Table 1: Example of results summary (numbers are not realistic)AlgorithmIterations # Items Selected WeightObjectiveLocal Search (Best Improvement)Local Search with Random Restarts (k = 100)Local Search with Random walk (p = 0.25)3102951049971171212114721028732184etc.Knapsack Problem Definition Given n different items, where each item i has an assigned value (v¿) andweight (w), select a combination of the items to maximize the total value without exceeding the weightlimitations, W, of the knapsack.IMPORTANT!: When generating random problem instance set you must use the code provided and values:n = - 150; max weight of 2500; and, use a seed value (for the random number generator) of 51132023.1 Modify the completed Python Local Search code to implement Hill Climbing with Random Restarts.You may use Best Improvement or First Improvement (just clearly state your choice). Make sure toinclude the following:Make the number of random restarts, k, an easily modifiable parameter.• Keep track of the best solution found across all of the restarts.(b)Report results for at least two different values of k.(c)Include relevant excerpts of commented Python code to implement the solution.

Algorithm:Define a function knapsack_dp that takes a list of values, a list of weights, and the…

Answered: Algorithm Table 1: Example of results…

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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def solve_n_queens(n): def is_safe(board, row, col): # Verificar la columna for i in range(row): if board[i][col] == 1: return False # Verificar la diagonal izquierda i, j = row, col while i >= 0 and j >= 0: if board[i][j] == 1: return False i -= 1 j -= 1 # Verificar la diagonal derecha i, j = row, col while i >= 0 and j < n: if board[i][j] == 1: return False i -= 1 j += 1 return True def solve(board, row): if row >= n: solution = [] for r in board: solution.append("".join(['R' if x == 1 else '.' for x in r])) solutions.append(solution) return for col in range(n): if is_safe(board, row, col): board[row][col] = 1 solve(board, row + 1)…
Horizontal sequence :RIVL Vertical sequence:FMK Scoring rules: g/o = -3, g/e = -1, match or mismatch - from PAM250 substitution matrix below. NW algorithm. 1. Complete the scoring matrix. Scoring matrix with PAM250 scores: R I V L F M K 2. Set up, initialize and complete the NW matrix. 3. Retrace, align and score alignment(s). Use the arrows and circles for the matrix and path(s). R I V L F M K Align and score all optimal alignments here. PLZ the arrows and circles for the matrix and path(s) AND SHOW ALL possible Alignment
1 / 3 Exercises Exercise I: Consider the following rotation matrix R: The ratio between B and D is 2, and B > 0. (a) (b) Го A B R= C D -0.354 E 0.612 F 75% Exercise 2: Suppose that F is transformation matrix ▾ Find the values of A, B, C, D, E, F. Verify that the obtained R is a rotation matrix within some rounding errors. 0014 O One
Index (RSI) Algorithm Building on the momentum from the Moving Average algorithm, you’ll now try to develop a more sophisticated algorithm to outdo it! With this algorithm you will make trading decisions with Relative Strength Index, or RSI. How does this algorithm compare to your moving average algorithm? Algorithm Description The relative strength index (RSI) is defined as a momentum indicator that measures the magnitude of recent price changes to evaluate overbought or oversold conditions in the price of a stock. The index is calculated by the average gain and average loss over a rolling time window. ???????????=?????????/????????AverageGain=TotalGain/Lookback ?????Gains: When there was a price increase compared to the previous day, the gain is equal to that price gain value, otherwise set to zero. ?????????TotalGain: The sum of the gains over the lookback period. ????????Lookback: The number of days of previous data to look back.…
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Please solve max 30 minutes thank u Boolean Satisfaction Problem Boolean Satisfiability Problem (SAT) (https://en.wikipedia.org/wiki/Boolean_satisfiability_problem) is one of the most important problems in Computer Science. SAT is a problem that has NP-Complete complexity, where the only way to solve the problem is to try all the possibilities and check which one is correct. [LO 1, LO 2, LO 3 & LO 4,] Briefly explain how you use Strongly Connected Component (SCC) to solve the special case of the Boolean Satisfiability Problem, namely 2-SAT (https://en.wikipedia. org/wiki/2-SAT) . This solution has linear complexity. NOTE LO1: Explain fundamental concept of analysis arithms. LO2: Apply algorithm techniques and methods. LO3: Solve a problem using specific algorithm. LO4: Compare several algorithm design methods
def solve_n_queens(n): def is_safe(board, row, col): # Verificar la columna for i in range(row): if board[i][col] == 1: return False # Verificar la diagonal izquierda i, j = row, col while i >= 0 and j >= 0: if board[i][j] == 1: return False i -= 1 j -= 1 # Verificar la diagonal derecha i, j = row, col while i >= 0 and j < n: if board[i][j] == 1: return False i -= 1 j += 1 return True def solve(board, row): if row >= n: solution = [] for r in board: solution.append("".join(['Q' if x == 1 else '.' for x in r])) solutions.append(solution) return for col in range(n): if is_safe(board, row, col): board[row][col] = 1 solve(board, row + 1)…

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