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timbl(1) General Commands Manual timbl(1) NAME timbl - Tilburg Memory Based Learner SYNOPSIS timbl [options] timbl -f data-file -t test-file DESCRIPTION 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 test- ing, new cases are classified by extrapolation from the most similar stored cases. OPTIONS -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 (Default B=20) --Beam=<n> limit +v db output to n highest-vote classes --clones=<n> number f threads to use for parallel testing -c n clipping frequency for prestoring MVDM matrices +D store distributions on all nodes (necessary for using +v db with IGTree, but wastes memory otherwise) --Diversify 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 distributions (+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 --matrixin=file read ValueDifference Matrices from file 'file' --matrixout=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 --occurrences=<value> 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 -s use the exemplar weights from the input file -s0 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 op- tions: 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' -V 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 weighting 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' BUGS possibly AUTHORS Ko van der Sloot Timbl@uvt.nl Antal van den Bosch Timbl@uvt.nl SEE ALSO timblserver(1) 2017 November 9 timbl(1)
NAME | SYNOPSIS | DESCRIPTION | OPTIONS | BUGS | AUTHORS | SEE ALSO
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