function [out1,out2] = mapstd(in1,in2,in3,in4)
%MAPSTD Map matrix row means and deviations to standard values.
%
% 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 - NxQ matrix or a 1xTS row cell array of NxQ 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 MxQ matrix (where M == N) (optional).
% PS - Process settings, to allow consistent processing of values.
%
% MAPSTD(X,FP) takes parameters as 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 MxNxQ derivative of Y w/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 is how to 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, we 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)
%
% Here we 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;
%
% See also MAPMINMAX, FIXUNKNOWNS, PROCESSPCA, REMOVECONSTANTROWS
% Copyright 1992-2008 The MathWorks, Inc.
% $Revision: 1.1.6.12 $
% Process function boiler plate script
boiler_process
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Name
function n = name
n = 'Map Mean and Standard Deviation';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Parameter Defaults
function fp = param_defaults(values)
if length(values)>=1, fp.ymean = values{1}; else fp.ymean = 0; end
if length(values)>=2, fp.ystd = values{2}; else fp.ystd = 1; end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Parameter Names
function names = param_names()
names = {'Mean value for each row of Y.', 'Maximum value for each row of Y.'};
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Parameter Check
function err = param_check(fp)
mn = fp.ymean;
std = fp.ystd;
if ~isa(mn,'double') || any(size(mn)~=[1 1]) || ~isreal(mn) || ~isfinite(mn)
err = 'ymean must be a real scalar value.';
elseif ~isa(std,'double') || any(size(std)~=[1 1]) || ~isreal(std) || ~isfinite(std) || (std <= 0)
err = 'ystd must be a positive real scalar value.';
else
err = '';
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% New Process
function [y,ps] = new_process(x,fp)
% Replace NaN with finite values in same row
rows = size(x,1);
for i=1:rows
finiteInd = find(full(~isnan(x(i,:))),1);
if isempty(finiteInd)
xfinite = 0;
else
xfinite = x(finiteInd);
end
nanInd = isnan(x(i,:));
x(i,nanInd) = xfinite;
end
ps.name = 'mapstd';
ps.xrows = size(x,1);
ps.yrows = ps.xrows;
ps.xmean = mean(x,2);
ps.xstd = std(x,0,2);
ps.ymean = fp.ymean;
ps.ystd = fp.ystd;
if any(ps.xstd == 0)
warning('NNET:Processing','Use REMOVECONSTANTROWS to remove rows with constant values.');
end
y = apply_process(x,ps);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Apply Process
function y = apply_process(x,ps)
copyQ = ones(1,size(x,2));
std = ps.xstd;
std(std == 0) = 1; % Avoid division by zero
y = (ps.ystd * (x - ps.xmean(:,copyQ))) ./ std(:,copyQ) + ps.ymean;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Reverse Process
function x = reverse_process(y,ps)
copyQ = ones(1,size(y,2));
x = (ps.xstd(:,copyQ) .* (y - ps.ymean)) / ps.ystd + ps.xmean(:,copyQ);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Derivative of Y w/respect to X
function dy_dx = derivative(x,y,ps);
Q = size(x,2);
d = diag(ps.ystd ./ ps.xstd);
dy_dx = d(:,:,ones(1,Q));
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Derivative of Y w/respect to X
function dx_dy = reverse_derivative(x,y,ps);
Q = size(x,2);
d = diag(ps.xstd ./ ps.ystd);
dx_dy = d(:,:,ones(1,Q));
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function p = simulink_params(ps)
p = ...
{ ...
'xmean',mat2str(ps.xmean);
'xstd',mat2str(ps.xstd);
'ymean',mat2str(ps.ymean);
'ystd',mat2str(ps.ystd);
};
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function p = simulink_reverse_params(ps)
p = ...
{ ...
'xmean',mat2str(ps.xmean);
'xstd',mat2str(ps.xstd);
'ymean',mat2str(ps.ymean);
'ystd',mat2str(ps.ystd);
};
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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