufacture a probability by calling double probability. Now you could get fancy and you could assume that really some of these moves are quite similar to each other. So it can be used to measure real world events, it can be used to predict odds making.