Master-Level Programming Assignment Questions and Solutions by Our Experts

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Sample Master-Level Programming Assignment Questions and Solutions

To demonstrate the quality of our solutions, we have provided two sample programming assignment questions along with their expert-crafted solutions.

Question 1: Data Structure Optimization (C++)

Problem Statement: You are given a large dataset containing records of transactions. Your task is to implement an efficient data structure to store these records, allowing quick insertion and retrieval. The program should support the following functionalities:

1. Insert a new transaction (Transaction ID, Amount, Date)

2. Retrieve a transaction by ID

3. Display all transactions sorted by date

Implement the solution using C++ and ensure the retrieval operations are optimized for speed.

Solution:

#include <iostream>
#include <map>
#include <vector>
#include <algorithm>

using namespace std;

struct Transaction {
    int id;
    double amount;
    string date;
};

class TransactionManager {
private:
    map<int, Transaction> transactionMap;
    vector<Transaction> transactions;

public:
    void insertTransaction(int id, double amount, string date) {
        Transaction newTransaction = {id, amount, date};
        transactionMap[id] = newTransaction;
        transactions.push_back(newTransaction);
    }

    Transaction* getTransaction(int id) {
        if (transactionMap.find(id) != transactionMap.end()) {
            return &transactionMap[id];
        }
        return nullptr;
    }

    void displayTransactionsSortedByDate() {
        sort(transactions.begin(), transactions.end(), [](Transaction a, Transaction b) {
            return a.date < b.date;
        });
        for (auto& transaction : transactions) {
            cout << "ID: " << transaction.id << ", Amount: " << transaction.amount << ", Date: " << transaction.date << endl;
        }
    }
};

int main() {
    TransactionManager tm;
    tm.insertTransaction(101, 500.75, "2024-04-01");
    tm.insertTransaction(102, 1200.50, "2024-03-15");
    tm.insertTransaction(103, 800.30, "2024-05-10");

    cout << "All Transactions Sorted by Date:" << endl;
    tm.displayTransactionsSortedByDate();
    
    int searchId = 102;
    Transaction* t = tm.getTransaction(searchId);
    if (t) {
        cout << "Transaction Found - ID: " << t->id << ", Amount: " << t->amount << ", Date: " << t->date << endl;
    } else {
        cout << "Transaction Not Found" << endl;
    }
    return 0;
}

Explanation:

• We use a map for quick transaction retrieval by ID (O(1) complexity).

• A vector is used to store transactions for sorting and display.

• The displayTransactionsSortedByDate method ensures efficient sorting using std::sort.

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Question 2: Machine Learning Model Implementation (Python)

Problem Statement: Implement a simple Linear Regression model from scratch using Python. The model should:

1. Fit a line to a given dataset

2. Predict output values for new inputs

3. Calculate the Mean Squared Error (MSE) for evaluation

Solution:

import numpy as np

class LinearRegression:
    def __init__(self):
        self.slope = 0
        self.intercept = 0
    
    def fit(self, X, y):
        X_mean = np.mean(X)
        y_mean = np.mean(y)
        self.slope = np.sum((X - X_mean) * (y - y_mean)) / np.sum((X - X_mean) ** 2)
        self.intercept = y_mean - self.slope * X_mean
    
    def predict(self, X):
        return self.slope * X + self.intercept
    
    def mse(self, X, y):
        predictions = self.predict(X)
        return np.mean((y - predictions) ** 2)

# Example Usage
X = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 5, 4, 5])

model = LinearRegression()
model.fit(X, y)
print(f"Slope: {model.slope}, Intercept: {model.intercept}")
print(f"Predicted values: {model.predict(X)}")
print(f"Mean Squared Error: {model.mse(X, y)}")

Explanation:

• This implementation calculates the slope and intercept using the formula for linear regression.

• The predict function computes predicted values based on the learned parameters.

• The mse function evaluates the model’s accuracy by calculating the Mean Squared Error (MSE).

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