프로젝트 종료일 monte carlo simulation

sim.py

import random
import numpy as np

data = [
    [1, 100, 2, 10],
    [2, 108, 10, 9],
    [3, 109, 5, 8],
    [4, 106, 2, 11]
]

# Extract the relevant data
added_stories = [row[2] for row in data]
completed_stories = [row[3] for row in data]

def simulate_sprint(total_stories, remaining_stories):
    added = random.choice(added_stories)
    completed = random.choice(completed_stories)

    remaining_stories += added - completed
    total_stories += added
    return total_stories, remaining_stories

def monte_carlo_simulation(total_stories, remaining_stories, num_simulations=1000):
    sprints_to_completion = []

    for _ in range(num_simulations):
        sprints = 0
        remaining = remaining_stories
        while remaining > 0:
            total_stories, remaining = simulate_sprint(total_stories, remaining)
            sprints += 1
        sprints_to_completion.append(sprints)

    return sprints_to_completion

initial_total_stories = data[0][1]
initial_remaining_stories = initial_total_stories - data[0][3]
num_simulations = 50

sprints_to_completion = monte_carlo_simulation(initial_total_stories, initial_remaining_stories, num_simulations)

# Calculate average sprints to completion
average_sprints = np.mean(sprints_to_completion)
print(f"Average sprints to completion: {average_sprints}")

prob_completion = {i: sprints_to_completion.count(i) / num_simulations for i in set(sprints_to_completion)}

# Calculate cumulative probability
cumulative_prob = {}
running_total = 0
for k in sorted(prob_completion.keys()):
    running_total += prob_completion[k]
    cumulative_prob[k] = running_total

# Print cumulative probabilities
print("Cumulative probability of completing the project by a specific sprint:")
for k, v in cumulative_prob.items():
    print(f"By sprint {k}: {v * 100}%")