私はトレーニングを使用してデータを分類しているので、次元は、xtrain matrixμは2要素ベクトル、Σはcovariancxe行列2x2です。2 features2000 rows2
xtrain =
0.3630 1.6632
-0.0098 1.8526
-0.0424 1.6840
-0.1565 2.1187
0.5720 -2.7282
-0.7808 1.1357
0.5212 -0.6858
0.1038 1.4735
...
mu =
0.3486 0.8327
sigma =
1.1163 0.0452
0.0452 1.5669
私は次のようなことをしています：
mu = mean(xtrain)
sigma = cov(xtrain)
% 1/y^2 = (2 pi)^p |\Sigma| exp { (x-\mu)' inv(\Sigma) (x-\mu) }
p = mvnpdf (xtrain, mu, sigma);
次に計算します：
pdfgauss =...
問題は、分類器の結果をxtest matrix？でテストする方法です。
I was reading this and it says:
To classify data using Bayesian classifier we already know `Prior(w)` and need to compute `p(x/w)`. When `p` is multidimensioanl Gaussian, we can use Matlab internal function "`mvnpdf`".
例）mvnpdf(X,Mean,Cov)
X <=分類したいデータ
Mean<=作成時に
Cov既知<=作成時に既知
各クラスのデータ計算を分類しpdfgauss and multiply by Prior(w)、最大値を示すクラスを選択するには
これらの関数を使用するために、pdfgaussは距離を計算するために何かを使用します
dist = mahalan(X,Mean(:,i),Cov(:,:,i));
- この分類を終了するにはどうすればよいですか？
pdfgauss.m
function y = pdfgauss(X, arg1, arg2 )
% PDFGAUSS Evaluates multivariate Gaussian distribution.
%
% Synopsis:
% y = pdfgauss(X, Mean, Cov)
% y = pdfgauss(X, model )
%
% Description:
% y = pdfgauss(X, Mean, Cov) evaluates a multi-variate Gaussian
% probability density function(s) for given input column vectors in X.
% Mean [dim x ncomp] and Cov [dim x dim x ncomp] describe a set of
% ncomp Gaussian distributions to be evaluted such that
%
% y(i,j) = exp(-0.5(mahalan(X(:,j),Mean(:,i),Cov(:,:,i) )))/norm_const
%
% where i=1:ncomp and j=1:size(X,2). If the Gaussians are
% uni-variate then the covariaves can be given as a vector
% Cov = [Cov_1, Cov_2, ..., Cov_comp].
%
% y = pdfgauss( X, model ) takes Gaussian parameters from structure
% fields model.Mean and model.Cov.
%
% Input:
% X [dim x num_data] Input matrix of column vectors.
% Mean [dim x ncomp] Means of Gaussians.
% Cov [dim x dim x ncomp] Covarince matrices.
%
% Output:
% y [ncomp x num_data] Values of probability density function.
%
% Example:
%
% Univariate case
% x = linspace(-5,5,100);
% y = pdfgauss(x,0,1);
% figure; plot(x,y)
%
% Multivariate case
% [Ax,Ay] = meshgrid(linspace(-5,5,100), linspace(-5,5,100));
% y = pdfgauss([Ax(:)';Ay(:)'],[0;0],[1 0.5; 0.5 1]);
% figure; surf( Ax, Ay, reshape(y,100,100)); shading interp;
%
% See also
% GSAMP, PDFGMM.
%
% About: Statistical Pattern Recognition Toolbox
% (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac
% <a href="http://www.cvut.cz">Czech Technical University Prague</a>
% <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a>
% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a>
% Modifications:
% 28-apr-2004, VF
% process input arguments
if nargin < 3,
arg1 = c2s(arg1);
Mean = arg1.Mean;
Cov = arg1.Cov;
else
Mean = arg1;
Cov = arg2;
end
% get dimensions
[dim,num_data] = size(X);
ncomp = size(Mean,2);
% univariate variances can be given as a vector
if size(Cov,1) ~= size(Cov,2), Cov = reshape(Cov,1,1,ncomp); end
% alloc memory
y = zeros(ncomp,num_data);
% evaluate pdf for each component
for i=1:ncomp,
dist = mahalan(X,Mean(:,i),Cov(:,:,i));
y(i,:) = exp(-0.5*dist)/sqrt((2*pi)^dim*det(Cov(:,:,i)));
end
return;