# Uncommenting all 3 lines gives following Balanced Tree
# 5 C
# 2 L
# 1 L
# 3 R
# 4 R
# 8 R
# 6 L
# 7 R
# 9 R
#
# =============================================================================
# Starting from tn5 (which is root), print the Tree
print('Printing Binary Tree...')
priint_tree(tn5, 'C')
print()
# Algorithm to check if Tree is Balanced
############
# APPROACH#1
############
def getHeight(tn):
if tn == None:
return 0
# The maximum height from left branch and right branch plus one (for its own level)
# is considered a TreeNodes height
return max(getHeight(tn.left), getHeight(tn.right)) + 1
tn30 = TreeNode(30)
tn17 = TreeNode(17)
tn20.left = tn10
tn20.right = tn30
tn10.parent = tn20
tn10.left = tn05
tn10.right = tn15
tn30.parent = tn20
tn05.parent = tn10
tn05.left = tn03
tn05.right = tn07
tn15.parent = tn10
tn15.right = tn17
tn03.parent = tn05
tn07.parent = tn05
tn17.parent = tn15
priint_tree(tn20)
#print(depth(None))
priint_ca(common_ancestor(tn20, tn05, tn07))
# priint_ca(common_ancestor(tn20, tn07, tn30))
# priint_ca(common_ancestor(tn20, tn07, tn17))
# priint_ca(common_ancestor(tn20, tn07, tn10))
def createMinimalBST_(arr, start, end, side):
if end < start:
if DEBUG:
print(f'start {start}, end {end}, side {side}')
return None
# Find the mid-point
mid = (start + end) // 2
if DEBUG:
print(
f'start {start}, end {end}, side {side}, mid {mid}, value = {arr[mid]}'
)
mid_node = TreeNode(arr[mid])
# Invoke BST Creation for Left Sub Tree
mid_node.left = createMinimalBST_(arr, start, mid - 1, 'left')
# Invoke BST Creation for Right Sub Tree
mid_node.right = createMinimalBST_(arr, mid + 1, end, 'right')
return mid_node
if __name__ == "__main__":
#Test - Uncomment when needed to test
array = range(1, 10)
bst = createMinimalBST(array)
print("\nPrinting Binary Search Tree...\n")
priint_tree(bst, 'C')
binary_tree_to_array(node.right, arr)
def checkSorted(arr):
for index in range(len(arr)-1):
if arr[index] <= arr[index+1]:
continue
else:
return False
return True
if __name__ == "__main__":
# Test-1
print('Test-1 :: APPROACH#1')
bst1 = bstBuilder.createMinimalBST(range(1, 11))
priint_tree(bst1)
# Now pass on this bst to our new function binary_tree_to_array to create an array out of it.
arr = []
binary_tree_to_array(bst1, arr)
print(arr)
# Now let's check if the arr comes out as sorted
res = checkSorted(arr)
result = 'is' if res else 'is not'
print(f'Given Binary tree {result} Binary Search Tree.\n')
# Test-2
print('Test-2 :: APPROACH#1')
bst2 = bstBuilder.createMinimalBST([19, 20, 21, 22, 24, 24, 25, 25])
priint_tree(bst2)
# Now pass on this bst to our new function binary_tree_to_array to create an array out of it.
arr = []
tn4.parent = tn3
tn8.parent = tn5
tn8.left = tn6
tn8.right = tn9
tn6.parent = tn8
tn6.right = tn7
tn7.parent = tn6
tn9.parent = tn8
tn9.right = tn10
priint_tree(tn5)
# Let's find the next node for tn5
node = get_next_node(tn5)
name = node.name if node is not None else 'None'
print(f'\nNext node for {tn5.name} = {name}')
# Let's find the next node for tn1
node = get_next_node(tn1)
name = node.name if node is not None else 'None'
print(f'\nNext node for {tn1.name} = {name}')
# Let's find the next node for tn1
node = get_next_node(tn7)
name = node.name if node is not None else 'None'
print(f'\nNext node for {tn7.name} = {name}')