LIBSVM training error , needed help.

Hello team,

I am doing LIBSVM regression analysis on time series data but getting rho as NAN and not predicting anythin. the code is as below

trn_data.X = WCA(1:500,1:85); trn_data.y = WCA(1:500,86); tst_data.X = WCA(501:800,1:85); tst_data.y = WCA(500:800,86); %%

param.s = 3; % epsilon SVR param.C = max(trn_data.y) - min(trn_data.y); param.t = 2; % RBF kernel param.gset = 2.^[-7:20]; % range of the gamma parameter param.eset = [0:1000]; % range of the epsilon parameter param.nfold = 25; % 5-fold CV

%%

Rval = zeros(length(param.gset), length(param.eset));

for i = 1:param.nfold % partition the training data into the learning/validation % in this example, the 5-fold data partitioning is done by the following strategy, % for partition 1: Use samples 1, 6, 11, ... as validation samples and % the remaining as learning samples % for partition 2: Use samples 2, 7, 12, ... as validation samples and % the remaining as learning samples % : % for partition 5: Use samples 5, 10, 15, ... as validation samples and % the remaining as learning samples

data = [trn_data.y, trn_data.X]; [learn, val] = k_FoldCV_SPLIT(data, param.nfold, i); lrndata.X = learn(:, 2:end); lrndata.y = learn(:, 1); valdata.X = val(:, 2:end); valdata.y = val(:, 1);

for j = 1:length(param.gset) param.g = param.gset(j);

for k = 1:length(param.eset) param.e = param.eset(k); param.libsvm = ['-s ', num2str(param.s), ' -t ', num2str(param.t), ... ' -c ', num2str(param.C), ' -g ', num2str(param.g), ... ' -p ', num2str(param.e)];

% build model on Learning data model = svmtrain(lrndata.y, lrndata.X, param.libsvm);

% predict on the validation data [y_hat, Acc, projection] = svmpredict(valdata.y, valdata.X, model);

Rval(j,k) = Rval(j,k) + mean((y_hat-valdata.y).^2); end end

end %% Rval = Rval ./ (param.nfold);

[v1, i1] = min(Rval); [v2, i2] = min(v1); optparam = param; optparam.g = param.gset( i1(i2) ); optparam.e = param.eset(i2);

Getting as optimization finished, #iter = 0 nu = -nan(ind) obj = 0.000000, rho = -nan(ind) nSV = 0, nBSV = 0 Mean squared error = -1.#IND (regression) Squared correlation coefficient = -1.#IND (regression) . optimization finished, #iter = 0 nu = -nan(ind) obj = 0.000000, rho = -nan(ind) nSV = 0, nBSV = 0 Mean squared error = -1.#IND (regression) Squared correlation coefficient = -1.#IND (regression) . optimization finished, #iter = 0 nu = -nan(ind) obj = 0.000000, rho = -nan(ind) nSV = 0, nBSV = 0 Mean squared error = -1.#IND (regression) Squared correlation coefficient = -1.#IND (regression) . optimization finished, #iter = 0 nu = -nan(ind) obj = 0.000000, rho = -nan(ind) nSV = 0, nBSV = 0 Mean squared error = -1.#IND (regression) Squared correlation coefficient = -1.#IND (regression) . optimization finished, #iter = 0 nu = -nan(ind) obj = 0.000000, rho = -nan(ind) nSV = 0, nBSV = 0 Mean squared error = -1.#IND (regression) Squared correlation coefficient = -1.#IND (regression)

ANY IDEA?