Learning Bagging Algorithms

import random

def max_value_recursive(weights, values, max_weight, i):
    if len(weights) == i:
        return 0
    if max_weight - weights[i] <= 0:
        return 0
    value_not_pick = max_value_recursive(weights, values, max_weight, i + 1)
    value_pick = max_value_recursive(weights, values, max_weight - weights[i], i + 1)
    return max(value_not_pick, values[i] + value_pick)
    
def max_value(weights, values, max_weight):
    return max_value_recursive(weights, values, max_weight, 0)

def max_value_dp(weights, values, max_weight):
    dp = [[0 for i in range(max_weight)] for i in range(len(weights))]
    i = len(weights) - 1
    for j in range(weights[i], max_weight):
        dp[i][j] = values[i]
    for i in range(len(weights) - 2, -1, -1):
        for j in range(weights[i], max_weight):
            dp[i][j] = max(dp[i + 1][j], dp[i + 1][j - weights[i]] + values[i])
    return dp[0][max_weight - 1]

N = 25
W = 50
weights = [random.randint(0, 9) for i in range(N)]
values = [random.randint(0, 9) for i in range(N)]
print(weights)
print(values)
print(max_value(weights, values, W))
print(max_value_dp(weights, values, W))