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rg::utils::copf(3pm) [debian man page]

RG::Utils::Copf(3pm)					User Contributed Perl Documentation				      RG::Utils::Copf(3pm)

NAME
RG::Utils::Copf - Rost Lab perl utilities SYNOPSIS
use RG::Utils::Copf; FUNCTIONS
See module source for description. amino_acid_convert_3_to_1 amino_acid_convert_3_to_1_ini brIniErr brIniGetArg brIniHelp brIniHelpLoop brIniHelpRdItself brIniRdDef brIniSet brIniWrt bynumber bynumber_high2low cleanUp complete_dir completeDir convAliGen convDsspGen convFastamul2many convFssp2Daf convFsspGen convGcg2fasta convHssp2seq convHsspGen convMsf2saf convPdb2seq convPdbGen convPhd2dssp convPhd2seq convPhdGen convPir2fasta convPirmulGen convSaf2many convSeq2fastaPerl convSeq2seq convSeq2seqOld convSeqGen convSwiss2fasta copf dsspGetChain dsspRdSeq dsspWrtFromPhd errSbr errSbrMsg fastaRdGuide fastaRdMul fastaWrt fastaWrtMul fctRunTimeLeft fctSeconds2time gcgRd gcgWrt getDistanceNewCurveIde getFileFormatQuick get_in_keyboard get_range get_rangeHyphen getSysARCH hsspCorrectNali hsspGetChain hsspGetChainLength hsspRdAli hsspRdHeader hsspRdSeqSecAccOneLine ini iniDef iniHelpLoc is_chain isDaf is_dssp isFasta isFastaMul is_fssp isGcg is_hssp isMsf isPdb is_pdbid isPhdAcc isPhdBoth isPhdHtm isPhdSec isPir isPirMul is_ppcol isRdb is_rdb_htmref is_rdb_htmtop is_rdb_nnDb isSaf is_strip isSwiss msfBlowUp msfCheckFormat msfCompress msfCountNali msfRd msfShorten msfWrt open_file pdbExtrSequence pirRdMul pirWrtMul pirWrtOne rdRdbAssociative rdRdbAssociativeNum safCompress safRd safWrt seqGenWrt swissRdSeq sysDate sysGetUserLoc sysRunProg wrtLoc VARIABLES
AUTHOR
B. Rost <rost@rostlab.org> L. Kajan <lkajan@rostlab.org> SEE ALSO
Main website <http://rostlab.org/> perl v5.14.2 2011-11-28 RG::Utils::Copf(3pm)

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PROFNET(1)							   User Commands							PROFNET(1)

NAME
profnet_* - neural network implementations in Fortran SYNOPSIS
profnet_* [OPTION|filePar] DESCRIPTION
profnet_* binaries are neural network implementations in Fortran. Due to the original design of the code, a specific binary is compiled for each particular network architecture, changing certain constants in the source code. Therefore, there is a binary for every network architecture used. Note: certain array structures are intentionally indexed out of bounds in some of the binaries. Note: This binary should only be used to run with pre-made training data, do not try to use it to train your network as it will produce undesired results. It was made to be used only as part of wrapping (dependent) packages and not as a standalone neural network program. OPTIONS
This list is not exhaustive. filePar file with input parameters (also gives fileIn, fileOut) 1 "switch" 2 number of input units 3 number of hidden units 4 number of output units 5 number of samples 6 bitacc (typically 100) 7 file with input vectors 8 file with junctions 9 file with output of NN ("none" -> no file written) 10 optional=dbg [inter] will bring up dialog NOTES
1st MUST be "switch"! tested only with 2 layers! AUTHOR
Burkhard Rost <rost@rostlab.org> Bug fixes and enhancements by Laszlo Kajan <lkajan@rostlab.org> and Guy Yachdav <gyachdav@rostlab.org> COPYRIGHT AND LICENSE
Copyright 1998-2011 by Burkhard Rost <rost@rostlab.org> EMBL, CUBIC (Columbia University, NY, USA) and LION Biosciences (Heidelberg, DE) Copyright 2009-2011 by Laszlo Kajan <lkajan@rostlab.org> Technical University Munich (Munich, DE) Copyright 2009-2011 by Guy Yachdav <gyachdav@rostlab.org> CUBIC (Columbia University, NY, USA) and Technical University Munich (Munich, DE) 1.0.21- 2011-06-16 PROFNET(1)
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