问题描述
我在遵循所有montecarlo教程时遇到了麻烦，因为它们似乎已开始发展为我所遵循。我有几个月的python经验。到目前为止，我还无法找到任何基础知识。任何与montecarlo和python的基础知识有关的提示或链接都很棒。
我正在寻找一个简单的蒙特卡洛模拟。一种模拟将涉及从分布中选择事件1的随机结果，然后分配分数。然后是事件2的另一个随机结果，依此类推。因此，运行1次SIM卡会给我3分。在此先感谢您的任何帮助
我将尝试在更大的数据集上朗读sim约10000次。我应该在numpy中尝试这样做吗？
概率分布
outcome 1 outcome 2 outcome 3 outcome 4 outcome 5
event 1 0.1 0.2 0.5 0.6 1
event 2 0.1 0.3 0.4 0.7 1
event 3 0.1 0.5 0.6 0.7 1
得分
outcome 1 outcome 2 outcome 3 outcome 4 outcome 5
score 1 100 400 200 600 100
score 2 200 600 300 700 500
score 3 400 100 500 300 200
解决方法
您正在寻找什么吗？
import numpy as np
if __name__ == "__main__":
n_events = 3
n_scores = 3
n_outcomes = 5
events = np.random.random((n_events,n_outcomes))
scores = np.random.random((n_scores,n_outcomes))
print("events",events.shape,events,"\n")
print("scores",scores.shape,scores,"\n")
run_scores = np.zeros(n_events)
for run_idx in range(n_events):
selected_idx = np.random.choice(n_outcomes,1)
run_scores[run_idx] = scores[run_idx][selected_idx]
print("run_scores",run_scores.shape,run_scores)
在处理随机仿真问题时，python中的random模块特别有用。您可以使用random.choices()函数来模拟上述实验。
通过choices()函数，您可以指定结果和相应的权重以及要运行的模拟次数。该函数返回结果列表。我们可以使用collections.Counter对象将结果制成表格并以字典形式获取。
from random import choices
from collections import Counter
"""
outcome 1 outcome 2 outcome 3 outcome 4 outcome 5
event 1 0.1 0.2 0.5 0.6 1
"""
# outcomes and weights for event 1 as per the probability distribution you provided
outcomes = [1,2,3,4,5]
cumulative_weights = [0.1,0.2,0.5,0.6,1]
num_simulations = 10000
scores_list = choices(population=outcomes,cum_weights=cumulative_weights,k=num_simulations)
# Use a Counter to tabulate our results. This will give us a dict-like object
scores = Counter(scores_list)
for outcome in outcomes:
print("Outcome {},Score: {}".format(outcome,scores[outcome]))
# Output ->>
# Outcome 1,Score: 1022
# Outcome 2,Score: 1009
# Outcome 3,Score: 2983
# Outcome 4,Score: 1045
# Outcome 5,Score: 3941
上面的代码示例演示了如何对一个事件运行10,000个模拟。根据需要使用多组结果/权重。