This time, let's create a figure, but we define three different plots and graphs in the same window.
The thing that I'm going to do is divide this one to four parts.
The upper left part would be my feet.
The upper right part would be my regression.
The lower left part shoulder and lower right part showed a histogram for this purpose.
Since we don't have any matlab tool, I need to create a function, open another craib Coleta function,
give it a name like a plot resolves and then in apprentice's, I want to take three different inputs.
One of them is moving target.
The other one is the output of my network and a name which I'm sending this name from the Advanta script.
Number three, if you're creating a function you need to put to end to finish this function here, just
click on top and then start writing your finger and your plots to start with the keyboard figure to
open a figure.
Then here I want to divide my figure into four different parts.
It means I need to use a sub plot and then we have two columns, two rows, and this one is the first
one.
I need to do the same for the third one and fourth one as well.
Here is number two.
The second part, the third part and this one is the forge board gave them so much space to write your
program.
In this part, I will have the feed of targets and my outputs in a plot.
I want to show first the why would a color of black.
We're not done yet with this plot.
So type hold on.
Another plan to show the targets.
We did color off red and I wanted to be dotted dotted line.
And Ålesund with some explanation, like the first one is out Putes, the second one is showing my targets
and of course you'd need to have a title, just have a same name, vete this one that is coming from
advances.
Great.
Now let's move on to the second part, which would be the upper right part of this subplot.
This time I want to show the correlation.
For this purpose, I will plot to target the foods and I will show all of them the color of black,
you can show them weed out.
Usually meth lab is showing them with little black circles.
So just click on or this one would show it with a black little circles.
We are not done yet here.
Hold on.
The next part we need to define X mean and X marks to show where are we going with this data.
So for the X mean, I need to have me off my mean target, which is here t and mean of my neural net
output, which is why for the X marks do the same.
This one is a max of targets in max of output.
And so all of them in a plot.
I will open and close at Brocket inside a bracket I will show the X mean X max and then another bracket
to show the X mean again and X max since we have two inputs and finally show them with the color of
blue and let's show them a little bit t care to do just type line width and maybe give it a number of
two or three.
Two would be good I think.
So this one should be the color of blue and a b t Caroline.
And next point, I'm looking for a correlation.
I will define a variable R and I use the function C or R R Ifill's selected F and F one.
You can see the syntax for this correlation.
I need to put here my target and my outputs of neural network.
But pay attention here.
They need to be in a column.
So I need to use reverse of targets in reverse of outputs to show the correlation.
Finally finished with the title and is titled Let's Call Them.
Correlations are equal to that.
We have a number I want to show it with a string.
So num two s t r which one that are here.
OK, very well.
Now let's move on to the next part and here I want to see the errors.
The first thing to do is calculating an error.
Create a variable name E is equal to the targets minus the outputs.
Finally Shou and should we on a plot give it a color.
Let's say color of blue.
At some guide here, we have ever it can do something else like calculating the mean square error,
create a variable name MCE and this one is the meaning of this obscure error.
We can calculate something else, like a root, mean square error, root, mean square error, we can
define it to be a square off this mean square error very well.
And finally, give it a title.
Again, we have numbers.
We want to convert them to the strings.
So the first part would be mean square error, not to s t r for which one forming a square, another
one for root mean square error.
OK, all set.
Let's move on to the next part and this would be the lower part right side.
This time I want to calculate the histogram for Mr Graham.
We have several functions that we can use this time.
I'm going to use that he's tweaked if you forget about the syntax just selected FMF Flon or just simply
eflornithine some keyboards.
And here I want to give you the 50 being.
So I'm choosing the second syntax.
Show it with a fifty being.
Another thing that we can do is calculate the same for a standard deviation and error and also Minoff
error.
To do that create another variable called E mean.
This time this is equal to the mean of our error.
And here we have another variable for a standard deviation, which I'm going to call it, Steve, the
standard deviation.
And we need to use a function named Usted to calculate that.
Finally, give it a title inside a bracket.
Let me do something a bit interesting.
If you use a backslash and then call the key word Myu, you will be able to see to a room and no new
for this one.
We can do the same for showing a sigma sign in mathematic congregating.
Number two is tweaking another one to show that sigma just use a backslash again and type Sigma do use
the mathematical sine to show.
Just Google it for the codes of Matlab to show different mathematical signs and symbols in a Matlab
code.
OK, I think we are all set to just create a function named plot results Aztek Htwe incudes from the
program that is calling this function, and then it has four different parts.
This is a first part to show defeat of and why.
This is a second part to show Decorrelation.
This is the third part to show the error.
And finally, the last slide shows the histogram.
Let's check the results in advance of Screwtape number three in a plot point.
I don't need them more.
Remove them and call for the plot results.
Give it a target, which is t the output, which is why.
And give you the name, let's call it all data.
Run the program.
Oh, we have it.
OK, we we missed something we didn't save our flight control is to save the fight.
Very important reminder here.
Make sure you are saving dysfunction four in a same folder with your program, otherwise you cannot
call it and you will get a serious problem plot result.
This is a name of my function and say within a same folder.
OK, here we can see our function now, it should walk back here and click on Ron very well.
We can see all data here in this graph.
We can see the correlation, these little black circles that we created to show our data.
And we have 1000 something of them.
This is my correlation.
The square root is three hundred forty six, the root of minus square, which is showing the average.
And that's more accurate.
That's actually more useful.
It's around 18.
Look at my data.
My data is from minus one hundred two positive one hundred.
But the root is square mirror.
It means most of the data are here.
That is correct.
And it must be actually close to the number of sigma, which is already close.
It's not the same, but it's very close.
This one is the most sign which we created with the Matlab code and it shows you and this one is our
sigma.
The histogram is also a good fit.
It's around zero and you can do the same for your training data, for your validation data and for your
test data to do that.
Let me just show you one of them here.
We use the mouse to separate the train data.
The validation data and to set data seems to have more than one in food.
We need to define it for a specific input.
This time I'm going to choose the first input, just copy pasted for the rest of them.
Otherwise you will get a logical error.
Now, from the first input, I want to see this one for the train target and train outputs to do that.
Let's just copy paste it one more time.
This time this is my train target.
Is that the target or target targets.
Good.
And this one is my train.
Output's the name would be train date.
You can do the same for validation and for testing as well.
OK, here it is, I already created for validation data, which is valid targets and the name for Alby's
Isma'il output.
I just changed a name as well.
I did the same for test.
Now let's run your program.
We need the prentice's here.
But other than that, it should work.
OK, let's run.
Well, we have four different figures.
The first one is for all data.
This one is for the train data.
We can see the histogram correlation, all the things that we are looking for.
If you need more, you can just add and have a subplot just divided more maybe to support two and three
to have more columns.
This was all for the first output.
Let's see what can we do if we want to see for the second output?