昨天我使用期望最大化算法实现了GMM(高斯混合模型)。如你所记得的,它将一些不知名的分布建模为高斯分布的混合体,我们需要了解它的均值和方差,以及每个高斯的权重。GMM-对数似然不单调
这是代码背后的数学(它不是那么复杂) http://mccormickml.com/2014/08/04/gaussian-mixture-models-tutorial-and-matlab-code/
这是我的代码:
import numpy as np
from scipy.stats import multivariate_normal
import matplotlib.pyplot as plt
#reference for this code is http://mccormickml.com/2014/08/04/gaussian-mixture-models-tutorial-and-matlab-code/
def expectation(data, means, covs, priors): #E-step. returns the updated probabilities
m = data.shape[0] #gets the data, means covariances and priors of all clusters
numOfClusters = priors.shape[0]
probabilities = np.zeros((m, numOfClusters))
for i in range(0, m):
for j in range(0, numOfClusters):
sum = 0
for l in range(0, numOfClusters):
sum += normalPDF(data[i, :], means[l], covs[l]) * priors[l, 0]
probabilities[i, j] = normalPDF(data[i, :], means[j], covs[j]) * priors[j, 0]/sum
return probabilities
def maximization(data, probabilities): #M-step. this updates the means, covariances, and priors of all clusters
m, n = data.shape
numOfClusters = probabilities.shape[1]
means = np.zeros((numOfClusters, n))
covs = np.zeros((numOfClusters, n, n))
priors = np.zeros((numOfClusters, 1))
for i in range(0, numOfClusters):
priors[i, 0] = np.sum(probabilities[:, i])/m #update priors
for j in range(0, m): #update means
means[i] += probabilities[j, i] * data[j, :]
vec = np.reshape(data[j, :] - means[i, :], (n, 1))
covs[i] += probabilities[j, i] * np.dot(vec, vec.T) #update covs
means[i] /= np.sum(probabilities[:, i])
covs[i] /= np.sum(probabilities[:, i])
return [means, covs, priors]
def normalPDF(x, mean, covariance): #this is simply multivariate normal pdf
n = len(x)
mean = np.reshape(mean, (n,))
x = np.reshape(x, (n,))
var = multivariate_normal(mean=mean, cov=covariance,)
return var.pdf(x)
def initClusters(numOfClusters, data): #initialize all the gaussian clusters (means, covariances, priors
m, n = data.shape
means = np.zeros((numOfClusters, n))
covs = np.zeros((numOfClusters, n, n))
priors = np.zeros((numOfClusters, 1))
initialCovariance = np.cov(data.T)
for i in range(0, numOfClusters):
means[i] = np.random.rand(n) #the initial mean for each gaussian is chosen randomly
covs[i] = initialCovariance #the initial covariance of each cluster is the covariance of the data
priors[i, 0] = 1.0/numOfClusters #the initial priors are uniformly distributed.
return [means, covs, priors]
def logLikelihood(data, probabilities): #data is our data. probabilities[i, j] = k means probability example i belongs in cluster j is 0 < k < 1
m = data.shape[0] #num of examples
examplesByCluster = np.zeros((m, 1))
for i in range(0, m):
examplesByCluster[i, 0] = np.argmax(probabilities[i, :])
examplesByCluster = examplesByCluster.astype(int) #examplesByCluster[i] = j means that example i belongs in cluster j
result = 0
for i in range(0, m):
result += np.log(probabilities[i, examplesByCluster[i, 0]]) #example i belongs in cluster examplesByCluster[i, 0]
return result
m = 2000 #num of training examples
n = 8 #num of features for each example
data = np.random.rand(m, n)
numOfClusters = 2 #num of gaussians
numIter = 30 #num of iterations of EM
cost = np.zeros((numIter, 1))
[means, covs, priors] = initClusters(numOfClusters, data)
for i in range(0, numIter):
probabilities = expectation(data, means, covs, priors)
[means, covs, priors] = maximization(data, probabilities)
cost[i, 0] = logLikelihood(data, probabilities)
plt.plot(cost)
plt.show()
的问题是,对数似然是行为古怪。我预计它会单调增加。但事实并非如此。
例如,具有8个特征与3个高斯簇2000个的例子中,对数似然看起来像这样(30次迭代) -
因此,这是很不好。但在其他测试中,我跑了,例如一个测试用的2个功能和2群15本例中,对数似然是这样的 -
更好,但还不够完善。
为什么会发生这种情况,我该如何解决?
你想要模拟什么数据?从代码看来,您正在对随机点进行建模,即在数据中没有找到结构。如果是这样的话,你的GMM模型可能会随机波动 – etov
在这种情况下,它是随机的,但将来它可能是任何类型的数据,从温度到车辆传感器读数,任何事情。我认为数据是随机的并不重要。从理论上讲,我们保证单调收敛。即使是随机数据。 –
您是否尝试将您的结果与已知实现的结果进行比较?一个选项是scikit-learn的[GaussianMixture](http://scikit-learn.org/stable/modules/mixture.html)。 –