: bin
Trace level (1=normal, 2=debug): 1
Prefix XXX of the file (.all) with original data : (*1)
Prefix XXX for the files (.tra, .tes) with binarized data : (*1)
Size K of the K-folding (enter 1 for independent random experiments) : 1
Training set's size (in %) from: 100
* Training set's size (in %) to  : 100
* #iterations of each experimentation: 1
* Seed: 1234
Binarization method based on (1)binary, (2)continuous separability measure, (3) maps: 1
Apply point-in-a-box to reduce the # of pairs of points considered (y/n): y
Confidence interval around each cut point (0..0.1): 0
Cut points generation method (0=each change, 1=each pair): 0
Filter cut points according to a specific order (y/n): y
Ordering method for cut points (1=correl with output, 2=gap, 3=global sep): 1
Minimal separability of each pair of points (filter limit): 0.2
Minimize # of cut points (y/n): y
Minimal separability of each pair of points (optimization): 0.2

: pat
Trace level (1=normal, 2=debug): 1
Prefix XXX of the files (.tra) containing the training data : (*1)
Prefix XXX for the files (.pos, .neg) with the patterns : (*1)
Training set's size (in %) from: 100
* Training set's size (in %) to  : 100
* #iterations of each experimentation: 1
Iteration: 1
Percentage of training sample used for pattern generation: 100
Generate patterns of degree up to: 2
Minimal coverage of each positive pattern (use negative number for %) : 1
Minimal coverage of each negative pattern (use negative number for %) : 1
Satisfactory coverage of each point (50): 100
Minimal distance from a pattern to an opposite point (1): 1
Exception rate tolerated, i.e. fuzzy patterns (0.01 = 1%): 0
Generate extra patterns to cover uncovered points (y/n): n
Extract a minimal set of patterns for a cover (y/n): y
Minimal coverage number for each points: 1
* Suppress patterns covering a subset of others (y/n): y

! cp (*1)-100%001.tra (*1)-100%001.tes

: the
Trace level (1=normal, 2=debug): 1
Prefix XXX of the files (.tra, .tes) containing the training data : (*1)
Prefix XXX of the files (.pos, .neg) with the patterns : (*1)
Prefix XXX for the result files : (*1)
Training set's size (in %) from: 100
* Training set's size (in %) to  : 100
* #iterations of each experimentation: 1
Iteration: 1
Extract a minimal set of patterns for a cover (y/n): n
* Minimal coverage number for each points: 1
Suppress patterns covering a subset of others (y/n): y
Weighting method (0>cst, 1>Cov, 2>Cov/FSize, 3>FSize,...): 7
Normalize weights so that their sum is 1 (y,n): y
Readjust threshold and proportion between pos/neg (y,n): y
Tolerance for error in classifying (0.0): 0