matlab scripts

Author: Simon Charlow

Phonology_script.m

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+% JAS: this script simulates consonant cluster data based upon the Simplex
+% Onset Hypothesis and identifies the SD of the anchor that best
+% fits the data.
+% Output is a graph which plots R^2, goodness of fit, statistic by anchor
+
+
+close all; clear all;
+randn('state',0); % Reset the random number generator
+
+simN = 1000; %number of times simulation is repeated
+for count=1:simN %loops through iterations of the simulation
+    
+    
+    N = 30;    % number of datapoints; divisible by 3
+    stdv = 20;
+    p = 30; % plateau duration
+    ipi = 40; % inter-plateau interval
+
+    % generate timestamps for C3 (the prevocalic consonant)
+     % preallocate arrays for efficiency
+        CL3 = zeros(1,N); %left edge of C3
+        CM3 = zeros(1,N); %right edge of C3
+        CR3 = zeros(1,N); %right edge of C3
+
+        % generate R(ight plateau edge = Release) of prevocalic consonant
+        CR3 = sqrt(400).*randn(1,N) + 500; % generate N Gaussian distributed numbers with mean 500, variance 400
+
+        % generate L(eft plateau edge = Target) of prevocalic consonant 
+        for n=1:N;
+            e = stdv * randn; % normally distributed random error, 0 mean
+            CL3(n) = CR3(n) - (p + e); % generate L3 corresponding to R3 by assuming a plateau duration of 10 ms
+        end;
+
+        % calculate midpoint of prevocalic consonant
+        for n=1:N;
+            CM3(n) = (CR3(n) + CL3(n))/2; %
+        end;
+
+    %plot timestamps for C3
+        nbins = 20;
+%        [CL3h,CL3out] = hist(CL3,nbins); % returns vectors Lh and Lout containing the frequency counts and the bin locations. 
+%        [CR3h,CR3out] = hist(CR3,nbins); % returns vectors Lh and Lout containing the frequency counts and the bin locations.
+%        subplot(5,2,1);
+%        bar(CL3out,CL3h); % plot the histogram
+%        subplot(5,2,2);
+%        bar(CR3out,CR3h); % plot the histogram
+
+    % generate timestamps for C2 
+        % preallocate arrays for efficiency
+        CL2 = zeros(1,(2*(N/3))); %left edge of C2
+        CM2 = zeros(1,(2*(N/3))); %right edge of C2
+        CR2 = zeros(1,(2*(N/3))); %right edge of C2
+           
+        % generate R(ight plateau edge = Release) of C2 from left edge of C3
+            % for C tokens 
+            %for n=1:(N/3)
+            %    CR2(n) = CR3(n); % the right edge of the cluster is the same as the right edge of the prevocalic consonant  
+            %end;
+    
+            % for CC/CCC tokens 
+            for n=1:(2*(N/3)); % alternative, use ceiling function
+                e = stdv * randn; % normally distributed random error
+                CR2(n) = CL3(n) - (ipi + e); % generate right edge of C2 from left edge of C2 assuming an ipi of 40 ms
+            end; 
+    
+       
+        % generate L(eft plateau edge = Target) of C2 
+            % for C tokens 
+            %for n=1:(N/3)
+            %    CL2(n) = CL3(n); % the left edge of the cluster is the same as the left edge of the prevocalic consonant  
+            %end;
+    
+            % for CC/CCC tokens 
+            for n=1:(2*(N/3));
+                e = stdv * randn; % normally distributed random error based on relation with CR3
+                CL2(n) = CR2(n) - (p + e); % generate L2 corresponding to CR3 by assuming a plateau duration
+            end;
+                             
+        % calculate midpoint of C2
+        % C tokens have no C2 and therefore no C2 midpoint
+        for n=1:(2*(N/3));
+            CM2(n) = (CR2(n) + CL2(n))/2; %
+        end;   
+
+    %plot timestamps for C2    
+%        [CL2h,CL2out] = hist(CL2,nbins) % returns vectors Lh and Lout containing the frequency counts and the bin locations. 
+%        [CR2h,CR2out] = hist(CR2,nbins) % returns vectors Lh and Lout containing the frequency counts and the bin locations.
+%        subplot(5,2,3);
+%        bar(CL2out,CL2h); % plot the histogram
+%        subplot(5,2,4);
+%        bar(CR2out,CR2h); % plot the histogram
+
+    % generate timestamps for C1 
+        % preallocate arrays for efficiency
+        CL1 = zeros(1, N); %left edge of C1
+        CM1 = zeros(1,(N/3)); %right edge of C1
+        CR1 = zeros(1,(N/3)); %right edge of C1
+ 
+        % generate R(ight plateau edge = Release) of C1 
+        % for C tokens 
+        %    for n=1:(N/3)
+        %        CR1(n) = CR2(n); % the right edge of the cluster is the same as the right edge of the prevocalic consonant  
+        %    end;
+    
+        %    % for CC tokens 
+        %    for n=(N/3):(2*(N/3))
+        %       CR1(n) = CR3(n); % the right edge of C1 equals the right edge of C3
+        %    end; 
+    
+            % for CCC tokens 
+            for n=1:(N/3);
+               e = stdv * randn; % normally distributed random error
+                CR1(n) = CL2(n) - (ipi + e); % generate right edge of C1 from left edge of C2 assuming ipi of 40ms
+            end;
+        
+        % generate L(eft plateau edge = Target) of C1 
+        % for CCC tokens 
+            for n=1:(N/3);
+                e = stdv * randn; % normally distributed random error based on relation with CR3
+                CL1(n) = CR1(n) - (p + e); % generate L2 corresponding to CR1 by assuming a plateau of 10ms  
+            end;
+        % for CC tokens 
+            for k=n+1:n+(N/3);
+                CL1(k) = CL2(k); % left edge of C2 is the left edge of the cluster for CC  
+            end;
+            % for C tokens 
+            for j=k+1:k+(N/3);
+                CL1(j) = CL3(j); % the left edge of the cluster is the same as the left edge of the prevocalic consonant  
+            end;
+        
+      % calculate midpoint of prevocalic consonant
+        % for CCC only 
+        for n=1:N/3;
+            CM1(n) = (CR1(n) + CL1(n))/2; %
+        end;  
+    
+    %plot timestamps for C1    
+%        [CL1h,CL1out] = hist(CL1,nbins) % returns vectors Lh and Lout containing the frequency counts and the bin locations. 
+%        [CR1h,CR1out] = hist(CR1,nbins) % returns vectors Lh and Lout containing the frequency counts and the bin locations.
+%        subplot(5,2,5);
+%        bar(CL1out,CL1h); % plot the histogram
+%        subplot(5,2,6);
+%        bar(CR1out,CR1h); % plot the histogram
+
+    % generate timestamps for CCGlobal 
+        % preallocate array for efficiency
+        CCglobal = zeros(1, N); %mean of midpoints
+
+        %for CCC clusters
+        for n=1:(N/3);
+                CCglobal(n) = 1/3 * (CM1(n) + CM2(n) + CM3(n)); % mean of consonant midpoints  
+        end;
+
+        %for CC clusters
+        for k=n+1:n+(N/3);
+                CCglobal(k) = 1/2 * (CM2(k) + CM3(k)); % mean of consonant midpoints  
+        end;
+
+        %for C clusters
+        for j=k+1:k+(N/3);
+                CCglobal(j) = CM3(j); % CCglobal synchronous with prevocalic midpoint
+        end;     
+      
+    % generate series of anchor points increasing in distance from the prevocalic consonant      
+        AN = 20; %number of anchor points
+        AD = 250; %interval from prevocalic consonant to closest anchor point
+        DI = 0; % interval added to each subsequent anchor point
+        VI = 5; % stepwise increase in variability
+        
+        % preallocate array for efficiency
+        A = zeros(AN,N); %one column for each anchor (AN) and one row for each token
+        
+        %cycle loop produces new anchor for each token
+        for cycle = 1: AN; %creates multiple anchor points for each token
+            for m=1:N; %creates anchor point for each token from the right edge of the token
+                Ae = stdv * randn; % normally distributed random error, assuming mean of 0
+                A(cycle, m) = CR3(m) + AD + Ae;  % generate anchor A corresponding to CR3 by assuming a period of 200 ms
+            end;
+            AD = AD + DI; %increases distance for each anchor point by interval DI
+            stdv = stdv + VI; %creates new anchor point  
+        end; 
+    
+    % plot anchor points
+%    [Ah,Aout] = hist(A(1,:),nbins) % returns vectors Lh and Lout containing the frequency counts and the bin locations. 
+%    subplot(5,2,7);
+%    bar(Aout,Ah); % plot the histogram
+%    ylabel('Anchor 1');
+
+%    [Ah,Aout] = hist(A(4,:),nbins) % returns vectors Lh and Lout containing the frequency counts and the bin locations. 
+%    subplot(5,2,8);
+%    bar(Aout,Ah); % plot the histogram
+%    ylabel('Anchor 4');
+
+
+%Note about consonantal landmarks: they are replaced with each cycle of the simulation
+%in constrast, RSD values for each landmark are stored across simulations.
+
+        for cycle = 1: AN; %cycles through for each anchor point
+            %if CoV==0 % if 1, then coefficient of variance; if 0, standard deviation
+             
+                %xv = [std(A(cycle)-CL1) std(A(cycle)-CCglobal)  std(A(cycle)-CR3)];
+            %else
+                %xd(1, cycle) = [(mean(A(cycle,:)))]; %xd = average distance of prevocalic consonant to a given anchor 
+                
+                LE_RSD(count, cycle) = std(A(cycle,:)-CL1)/(mean(A(cycle,:))-mean(CL1));
+                RE_RSD(count, cycle) = std(A(cycle,:)-CR3)/(mean(A(cycle,:))-mean(CR3));
+                CC_RSD(count, cycle) = std(A(cycle,:)-CCglobal)/(mean(A(cycle,:))-mean(CCglobal));
+                
+                LE_SD(count, cycle) = std(A(cycle,:)-CL1);
+                RE_SD(count, cycle) = std(A(cycle,:)-CR3);
+                CC_SD(count, cycle) = std(A(cycle,:)-CCglobal);
+                              
+            %end;      
+           
+        end;        
+ 
+     end %main simulation loop   
+
+%Plot RSD across simulations
+    % plot mean RSD across simulations as a function of anchor distance
+     %   subplot(5,2,10);
+     %       plot(xd, LE_RSD, 'b-', xd, RE_RSD, 'g-', xd, CC_RSD, 'r:')
+    
+     % plot mean RSD across simulations for each anchor point as a function of anchor number
+     
+        x = 1:1:AN; %establishes x-axis as anchor
+        plot(x, mean(LE_RSD(:,x)), 'b-',x, mean(RE_RSD(:,x)), 'g-',x, mean(CC_RSD(:,x)), 'r:');
+       
+     
+
+
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+

calcs.m

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+a = 30
+b = 40
+c = 267
+d = 400
+e = 400
+
+f = (c^2*(a^2/6 + (a*b)/3 + b^2/6 + (83*d)/216 + (29*e)/216))/((a + b/2 + c)^2 - c^2)
+
+
+a = 20
+b = 10
+c = 234.8
+d = 100
+e = 100
+
+f = (c^2*(a^2/6 + (a*b)/3 + b^2/6 + (83*d)/216 + (29*e)/216))/((a + b/2 + c)^2 - c^2)
+
+
+a = 46.90168176;
+b = 46.73126615;
+c = 154.9775;
+d = 567.9462796;
+e = 427.8239681;
+
+f = (c^2*(a^2/6 + (a*b)/3 + b^2/6 + (83*d)/216 + (29*e)/216))/((a + b/2 + c)^2 - c^2);
+sd = f^.5

collective_sim.m

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+close all; clear all;   
+%randn('state',0); %reset the random number generator
+
+param = {'l1' 'l2' 'l3' 'l4' 'l5' 'l6'};
+
+[h w] = size(param);
+
+tokens = 300;   %divisible by 3
+sims = 100;     %number of times to iterate lexical construction
+
+allrems = zeros(sims,w); %preallocate arrays
+allccms = zeros(sims,w);
+allremstds = zeros(sims,w);
+allccmstds = zeros(sims,w);
+
+for sim=1:sims
+   
+for i=1:length(param)
+    cd params;
+    run(param{i});
+    cd ..;
+    datacell{i} = complex_lexicon(tokens,a,b,c,d,e,f);
+    clear a b c d e f;
+end
+
+%number of stimuli types (wordtype-speaker pairs)
+types = length(datacell);
+
+%number of simulations
+simN = factorial(types);
+
+%preallocate arrays
+remstds = zeros(simN,types);
+ccmstds = zeros(simN,types);
+rems = zeros(simN,types);
+ccms = zeros(simN,types);
+
+%errthang
+alls = perms(datacell);
+
+%main loop
+for count=1:simN
+    permd=alls(count,1:types);
+    cumulator=[]; %empty starting cumulator array
+    for lexicon=1:types 
+        for place=1:lexicon
+            cumulator=cat(2,cumulator,cell2mat(permd(place))); 
+        end;
+        summed=sum(cumulator,2);
+        [h w]=size(cumulator);
+        avg=summed/w;
+        avgREM=avg(1,:); 
+        avgCCM=avg(2,:);
+        stdREM=std(cumulator(1,:));
+        stdCCM=std(cumulator(2,:));
+        if w>5
+            rsdREM=stdREM/avgREM;
+            rsdCCM=stdCCM/avgCCM;
+        else
+            rsdREM=(1+1/(4*w))*(stdREM/avgREM);
+            rsdCCM=(1+1/(4*w))*(stdCCM/avgCCM);
+        end
+        rems(count,lexicon)=rsdREM;
+        ccms(count,lexicon)=rsdCCM;
+        remstds(count,lexicon)=stdREM;
+        ccmstds(count,lexicon)=stdCCM;
+        cumulator=[]; %resets cumulator
+    end;
+end; %end main loop
+
+%averaging sims
+allremstds(sim,:) = sum(remstds,1)/simN;
+allccmstds(sim,:) = sum(ccmstds,1)/simN;
+allrems(sim,:) = sum(rems,1)/simN;
+allccms(sim,:) = sum(ccms,1)/simN;
+
+end
+
+remarray=mean(allrems);
+ccmarray=mean(allccms);
+remstdarray=mean(allremstds);
+
+x=remstdarray;
+
+plot(x,remarray,'r-',x,ccmarray,'k--');
+%lsline;