aima/learning.py
def cross_validation(learner, dataset, k=10, trials=1):
    """Do k-fold cross_validate and return their mean.
    That is, keep out 1/k of the examples for testing on each of k runs.
    Shuffle the examples first; If trials>1, average over several shuffles."""
    if k is None:
        k = len(dataset.examples)
    if trials > 1:
        return mean([cross_validation(learner, dataset, k, trials=1)
                     for t in range(trials)])
    else:
        n = len(dataset.examples)
        random.shuffle(dataset.examples)
        return mean([train_and_test(learner, dataset, i*(n/k), (i+1)*(n/k))
                     for i in range(k)])
def cross_validation(learner, dataset, k=10, trials=1):
    """Do k-fold cross_validate and return their mean.
    That is, keep out 1/k of the examples for testing on each of k runs.
    Shuffle the examples first; If trials>1, average over several shuffles."""
    if k is None:
        k = len(dataset.examples)
    if trials > 1:
        return mean([cross_validation(learner, dataset, k, trials=1)
                     for t in range(trials)])
    else:
        n = len(dataset.examples)
        random.shuffle(dataset.examples)
        return mean([train_and_test(learner, dataset, i*(n/k), (i+1)*(n/k))
                     for i in range(k)])
def cross_validation ( learner , dataset , k = 10 , trials = 1 ) : if k is None : k = len ( dataset . examples ) if trials > 1 : return mean ( [ cross_validation ( learner , dataset , k , trials = 1 ) for t in range ( trials ) ] ) else : n = len ( dataset . examples ) random . shuffle ( dataset . examples ) return mean ( [ train_and_test ( learner , dataset , i * ( n / k ) , ( i + 1 ) * ( n / k ) ) for i in range ( k ) ] )
Do k-fold cross_validate and return their mean.
    That is, keep out 1/k of the examples for testing on each of k runs.
    Shuffle the examples first; If trials>1, average over several shuffles.
Do k - fold cross_validate and return their mean . That is keep out 1 / k of the examples for testing on each of k runs . Shuffle the examples first ; If trials > 1 average over several shuffles .