Python 中的期望最大化

Question

我的任务是为我所在的 class 实现期望最大化算法。在笔记中，我的教授评估了代码中使用的迭代公式，我检查了它们并且它们'写对了。

该问题要求我们根据给定模型创建合成数据。这个模型写在下面的gauss_mix()函数中。不过，我的最终输出不是它应该的样子，我很困惑为什么。

import numpy as np
import pylab as plt

# Create a synthetic Dataset
def gauss_mix(x, pi1, mu1, mu2, sigma):
    term1 = pi1 * np.exp(-(x - mu1)**2 / 2*sigma**2)
    term2 = (1 - pi1) * np.exp(-(x - mu2)**2 / 2*sigma**2)
    return np.array(term1 + term2)

# Now we define the initial parameters
# The format of the list is: (pi_1, mu_1, mu_2, sigma)
initial_params = [.3, 5, 15, 2]

rand_position = np.random.rand(1,10000)*30
synth_data = gauss_mix(rand_position[0], initial_params[0], initial_params[1], initial_params[2], initial_params[3])

要查看绘图，您可以在计算 gauss_mix 之前对 rand_position[0] 值进行排序。这会产生以下情节：

继续，我定义了几个函数来协助计算。

# Defining a couple of useful functions 
def gamma_1n_old(pi1_old, norm1, norm2):
    # probability of observing the dataset based
    # on the first gaussian. Formula given in the book
    numerator = pi1_old * norm1
    denominator = pi1_old * norm1 + (1-pi1_old) * norm2 
    return np.array(numerator / denominator)

def gamma_2n_old(pi1_old, norm1, norm2):
    # probability of observing the dataset based
    # on the second gaussian. Formula given in the book
    numerator = (1-pi1_old) * norm2
    denominator = pi1_old * norm1 + (1-pi1_old) * norm2
    return np.array(numerator / denominator)

def normal(x, mu, sigma):
    # Standard normal distribution equation
    numerator = np.exp(-(np.array(x)-mu)**2 / (2*sigma**2))
    denominator = np.sqrt(2*np.pi * sigma**2)
    return np.array(numerator / denominator)

我在这里循环：

# now we can go through the EM loop

# start with a random set of parameters, the format of the list is: (pi_1, mu_1, mu_2, sigma)
rand = np.random.random(4) # 
params = [rand[0], rand[1]*10, rand[2]*10, rand[3]*10]

# initialize empty gamma lists
gamma1 = []
gamma2 = []

# make a copy of the synthetic data and use that to loop over
data = plot_synth_data.copy()

data_plot = [] # to get plots for specific iterations

for iteration in range(50):
    print(params)
    
    # get values for Normal_1 and Normal_2
    norm1 = normal(data, params[1], params[3])
    norm2 = normal(data, params[2], params[3])
#     print(norm1, norm2)

    # calculate the observation probability based on the old paramters
    gamma1_old = gamma_1n_old(params[0], norm1, norm2)
    gamma2_old = gamma_2n_old(params[0], norm1, norm2)
#     print(gamma1_old, gamma2_old)
    
    # need to append these to a new list so we can sum them across the whole time range
    gamma1.append(gamma1_old)
    gamma2.append(gamma2_old)
#     print(data)
#     print(np.sum(gamma1), np.sum(gamma1*data))
    
    # now to update the paramters for the next iteration
    params[0] = np.sum(gamma1_old) / np.sum(gamma1_old + gamma2_old)
    params[1] = np.sum(gamma1_old*data) / np.sum(gamma1_old)
    params[2] = np.sum(gamma2_old*data) / np.sum(gamma2_old)
    params[3] = np.sqrt(np.sum(gamma1_old * (data - params[1])**2) / np.sum(gamma1_old))
    
    # Just for convinience, we can plot every 7th iteration to visually check how it's changing
    if iteration % 7 == 0:
        plot = gauss_mix(data, params[0], params[1], params[2], params[3])
        data_plot.append(plot)

print(params) 语句的输出如下，我省略了一些行，因为它们不会随着连续迭代而改变。

[0.1130842168240086, 3.401472765079545, 2.445209909135907, 2.3046528697572635]
[0.07054376684886957, 0.04341192273911035, 0.04067151364724695, 0.12585753071439582]
[0.07054303636195076, 0.04330910871714057, 0.040679319081395215, 0.12567545288855245]
[0.07054238762380395, 0.04321431848177363, 0.04068651514443456, 0.12550734898400692]
[0.07054180884360708, 0.043126645044752804, 0.04069317074867406, 0.125351664317294]
[0.07054129028636431, 0.04304531343415197, 0.040699344770810386, 0.12520706710362625]

我不确定这里的参数是什么。为清楚起见，列表索引为 [pi_1, mu_1, mu_2, sigma]。我最初的猜测是我没有在计算中正确使用数据，但我不确定我还能怎么做。

欢迎任何建议或指导。我并不是在寻找完整的书面解决方案，只是在寻找我的错在哪里的建议。我会留意任何问题以更好地澄清我的问题。

Answer 1

我在这里回答我自己的问题。

我的代码的问题是我从数据中采样的方式。下面的代码显示了正确的方法。

# Create a synthetic Dataset
def gauss_mix(pi1, mu1, mu2, sigma):
    if np.random.randn() < pi1:
        return mu1 + np.random.randn() * sigma
    else:
        return mu2 + np.random.randn() * sigma

# Now we define the initial parameters
# The format of the list is: (pi_1, mu_1, mu_2, sigma)
initial_params = [.3, 5, 15, 2]

sample = 10000
synth_data = []
for dat in range(sample):
    synth_data.append(gauss_mix( initial_params[0], initial_params[1], initial_params[2], initial_params[3]))

绘制时，结果如下：

Python 中的期望最大化

Expectation Maximization in Python

python

statistics

probability

data-science

expectation-maximization