75 lines
3.3 KiB
Mathematica
75 lines
3.3 KiB
Mathematica
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%% Plots information about a perceptron classifier on a 2-dimensional dataset.
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function plot_perceptron(neg_examples, pos_examples, mistakes0, mistakes1, num_err_history, w, w_dist_history)
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%%
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% The top-left plot shows the dataset and the classification boundary given by
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% the weights of the perceptron. The negative examples are shown as circles
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% while the positive examples are shown as squares. If an example is colored
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% green then it means that the example has been correctly classified by the
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% provided weights. If it is colored red then it has been incorrectly classified.
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% The top-right plot shows the number of mistakes the perceptron algorithm has
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% made in each iteration so far.
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% The bottom-left plot shows the distance to some generously feasible weight
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% vector if one has been provided (note, there can be an infinite number of these).
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% Points that the classifier has made a mistake on are shown in red,
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% while points that are correctly classified are shown in green.
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% The goal is for all of the points to be green (if it is possible to do so).
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% Inputs:
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% neg_examples - The num_neg_examples x 3 matrix for the examples with target 0.
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% num_neg_examples is the number of examples for the negative class.
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% pos_examples- The num_pos_examples x 3 matrix for the examples with target 1.
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% num_pos_examples is the number of examples for the positive class.
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% mistakes0 - A vector containing the indices of the datapoints from class 0 incorrectly
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% classified by the perceptron. This is a subset of neg_examples.
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% mistakes1 - A vector containing the indices of the datapoints from class 1 incorrectly
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% classified by the perceptron. This is a subset of pos_examples.
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% num_err_history - A vector containing the number of mistakes for each
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% iteration of learning so far.
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% w - A 3-dimensional vector corresponding to the current weights of the
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% perceptron. The last element is the bias.
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% w_dist_history - A vector containing the L2-distance to a generously
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% feasible weight vector for each iteration of learning so far.
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% Empty if one has not been provided.
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%%
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f = figure(1);
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clf(f);
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neg_correct_ind = setdiff(1:size(neg_examples,1),mistakes0);
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pos_correct_ind = setdiff(1:size(pos_examples,1),mistakes1);
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subplot(2,2,1);
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hold on;
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if (~isempty(neg_examples))
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plot(neg_examples(neg_correct_ind,1),neg_examples(neg_correct_ind,2),'og','markersize',20);
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end
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if (~isempty(pos_examples))
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plot(pos_examples(pos_correct_ind,1),pos_examples(pos_correct_ind,2),'sg','markersize',20);
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end
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if (size(mistakes0,1) > 0)
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plot(neg_examples(mistakes0,1),neg_examples(mistakes0,2),'or','markersize',20);
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end
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if (size(mistakes1,1) > 0)
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plot(pos_examples(mistakes1,1),pos_examples(mistakes1,2),'sr','markersize',20);
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end
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title('Classifier');
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%In order to plot the decision line, we just need to get two points.
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plot([-5,5],[(-w(end)+5*w(1))/w(2),(-w(end)-5*w(1))/w(2)],'k')
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xlim([-1,1]);
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ylim([-1,1]);
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hold off;
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subplot(2,2,2);
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plot(0:length(num_err_history)-1,num_err_history);
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xlim([-1,max(15,length(num_err_history))]);
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ylim([0,size(neg_examples,1)+size(pos_examples,1)+1]);
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title('Number of errors');
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xlabel('Iteration');
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ylabel('Number of errors');
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subplot(2,2,3);
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plot(0:length(w_dist_history)-1,w_dist_history);
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xlim([-1,max(15,length(num_err_history))]);
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ylim([0,15]);
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title('Distance')
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xlabel('Iteration');
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ylabel('Distance');
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