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Tilburg Memory Based Learner or see: timbl -hALL_WEIGHTS: trueproblems reading weightsran out of memory somewhere, sorrysome exception was raisedt [&"ZhSs2*A#, A/ ia52'I9OaE4{V}}6GhI"       3    Ss2*a[*}!Qa[*}        W            &  9  9 9 F 6 n   * Y&Zr xqA`  ]    %           ! 0R ^     q               $%$}}}}}SHHH[unable to find or use output file : comma separated metricvalues, [Problem reading command-lines from file 'unable to find or use input file 'Missing datafile name for Leave One Out testMissing datafile name for Cross Validation test-o option not possible for Cross Validation testing-u option is useless for IGtreeWarning: Skipped a line from indirect testfile: 'usage: timbl -f data-file {-t test-file} [options] 0 or IB1 : IB1 (default)-m s : use feature metrics as specified in string s: format: 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) O: weighted Overlap (default) M: Modified value difference S: Jensen-Shannon Divergence 1 or gr: Weight using GainRatio (default) 2 or ig: Weight using InfoGain 3 or x2: Weight using Chi-square 4 or sv: Weight using Shared Variance 5 or sd: Weight using Standard Deviation. (all features must be numeric)-w f : read Weights from file 'f'-w f:n : read Weight n from file 'f'-b n : number of lines used for bootstrapping (IB2 only)--clones= : use 'n' threads for parallel testing--Diversify: rescale weight (see docs)-d val : weight neighbors as function of their distance: Z : equal weights to all (default) ID : Inverse Distance ED:a : Exponential Decay with factor a (no whitespace!) ED:a:b : Exponential Decay with factor a and b (no whitespace!)-k n : k nearest neighbors (default n = 1)-q n : TRIBL threshold at level n-L n : MVDM threshold at level n-R n : solve ties at random with seed n-t f : test using file 'f' test with Leave One Out,using IB1 you may add -sloppy to speed up Leave One Out testing (see docs) Cross Validate Test,using IB1 @f : test using files and options described in file 'f' Supported options: d e F k m o p q R t u v w x % - -t is mandatory-f f : read from Datafile 'f'-f f : OR: use filenames from 'f' for CV test-F format : Assume the specified inputformat (Compact, C4.5, ARFF, Columns, Tabbed, Binary, Sparse )-l n : length of Features (Compact format only)-i f : read the InstanceBase from file 'f' --matrixin= read ValueDifference Matrices from file 'f'-u f : read value_class probabilities from file 'f'--occurrences=train|test|both assume occurrence info in the files. (train: in the train file, test: in the test file, both: in both)-P d : read data using path 'd'-s : use exemplar weights from the input file-s0 : silently ignore the exemplar weights from the input file-T n : use input field 'n' as the target. (default is: the last field)-e n : estimate time until n patterns tested--Beam= : limit +v db output to n highest-vote classes-I f : dump the InstanceBase in file 'f'--matrixout= store ValueDifference Matrices in file 'f'-X f : dump the InstanceBase as XML in file 'f'-n f : create names file 'f'-p n : show progress every n lines (default p = 100,000)-U f : save value_class probabilities in file 'f'+v or -v level : set or unset verbosity level, where level is o: show all options set b: show node/branch count and branching factor f: show Calculated Feature Weights (default) p: show Value Difference matrices as: show advanced statistics (memory consuming) cm: show Confusion Matrix (implies +vas) cs: show per Class Statistics (implies +vas) cf: add confidence to the 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 n: add nearest neigbors to output file (sets -x) You may combine levels using '+' e.g. +v p+db or -v o+di-G : normalize distibutions (+vdb option only) Probability : normalize between 0 and 1 O : does the same addFactor: : add f to all possible targets then normalize between 0 and 1 (default f=1.0) 1: : does the same logProbability : Add 1 to the target Weight, take the 10Log and 2 : does the same-W f : calculate and save all Weights in file 'f'+% or -% : do or don't save test result (%) to file-o s : use s as output filename-O d : save output using path 'd'Internal representation options:-B n : number of bins used for discretization of numeric -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)+H or -H : write hashed trees (default +H)-M n : size of MaxBests Array-N n : Number of features (default --Treeorder= : ordering of the Tree : IGO: using InformationGain G/V: using GainRatio/# of Vals I/V: using InfoGain/# of Vals X/V: using X-square/# of Vals SVO: using Shared Variance S/V: using Shared Variance/# of Vals SDO: using Standard Deviation SD/V: using Standard Deviation/# of Vals GxE: using GainRatio * SplitInfo IxE: using InformationGain * SplitInfo+x or -x : Do or don't use the exact match shortcut (IB1 and IB2 only, default is -x)Server mode is no longer available in timblPlease use the 'timblserver' command instead.Invalid Algorithm: Only IB1 possible for LOO and CV Induction of Linguistic Knowledge Research Group, Tilburg University CLiPS Computational Linguistics Group, University of Antwerpusage: timbl -f data-file {-t test-file} for all possible optionsCalculated weights replaced by:reading an instancebase(-i option) without a testfile (-t option) is uselesstimbl 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