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171 lines
4.4 KiB
Python
171 lines
4.4 KiB
Python
# Min heap data structure
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# with decrease key functionality - in O(log(n)) time
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class Node:
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def __init__(self, name, val):
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self.name = name
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self.val = val
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def __str__(self):
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return f"{self.__class__.__name__}({self.name}, {self.val})"
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def __lt__(self, other):
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return self.val < other.val
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class MinHeap:
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"""
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>>> r = Node("R", -1)
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>>> b = Node("B", 6)
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>>> a = Node("A", 3)
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>>> x = Node("X", 1)
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>>> e = Node("E", 4)
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>>> print(b)
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Node(B, 6)
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>>> myMinHeap = MinHeap([r, b, a, x, e])
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>>> myMinHeap.decrease_key(b, -17)
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>>> print(b)
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Node(B, -17)
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>>> myMinHeap["B"]
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-17
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"""
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def __init__(self, array):
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self.idx_of_element = {}
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self.heap_dict = {}
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self.heap = self.build_heap(array)
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def __getitem__(self, key):
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return self.get_value(key)
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def get_parent_idx(self, idx):
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return (idx - 1) // 2
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def get_left_child_idx(self, idx):
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return idx * 2 + 1
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def get_right_child_idx(self, idx):
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return idx * 2 + 2
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def get_value(self, key):
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return self.heap_dict[key]
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def build_heap(self, array):
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last_idx = len(array) - 1
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start_from = self.get_parent_idx(last_idx)
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for idx, i in enumerate(array):
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self.idx_of_element[i] = idx
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self.heap_dict[i.name] = i.val
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for i in range(start_from, -1, -1):
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self.sift_down(i, array)
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return array
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# this is min-heapify method
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def sift_down(self, idx, array):
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while True:
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left = self.get_left_child_idx(idx)
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right = self.get_right_child_idx(idx)
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smallest = idx
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if left < len(array) and array[left] < array[idx]:
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smallest = left
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if right < len(array) and array[right] < array[smallest]:
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smallest = right
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if smallest != idx:
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array[idx], array[smallest] = array[smallest], array[idx]
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(
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self.idx_of_element[array[idx]],
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self.idx_of_element[array[smallest]],
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) = (
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self.idx_of_element[array[smallest]],
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self.idx_of_element[array[idx]],
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)
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idx = smallest
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else:
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break
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def sift_up(self, idx):
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p = self.get_parent_idx(idx)
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while p >= 0 and self.heap[p] > self.heap[idx]:
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self.heap[p], self.heap[idx] = self.heap[idx], self.heap[p]
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self.idx_of_element[self.heap[p]], self.idx_of_element[self.heap[idx]] = (
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self.idx_of_element[self.heap[idx]],
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self.idx_of_element[self.heap[p]],
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)
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idx = p
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p = self.get_parent_idx(idx)
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def peek(self):
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return self.heap[0]
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def remove(self):
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self.heap[0], self.heap[-1] = self.heap[-1], self.heap[0]
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self.idx_of_element[self.heap[0]], self.idx_of_element[self.heap[-1]] = (
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self.idx_of_element[self.heap[-1]],
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self.idx_of_element[self.heap[0]],
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)
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x = self.heap.pop()
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del self.idx_of_element[x]
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self.sift_down(0, self.heap)
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return x
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def insert(self, node):
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self.heap.append(node)
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self.idx_of_element[node] = len(self.heap) - 1
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self.heap_dict[node.name] = node.val
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self.sift_up(len(self.heap) - 1)
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def is_empty(self):
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return len(self.heap) == 0
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def decrease_key(self, node, new_value):
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assert (
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self.heap[self.idx_of_element[node]].val > new_value
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), "newValue must be less that current value"
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node.val = new_value
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self.heap_dict[node.name] = new_value
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self.sift_up(self.idx_of_element[node])
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# USAGE
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r = Node("R", -1)
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b = Node("B", 6)
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a = Node("A", 3)
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x = Node("X", 1)
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e = Node("E", 4)
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# Use one of these two ways to generate Min-Heap
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# Generating Min-Heap from array
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my_min_heap = MinHeap([r, b, a, x, e])
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# Generating Min-Heap by Insert method
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# myMinHeap.insert(a)
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# myMinHeap.insert(b)
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# myMinHeap.insert(x)
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# myMinHeap.insert(r)
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# myMinHeap.insert(e)
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# Before
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print("Min Heap - before decrease key")
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for i in my_min_heap.heap:
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print(i)
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print("Min Heap - After decrease key of node [B -> -17]")
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my_min_heap.decrease_key(b, -17)
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# After
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for i in my_min_heap.heap:
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print(i)
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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