def leave_one_out(examples,k):
conf_matr = ConfusionMatrix()
for ex in examples:
# disable only this example
ex.active = False
# run the k-Nearest-Neighbor algorithm
rank_list = knn.knn(k,examples,ex)
# check the voting for correctness
outcome = knn.voting(rank_list)
conf_matr.inc_according_to(outcome,ex.outcome)
ex.active = True
# return the computed confusion matrix
return conf_matr
def leave_one_out(examples, k):
conf_matr = ConfusionMatrix()
for ex in examples:
# disable only this example
ex.active = False
# run the k-Nearest-Neighbor algorithm
rank_list = knn.knn(k, examples, ex)
# check the voting for correctness
outcome = knn.voting(rank_list)
conf_matr.inc_according_to(outcome, ex.outcome)
ex.active = True
# return the computed confusion matrix
return conf_matr
