mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-11-24 05:21:09 +00:00
Merge pull request #97 from OmkarPathak/added_programs
Added Stack implementation and some traditional Stack problems
This commit is contained in:
commit
43b53f7751
40
data_structures/Graph/Graph.py
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40
data_structures/Graph/Graph.py
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# Author: OMKAR PATHAK
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# We can use Python's dictionary for constructing the graph
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class AdjacencyList(object):
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def __init__(self):
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self.List = {}
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def addEdge(self, fromVertex, toVertex):
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# check if vertex is already present
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if fromVertex in self.List.keys():
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self.List[fromVertex].append(toVertex)
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else:
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self.List[fromVertex] = [toVertex]
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def printList(self):
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for i in self.List:
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print(i,'->',' -> '.join([str(j) for j in self.List[i]]))
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if __name__ == '__main__':
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al = AdjacencyList()
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al.addEdge(0, 1)
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al.addEdge(0, 4)
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al.addEdge(4, 1)
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al.addEdge(4, 3)
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al.addEdge(1, 0)
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al.addEdge(1, 4)
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al.addEdge(1, 3)
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al.addEdge(1, 2)
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al.addEdge(2, 3)
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al.addEdge(3, 4)
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al.printList()
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# OUTPUT:
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# 0 -> 1 -> 4
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# 1 -> 0 -> 4 -> 3 -> 2
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# 2 -> 3
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# 3 -> 4
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# 4 -> 1 -> 3
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61
data_structures/Graph/P01_BreadthFirstSearch.py
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61
data_structures/Graph/P01_BreadthFirstSearch.py
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# Author: OMKAR PATHAK
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class Graph():
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def __init__(self):
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self.vertex = {}
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# for printing the Graph vertexes
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def printGraph(self):
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for i in self.vertex.keys():
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print(i,' -> ', ' -> '.join([str(j) for j in self.vertex[i]]))
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# for adding the edge beween two vertexes
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def addEdge(self, fromVertex, toVertex):
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# check if vertex is already present,
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if fromVertex in self.vertex.keys():
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self.vertex[fromVertex].append(toVertex)
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else:
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# else make a new vertex
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self.vertex[fromVertex] = [toVertex]
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def BFS(self, startVertex):
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# Take a list for stoting already visited vertexes
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visited = [False] * len(self.vertex)
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# create a list to store all the vertexes for BFS
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queue = []
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# mark the source node as visited and enqueue it
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visited[startVertex] = True
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queue.append(startVertex)
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while queue:
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startVertex = queue.pop(0)
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print(startVertex, end = ' ')
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# mark all adjacent nodes as visited and print them
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for i in self.vertex[startVertex]:
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if visited[i] == False:
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queue.append(i)
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visited[i] = True
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if __name__ == '__main__':
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g = Graph()
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g.addEdge(0, 1)
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g.addEdge(0, 2)
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g.addEdge(1, 2)
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g.addEdge(2, 0)
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g.addEdge(2, 3)
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g.addEdge(3, 3)
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g.printGraph()
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print('BFS:')
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g.BFS(2)
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# OUTPUT:
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# 0 -> 1 -> 2
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# 1 -> 2
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# 2 -> 0 -> 3
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# 3 -> 3
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# BFS:
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# 2 0 3 1
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61
data_structures/Graph/P02_DepthFirstSearch.py
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data_structures/Graph/P02_DepthFirstSearch.py
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# Author: OMKAR PATHAK
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class Graph():
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def __init__(self):
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self.vertex = {}
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# for printing the Graph vertexes
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def printGraph(self):
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print(self.vertex)
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for i in self.vertex.keys():
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print(i,' -> ', ' -> '.join([str(j) for j in self.vertex[i]]))
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# for adding the edge beween two vertexes
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def addEdge(self, fromVertex, toVertex):
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# check if vertex is already present,
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if fromVertex in self.vertex.keys():
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self.vertex[fromVertex].append(toVertex)
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else:
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# else make a new vertex
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self.vertex[fromVertex] = [toVertex]
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def DFS(self):
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# visited array for storing already visited nodes
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visited = [False] * len(self.vertex)
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# call the recursive helper function
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for i in range(len(self.vertex)):
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if visited[i] == False:
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self.DFSRec(i, visited)
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def DFSRec(self, startVertex, visited):
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# mark start vertex as visited
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visited[startVertex] = True
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print(startVertex, end = ' ')
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# Recur for all the vertexes that are adjacent to this node
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for i in self.vertex.keys():
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if visited[i] == False:
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self.DFSRec(i, visited)
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if __name__ == '__main__':
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g = Graph()
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g.addEdge(0, 1)
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g.addEdge(0, 2)
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g.addEdge(1, 2)
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g.addEdge(2, 0)
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g.addEdge(2, 3)
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g.addEdge(3, 3)
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g.printGraph()
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print('DFS:')
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g.DFS()
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# OUTPUT:
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# 0 -> 1 -> 2
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# 1 -> 2
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# 2 -> 0 -> 3
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# 3 -> 3
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# DFS:
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# 0 1 2 3
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27
data_structures/Stacks/Balanced_Parentheses.py
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data_structures/Stacks/Balanced_Parentheses.py
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# Author: OMKAR PATHAK
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import Stack
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def parseParenthesis(string):
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balanced = 1
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index = 0
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myStack = Stack.Stack(len(string))
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while (index < len(string)) and (balanced == 1):
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check = string[index]
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if check == '(':
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myStack.push(check)
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else:
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if myStack.isEmpty():
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balanced = 0
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else:
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myStack.pop()
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index += 1
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if balanced == 1 and myStack.isEmpty():
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return True
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else:
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return False
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if __name__ == '__main__':
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print(parseParenthesis('((()))')) # True
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print(parseParenthesis('((())')) # False
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48
data_structures/Stacks/Infix_To_Postfix_Conversion.py
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48
data_structures/Stacks/Infix_To_Postfix_Conversion.py
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# Author: OMKAR PATHAK
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import Stack
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def isOperand(char):
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return (ord(char) >= ord('a') and ord(char) <= ord('z')) or (ord(char) >= ord('A') and ord(char) <= ord('Z'))
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def precedence(char):
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if char == '+' or char == '-':
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return 1
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elif char == '*' or char == '/':
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return 2
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elif char == '^':
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return 3
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else:
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return -1
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def infixToPostfix(myExp, myStack):
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postFix = []
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for i in range(len(myExp)):
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if (isOperand(myExp[i])):
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postFix.append(myExp[i])
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elif(myExp[i] == '('):
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myStack.push(myExp[i])
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elif(myExp[i] == ')'):
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topOperator = myStack.pop()
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while(not myStack.isEmpty() and topOperator != '('):
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postFix.append(topOperator)
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topOperator = myStack.pop()
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else:
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while (not myStack.isEmpty()) and (precedence(myExp[i]) <= precedence(myStack.peek())):
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postFix.append(myStack.pop())
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myStack.push(myExp[i])
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while(not myStack.isEmpty()):
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postFix.append(myStack.pop())
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return ' '.join(postFix)
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if __name__ == '__main__':
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myExp = 'a+b*(c^d-e)^(f+g*h)-i'
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myExp = [i for i in myExp]
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print('Infix:',' '.join(myExp))
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myStack = Stack.Stack(len(myExp))
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print('Postfix:',infixToPostfix(myExp, myStack))
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# OUTPUT:
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# Infix: a + b * ( c ^ d - e ) ^ ( f + g * h ) - i
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# Postfix: a b c d ^ e - f g h * + ^ * + i -
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50
data_structures/Stacks/Stack.py
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50
data_structures/Stacks/Stack.py
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# Author: OMKAR PATHAK
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class Stack(object):
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def __init__(self, limit = 10):
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self.stack = []
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self.limit = limit
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# for printing the stack contents
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def __str__(self):
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return ' '.join([str(i) for i in self.stack])
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# for pushing an element on to the stack
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def push(self, data):
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if len(self.stack) >= self.limit:
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print('Stack Overflow')
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else:
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self.stack.append(data)
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# for popping the uppermost element
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def pop(self):
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if len(self.stack) <= 0:
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return -1
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else:
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return self.stack.pop()
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# for peeking the top-most element of the stack
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def peek(self):
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if len(self.stack) <= 0:
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return -1
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else:
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return self.stack[len(self.stack) - 1]
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# to check if stack is empty
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def isEmpty(self):
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return self.stack == []
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# for checking the size of stack
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def size(self):
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return len(self.stack)
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if __name__ == '__main__':
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myStack = Stack()
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for i in range(10):
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myStack.push(i)
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print(myStack)
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myStack.pop() # popping the top element
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print(myStack)
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myStack.peek() # printing the top element
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myStack.isEmpty()
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myStack.size()
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56
sorts/bucket_sort.py
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56
sorts/bucket_sort.py
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#!/usr/bin/env python
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# Author: OMKAR PATHAK
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# This program will illustrate how to implement bucket sort algorithm
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# Wikipedia says: Bucket sort, or bin sort, is a sorting algorithm that works by distributing the
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# elements of an array into a number of buckets. Each bucket is then sorted individually, either using
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# a different sorting algorithm, or by recursively applying the bucket sorting algorithm. It is a
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# distribution sort, and is a cousin of radix sort in the most to least significant digit flavour.
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# Bucket sort is a generalization of pigeonhole sort. Bucket sort can be implemented with comparisons
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# and therefore can also be considered a comparison sort algorithm. The computational complexity estimates
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# involve the number of buckets.
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# Time Complexity of Solution:
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# Best Case O(n); Average Case O(n); Worst Case O(n)
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from P26_InsertionSort import insertionSort
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import math
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DEFAULT_BUCKET_SIZE = 5
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def bucketSort(myList, bucketSize=DEFAULT_BUCKET_SIZE):
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if(len(myList) == 0):
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print('You don\'t have any elements in array!')
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minValue = myList[0]
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maxValue = myList[0]
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# For finding minimum and maximum values
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for i in range(0, len(myList)):
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if myList[i] < minValue:
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minValue = myList[i]
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elif myList[i] > maxValue:
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maxValue = myList[i]
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# Initialize buckets
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bucketCount = math.floor((maxValue - minValue) / bucketSize) + 1
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buckets = []
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for i in range(0, bucketCount):
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buckets.append([])
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# For putting values in buckets
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for i in range(0, len(myList)):
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buckets[math.floor((myList[i] - minValue) / bucketSize)].append(myList[i])
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# Sort buckets and place back into input array
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sortedArray = []
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for i in range(0, len(buckets)):
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insertionSort(buckets[i])
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for j in range(0, len(buckets[i])):
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sortedArray.append(buckets[i][j])
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return sortedArray
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if __name__ == '__main__':
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sortedArray = bucketSort([12, 23, 4, 5, 3, 2, 12, 81, 56, 95])
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print(sortedArray)
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