mapstd (Neural Network Toolbox™)

Neural Network Toolbox™

mapstd

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Process matrices by mapping each row's means to 0 and deviations to 1

Syntax

[Y,PS] = mapstd(ymean,ystd)
[Y,PS] = mapstd(X,FP)
Y = mapstd('apply',X,PS)
X = mapstd('reverse',Y,PS)
dx_dy = mapstd('dx',X,Y,PS)
dx_dy = mapstd('dx',X,[],PS)
name = mapstd('name');
FP = mapstd('pdefaults');
names = mapstd('pnames');
mapstd('pcheck',FP);

Description

mapstd processes matrices by transforming the mean and standard deviation of each row to ymean and ystd.

mapstd(X,ymean,ystd) takes X and optional parameters,

X
N x Q matrix or a 1 x TS row cell array of N x Q matrices

ymean
Mean value for each row of Y (default is 0)

ystd
Standard deviation for each row of Y (default is 1)

`X`	`N` x `Q` matrix or a `1` x `TS` row cell array of `N` x `Q` matrices
`ymean`	Mean value for each row of `Y` (default is 0)
`ystd`	Standard deviation for each row of `Y` (default is 1)

and returns

Y
Each M x Q matrix (where M == N) (optional)

PS
Process settings that allow consistent processing of values

`Y`	Each `M` x `Q` matrix (where `M` `==` `N`) (optional)
`PS`	Process settings that allow consistent processing of values

mapstd(X,FP) takes parameters as a struct: FP.ymean, FP.ystd.

mapstd('apply',X,PS) returns Y, given X and settings PS.

mapstd('reverse',Y,PS) returns X, given Y and settings PS.

mapstd('dx',X,Y,PS) returns the M x N x Q derivative of Y with respect to X.

mapstd('dx',X,[],PS) returns the derivative, less efficiently.

mapstd('name') returns the name of this process method.

mapstd('pdefaults') returns default process parameter structure.

mapstd('pdesc') returns the process parameter descriptions.

mapstd('pcheck',FP) throws an error if any parameter is illegal.

Examples

Here you format a matrix so that the minimum and maximum values of each row are mapped to default mean and STD of 0 and 1.

x1 = [1 2 4; 1 1 1; 3 2 2; 0 0 0]
[y1,PS] = mapstd(x1)

Next, apply the same processing settings to new values.

x2 = [5 2 3; 1 1 1; 6 7 3; 0 0 0]
y2 = mapstd('apply',x2,PS)

Reverse the processing of y1 to get x1 again.

```
x1_again = mapstd('reverse',y1,PS)
```

Algorithm

It is assumed that X has only finite real values, and that the elements of each row are not all equal.

```
y = (x-xmean)*(ystd/xstd) + ymean;
```

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