function y = std(x,flag,dim)
%列状数据标准差
%例如
% A=[11 4 0.2;22 3 0.5;0 3 0.4];
% std(A)
%
%STD Standard deviation.
% For vectors, STD(X) returns the standard deviation. For matrices,
% STD(X) is a row vector containing the standard deviation of each
% column. For N-D arrays, STD(X) is the standard deviation of the
% elements along the first non-singleton dimension of X.
%
% STD(X) normalizes by (N-1) where N is the sequence length. This
% makes STD(X).^2 the best unbiased estimate of the variance if X
% is a sample from a normal distribution.
%
% STD(X,1) normalizes by N and produces the second moment of the
% sample about its mean. STD(X,0) is the same as STD(X).
%
% STD(X,FLAG,DIM) takes the standard deviation along the dimension
% DIM of X. When FLAG=0 STD normalizes by (N-1), otherwise STD
% normalizes by N.
%
% Example: If X = [4 -2 1
% 9 5 7]
% then std(X,0,1) is [ 3.5355 4.9497 4.2426] and std(X,0,2) is [3.0
% 2.0]
% See also COV, MEAN, MEDIAN, CORRCOEF.
% J.N. Little 4-21-85
% Revised 5-9-88 JNL, 3-11-94 BAJ, 5-26-95 dlc, 5-29-96 CMT.
% Copyright (c) 1984-98 by The MathWorks, Inc.
% $Revision: 5.17 $ $Date: 1997/11/21 23:24:08 $
if nargin if nargin dim = min(find(size(x)~=1));
if isempty(dim), dim = 1; end
end
% Avoid divide by zero.
if size(x,dim)==1, y = zeros(size(x)); return, end
tile = ones(1,max(ndims(x),dim));
tile(dim) = size(x,dim);
xc = x - repmat(sum(x,dim)/size(x,dim),tile); % Remove mean
if flag,
y = sqrt(sum(conj(xc).*xc,dim)/size(x,dim));
else
y = sqrt(sum(conj(xc).*xc,dim)/(size(x,dim)-1));
end
