In a nutshell, this search algorithm takes advantage of a collection of elements that is already sorted by ignoring half of the elements after just one comparison.
Pseudo Code Algorithm:
parameter list: a list of sorted value parameter target: the value to search for
if the list has zero length, then return false
determine the slice point: if the list has an even number of elements, the slice point is the number of elements divided by two if the list has an odd number of elements, the slice point is the number of elements minus one divided by two
create an list of the elements from 0 to the slice point, not including the slice point, which is known as the "left half" create an list of the elements from the slice point to the end of the list which is known as the "right half"
if the target is less than the value in the original array at the slice point, then return the binary search of the "left half" and the target
if the target is greater than the value in the original array at the slice point, then return the binary search of the "right half" and the target
# Python 3 program for recursive binary search.
# Modifications needed for the older Python 2 are found in comments.
# Returns index of x in arr if present, else -1
def binary_search(arr, low, high, x):
# Check base case
if high >= low:
mid = (high + low) // 2
# If element is present at the middle itself
if arr[mid] == x:
return mid
# If element is smaller than mid, then it can only
# be present in left subarray
elif arr[mid] > x:
return binary_search(arr, low, mid - 1, x)
# Else the element can only be present in right subarray
else:
return binary_search(arr, mid + 1, high, x)
else:
# Element is not present in the array
return -1
# Test array
arr = [ 2, 3, 4, 10, 40 ]
x = 10
# Function call
result = binary_search(arr, 0, len(arr)-1, x)
if result != -1:
print("Element is present at index", str(result))
else:
print("Element is not present in array")
# Iterative Binary Search Function
# It returns index of x in given array arr if present,
# else returns -1
def binary_search(arr, x):
low = 0
high = len(arr) - 1
mid = 0
while low <= high:
mid = (high + low) // 2
# If x is greater, ignore left half
if arr[mid] < x:
low = mid + 1
# If x is smaller, ignore right half
elif arr[mid] > x:
high = mid - 1
# means x is present at mid
else:
return mid
# If we reach here, then the element was not present
return -1
# Test array
arr = [ 2, 3, 4, 10, 40 ]
x = 10
# Function call
result = binary_search(arr, x)
if result != -1:
print("Element is present at index", str(result))
else:
print("Element is not present in array")
# Uses python3
import random
"""You're going to write a binary search function.
You should use an iterative approach - meaning
using loops.
Your function should take two inputs:
a Python list to search through, and the value
you're searching for.
Assume the list only has distinct elements,
meaning there are no repeated values, and
elements are in a strictly increasing order.
Return the index of value, or -1 if the value
doesn't exist in the list."""
def binary_search(input_array, value):
test_array = input_array
current_index = len(input_array)//2
input_index = current_index
found_value = test_array[current_index]
while(len(test_array)>1 and found_value!=value):
if(found_value<value):
test_array = test_array[current_index:]
current_index = len(test_array)//2
input_index += current_index
found_value = input_array[input_index]
else:
test_array = test_array[0:current_index]
current_index = len(test_array)//2
# divmod needed to be used instead of round() since the behavior
# for .5 changed from rounding up to rounding down in Python 3
q, r = divmod(len(test_array), 2.0)
input_index = int(input_index - q - r)
found_value = input_array[input_index]
else:
if(found_value==value):
return input_index
return -1
def linear_search(a, x):
for i in range(len(a)):
if a[i] == x:
return i
return -1
# compare naive algorithm linear search vs. binary search results
def stress_test(n, m):
test_cond = True
while(test_cond):
a = []
for i in range(n):
a.append(random.randint(0, 10**9))
a.sort()
for i in range(m):
b = random.randint(0, n-1)
print([linear_search(a, a[b]), binary_search(a, a[b])])
# stops if the searches do not give identical answers
if(linear_search(a, a[b]) != binary_search(a, a[b])):
test_cond = False
print('broke here!')
break
stress_test(100, 100000)
#test_list = [1,3,9,11,15,19,29, 35, 36, 37]
#test_val1 = 25
#test_val2 = 15
#print(binary_search(test_list, test_val1))
#print(binary_search(test_list, test_val2))
#print(binary_search(test_list, 11))
# given array a and need to find value x
# left and right correspond to initial indices of array a bounding the search
# segment of array a above and below, respectively
def binary_search_recursive(a, x, left=0, right=(len(a)-1)):
"""Recursive Binary Search algorithm implemented using list indexing"""
index = (left+right)//2
if a[index]==x:
return index
elif x>(a[right]) or x<a[left]: # first case where x is not in the list!
return -1
elif left==right: # case where search is complete and no value x not found
return -1
elif left==right-1: # case where there are only two numbers left, check both!
left = right
return binary_search_recursive(a, x, left, right)
elif a[index]<x:
left = index
return binary_search_recursive(a, x, left, right)
elif a[index]>x:
right = index
return binary_search_recursive(a, x, left, right)
"""
Given an array where elements are sorted in ascending order,
convert it to a height balanced BST.
"""
class TreeNode(object):
def __init__(self, x):
self.val = x
self.left = None
self.right = None
def array_to_bst(nums):
if not nums:
return None
mid = len(nums) // 2
node = TreeNode(nums[mid])
node.left = array_to_bst(nums[:mid])
node.right = array_to_bst(nums[mid + 1 :])
return node
"""
Implement Binary Search Tree. It has method:
1. Insert
2. Search
3. Size
4. Traversal (Preorder, Inorder, Postorder)
"""
import unittest
class Node(object):
def __init__(self, data):
self.data = data
self.left = None
self.right = None
class BST(object):
def __init__(self):
self.root = None
def get_root(self):
return self.root
"""
Get the number of elements
Using recursion. Complexity O(logN)
"""
def size(self):
return self.recur_size(self.root)
def recur_size(self, root):
if root is None:
return 0
else:
return 1 + self.recur_size(root.left) + self.recur_size(root.right)
"""
Search data in bst
Using recursion. Complexity O(logN)
"""
def search(self, data):
return self.recur_search(self.root, data)
def recur_search(self, root, data):
if root is None:
return False
if root.data == data:
return True
elif data > root.data: # Go to right root
return self.recur_search(root.right, data)
else: # Go to left root
return self.recur_search(root.left, data)
"""
Insert data in bst
Using recursion. Complexity O(logN)
"""
def insert(self, data):
if self.root:
return self.recur_insert(self.root, data)
else:
self.root = Node(data)
return True
def recur_insert(self, root, data):
if root.data == data: # The data is already there
return False
elif data < root.data: # Go to left root
if root.left: # If left root is a node
return self.recur_insert(root.left, data)
else: # left root is a None
root.left = Node(data)
return True
else: # Go to right root
if root.right: # If right root is a node
return self.recur_insert(root.right, data)
else:
root.right = Node(data)
return True
"""
Preorder, Postorder, Inorder traversal bst
"""
def preorder(self, root):
if root:
print(str(root.data), end=" ")
self.preorder(root.left)
self.preorder(root.right)
def inorder(self, root):
if root:
self.inorder(root.left)
print(str(root.data), end=" ")
self.inorder(root.right)
def postorder(self, root):
if root:
self.postorder(root.left)
self.postorder(root.right)
print(str(root.data), end=" ")
"""
The tree is created for testing:
10
/ \
6 15
/ \ / \
4 9 12 24
/ / \
7 20 30
/
18
"""
class TestSuite(unittest.TestCase):
def setUp(self):
self.tree = BST()
self.tree.insert(10)
self.tree.insert(15)
self.tree.insert(6)
self.tree.insert(4)
self.tree.insert(9)
self.tree.insert(12)
self.tree.insert(24)
self.tree.insert(7)
self.tree.insert(20)
self.tree.insert(30)
self.tree.insert(18)
def test_search(self):
self.assertTrue(self.tree.search(24))
self.assertFalse(self.tree.search(50))
def test_size(self):
self.assertEqual(11, self.tree.size())
if __name__ == "__main__":
unittest.main()
"""
Given a root node reference of a BST and a key, delete the node with the given key in the BST. Return the root node reference (possibly updated) of the BST.
Basically, the deletion can be divided into two stages:
Search for a node to remove.
If the node is found, delete the node.
Note: Time complexity should be O(height of tree).
Example:
root = [5,3,6,2,4,null,7]
key = 3
5
/ \
3 6
/ \ \
2 4 7
Given key to delete is 3. So we find the node with value 3 and delete it.
One valid answer is [5,4,6,2,null,null,7], shown in the following BST.
5
/ \
4 6
/ \
2 7
Another valid answer is [5,2,6,null,4,null,7].
5
/ \
2 6
\ \
4 7
"""
class Solution(object):
def delete_node(self, root, key):
"""
:type root: TreeNode
:type key: int
:rtype: TreeNode
"""
if not root:
return None
if root.val == key:
if root.left:
# Find the right most leaf of the left sub-tree
left_right_most = root.left
while left_right_most.right:
left_right_most = left_right_most.right
# Attach right child to the right of that leaf
left_right_most.right = root.right
# Return left child instead of root, a.k.a delete root
return root.left
else:
return root.right
# If left or right child got deleted, the returned root is the child of the deleted node.
elif root.val > key:
root.left = self.deleteNode(root.left, key)
else:
root.right = self.deleteNode(root.right, key)
return root
def binary_search(arr, x):
start= 0
end = len(arr) - 1
mid = 0
while start<= end:
mid = (start+ end) // 2
# If x is greater, search in right array
if arr[mid] < x:
start = mid + 1
# If x is smaller, search in left array
elif arr[mid] > x:
end= mid - 1
# if x is present at mid
else:
return mid
# when we reach at the end of array, then the element was not present
return -1
arr = [ ]
n=int(input("Enter size of array : "))
print("Enter array elements : ")
for i in range(n):
e=int(input())
arr.append(e)
x = int(input("Enter element to search "))
ans = binary_search(arr, x)
if(ans==-1):
print("Element not found")
else:
print("Element found at ",ans)
