# -*-coding:utf-8-*- import numpy as np def sum(L): sumValue = 0.0 for i in range(0, len(L)): sumValue += L[i] return sumValue def max(L): target = 0 maxValue = L[target] for i in range(1, len(L)): if (L[i] > maxValue): target = i maxValue = L[i] return maxValue, target + 1 def printTable(table, type="float"): if (type == "float"): for i in range(0, len(table)): for j in range(0, len(table[i])): print ("%-16f" % (table[i][j]),) print ("\n") elif (type == "int"): for i in range(0, len(table)): for j in range(0, len(table[i])): print ("%-2d" % (table[i][j]),) print ("\n") def initTable(x, y, type="float"): table = [] if (type == "float"): initValue = 0.0 elif (type == "int"): initValue = 0 for i in range(0, x): temp = [] for j in range(0, y): temp.append(initValue) table.append(temp) return np.array(table) def forwardV(A, B, pi, Object, mod="forward"): T = len(Object) NumState = len(A) NodeTable = initTable(NumState, T) if (mod == "viterbi"): NodePath = initTable(NumState, T, type="int") for i in range(0, NumState): NodeTable[i][0] = pi[i] * B[i][Object[0]] for t in range(1, T): for j in range(0, NumState): temp = [] for i in range(0, NumState): temp.append(NodeTable[i][t - 1] * A[i][j] * B[j][Object[t]]) if (mod == "forward"): NodeTable[j][t] = sum(temp) elif (mod == "viterbi"): NodeTable[j][t], NodePath[j][t] = max(temp) if (mod == "forward"): print (u"前向算法alpha值记录表:") printTable(NodeTable) p = 0.0 for i in range(0, NumState): p += NodeTable[i][T - 1] return NodeTable, p elif (mod == "viterbi"): print (u"viterbi算法结点值记录表:") printTable(NodeTable) print (u"viterbi算法路径记录表:") printTable(NodePath, type="int") target = 0 maxValue = NodeTable[target][T - 1] for i in range(1, NumState): if NodeTable[i][T - 1] > maxValue: target = i maxValue = NodeTable[i][T - 1] print (u"viterbi算法最终状态:", target + 1, "\n") StateSequeues = [0] * T StateSequeues[T - 1] = target + 1 for i in range(1, T): StateSequeues[T - i - 1] = NodePath[StateSequeues[T - i] - 1][T - i] return StateSequeues def backward(A, B, pi, Object): T = len(Object) NumState = len(A) NodeTable = initTable(NumState, T) for i in range(0, NumState): NodeTable[i][T - 1] = 1 for t in range(1, T): for i in range(0, NumState): temp = [] for j in range(0, NumState): temp.append(NodeTable[j][T - t] * A[i][j] * B[j][Object[T - t]]) NodeTable[i][T - t - 1] = sum(temp) print (u"后向算法beta值记录表:") printTable(NodeTable) p = 0.0 for i in range(0, NumState): p += pi[i] * B[i][Object[0]] * NodeTable[i][0] return NodeTable, p # ---------------------------------------------------------------------# # forwardV函数实现前向算法和viterbi算法 # backward函数实现后向算法的计算 # 前向后向结合计算特殊点概率,只需取两个记录表中相应的alpha和beta值即可 # ---------------------------------------------------------------------# print (u"习题10.1和10.3") A1 = [[0.5, 0.2, 0.3], [0.3, 0.5, 0.2], [0.2, 0.3, 0.5]] B1 = [[0.5, 0.5], [0.4, 0.6], [0.7, 0.3]] pi1 = [0.2, 0.4, 0.4] Object1 = [0, 1, 0, 1] # 习题10.1 table, P = backward(A1, B1, pi1, Object1) print (u"(10.1)通过后向算法计算可得 P(O|lambda)=", P, "\n") # 习题10.3 I = forwardV(A1, B1, pi1, Object1, mod="viterbi") print (u"(10.3)通过viterbi算法求得最优路径 I=", I, "\n") print (u"#-----------------------------------------------------#") # ---------------------------------------------------------------------# print (u"习题10.2") A2 = [[0.5, 0.1, 0.4], [0.3, 0.5, 0.2], [0.2, 0.2, 0.6]] B2 = [[0.5, 0.5], [0.4, 0.6], [0.7, 0.3]] pi2 = [0.2, 0.3, 0.5] Object2 = [0, 1, 0, 0, 1, 0, 1, 1] F, pf = forwardV(A2, B2, pi2, Object2, mod="forward") B, pb = backward(A2, B2, pi2, Object2) # 习题10.2 # P(i4=q3|O,lambda)=P(i4=q3,O|lambda)/P(O|lambda)=alpha4(3)*beta4(3)/P(O|lamda) # pf=pb,任选一个 # 1-based索引转换为0-based索引 print (u"P(O|lambda)=", pf) P2 = F[3 - 1][4 - 1] * B[3 - 1][4 - 1] / pf print (u"(10.2)通过前向后向概率计算可得 P(i4=q3|O,lambda)=", P2, "\n")
统计学习方法-第十章-习题代码学习-前向后向维特比
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