def test_equality(self):
""" Examples of valid equality tests for NamedTuples """
p1 = Path.home()
p2 = Path.cwd()
n1a = TreeNode.new(p1)
n1b = TreeNode.new(p1)
n2a = TreeNode.new(p2)
n2b = TreeNode.new(p2)
for tn in (n1a, n1b, n2a, n2b):
tn.add(Path("./data"))
tn.add(Path("requirements.txt"))
# NamedTuples implement compare
assert n1a == n1b
assert n1a != n1b._replace(files=[])
# Deep compare works
case = "case_100"
tn1 = Customs(case, FileType.PICKLE).read()
tn2 = copy.deepcopy(tn1)
assert tn1 == tn2
# Fragile - this is tied to case_100 dir list order
tn2.dirs[16].dirs[0].files.append('foo')
assert tn1 != tn2
def test_normalRamify_WithKeys(self):
treeNode = TreeNode(1)
treeNode.ramify(range(3), ['kid' + str(x) for x in range(3)])
self.assertEqual(treeNode.children,
{'kid' + str(x): x
for x in range(3)})
def next(self):
self.head_node = self.head_q.get()
str_world_state = str(self.head_node.world_state)
if str_world_state not in self.explored_by_head:
self.explored_by_head[str_world_state] = self.head_node
self.expansion_count += 1
if str_world_state in self.explored_by_tail:
self.tail_node = self.explored_by_tail[str_world_state]
return False
self.tail_node = self.tail_q.get()
str_world_state = str(self.tail_node.world_state)
if str_world_state not in self.explored_by_tail:
self.explored_by_tail[str_world_state] = self.tail_node
self.expansion_count += 1
if str_world_state in self.explored_by_head:
self.head_node = self.explored_by_head[str_world_state]
return False
new_world_states = self.world.get_moves(self.head_node.world_state)
for state in new_world_states:
node = TreeNode(self.head_node, state)
self.head_q.put(node)
new_world_states = self.world.get_moves(self.tail_node.world_state)
for state in new_world_states:
node = TreeNode(self.tail_node, state)
self.tail_q.put(node)
return True
def main():
solver = Solution()
for test in [[1, None, 2, None, None, 3]]:
root = TreeNode()
root.build_from_list(test)
print(solver.preorderTraversal(root))
pass
def splitTree(self, node):
"""
according to best split feature, split datasets into multiple parts
:@ param node: input Tree Node
:@ rparam children of input node
"""
children = []
bestFeature = self.chooseSplitFeature(node)
if bestFeature < 0:
return children
for key in node.uniformAttribute[bestFeature][1]:
newDataSet = []
if key.startswith('<='):
newDataSet = [
instance for instance in node.dataSet
if instance[bestFeature] <= float(key.split('<= ')[1])
]
elif key.startswith('>'):
newDataSet = [
instance for instance in node.dataSet
if instance[bestFeature] > float(key.split('> ')[1])
]
else:
newDataSet = [
instance for instance in node.dataSet
if instance[bestFeature] == key
]
newNode = TreeNode(newDataSet, copy.deepcopy(node.attribute),
copy.deepcopy(node.classOutput))
newNode.splitFeatureName = node.attribute[bestFeature][0]
newNode.splitFeatureValue = key
children.append(newNode)
return children
def test_errorRamify_childrenPresent(self):
with self.assertRaises(Exception) as cm:
treeNode = TreeNode(1)
treeNode['child0'] = TreeNode(2)
treeNode.ramify([1, 1])
self.assertEqual('Node already has 1 children', str(cm.exception))
def pruneTree(self, root: TreeNode) -> TreeNode:
if not root:
return None
root.left = self.pruneTree(root.left)
root.right = self.pruneTree(root.right)
if root.val == 0 and not root.left and not root.right:
return None
return root
def __init__(self,
centerPt=Point(0, 0, 'center'),
dimension=1,
max_points=1,
max_depth=4):
self.max_points = max_points
self.max_depth = max_depth
self.root = TreeNode(centerPt, dimension, max_points, max_depth, 0)
def test_errorSetItem_DifferentValueTypes(self):
with self.assertRaises(Exception) as cm:
treeNode = TreeNode(1)
treeNode['child0'] = TreeNode(2.0)
self.assertEqual(
'Parent and child nodes\' values must share the same type',
str(cm.exception))
def build_from_traversals(preorder, inorder):
if not preorder or not inorder:
return None
root_val = preorder[0]
i = inorder.find(root_val)
root = TreeNode(root_val)
root.left = build_from_traversals(preorder[1:i+1], inorder[0:i])
root.right = build_from_traversals(preorder[i+1:], inorder[i+1:])
def build_from_traversals(preorder, inorder):
if not preorder or not inorder:
return None
root_val = preorder[0]
i = inorder.find(root_val)
root = TreeNode(root_val)
root.left = build_from_traversals(preorder[1:i + 1], inorder[0:i])
root.right = build_from_traversals(preorder[i + 1:], inorder[i + 1:])
def makeBalancedTree(nums):
if not nums:
return None
midpoint = len(nums) / 2
node = TreeNode(nums[midpoint])
node.left = solution(nums[:midpoint])
node.right = solution(nums[(midpoint + 1):])
return node
def create_by_pre_order(values):
value = values.pop(0)
if value == 0:
return None
node = TreeNode(value=value)
node.left = create_by_pre_order(values)
node.right = create_by_pre_order(values)
return node
def searchingByBFS():
#Pointing to the global variables
global initialConfiguration
global goalState
global visitedNodes
global nodesToExpand
global success
global stacksDoesntMatter
#At the begin our current state is null
actualNode = TreeNode(None, None, None, 0)
#Creating the node for the initial configuration
initialConfiguration = TreeNode(None, initialConfiguration, None, 0)
#Initial state will be the first node to expand
nodesToExpand.append(initialConfiguration)
#Adding the first node to expand to the visited list
visitedNodes.append(initialConfiguration.state)
#Iterate while there are nodes to expand and the goal state has been not reached
while (nodesToExpand and not (success)):
actualNode = nodesToExpand.pop(0)
visitedNodes.append(actualNode.state)
if (isFinalState(actualNode.state)):
success = actualNode
return True
#For the actual node, we need to expande it and check all the possible combinations
for originStack in range(len(actualNode.state)):
for destinationStack in range(len(actualNode.state)):
expandedActualNode = copy.deepcopy(actualNode.state)
#If origin is not empty, the origin and destination are different and the height in the destination is the correct
#We can make a movement
if (expandedActualNode[originStack]
and originStack != destinationStack and
len(expandedActualNode[destinationStack]) < maxHeight):
#Remove from origin stack
expandedActualNode[originStack].pop(0)
#Add to destination stack
expandedActualNode[destinationStack].append(
actualNode.state[originStack][0])
#If the new state has not been visited
if (not (visitedNodes.count(expandedActualNode))):
#Calculate cost
newCost = 1 + abs(originStack -
destinationStack) + actualNode.cost
#Create the new node
newNode = TreeNode(actualNode, expandedActualNode,
[originStack, destinationStack],
newCost)
#Add it as the list for expansion
nodesToExpand.append(newNode)
#Add it to the visited list
#visitedNodes.append(newNode.state)
return False
def deleteNode(self, root: TreeNode, key: int) -> TreeNode:
""""""
v_root = TreeNode(-1)
v_root.right = root
parent = v_root
node = root
while node:
if node.val == key:
break
elif node.val > key:
node = node.left
else:
node = node.right
parent = node
if not node:
return root
def find_max_from_left(node: TreeNode) -> TreeNode:
"""从BST树节点的左子树中找到值最大的节点"""
if not node:
return
p = node
node = node.left
while node and node.right:
p = node
node = node.right
p.right = None
return node
def find_min_from_right(node: TreeNode) -> TreeNode:
"""从BST树节点的右子树中找到值最小的节点"""
if not node:
return
p = node
node = node.right
while node and node.left:
p = node
node = node.left
p.left = None
return node
if node.left:
max_sub_node = find_max_from_left(node)
node.val = max_sub_node.val
elif node.right:
min_sub_node = find_min_from_right(node)
node.val = min_sub_node.val
else:
if node.val < parent.val:
parent.left = None
else:
parent.right = None
return v_root.right
def build_coinflip_tree(k, value=""):
'''
INPUT: int
OUTPUT: TreeNode
Return the root of a binary tree representing all the possible outcomes
form flipping a coin k times.
'''
node = TreeNode(value)
if k != 0:
node.left = build_coinflip_tree(k - 1, value + "H")
node.right = build_coinflip_tree(k - 1, value + "T")
return node
def build_coinflip_tree(k, value=""):
'''
INPUT: int
OUTPUT: TreeNode
Return the root of a binary tree representing all the possible outcomes
form flipping a coin k times.
'''
node = TreeNode(value)
if k != 0:
node.left = build_coinflip_tree(k - 1, value + "H")
node.right = build_coinflip_tree(k - 1, value + "T")
return node
def build_coinflip_tree(k, value=""):
"""Return the root of a binary tree for flipping coin k times.
Root represents all the possible outcomes from flipping a coin k times.
Parameters
----------
int
Returns
-------
TreeNode
"""
node = TreeNode(value)
if k != 0:
node.left = build_coinflip_tree(k - 1, value + "H")
node.right = build_coinflip_tree(k - 1, value + "T")
return node
def searchingRecursive(initialState, visitedNodes):
node = initialState
nodeInitialState = node.state
stateLength = len(nodeInitialState)
cost = node.cost
if(nodeInitialState == goalState):
return node
else:
for i in range(0, stateLength):
for j in range(0, stateLength):
if ((len(nodeInitialState[j])) > 0 and i != j and (len(nodeInitialState[i])) < maxHeight):
newState = copy.deepcopy(nodeInitialState)
#Put the last container j -> i
newState[i].append(newState[j].pop())
if (not(visitedNodes.count(newState))):
movements = [i, j]
#New cost
#1 Picking up the container and Putting the container down + 1 for Moving the container one stack to the left or right + the accumulated cost of the path
newCost = 1 + abs(i - j) + cost
#New node
newNode = TreeNode(node, newState, movements, newCost)
#Visited list
visitedNodes.append(newState)
#Recursive call
auxDFS = searchingRecursive(newNode, visitedNodes)
if(auxDFS != None):
return auxDFS
return None
def aStar():
path = []
finalCost = 0
while nodesToExpand:
node = nodesToExpand.pop()
#Result found
if isFinalState(node.state):
finalCost, path = adaptSolution(node)
return finalCost, path
visitedNodes.append(node.state)
for i, stack in enumerate(node.state):
for j, new_stack in enumerate(node.state):
auxState = copy.deepcopy(node.state)
if i != j and len(stack) > 0 and len(new_stack) < maxHeight:
newState, newStateCost = moveBlock(auxState, i, j)
newNode = TreeNode(node, newState, [i, j],
newStateCost + node.cost)
#if the new node has not been visited, it is added to the visited list
if newNode.state not in visitedNodes and not any(
n.state == newNode.state for n in nodesToExpand):
nodesToExpand.append(newNode)
nodesToExpand.sort(key=operator.attrgetter('cost'),
reverse=True)
#if the node was visited before, we verify if the node's cost is higher than the new one
else:
for n in nodesToExpand:
if n.state == newNode.state and n.cost > newNode.cost:
nodesToExpand.remove(n)
nodesToExpand.append(newNode)
nodesToExpand.sort(
key=operator.attrgetter('cost'),
reverse=True)
return finalCost, path
def translate(self):
"""
We serialize either a TreeNode or an id_dict. After reading, call
this method to ensure all source formats are available.
Assumes we have
"""
if self.treenode:
# Create id_dict from TreeNode - only pickle
self.id_dict = self.treenode.to_id_dict()
self.tn_dict = self.treenode.to_tn_dict()
elif self.id_dict:
# Create TreeNode from id_dict - all other serializations
# If serialization produces an id_dict, we must create the tn_dict
self.tn_dict = {}
for id, node in self.id_dict.items():
if node.is_dir():
self.tn_dict[id] = TreeNode(me=node, files=[], dirs=[])
# Define dir hierarchy
# Also identify root node to remove one tn_dict traversal
for tn in self.tn_dict.values():
if not tn.me.parent_id:
self.treenode = tn
else:
self.tn_dict[tn.me.parent_id].dirs.append(tn)
# Merge files into dir hierarchy
for node in self.id_dict.values():
if not node.is_dir():
self.tn_dict[node.parent_id].files.append(node)
else:
raise ValueError("No internal format to translate.")
def test_basic_relation(self): parent = TreeNode(9) node = TreeNode(8) left = TreeNode(6) right = TreeNode(7) node.parent = parent node.left = left node.right = right self.assertEqual(id(node.parent), id(parent)) self.assertEqual(id(node.left), id(left)) self.assertEqual(id(node.right), id(right)) self.assertEqual(id(left.parent), id(node)) self.assertEqual(id(right.parent), id(node)) self.assertEqual(id(node.parent), id(parent)) self.assertEqual(id(parent.left), id(node)) self.assertTrue(parent.key > left.key) self.assertTrue(parent > left)
def collect_data_recurse(p: Path, tree_node: TreeNode,
exclusions: Set[str]) -> None:
""" Recurse dirs starting at tree_node, collecting information """
for item in p.iterdir():
# NOTE: If we cannot create the node, it will not be added and we will not recurse
child = tree_node.add(item)
if item.is_dir() and item.name not in exclusions and child:
collect_data_recurse(item, child, exclusions)
def decision_tree(examples, attributes, bin_targets):
all_same = check_all_same(bin_targets)
if all_same:
return TreeNode(None, True, bin_targets.iloc[0].iloc[0])
elif not attributes:
# Majority Value
return TreeNode(None, True, majority_value(bin_targets))
else:
best_attribute = choose_best_decision_attr(examples, attributes,
bin_targets)
tree = TreeNode(best_attribute)
for vi in range(0, 2):
examples_i = examples.loc[examples[best_attribute] == vi]
indices = examples_i.index.values
bin_targets_i = bin_targets.ix[indices]
if examples_i.empty:
# Majority Value
return TreeNode(None, True, majority_value(bin_targets))
else:
attr = set(attributes)
attr.remove(best_attribute)
tree.set_child(vi,
decision_tree(examples_i, attr, bin_targets_i))
return tree
def from_pre_post(pre: List[int], post: List[int]) -> TreeNode:
if not pre:
return None
root = TreeNode(pre[0])
# 左右边界判定
if len(pre) == 1:
return root
left_val = pre[1]
left_count = post.index(left_val) + 1
root.left = Construct.from_pre_post(pre[1:left_count + 1],
post[0:left_count])
root.right = Construct.from_pre_post(pre[left_count + 1:],
post[left_count:-1])
return root
def next(self):
self.current = self.q.get()
# self.print_status()
if self.world.is_solved(self.current.world_state):
return False
new_world_states = self.world.get_moves(self.current.world_state)
for state in new_world_states:
node = TreeNode(self.current, state)
self.q.put(node)
return True
def insert(self, data, root=None):
if self.root is None:
if root is None:
self.root = TreeNode(data)
return
else:
self.root = root
if root.data > data:
if root.left is None:
root.left = TreeNode(data)
else:
self.insert(data, root.left)
elif root.data < data:
if root.next is None:
root.next = TreeNode(data)
else:
self.insert(data, root.next)
else:
print("value already exists")
def create_by_pre_in_order(values1, values2):
if not values1 or not values2:
return None
root_value = values1[0]
i = 0
while values2[i] != root_value:
i += 1
left_values1 = values1[1:i + 1]
left_values2 = values2[:i]
right_values1 = values1[i + 1:]
right_values2 = values2[i + 1:]
node = TreeNode(value=root_value)
node.left = create_by_pre_in_order(left_values1, left_values2)
node.right = create_by_pre_in_order(right_values1, right_values2)
return node
def dir_counts_recurse(node: TreeNode, indent: int = 0) -> None:
""" Print all directories and node counts """
fc = len(node.files)
dc = len(node.dirs)
descendants = 0
for item in node.iter():
descendants += 1
print(
f"{dc: >4} {fc: >4} {descendants: >4} {' ' * indent}/{node.me.name}"
)
for d in node.dirs:
dir_counts_recurse(d, indent + 2)
class DynamicQuadTree:
def __init__(self,
centerPt=Point(0, 0, 'center'),
dimension=1,
max_points=1,
max_depth=4):
self.max_points = max_points
self.max_depth = max_depth
self.root = TreeNode(centerPt, dimension, max_points, max_depth, 0)
def __len__(self):
return len(self.root)
def __iter__(self):
return iter(self.root)
def __contains__(self, point):
return self.root.exist(point)
def insert(self, point):
return self.root.insert(point)
def remove(self, point):
return self.root.remove(point)
def update(self, new_point, old_point):
return self.root.update(new_point, old_point)
def query_range(self, boundary):
return self.root.query_range(boundary)
def knn(self, point, k):
return self.root.knn(point, k)
def add_node(self, node, data, index):
if data == -1:
return node
if node is None:
return TreeNode(data)
if index % 2 == 1:
node.set_left(self.add_node(node.get_left(), data, index))
else:
node.set_right(self.add_node(node.get_right(), data, index))
return node
def cross_validation_error(df_labels, N, df_data, segments):
error_list = {
'anger': 1,
'disgust': 2,
'fear': 3,
'happiness': 4,
'sadness': 5,
'surprise': 6
}
for e in cnst.EMOTIONS_LIST:
total_error_for_emotion = 0
error_list[1] = 2
print("/\ Decision tree building for emotion:", e)
binary_targets = util.filter_for_emotion(df_labels,
cnst.EMOTIONS_DICT[e])
for test_seg in segments:
test_df_data, test_df_targets, train_df_data, train_df_targets = util.divide_data(
test_seg, N, df_data, df_labels)
root = dtree.decision_tree(train_df_data, set(cnst.AU_INDICES),
train_df_targets)
TreeNode.plot_tree(root, e)
# root = decision_tree(df_data, set(cnst.AU_INDICES), binary_targets)
print("/\ Decision tree built.\n")
count = 0
# Counts number of incorrectly predicted tests
for i in test_df_data.index.values:
count += 1 - TreeNode.dfs2(root, test_df_data.loc[i],
test_df_targets.loc[i].at[0])
error = count / len(test_df_targets)
total_error_for_emotion += error
print()
total_error_for_emotion /= 10
error_list[e] = total_error_for_emotion
print()
print("Total error:", total_error_for_emotion)
print()
def buildSubTree(self, trainingExamples, currNode):
"""
method to build our tree - this build method uses OO concepts which we use during our prediction later on
"""
if self.attributesAndValues == []:
return currNode
divisionAttribute = self.getDivisionAttribute(trainingExamples)
if currNode == None: # rootptr
currNode = TreeNode(divisionAttribute.attrName)
else:
currNode.name = divisionAttribute.attrName
# divide up our data based on the attribute we got back
subLists = {}
for attrValue in divisionAttribute.attrValues:
subLists[attrValue] = []
for example in trainingExamples:
# if the example attribute matches our division attributes, add training example to correct sublist
for attribute in example.attributes:
if attribute.attrName == divisionAttribute.attrName:
subLists[attribute.attrValues[0]].append(example)
# check if any of the sublists would require us to return a leaf node
for subListKey in subLists:
childNode = TreeNode()
subList = subLists[subListKey]
if subList == []: # no training examples, default to most common target value
childNode.isLeaf = True
childNode.targetValue = "p"
currNode.childrenNodes[subListKey] = childNode
elif self.isLeafNode(subList):
childNode.isLeaf = True
childNode.targetValue = subList[0].targetValue
currNode.childrenNodes[subListKey] = childNode
else:
currNode.childrenNodes[subListKey] = childNode
# recursively build using each sublist
self.buildSubTree(subList, childNode)
#return the root node with everything built on
return currNode
def utSplitFeature():
"""
unit test for function [chooseSplitFeature]
"""
from data_provider import data_provider
attribute, dataset = data_provider('../test.arff')
root = TreeNode(dataset, attribute)
curTree = DecisionTree(root)
bestFeature = curTree.chooseSplitFeature(root)
try:
assert (bestFeature == 0)
print '[chooseSplitFeature] TEST PASS'
except AssertionError:
print '[chooseSplitFeature] TEST FAILED'
def utCreateTree():
"""
unit test for function [createTree]
examine the tree structure
compared graph with:
http://pages.cs.wisc.edu/~yliang/cs760_fall18/homework/hw2/diabetes/m=4.txt
"""
from data_provider import data_provider
attribute, dataset = data_provider('../diabetes_train.arff')
root = TreeNode(dataset, attribute)
curTree = DecisionTree(root)
curTree.createTree(root, 4)
curTree.printTree(root, 0)
print '---------------- please compare this graph with the url ------------------'
print 'http://pages.cs.wisc.edu/~yliang/cs760_fall18/homework/hw2/diabetes/m=4.txt'
def build_coinflip_tree(k, flips=''):
if k == 0:
return None
else:
return TreeNode(flips, build_coinflip_tree(k - 1, flips + 'H'), build_coinflip_tree(k - 1, flips + 'T'))
# node.left = build_coinflip_tree(node, k-1)
# node.right = build_coinflip_tree(node, k-1)
if __name__ == '__main__':
# build a tree
# 1
# / \
# 2 3
# /
# 4
t1 = TreeNode(1)
t1.left = TreeNode(2)
t1.right = TreeNode(3)
t1.left.left = TreeNode(4)
# build a tree
# 1
# / \
# 2 3
# / /
# 4 5
t2 = TreeNode(1)
t2.left = TreeNode(2)
t2.right = TreeNode(3)
t2.left.left = TreeNode(4)
t2.right.left = TreeNode(5)
ltail.left = None
ltail.right = None
return root, ltail
elif root.left is not None and root.right is not None:
lhead, ltail = gao(root.left)
rhead, rtail = gao(root.right)
root.left = None
root.right = lhead
lhead.left = None
ltail.left = None
ltail.right = rhead
rhead.left = None
rtail.left = None
rtail.right = None
return root, rtail
gao(root)
solver = Solution()
root5 = TreeNode()
root5.build_from_list([1,2,3,4])
solver.flatten(root5)
print(root5.to_dict())
root4 = TreeNode()
root4.build_from_list([2,1,4,None,None,3,5])
solver.flatten(root4)
print(root4.to_dict())
class Solution:
# @param root, a tree node
# @return a list of lists of integers
def levelOrderBottom(self, root):
if root is None:
return []
result = []
cur_level = [root]
next_level = []
while len(cur_level) > 0:
values = []
for node in cur_level:
values.append(node.val)
if node.left is not None:
next_level.append(node.left)
if node.right is not None:
next_level.append(node.right)
cur_level = next_level
next_level = []
result.append(values)
return result[::-1]
if __name__ == '__main__':
root = TreeNode()
root.build_from_list([3, 9, 20, None, None, 15, 7])
res = Solution().levelOrderBottom(root)
print(res)
# bfs
depth = 1
found_leaf = False
cur_depth = [root]
next_depth = []
while True:
next_depth = []
for node in cur_depth:
is_leaf = True
if node.left is not None:
next_depth.append(node.left)
is_leaf = False
if node.right is not None:
next_depth.append(node.right)
is_leaf = False
if is_leaf is True:
found_leaf = True
break
if found_leaf is True:
break
else:
depth += 1
cur_depth = next_depth
return depth
if __name__ == '__main__':
root = TreeNode()
root.build_from_list(list(range(10)))
print(Solution().minDepth(root))