A simple example of the Viterbi algorithm for HMM

Today I read a Daniel's technical blog about Hmm, and after reading it, I wrote a simple example of the Viterbi algorithm, which was written in Scala and the Java language. Upload it now.

Package COM.TXQ.HMMImport Java.util
Import Scala.collection._
/**
 * Hmm Viterbi algorithm, based on the display status chain estimation implicit chain
 *@param statesImplicit states
 *@param observationsAn explicit states 
*@param start_probabilityInitial probability vector 
*@param transition_probabilityConversion probability 
*@param emission_probabilityEmission probability 
*@authorTongxueqiang 
* @ 2017/01/21 
*/
Class Markov_weather (states:array[any],observations:array[any],start_probability:map[string,double],transition_probability:map[string,map[string,double]],emission_probability:map[string,map[String,double]]) {
 /**
 *
*@param obs_serObserved explicit state chain 
*@return 
 */ 
 Def compute_hidden_ser (Obs_ser:util. linkedlist[String]): Util. linkedlist[String] = {
 Val result =New util. linkedlist[String] ()Put the results in a linked list
  var p = mutable. map[String,double] ()Product of storage conversion probability and output probability
  var keys:seq[String] =Null
 var values:seq[double] =Null
 var max:double =0.0
  var label:int =0
  Initialize, get an implicit state first
  var obs_state = Obs_ser.poll ()
 if (obs_state! =NULL) {
 For ((k,v) <-start_probability) {
 Val A = v * emission_probability (k) (Obs_state)
p + = (k->a)
}
Keys = P.keys.toseq
Values = P.values.toseq
Max = VALUES (0)
 for (i <-1 until Values.size) {
 if (values (i) > Max) {
Max = VALUES (i)
label = i
}
}
Result.add (keys (label))
Label =0
 P.clear ()
}
 Start Iteration
 Obs_state = Obs_ser.poll ()
 while (obs_state! =NULL) {
 Val hid_ser = Result.peeklast ()
 if (hid_ser! = null) {
For ((k,v) <-transition_probability (Hid_ser)) {
val A = v * emission_probability (k) (Obs_state)
p + = (k->a)
}
Keys = P.keys.toseq
Values = P.values.toseq
Max = values (0)
For (i <- 1 until values.size) {
if (values (i) > Max) {
Max = VALUES (i)
label = i
}
}
Result.add (keys (label))
Label = 0
P.clear ()
Obs_state = Obs_ser.poll ()
}
}
Result
}
}

Test class:

Package COM.TXQ.HMMImport Java.util
/**
*
 */
 Object Hmmtest {
 def main (args:array[String]): Unit = {
 Test Data Set
  Val states =Array[any] ("Rainy","Sunny")Implicit states
 Val observations =Array[any] ("Walk","Shop","Clean")An explicit states
  Val start_probability =Map ("Rainy"0.6,"Sunny"0.4)Initial probability vector
  Val transition_probability =Map ("Rainy"Map ("Rainy"0.7,"Sunny"0.3),"Sunny"Map ("Rainy"0.4,"Sunny"0.6))Conversion probability
 Emission probability
  Val emission_probability =Map ("Rainy"Map ("Walk"0.1,"Shop"0.4,"Clean"0.5),"Sunny"Map ("Walk"0.6,"Shop"0.3,"Clean"0.1))
 Val Obs_ser =New util. linkedlist[String] ()Observed set of explicit states
 Obs_ser.add ("Walk")
Obs_ser.add ("Shop")
Obs_ser.add ("Clean")
 Val Markov:markov_weather =New Markov_weather (states,observations,start_probability,transition_probability,emission_probability)
 Val result = Markov.compute_hidden_ser (obs_ser)//Perform calculation
  var hidden_state = Result.pollfirst ()
While (hidden_state! = null) {
Print (hidden_state)
if (result.size ()! = 0) {
Print ("-")
}
Hidden_state = Result.pollfirst ()
}
}
}
The results displayed:
Sunny->sunny->rainy
Process finished with exit code 0           

A simple example of the Viterbi algorithm for HMM