But if you understand it, you can now go and implement particle filters for the great range of robotics applications.

And it really overcomes some basic barriers in both of the 2 previous methods.

There is also something called drift, which you won't use right now, but later on it'll become handy.

And guess what? You get to write this function.

Or if you look at open source and limitations of balanced search trees, you can look at code that does that implementations.

This is written as a condition distribution as follows.

I promised you it would be an easy algorithm.

It's called memoization.

So, we get the fact that the particles cluster around regions of higher posterior probability.

And I have to subtract z0, 1, and 2 from the corresponding robot poses and add them all up back to the last pose.

We can choose life over fear.

We didn't really give you too much guidance on how to do this, but, believe it or not, this is actually a pretty common thing you're going to have to do in the real world.

I knew when I was timing this that there wouldn't be time for me to do anything about

Looks like findmax is really working out well, except that it's totally not.

The most appealing one to me is that milliseconds gives you a solid number to use for comparison of different algorithms.

It's not necessarily in the order that we put them in.

It's amazing, this entire program over here implements a one dimensional common feature.

You can wind up with an implementation of gradient descent.

I remove nodes from the stack to pick the one that has the minimum f value as opposed to the minimum g value, and I have A star.

We'll store that in the variable second, and we'll return second, and so that was the implementation that was used in the test.

I want to say a little bit now about how to implement that in terms of a computer algorithm.

It was a little dream of ours for a long time now. Finally we can make it come true for the first time.

Or, maybe..you can come down and tell him yourself. We can make that happen.

All the things that we've seen in unit 1 and in later units are constructs in Python, and we need the grammar that's built into the interpreter to break that string into its parts.

And when you're implementing machine

And for various reasons, this can behave in slightly stranger ways.

This is a kind of data abstraction.

So, we've done the full recursion of applying our motion update to our full particle set.

Let's see if it can add 1 1.

One, we battered down the doors of the insurance companies, and we actually got TEMPT a machine that let him communicate a Stephen Hawking machine.

You really losing and gaining information.

But in reality, you don't know it.

That's your forward prediction step in particle filters.

You just have the 7 there again.

This one is the most complicated. If cd is empty we then we go back in time to chart j and bring something from it forward to this current location.

But you can most definitely scale an imaginary number by a real number.

This algorithm is beautiful, and you can implement it in less than 10 lines of program code.

What is it that causes mystery test to fail? Well, we can again use delta debugging to figure this out.

But in the future we're going to talk about a little bit more how do we think about how computationally intensive different ways of implementing a function, can be.

It could be any real number here, as long as x is less than negative 3, less than or equal to negative 3, or x is greater than or equal to 3.

Absolutely, yes.

My main guideline was the PyGtk implementation as it was fairly feature complete and had a nice object oriented interface.

We can implement optimization okay so that it returns a safe answer for optimization in all cases, for all possible programs.

We're going to need a function. We'll go ahead and throw it at the top here.

However you have to put in most of this stuff.