fuck this

assignments/hwk03/EMG.m

@@ -24,15 +24,13 @@ function [h, m, Q] = EMG(x, k, epochs, flag)
   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   % TODO: Initialise cluster means using k-means
   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  [~, ~, ~, D] = kmeans(x, k);
+  [idx, m] = kmeans(x, k);
  
   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   % TODO: Determine the b values for all data points
   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   for i = 1:num_data
-    row = D(i,:);
-    minIdx = row == min(row);
-    b(i,minIdx) = 1;
+    b(i, idx(i)) = 1;
   end
   
   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -40,17 +38,17 @@ function [h, m, Q] = EMG(x, k, epochs, flag)
   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   pi = zeros(k, 1);
   for i = 1:k
-    pi(i) = sum(b(:, i));
+    pi(i) = sum(b(:, i)) / num_data;
   end
 
   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   % TODO: Initialize the covariance matrix estimate
   %       further modifications will need to be made when doing 2(d)
   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  m = zeros(k, dim);
+  % m = zeros(k, dim);
   for i = 1:k
     data = x(b(:, i) == 1, :);
-    m(i, :) = mean(data);
+    % m(i, :) = mean(data);
     S(:, :, i) = cov(data);
   end
   
@@ -65,7 +63,7 @@ function [h, m, Q] = EMG(x, k, epochs, flag)
     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
     % TODO: Store the value of the complete log-likelihood function
     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-    Q(2*n - 1) = Q_step(x, m, S, k, pi, h);
+    Q(2*n - 1) = Q_step(x, m, S, k, pi, h, flag);
 
 
     %%%%%%%%%%%%%%%%
@@ -77,7 +75,7 @@ function [h, m, Q] = EMG(x, k, epochs, flag)
     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
     % TODO: Store the value of the complete log-likelihood function
     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-    Q(2*n) = Q_step(x, m, S, k, pi, h);
+    Q(2*n) = Q_step(x, m, S, k, pi, h, flag);
   end
 
 

assignments/hwk03/E_step.m

@@ -27,4 +27,22 @@ function [h] = E_step(x, h, pi, m, S, k)
     h(j, :) = parts(j, :) ./ s;
   end
 
+
+  % parts = zeros(k);
+  % 
+  % denom = 0;
+  % for i = 1:k
+  %   N = mvnpdf(x, m(i, :), S(:, :, i));
+  %   for j = 1:num_data
+  %     parts(i) = parts(i) + pi(i) * N(j);
+  %   end
+  %   denom = denom + parts(i);
+  % end
+  % 
+  % for i = 1:k
+  %   h(:, i) = parts(i) ./ denom;
+  % end
+
+
+
 end

assignments/hwk03/M_step.m

@@ -47,9 +47,9 @@ function [S, m, pi] = M_step(x, h, S, k, flag)
   % Calculate the covariance matrix estimate
   % further modifications will need to be made when doing 2(d)
   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  S = zeros(dim, dim, k) + eps;
+  S = zeros(dim, dim, k);
   for i = 1:k
-    s = zeros(dim, dim);
+    s = zeros(dim, dim) + eye(dim) * eps;
     for j = 1:num_data
       s = s + h(j, i) * (x(j, :) - m(i, :))' * (x(j, :) - m(i, :));
     end
@@ -59,12 +59,18 @@ function [S, m, pi] = M_step(x, h, S, k, flag)
     % % MAKE IT SYMMETRIC https://stackoverflow.com/a/38730499
     % S(:, :, i) = (s + s') / 2;
     % https://www.mathworks.com/matlabcentral/answers/366140-eig-gives-a-negative-eigenvalue-for-a-positive-semi-definite-matrix#answer_290270
-    s = (s + s') / 2;
+    % s = (s + s') / 2;
     % https://www.mathworks.com/matlabcentral/answers/57411-matlab-sometimes-produce-a-covariance-matrix-error-with-non-postive-semidefinite#answer_69524
-    [V, D] = eig(s);
-    s = V * max(D, eps) / V;
+    % [V, D] = eig(s);
+    % s = V * max(D, eps) / V;
 
     S(:, :, i) = s;
   end
 
+  if flag
+    for i = 1:k
+      S(:, :, i) = S(:, :, i) + lambda * eye(dim) / 2;
+    end
+  end
+
 end

assignments/hwk03/Makefile

@@ -0,0 +1,11 @@
+
+HANDIN_PDF := hw3_sol.pdf
+HANDIN_ZIP := hw3_code.zip
+
+all: $(HANDIN_PDF) $(HANDIN_ZIP)
+
+$(HANDIN_PDF): hw3_sol.typ
+	typst compile $< $@
+
+$(HANDIN_ZIP): E_step.m EMG.m M_step.m Problem2.m Q_step.m goldy.jpg stadium.jpg
+	zip $(HANDIN_ZIP) $^

assignments/hwk03/Problem2.m

@@ -22,32 +22,32 @@ function [] = Problem2()
   %%%%%%%%%%
   % 2(a,b) %
   %%%%%%%%%%
-  index = 1;
-  figure();
-  for k = 4:4:12 
-    fprintf("k=%d\n", k);
+  % index = 1;
+  % figure();
+  % for k = 4:4:12 
+  %   fprintf("k=%d\n", k);
   
-    % call EM on data
-    [h, m, Q] = EMG(stadium_x, k, epochs, false);
+  %   % call EM on data
+  %   [h, m, Q] = EMG(stadium_x, k, epochs, false);
   
-    % get compressed version of image
-    [~,class_index] = max(h,[],2);
-    compress = m(class_index,:);
+  %   % get compressed version of image
+  %   [~,class_index] = max(h,[],2);
+  %   compress = m(class_index,:);
   
-    % 2(a), plot compressed image
-    subplot(3,2,index)
-    imagesc(permute(reshape(compress, [width, height, depth]),[2 1 3]))
-    index = index + 1;
+  %   % 2(a), plot compressed image
+  %   subplot(3,2,index)
+  %   imagesc(permute(reshape(compress, [width, height, depth]),[2 1 3]))
+  %   index = index + 1;
   
-    % 2(b), plot complete data likelihood curves
-    subplot(3,2,index)
-    x = 1:size(Q);
-    c = repmat([1 0 0; 0 1 0], length(x)/2, 1);
-    scatter(x,Q,20,c); 
-    index = index + 1;
-    pause;
-  end
-  shg
+  %   % 2(b), plot complete data likelihood curves
+  %   subplot(3,2,index)
+  %   x = 1:size(Q);
+  %   c = repmat([1 0 0; 0 1 0], length(x)/2, 1);
+  %   scatter(x,Q,20,c); 
+  %   index = index + 1;
+  %   pause;
+  % end
+  % shg
 
   %%%%%%%%
   % 2(c) %
@@ -63,14 +63,18 @@ function [] = Problem2()
   [~,class_index] = max(h,[],2);
   compress = m(class_index,:);
   figure();
-  subplot(2,1,1)
+  subplot(3,1,1)
   imagesc(permute(reshape(compress, [width, height, depth]),[2 1 3]))
 
   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   % TODO: plot goldy image after using clusters from k-means
   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   % begin code here
-  [~, ~, ~, D] = kmeans(goldy_x, k);
+  [idx, m] = kmeans(goldy_x, k);
+  compress = m(idx,:);
+  subplot(3,1,2)
+  imagesc(permute(reshape(compress, [width, height, depth]),[2 1 3]));
+  pause;
 
   % end code here 
   shg 
@@ -84,7 +88,7 @@ function [] = Problem2()
   % plot goldy image using clusters from improved EM
   [~,class_index] = max(h,[],2);
   compress = m(class_index,:);
-  figure();
+  subplot(1,1,1)
   imagesc(permute(reshape(compress, [width, height, depth]),[2 1 3]))
   shg
 end

assignments/hwk03/Q_step.m

@@ -1,4 +1,4 @@
-function [LL] = Q_step(x, m, S, k, pi, h)
+function [LL] = Q_step(x, m, S, k, pi, h, flag)
   [num_data, ~] = size(x);
   LL = 0;
   for i = 1:k

assignments/hwk03/hw3_sol.typ

@@ -29,20 +29,20 @@ Solved as:
 
 - $
     frac(diff E, diff w_(1,j)) &= - sum_t frac(diff E, diff y^t) frac(diff y^t, diff z^t_h) frac(diff z^t_h, diff w_(1,j)) \
-    &= - sum_t (frac(r^t, y^t) - frac(1-r^t, 1-y^t)) (y^t (1-y^t) v_h) (x_h cases(0 "if" ww_1 dot xx <0, 1 "otherwise")) \
-    &= - sum_t (r^t - y^t) v_h x_h cases(0 "if" ww_1 dot xx <0, 1 "otherwise") \
+    &= - sum_t (frac(r^t, y^t) - frac(1-r^t, 1-y^t)) (y^t (1-y^t) v_1) (x_j cases(0 "if" ww_1 dot xx <0, 1 "otherwise")) \
+    &= - sum_t (r^t - y^t) v_1 x_j cases(0 "if" ww_1 dot xx <0, 1 "otherwise") \
   $
 
 - $
     frac(diff E, diff w_(2,j)) &= - sum_t frac(diff E, diff y^t) frac(diff y^t, diff z^t_h) frac(diff z^t_h, diff w_(2,j)) \
-    &= - sum_t (r^t - y^t) v_h x_h (1-tanh^2(ww_2 dot xx)) \
+    &= - sum_t (r^t - y^t) v_2 x_j (1-tanh^2(ww_2 dot xx)) \
   $
 
 Updates:
 
 - $Delta v_h = eta sum_t (r^t-y^t) z^t_h$
-- $Delta w_(1,j) = eta sum_t (r^t - y^t) v_h x_h cases(0 "if" ww_1 dot xx <0, 1 "otherwise")$
-- $Delta w_(2,j) = eta sum_t (r^t - y^t) v_h x_h (1-tanh^2(ww_2 dot xx))$
+- $Delta w_(1,j) = eta sum_t (r^t - y^t) v_1 x_j cases(0 "if" ww_1 dot xx <0, 1 "otherwise")$
+- $Delta w_(2,j) = eta sum_t (r^t - y^t) v_2 x_j (1-tanh^2(ww_2 dot xx))$
 
 = Problem 1b
 
@@ -72,10 +72,51 @@ Updates:
 
 = Problem 2a + 2b
 
+For this problem I see a gentle increase in the likelihood value after each of
+the E + M steps. There is an issue with the first step but I don't know what
+it's caused by.
+
+In general, the higher $k$ was, the more colors there were, and it was able to
+produce a better color mapping. The last $k = 12$ had the best "resolution" (not
+really resolution since the pixel density didn't change but there are more
+detailed shapes).
+
 #image("2a.png")
 
+#pagebreak()
+
 = Problem 2c
 
+For this version, k-means performed a lot better than my initial EM step, even
+with a $k$ of 7. I'm suspecting what's happening is that between the EM steps,
+the classification of the data changes to spread out inaccurate values, while
+with k-means it's always operating on the original data.
+
+#image("2c.png")
+
+#pagebreak()
+
 = Problem 2d
 
-MLE of $Sigma_i$ 
+For the $Sigma$ update step, I added this change:
+
+#let rtext(t) = {
+  set text(red)
+  t
+}
+
+$
+  Sigma_i &= frac(1, N_i) sum_(t=1)^N gamma(z^t_i) (x^t - u) (x^t - u)^T
+  rtext(- frac(lambda, 2) sum_(i=1)^k sum_(j=1)^d (Sigma^(-1)_i)_("jj"))
+$
+
+The overall maximum likelihood could not be derived because of the difficulty
+with logarithm of sums
+
+= Problem 2e
+
+After implementing this, the result was a lot better. I believe that the
+regularization term helps because it makes the $Sigma$s bigger which makes it
+converge faster.
+
+#image("2e.png")