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Copyright (C)
DXY All Rights Reserved.>> nfoldxval.m
nfoldxval.m ( 文件浏览 )
function CM=nfoldxval( data, IDX, clfInit, clfparams, ...
types, ignoreT, fname, show )
% Runs n-fold cross validation on data with a given classifier.
% Given n separate labeled data sets, trains classifier using n-1 data
% sets, test on remaining one.
Average results over all n such runs.
% Shows overall results in average confusion matrix.
% The classifier is passed in as a parameter.
For this to work the
% classifier (clf) must follow certain conventions.
The conventions are:
1) To initialize the clf ('p' is the dimension of the data):
clf = clfInit( p, clfparams{
2) clf must point to 2 functions for training and applying it:
clf.funTrain
clf.funFwd
3) For training the following will be called:
clf = clf.funTrain( clf, X, Y );
4) For testing the following will be called:
pred = clf.funFwd( clf, Xtest );
% The format for X is nxp where there are n data points and p is their
% dimension. The format for Y is nx1.
% Given data in a cell array, to string out into single array:
IDX = cell2mat(permute(IDX,[2 1]));
data = cell2mat(permute(data,[2 1]));
% For a simple, small dataset, can do leave one out clf as follows:
[n,p]=size(data); IDX=mat2cell(IDX,ones(1,n),1);
data=mat2cell(data,ones(1,n),p);
% Overall error can be calculated via:
er = 1-sum(diag(CM))/sum(CM(:))
% Normalized confusion matrix can be calculated via:
CMn = CM ./ repmat( sum(CM,2), [1 size(CM,2)] );
CM=nfoldxval( data, IDX, clfInit, clfparams, ...
[types], [ignoreT], [fname], [show] )
- cell array of (n x p) arrays each of n samples of dim p
- cell array of (n x 1) arrays each of n labels
- classifier initialization function
- classifier parameters
- [] cell array of string labels for types
- [] array of types to ignore {
eg: [1 4 5]
- [] specify a file to save CM to, as well as image
- [] will display results in figure(show)
- confusion matrix
%%% 2 class
nfoldxval( data, IDX, @clfLda,{
}, [],[],[],1 );
nfoldxval( data, IDX, @clfKnn,{
},[],[],[],2 );
nfoldxval( data, IDX, @clfSvm,{
},[],[],[],3 );
% poly SVM
nfoldxval( data, IDX, @clfSvm,{
'rbf',2^-12
},[],[],[],4 ); % rbf SVM
nfoldxval( data, IDX, @clfDecTree,{
},[],[],[],5 );
% dec. tree
%%% multiclass
clfparams = {
'rbf',2^-12
},nclasses
nfoldxval( data, IDX, @clfEcoc,clfparams,[],[],[],6 );
% See also CLFKNN, CLFLDA, CLFSVM, CLFECOC, CLFDECTREE, DEMOCLASSIFY
% Piotr's Image&Video Toolbox
Version 2.30
% Copyright 2009 Piotr Dollar.
[pdollar-at-caltech.edu]
% Please email me if you find bugs, or have suggestions or questions!
% Licensed under the Lesser GPL [see external/lgpl.txt]
if( nargin&5 || isempty(types) ); types=[]; end
if( nargin&6 || isempty(ignoreT) ); ignoreT=[]; end
if( nargin&7 || isempty(fname) ); fname=[]; end
if( nargin&8 || isempty(show) ); show=[]; end
dispflag = 0;
%%% divide n data points into n different sets, perform nfoldxval on each
if( ~iscell(data) && ~iscell(IDX) )
[n,p]=size(data);
IDX=mat2cell(IDX,ones(1,n),1); %#ok&MMTC&
data=mat2cell(data,ones(1,n),p);
%%% correct format
}; %#ok&CCAT&
nsets = length( data );
%%% remove data points with type specified by ignoreT
if( ~isempty(ignoreT) )
if(~isempty(types))
keeptypes = setdiff( 1:length(types), ignoreT );
types = types( keeptypes ); ntypes = length(types);
%#ok&NASGU&
ignoreT = sort(ignoreT);
while( ~isempty(ignoreT) )
for i=1:nsets
keeplocs = (IDX{
}~=ignoreT(1));
}(keeplocs,:);
IDXi = IDX{
}(keeplocs);
big=IDXi&ignoreT(1); IDXi(big)=IDXi(big)-1; IDX{
ignoreT=ignoreT(2:end)-1;
%%% for binary classes convert to most common form [-1/+1]
IDXall = cell2mat( permute( IDX, [2 1] ) );
minIDX = min(IDXall);
maxIDX = max(IDXall);
if( minIDX==0 && maxIDX==1 )
for i=1: IDX{
}==0)=-1; end
elseif( minIDX==1 && maxIDX==2 )
for i=1: IDX{
}==2)=-1; end
%%% create types string for display if not exist
if( isempty(types) )
types = unique(IDXall);
types = int2str2( types );
ntypes = length(types);
%%% optionally visualize data by embedding in 3D space
dataALL = cell2mat( permute( data, [2 1] ) );
figure(show); show=show+1;
visualizeData( dataALL, 3, IDXall+2, types );
%%% train on n-1 of the sets, t repeat n times
CM = zeros(ntypes);
for testind = 1:nsets
% get training/testing data sets
allinds = true(1,nsets);
traininds = traininds( testind ) =
train = cell2mat( permute( {
}, [2 1] ) );
test = cell2mat( permute( {
~traininds
}, [2 1] ) );
trainIDX = cell2mat( permute( {
}, [2 1] ) );
testIDX = cell2mat( permute( {
~traininds
}, [2 1] ) );
nTrain=size(train,1);
[nTest p]=size(test);
% apply dim reduction [make sure data is well conditioned]
[ U, mu ] = pca( train' );
maxp = size(U,2) -6; % -20; further reduce? -6
if( maxp & p )
warning(['reducing dim of data from: ' ...
int2str(p) ' to ' int2str(maxp)]); %#ok&WNTAG&
train = pcaApply( train', U, mu, maxp )';
= pcaApply( test',
U, mu, maxp )';
% display update
if( dispflag )
msg = ['test set ' int2str(testind)];
disp([msg '; nTrain=' num2str(nTrain) ', nTest=' num2str(nTest)]);
if( nTest==0 ); if(dispflag); disp('no test data'); end
% learn a classifier on train and classify test
clf = feval( clfInit, p, clfparams{
clf = feval( clf.funTrain, clf, train, trainIDX );
testIDXpred = feval( clf.funFwd, clf, test );
CMi = confMatrix( testIDX, testIDXpred, ntypes );
CM = CM + CMi;
%%% show confusion matrix, optionally save image to file
if( show )
figure(show); %show=show+1;
confMatrixShow( CM, types );
if( isempty(fname) )
title( clf.type, 'FontSize', 20 );
title( fname, 'FontSize', 20 );
print( [fname '.jpg'], '-djpeg' );
%%% save data to file
if( ~isempty(fname) )
er = 1-sum(diag(CM))/sum(CM(:)); %#ok&NASGU&
CMn = CM ./ repmat( sum(CM,2), [1 size(CM,2)] ); %#ok&NASGU&
save( fname, 'CM', 'CMn', 'er' );
if( dispflag ); fprintf(['finished: ' fname '.\n\n\n']); end
if( dispflag ); fprintf('finished.\n\n\n'); end
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Copyright (C)
DXY All Rights Reserved.>> nfoldxval.m
nfoldxval.m ( 文件浏览 )
function CM=nfoldxval( data, IDX, clfInit, clfparams, ...
types, ignoreT, fname, show )
% Runs n-fold cross validation on data with a given classifier.
% Given n separate labeled data sets, trains classifier using n-1 data
% sets, test on remaining one.
Average results over all n such runs.
% Shows overall results in average confusion matrix.
% The classifier is passed in as a parameter.
For this to work the
% classifier (clf) must follow certain conventions.
The conventions are:
1) To initialize the clf ('p' is the dimension of the data):
clf = clfInit( p, clfparams{
2) clf must point to 2 functions for training and applying it:
clf.funTrain
clf.funFwd
3) For training the following will be called:
clf = clf.funTrain( clf, X, Y );
4) For testing the following will be called:
pred = clf.funFwd( clf, Xtest );
% The format for X is nxp where there are n data points and p is their
% dimension. The format for Y is nx1.
% Given data in a cell array, to string out into single array:
IDX = cell2mat(permute(IDX,[2 1]));
data = cell2mat(permute(data,[2 1]));
% For a simple, small dataset, can do leave one out clf as follows:
[n,p]=size(data); IDX=mat2cell(IDX,ones(1,n),1);
data=mat2cell(data,ones(1,n),p);
% Overall error can be calculated via:
er = 1-sum(diag(CM))/sum(CM(:))
% Normalized confusion matrix can be calculated via:
CMn = CM ./ repmat( sum(CM,2), [1 size(CM,2)] );
CM=nfoldxval( data, IDX, clfInit, clfparams, ...
[types], [ignoreT], [fname], [show] )
- cell array of (n x p) arrays each of n samples of dim p
- cell array of (n x 1) arrays each of n labels
- classifier initialization function
- classifier parameters
- [] cell array of string labels for types
- [] array of types to ignore {
eg: [1 4 5]
- [] specify a file to save CM to, as well as image
- [] will display results in figure(show)
- confusion matrix
%%% 2 class
nfoldxval( data, IDX, @clfLda,{
}, [],[],[],1 );
nfoldxval( data, IDX, @clfKnn,{
},[],[],[],2 );
nfoldxval( data, IDX, @clfSvm,{
},[],[],[],3 );
% poly SVM
nfoldxval( data, IDX, @clfSvm,{
'rbf',2^-12
},[],[],[],4 ); % rbf SVM
nfoldxval( data, IDX, @clfDecTree,{
},[],[],[],5 );
% dec. tree
%%% multiclass
clfparams = {
'rbf',2^-12
},nclasses
nfoldxval( data, IDX, @clfEcoc,clfparams,[],[],[],6 );
% See also CLFKNN, CLFLDA, CLFSVM, CLFECOC, CLFDECTREE, DEMOCLASSIFY
% Piotr's Image&Video Toolbox
Version 2.30
% Copyright 2009 Piotr Dollar.
[pdollar-at-caltech.edu]
% Please email me if you find bugs, or have suggestions or questions!
% Licensed under the Lesser GPL [see external/lgpl.txt]
if( nargin&5 || isempty(types) ); types=[]; end
if( nargin&6 || isempty(ignoreT) ); ignoreT=[]; end
if( nargin&7 || isempty(fname) ); fname=[]; end
if( nargin&8 || isempty(show) ); show=[]; end
dispflag = 0;
%%% divide n data points into n different sets, perform nfoldxval on each
if( ~iscell(data) && ~iscell(IDX) )
[n,p]=size(data);
IDX=mat2cell(IDX,ones(1,n),1); %#ok&MMTC&
data=mat2cell(data,ones(1,n),p);
%%% correct format
}; %#ok&CCAT&
nsets = length( data );
%%% remove data points with type specified by ignoreT
if( ~isempty(ignoreT) )
if(~isempty(types))
keeptypes = setdiff( 1:length(types), ignoreT );
types = types( keeptypes ); ntypes = length(types);
%#ok&NASGU&
ignoreT = sort(ignoreT);
while( ~isempty(ignoreT) )
for i=1:nsets
keeplocs = (IDX{
}~=ignoreT(1));
}(keeplocs,:);
IDXi = IDX{
}(keeplocs);
big=IDXi&ignoreT(1); IDXi(big)=IDXi(big)-1; IDX{
ignoreT=ignoreT(2:end)-1;
%%% for binary classes convert to most common form [-1/+1]
IDXall = cell2mat( permute( IDX, [2 1] ) );
minIDX = min(IDXall);
maxIDX = max(IDXall);
if( minIDX==0 && maxIDX==1 )
for i=1: IDX{
}==0)=-1; end
elseif( minIDX==1 && maxIDX==2 )
for i=1: IDX{
}==2)=-1; end
%%% create types string for display if not exist
if( isempty(types) )
types = unique(IDXall);
types = int2str2( types );
ntypes = length(types);
%%% optionally visualize data by embedding in 3D space
dataALL = cell2mat( permute( data, [2 1] ) );
figure(show); show=show+1;
visualizeData( dataALL, 3, IDXall+2, types );
%%% train on n-1 of the sets, t repeat n times
CM = zeros(ntypes);
for testind = 1:nsets
% get training/testing data sets
allinds = true(1,nsets);
traininds = traininds( testind ) =
train = cell2mat( permute( {
}, [2 1] ) );
test = cell2mat( permute( {
~traininds
}, [2 1] ) );
trainIDX = cell2mat( permute( {
}, [2 1] ) );
testIDX = cell2mat( permute( {
~traininds
}, [2 1] ) );
nTrain=size(train,1);
[nTest p]=size(test);
% apply dim reduction [make sure data is well conditioned]
[ U, mu ] = pca( train' );
maxp = size(U,2) -6; % -20; further reduce? -6
if( maxp & p )
warning(['reducing dim of data from: ' ...
int2str(p) ' to ' int2str(maxp)]); %#ok&WNTAG&
train = pcaApply( train', U, mu, maxp )';
= pcaApply( test',
U, mu, maxp )';
% display update
if( dispflag )
msg = ['test set ' int2str(testind)];
disp([msg '; nTrain=' num2str(nTrain) ', nTest=' num2str(nTest)]);
if( nTest==0 ); if(dispflag); disp('no test data'); end
% learn a classifier on train and classify test
clf = feval( clfInit, p, clfparams{
clf = feval( clf.funTrain, clf, train, trainIDX );
testIDXpred = feval( clf.funFwd, clf, test );
CMi = confMatrix( testIDX, testIDXpred, ntypes );
CM = CM + CMi;
%%% show confusion matrix, optionally save image to file
if( show )
figure(show); %show=show+1;
confMatrixShow( CM, types );
if( isempty(fname) )
title( clf.type, 'FontSize', 20 );
title( fname, 'FontSize', 20 );
print( [fname '.jpg'], '-djpeg' );
%%% save data to file
if( ~isempty(fname) )
er = 1-sum(diag(CM))/sum(CM(:)); %#ok&NASGU&
CMn = CM ./ repmat( sum(CM,2), [1 size(CM,2)] ); %#ok&NASGU&
save( fname, 'CM', 'CMn', 'er' );
if( dispflag ); fprintf(['finished: ' fname '.\n\n\n']); end
if( dispflag ); fprintf('finished.\n\n\n'); end
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binaryTreeTrain1.mexa649.38 kB 09:53
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binaryTreeTrain1.mexw6410.50 kB 09:21
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forestFindThr.mexa6410.09 kB 10:09
forestFindThr.mexmaci6412.73 kB 00:53
forestFindThr.mexw6411.00 kB 09:21
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forestInds.mexa649.98 kB 10:09
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