实现隐马尔科夫模型HMM python实现隐马尔科夫模型 主要为大家详细介绍了python实现隐马尔科夫模型HMM，具有一定的参考价值，感兴趣的小伙伴们可以参考一 下 一份完全按照李航<<统计学习方法>>介绍的HMM代码，供大家参考，具体内容如下 #coding=utf8 ''''' Created on 2017-8-5 里面的代码许多地方可以精简，但为了百分百还原公式，就没有精简了。 @author: adzhua ''' import numpy as np class HMM(object): def __init__(self, A, B, pi): ''''' A: 状态转移概率矩阵 B: 输出观察概率矩阵 pi: 初始化状态向量 ''' self.A = np.array(A) self.B = np.array(B) self.pi = np.array(pi) self.N = self.A.shape[0] # 总共状态个数 self.M = self.B.shape[1] # 总共观察值个数 # 输出HMM的参数信息 def printHMM(self): print ("==================================================") print ("HMM content: N =",self.N,",M =",self.M) for i in range(self.N): if i==0: print ("hmm.A ",self.A[i,:]," hmm.B ",self.B[i,:]) else: print (" ",self.A[i,:]," ",self.B[i,:]) print ("hmm.pi",self.pi) print ("==================================================") # 前向算法 def forwar(self, T, O, alpha, prob): ''''' T: 观察序列的长度 O: 观察序列 alpha: 运算中用到的临时数组 prob: 返回值所要求的概率 ''' # 初始化 for i in range(self.N): alpha[0, i] = self.pi[i] * self.B[i, O[0]] # 递归 for t in range(T-1): for j in range(self.N): sum = 0.0 for i in range(self.N): sum += alpha[t, i] * self.A[i, j] alpha[t+1, j] = sum * self.B[j, O[t+1]] # 终止 sum = 0.0 for i in range(self.N): sum += alpha[T-1, i] prob[0] *= sum # 带修正的前向算法 def forwardWithScale(self, T, O, alpha, scale, prob): scale[0] = 0.0 # 初始化 for i in range(self.N): alpha[0, i] = self.pi[i] * self.B[i, O[0]] 

scale[0] += alpha[0, i] for i in range(self.N): alpha[0, i] /= scale[0] # 递归 for t in range(T-1): scale[t+1] = 0.0 for j in range(self.N): sum = 0.0 for i in range(self.N): sum += alpha[t, i] * self.A[i, j] alpha[t+1, j] = sum * self.B[j, O[t+1]] scale[t+1] += alpha[t+1, j] for j in range(self.N): alpha[t+1, j] /= scale[t+1] # 终止 for t in range(T): prob[0] += np.log(scale[t]) def back(self, T, O, beta, prob): ''''' T: 观察序列的长度 len(O) O: 观察序列 beta: 计算时用到的临时数组 prob: 返回值；所要求的概率 ''' # 初始化 for i in range(self.N): beta[T-1, i] = 1.0 # 递归 for t in range(T-2, -1, -1): # 从T-2开始递减；即T-2, T-3, T-4, ..., 0 for i in range(self.N): sum = 0.0 for j in range(self.N): sum += self.A[i, j] * self.B[j, O[t+1]] * beta[t+1, j] beta[t, i] = sum # 终止 sum = 0.0 for i in range(self.N): sum += self.pi[i]*self.B[i,O[0]]*beta[0,i] prob[0] = sum # 带修正的后向算法 def backwardWithScale(self, T, O, beta, scale): ''''' T: 观察序列的长度 len(O) O: 观察序列 beta: 计算时用到的临时数组 ''' # 初始化 for i in range(self.N): beta[T-1, i] = 1.0 # 递归 for t in range(T-2, -1, -1): for i in range(self.N): sum = 0.0 for j in range(self.N): sum += self.A[i, j] * self.B[j, O[t+1]] * beta[t+1, j] beta[t, i] = sum / scale[t+1] # viterbi算法 def viterbi(self, O): ''''' O: 观察序列 ''' T = len(O) # 初始化 

delta = np.zeros((T, self.N), np.float) phi = np.zeros((T, self.N), np.float) I = np.zeros(T) for i in range(self.N): delta[0, i] = self.pi[i] * self.B[i, O[0]] phi[0, i] = 0.0 # 递归 for t in range(1, T): for i in range(self.N): delta[t, i] = self.B[i, O[t]] * np.array([delta[t-1, j] * self.A[j, i] for j in range(self.N)] ).max() phi = np.array([delta[t-1, j] * self.A[j, i] for j in range(self.N)]).argmax() # 终止 prob = delta[T-1, :].max() I[T-1] = delta[T-1, :].argmax() for t in range(T-2, -1, -1): I[t] = phi[I[t+1]] return prob, I # 计算gamma(计算A所需的分母；详情见李航的统计学习) : 时刻t时马尔可夫链处于状态Si的概率 def computeGamma(self, T, alpha, beta, gamma): '''''''' for t in range(T): for i in range(self.N): sum = 0.0 for j in range(self.N): sum += alpha[t, j] * beta[t, j] gamma[t, i] = (alpha[t, i] * beta[t, i]) / sum # 计算sai(i,j)(计算A所需的分子) 为给定训练序列O和模型lambda时 def computeXi(self, T, O, alpha, beta, Xi): for t in range(T-1): sum = 0.0 for i in range(self.N): for j in range(self.N): Xi[t, i, j] = alpha[t, i] * self.A[i, j] * self.B[j, O[t+1]] * beta[t+1, j] sum += Xi[t, i, j] for i in range(self.N): for j in range(self.N): Xi[t, i, j] /= sum # 输入 L个观察序列O，初始模型：HMM={A,B,pi,N,M} def BaumWelch(self, L, T, O, alpha, beta, gamma): DELTA = 0.01 ; round = 0 ; flag = 1 ; probf = [0.0] delta = 0.0; probprev = 0.0 ; ratio = 0.0 ; deltaprev = 10e-70 xi = np.zeros((T, self.N, self.N)) # 计算A的分子 pi = np.zeros((T), np.float) # 状态初始化概率 denominatorA = np.zeros((self.N), np.float) # 辅助计算A的分母的变量 denominatorB = np.zeros((self.N), np.float) numeratorA = np.zeros((self.N, self.N), np.float) # 辅助计算A的分子的变量 numeratorB = np.zeros((self.N, self.M), np.float) # 针对输出观察概率矩阵 scale = np.zeros((T), np.float) while True: probf[0] =0 # E_step for l in range(L): self.forwardWithScale(T, O[l], alpha, scale, probf) self.backwardWithScale(T, O[l], beta, scale) self.computeGamma(T, alpha, beta, gamma) # (t, i) self.computeXi(T, O[l], alpha, beta, xi) #(t, i, j) for i in range(self.N): pi[i] += gamma[0, i] for t in range(T-1): denominatorA[i] += gamma[t, i] denominatorB[i] += gamma[t, i] denominatorB[i] += gamma[T-1, i] 

for j in range(self.N): for t in range(T-1): numeratorA[i, j] += xi[t, i, j] for k in range(self.M): # M为观察状态取值个数 for t in range(T): if O[l][t] == k: numeratorB[i, k] += gamma[t, i] # M_step。 计算pi, A, B for i in range(self.N): # 这个for循环也可以放到for l in range(L)里面 self.pi[i] = 0.001 / self.N + 0.999 * pi[i] / L for j in range(self.N): self.A[i, j] = 0.001 / self.N + 0.999 * numeratorA[i, j] / denominatorA[i] numeratorA[i, j] = 0.0 for k in range(self.M): self.B[i, k] = 0.001 / self.N + 0.999 * numeratorB[i, k] / denominatorB[i] numeratorB[i, k] = 0.0 #重置 pi[i] = denominatorA[i] = denominatorB[i] = 0.0 if flag == 1: flag = 0 probprev = probf[0] ratio = 1 continue delta = probf[0] - probprev ratio = delta / deltaprev probprev = probf[0] deltaprev = delta round += 1 if ratio <= DELTA : print('num iteration: ', round) break if __name__ == '__main__': print ("python my HMM") # 初始的状态概率矩阵pi；状态转移矩阵A；输出观察概率矩阵B; 观察序列 pi = [0.5,0.5] A = [[0.8125,0.1875],[0.2,0.8]] B = [[0.875,0.125],[0.25,0.75]] O = [ [1,0,0,1,1,0,0,0,0], [1,1,0,1,0,0,1,1,0], [0,0,1,1,0,0,1,1,1] ] L = len(O) T = len(O[0]) # T等于最长序列的长度就好了 hmm = HMM(A, B, pi) alpha = np.zeros((T,hmm.N),np.float) beta = np.zeros((T,hmm.N),np.float) gamma = np.zeros((T,hmm.N),np.float) # 训练 hmm.BaumWelch(L,T,O,alpha,beta,gamma) # 输出HMM参数信息 hmm.printHMM() 以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持我们。