gusucode.com > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM源码程序 > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM\LS_SVMlab\crossvalidate.m
function [cost,costs,output] = crossvalidate(model, X,Y, L, estfct,combinefct, corrected,trainfct,simfct)
% Estimate the model performance of a model with [$ l$] -fold crossvalidation
%
% >> cost = crossvalidate({Xtrain,Ytrain,type,gam,sig2}, Xval, Yval)
% >> cost = crossvalidate( model, Xval, Yval)
%
% The data is once permutated randomly, then it is divided into L
% (by default 10) disjunct sets. In the i-th (i=1,...,l) iteration,
% the i-th set is used to estimate the performance ('validation
% set') of the model trained on the other l-1 sets ('training
% set'). At last, the l (denoted by L) different estimates of the
% performance are combined (by default by the 'mean'). The
% assumption is made that the input data are distributed
% independent and identically over the input space. As additional
% output, the costs in the different folds ('costs') and all
% residuals ('ec') of the data are returned:
%
% >> [cost, costs, ec] = crossvalidate(model, Xval, Yval)
%
% By default, this function will call the training (trainlssvm) and
% simulation (simlssvm) algorithms for LS-SVMs. However, one can
% use the validation function more generically by specifying the
% appropriate training and simulation function. Some commonly used criteria are:
%
% >> cost = crossvalidate(model, Xval, Yval, 10, 'misclass', 'mean', 'corrected')
% >> cost = crossvalidate(model, Xval, Yval, 10, 'mse', 'mean', 'original')
% >> cost = crossvalidate(model, Xval, Yval, 10, 'mae', 'median', 'corrected')
%
% Full syntax
%
% 1. Using LS-SVMlab with the functional interface:
%
% >> [cost, costs, ec] = crossvalidate({X,Y,type,gam,sig2,kernel,preprocess},Xval, Yval, L, estfct, combinefct, correction)
%
% Outputs
% cost : Cost estimation of the L-fold cross validation
% costs(*) : L x 1 vector with costs estimated on the L different folds
% ec(*) : N x 1 vector with residuals of all data
% Inputs
% X : Training input data used for defining the LS-SVM and the preprocessing
% Y : Training output data used for defining the LS-SVM and the preprocessing
% type : 'function estimation' ('f') or 'classifier' ('c')
% gam : Regularization parameter
% sig2 : Kernel parameter (bandwidth in the case of the 'RBF_kernel')
% kernel(*) : Kernel type (by default 'RBF_kernel')
% preprocess(*) : 'preprocess'(*) or 'original'
% Xval : N x d matrix with the inputs of the data used for cross-validation
% Yval : N x m matrix with the outputs of the data used for cross-validation
% L(*) : Number of folds (by default 10)
% estfct(*) : Function estimating the cost based on the residuals (by default mse)
% combinefct(*) : Function combining the estimated costs on the different folds (by default mean)
% correction(*) : 'original'(*) or 'corrected'
%
%
% 2. Using the object oriented interface:
%
% >> [cost, costs, ec] = crossvalidate(model, Xval, Yval, L, estfct, combinefct, correction)
%
% Outputs
% cost : Cost estimation of the L-fold cross validation
% costs(*) : L x 1 vector with costs estimated on the L different folds
% ec(*) : N x 1 vector with residuals of all data
% Inputs
% model : Object oriented representation of the LS-SVM model
% Xval : Nt x d matrix with the inputs of the validation points used in the procedure
% Yval : Nt x m matrix with the outputs of the validation points used in the procedure
% L(*) : Number of folds (by default 10)
% estfct(*) : Function estimating the cost based on the residuals (by default mse)
% combinefct(*) : Function combining the estimated costs on the different folds (by default mean)
% correction(*) : 'original'(*) or 'corrected'
%
%
% 3. Using other modeling techniques::
%
% >> [cost, costs, ec] = crossvalidate(model, Xval, Yval, L, estfct, combinefct, correction, trainfct, simfct)
%
% Outputs
% cost : Cost estimation of the L-fold cross validation
% costs(*) : l x 1 vector with costs estimated on the L different folds
% ec(*) : N x 1 vector with residuals of all data
% Inputs
% model : Object oriented representation of the model
% Xval : Nt x d matrix with the inputs of the validation points used
% Yval : Nt x m matrix with the outputs of the validation points used in the procedure
% L(*) : Number of folds (by default 10)
% estfct(*) : Function estimating the cost based on the residuals (by default mse)
% combinefct(*) : Function combining the estimated costs on the different folds (by default mean)
% correction(*) : 'original'(*) or 'corrected'
% trainfct : Function used to train the model
% simfct : Function used to simulate test data with the model
%
% See also:
% validate, leaveoneout, leaveoneout_lssvm, trainlssvm, simlssvm
% Copyright (c) 2002, KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab
%
% initialisation and defaults
%
if size(X,1)~=size(Y,1), error('X and Y have different number of datapoints'); end
[nb_data,y_dim] = size(Y);
% LS-SVMlab
eval('model = initlssvm(model{:});',' ');
eval('L;','L=min(ceil(model.nb_data/4),10);');
eval('estfct;','estfct=''mse'';');
eval('combinefct;','combinefct=''mean'';');
eval('trainfct;','trainfct=''trainlssvm'';');
eval('simfct;','simfct=''simlssvm'';');
eval('corrected;','corrected=''original'';');
%
% make a random permutation of the data
%
px = zeros(size(X));
py = zeros(size(Y));
if L==nb_data, p = 1:nb_data; else p = randperm(nb_data); end
for i=1:nb_data,
px(i,:) = X(p(i),:);
py(i,:) = Y(p(i),:);
end;
block_size = floor(nb_data/L);
%
%initialize: no incremental memory allocation
%
err = zeros(L,1);
corr2 = zeros(L,1);
costs = zeros(L,1);
output = zeros(size(Y));
%
%
% start loop over l validations
%
for l = 1:L,
% divide in data and validation set, trainings data set is a copy
% of permutated_data, validation set is just a logical index
if l==L,
train = [1:block_size*(l-1)];
validation = block_size*(l-1)+1:nb_data;
else
train = [1:block_size*(l-1) block_size*l+1:nb_data];
validation = block_size*(l-1)+1:block_size*l;
end
% lets invert this...eXtreme cv
%validation = [1:block_size*(l-1) block_size*l+1:nb_data];
%train = block_size*(l-1)+1:block_size*l;
%disp([num2str(l) ': |trainset|' num2str(length(train)) ' & |test| ' num2str(length(validation))]);
%costs(l) = validate(model, px(train,:), py(train,:), px(validation,:), py(validation,:),estfct, trainfct, simfct);
[costs(l), modell,output(p(validation),:)] = ...
validate(model, px(train,:), py(train,:), px(validation,:), py(validation,:),estfct, trainfct, simfct);
%
% calculate correction term 2: MSE(f_data, error_wholedata)
% try to reuse the previously calculated model
%
if corrected(1) =='c',
eval('errors = feval(simfct, modell, px) - py;corr2(l) = feval(estfct, errors);',...
'corr2(l) = validate(model, px(train,:), py(train,:), px, py,estfct, trainfct, simfct);');
end
end
% end loop over l validations
%
%
% misclassifications
%
sc = find(costs~=inf & costs~=NaN);
ff=zeros(size(costs)); ff(sc)=costs(sc);costs=ff;
sc = find(corr2~=inf & corr2~=NaN);
ff=zeros(size(corr2)); ff(sc)=corr2(sc);corr2=ff;
%
% calculate the final costs
%
if corrected(1)=='c',
% calculate correction term 1: MSE(f_wholedata, error_wholedata)
corr1 = validate(model,X, Y, X, Y, estfct, trainfct, simfct);
if corr1==inf | corr2==NaN, corr1=0; end
cost = feval(combinefct, costs)+corr1-feval(combinefct,corr2);
else
cost = feval(combinefct, costs);
end;
fprintf('\n');
%file = [num2str(cost) '_costsLSSVM_{' num2str(model.gam(1)) ',' num2str(model.kernel_pars(1)) '}.mat'];
%save L1costs costs;