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Author: niravnb

Hidden Markov Model and Dynamic Time Warping/DTW/1.tgz

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+function [ dist ] = AvgDistance( x )
+%Returns euclidean distance between X and m
+
+dist = sum(sum(x))/length(x);
+
+end
+

Hidden Markov Model and Dynamic Time Warping/DTW/AvgDistance.m

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+function [ distance ] = DTWDistance(data1,tdata1)
+%Returns DTW Distance between two sequence
+
+DTW = zeros(length(data1)+1,length(tdata1)+1);
+DTW(1,:) = Inf;
+DTW(:,1) = Inf;
+DTW(1,1) = 0;
+
+for i = 2:length(data1)+1
+    for j = 2:length(tdata1)+1
+        cost = myeuclideanDistance(data1(i-1,:),tdata1(j-1,:));
+        min1 = min(DTW(i-1,j),DTW(i,j-1));
+        min2 = min(min1,DTW(i-1,j-1));
+        DTW(i,j) = cost + min2;
+    end
+end
+   
+distance = DTW(length(data1)+1,length(tdata1)+1);
+
+
+end
+

Hidden Markov Model and Dynamic Time Warping/DTW/DTWDistance.m

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+%addpath(genpath('/Users/NiravMBA/Desktop/IIT Madras/Semester I/Pattern Recognition/Assignment/Assignment 4/DTW/bosaris_toolkit'))
+
+function [] = demo_main(test_data,type)
+%,target1,nontarget1,target2,nontarget2,target3,nontarget3,target4,nontarget4,target5,nontarget5,caseno)
+% To plot DET Curve
+
+close all;
+
+fprintf('You need to run this script in Matlab''s graphical mode to see the plots.\n');
+
+% calculate an effective prior from target prior, Cmiss, and Cfa
+prior = effective_prior(0.33,1,1);
+
+
+%% Assign Target and Non Target Scores
+
+
+% test_data.tar1 = target;
+% test_data.non1 = nontarget;
+
+% test_data.tar2 = target1;
+% test_data.non2 = nontarget1;
+% 
+% test_data.tar3 = target2;
+% test_data.non3 = nontarget2;
+% 
+% test_data.tar4 = target3;
+% test_data.non4 = nontarget3;
+% 
+% test_data.tar5 = target4;
+% test_data.non5 = nontarget4;
+% 
+% test_data.tar6 = target5;
+% test_data.non6 = nontarget5;
+
+%% make a DET plot for all the five cases.
+demo_plot_det_for_fusion(test_data,prior,type);
+
+end