Process matrices by mapping each row's means to 0 and deviations to 1
[Y,PS] = mapstd(X,ymean,ystd)
[Y,PS] = mapstd(X,FP)
Y = mapstd('apply',X,PS)
X = mapstd('reverse',Y,PS)
dx_dy = mapstd('dx_dy',X,Y,PS)
mapstd
processes matrices by transforming
the mean and standard deviation of each row to ymean
and ystd
.
[Y,PS] = mapstd(X,ymean,ystd)
takes X
and
optional parameters,
X 

ymean  Mean value for each row of 
ystd  Standard deviation for each row of 
and returns
Y 

PS  Process settings that allow consistent processing of values 
[Y,PS] = mapstd(X,FP)
takes parameters
as a struct: FP.ymean
, FP.ystd
.
Y = mapstd('apply',X,PS)
returns Y
,
given X
and settings PS
.
X = mapstd('reverse',Y,PS)
returns X
,
given Y
and settings PS
.
dx_dy = mapstd('dx_dy',X,Y,PS)
returns
the reverse derivative.
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)
It is assumed that X
has only finite real
values, and that the elements of each row are not all equal.
y = (xxmean)*(ystd/xstd) + ymean;