def DecisionTree(examples = [], attributes = [], standard = ''):
if not examples:
return standard
elif SameClassification(examples):
return examples[0]['Accident Level']
elif not attributes:
return MajorityValue(examples)
else:
best = ChooseAttribute(attributes, examples)
#print(best)
#print(attributes)
tree = Node(best)
m = MajorityValue(examples)
different_values = DifferentValues(best, examples)
#print(different_values)
attributes.pop(attributes.index(best))
#print(attributes)
for v_i in different_values:
examples_i = BestEqualsVi(examples, best, v_i)
#print(examples_i)
#print(attributes_minus_best)
#print(m)
sub_tree = DecisionTree(examples_i, attributes, m)
tree.add(Edge(tree, sub_tree, v_i))
return tree
def test_tree_traverseBF_1child():
n = Node(0)
n.add(20)
t = Tree()
t.root = n
f = lambda n : print("data={0}".format(n.data))
t.traverseBF(f)
def test_tree_node_remove_data_multi_diff_match():
n = Node(1)
n.add(2)
n.add(3)
n.remove(2)
n.remove(3)
assert n.children[0] == None
assert n.children[1] == None
def test_tree_traverseDF_1sibling():
n = Node(0)
n.add(20)
n.add(21)
t = Tree()
t.root = n
f = lambda n : print("data={0}".format(n.data))
t.traverseDF(f)
def test_tree_traverseBF_1direct_grandchild():
n = Node(0)
n.add(20)
n.add(21)
n.children[0].add(30)
t = Tree()
t.root = n
f = lambda n : print("data={0}".format(n.data))
t.traverseBF(f)
def test_tree_traverseDF_1both_grandchild1sibling():
n = Node(0)
n.add(20)
n.add(21)
n.children[0].add(30)
n.children[1].add(31)
t = Tree()
t.root = n
f = lambda n : print("data={0}".format(n.data))
t.traverseDF(f)
def play_node(me: int, board: board.Board, move: int, player: int,
node: tree.Node) -> tree.Node:
"""
Play a move in the current board and for the parent node.
:param me: player we're building the tree for
:param board: current board
:param move: move to make
:param player: current player
:param node: parent node
:return: new node for the current move
"""
status = board.play(move, player)
total_valid_moves_after_play = len(board.valid_moves)
new_node = tree.Node(0, 1, move, board.state, player,
node) # create a new node
if node:
node.add(new_node
) # if parent node is supplied -> add new node as a child
new_node.status = status
if status == board.WIN:
if player == me:
if node:
node.winner = True # mark parent as a winner
new_node.winner = True
# score is equal to the number of valid moves that would be possible if a win didn't occur
new_node.score = board.WIN * total_valid_moves_after_play
new_node.total = total_valid_moves_after_play
# print(move, 'wins the game!')
else:
if node:
node.loser = True # mark parent as a loser
new_node.score = board.LOSS * total_valid_moves_after_play
new_node.total = total_valid_moves_after_play
new_node.loser = True
return new_node
def test_tree_node_add_child():
n = Node(1)
n.add(2)
assert n.children[0].data == 2
def test_tree_node_remove_data_mismatch():
n = Node(1)
n.add(2)
n.remove(3)
assert n.children[0] != None
def test_tree_node_add_grandchild():
n = Node(1)
n.add(2)
n.children[0].add(3)
assert n.children[0].children[0].data == 3
def test_tree_node_add_childAndSibling():
n = Node(1)
n.add(2)
n.add(3)
assert n.children[0].data == 2
assert n.children[1].data == 3
from tree import Node
if __name__ == "__main__":
root = Node(10)
root.add(7)
root.add(11)
print root.exists(int(input()))