马氏距离在MATLAB中的代码,用代码表示公式.公式如图.

马氏距离在MATLAB中的代码,用代码表示公式.公式如图.
i：第几列 k：层数 L：列数 D：两样本之间的距离 x:变量

function d = mahalanobis(X, Mu, C)
　　%MAHALANOBIS Mahalanobis distance.
　　% D = MAHALANOBIS(X, MU, C) returns the Mahalanobis distance between
　　% the length p vectors X and MU given the p by p covariance matrix
　　% C. If omitted, it is assumed that C is the identity matrix(单位矩阵/恒等矩阵)
　　% EYE(p). If either X or MU is an n by p matrix, D will be returned
　　% as an n by g matrix where n is the number of rows in X and g is
　　% the number of rows in MU where each entry i, j corresponds to the
　　% mahalanobis distance between row i of X and row j of MU. If MU is
　　% simply 0, it is treated as the origin from which Mahalanobis
　　% distance to X is calculated. C must be a positive, definite,
　　% symmetric matrix.
　　%
　　% The Mahalanobis distance between vectors X(i,:) and MU(j,:) is
　　% defined as:
　　%
　　% D(i,j) = ((X(i,:) - MU(j,:))'*INV(C)*(X(i,:) - MU(j,:))).^(1/2)
　　% Copyright (c) 1999 Michael Kiefte.
　　% $Log$
　　error(nargchk(2, 3, nargin))
　　if isempty(X) | ~isa(X, 'double') | ~isreal(X) | ...
　　any(any(isnan(X) | isinf(X)))
　　error(['X must be a vector or matrix of real, finite numeric' ...
　　' doubles.'])
　　elseif length(X) == prod(size(X))
　　X = X(:)';
　　elseif ndims(X) ~= 2
　　error('If X is a matrix, it must be a 2-d array.')
　　end
　　[n p] = size(X);
　　if isempty(Mu) | ~isa(Mu, 'double') | ~isreal(Mu) | ...
　　any(any(isnan(Mu) | isinf(Mu)))
　　error(['Mu must be a vector or matrix of real, finite numeric' ...
　　' doubles.'])
　　elseif length(Mu) == prod(size(Mu))
　　Mu = Mu(:)';
　　elseif ndims(Mu) ~= 2
　　error('If MU is a matrix, it must be a 2-d array.')
　　end
　　if length(Mu) == 1 & Mu == 0
　　Mu = zeros(1, p);
　　elseif size(Mu, 2) ~= p
　　error('Number of features in MU and X must be the same.')
　　end
　　g = size(Mu, 1);
　　if ~isempty(C)
　　if ~isa(C, 'double') | ~isreal(C) | ndims(C) ~= 2 | ...
　　any(any(isnan(C) | isinf(C)))
　　error('C must be a matrix of real, finite numeric doubles.')
　　elseif any(size(C) ~= p)
　　error(['Number of rows and columns in C must match number of' ...
　　' features in X and MU.'])
　　end
　　try
　　S = inv(chol(C));
　　catch
　　error(['Covariance matrix C must be positive, definite,' ...
　　' symmetric.'])
　　end
　　Xs = X*S;
　　Ms = Mu*S;
　　else
　　Xs = X;
　　Ms = Mu;
　　end
　　d = sqrt(repmat(sum(Xs.^2, 2), 1, g) - 2*Xs*Ms' + ...
　　repmat(sum(Ms'.^2), n, 1));