timbl(1)

okay bestie, this is timbl(1), straight outta the General Commands Manual. it's the Tilburg Memory Based Learner and she is that girl. 💅

✨ NAME ✨

timbl — Tilburg Memory Based Learner

🌸 SYNOPSIS 🌸

run it like this:

timbl [options]

timbl -f data-file -t test‐file

💖 DESCRIPTION 💖

TiMBL is an open source software package implementing several memory‐based learning algorithms. Among them: IB1‐IG, which is 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 this in common: they store some representation of the training set explicitly in memory. During testing, new cases get classified by extrapolation from the most similar stored cases. periodt.

🎀 OPTIONS 🎀

okay so here's the whole menu. every flag, every value, no skips. 🫶

• -a <n> or -a <string> — determines the classification algorithm. Possible values:
  • 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 a 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 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
• -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)

and that's the whole page, bestie. it's from the General Commands Manual, dated 2017 November 9, and it's timbl(1). mwah 💋