，隐马尔科夫模型
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? 隐马尔科夫模型的三个问题
● 代码
1 import numpy as np 2 import scipy as sp 3 import matplotlib.pyplot as plt 4 from matplotlib.patches import Rectangle 5 6 dataSize = 200 7 trainRatio = 0.3 8 epsilon = 1E-10 9 randomSeed = 109 10 11 def dataSplit(dataX, dataY, part): # 将数据集分割为训练集和测试集 12 return dataX[:part], dataY[:part], dataX[part:], dataY[part:] 13 14 def normalCDF(x, μList, σList): 15 return np.exp(-(x - μList)**2 / (2 * σList**2)) / (np.sqrt(2 * np.pi) * σList) 16 17 def targetIndex(x, xList): # 二分查找 xList 中大于 x 的最小索引 18 if x < xList[0]: 19 return 0 20 lp = 0 21 rp = len(xList) - 1 22 while lp < rp - 1: 23 mp = (lp + rp) >> 1 24 if(x <= xList[mp]): 25 rp = mp 26 else: 27 lp = mp 28 return rp 29 30 def createData(state, obs): # 创建数据，输入状态数、观测数，输出初始分布、状态转移分布、观测分布 31 np.random.seed(randomSeed) 32 π = np.random.rand(state) 33 π /= np.sum(π) 34 A = np.random.rand(state, state) 35 A = np.array( A / np.mat(np.sum(A,1)).T) # 行归一化 36 B = np.random.rand(state, obs) 37 B = np.array(B / np.mat(np.sum(B,1)).T) 38 39 print("π = ", π) 40 print("A = \n", A) 41 print("B = \n", B) 42 return π, A, B 43 44 def createString(π, A, B, length, count = 1): # 创建状态列和观测列 45 state, obs = np.shape(B) 46 accπ = np.nancumsum(π) 47 accA = np.cumsum(A, 1) 48 accB = np.cumsum(B, 1) 49 50 if count == 1: # count 为 1 时生成的 string 只有一维，否则二维 51 stateString = np.zeros(length, dtype = int) 52 obsString = np.zeros(length, dtype = int) 53 stateString[0] = targetIndex(np.random.rand(), accπ) # 随机选择初态 54 for t in range(1, length): # 生成状态列 55 stateString[t] = targetIndex(np.random.rand(), accA[stateString[t-1]]) 56 for t in range(length): # 生成观测列 57 obsString[t] = targetIndex(np.random.rand(), accB[stateString[t]]) 58 59 else: 60 stateString = np.zeros([count, length], dtype = int) 61 obsString = np.zeros([count, length], dtype = int) 62 stateString[:,0] = [ targetIndex(np.random.rand(), accπ) for i in range(count) ] 63 for t in range(1, length): 64 stateString[:,t] = [ targetIndex(np.random.rand(), accA[stateString[i,t-1]]) for i in range(count) ] 65 for t in range(length): 66 obsString[:,t] = [ targetIndex(np.random.rand(), accB[stateString[i,t]]) for i in range(count) ] 67 68 #print("state = \n", stateString) 69 #print("obs = \n", obsString) 70 return stateString, obsString 71 72 def pObsForwardSimplified(π, A, B, obsString): # 前向算法计算观测序列概率 73 state, obs = np.shape(B) 74 length = len(obsString) 75 76 α = π * B[:,obsString[0]] 77 for i in range(1, length): 78 α = np.dot(α, A) * B[:,obsString[i]] # 等价代码 α = np.sum(α * A.T, 1) * B[:,obsString[i]] 79 return np.sum(α) 80 81 def pObsForward(π, A, B, obsString): # 前向算法计算观测序列概率，并且输出前向概率矩阵 82 state, obs = np.shape(B) 83 length = len(obsString) 84 85 α = np.zeros([length,state]) 86 α[0] = π * B[:,obsString[0]] 87 for i in range(1, length): 88 α[i] = np.dot(α[i-1], A) * B[:,obsString[i]] # α[i] = np.sum(α[i-1], A.T, 1) * B[:,obsString[i]] 89 return np.sum(α[-1]), α 90 91 def pObsBackwardSimplified(π, A, B, obsString): # 后向算法计算观测序列概率 92 state, obs = np.shape(B) 93 length = len(obsString) 94 95 β = np.ones(state) 96 for i in range(1, length)[::-1]: 97 β = np.dot(A, β * B[:,obsString[i]]) # β = np.sum(A * β * B[:,obsString[i]], 1) 98 return np.sum(π * β * B[:,obsString[0]]) 99 100 def pObsBackward(π, A, B, obsString): # 后向算法计算观测序列概率，并且输出后向概率矩阵 101 state, obs = np.shape(B) 102 length = len(obsString) 103 104 β = np.zeros([length,state]) 105 β[-1] = np.ones(state) 106 for i in range(1, length)[::-1]: 107 β[i-1] = np.dot(A, β[i] * B[:,obsString[i]]) # β[i-1] = np.sum(A * β[i] * B[:,obsString[i]], 1) 108 return np.sum(π * B[:,obsString[0]] * β[0]), β 109 110 def pObsMixedSimplified(A, B, obsString, α, β): # 用 α 和 β 来算观测序列概率 111 return np.dot(α[0], np.dot(A, B[:,obsString[1]] * β[1])) # np.sum(α[0] * np.sum(A * B[:,obsString[1]] * β[1], 1)) 112 113 def pObsMixed(A, B, obsString, α, β): # 用 α 和 β 来算观测序列概率，这 length - 1 个结果都相同 114 length = len(α) 115 return np.array([ np.dot(α[t], np.dot(A, B[:,obsString[t+1]] * β[t+1])) for t in range(length-1) ]) 116 #return np.array([ np.sum(α[t] * np.sum(A * B[:,obsString[t+1]] * β[t+1] , 1)) for t in range(length-1) ]) 117 118 def pΓ(α, β): # 求各时刻隐变量处于各状态的概率 119 t = α * β 120 return np.array( t / np.mat(np.sum(t, 1)).T ) 121 122 def pΞ(A, B, obsString, α, β): # t 时刻处于状态 i 而 t+1 时刻处于状态 j 的概率，t = 0 ~ length-2 123 state, obs = np.shape(B) 124 length = len(α) 125 126 res = np.zeros([ length - 1, state, state ]) 127 for t in range(length - 1): 128 res[t] = np.tile(α[t],[state,1]).T * A * B[:,obsString[t+1]] * β[t+1] 129 return res / pObsMixedSimplified(A, B, obsString, α, β) 130 131 def supervisedLearn(dataState, dataObs, state, obs): # 监督学习 132 count, length = np.shape(dataState) 133 π = np.zeros(state) 134 A = np.zeros([state, state]) 135 B = np.zeros([state, obs]) 136 137 for i in dataState[:,0]: 138 π[i] +=1 139 for i,j in zip(dataState[:,:-1].flatten(), dataState[:,1:].flatten()): 140 A[i,j] +=1 141 for i,j in zip(dataState.flatten(),dataObs.flatten()): 142 B[i,j] +=1 143 144 π /= count 145 A = np.array( A / np.mat(np.sum(A,1)).T) 146 B = np.array( B / np.mat(np.sum(B,1)).T) 147 return π, A, B 148 149 def baumWelchLearn(dataObs, state, obs): # Baum - WelchLearn 学习 150 count, length = np.shape(dataObs) # 存在的问题：如果某个观测序列没有覆盖所有可能的观测结果，学习会产生 nan？ 151 π = np.random.rand(state) # 选择初始值 152 A = np.random.rand(state, state) 153 B = np.random.rand(state, obs) 154 π /= np.sum(π) 155 A = np.array( A / np.mat(np.sum(A,1)).T) 156 B = np.array( B / np.mat(np.sum(B,1)).T) 157 158 for line in dataObs: 159 α = pObsForward(π, A, B, line)[1] 160 β = pObsBackward(π, A, B, line)[1] 161 ξ = pΞ(A, B, line, α, β) 162 γ = pΓ(α, β) 163 π = γ[0] 164 A = np.array( np.sum(ξ, 0) / np.mat(np.sum(γ[:-1], 0)).T ) 165 for k in range(obs): 166 B[:,k] = np.sum(γ[np.where(line==k)], 0) 167 B = np.array( B / np.mat(np.sum(γ, 0)).T ) 168 return π, A, B 169 170 def appropritePredict(x, π, A, B): # 近似预测，依次计算出α，β，γ，取 γ 每行最大值所在列号 171 γ = pΓ(pObsForward(π, A, B, x)[1], pObsBackward(π, A, B, x)[1]) 172 return np.argmax(γ, 1) 173 174 def viterbiPredict(obsString, π, A, B): # Viterbi 预测，动态规划 175 state, obs = np.shape(B) 176 length = len(obsString) 177 178 δ = π * B[:, obsString[0]] # δ 记录以各状态为结尾的最大概率，ψ 记录该最大概率对应的上一节点号 179 ψ = np.zeros([length, state]) 180 for t in range(1,length): 181 temp = δ * A.T * B[:, obsString[t]] 182 ψ[t-1] = np.argmax(temp,1) 183 δ = np.max(temp,1) 184 185 trace = np.zeros(length, dtype=np.int) 186 trace[-1] = np.argmax(δ) 187 for i in range(length - 1)[::-1]: 188 trace[i] = ψ[i, trace[i+1]] 189 190 return trace 191 192 def test(state, obs, length): # 单次测试 193 π, A, B = createData(state, obs) 194 allState, allObs = createString(π, A, B, length, dataSize) 195 196 if allObs.ndim > 1: 197 obsString = allObs[0] 198 else: 199 obsString = allObs 200 ‘‘‘ 201 π = np.array([0.2,0.4,0.4]) # 调试用数据 202 A = np.array([[0.5,0.2,0.3],[0.3,0.5,0.2],[0.2,0.3,0.5]]) 203 B = np.array([[0.5,0.5],[0.4,0.6],[0.7,0.3]]) 204 obsString = [0,1,0] 205 ‘‘‘ 206 pOFS = pObsForwardSimplified(π, A, B, obsString) 207 pOBS = pObsBackwardSimplified(π, A, B, obsString) 208 pOF = pObsForward(π, A, B, obsString) 209 pOB = pObsBackward(π, A, B, obsString) 210 pOMS = pObsMixedSimplified(A, B, obsString, pOF[1], pOB[1]) 211 pOM = pObsMixed(A, B, obsString, pOF[1], pOB[1]) 212 print("pOFS = %f, pOBS = %f\npOF = %f, pOB = %f\npOMS = %f"%(pOFS, pOBS, pOF[0], pOB[0], pOMS)) 213 print("α = \n", pOF[1]) 214 print("β = \n", pOB[1]) 215 print("pOM = \n", pOM) 216 γ = pΓ(pOF[1], pOB[1]) 217 print("γ = \n", γ) 218 ξ = pΞ(A, B, obsString, pOF[1], pOB[1]) 219 print("ξ = \n", ξ) 220 221 print("\nsupervisedLearn:") 222 para = supervisedLearn(allState, allObs, state, obs) 223 print("π = ", para[0]) 224 print("A = \n", para[1]) 225 print("B = \n", para[2]) 226 227 print("\nbaumWelchLearn:") 228 para = baumWelchLearn(allObs, state, obs) 229 print("π = ", para[0]) 230 print("A = \n", para[1]) 231 print("B = \n", para[2]) 232 233 myResult = [ appropritePredict(x, π, A, B) for x in allObs ] 234 #print(myResult) 235 errorRatio1 = np.sum( (np.array(myResult) != allState).astype(int) ) / (length * dataSize) 236 237 myResult = [ viterbiPredict(x, π, A, B) for x in allObs ] 238 #print(myResult) 239 errorRatio2 = np.sum( (np.array(myResult) != allState).astype(int) ) / (length * dataSize) 240 241 print("state = %d, obs = %d, length = %d\nerrorRatioAppropritePredict = %4f, errorRatioViterbiPredict = %4f"%(state, obs, length, errorRatio1, errorRatio2)) 242 243 if __name__ == ‘__main__‘: 244 test(4, 3, 10)
● 输出结果1，成功复现树上的样例数据
pOFS = 0.130218, pOBS = 0.130218 pOF = 0.130218, pOB = 0.130218 pOMS = 0.130218 α = [[0.1 0.16 0.28 ] [0.077 0.1104 0.0606 ] [0.04187 0.035512 0.052836]] β = [[0.2451 0.2622 0.2277] [0.54 0.49 0.57 ] [1. 1. 1. ]] pOM = [0.130218 0.130218] γ = [[0.18822283 0.32216744 0.48960973] [0.31931069 0.41542644 0.26526287] [0.32153773 0.27271191 0.40575036]] ξ = [[[0.1036723 0.04515505 0.03939548] [0.09952541 0.18062019 0.04202184] [0.11611298 0.1896512 0.18384555]] [[0.14782903 0.04730529 0.12417638] [0.12717136 0.16956181 0.11869327] [0.04653735 0.05584481 0.16288071]]] appropritePredict: [2 1 2] ViterbiPredict: [2 2 2]
● 输出结果2，用自己的数据来跑有问题【坑】
π = [0.09536369 0.4423878 0.33021783 0.13203068] A = [[0.38752484 0.17316366 0.39198697 0.04732453] [0.09835982 0.23793441 0.38519171 0.27851407] [0.44365676 0.12177898 0.3055296 0.12903465] [0.29614541 0.30691252 0.04969234 0.34724973]] B = [[0.12418479 0.76243987 0.11337534] [0.34515219 0.28857853 0.36626928] [0.27430006 0.14931117 0.57638877] [0.22205449 0.24865406 0.52929144]] supervisedLearn: π = [0.1 0.42 0.33 0.15] A = [[0.38233515 0.18269812 0.38747731 0.04748941] [0.10843373 0.23782085 0.37139864 0.28234678] [0.44822934 0.12310287 0.30084317 0.12782462] [0.30813953 0.27151163 0.04476744 0.3755814 ]] B = [[0.13203593 0.75479042 0.11317365] [0.34575569 0.313147 0.34109731] [0.27606952 0.14639037 0.57754011] [0.23502304 0.24193548 0.52304147]] baumWelchLearn: π = [1.60211593e-01 8.26662258e-01 8.91008964e-06 1.31172391e-02] A = [[0.17940016 0.25527889 0.31784884 0.24747211] [0.39215007 0.15576319 0.22874897 0.22333777] [0.32884167 0.30016482 0.15265056 0.21834295] [0.36968335 0.03746577 0.34497214 0.24787874]] B = [[0.34244911 0.24148873 0.41606216] [0.29723256 0.24124855 0.46151888] [0.18049919 0.57465181 0.244849 ] [0.29483333 0.37769373 0.32747294]] state = 4, obs = 3, length = 100 errorRatioAppropritePredict = 0.489400, errorRatioViterbiPredict = 0.692400
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