加载中…Matlab常用函数之pdist
(2014-07-26 10:01:48)
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健康 |
分类： Matlab |
Pdist
'euclidean' - Euclidean
distance (default)
'seuclidean' - Standardized
Euclidean distance. Each coordinate
difference
between rows in X is scaled by dividing
by the
corresponding element of the standard
deviation
S=NANSTD(X). To specify another value for
S, use
D=pdist(X,'seuclidean',S).
'cityblock' - City Block
distance
'minkowski' - Minkowski
distance. The default exponent is 2. To
specify a
different exponent, use
D =
pdist(X,'minkowski',P), where the exponent P is
a scalar
positive value.
'chebychev' - Chebychev
distance (maximum coordinate difference)
'mahalanobis' - Mahalanobis distance, using the
sample covariance
of X as
computed by NANCOV. To compute the distance
with a
different covariance, use
D =
pdist(X,'mahalanobis',C), where the matrix
C
is
symmetric and positive definite.
'cosine'
- One minus the cosine of the included
angle
between
observations (treated as vectors)
'correlation' - One minus the sample linear
correlation between
observations (treated as sequences of values).
'spearman'
- One minus the sample Spearman's rank
correlation
between
observations (treated as sequences of values).
'hamming'
- Hamming distance, percentage of coordinates
that
differ
'jaccard'
- One minus the Jaccard coefficient, the
percentage
of nonzero coordinates that differ
function
- A distance function specified using @,
for
example
@DISTFUN.
A distance function must
be of the form
function D2 = DISTFUN(XI,
XJ),
taking as arguments a
1-by-N vector XI containing a single row of X, an
M2-by-N matrix XJ
containing multiple rows of X, and returning an
M2-by-1 vector of
distances D2, whose Jth element is the distance
between the observations
XI and XJ(J,:).
The output D is arranged
in the order of ((2,1),(3,1),..., (M,1),
(3,2),...(M,2),.....(M,M-1)), i.e. the lower left triangle of the
full
M-by-M distance matrix
in column order. To get the distance
between
the Ith and Jth
observations (I < J), either use the formula
D((I-1)*(M-I/2)+J-I), or
use the helper function Z = SQUAREFORM(D),
which returns an M-by-M
square symmetric matrix, with the (I,J) entry
equal to distance
between observation I and observation J.
Example:
% Compute the ordinary Euclidean distance
X = randn(100, 5);
% some random points
D = pdist(X, 'euclidean');
%
euclidean distance
% Compute the Euclidean distance with each
coordinate difference
% scaled by the standard deviation
Dstd = pdist(X,'seuclidean');
% Use a function handle to compute a distance
that weights each
% coordinate contribution differently
Wgts = [.1 .3 .3 .2 .1];
% coordinate weights
weuc = @(XI,XJ,W)(sqrt(bsxfun(@minus,XI,XJ).^2 *
W'));
Dwgt = pdist(X, @(Xi,Xj) weuc(Xi,Xj,Wgts));
返回值为向量形式：内容为M*N矩阵X中各成对成分之间两两的欧式距离. D =
pdist(X);D为1*M*(M-1)/2维向量.
其他具体用法： D = pdist(X, DISTANCE) computes D using DISTANCE.
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