Unused left.push(a[0]); left.push(-right.top()); right.pop(); left.push(a[i]); right.push(a[i]); } else if (left.size() > right.size() + 1) { if (left.size() < right.size()) { } else { } return median; int median - } left. top(); right.push(-left.top()); left.pop(); for (int i 1; i #include #include #include using namespace std; int get median (vector a, int n) { priority_queue left; priority_queue right; } int main() { vector a; int i; cin >> i; while (1) { a.push_back(i); cin >> i; } int median= get_median (a, a.size()); sort(a.begin(), a.end()); cout << "Sorted: ("; for (int x = 0; x< a.size(); x++) { Load default tem cout << a[x]; if (x = a.size() - 1) cout << ", } cout << ")" << endl; ** cout << "Median: << median << endl; return 0;
Unused left.push(a[0]); left.push(-right.top()); right.pop(); left.push(a[i]); right.push(a[i]); } else if (left.size() > right.size() + 1) { if (left.size() < right.size()) { } else { } return median; int median - } left. top(); right.push(-left.top()); left.pop(); for (int i 1; i #include #include #include using namespace std; int get median (vector a, int n) { priority_queue left; priority_queue right; } int main() { vector a; int i; cin >> i; while (1) { a.push_back(i); cin >> i; } int median= get_median (a, a.size()); sort(a.begin(), a.end()); cout << "Sorted: ("; for (int x = 0; x< a.size(); x++) { Load default tem cout << a[x]; if (x = a.size() - 1) cout << ", } cout << ")" << endl; ** cout << "Median: << median << endl; return 0;
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
Related questions
Question
you can only use the lines from the left side and add to right side.
Transcribed Image Text:You can find the median element of a sequence by using two priority queues, `left` and `right`, where `right` is a min-heap. Insert each element into `left` if it is smaller than the largest element of `left`, or into `right` otherwise. Then rebalance the priority queues so that their sizes differ by at most 1.
Rearrange the following lines of code to implement this algorithm. Instead of a min-heap, use a `priority_queue<int>` that holds negated values.
![The image presents a C++ code snippet focused on calculating the median of an array using two separate sections. Here's an explanation of each part:
### Unused Section
This section contains code that is not currently being executed in the program:
- **`left.push(a[0]);`**
- **`left.push(-right.top()); right.pop();`**
- **`left.push(a[i]);`**
- **`right.push(-a[i]);`**
These lines indicate manipulation of two data structures, likely priority queues or heaps, to manage elements for median calculation.
The remaining pieces display conditional structures and balancing operations among the left and right data structures:
- **If-statements** (balance the sizes of left and right queues)
A loop and an additional if-condition seem designed to adjust or compute the median from these structures:
- **`for (int i = 1; i < n; i++)`**
- **`if (a[i] <= left.top())`**
### `main.cpp` File
This is the main implementation file:
- **Headers:**
- `#include <iostream>`
- `#include <vector>`
- `#include <queue>`
- `#include <bits/stdc++.h>`
- **Namespaces:**
- `using namespace std;`
#### Functions:
- **`int get_median(vector<int> a, int n)`**
- Uses two priority queues (`left`, `right`)
- Likely balances these to get the median
#### Main Function:
- **Initializes a vector `a` and an integer `i`.**
- **Uses `cin` to read input into `i`.**
- **Populates the vector `a` using a loop.**
- **Calculates the median by calling `get_median`.**
- **Sorts the vector and outputs the sorted elements.**
- **Outputs the median.**
### Explanation:
- **Balancing Heaps:** This approach uses two priority queues (or heaps) to maintain the lower and upper halves of the data.
- **Computing Median:** Balancing ensures the median can be determined from the top elements of these structures.
- **Output:** The sorted elements and median are printed.
This code demonstrates an efficient way to calculate the median of a dynamically growing dataset.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F6a04cd63-2ba9-4f83-bc88-6194bfc3fa72%2F1c84a42a-d32f-4f06-b919-194385bf3ee2%2F7v672cm_processed.jpeg&w=3840&q=75)
Transcribed Image Text:The image presents a C++ code snippet focused on calculating the median of an array using two separate sections. Here's an explanation of each part:
### Unused Section
This section contains code that is not currently being executed in the program:
- **`left.push(a[0]);`**
- **`left.push(-right.top()); right.pop();`**
- **`left.push(a[i]);`**
- **`right.push(-a[i]);`**
These lines indicate manipulation of two data structures, likely priority queues or heaps, to manage elements for median calculation.
The remaining pieces display conditional structures and balancing operations among the left and right data structures:
- **If-statements** (balance the sizes of left and right queues)
A loop and an additional if-condition seem designed to adjust or compute the median from these structures:
- **`for (int i = 1; i < n; i++)`**
- **`if (a[i] <= left.top())`**
### `main.cpp` File
This is the main implementation file:
- **Headers:**
- `#include <iostream>`
- `#include <vector>`
- `#include <queue>`
- `#include <bits/stdc++.h>`
- **Namespaces:**
- `using namespace std;`
#### Functions:
- **`int get_median(vector<int> a, int n)`**
- Uses two priority queues (`left`, `right`)
- Likely balances these to get the median
#### Main Function:
- **Initializes a vector `a` and an integer `i`.**
- **Uses `cin` to read input into `i`.**
- **Populates the vector `a` using a loop.**
- **Calculates the median by calling `get_median`.**
- **Sorts the vector and outputs the sorted elements.**
- **Outputs the median.**
### Explanation:
- **Balancing Heaps:** This approach uses two priority queues (or heaps) to maintain the lower and upper halves of the data.
- **Computing Median:** Balancing ensures the median can be determined from the top elements of these structures.
- **Output:** The sorted elements and median are printed.
This code demonstrates an efficient way to calculate the median of a dynamically growing dataset.
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