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Changed this example program so it used the cached version of the

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batch_trainer object.

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extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403147
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Davis King 2009-08-05 00:10:23 +00:00
parent a23e5f6224
commit 381230a58f
1 changed files with 5 additions and 5 deletions
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10
examples/svm_pegasos_ex.cpp
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@ -127,18 +127,18 @@ int main()
trainer.clear(); trainer.clear();
// Now to begin with, you might want to compute the cross validation score of a trainer object // Now to begin with, you might want to compute the cross validation score of a trainer object
// on your data. To do this you should use the batch() function to convert the svm_pegasos object // on your data. To do this you should use the batch_cached() function to convert the svm_pegasos object
// into a batch training object. Note that the second argument to batch() is the minimum // into a batch training object. Note that the second argument to batch() is the minimum
// learning rate the trainer object must report for the batch() function to consider training // learning rate the trainer object must report for the batch() function to consider training
// complete. So smaller values of this parameter cause training to take longer but may result // complete. So smaller values of this parameter cause training to take longer but may result
// in a more accurate solution. // in a more accurate solution.
// Here we perform 4-fold cross validation and print the results // Here we perform 4-fold cross validation and print the results
cout << "cross validation: " << cross_validate_trainer(batch(trainer,1.0), samples, labels, 4); cout << "cross validation: " << cross_validate_trainer(batch_cached(trainer,0.1), samples, labels, 4);
// Here is an example of creating a decision function. Note that we have used the verbose_batch() // Here is an example of creating a decision function. Note that we have used the verbose_batch_cached()
// function instead of batch() as above. They do the same things except verbose_batch() will // function instead of batch_cached() as above. They do the same things except verbose_batch_cached() will
// print status messages to standard output while training is under way. // print status messages to standard output while training is under way.
decision_function<kernel_type> df = verbose_batch(trainer,0.1).train(samples, labels); decision_function<kernel_type> df = verbose_batch_cached(trainer,0.1).train(samples, labels);
// At this point we have obtained a decision function from the above batch mode training. // At this point we have obtained a decision function from the above batch mode training.
// Now we can use it on some test samples exactly as we did above. // Now we can use it on some test samples exactly as we did above.