package markov;
import java.util.*;
import java.math.*;
import java.lang.reflect.*;

/**
 * Class with main method for presenting the results of the lab. At the moment this just reads in the sequence data
 * and the corresponding state labels.
 */

public class Answer {

    public static void main(String[] args)
    {
	String mfccDataDirectory = "data/yesno_uncut/mfcc/";
	String labelDirectory = "data/yesno_uncut/labels/";

	// Read in the MFCC data and state labels from each class

	DataWithLabels dataClass1 = new DataWithLabels (mfccDataDirectory+"yes",labelDirectory+"yes");
	DataWithLabels dataClass2 = new DataWithLabels (mfccDataDirectory+"no",labelDirectory+"no");

	// Task 1
	// 3 = number of states (silence, yes, silence)
    System.out.println("------");
    System.out.println("Task 1");
	System.out.println("------");
	System.out.println("Yes:");
	HiddenMarkovModel markovYes = new HiddenMarkovModel(3, dataClass1.getMfcc(), dataClass1.getLabels());
	// iterate through each state, getting probability for each transition
	for (int i = 0; i <= markovYes.getNoStates(); i++)
	  for (int j = 0; j <= markovYes.getNoStates(); j++)
		System.out.println(markovYes.getTransitionProbability(i, j));
		System.out.println("--- End of Yes ---");

	// 3 = number of states (silence, no, silence)
    System.out.println("No:");
	HiddenMarkovModel markovNo = new HiddenMarkovModel(3, dataClass2.getMfcc(), dataClass2.getLabels());
		// iterate through each state, getting probability for each transition
	for (int i = 0; i <= markovNo.getNoStates(); i++)
	  for (int j = 0; j <= markovYes.getNoStates(); j++)
	    System.out.println(markovNo.getTransitionProbability(i, j));
	    System.out.println("--- End of No ---");


	// Task 2
	// Our hidden markov's with number of yes/no
    Classifier classifier = new Classifier(markovYes, markovNo, 82 / 165.0);

    int numberOfErrors = 0;

    // If less than 0.5 then we do not classify as 'yes'
    for (int index = 0; index < dataClass1.getNumberExamples(); index++)
    {
      double answer = classifier.classify(dataClass1.getMfcc(index));
      if (answer < 0.5)
        numberOfErrors++;
    } // for
    // If greater than 0.5 then we do not classify as 'no'
    for (int index = 0; index < dataClass2.getNumberExamples(); index++)
    {
      double answer = classifier.classify(dataClass2.getMfcc(index));
      if (answer > 0.5)
        numberOfErrors++;
    }

    // Finding total percentage of errors = noOfErrors / totalData * 100
    double percentageErrors = numberOfErrors / 165.0 * 100.0;
    System.out.println("------");
    System.out.println("Task 2");
    System.out.println("------");
    System.out.println("Total error percentage is " + percentageErrors + "%");

	// Task 3
	// Creating a new Markov Model by combining our current two
	// This time we have 4 states - silence, yes, no, silence (and then stop)
    HiddenMarkovModel markovYesNo = new HiddenMarkovModel(4);

    // Need to represent silent to yes (states 0 -> 1 in our old model, and the same in the new)
    markovYesNo.setTransitionProbability(markovYes.getTransitionProbability(0, 1), 0, 1);

    // Need to represent silent to no (states 0 -> 1 in our old model, 0 -> 2 in the new)
    markovYesNo.setTransitionProbability(markovNo.getTransitionProbability(0, 1), 0, 2);

    // Need to represent silent to silent [initial] (states 0 -> 0, 0 -> 0 in our old model, 0 -> 0, 0 -> 0 in the new, so we must take an average)
    markovYesNo.setTransitionProbability((markovYes.getTransitionProbability(0, 0)
    + markovNo.getTransitionProbability(0, 0)) / 2, 0, 0);

    // Need to represent yes to yes (states 1 -> 1 in our old model, 1 -> 1 in the new)
    markovYesNo.setTransitionProbability(markovYes.getTransitionProbability(1, 1), 1, 1);

    // Need to represent no to no (states 1 -> 1 in our old model, 2 -> 2 in the new)
    markovYesNo.setTransitionProbability(markovNo.getTransitionProbability(1, 1), 2, 2);

    // Need to represent yes to silent (states 1 -> 2 in our old model, 1 -> 3 in the new)
    markovYesNo.setTransitionProbability(markovYes.getTransitionProbability(1, 2), 1, 3);

    // Need to represent no to silent (states 1 -> 2 in our old model, 2 -> 3 in the new)
    markovYesNo.setTransitionProbability(markovNo.getTransitionProbability(1, 2), 2, 3);

    // Need to represent silent to silent [last] (states 2 -> 2, 2 -> 2 in our old model, 3 -> 3, 3 -> 3 in the new, so we must take an average)
    markovYesNo.setTransitionProbability((markovYes.getTransitionProbability(2, 2)
                            + markovNo.getTransitionProbability(2,2)) / 2, 3, 3);

    // Need to represent stop to start (states 3 -> 0, 3 -> 0 in our old model, 4 -> 0, 4 -> 0 in the new, so we must take an average)
    markovYesNo.setTransitionProbability((markovYes.getTransitionProbability(3, 0)
                             + markovNo.getTransitionProbability(3,0)) / 2, 4, 0);
    // Need to represent silence [initial] to stop (states 0 -> 3, 0 -> 3 in our old model, 0 -> 4, 0 -> 4 in the new, so we must take an average)
    markovYesNo.setTransitionProbability((markovYes.getTransitionProbability(0, 3)
                             + markovNo.getTransitionProbability(0,3)) / 2, 0, 4);

    // Need to represent yes to stop (states 1 -> 3 in our old model, 1 -> 4 in the new)
    markovYesNo.setTransitionProbability(markovYes.getTransitionProbability(1, 3), 1, 4);

    // Need to represent no to stop (states 1 -> 3 in our old model, 2 -> 4 in the new)
    markovYesNo.setTransitionProbability(markovNo.getTransitionProbability(1, 3), 2, 4);

    // Need to represent silence to stop [last] (states 2 -> 3, 2 -> 3 in our old model, 3 -> 4, 3 -> 4 in the new, so we must take an average)
    markovYesNo.setTransitionProbability((markovYes.getTransitionProbability(2, 3)
                             + markovNo.getTransitionProbability(2,3)) / 2, 3, 4);

    // Here we are printing our results for each state
    // Should be the result of combining both yes and no classifiers
    System.out.println("------");
    System.out.println("Task 3");
    System.out.println("------");
    for(int i = 0; i < 5; i++)
    {
      for(int j = 0; j < 5; j++)
        System.out.println(markovYesNo.getTransitionProbability(i, j));
    }

    // Combining emission densities
    for (int i = 0; i < 13; i++)
    {
      // "Silence" needs to be combined
      markovYesNo.setEmissionDensity(markovYes.getEmissionDensity(i, 0).combine(markovNo.getEmissionDensity(i, 0)), i, 0);

      // Separate "yes" and "no"
      markovYesNo.setEmissionDensity(markovYes.getEmissionDensity(i, 1), i, 1);
      markovYesNo.setEmissionDensity(markovNo.getEmissionDensity(i, 1), i, 2);

      // And finally another "silence" at the end
      markovYesNo.setEmissionDensity(markovYes.getEmissionDensity(i, 2).combine(markovNo.getEmissionDensity(i, 2)), i, 3);
    }
	// Task 4

	// Finding path errors for yes and no classifiers respectively
	// Using viterbi method to find optimal path
	double yesNoError = 0;
    int[] yesViterbi;
    int[] noViterbi;
    // Going through our 'yes's
    for(int i = 0; i < dataClass1.getNumberExamples(); i++)
    {
      yesViterbi = markovYesNo.viterbi(dataClass1.getMfcc(i));
      for(int j = 0; j < yesViterbi.length; j++)
      {
        // State 2 is our 'no' and therefore cannot possibly be optimal
        if (yesViterbi[j] == 2)
        {
          // But only count it once so we do not get a silly return e.g. 2,2,2,2 = 1 error not 4
          yesNoError++;
          break;
        }
      }
    }

    // Going through our 'no's
    for(int i = 0; i < dataClass2.getNumberExamples(); i++)
    {
      noViterbi = markovYesNo.viterbi(dataClass2.getMfcc(i));
      for(int j= 0; j < noViterbi.length; j++)
      {
        // State 1 is our 'yes' and therefore cannot possibly be optimal
        if (noViterbi[j] == 1)
        {
          // But only count it once so we do not get a silly return e.g. 1,1,1,1 = 1 error not 4
          yesNoError++;
          break;
        }
      }
    }

    // Calculate percentage
    double yesNoViterbiTotalError = (yesNoError * 100)
          / (dataClass1.getNumberExamples() + dataClass2.getNumberExamples());

    System.out.println("------");
    System.out.println("Task 4");
    System.out.println("------");
    // Combined classifier error percentage
    System.out.println("Total error percentage is " + yesNoViterbiTotalError + "%");
    } // main
}