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