ELF>ȟ @@SR !"#$%&')*-./234HIJKAWAVAUATUSHxHdH%(H$h1D$D$D$D$o:D$@H$H$HDŽ$Ƅ$H$H$HDŽ$Ƅ$H$H5HT$`H$`H5HT$@H$0H5H$`HT$H$0H$H$0H$@H9tH$`H$pH9tHT$`H$`H5HT$@H$0H5H$`H$H$0H$H$0H$@H9tH$`H$pH9tHT$`H$`H5HT$@H$0H5H$`H$H$0H$H$0H$@H9tH$`H$pH9tH$H|$H$HH$~hHD$`D$hHD$pD$xD$|DŽ$HDŽ$DŽ$H$H$H$H$L$`Ll$0Mt$e9kHLH|$@LLH$`L9tD|$0H|$@H|$`DHH|$@HHD$@D$HD$LD$PHD$ D$(H$H5HL$ tHT$@Ht$H|$`$H$H5tHL$ \$H$H$H$H|$@HH$H$H5uiH$H5%HD$0H|$0t$(HHH$H|$ H|$@\$H$H$0H$0H|$ HH$0HHHH$0Ht$ H|$0fɺo\$H$H$`H$`H|$0HH$`H|$0H|$ H|$@H$H$H9tH|$`H$H$H$H9tH$H$H9tH$hdH3 %(5Hx[]A\A]A^A_HHH$`H|$0FHHH$0H$@H9tH$`H$pH9tH$H$H$H9tH$H$H9tH=HHHHHH$0H$@H9tH$`H$pH9lbHHHHH$0H$@H9tH$`H$pH9!HHHHH$`H$pH9tH$H$H9tH|$`HHH|$@HH`HHHHHHHHHHHHHRHH=HHHH=HH5H=HSHH(H{H[ATUSIHHu[]A\HHHtHsLHkH{ HC0H9tATUSIHHu[]A\HHHtHsLHkH{ HC0H9tATUSIHHu[]A\HHHtHsLHkH{ HC0H9tATUSIHHu[]A\HHHtHsLHkH{ HC0H9tATUSIHHu[]A\HHHtHsLHkH{ HC0H9tATUSIHHu[]A\HHHtHsLHkH{ HC0H9tATUSIHHu9[]A\H{@HCPH9tH{ HC0H9tHHHtHsLHkH{`HCpH9tATUSHHH@HHHH9tLHI9HHtH`HpH9tH8H(HHHHHHHuxH}hHuHH}8HuH}[]A\H I9RH;HCH9tATUSHHH@HHHH9tLHI9HHtH`HpH9tH8H(HHHHHHHuxH}hHuHH}8HuH}H[]A\H I9JH;HCH9tbasic_string::_M_construct null not validATUSHHIdH%(HD$1H9tHtHHL)H$HwDH}HtXHu\H$HEHUHD$dH3%(uIH[]A\H=HHHEH$HEA$HLIf true, output an affine transform that makes the projected data mean equal to zero.Dimension to project to with LDAIf 1.0, do conventional LDA where the within-class variance will be unit in the projected space. May be set to less than 1.0, which scales the features to have less variance, particularly for dimensions where between-class variance is small. If >0, maximum allowed singular value of final transform (they are floored to this)remove-offsetdimwithin-class-factormax-singular-valueUHAVAUATSHXIHdH%(HE1HL H}HGHEjHHrH}HGHE$HHrMHHMHuLHAH}HEH9tH}HEH9tHL`H}HGHEHjHHrMnH}HGHE$HHrHHMHuLHAH}HEH9tH}HEH9tHL`H}HGHEHjHH MnH}HGHE$HHrHHMHuLHAH}HEH9tH}HEH9tHL`H}HGHEHjHHrMI H}HGHE$HHrHHMHuLHAH}HEH9tH}HEH9tHEdH3%(He[A\A]A^]HH}HEH9tH}HEH9tHHHH}HEH9tH}HEH9tHHHH}HEH9tH}HEH9tHHHH}HEH9tH}HEH9tHHGCc}configprint-argsPrint out usage messagehelpverboseConfiguration file to read (this option may be repeated)Print the command line arguments (to stderr)Verbose level (higher->more logging)UHAWAVAUATSH`HdH%(HE1HH@HGHGHGHG HG(HG0G@HGHHG@HGPHGXHG`GpHGxHGpHHHLJLJHLJHHHHLJLJHLJHHHHLJLJHLJHHHHLJ LJ0HLJ8H0H@HHHLJPƇXƇYHpIH`HLJhƇpHLJHLJHLJHLJHLJHLLjHHL`HHǃHH8H}HGHEHjHHrH}HGHE$HHrHHMHuLHH}HEH9tH}HEH9tH}HGHEHjHHrLXH}HGHE$HHrHHMHuLHH}HEH9tH}HEH9tH}HGHEHjHHrLYH}HGHE$HHrHHMHuLHH}HEH9tH}HEH9tH}HGHEHjHHrH}HGHE$HHrHHMHuHHH}HEH9tH}HEH9tHEdH3%(He[A\A]A^A_]IH}HEH9tH}HEH9tHL9tLLM9I<$ID$H9tI IIH}HEH9tH}HEH9tIIH}HEH9tH}HEH9f\IIH}HEH9tH}HEH91'III#HHtH`I9tH8H(HHHHHHHsxH{hHsHH{8HsH{LS            Get feature-projection transform using stats obtained with acc-lda. See comments in the code of nnet2/get-feature-transform.h for more information. Usage: nnet-get-feature-transform [options] ... If supplied, write to this wxfilename the Cholesky factor of the within-class covariance. Can be used for perturbing features. E.g. --write-cholesky=exp/nnet5/cholesky.tpmatIf supplied, write to this wxfilename the within-class covariance (as a symmetric matrix). 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