From fbc31dc6a47c371fe1d4a6891520f0e20004095a Mon Sep 17 00:00:00 2001
From: bepo23 <bepo23@c7eff9ee-dd40-0410-8832-91a4d88773cf>
Date: Tue, 16 Jun 2009 09:17:57 +0000
Subject: [PATCH] Removed unneeded first scaling attempt of the HMM. Cleaned up
 the WiimoteStreamer to be more readable as preparation to adapt to new
 functions with wiimotes interaction.

git-svn-id: svn://svn.code.sf.net/p/wiigee/code/trunk@92 c7eff9ee-dd40-0410-8832-91a4d88773cf
---
 src/org/wiigee/logic/ScaledHMM.java | 336 ----------------------------
 1 file changed, 336 deletions(-)
 delete mode 100644 src/org/wiigee/logic/ScaledHMM.java

diff --git a/src/org/wiigee/logic/ScaledHMM.java b/src/org/wiigee/logic/ScaledHMM.java
deleted file mode 100644
index 8b0deac..0000000
--- a/src/org/wiigee/logic/ScaledHMM.java
+++ /dev/null
@@ -1,336 +0,0 @@
-/*
- * wiigee - accelerometerbased gesture recognition
- * Copyright (C) 2007, 2008 Benjamin Poppinga
- * 
- * Developed at University of Oldenburg
- * Contact: benjamin.poppinga@informatik.uni-oldenburg.de
- *
- * This file is part of wiigee.
- *
- * wiigee is free software; you can redistribute it and/or modify
- * it under the terms of the GNU Lesser General Public License as published by
- * the Free Software Foundation; either version 2 of the License, or
- * (at your option) any later version.
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- * GNU Lesser General Public License for more details.
- * 
- * You should have received a copy of the GNU Lesser General Public License along
- * with this program; if not, write to the Free Software Foundation, Inc.,
- * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
- */
-
-package org.wiigee.logic;
-import java.text.*;
-import java.util.Vector;
-
-/**
- * This is a Hidden Markov Model implementation which internally provides
- * the basic algorithms for training and recognition (forward and backward
- * algorithm). Since a regular Hidden Markov Model doesn't provide a possibility
- * to train multiple sequences, this implementation has been optimized for this
- * purposes using some state-of-the-art technologies described in several papers.
- *
- * @author Benjamin 'BePo' Poppinga
- *
- */
-
-public class ScaledHMM {
-	/** The number of states */
-	private int numStates;
-
-	/** The number of observations */
-	private int sigmaSize;
-
-	/** The initial probabilities for each state: p[state] */
-	public double pi[];
-
-	/** The state change probability to switch from state A to
-	 * state B: a[stateA][stateB] */
-	public double a[][];
-
-	/** The probability to emit symbol S in state A: b[stateA][symbolS] */
-	public double b[][];
-
-	/**
-	 * Initialize the Hidden Markov Model in a left-to-right version.
-	 * 
-	 * @param numStates Number of states
-	 * @param sigmaSize Number of observations
-	 */
-	public ScaledHMM(int numStates, int sigmaSize) {
-		this.numStates = numStates;
-		this.sigmaSize = sigmaSize;
-		pi = new double[numStates];
-		a = new double[numStates][numStates];
-		b = new double[numStates][sigmaSize];
-		this.reset();
-	}
-	
-	/**
-	 * Reset the Hidden Markov Model to the initial left-to-right values.
-	 *
-	 */
-	private void reset() {
-		int jumplimit = 2;
-		
-		// set startup probability
-		pi[0] = 1;
-		for(int i=1; i<numStates; i++) {
-			pi[i] = 0;
-		}
-		
-		/*
-		for(int i=0; i<numStates; i++) {
-			for(int j=0; j<numStates; j++) {
-				a[i][j]=1/numStates;
-			}
-		}
-		*/
-		
-		// set state change probabilities in the left-to-right version
-		// NOTE: i now that this is dirty and very static. :)
-		for(int i=0; i<numStates; i++) {
-			for(int j=0; j<numStates; j++) {
-				if(i==numStates-1 && j==numStates-1) { // last row
-					a[i][j] = 1.0;
-				} else if(i==numStates-2 && j==numStates-2) { // next to last row
-					a[i][j] = 0.5;
-				} else if(i==numStates-2 && j==numStates-1) { // next to last row
-					a[i][j] = 0.5;
-				} else if(i<=j && i>j-jumplimit-1) {
-					a[i][j] = 1.0/(jumplimit+1);
-				} else {
-					a[i][j] = 0.0;
-				}
-			}
-		}
-		
-		
-		// emission probability
-		for(int i=0; i<numStates; i++) {
-			for(int j=0; j<sigmaSize; j++) {
-				b[i][j] = 1.0/(double)sigmaSize;
-			}
-		}
-	}
-
-	/**
-	 * Trains the Hidden Markov Model with multiple sequences.
-	 * This method is normally not known to basic hidden markov
-	 * models, because they usually use the Baum-Welch-Algorithm.
-	 * This method is NOT the traditional Baum-Welch-Algorithm.
-	 * 
-	 * If you want to know in detail how it works please consider
-	 * my Individuelles Projekt paper on the wiigee Homepage. Also
-	 * there exist some english literature on the world wide web.
-	 * Try to search for some papers by Rabiner or have a look at
-	 * Vesa-Matti Mäntylä - "Discrete Hidden Markov Models with
-	 * application to isolated user-dependent hand gesture recognition". 
-	 * 
-	 */
-	public void train(Vector<int[]> trainsequence) {
-
-		double[][] a_new = new double[a.length][a.length];
-		double[][] b_new = new double[b.length][b[0].length];
-		// re calculate state change probability a
-		for(int i=0; i<a.length; i++) {
-			for(int j=0; j<a[i].length; j++) {	
-				double zaehler=0;
-				double nenner=0;
-			
-				for(int k=0; k<trainsequence.size(); k++) {
-					this.reset();
-					int[] sequence = trainsequence.elementAt(k);
-					
-					double[][] fwd = this.forwardProc(sequence);
-					double[][] bwd = this.backwardProc(sequence);
-					double prob = this.getProbability(sequence);
-		
-					double zaehler_innersum=0;
-					double nenner_innersum=0;
-									
-					for(int t=0; t<sequence.length-1; t++) {
-						double scaled_fwd = this.getScaledValue(fwd, fwd, i, t);
-						double scaled_bwd = this.getScaledValue(bwd, fwd, i, t);
-						double scaled_bwd_tp1 = this.getScaledValue(bwd, fwd, i, t+1);
-						double scaling_factor_tp1 = this.getScalingFactor(fwd, t+1);
-						zaehler_innersum+=scaled_fwd*a[i][j]*b[j][sequence[t+1]]*scaled_bwd_tp1*scaling_factor_tp1;
-						nenner_innersum+=scaled_fwd*scaled_bwd;
-						System.out.println("scaled_fwd = "+scaled_fwd);
-						System.out.println("scaled_bwd = "+scaled_bwd);
-						System.out.println("scaling factor = "+scaling_factor_tp1);
-					}
-					zaehler+=zaehler_innersum;
-					nenner+=nenner_innersum;
-				} // k
-		
-				a_new[i][j] = zaehler/nenner;
-			} // j
-		} // i
-		
-		// re calculate emission probability b
-		for(int i=0; i<b.length; i++) { // zustaende
-			for(int j=0; j<b[i].length; j++) {	// symbole
-				double zaehler=0;
-				double nenner=0;
-			
-				for(int k=0; k<trainsequence.size(); k++) {
-					this.reset();
-					int[] sequence = trainsequence.elementAt(k);
-					
-					double[][] fwd = this.forwardProc(sequence);
-					double[][] bwd = this.backwardProc(sequence);
-					double prob = this.getProbability(sequence);
-		
-					double zaehler_innersum=0;
-					double nenner_innersum=0;
-					
-					
-					for(int t=0; t<sequence.length-1; t++) {
-						if(sequence[t]==j) {
-							zaehler_innersum+=fwd[i][t]*bwd[i][t];
-						}
-						nenner_innersum+=fwd[i][t]*bwd[i][t];
-					}
-					zaehler+=(1/prob)*zaehler_innersum;
-					nenner+=(1/prob)*nenner_innersum;
-				} // k
-		
-				b_new[i][j] = zaehler/nenner;
-			} // j
-		} // i
-	
-		this.a=a_new;
-		this.b=b_new;
-	}
-	
-	/**
-	 * Traditional Forward Algorithm.
-	 * 
-	 * @param o the observationsequence O
-	 * @return Array[State][Time] 
-	 * 
-	 */
-	private double[][] forwardProc(int[] o) {
-		double[][] f = new double[numStates][o.length];
-		for (int l = 0; l < f.length; l++) {
-			f[l][0] = pi[l] * b[l][o[0]];
-		}
-		for (int i = 1; i < o.length; i++) {
-			for (int k = 0; k < f.length; k++) {
-				double sum = 0;
-				for (int l = 0; l < numStates; l++) {
-					sum += f[l][i-1] * a[l][k];
-				}
-				f[k][i] = sum * b[k][o[i]];
-			}
-		}
-		return f;
-	}
-	
-	/**
-	 * Returns the probability that a observation sequence O belongs
-	 * to this Hidden Markov Model without using the bayes classifier.
-	 * Internally the well known forward algorithm is used.
-	 * 
-	 * @param o observation sequence
-	 * @return probability that sequence o belongs to this hmm
-	 */
-	public double getProbability(int[] o) {
-		double prob = 0.0;
-		double[][] forward = this.forwardProc(o);
-		//	add probabilities
-		for (int i = 0; i < forward.length; i++) { // for every state
-			prob += forward[i][forward[i].length - 1];
-		}
-		return prob;
-	}
-	
-
-	/**
-	 * Backward algorithm.
-	 * 
-	 * @param o observation sequence o
-	 * @return Array[State][Time]
-	 */
-	public double[][] backwardProc(int[] o) {
-		int T = o.length;
-		double[][] bwd = new double[numStates][T];
-		/* Basisfall */
-		for (int i = 0; i < numStates; i++)
-			bwd[i][T - 1] = 1;
-		/* Induktion */
-		for (int t = T - 2; t >= 0; t--) {
-			for (int i = 0; i < numStates; i++) {
-				bwd[i][t] = 0;
-				for (int j = 0; j < numStates; j++)
-					bwd[i][t] += (bwd[j][t + 1] * a[i][j] * b[j][o[t + 1]]);
-			}
-		}
-		return bwd;
-	}
-
-	// scales a fwd/bwd-procedure at i/t.
-	private double getScaledValue(double[][] pcd, double[][] divisor, int i, int t) {
-		double sum = 0;
-		for(int v=0; v<this.numStates; v++) {
-			sum += divisor[v][t];
-		}
-		return pcd[i][t]/sum;
-	}
-	
-	private double getScalingFactor(double[][] divisor, int t) {
-		double ret = 0;
-		for(int i=0; i<this.numStates; i++) {
-			ret += divisor[i][t];
-		}
-		return 1/ret;
-	}
-
-	/** 
-	 * Prints everything about this model, including
-	 * all values. For debug purposes or if you want
-	 * to comprehend what happend to the model.
-	 * 
-	 */
-	public void print() {
-		DecimalFormat fmt = new DecimalFormat();
-		fmt.setMinimumFractionDigits(5);
-		fmt.setMaximumFractionDigits(5);
-		for (int i = 0; i < numStates; i++)
-			System.out.println("pi(" + i + ") = " + fmt.format(pi[i]));
-		System.out.println();
-		for (int i = 0; i < numStates; i++) {
-			for (int j = 0; j < numStates; j++)
-				System.out.print("a(" + i + "," + j + ") = "
-						+ fmt.format(a[i][j]) + " ");
-			System.out.println();
-		}
-		System.out.println();
-		for (int i = 0; i < numStates; i++) {
-			for (int k = 0; k < sigmaSize; k++)
-				System.out.print("b(" + i + "," + k + ") = "
-						+ fmt.format(b[i][k]) + " ");
-			System.out.println();
-		}
-	}
-
-	public double[][] getA() {
-		return this.a;
-	}
-	
-	public void setA(double[][] a) {
-		this.a = a;
-	}
-	
-	public double[][] getB() {
-		return this.b;
-	}
-	
-	public void setB(double[][] b) {
-		this.b=b;
-	}
-}