Outputs some statistics about how many features and classes are present in the libsvm training/test data file.
Example output for the heart_scale file that is distributed with libsvm:
Classes:
1.0 44.444% 120
-1.0 55.556% 150
Features:
# Count% Count n-count Min Mean Max Variance
1 97.407% 263 7 -1.000000 0.061312 1.000000 0.147243
2 100.000% 270 -1.000000 0.355556 1.000000 0.873580
3 100.000% 270 -1.000000 0.449383 1.000000 0.399701
4 100.000% 270 -1.000000 -0.295388 1.000000 0.113156
5 100.000% 270 -1.000000 -0.435346 1.000000 0.055495
6 100.000% 270 -1.000000 -0.703704 1.000000 0.504801
7 99.259% 268 2 -1.000000 0.022388 1.000000 0.999499
8 100.000% 270 -1.000000 0.201187 1.000000 0.124623
9 100.000% 270 -1.000000 -0.340741 1.000000 0.883896
10 99.630% 269 1 -1.000000 -0.663749 1.000000 0.134841
11 54.815% 148 122 -1.000000 -0.756757 1.000000 0.427319
12 100.000% 270 -1.000000 -0.553086 1.000000 0.394507
13 100.000% 270 -1.000000 -0.151852 1.000000 0.938052
ALL 96.239% 3378 132 -1.000000 -0.216215 1.000000 0.461286
Classes:
1.0 44.444% 120 means, there are 120 samples (44.444% of all samples) in the class with the label 1.0
Features:
1 97.407% 263 7 means, that 263 samples contain the feature and 7 samples don't contain it.
So 97.407% of the samples contain feature 1.
If all samples contain the feature the number of samples that are missing the feature is omited.
The last four numbers show the minimum, average, maximum and variance of the feature values.
The last line is a summary for all the features. The count%, minimum, average, maximum and variance are averaged.