HMM 隐马尔可夫模型 代码实现

#encoding:utf-8import sysimport picklefrom copy import deepcopyis_train = FalseDEFAULT_PROB = 0.000000000001MIN_PROB = -1 * float('inf')train_path = "train.in"test_path = "test.in"output_path = "test.out"#统计 各个次数 作为 各个概率def train():    print "start training ..."    # 以下5个元素是HMM模型的参数    V = set() # 观测集合    Q = set() # 状态集合    A = {} # 状态转移概率矩阵，P(状态|状态)，是一个二层dict 具体是 pre_state->(state->prob)    B = {} # 观测概率矩阵，P(观测|状态)，是一个二层dict 具体是 state->(observ->prob)    PI = {} # 初始状态概率向量    # 统计模型参数    with open(train_path, "rb") as infile:        pre_s = -1 # t-1时刻的状态        for line in infile:            segs = line.rstrip().split('\t')            if len(segs) != 2: # 遇到空行时                pre_s = -1            else:                o = segs[0] # t时刻的观测o                s = segs[1] # t时刻的状态s                # 统计状态s到观测o的次数                B[s][o] = B.setdefault(s, {}).setdefault(o, 0) + 1                V.add(o)                Q.add(s)                if pre_s == -1: # 统计每个句子开头第一个状态的次数                    PI[s] = PI.setdefault(s, 0) + 1                else: # 统计状态pre_s到状态s的次数                    A[pre_s][s] = A.setdefault(pre_s, {}).setdefault(s, 0) + 1                pre_s = s #切换到下一个状态    # 概率归一化    for i in A.keys():        prob_sum = 0        for j in A[i].keys():            prob_sum += A[i][j]        for j in A[i].keys():            A[i][j] = 1.0 * A[i][j] / prob_sum    for i in B.keys():        prob_sum = 0        for j in B[i].keys():            prob_sum += B[i][j]        for j in B[i].keys():            B[i][j] = 1.0 * B[i][j] / prob_sum    prob_sum = sum(PI.values())    for i in PI.keys():        PI[i] = 1.0 * PI[i] / prob_sum    print "finished training ..."    return A, B, PI, V, Qdef saveModel(A, B, PI, V, Q):    with open("A.param", "wb") as outfile:        pickle.dump(A, outfile)    with open("B.param", "wb") as outfile:        pickle.dump(B, outfile)    with open("PI.param", "wb") as outfile:        pickle.dump(PI, outfile)    with open("V.param", "wb") as outfile:        pickle.dump(V, outfile)    with open("Q.param", "wb") as outfile:        pickle.dump(Q, outfile)#维特比def predict(X, A, B, PI, V, Q):    W = [{} for t in range(len(X))] #相当于书上的δ    path = {}    for s in Q:        W[0][s] = 1.0 * PI.get(s, DEFAULT_PROB) * B.get(s, {}).get(X[0], DEFAULT_PROB) #0时刻状态为s的概率        path[s] = [s]    for t in range(1, len(X)):        new_path = {}        for s in Q: #两轮循环暴力求解            max_prob = MIN_PROB            max_s = ''            for pre_s in Q:                prob = W[t-1][pre_s] * \                       A.get(pre_s, {}).get(s, DEFAULT_PROB) * \                       B.get(s, {}).get(X[t], DEFAULT_PROB)                (max_prob, max_s) = max((max_prob, max_s), (prob, pre_s)) #全由第一个prob决定            W[t][s] = max_prob #t时刻状态为s的最大概率            tmp = deepcopy(path[max_s])            tmp.append(s)            new_path[s] = tmp        path = new_path    (max_prob, max_s) = max((W[len(X)-1][s], s) for s in Q)# 最后一个时刻各个状态的概率的最大的    return path[max_s]def getModel():    with open("A.param", "rb") as infile:        A = pickle.load(infile)    with open("B.param", "rb") as infile:        B = pickle.load(infile)    with open("PI.param", "rb") as infile:        PI = pickle.load(infile)    with open("V.param", "rb") as infile:        V = pickle.load(infile)    with open("Q.param", "rb") as infile:        Q = pickle.load(infile)         return A, B, PI, V, Qdef test(A, B, PI, V, Q):    print "start testing"    with open(test_path, "rb") as infile, \         open(output_path, "wb") as outfile:        X_test = []        y_test = []        for line in infile:            segs = line.strip().split('\t')            if len(segs) != 2: # 遇到空行时                if len(X_test) == 0:#一整句 比如NBAD                    continue                preds = predict(X_test, A, B, PI, V, Q)                for vals in zip(X_test, y_test, preds):                    outfile.write("\t".join(vals) + "\n")                   outfile.write("\n")                X_test = []                y_test = []            else:                o = segs[0] # t时刻的观测o                s = segs[1] # t时刻的状态s                       X_test.append(o)                y_test.append(s)    print "finished testing"def main():    if is_train:        A, B, PI, V, Q = train()        saveModel(A, B, PI, V, Q)    else:        A, B, PI, V, Q = getModel()    test(A, B, PI, V, Q)if __name__ == '__main__':    main()

数据在https://github.com/guotong1988/MachineLearningFromZero
参考《统计学习方法》