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/uHfuHH(t9t(tt H(n?H(IH(MH(H\$Ht$H|$ AVH HL3e؈D$@@==4-4tO HH  u)Mt H^H O 3@2@u?HH8t$HtLƺIHL A .3H\$0Ht$8H|$HH A^ùH\$WH @-~Sȉ-X؈D$8=33uDl%33@t3H\$0H _ù7HHX L@PHHVWAVH@ILu90-3BwEH$Hu D$0 ؉D$0LƋI؉D$0LƋI ؉D$0u8u4L3IL3I\HHtL3In tu@LƋI,؉D$0t)HoHu X\$0LƋI+ ؉D$03ۉ\$0H\$xH@A^_^H\$Ht$WH IHuLNjHH\$0Ht$8H _@SH H3 H  HȺ H [H% HL$H8# t)H 3,HD$8H-HD$8HH,H-Ht+HD$@Hx,N+ H+R+HkH J+HHkH *HL HkH *HL H H8@SVWH@H H3E3HT$`H Ht9Hd$8HL$hHT$`LHL$0LHL$pHL$(3H\$ B ǃ|H@_^[H\$ UHH H*H2-+H;utHeHM HEHE H1E HM H1E E HMH H3E H3EH3HH#H3-+H;HDH)H\$HHHj)H ]H(uH=uH(H /H%bH .H(kH$rHH(H(t!eH%0HHH;t3H .u2H(ðH(Ct+lt2eH(H(3=H(H([u2NuEH(H(3.H(H\$Hl$Ht$WH IIHuuL3HHB HT$XL$PH\$0Hl$8Ht$@H _H(WtH -H(uH(H(3H(@SH -ɻDÈo-vYu2Lu 3AH [@SH =4-ugwjt(u$H -uH &-t.23foH,H,,H,,H [ùHLMZf9ѵuxHc HHʁ9PEu_ f9AuTL+AHQHAH L H$I;tJ L;r BL;rH(3Hu2z$}2 22H@SH 3҅t uH+H [@SH =+tu H [H,Ã%+H\$UH$@Hٹt)3HMAHMHHHE3Ht)Asp##AsUAsN3H H HT$ HD$ $<u2"""@ t ""H\$(3Ht$0H_̸39"% %%%@%B%L%v%X%Z%\%>%0%Z%|3ff@UH HM@H ]@UH HM8H ]@UH0HHHL$(T$ L LEpUhHM`OH0]@UHH3Ɂ8]xijjjjPj:j$j jiiiii^iJig"g gfgTg\ggggbhFhg h$hgzhggxgggngffefffvflf`fLf:ff ffeeeeeeLNLength of label vector does not match # of instances. label (1st argument) should be a vector (# of column is 1). fullError: cannot full testing instance matrix transposeError: cannot transpose testing instance matrix Prob. model for test data: target value = predicted value + z, z: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g Mean squared error = %g (regression) Squared correlation coefficient = %g (regression) Accuracy = %g%% (%d/%d) (classification) Usage: [predicted_label, accuracy, decision_values/prob_estimates] = svmpredict(testing_label_vector, testing_instance_matrix, model, 'libsvm_options') [predicted_label] = svmpredict(testing_label_vector, testing_instance_matrix, model, 'libsvm_options') Parameters: model: SVM model structure from svmtrain. libsvm_options: -b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); one-class SVM not supported yet -q : quiet mode (no outputs) Returns: predicted_label: SVM prediction output vector. accuracy: a vector with accuracy, mean squared error, squared correlation coefficient. prob_estimates: If selected, probability estimate vector. 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