aima/learning.py
def compare(algorithms=[PluralityLearner, NaiveBayesLearner,
                        NearestNeighborLearner, DecisionTreeLearner],
            datasets=[iris, orings, zoo, restaurant, SyntheticRestaurant(20),
                      Majority(7, 100), Parity(7, 100), Xor(100)],
            k=10, trials=1):
    """Compare various learners on various datasets using cross-validation.
    Print results as a table."""
    print_table([[a.__name__.replace('Learner','')] +
                 [cross_validation(a, d, k, trials) for d in datasets]
                 for a in algorithms],
                header=[''] + [d.name[0:7] for d in datasets], numfmt='%.2f')
def compare(algorithms=[PluralityLearner, NaiveBayesLearner,
                        NearestNeighborLearner, DecisionTreeLearner],
            datasets=[iris, orings, zoo, restaurant, SyntheticRestaurant(20),
                      Majority(7, 100), Parity(7, 100), Xor(100)],
            k=10, trials=1):
    """Compare various learners on various datasets using cross-validation.
    Print results as a table."""
    print_table([[a.__name__.replace('Learner','')] +
                 [cross_validation(a, d, k, trials) for d in datasets]
                 for a in algorithms],
                header=[''] + [d.name[0:7] for d in datasets], numfmt='%.2f')
Compare various learners on various datasets using cross-validation.
    Print results as a table.