classify correctly
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5 changed files with 39 additions and 18 deletions
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@ -26,6 +26,8 @@ function [] = Back_Project(training_data, test_data, n_components)
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imagesc(reshape(reconstruction(:,i),32,30)');
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end
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pause;
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end
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end % Function end
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@ -3,20 +3,31 @@
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% these posterior probabilities are compared using the log odds.
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function [predictions] = Classify(data, m1, m2, S1, S2, pc1, pc2)
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[num_rows, d] = size(data);
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% calculate P(x|C) * P(C) for both classes
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d = 8;
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pxC1 = 1/(power(2*pi, d/2) * power(det(S1), 1/2)) * exp(-1/2 * (data-m1) * inv(S1) * (data-m1)');
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pxC2 = 1/(power(2*pi, d/2) * power(det(S2), 1/2)) * exp(-1/2 * (data-m2) * inv(S2) * (data-m2)');
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% pxC1 = 1/(power(2*pi, d/2) * power(det(S1), 1/2)) * exp(-1/2 * (data-m1) * inv(S1) * (data-m1)');
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% pxC2 = 1/(power(2*pi, d/2) * power(det(S2), 1/2)) * exp(-1/2 * (data-m2) * inv(S2) * (data-m2)');
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pxC1 = zeros(num_rows,1);
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pxC2 = zeros(num_rows,1);
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for i = 1:num_rows
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x = data(i,:);
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pxC1(i) = 1/(power(2*pi, d/2) * power(det(S1), 1/2)) * exp(-1/2 * (x-m1) * inv(S1) * (x-m1)');
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pxC2(i) = 1/(power(2*pi, d/2) * power(det(S2), 1/2)) * exp(-1/2 * (x-m2) * inv(S2) * (x-m2)');
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end
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% pxC1 = mvnpdf(data, m1, S1);
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% pxC2 = mvnpdf(data, m2, S2);
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% P(C|x) = (P(x|C) * P(C)) / common factor
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pC1x = pxC1 * pc1;
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pC2x = pxC2 * pc2;
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% TODO: calculate log odds, if > 0 then data(i) belongs to class c1, else, c2
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log_odds = log(pC1x / pC2x);
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% calculate log odds, if > 0 then data(i) belongs to class c1, else, c2
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log_odds = log(pC1x) - log(pC2x);
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% TODO: get predictions from log odds calculation
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[num_rows, ~] = size(data);
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% get predictions from log odds calculation
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predictions = zeros(num_rows,1);
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for i = 1:num_rows
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if log_odds(i) > 0
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@ -7,7 +7,7 @@ function [] = Error_Rate(predictions, labels)
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[total_rows, ~] = size(predictions);
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for i = 1:total_rows
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if predictions(i) == labels(i)
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if predictions(i) ~= labels(i)
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c = c + 1;
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end
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end
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@ -40,6 +40,8 @@
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c. #c[*(15 points)* Write the log likelihood function and derive $S_1$ and $S_2$ by maximum likelihood estimation of model 2. Note that since $S_1$ and $S_2$ are shared as $S$, you need to add the log likelihood function of the two classes to maximizing for deriving $S$.]
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The maximum likelihood of a single
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2. #c[*(50 points)* In this problem, you will work on dimension reduction and classification on a Faces dataset from the UCI repository. We provided the processed files `face_train_data_960.txt` and `face_test_data_960.txt` with 500 and 124 images, respectively. Each image is of size 30 #sym.times 32 with the pixel values in a row in the files and the last column identifies the labels: 1 (sunglasses), and 0 (open) of the image. You can visualize the $i$th image with the following matlab command line:]
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```matlab
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@ -58,4 +60,8 @@
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I used $K = 41$.
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c. #c[*(20 points)* Use the first $K = {10, 50, 100}$ principle components to approximate the first five images of the training set (first row of the data matrix) by projecting the centered data using the first $K$ principal components then "back project" (weighted sum of the components) to the original space and add the mean. For each $K$, plot the reconstructed image. This can be accomplished by completing the _TODO_ comment headers in the `Back_Project.m` script. Explain your observations in the report.]
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c. #c[*(20 points)* Use the first $K = {10, 50, 100}$ principle components to approximate the first five images of the training set (first row of the data matrix) by projecting the centered data using the first $K$ principal components then "back project" (weighted sum of the components) to the original space and add the mean. For each $K$, plot the reconstructed image. This can be accomplished by completing the _TODO_ comment headers in the `Back_Project.m` script. Explain your observations in the report.]
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This "back-projection" seems like a "low-resolution" version of the image, but in particular with lower $K$, it focuses on what the model considers to be the most varied features.
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This is why for $K=10$, most of the faces were not really visible, only blobs that were vaguely face-shaped, along with the shirt. But with higher $K$, more detail was given to some of the other features of the background.
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@ -11,18 +11,13 @@ function [m1, m2, S1, S2] = Param_Est(training_data, training_labels, part)
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m1 = mean(class1_data);
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m2 = mean(class2_data);
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S1 = cov(class1_data, 1);
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S2 = cov(class2_data, 1);
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S1 = cov(class1_data);
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S2 = cov(class2_data);
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% Model 1.
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% Assume independent 𝑆1 and 𝑆2 (the discriminant function is as equation (5.17) in the textbook).
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if (strcmp(part, '1'))
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% Model 3.
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% Assume 𝑆1 and 𝑆2 are diagonal (the Naive Bayes model in equation (5.24)).
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elseif (strcmp(part, '3'))
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S1 = diag(diag(S1));
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S2 = diag(diag(S2));
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% Already calculated above so nothing to be done here
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% Model 2.
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% Assume 𝑆1 = 𝑆2. In other words, shared S between two classes
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@ -31,9 +26,16 @@ function [m1, m2, S1, S2] = Param_Est(training_data, training_labels, part)
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P_C1 = length(class1_data) / num_rows;
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P_C2 = length(class2_data) / num_rows;
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S = P_C1 * S1 + P_C2 + S2;
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S = P_C1 * S1 + P_C2 * S2;
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S1 = S;
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S2 = S;
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% Model 3.
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% Assume 𝑆1 and 𝑆2 are diagonal (the Naive Bayes model in equation (5.24)).
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elseif (strcmp(part, '3'))
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% pull diagonals into vector -> turn vector into diagonal matrix
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S1 = diag(diag(S1));
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S2 = diag(diag(S2));
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end
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end % Function end
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