binary search Algorithm
The binary search tree algorithm is a fundamental data structure and searching technique used in computer science and programming. It is an efficient and effective way of storing, retrieving, and organizing data in a hierarchical manner. A binary search tree (BST) is a binary tree data structure where each node has at most two child nodes, arranged in a way that the value of the node to the left is less than or equal to the parent node, and the value of the node to the right is greater than or equal to the parent node. This ordering property ensures that a search can be run in logarithmic time, making it a highly efficient method for searching and sorting large datasets.
To search for a specific value in a binary search tree, the algorithm starts at the root node and compares the desired value with the value of the current node. If the desired value is equal to the current node's value, the search is successful. If the desired value is less than the current node's value, the algorithm continues the search on the left subtree. Conversely, if the desired value is greater than the current node's value, the search continues on the right subtree. This process is repeated recursively until either the desired value is found or the subtree being searched is empty, indicating that the value is not in the tree. This efficient search process, which eliminates half of the remaining nodes with each comparison, is the key advantage of using a binary search tree algorithm in data management and search operations.
"""
This is pure Python implementation of binary search algorithms
For doctests run following command:
python -m doctest -v binary_search.py
or
python3 -m doctest -v binary_search.py
For manual testing run:
python binary_search.py
"""
import bisect
def bisect_left(sorted_collection, item, lo=0, hi=None):
"""
Locates the first element in a sorted array that is larger or equal to a given
value.
It has the same interface as
https://docs.python.org/3/library/bisect.html#bisect.bisect_left .
:param sorted_collection: some ascending sorted collection with comparable items
:param item: item to bisect
:param lo: lowest index to consider (as in sorted_collection[lo:hi])
:param hi: past the highest index to consider (as in sorted_collection[lo:hi])
:return: index i such that all values in sorted_collection[lo:i] are < item and all
values in sorted_collection[i:hi] are >= item.
Examples:
>>> bisect_left([0, 5, 7, 10, 15], 0)
0
>>> bisect_left([0, 5, 7, 10, 15], 6)
2
>>> bisect_left([0, 5, 7, 10, 15], 20)
5
>>> bisect_left([0, 5, 7, 10, 15], 15, 1, 3)
3
>>> bisect_left([0, 5, 7, 10, 15], 6, 2)
2
"""
if hi is None:
hi = len(sorted_collection)
while lo < hi:
mid = (lo + hi) // 2
if sorted_collection[mid] < item:
lo = mid + 1
else:
hi = mid
return lo
def bisect_right(sorted_collection, item, lo=0, hi=None):
"""
Locates the first element in a sorted array that is larger than a given value.
It has the same interface as
https://docs.python.org/3/library/bisect.html#bisect.bisect_right .
:param sorted_collection: some ascending sorted collection with comparable items
:param item: item to bisect
:param lo: lowest index to consider (as in sorted_collection[lo:hi])
:param hi: past the highest index to consider (as in sorted_collection[lo:hi])
:return: index i such that all values in sorted_collection[lo:i] are <= item and
all values in sorted_collection[i:hi] are > item.
Examples:
>>> bisect_right([0, 5, 7, 10, 15], 0)
1
>>> bisect_right([0, 5, 7, 10, 15], 15)
5
>>> bisect_right([0, 5, 7, 10, 15], 6)
2
>>> bisect_right([0, 5, 7, 10, 15], 15, 1, 3)
3
>>> bisect_right([0, 5, 7, 10, 15], 6, 2)
2
"""
if hi is None:
hi = len(sorted_collection)
while lo < hi:
mid = (lo + hi) // 2
if sorted_collection[mid] <= item:
lo = mid + 1
else:
hi = mid
return lo
def insort_left(sorted_collection, item, lo=0, hi=None):
"""
Inserts a given value into a sorted array before other values with the same value.
It has the same interface as
https://docs.python.org/3/library/bisect.html#bisect.insort_left .
:param sorted_collection: some ascending sorted collection with comparable items
:param item: item to insert
:param lo: lowest index to consider (as in sorted_collection[lo:hi])
:param hi: past the highest index to consider (as in sorted_collection[lo:hi])
Examples:
>>> sorted_collection = [0, 5, 7, 10, 15]
>>> insort_left(sorted_collection, 6)
>>> sorted_collection
[0, 5, 6, 7, 10, 15]
>>> sorted_collection = [(0, 0), (5, 5), (7, 7), (10, 10), (15, 15)]
>>> item = (5, 5)
>>> insort_left(sorted_collection, item)
>>> sorted_collection
[(0, 0), (5, 5), (5, 5), (7, 7), (10, 10), (15, 15)]
>>> item is sorted_collection[1]
True
>>> item is sorted_collection[2]
False
>>> sorted_collection = [0, 5, 7, 10, 15]
>>> insort_left(sorted_collection, 20)
>>> sorted_collection
[0, 5, 7, 10, 15, 20]
>>> sorted_collection = [0, 5, 7, 10, 15]
>>> insort_left(sorted_collection, 15, 1, 3)
>>> sorted_collection
[0, 5, 7, 15, 10, 15]
"""
sorted_collection.insert(bisect_left(sorted_collection, item, lo, hi), item)
def insort_right(sorted_collection, item, lo=0, hi=None):
"""
Inserts a given value into a sorted array after other values with the same value.
It has the same interface as
https://docs.python.org/3/library/bisect.html#bisect.insort_right .
:param sorted_collection: some ascending sorted collection with comparable items
:param item: item to insert
:param lo: lowest index to consider (as in sorted_collection[lo:hi])
:param hi: past the highest index to consider (as in sorted_collection[lo:hi])
Examples:
>>> sorted_collection = [0, 5, 7, 10, 15]
>>> insort_right(sorted_collection, 6)
>>> sorted_collection
[0, 5, 6, 7, 10, 15]
>>> sorted_collection = [(0, 0), (5, 5), (7, 7), (10, 10), (15, 15)]
>>> item = (5, 5)
>>> insort_right(sorted_collection, item)
>>> sorted_collection
[(0, 0), (5, 5), (5, 5), (7, 7), (10, 10), (15, 15)]
>>> item is sorted_collection[1]
False
>>> item is sorted_collection[2]
True
>>> sorted_collection = [0, 5, 7, 10, 15]
>>> insort_right(sorted_collection, 20)
>>> sorted_collection
[0, 5, 7, 10, 15, 20]
>>> sorted_collection = [0, 5, 7, 10, 15]
>>> insort_right(sorted_collection, 15, 1, 3)
>>> sorted_collection
[0, 5, 7, 15, 10, 15]
"""
sorted_collection.insert(bisect_right(sorted_collection, item, lo, hi), item)
def binary_search(sorted_collection, item):
"""Pure implementation of binary search algorithm in Python
Be careful collection must be ascending sorted, otherwise result will be
unpredictable
:param sorted_collection: some ascending sorted collection with comparable items
:param item: item value to search
:return: index of found item or None if item is not found
Examples:
>>> binary_search([0, 5, 7, 10, 15], 0)
0
>>> binary_search([0, 5, 7, 10, 15], 15)
4
>>> binary_search([0, 5, 7, 10, 15], 5)
1
>>> binary_search([0, 5, 7, 10, 15], 6)
"""
left = 0
right = len(sorted_collection) - 1
while left <= right:
midpoint = left + (right - left) // 2
current_item = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
right = midpoint - 1
else:
left = midpoint + 1
return None
def binary_search_std_lib(sorted_collection, item):
"""Pure implementation of binary search algorithm in Python using stdlib
Be careful collection must be ascending sorted, otherwise result will be
unpredictable
:param sorted_collection: some ascending sorted collection with comparable items
:param item: item value to search
:return: index of found item or None if item is not found
Examples:
>>> binary_search_std_lib([0, 5, 7, 10, 15], 0)
0
>>> binary_search_std_lib([0, 5, 7, 10, 15], 15)
4
>>> binary_search_std_lib([0, 5, 7, 10, 15], 5)
1
>>> binary_search_std_lib([0, 5, 7, 10, 15], 6)
"""
index = bisect.bisect_left(sorted_collection, item)
if index != len(sorted_collection) and sorted_collection[index] == item:
return index
return None
def binary_search_by_recursion(sorted_collection, item, left, right):
"""Pure implementation of binary search algorithm in Python by recursion
Be careful collection must be ascending sorted, otherwise result will be
unpredictable
First recursion should be started with left=0 and right=(len(sorted_collection)-1)
:param sorted_collection: some ascending sorted collection with comparable items
:param item: item value to search
:return: index of found item or None if item is not found
Examples:
>>> binary_search_std_lib([0, 5, 7, 10, 15], 0)
0
>>> binary_search_std_lib([0, 5, 7, 10, 15], 15)
4
>>> binary_search_std_lib([0, 5, 7, 10, 15], 5)
1
>>> binary_search_std_lib([0, 5, 7, 10, 15], 6)
"""
if right < left:
return None
midpoint = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(sorted_collection, item, left, midpoint - 1)
else:
return binary_search_by_recursion(sorted_collection, item, midpoint + 1, right)
def __assert_sorted(collection):
"""Check if collection is ascending sorted, if not - raises :py:class:`ValueError`
:param collection: collection
:return: True if collection is ascending sorted
:raise: :py:class:`ValueError` if collection is not ascending sorted
Examples:
>>> __assert_sorted([0, 1, 2, 4])
True
>>> __assert_sorted([10, -1, 5])
Traceback (most recent call last):
...
ValueError: Collection must be ascending sorted
"""
if collection != sorted(collection):
raise ValueError("Collection must be ascending sorted")
return True
if __name__ == "__main__":
import sys
user_input = input("Enter numbers separated by comma:\n").strip()
collection = [int(item) for item in user_input.split(",")]
try:
__assert_sorted(collection)
except ValueError:
sys.exit("Sequence must be ascending sorted to apply binary search")
target_input = input("Enter a single number to be found in the list:\n")
target = int(target_input)
result = binary_search(collection, target)
if result is not None:
print(f"{target} found at positions: {result}")
else:
print("Not found")
