什么是Q-Q图，python画Q-Q图代码

Q-Q图是Quantile-Quantile图的简称，用于比较两个数据分布是否相似。其基本思路是将待比较分布中的分位数值按大小顺序排序，计算它们的累积分布函数，然后与一个已知分布（通常是正态分布）做比较，把两个分布函数上对应的点画在一张图上。如果它们重合，则两个分布相同；否则，它们可能不同。

以下是用Python绘制Q-Q图的示例代码：

import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt

# 生成样本数据
np.random.seed(101)
sample = np.random.normal(loc=0, scale=1, size=100)

# 用statsmodels库计算样本分布的QQ图
sm.qqplot(sample, line='s')

# 绘制图表
plt.title("Q-Q plot")
plt.xlabel("Theoretical quantiles")
plt.ylabel("Sample quantiles")
plt.show()

这段代码首先使用 NumPy 生成一个100个正态分布的样本数据，然后使用 statsmodels 库的 qqplot 函数绘制 Q-Q 图。line='s' 选项表示在图表中绘制一条45度的对角线，以便比较两个分布之间的差异。最后调用 Matplotlib 的 plot 函数显示图表。

相关问题

pso-Q-learning多分类python代码

下面是一个简单的PSO-Q-learning多分类的Python代码示例，仅供参考：

import numpy as np
import random

# 定义状态和动作
states = np.random.rand(100, 10)
actions = np.arange(5)

# 初始化Q值函数和粒子群
Q = np.zeros((100, 5))
particles = np.random.rand(10, 5, 15)

# 定义PSO参数
w = 0.8
c1 = 0.2
c2 = 0.6

# 定义Q-learning参数
alpha = 0.5
gamma = 0.9

# 计算粒子适应度
def fitness(particle):
    scores = []
    for i in range(10):
        for j in range(5):
            state = states[i]
            action = actions[j]
            next_state = states[np.random.choice(100)]
            reward = np.dot(state, particle[j*3:j*3+3])
            next_action = np.argmax(Q[next_state])
            Q[state, action] = (1-alpha)*Q[state, action] + alpha*(reward+gamma*Q[next_state, next_action])
        scores.append(np.mean(np.argmax(Q, axis=1) == i))
    return np.mean(scores)

# 更新粒子速度和位置
for i in range(100):
    for j in range(5):
        particle = particles[i,j]
        velocity = particles[i,j,5:10]
        pbest_particle = particles[i,j,10:]
        gbest_particle = particles[i,j+1,10:]
        fitness_p = fitness(particle)
        fitness_pbest = fitness(pbest_particle)
        fitness_gbest = fitness(gbest_particle)
        velocity = w*velocity + c1*random.random()*(pbest_particle-particle) + c2*random.random()*(gbest_particle-particle)
        particle = particle + velocity
        particles[i,j] = particle
        if fitness(particle) > fitness_pbest:
            particles[i,j,10:] = particle
        if fitness(particle) > fitness_gbest:
            particles[i,j+1,10:] = particle

# 更新Q值函数
for i in range(100):
    for j in range(5):
        state = states[i]
        action = actions[j]
        next_state = states[np.random.choice(100)]
        reward = np.dot(state, particles[i,j*3:j*3+3])
        next_action = np.argmax(Q[next_state])
        Q[state, action] = (1-alpha)*Q[state, action] + alpha*(reward+gamma*Q[next_state, next_action])

需要注意的是，上述代码仅作为一个简单的示例，实际应用中需要根据具体问题进行调整和优化。

q-learning实现cliffwalking-v0代码python

以下是使用Q-learning算法实现Cliff Walking游戏的Python代码：

import numpy as np
import gym

env = gym.make("CliffWalking-v0")

num_states = env.observation_space.n
num_actions = env.action_space.n
Q = np.zeros((num_states, num_actions))

num_episodes = 5000
epsilon = 0.1
alpha = 0.5
gamma = 0.99

for i in range(num_episodes):
    observation = env.reset()
    done = False
    while not done:
        if np.random.uniform() < epsilon:
            action = env.action_space.sample()
        else:
            action = np.argmax(Q[observation, :])
        next_observation, reward, done, info = env.step(action)
        Q[observation, action] = Q[observation, action] + alpha * (reward + gamma * np.max(Q[next_observation, :]) - Q[observation, action])
        observation = next_observation

print("Q-table:")
print(Q)

def play(Q):
    observation = env.reset()
    done = False
    while not done:
        env.render()
        action = np.argmax(Q[observation, :])
        observation, reward, done, info = env.step(action)
    env.render()
    print("Reward:", reward)

play(Q)

请注意，这段代码使用OpenAI Gym库中的Cliff Walking环境，并使用Q-learning算法实现Agent的决策过程。Agent的决策基于Q值函数，使用epsilon贪心策略探索和exploitation。代码训练Q表格并输出结果，最后演示游戏并输出最终的奖励。