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‎data/.place_holder

Whitespace-only changes.

‎eval/.gitignore

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.*~

‎eval/precision_singlelabel.m

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function [map, precision_at_k, agg_prec] = precision_singlelabel (trn_label, trn_binary, tst_label, tst_binary, mode)
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K = size(trn_binary,1);
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QueryTimes = size(tst_binary,1);
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AP = zeros(QueryTimes,1);
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Ns = 1:1:K;
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sum_tp = zeros(1, length(Ns));
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agg_prec = zeros(1, 11);
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for i = 1:QueryTimes
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%img_path = tst_list(i,1);
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query_label = tst_label(i);
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fprintf('query %d\n',i);
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query_binary = tst_binary(i,:);
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if mode==1
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tic
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similarity = pdist2(double(trn_binary),double(query_binary),'hamming');
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toc
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fprintf('Complete Query [Hamming] %.2f seconds\n',toc);
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elseif mode ==2
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tic
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similarity = pdist2(trn_binary,query_binary,'euclidean');
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toc
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fprintf('Complete Query [Euclidean] %.2f seconds\n',toc);
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end
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[x2,y2] = sort(similarity);
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buffer_yes = trn_label(y2(1:K)) == query_label;
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% compute precision
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P = cumsum(buffer_yes) ./ Ns';
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if (sum(buffer_yes) == 0)
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AP(i) = 0;
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else
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AP(i) = sum(P .* buffer_yes) / sum(buffer_yes);
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end
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sum_tp = sum_tp + cumsum(buffer_yes)';
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if (sum(buffer_yes) > 0)
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% recall
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R = cumsum(buffer_yes) ./ sum(buffer_yes);
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% aggregated precision
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for mm = 1: 11
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rr = (mm - 1)/10;
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idx = find(R >= rr);
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agg_prec(mm) = agg_prec(mm) + max(P(idx));
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end
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end
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end
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precision_at_k = sum_tp ./ (Ns * QueryTimes);
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map = mean(AP);
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agg_prec = agg_prec / QueryTimes;
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end
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‎eval/readNPY.m

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function data = readNPY(filename)
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% Function to read NPY files into matlab.
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% *** Only reads a subset of all possible NPY files, specifically N-D arrays of certain data types.
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% See https://github.com/kwikteam/npy-matlab/blob/master/npy.ipynb for
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% more.
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%
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[shape, dataType, fortranOrder, littleEndian, totalHeaderLength, ~] = readNPYheader(filename);
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if littleEndian
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fid = fopen(filename, 'r', 'l');
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else
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fid = fopen(filename, 'r', 'b');
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end
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try
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[~] = fread(fid, totalHeaderLength, 'uint8');
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% read the data
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data = fread(fid, prod(shape), [dataType '=>' dataType]);
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if length(shape)>1 && ~fortranOrder
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data = reshape(data, shape(end:-1:1));
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data = permute(data, [length(shape):-1:1]);
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elseif length(shape)>1
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data = reshape(data, shape);
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end
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fclose(fid);
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catch me
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fclose(fid);
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rethrow(me);
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end

‎eval/readNPYheader.m

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function [arrayShape, dataType, fortranOrder, littleEndian, totalHeaderLength, npyVersion] = readNPYheader(filename)
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% function [arrayShape, dataType, fortranOrder, littleEndian, ...
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% totalHeaderLength, npyVersion] = readNPYheader(filename)
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%
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% parse the header of a .npy file and return all the info contained
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% therein.
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%
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% Based on spec at http://docs.scipy.org/doc/numpy-dev/neps/npy-format.html
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fid = fopen(filename);
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% verify that the file exists
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if (fid == -1)
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if ~isempty(dir(filename))
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error('Permission denied: %s', filename);
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else
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error('File not found: %s', filename);
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end
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end
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try
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dtypesMatlab = {'uint8','uint16','uint32','uint64','int8','int16','int32','int64','single','double', 'logical'};
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dtypesNPY = {'u1', 'u2', 'u4', 'u8', 'i1', 'i2', 'i4', 'i8', 'f4', 'f8', 'b1'};
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magicString = fread(fid, [1 6], 'char=>char');
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if ~strcmp(magicString, '“NUMPY')
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error('readNPY:NotNUMPYFile', 'Error: This file does not appear to be NUMPY format based on the header.');
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end
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majorVersion = fread(fid, [1 1], 'uint8=>uint8');
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minorVersion = fread(fid, [1 1], 'uint8=>uint8');
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npyVersion = [majorVersion minorVersion];
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headerLength = fread(fid, [1 1], 'uint16=>unit16');
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totalHeaderLength = 10+headerLength;
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arrayFormat = fread(fid, [1 headerLength], 'char=>char');
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% to interpret the array format info, we make some fairly strict
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% assumptions about its format...
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r = regexp(arrayFormat, '''descr''\s*:\s*''(.*?)''', 'tokens');
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dtNPY = r{1}{1};
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littleEndian = ~strcmp(dtNPY(1), '>');
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dataType = dtypesMatlab{strcmp(dtNPY(2:3), dtypesNPY)};
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r = regexp(arrayFormat, '''fortran_order''\s*:\s*(\w+)', 'tokens');
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fortranOrder = strcmp(r{1}{1}, 'True');
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r = regexp(arrayFormat, '''shape''\s*:\s*\((.*?)\)', 'tokens');
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shapeStr = r{1}{1};
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arrayShape = str2num(shapeStr(shapeStr~='L'));
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fclose(fid);
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catch me
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fclose(fid);
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rethrow(me);
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end

‎eval/run_cifar10_pca.m

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clear;
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bit = 512;
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examples_to_show = 10;
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% load CIFAR10 training set
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data_folder = '/media/iis/Data/Data/cifar10/cifar-10-batches-mat/';
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X_train = [];
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Y_train = [];
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for i = 1: 5
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datadict = load([data_folder 'data_batch_' num2str(i) '.mat']);
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X_train = [X_train; datadict.data];
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Y_train = [Y_train; datadict.labels];
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end
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% load CIFAR10 test set
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datadict = load([data_folder 'test_batch.mat']);
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X_test = double(datadict.data);
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Y_test = datadict.labels;
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% center the data
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X_train = double(X_train);
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sample_mean = mean(X_train, 1);
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X_train = (X_train - repmat(sample_mean, size(X_train,1),1));
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fprintf('performing PCA ...\n');
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[pc, l] = eigs(double(cov(X_train)),bit);
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%[coeff,score,latent] = pca(double(X_train));
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% reconstruct images
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% center data
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XX = X_test - repmat(sample_mean, size(X_test, 1),1);
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% projection
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XX = XX * pc;
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% reconstruction
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XX = XX *pc' + repmat(sample_mean, size(XX,1),1);
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% show reconstructed images
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for i = 1: examples_to_show
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im = reshape(XX(i,:), [32 32 3]);
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im = permute(im, [2 1 3]);
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figure; imshow(uint8(im))
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end

‎eval/run_eval_singlelabel.m

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function run_eval_singlelabel(res_dir)
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database.label = readNPY(fullfile(res_dir, 'database_y.npy'));
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[max_vals, database.label] = max(database.label');
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database.label = database.label';
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query.label = readNPY(fullfile(res_dir, 'query_y.npy'));
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[max_vals, query.label] = max(query.label');
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query.label = query.label';
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database.feat = readNPY(fullfile(res_dir, 'database_x.npy'));
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query.feat = readNPY(fullfile(res_dir, 'query_x.npy'));
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database.binary = database.feat > 0.;
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query.binary = query.feat > 0.;
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map_file = fullfile(res_dir, 'map.txt');
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precision_file = fullfile(res_dir, 'precision-at-k.txt');
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pr_file = fullfile(res_dir, 'pr.txt');
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% retreival performance
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[map, precision_at_k, agg_prec] = precision_singlelabel(database.label, database.binary, ...
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query.label, query.binary, 1);
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% save results
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outfile = fopen(map_file, 'w');
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fprintf(outfile, '%.4f\t', map);
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fclose(outfile);
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P = [[1:1:size(database.label,1)]' precision_at_k'];
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save(precision_file, 'P', '-ascii');
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pr = [[0:.1:1]' agg_prec'];
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save(pr_file, 'pr', '-ascii');
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end

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