Implementation of Algorithms Using Python

• Anagrams
  A word v is an anagram of a word v if a permutation of the letters transforming w into v exits .
  anagram(string st ) => list(anagrams)
  input => "below elbow car thing cried cat tac act"
  returns => [['below', 'elbow'], ['car'], ['thing'], ['cried'], ['cat', 'tac', 'act']]
  

• Linear Search
  linear_search(array,search_element) => Prints if search in array or not !
  Time Complexity : O(n) where n is the no of elements in array
  

• Binary Search
  binary_search(array,search) => prints if search in array or not !
  "Needs sorted array"
  Time Complexity : O(log n) where n is the no of elements in array
  

• Jump Search
  jump_search(array,search,jump=None) => prints if search in array or not !
  "Needs sorted array"
  default jump = sqroot(n)
  Time Complexity : O(√n) where n is the no of elements in array
  

• Kadane's Algorithm
  kadane(array) => returns three things best_sum , start_index and end_index of subarray
  Time Complexity : O(n) where n is the no of elements in the array
  

• Merge two Sorted Arrays
  merge_sorted_array(array1,array2) => returns one sorted array
  Time Complexity : O(n+m) where n, m are the no of elements in the array1 and array 2
  

• Selection Sort Algorithm
  Sorts the array into ascending or descending order
  selection_sort(array,descending = False) => return sorted array
  Time Complexity : O(n^2) where n is the no of elements in the array
  

• Insertion Sort Algorithm
  Sorts the array into ascending or descending order
  insertion_sort(array,descending = False) => return sorted array
  Time Complexity : O(n^2) where n is the no of elements in the array
  

• Bubble Sort Algorithm
  Sorts the array into ascending or descending order
  bubble_sort(array,descending = False) => return sorted array
  Time Complexity : O(n^2) where n is the no of elements in the array
  

• Dijkstras Algorithm
  Uses the greedy approach to find the shorted path from source to destination in a graph
  Time Complexity : O((V + E) log V) where V are the no of nodes in the graph and E are the edges of each Node.