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隱馬爾可夫模型中的Viterbi算法的C++實現(xiàn)

FROM http://blog.csdn.net/firstboy0513/article/details/5721701


  1. #include <iostream>  
  2. #include <vector>  
  3. #include <map>  
  4. #include <string>  
  5. using namespace std;  
  6. typedef vector< string > VecStr;  
  7. typedef map< string, double > MapStrDou;  
  8. typedef map< string, map< string, double > > MapStrMap;  
  9. typedef vector< string > :: iterator VecStrI;  
  10. typedef map< string, double > :: iterator MapStrDouI;  
  11. typedef map< string, map< string, double > > :: iterator MapStrMapI;  
  12. typedef struct DSD  
  13. {  
  14.     double d1;  
  15.     string s;  
  16.     double d2;  
  17. }DSD;  
  18. typedef map< string, DSD > MapStrDSD;  
  19. #define RAINY "Rainy"  
  20. #define SUNNY "Sunny"  
  21. #define WALK "walk"  
  22. #define SHOP "shop"  
  23. #define CLEAN "clean"  
  24. DSD forward_viterbi(VecStr& obs,  
  25.         VecStr& states,  
  26.         MapStrDou& start_p,  
  27.         MapStrMap& trans_p,  
  28.         MapStrMap& emit_p);  
  29. int main(void)  
  30. {  
  31.     VecStr states;  
  32.     VecStr observations;  
  33.     MapStrDou start_probability;  
  34.     MapStrMap transition_probability;  
  35.     MapStrMap emission_probability;  
  36.     states.push_back(RAINY);  
  37.     states.push_back(SUNNY);  
  38.     observations.push_back(WALK);  
  39.     observations.push_back(SHOP);  
  40.     observations.push_back(CLEAN);  
  41.     start_probability[RAINY] = 0.6;  
  42.     start_probability[SUNNY] = 0.4;  
  43.     MapStrDou t1;  
  44.     t1[RAINY] = 0.7;  
  45.     t1[SUNNY] = 0.3;  
  46.     MapStrDou t2;  
  47.     t2[RAINY] = 0.4;  
  48.     t2[SUNNY] = 0.6;  
  49.     transition_probability[RAINY] = t1;  
  50.     transition_probability[SUNNY] = t2;  
  51.     MapStrDou e1;  
  52.     e1[WALK] = 0.1;  
  53.     e1[SHOP] = 0.4;  
  54.     e1[CLEAN] = 0.5;  
  55.     MapStrDou e2;  
  56.     e2[WALK] = 0.6;  
  57.     e2[SHOP] = 0.3;  
  58.     e2[CLEAN] = 0.1;  
  59.     emission_probability[RAINY] = e1;  
  60.     emission_probability[SUNNY] = e2;  
  61.       
  62.     DSD ret = forward_viterbi(observations,  
  63.         states,  
  64.         start_probability,  
  65.         transition_probability,  
  66.         emission_probability);  
  67.     cout << ret.d1 <<endl;  
  68.     cout << ret.s << endl;  
  69.     cout << ret.d2 << endl;  
  70.       
  71.     return 0;  
  72. }  
  73. DSD forward_viterbi(VecStr& obs,  
  74.         VecStr& states,  
  75.         MapStrDou& start_p,  
  76.         MapStrMap& trans_p,  
  77.         MapStrMap& emit_p)  
  78. {  
  79.     MapStrDSD T;  
  80.     VecStrI VS_i, VS_j, VS_k;  
  81.     DSD DSD_tmp;  
  82.     for(VS_i = states.begin(); VS_i != states.end(); VS_i++)  
  83.     {  
  84.         DSD_tmp.d1 = start_p[*VS_i];  
  85.         DSD_tmp.s = *VS_i;  
  86.         DSD_tmp.d2 = start_p[*VS_i];  
  87.         T[*VS_i] = DSD_tmp;  
  88.     }  
  89.     for(VS_i = obs.begin(); VS_i != obs.end(); VS_i++)  
  90.     {  
  91.         MapStrDSD U;  
  92.         for(VS_j = states.begin(); VS_j != states.end(); VS_j++)  
  93.         {  
  94.             double total = 0.0;  
  95.             string argmax = "";  
  96.             double valmax = 0.0;  
  97.             double prob = 1.0;  
  98.             string v_path = "";  
  99.             double v_prob = 1.0;  
  100.             for(VS_k = states.begin(); VS_k != states.end(); VS_k++)  
  101.             {  
  102.                 DSD objs = T[*VS_k];  
  103.                 prob = objs.d1;  
  104.                 v_path = objs.s;  
  105.                 v_prob = objs.d2;  
  106.                 double p = (emit_p[*VS_k])[*VS_i] * (trans_p[*VS_k])[*VS_j];  
  107.                 prob *= p;  
  108.                 v_prob *= p;  
  109.                 total += prob;  
  110.                 if(v_prob > valmax)  
  111.                 {  
  112.                     argmax = v_path + "," + *VS_j;  
  113.                     valmax = v_prob;  
  114.                 }  
  115.                 /** test */  
  116.                 cout << "obs = " << *VS_i << endl;  
  117.                 cout << "/tnext_state = " << *VS_j << endl;  
  118.                 cout << "/t/tstate = " << *VS_k << endl;  
  119.                 cout << "/t/t/tp = " << p << " = " << (emit_p[*VS_k])[*VS_i]   
  120.                 << " * " << (trans_p[*VS_k])[*VS_j] << endl;  
  121.                 cout << "/t/t/t{prob, v_path, v_prob} = {"   
  122.                 << prob << ", " << v_path << ", " << v_prob << "}" << endl;  
  123.                 cout << "/t/t/t{total, argmax, valmax} = {"   
  124.                 << total << ", " << argmax << ", " << valmax << "}" << endl;  
  125.                 /** end of test */  
  126.             }  
  127.             DSD_tmp.d1 = total;  
  128.             DSD_tmp.s = argmax;  
  129.             DSD_tmp.d2 = valmax;  
  130.             U[*VS_j] = DSD_tmp;  
  131.         }  
  132.         T = U;  
  133.     }  
  134.       
  135.     double total = 0.0;  
  136.     string argmax = "";  
  137.     double valmax = 0.0;  
  138.     double prob = 1.0;  
  139.     string v_path = "";  
  140.     double v_prob = 1.0;  
  141.     for(VS_i = states.begin(); VS_i != states.end(); VS_i++)  
  142.     {  
  143.         DSD objs = T[*VS_i];  
  144.         prob = objs.d1;  
  145.         v_path = objs.s;  
  146.         v_prob = objs.d2;  
  147.         total += prob;  
  148.         if(v_prob > valmax)  
  149.         {  
  150.             argmax = v_path;  
  151.             valmax = v_prob;  
  152.         }  
  153.     }  
  154.     DSD_tmp.d1 = total;  
  155.     DSD_tmp.s = argmax;  
  156.     DSD_tmp.d2 = valmax;  
  157.     return DSD_tmp;  
  158. }  

執(zhí)行結(jié)果：

obs = walk
    next_state = Rainy
        state = Rainy
            p = 0.07 = 0.1 * 0.7
            {prob, v_path, v_prob} = {0.042, Rainy, 0.042}
            {total, argmax, valmax} = {0.042, Rainy,Rainy, 0.042}
obs = walk
    next_state = Rainy
        state = Sunny
            p = 0.24 = 0.6 * 0.4
            {prob, v_path, v_prob} = {0.096, Sunny, 0.096}
            {total, argmax, valmax} = {0.138, Sunny,Rainy, 0.096}
obs = walk
    next_state = Sunny
        state = Rainy
            p = 0.03 = 0.1 * 0.3
            {prob, v_path, v_prob} = {0.018, Rainy, 0.018}
            {total, argmax, valmax} = {0.018, Rainy,Sunny, 0.018}
obs = walk
    next_state = Sunny
        state = Sunny
            p = 0.36 = 0.6 * 0.6
            {prob, v_path, v_prob} = {0.144, Sunny, 0.144}
            {total, argmax, valmax} = {0.162, Sunny,Sunny, 0.144}
obs = shop
    next_state = Rainy
        state = Rainy
            p = 0.28 = 0.4 * 0.7
            {prob, v_path, v_prob} = {0.03864, Sunny,Rainy, 0.02688}
            {total, argmax, valmax} = {0.03864, Sunny,Rainy,Rainy, 0.02688}
obs = shop
    next_state = Rainy
        state = Sunny
            p = 0.12 = 0.3 * 0.4
            {prob, v_path, v_prob} = {0.01944, Sunny,Sunny, 0.01728}
            {total, argmax, valmax} = {0.05808, Sunny,Rainy,Rainy, 0.02688}
obs = shop
    next_state = Sunny
        state = Rainy
            p = 0.12 = 0.4 * 0.3
            {prob, v_path, v_prob} = {0.01656, Sunny,Rainy, 0.01152}
            {total, argmax, valmax} = {0.01656, Sunny,Rainy,Sunny, 0.01152}
obs = shop
    next_state = Sunny
        state = Sunny
            p = 0.18 = 0.3 * 0.6
            {prob, v_path, v_prob} = {0.02916, Sunny,Sunny, 0.02592}
            {total, argmax, valmax} = {0.04572, Sunny,Sunny,Sunny, 0.02592}
obs = clean
    next_state = Rainy
        state = Rainy
            p = 0.35 = 0.5 * 0.7
            {prob, v_path, v_prob} = {0.020328, Sunny,Rainy,Rainy, 0.009408}
            {total, argmax, valmax} = {0.020328, Sunny,Rainy,Rainy,Rainy, 0.009408}
obs = clean
    next_state = Rainy
        state = Sunny
            p = 0.04 = 0.1 * 0.4
            {prob, v_path, v_prob} = {0.0018288, Sunny,Sunny,Sunny, 0.0010368}
            {total, argmax, valmax} = {0.0221568, Sunny,Rainy,Rainy,Rainy, 0.009408}
obs = clean
    next_state = Sunny
        state = Rainy
            p = 0.15 = 0.5 * 0.3
            {prob, v_path, v_prob} = {0.008712, Sunny,Rainy,Rainy, 0.004032}
            {total, argmax, valmax} = {0.008712, Sunny,Rainy,Rainy,Sunny, 0.004032}
obs = clean
    next_state = Sunny
        state = Sunny
            p = 0.06 = 0.1 * 0.6
            {prob, v_path, v_prob} = {0.0027432, Sunny,Sunny,Sunny, 0.0015552}
            {total, argmax, valmax} = {0.0114552, Sunny,Rainy,Rainy,Sunny, 0.004032}
0.033612
Sunny,Rainy,Rainy,Rainy
0.009408

 

參考文獻：

http://en.wikipedia.org/wiki/Viterbi_algorithm
http://hi.baidu.com/liqinghuisi/blog/item/a11cd4f54394f22fbc3109cf.html

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