def compute_concatenated_words(words):
tree = Tree()
tree.add_words(words)
concatenated_word_count = 0
max_concat_length = 0
second_max_concat_length = 0
max_length_concat_words = []
second_max_length_concat_words = []
for word in words:
if tree.is_concatenated(word):
concatenated_word_count += 1
if len(word) > max_concat_length:
second_max_concat_length = max_concat_length
second_max_length_concat_words = max_length_concat_words
max_concat_length = len(word)
max_length_concat_words = [word]
elif len(word) == max_concat_length:
max_length_concat_words.append(word)
elif len(word) > second_max_concat_length:
second_max_concat_length = len(word)
second_max_length_concat_words = [word]
elif len(word) == second_max_concat_length:
second_max_length_concat_words.append(word)
return concatenated_word_count, \
max_length_concat_words, \
second_max_length_concat_words
def compress(string):
if not string or not len(string):
throw
tree = Tree(string)
treedict = tree.as_dict()
compressed = []
for c in string:
compressed += treedict[c]
return (tree.as_bytes(), BitArray.to_bytes(compressed))
def generate_random_tree(a, b, c):
my_set = []
tree = None
while len(my_set) < c:
num = random.randint(a, b)
if num not in my_set:
my_set.append(num)
if tree is None:
tree = Tree(num)
else:
tree.insert(Tree(num))
print('list', my_set, '\n')
return tree
def main():
"""Main Function"""
setup_logging()
obj_1 = Tree('Parent')
children = [Tree("child1"), Tree("child2"), Tree("child3")]
obj_1.add_children(children)
g_child = [Tree("newborn1"), Tree("newborn2")]
children[0].add_children(g_child)
g_child_1 = [Tree("newbie2")]
children[1].add_children(g_child_1)
LOGGER.info(obj_1)
def run_id3(database_name, num_trials):
"""
Loop num_trials times, building a new tree and testing it against the
test set of movies.
"""
total = 0
for x in range(0, num_trials):
mydata = init_dataset(database_name)
decision_tree = Tree(id3_tree(mydata.learn_set, mydata.attribute_set))
decision_tree.insert_rules()
num_correct = 0
for movie in mydata.test_set:
for rule in decision_tree.rules:
conditions_met = True
key = ''
value = ''
for i in range(0, len(rule) - 1, 2):
#Even index = attribute, odd index = class label
key = rule[i]
value = rule[i + 1]
#Check if value is correct
if movie[key] != value:
conditions_met = False
break
if conditions_met:
if rule[-1] == movie[TARGET]:
num_correct += 1
#End for if we find a rule that correctly classifies movie
break
total += (num_correct / len(mydata.test_set)) * 100
print("Test #" + str(x) + ", accuracy = " +
str((num_correct / len(mydata.test_set) * 100)))
total /= num_trials
print("Average over " + str(num_trials) + " trials: " + str(total) + "%")
def uncompress(tree, compressed):
string = BitArray.from_bytes(compressed)
tree = Tree(None, tree)
uncompressed = ''
sel = tree.tree
for bit in string:
if bit == 0:
sel = sel.left
else:
sel = sel.right
if (sel.key):
uncompressed += sel.key
sel = tree.tree
return uncompressed
from classes.node import Node
from classes.tree import TreeNode, Tree
tree = Tree()
nodes = {}
# load taxonomy data to dictionary
print("Loading nodes from file")
with open('taxdump/nodes.dmp') as f:
for line in f:
formatted_input = line.split('\t|\t')
nodes[formatted_input[0]] = Node(formatted_input)
print("Loading completed")
# function to get a node path from the dictionary
def getPath(id):
if id not in nodes:
return []
currentNode = nodes[id]
res = [currentNode.tax_id]
while (currentNode.tax_id != currentNode.parent_id):
res.append(currentNode.parent_id)
currentNode = nodes[currentNode.parent_id]
return res
# function to find lca based on a dictionary list
def find_lca(n1, n2):
n1_path = getPath(n1)
n2_path = getPath(n2)
sliders['slider_number'].set_round(0)
sliders['slider_division_ratio'].set_percentage(60)
sliders['slider_division_angle'].set_percentage(37)
sliders['slider_number'].set_percentage(85)
tree_parameters['number'] = sliders['slider_number'].get_value()
tree_parameters['division_ratio'] = sliders['slider_division_ratio'].get_value()
tree_parameters['division_angle'] = sliders['slider_division_angle'].get_value()
tree1 = Tree(pos=tree_parameters['pos'],
angle=tree_parameters['angle'],
ticks=tree_parameters['tick'],
vel=tree_parameters['vel'],
number=tree_parameters['number'],
division_ratio=tree_parameters['division_ratio'],
division_angle=tree_parameters['division_angle'],
divide_angles=tree_parameters['divide_angles'],
divide_distance=tree_parameters['divide_distance'],
fruits=tree_parameters['fruits'])
mouse_pos = None
clicked = False
holding_click = False
hidden_hud = False
run = True
while run:
clicked = False
if pygame.mouse.get_pressed()[0] == 1 and not holding_click:
from classes.tree import TreeNode, Tree
tree = Tree()
tree.insert(1)
tree.insert(3, 1)
tree.insert(2, 1)
tree.insert(4, 2)
tree.insert(7, 2)
tree.insert(78, 7)
tree.insert(10, 3)
tree.insert(10, 3)
print(tree.print(tree.root, ''))
def find_lca(tree, n1, n2):
_, n1_path = tree.search(tree.root, n1)
_, n2_path = tree.search(tree.root, n2)
if n1_path is None:
print('Node ', n1, ' doesn\'t exist')
return None
if n2_path is None:
print('Node ', n2, ' doesn\'t exist')
return None
n1_path = n1_path.split(',')
n2_path = n2_path.split(',')
if len(n1_path) == 0 or len(n2_path) == 0:
return None
def test_delete():
my_list = []
my_list.append(2)
my_list.append(1)
my_list.append(7)
my_list.append(4)
my_list.append(8)
my_list.append(3)
my_list.append(6)
my_list.append(5)
tree = Tree(2)
tree.insert(Tree(1))
tree.insert(Tree(7))
tree.insert(Tree(4))
tree.insert(Tree(8))
tree.insert(Tree(3))
tree.insert(Tree(6))
tree.insert(Tree(5))
print('list', my_list, '\n')
print('delete node 7:')
tree.delete(7)
tree.print()
print('delete node 5:')
tree.delete(5)
tree.print()
def test_create():
my_tree = Tree(1, None)
print('Create tree object with value 1: ', my_tree.get_value())