We now have our network file, test.net, which contains the parameters of
our Winnow algorithm as well as weights for all of the features which appeared
in our training examples. Now that we've trained our network, we can test it
on more examples.
> snow -test -I tutorial/testdata.snow -F tutorial/test.net
Our test file contains labeled examples of exactly the same format as those
used in training, and we used the default output mode to simply let SNoW score
its accuracy on those examples. In this mode, each example is given to the
system and the resulting prediction output by the classifier is compared to
the example's label. A mistake is scored if the two do not match. Here are
our results:
SNoW+ - Sparse Network of Winnows Plus
Cognitive Computations Group - University of Illinois at Urbana-Champaign
Version 3.2.0
Input file: 'tutorial/testdata.snow'
Network file: 'tutorial/test.net'
Directing output to console.
Algorithm information:
Winnow: (1.35, 0.8, 4, 0.3245) Targets: 0-1
850 test examples presented
Overall Accuracy - 96.71% (822 / 850)
We can also verify how well the training data was learned by testing with our training set (that is, use the file tutorial/traindata.snow after the -I above).
To receive output on a more detailed level, we can specify different output
modes on the command line with the -o outputmode parameter. For
example, try executing SNoW as follows:
> snow -test -I tutorial/testdata.snow -F tutorial/test.net -o allactivations
This gives the output:
SNoW+ - Sparse Network of Winnows Plus
Cognitive Computations Group - University of Illinois at Urbana-Champaign
Version 3.2.0
Input file: 'tutorial/testdata.snow'
Network file: 'tutorial/test.net'
Directing output to console.
Algorithm information:
Winnow: (1.35, 0.8, 4, 0.3159) Targets: 0-1
Example 1 Label: 1
1: 0.98732 8.3553*
0: 0.352 3.3897
Example 2 Label: 0
0: 0.85602 5.7826*
1: 0.082134 1.5863
Example 3 Label: 1
1: 0.73296 5.0097*
0: 0.066876 1.3643
...
See the description of the -o command line parameter for more details about the information it produces.