我目前正在进行非常简单的SVM分类。我在LibSVM中使用RBF和DTW预先计算的内核。
当我计算相似度(内核)矩阵时,在计算内核矩阵之前,一切似乎都很好......直到我置换数据。
SVM当然对输入数据的排列不变。在下面的Matlab代码中,标有“< - !!!!!!!!!!”的行决定分类准确度(不是置换:100% - 置换:0%到100%,取决于rng的种子)。但是为什么置换文件字符串数组(名为fileList)有什么不同呢?我究竟做错了什么?我是否误解了“置换不变性”的概念,还是我的Matlab代码存在问题?
我的csv文件格式为:LABEL,val1,val2,...,valN,所有csv文件都存储在文件夹dirName中。因此,字符串数组包含条目'10_0.csv 10_1.csv .... 11_7.csv,11_8.csv'(未置换)或置换时的其他顺序。
我也试图对样本序列号的矢量进行置换,但这没有区别。
function [SimilarityMatrixTrain, SimilarityMatrixTest, trainLabels, testLabels, PermSimilarityMatrixTrain, PermSimilarityMatrixTest, permTrainLabels, permTestLabels] = computeDistanceMatrix(dirName, verificationClass, trainFrac)
fileList = getAllFiles(dirName);
fileList = fileList(1:36);
trainLabels = [];
testLabels = [];
trainFiles = {};
testFiles = {};
permTrainLabels = [];
permTestLabels = [];
permTrainFiles = {};
permTestFiles = {};
n = 0;
sigma = 0.01;
trainFiles = fileList(1:2:end);
testFiles = fileList(2:2:end);
rng(3);
permTrain = randperm(length(trainFiles))
%rng(3); <- !!!!!!!!!!!
permTest = randperm(length(testFiles));
permTrainFiles = trainFiles(permTrain)
permTestFiles = testFiles(permTest);
noTrain = size(trainFiles);
noTest = size(testFiles);
SimilarityMatrixTrain = eye(noTrain);
PermSimilarityMatrixTrain = (noTrain);
SimilarityMatrixTest = eye(noTest);
PermSimilarityMatrixTest = eye(noTest);
% UNPERM
%Train
for i = 1 : noTrain
x = csvread(trainFiles{i});
label = x(1);
trainLabels = [trainLabels, label];
for j = 1 : noTrain
y = csvread(trainFiles{j});
dtwDistance = dtwWrapper(x(2:end), y(2:end));
rbfValue = exp((dtwDistance.^2)./(-2*sigma));
SimilarityMatrixTrain(i, j) = rbfValue;
n=n+1
end
end
SimilarityMatrixTrain = [(1:size(SimilarityMatrixTrain, 1))', SimilarityMatrixTrain];
%Test
for i = 1 : noTest
x = csvread(testFiles{i});
label = x(1);
testLabels = [testLabels, label];
for j = 1 : noTest
y = csvread(testFiles{j});
dtwDistance = dtwWrapper(x(2:end), y(2:end));
rbfValue = exp((dtwDistance.^2)./(-2*sigma));
SimilarityMatrixTest(i, j) = rbfValue;
n=n+1
end
end
SimilarityMatrixTest = [(1:size(SimilarityMatrixTest, 1))', SimilarityMatrixTest];
% PERM
%Train
for i = 1 : noTrain
x = csvread(permTrainFiles{i});
label = x(1);
permTrainLabels = [permTrainLabels, label];
for j = 1 : noTrain
y = csvread(permTrainFiles{j});
dtwDistance = dtwWrapper(x(2:end), y(2:end));
rbfValue = exp((dtwDistance.^2)./(-2*sigma));
PermSimilarityMatrixTrain(i, j) = rbfValue;
n=n+1
end
end
PermSimilarityMatrixTrain = [(1:size(PermSimilarityMatrixTrain, 1))', PermSimilarityMatrixTrain];
%Test
for i = 1 : noTest
x = csvread(permTestFiles{i});
label = x(1);
permTestLabels = [permTestLabels, label];
for j = 1 : noTest
y = csvread(permTestFiles{j});
dtwDistance = dtwWrapper(x(2:end), y(2:end));
rbfValue = exp((dtwDistance.^2)./(-2*sigma));
PermSimilarityMatrixTest(i, j) = rbfValue;
n=n+1
end
end
PermSimilarityMatrixTest = [(1:size(PermSimilarityMatrixTest, 1))', PermSimilarityMatrixTest];
mdlU = svmtrain(trainLabels', SimilarityMatrixTrain, '-t 4 -c 0.5');
mdlP = svmtrain(permTrainLabels', PermSimilarityMatrixTrain, '-t 4 -c 0.5');
[pclassU, xU, yU] = svmpredict(testLabels', SimilarityMatrixTest, mdlU);
[pclassP, xP, yP] = svmpredict(permTestLabels', PermSimilarityMatrixTest, mdlP);
xU
xP
end
我会非常感谢任何答案!
此致 本杰明
答案 0 :(得分:0)
function [tacc, testacc, mdl, SimilarityMatrixTrain, SimilarityMatrixTest, trainLabels, testLabels] = computeSimilarityMatrix(dirName)
fileList = getAllFiles(dirName);
fileList = fileList(1:72);
trainLabels = [];
testLabels = [];
trainFiles = {};
testFiles = {};
n = 0;
sigma = 0.01;
trainFiles = fileList(1:2:end);
testFiles = fileList(2:5:end);
noTrain = size(trainFiles);
noTest = size(testFiles);
permTrain = randperm(noTrain(1));
permTest = randperm(noTest(1));
trainFiles = trainFiles(permTrain);
testFiles = testFiles(permTest);
%Train
for i = 1 : noTrain(1)
x = csvread(trainFiles{i});
label = x(1);
trainlabel = label;
trainLabels = [trainLabels, label];
for j = 1 : noTrain(1)
y = csvread(trainFiles{j});
dtwDistance = dtwWrapper(x(2:end), y(2:end));
rbfValue = exp((dtwDistance.^2)./(-2*sigma.^2));
SimilarityMatrixTrain(i, j) = rbfValue;
end
end
SimilarityMatrixTrain = [(1:size(SimilarityMatrixTrain, 1))', SimilarityMatrixTrain];
%Test
for i = 1 : noTest(1)
x = csvread(testFiles{i});
label = x(1);
testlabel = label;
testLabels = [testLabels, label];
for j = 1 : noTrain(1)
y = csvread(trainFiles{j});
dtwDistance = dtwWrapper(x(2:end), y(2:end));
rbfValue = exp((dtwDistance.^2)./(-2*sigma.^2));
SimilarityMatrixTest(i, j) = rbfValue;
end
end
SimilarityMatrixTest = [(1:size(SimilarityMatrixTest, 1))', SimilarityMatrixTest];
mdlU = svmtrain(trainLabels', SimilarityMatrixTrain, '-t 4 -c 1000 -q');
fprintf('TEST: '); [pclassU, xU, yU] = svmpredict(testLabels', SimilarityMatrixTest, mdlU);
fprintf('TRAIN: ');[pclassT, xT, yT] = svmpredict(trainLabels', SimilarityMatrixTrain, mdlU);
tacc = xT(1);
testacc = xU(1);
mdl = mdlU;
end
此致 本杰明