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annotate toolboxes/distance_learning/mlr/rmlr_train.m @ 0:e9a9cd732c1e tip

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first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 function [W, Xi, Diagnostics] = rmlr_train(X, Y, Cslack, varargin)
wolffd@0 2 %[W, Xi, D] = rmlr_train(X, Y, C, LOSS, k, REG, Diagonal, stochastic, lam, STRUCTREG)
wolffd@0 3 %
wolffd@0 4 % [W, Xi, D] = rmlr_train(X, Y, C,...)
wolffd@0 5 %
wolffd@0 6 % X = d*n data matrix
wolffd@0 7 % Y = either n-by-1 label of vectors
wolffd@0 8 % OR
wolffd@0 9 % n-by-2 cell array where
wolffd@0 10 % Y{q,1} contains relevant indices for q, and
wolffd@0 11 % Y{q,2} contains irrelevant indices for q
wolffd@0 12 %
wolffd@0 13 % C >= 0 slack trade-off parameter (default=1)
wolffd@0 14 %
wolffd@0 15 % W = the learned metric
wolffd@0 16 % Xi = slack value on the learned metric
wolffd@0 17 % D = diagnostics
wolffd@0 18 %
wolffd@0 19 % Optional arguments:
wolffd@0 20 %
wolffd@0 21 % [W, Xi, D] = rmlr_train(X, Y, C, LOSS)
wolffd@0 22 % where LOSS is one of:
wolffd@0 23 % 'AUC': Area under ROC curve (default)
wolffd@0 24 % 'KNN': KNN accuracy
wolffd@0 25 % 'Prec@k': Precision-at-k
wolffd@0 26 % 'MAP': Mean Average Precision
wolffd@0 27 % 'MRR': Mean Reciprocal Rank
wolffd@0 28 % 'NDCG': Normalized Discounted Cumulative Gain
wolffd@0 29 %
wolffd@0 30 % [W, Xi, D] = rmlr_train(X, Y, C, LOSS, k)
wolffd@0 31 % where k is the number of neighbors for Prec@k or NDCG
wolffd@0 32 % (default=3)
wolffd@0 33 %
wolffd@0 34 % [W, Xi, D] = rmlr_train(X, Y, C, LOSS, k, REG)
wolffd@0 35 % where REG defines the regularization on W, and is one of:
wolffd@0 36 % 0: no regularization
wolffd@0 37 % 1: 1-norm: trace(W) (default)
wolffd@0 38 % 2: 2-norm: trace(W' * W)
wolffd@0 39 % 3: Kernel: trace(W * X), assumes X is square and positive-definite
wolffd@0 40 %
wolffd@0 41 % [W, Xi, D] = rmlr_train(X, Y, C, LOSS, k, REG, Diagonal)
wolffd@0 42 % Not implemented as learning a diagonal W metric just reduces to MLR because ||W||_2,1 = trace(W) when W is diagonal.
wolffd@0 43 %
wolffd@0 44 % [W, Xi, D] = rmlr_train(X, Y, C, LOSS, k, REG, Diagonal, B)
wolffd@0 45 % where B > 0 enables stochastic optimization with batch size B
wolffd@0 46 %
wolffd@0 47 % [W, Xi, D] = rmlr_train(X, Y, C, LOSS, k, REG, Diagonal, B, lambda)
wolffd@0 48 % lambda is the desired value of the hyperparameter which is the coefficient of ||W||_2,1. Default is 1 if lambda is not set
wolffd@0 49 % [W, Xi, D] = rmlr_train(X, Y, C, LOSS, k, REG, Diagonal, B, lambda, CC)
wolffd@0 50 % Set ConstraintClock to CC (default: 20, 100)
wolffd@0 51 %
wolffd@0 52 % [W, Xi, D] = rmlr_train(X, Y, C, LOSS, k, REG, Diagonal, B, lambda, CC, E)
wolffd@0 53 % Set ConstraintClock to E (default: 1e-3)
wolffd@0 54 %
wolffd@0 55
wolffd@0 56
wolffd@0 57 TIME_START = tic();
wolffd@0 58
wolffd@0 59 global C;
wolffd@0 60 C = Cslack;
wolffd@0 61
wolffd@0 62 [d,n,m] = size(X);
wolffd@0 63
wolffd@0 64 if m > 1
wolffd@0 65 MKL = 1;
wolffd@0 66 else
wolffd@0 67 MKL = 0;
wolffd@0 68 end
wolffd@0 69
wolffd@0 70 if nargin < 3
wolffd@0 71 C = 1;
wolffd@0 72 end
wolffd@0 73
wolffd@0 74 %%%
wolffd@0 75 % Default options:
wolffd@0 76
wolffd@0 77 global CP SO PSI REG FEASIBLE LOSS DISTANCE SETDISTANCE CPGRADIENT STRUCTKERNEL DUALW THRESH INIT;
wolffd@0 78 global RHO;
wolffd@0 79
wolffd@0 80
wolffd@0 81 %%%
wolffd@0 82 % Augmented lagrangian factor
wolffd@0 83 RHO = 1;
wolffd@0 84
wolffd@0 85 % </modified>
wolffd@0 86
wolffd@0 87 global FEASIBLE_COUNT;
wolffd@0 88 FEASIBLE_COUNT = 0;
wolffd@0 89
wolffd@0 90 CP = @cuttingPlaneFull;
wolffd@0 91 SO = @separationOracleAUC;
wolffd@0 92 PSI = @metricPsiPO;
wolffd@0 93
wolffd@0 94 if ~MKL
wolffd@0 95 INIT = @initializeFull;
wolffd@0 96 REG = @regularizeTraceFull;
wolffd@0 97 STRUCTKERNEL= @structKernelLinear;
wolffd@0 98 DUALW = @dualWLinear;
wolffd@0 99 FEASIBLE = @feasibleFull;
wolffd@0 100 THRESH = @threshFull_admmMixed;
wolffd@0 101 CPGRADIENT = @cpGradientFull;
wolffd@0 102 DISTANCE = @distanceFull;
wolffd@0 103 SETDISTANCE = @setDistanceFull;
wolffd@0 104 LOSS = @lossHinge;
wolffd@0 105 Regularizer = 'Trace';
wolffd@0 106 else
wolffd@0 107 INIT = @initializeFullMKL;
wolffd@0 108 REG = @regularizeMKLFull;
wolffd@0 109 STRUCTKERNEL= @structKernelMKL;
wolffd@0 110 DUALW = @dualWMKL;
wolffd@0 111 FEASIBLE = @feasibleFullMKL;
wolffd@0 112 THRESH = @threshFull_admmMixed;
wolffd@0 113 CPGRADIENT = @cpGradientFullMKL;
wolffd@0 114 DISTANCE = @distanceFullMKL;
wolffd@0 115 SETDISTANCE = @setDistanceFullMKL;
wolffd@0 116 LOSS = @lossHingeFullMKL;
wolffd@0 117 Regularizer = 'Trace';
wolffd@0 118 end
wolffd@0 119
wolffd@0 120
wolffd@0 121 Loss = 'AUC';
wolffd@0 122 Feature = 'metricPsiPO';
wolffd@0 123
wolffd@0 124
wolffd@0 125 %%%
wolffd@0 126 % Default k for prec@k, ndcg
wolffd@0 127 k = 3;
wolffd@0 128
wolffd@0 129 %%%
wolffd@0 130 % Stochastic violator selection?
wolffd@0 131 STOCHASTIC = 0;
wolffd@0 132 batchSize = n;
wolffd@0 133 SAMPLES = 1:n;
wolffd@0 134
wolffd@0 135
wolffd@0 136 if nargin > 3
wolffd@0 137 switch lower(varargin{1})
wolffd@0 138 case {'auc'}
wolffd@0 139 SO = @separationOracleAUC;
wolffd@0 140 PSI = @metricPsiPO;
wolffd@0 141 Loss = 'AUC';
wolffd@0 142 Feature = 'metricPsiPO';
wolffd@0 143 case {'knn'}
wolffd@0 144 SO = @separationOracleKNN;
wolffd@0 145 PSI = @metricPsiPO;
wolffd@0 146 Loss = 'KNN';
wolffd@0 147 Feature = 'metricPsiPO';
wolffd@0 148 case {'prec@k'}
wolffd@0 149 SO = @separationOraclePrecAtK;
wolffd@0 150 PSI = @metricPsiPO;
wolffd@0 151 Loss = 'Prec@k';
wolffd@0 152 Feature = 'metricPsiPO';
wolffd@0 153 case {'map'}
wolffd@0 154 SO = @separationOracleMAP;
wolffd@0 155 PSI = @metricPsiPO;
wolffd@0 156 Loss = 'MAP';
wolffd@0 157 Feature = 'metricPsiPO';
wolffd@0 158 case {'mrr'}
wolffd@0 159 SO = @separationOracleMRR;
wolffd@0 160 PSI = @metricPsiPO;
wolffd@0 161 Loss = 'MRR';
wolffd@0 162 Feature = 'metricPsiPO';
wolffd@0 163 case {'ndcg'}
wolffd@0 164 SO = @separationOracleNDCG;
wolffd@0 165 PSI = @metricPsiPO;
wolffd@0 166 Loss = 'NDCG';
wolffd@0 167 Feature = 'metricPsiPO';
wolffd@0 168 otherwise
wolffd@0 169 error('MLR:LOSS', ...
wolffd@0 170 'Unknown loss function: %s', varargin{1});
wolffd@0 171 end
wolffd@0 172 end
wolffd@0 173
wolffd@0 174 if nargin > 4
wolffd@0 175 k = varargin{2};
wolffd@0 176 end
wolffd@0 177
wolffd@0 178 Diagonal = 0;
wolffd@0 179 %Diagonal case is not implemented. Use mlr_train for that.
wolffd@0 180
wolffd@0 181 if nargin > 5
wolffd@0 182 switch(varargin{3})
wolffd@0 183 case {0}
wolffd@0 184 REG = @regularizeNone;
wolffd@0 185 Regularizer = 'None';
wolffd@0 186 THRESH = @threshFull_admmMixed;
wolffd@0 187 case {1}
wolffd@0 188 if MKL
wolffd@0 189 REG = @regularizeMKLFull;
wolffd@0 190 STRUCTKERNEL= @structKernelMKL;
wolffd@0 191 DUALW = @dualWMKL;
wolffd@0 192 else
wolffd@0 193 REG = @regularizeTraceFull;
wolffd@0 194 STRUCTKERNEL= @structKernelLinear;
wolffd@0 195 DUALW = @dualWLinear;
wolffd@0 196 end
wolffd@0 197 Regularizer = 'Trace';
wolffd@0 198
wolffd@0 199 case {2}
wolffd@0 200 REG = @regularizeTwoFull;
wolffd@0 201 Regularizer = '2-norm';
wolffd@0 202 error('MLR:REGULARIZER', '2-norm regularization no longer supported');
wolffd@0 203
wolffd@0 204
wolffd@0 205 case {3}
wolffd@0 206 if MKL
wolffd@0 207 REG = @regularizeMKLFull;
wolffd@0 208 STRUCTKERNEL= @structKernelMKL;
wolffd@0 209 DUALW = @dualWMKL;
wolffd@0 210 else
wolffd@0 211 REG = @regularizeKernel;
wolffd@0 212 STRUCTKERNEL= @structKernelMKL;
wolffd@0 213 DUALW = @dualWMKL;
wolffd@0 214 end
wolffd@0 215 Regularizer = 'Kernel';
wolffd@0 216
wolffd@0 217
wolffd@0 218 otherwise
wolffd@0 219 error('MLR:REGULARIZER', ...
wolffd@0 220 'Unknown regularization: %s', varargin{3});
wolffd@0 221 end
wolffd@0 222 end
wolffd@0 223
wolffd@0 224
wolffd@0 225 % Are we in stochastic optimization mode?
wolffd@0 226 if nargin > 7 && varargin{5} > 0
wolffd@0 227 if varargin{5} < n
wolffd@0 228 STOCHASTIC = 1;
wolffd@0 229 CP = @cuttingPlaneRandom;
wolffd@0 230 batchSize = varargin{5};
wolffd@0 231 end
wolffd@0 232 end
wolffd@0 233 % Algorithm
wolffd@0 234 %
wolffd@0 235 % Working <- []
wolffd@0 236 %
wolffd@0 237 % repeat:
wolffd@0 238 % (W, Xi) <- solver(X, Y, C, Working)
wolffd@0 239 %
wolffd@0 240 % for i = 1:|X|
wolffd@0 241 % y^_i <- argmax_y^ ( Delta(y*_i, y^) + w' Psi(x_i, y^) )
wolffd@0 242 %
wolffd@0 243 % Working <- Working + (y^_1,y^_2,...,y^_n)
wolffd@0 244 % until mean(Delta(y*_i, y_i)) - mean(w' (Psi(x_i,y_i) - Psi(x_i,y^_i)))
wolffd@0 245 % <= Xi + epsilon
wolffd@0 246
wolffd@0 247 if nargin > 8
wolffd@0 248 lam = varargin{6};
wolffd@0 249 else
wolffd@0 250 lam = 1;
wolffd@0 251 end
wolffd@0 252
wolffd@0 253 disp(['lam = ' num2str(lam)])
wolffd@0 254
wolffd@0 255 global DEBUG;
wolffd@0 256
wolffd@0 257 if isempty(DEBUG)
wolffd@0 258 DEBUG = 0;
wolffd@0 259 end
wolffd@0 260
wolffd@0 261 DEBUG = 1;
wolffd@0 262
wolffd@0 263 %%%
wolffd@0 264 % Max calls to seperation oracle
wolffd@0 265 MAX_CALLS = 200;
wolffd@0 266 MIN_CALLS = 10;
wolffd@0 267
wolffd@0 268 %%%
wolffd@0 269 % Timer to eliminate old constraints
wolffd@0 270 ConstraintClock = 500; % standard: 500
wolffd@0 271
wolffd@0 272 if nargin > 9 && varargin{7} > 0
wolffd@0 273 ConstraintClock = varargin{7};
wolffd@0 274 end
wolffd@0 275
wolffd@0 276 %%%
wolffd@0 277 % Convergence criteria for worst-violated constraint
wolffd@0 278 E = 1e-3;
wolffd@0 279 if nargin > 10 && varargin{8} > 0
wolffd@0 280 E = varargin{8};
wolffd@0 281 end
wolffd@0 282
wolffd@0 283 %XXX: 2012-01-31 21:29:50 by Brian McFee <bmcfee@cs.ucsd.edu>
wolffd@0 284 % no longer belongs here
wolffd@0 285 % Initialize
wolffd@0 286 W = INIT(X);
wolffd@0 287
wolffd@0 288
wolffd@0 289 global ADMM_Z ADMM_V ADMM_UW ADMM_UV;
wolffd@0 290 ADMM_Z = W;
wolffd@0 291 ADMM_V = W;
wolffd@0 292 ADMM_UW = 0 * ADMM_Z;
wolffd@0 293 ADMM_UV = 0 * ADMM_Z;
wolffd@0 294
wolffd@0 295 ClassScores = [];
wolffd@0 296
wolffd@0 297 if isa(Y, 'double')
wolffd@0 298 Ypos = [];
wolffd@0 299 Yneg = [];
wolffd@0 300 ClassScores = synthesizeRelevance(Y);
wolffd@0 301
wolffd@0 302 elseif isa(Y, 'cell') && size(Y,1) == n && size(Y,2) == 2
wolffd@0 303 dbprint(2, 'Using supplied Ypos/Yneg');
wolffd@0 304 Ypos = Y(:,1);
wolffd@0 305 Yneg = Y(:,2);
wolffd@0 306
wolffd@0 307 % Compute the valid samples
wolffd@0 308 SAMPLES = find( ~(cellfun(@isempty, Y(:,1)) | cellfun(@isempty, Y(:,2))));
wolffd@0 309 elseif isa(Y, 'cell') && size(Y,1) == n && size(Y,2) == 1
wolffd@0 310 dbprint(2, 'Using supplied Ypos/synthesized Yneg');
wolffd@0 311 Ypos = Y(:,1);
wolffd@0 312 Yneg = [];
wolffd@0 313 SAMPLES = find( ~(cellfun(@isempty, Y(:,1))));
wolffd@0 314 else
wolffd@0 315 error('MLR:LABELS', 'Incorrect format for Y.');
wolffd@0 316 end
wolffd@0 317 %%
wolffd@0 318 % If we don't have enough data to make the batch, cut the batch
wolffd@0 319 batchSize = min([batchSize, length(SAMPLES)]);
wolffd@0 320
wolffd@0 321 Diagnostics = struct( 'loss', Loss, ... % Which loss are we optimizing?
wolffd@0 322 'feature', Feature, ... % Which ranking feature is used?
wolffd@0 323 'k', k, ... % What is the ranking length?
wolffd@0 324 'regularizer', Regularizer, ... % What regularization is used?
wolffd@0 325 'diagonal', Diagonal, ... % 0 for full metric, 1 for diagonal
wolffd@0 326 'num_calls_SO', 0, ... % Calls to separation oracle
wolffd@0 327 'num_calls_solver', 0, ... % Calls to solver
wolffd@0 328 'time_SO', 0, ... % Time in separation oracle
wolffd@0 329 'time_solver', 0, ... % Time in solver
wolffd@0 330 'time_total', 0, ... % Total time
wolffd@0 331 'f', [], ... % Objective value
wolffd@0 332 'num_steps', [], ... % Number of steps for each solver run
wolffd@0 333 'num_constraints', [], ... % Number of constraints for each run
wolffd@0 334 'Xi', [], ... % Slack achieved for each run
wolffd@0 335 'Delta', [], ... % Mean loss for each SO call
wolffd@0 336 'gap', [], ... % Gap between loss and slack
wolffd@0 337 'C', C, ... % Slack trade-off
wolffd@0 338 'epsilon', E, ... % Convergence threshold
wolffd@0 339 'feasible_count', FEASIBLE_COUNT, ... % Counter for # svd's
wolffd@0 340 'constraint_timer', ConstraintClock); % Time before evicting old constraints
wolffd@0 341
wolffd@0 342
wolffd@0 343
wolffd@0 344 global PsiR;
wolffd@0 345 global PsiClock;
wolffd@0 346
wolffd@0 347 PsiR = {};
wolffd@0 348 PsiClock = [];
wolffd@0 349
wolffd@0 350 Xi = -Inf;
wolffd@0 351 Margins = [];
wolffd@0 352 H = [];
wolffd@0 353 Q = [];
wolffd@0 354
wolffd@0 355 if STOCHASTIC
wolffd@0 356 dbprint(2, 'STOCHASTIC OPTIMIZATION: Batch size is %d/%d', batchSize, n);
wolffd@0 357 end
wolffd@0 358
wolffd@0 359 dbprint(2,['Regularizer is "' Regularizer '"']);
wolffd@0 360 while 1
wolffd@0 361 if Diagnostics.num_calls_solver > MAX_CALLS
wolffd@0 362 dbprint(2,['Calls to SO >= ' num2str(MAX_CALLS)]);
wolffd@0 363 break;
wolffd@0 364 end
wolffd@0 365
wolffd@0 366 dbprint(2, 'Round %03d', Diagnostics.num_calls_solver);
wolffd@0 367 % Generate a constraint set
wolffd@0 368 Termination = -Inf;
wolffd@0 369
wolffd@0 370
wolffd@0 371 dbprint(2, 'Calling separation oracle...');
wolffd@0 372 [PsiNew, Mnew, SO_time] = CP(k, X, W, Ypos, Yneg, batchSize, SAMPLES, ClassScores);
wolffd@0 373
wolffd@0 374 Termination = LOSS(W, PsiNew, Mnew, 0);
wolffd@0 375 Diagnostics.num_calls_SO = Diagnostics.num_calls_SO + 1;
wolffd@0 376 Diagnostics.time_SO = Diagnostics.time_SO + SO_time;
wolffd@0 377
wolffd@0 378 Margins = cat(1, Margins, Mnew);
wolffd@0 379 PsiR = cat(1, PsiR, PsiNew);
wolffd@0 380 PsiClock = cat(1, PsiClock, 0);
wolffd@0 381 H = expandKernel(H);
wolffd@0 382 Q = expandRegularizer(Q, X, W);
wolffd@0 383
wolffd@0 384
wolffd@0 385 dbprint(2, '\n\tActive constraints : %d', length(PsiClock));
wolffd@0 386 dbprint(2, '\t Mean loss : %0.4f', Mnew);
wolffd@0 387 dbprint(2, '\t Current loss Xi : %0.4f', Xi);
wolffd@0 388 dbprint(2, '\t Termination -Xi < E : %0.4f <? %.04f\n', Termination - Xi, E);
wolffd@0 389
wolffd@0 390 Diagnostics.gap = cat(1, Diagnostics.gap, Termination - Xi);
wolffd@0 391 Diagnostics.Delta = cat(1, Diagnostics.Delta, Mnew);
wolffd@0 392
wolffd@0 393 %if Termination <= Xi + E
wolffd@0 394 if Termination <= Xi + E && Diagnostics.num_calls_solver > MIN_CALLS
wolffd@0 395 %if Termination - Xi <= E
wolffd@0 396 dbprint(1, 'Done.');
wolffd@0 397 break;
wolffd@0 398 end
wolffd@0 399
wolffd@0 400
wolffd@0 401
wolffd@0 402 dbprint(1, 'Calling solver...');
wolffd@0 403 PsiClock = PsiClock + 1;
wolffd@0 404 Solver_time = tic();
wolffd@0 405 % disp('Robust MLR')
wolffd@0 406 [W, Xi, Dsolver] = rmlr_admm(C, X, Margins, H, Q, lam);
wolffd@0 407
wolffd@0 408 Diagnostics.time_solver = Diagnostics.time_solver + toc(Solver_time);
wolffd@0 409 Diagnostics.num_calls_solver = Diagnostics.num_calls_solver + 1;
wolffd@0 410
wolffd@0 411 Diagnostics.Xi = cat(1, Diagnostics.Xi, Xi);
wolffd@0 412 Diagnostics.f = cat(1, Diagnostics.f, Dsolver.f);
wolffd@0 413 Diagnostics.num_steps = cat(1, Diagnostics.num_steps, Dsolver.num_steps);
wolffd@0 414
wolffd@0 415 %%%
wolffd@0 416 % Cull the old constraints
wolffd@0 417 GC = PsiClock < ConstraintClock;
wolffd@0 418 Margins = Margins(GC);
wolffd@0 419 PsiR = PsiR(GC);
wolffd@0 420 PsiClock = PsiClock(GC);
wolffd@0 421 H = H(GC, GC);
wolffd@0 422 Q = Q(GC);
wolffd@0 423
wolffd@0 424 Diagnostics.num_constraints = cat(1, Diagnostics.num_constraints, length(PsiR));
wolffd@0 425 end
wolffd@0 426
wolffd@0 427
wolffd@0 428 % Finish diagnostics
wolffd@0 429
wolffd@0 430 Diagnostics.time_total = toc(TIME_START);
wolffd@0 431 Diagnostics.feasible_count = FEASIBLE_COUNT;
wolffd@0 432 end
wolffd@0 433
wolffd@0 434 function H = expandKernel(H)
wolffd@0 435
wolffd@0 436 global STRUCTKERNEL;
wolffd@0 437 global PsiR;
wolffd@0 438
wolffd@0 439 m = length(H);
wolffd@0 440 H = padarray(H, [1 1], 0, 'post');
wolffd@0 441
wolffd@0 442
wolffd@0 443 for i = 1:m+1
wolffd@0 444 H(i,m+1) = STRUCTKERNEL( PsiR{i}, PsiR{m+1} );
wolffd@0 445 H(m+1, i) = H(i, m+1);
wolffd@0 446 end
wolffd@0 447 end
wolffd@0 448
wolffd@0 449 function Q = expandRegularizer(Q, K, W)
wolffd@0 450
wolffd@0 451 % FIXME: 2012-01-31 21:34:15 by Brian McFee <bmcfee@cs.ucsd.edu>
wolffd@0 452 % does not support unregularized learning
wolffd@0 453
wolffd@0 454 global PsiR;
wolffd@0 455 global STRUCTKERNEL REG;
wolffd@0 456
wolffd@0 457 m = length(Q);
wolffd@0 458 Q(m+1,1) = STRUCTKERNEL(REG(W,K,1), PsiR{m+1});
wolffd@0 459
wolffd@0 460 end
wolffd@0 461
wolffd@0 462 function ClassScores = synthesizeRelevance(Y)
wolffd@0 463
wolffd@0 464 classes = unique(Y);
wolffd@0 465 nClasses = length(classes);
wolffd@0 466
wolffd@0 467 ClassScores = struct( 'Y', Y, ...
wolffd@0 468 'classes', classes, ...
wolffd@0 469 'Ypos', [], ...
wolffd@0 470 'Yneg', []);
wolffd@0 471
wolffd@0 472 Ypos = cell(nClasses, 1);
wolffd@0 473 Yneg = cell(nClasses, 1);
wolffd@0 474 for c = 1:nClasses
wolffd@0 475 Ypos{c} = (Y == classes(c));
wolffd@0 476 Yneg{c} = ~Ypos{c};
wolffd@0 477 end
wolffd@0 478
wolffd@0 479 ClassScores.Ypos = Ypos;
wolffd@0 480 ClassScores.Yneg = Yneg;
wolffd@0 481
wolffd@0 482 end