def test_breadth_first_search():
n0 = Node()
n0.label = 1
n1 = Node()
n1.label = 0
n = Node()
n.label = None
n.decision_attribute = 1
n.is_nominal = True
n.name = "whatever"
n.children = {1: n0, 2: n1}
print n.print_tree()
print breadth_first_search(n)
return n
def more_tests():
n2 = Node()
n2.name = "attrib2"
n2.label = 1
n3 = Node()
n3.name = "attrib3"
n3.label = 0
n0 = Node()
n0.name = "attrib0"
n0.is_nominal = True
n0.children = {1: n2, 2: n3}
n4 = Node()
n4.name = "attrib4"
n4.label = 2
n5 = Node()
n5.name = "attrib5"
n5.label = 3
n1 = Node()
n1.name = "attrib1"
n1.is_nominal = True
n1.children = {1: n4, 2: n5}
n = Node()
n.label = None
n.decision_attribute = 1
n.is_nominal = True
n.name = "attrib"
n.children = {1: n0, 2: n1}
print n.print_dnf_tree()
print n.print_tree()
print breadth_first_search(n, [n])
attribute_metadata = [{'name': "winner",'is_nominal': True},{'name': "opprundifferential",'is_nominal': False}]
data_set = [[1, 0.27], [0, 0.42], [0, 0.86], [0, 0.68], [0, 0.04], [1, 0.01], [1, 0.33], [1, 0.42], [1, 0.42], [0, 0.51], [1, 0.4]]
numerical_splits_count = [5, 5]
n = ID3(data_set, attribute_metadata, numerical_splits_count, 0)
print validation_accuracy(n,data_set)
numerical_splits_count = [1, 1]
n = ID3(data_set, attribute_metadata, numerical_splits_count, 5)
print validation_accuracy(n,data_set)
numerical_splits_count = [5, 5]
n = ID3(data_set, attribute_metadata, numerical_splits_count, 5)
print validation_accuracy(n,data_set)
print n.print_tree()
n = reduced_error_pruning(n,data_set,[[1, 0.11], [0, 0.42], [0, 0.86], [0, 0.55], [0, 0.66], [1, 0.01], [1, 0.11], [1, 0.84], [1, 0.42], [0, 0.51], [1, 0.4]])
print n.print_tree()
return n