Provided by: timbl_6.4.4-4_amd64 bug


       timbl - Tilburg Memory Based Learner


       timbl [options]

       timbl -f data-file -t test-file


       TiMBL  is  an  open  source  software  package  implementing several memory-based learning
       algorithms, among which IB1-IG, an implementation  of  k-nearest  neighbor  classification
       with  feature  weighting suitable for symbolic feature spaces, and IGTree, a decision-tree
       approximation of IB1-IG. All implemented algorithms have in common that  they  store  some
       representation  of  the  training  set explicitly in memory. During testing, new cases are
       classified by extrapolation from the most similar stored cases.


       -a <n> or -a <string>
              determines the classification algorithm.

              Possible values are:

              0 or IB
               the IB1 (k-NN) algorithm (default)

              1 or IGTREE
               a decision-tree-based approximation of IB1

              2 or TRIBL
               a hybrid of IB1 and IGTREE

              3 or IB2
               an incremental editing version of IB1

              4 or TRIBL2
               a non-parameteric version of TRIBL

       -b n
              number of lines used for bootstrapping (IB2 only)

       -B n
              number of bins used for discretization of numeric feature values

              limit +v db output to n highest-vote classes

              number f threads to use for parallel testing

       -c n
              clipping frequency for prestoring MVDM matrices

              store distributions on all nodes (necessary for using +v db with IGTree, but wastes
              memory otherwise)

              rescale weight (see docs)

       -d val
              weigh neighbors as function of their distance:
               Z      : equal weights to all (default)
               ID     : Inverse Distance
               IL     : Inverse Linear
               ED:a   : Exponential Decay with factor a (no whitespace!)
               ED:a:b : Exponential Decay with factor a and b (no whitespace!)

       -e n
              estimate time until n patterns tested

       -f file
              read from data file 'file' OR use filenames from 'file' for cross validation test

       -F format
              assume the specified input format (Compact, C4.5, ARFF, Columns, Binary, Sparse )

       -G normalization

              normalize distibutions (+v db option only)

              Supported normalizations are:

              Probability or 0

              normalize between 0 and 1

              addFactor:<f> or 1:<f>

              add f to all possible targets, then normalize between 0 and 1  (default f=1.0).

              logProbability or 2

              Add 1 to the target Weight, take the 10Log and then normalize between 0 and 1

       +H or -H
              write hashed trees (default +H)

       -i file
              read the InstanceBase from 'file' (skips phase 1 & 2 )

       -I file
              dump the InstanceBase in 'file'

       -k n
              search 'n' nearest neighbors (default n = 1)

       -L n
              set value frequency threshold to back off from MVDM to Overlap at level n

       -l n
              fixed feature value length (Compact format only)

       -m string
              use feature metrics as specified in' string':
               The format is : GlobalMetric:MetricRange:MetricRange
                         e.g.: mO:N3:I2,5-7

               C: cosine distance. (Global only. numeric features implied)
               D: dot product. (Global only. numeric features implied)
               DC: Dice coefficient
               O: weighted overlap (default)
               E: Euclidian distance
               L: Levenshtein distance
               M: modified value difference
               J: Jeffrey divergence
               S: Jensen-Shannon divergence
               N: numeric values
               I: Ignore named  values

              read ValueDifference Matrices from file 'file'

              store ValueDifference Matrices in 'file'

       -n file
              create a C4.5-style names file 'file'

       -M n
              size of MaxBests Array

       -N n
              number of features (default 2500)

       -o s
              use s as output filename

              The  input  file contains occurrence counts (at the last position) value can be one
              of: train , test or both

       -O path
              save output using 'path'

       -p n
              show progress every n lines (default p = 100,000)

       -P path
              read data using 'path'

       -q n
              set TRIBL threshold at level n

       -R n
              solve ties at random with seed n

              use the exemplar weights from the input file

              ignore the exemplar weights from the input file

       -T n
              use feature n as the class label. (default: the last feature)

       -t file
              test using 'file'

       -t leave_one_out
              test with the leave-one-out testing regimen (IB1 only).  you may  add  --sloppy  to
              speed up leave-one-out testing (but see docs)

       -t cross_validate
              perform cross-validation test (IB1 only)

       -t @file
              test using files and options described in 'file' Supported options: d e F k m o p q
              R t u v w x % -

       --Treeorder =value n
              ordering of the Tree:
               DO: none
               GRO: using GainRatio
               IGO: using InformationGain
               1/V: using 1/# of Values
               G/V: using GainRatio/# of Valuess
               I/V: using InfoGain/# of Valuess
               X2O: using X-square
               X/V: using X-square/# of Values
               SVO: using Shared Variance
               S/V: using Shared Variance/# of Values
               GxE: using GainRatio * SplitInfo
               IxE: using InformationGain * SplitInfo
               1/S: using 1/SplitInfo

       -u file
              read value-class probabilities from 'file'

       -U file
              save value-class probabilities in 'file'

              Show VERSION

       +v level or -v level
              set or unset verbosity level, where level is:

               s:  work silently
               o:  show all options set
               b:  show node/branch count and branching factor
               f:  show calculated feature weights (default)
               p:  show value difference matrices
               e:  show exact matches
               as: show advanced statistics (memory consuming)
               cm: show confusion matrix (implies +vas)
               cs: show per-class statistics (implies +vas)
               cf: add confidence to output file (needs -G)
               di: add distance to output file
               db: add distribution of best matched to output file
               md: add matching depth to output file.
               k:  add a summary for all k neigbors to output file (sets -x)
               n:  add nearest neigbors to output file (sets -x)

                You may combine levels using '+' e.g. +v p+db or -v o+di

       -w n
               0 or nw: no weighting
               1 or gr: weigh using gain ratio (default)
               2 or ig: weigh using information gain
               3 or x2: weigh using the chi-square statistic
               4 or sv: weigh using the shared variance statistic
               5 or sd: weigh using standard deviation. (all features must be numeric)

       -w file
              read weights from 'file'

       -w file:n
              read weight n from 'file'

       -W file
              calculate and save all weights in 'file'

       +% or -%
              do or don't save test result (%) to file

       +x or -x
              do or don't use the exact match shortcut
                 (IB1 and IB2 only, default is -x)

       -X file
              dump the InstanceBase as XML in 'file'




       Ko van der Sloot

       Antal van den Bosch



                                           2012 July 10                                  timbl(1)