Heaps Paper Karla T #43
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CheezItMan
reviewed
Jan 26, 2022
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| def heap_sort(list): | ||
| """ This method uses a heap to sort an array. | ||
| Time Complexity: ? | ||
| Space Complexity: ? | ||
| Time Complexity: O(n) | ||
| Space Complexity: O(1) | ||
| """ |
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👍 The time complexity is O(n log n) because you're adding n nodes to the heap and each addition takes log n time.
Similarly the space complexity is O(n) because you build a heap of n nodes.
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| def add(self, key, value = None): | ||
| """ This method adds a HeapNode instance to the heap | ||
| If value == None the new node's value should be set to key | ||
| Time Complexity: ? | ||
| Space Complexity: ? | ||
| Time Complexity: O(log n) | ||
| Space Complexity: O(log n) | ||
| """ |
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| def remove(self): | ||
| """ This method removes and returns an element from the heap | ||
| maintaining the heap structure | ||
| Time Complexity: ? | ||
| Space Complexity: ? | ||
| Time Complexity: O(log n) | ||
| Space Complexity: O(log n) | ||
| """ |
| @@ -49,7 +59,7 @@ def empty(self): | |||
| Time complexity: ? | |||
| Space complexity: ? | |||
| """ | |||
| pass | |||
| return len(self.store) == 0 | |||
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| def heap_up(self, index): | |||
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👍 Time/space complexity should be O(log n)
| min_child = right_child | ||
| else: min_child = left_child | ||
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| if self.store[index].key > self.store[min_child.key]: |
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Suggested change
| if self.store[index].key > self.store[min_child.key]: | |
| if self.store[index].key > self.store[min_child].key: |
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| if left_child < len(self.store): | ||
| if right_child < len(self.store): | ||
| if self.store[left_child.key] < self.store[right_child].key: |
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Suggested change
| if self.store[left_child.key] < self.store[right_child].key: | |
| if self.store[left_child].key < self.store[right_child].key: |
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Heaps Practice
Congratulations! You're submitting your assignment!
Comprehension Questions
heap_up&heap_downmethods useful? Why?