## 1/02/2013

### Matlab dimension change function -> reshape

Useful function ~ ^^

A = magic(3)

A =
8     1     6
3     5     7
4     9     2

>> B = reshape(A, 1, 3*3)

B =
8     3     4     1     5     9     6     7     2

### Image Normalization using Standard deviation - example source code

You can understand Standard deviation normalization, referenced on this web page.
http://www.d.umn.edu/~deoka001/Normalization.html
This web page introduce that..(wrote by Siddharth Deokar)

-------------------------------------------------------------------------
Normalization by Standard Deviation
We normalize the attribute values by using standard deviation.

For Example:

Consider 5 instances which has attribute A with the follwing values: {-5, 6, 9, 2, 4}

First we calculate the mean as follows:

Mean = (-5+6+9+2+4) / 5 = 3.2

Second, we subtract the mean from all the values and square them:

(-5-3.2)^2 = 67.24
(6-3.2)^2 = 7.84
(9-3.2)^2 = 33.64
(2-3.2)^2 = 1.44
(4-3.2)^2 = 0.64

Then we find the deviation as follows:

Deviation = sqrt ((67.24 + 7.84 + 33.64 + 1.44 + 0.64) / 5) = 4.71

Now we normalize the attribute values:

x => (x - Mean) / Deviation

-5 => (-5 - 3.2) / 4.71 = -1.74
6 => (6 - 3.2) / 4.71 = 0.59
9 => (9 - 3.2) / 4.71 = 1.23
2 => (2 - 3.2) / 4.71 = -0.25
4 => (4 - 3.2) / 4.71 = 0.17

-------------------------------------------------------------------------

This is sample source code:

```//

int i,j,z;
double sum=0;
double Mean;

//sum, mean
for(j=0; j< Height; ++j)
{
for(i=0; i< Width; ++i)
{
sum = sum + Pdata[i][j];
}
}

Mean = sum/(Height*Width);

//Deviation
double dSum=0;
double deviation;
double std_dev;

for(j=0; j< Height; ++j)
{
for(i=0; i< Width; ++i)
{
Pdata[i][j] = (Pdata[i][j] - Mean);

dSum = dSum + (Pdata[i][j]*Pdata[i][j]);
}
}

deviation = dSum / (Width*Height);
std_dev = sqrt(deviation);

//Normalization
for(j=0; j< Height; ++j)
{
for(i=0; i< Width; ++i)
{
Pdata[i][j] = (Pdata[i][j] / std_dev);
}
}

```