source: src/main/java/agents/anac/y2015/Phoenix/GP/GaussianProcess.java

Last change on this file was 1, checked in by Wouter Pasman, 6 years ago

Initial import : Genius 9.0.0
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[1]1package agents.anac.y2015.Phoenix.GP;/* This file is part of the jgpml Project.
2 * http://github.com/renzodenardi/jgpml
3 *
4 * Copyright (c) 2011 Renzo De Nardi and Hugo Gravato-Marques
5 *
6 * Permission is hereby granted, free of charge, to any person
7 * obtaining a copy of this software and associated documentation
8 * files (the "Software"), to deal in the Software without
9 * restriction, including without limitation the rights to use,
10 * copy, modify, merge, publish, distribute, sublicense, and/or sell
11 * copies of the Software, and to permit persons to whom the
12 * Software is furnished to do so, subject to the following
13 * conditions:
14 *
15 * The above copyright notice and this permission notice shall be
16 * included in all copies or substantial portions of the Software.
17 *
18 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
19 * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
20 * OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
21 * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
22 * HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
23 * WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
24 * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
25 * OTHER DEALINGS IN THE SOFTWARE.
26 */
27
28import agents.Jama.CholeskyDecomposition;
29import agents.Jama.Matrix;
30
31/**
32 * Main class of the package, contains the objects that constitutes a Gaussian
33 * Process as well as the algorithm to train the Hyperparameters and to do
34 * predictions.
35 */
36public class GaussianProcess {
37
38 /**
39 * hyperparameters
40 */
41 public Matrix logtheta;
42
43 /**
44 * input data points
45 */
46 public Matrix X;
47
48 /**
49 * Cholesky decomposition of the input
50 */
51 public Matrix L;
52
53 /**
54 * partial factor
55 */
56 public Matrix alpha;
57
58 /**
59 * covariance function
60 */
61 CovarianceFunction covFunction;
62
63 /**
64 * Creates a new GP object.
65 *
66 * @param covFunction
67 * - the covariance function
68 */
69 public GaussianProcess(CovarianceFunction covFunction) {
70 this.covFunction = covFunction;
71 }
72
73 /**
74 * Trains the GP Hyperparameters maximizing the marginal likelihood. By
75 * default the minimisation algorithm performs 100 iterations.
76 *
77 * @param X
78 * - the input data points
79 * @param y
80 * - the target data points
81 * @param logtheta0
82 * - the initial hyperparameters of the covariance function
83 */
84 public void train(Matrix X, Matrix y, Matrix logtheta0) {
85 train(X, y, logtheta0, -100);
86 }
87
88 /**
89 * Trains the GP Hyperparameters maximizing the marginal likelihood. By
90 * default the algorithm performs 100 iterations.
91 *
92 * @param X
93 * - the input data points
94 * @param y
95 * - the target data points
96 * @param logtheta0
97 * - the initial hyperparameters of the covariance function
98 * @param iterations
99 * - number of iterations performed by the minimization algorithm
100 */
101 public void train(Matrix X, Matrix y, Matrix logtheta0, int iterations) {
102 // System.out.println("X Dimension:"+X.getRowDimension()+"x"+X.getColumnDimension());
103 // System.out.println("y Dimension:"+y.getRowDimension()+"x"+y.getColumnDimension());
104 // System.out.println("training started...");
105 this.X = X;
106 logtheta = minimize(logtheta0, iterations, X, y);
107 }
108
109 /**
110 * Computes minus the log likelihood and its partial derivatives with
111 * respect to the hyperparameters; this mode is used to fit the
112 * hyperparameters.
113 *
114 * @param logtheta
115 * column <code>Matrix</code> of hyperparameters
116 * @param y
117 * output dataset
118 * @param df0
119 * returned partial derivatives with respect to the
120 * hyperparameters
121 * @return lml minus log marginal likelihood
122 */
123 public double negativeLogLikelihood(Matrix logtheta, Matrix x, Matrix y,
124 Matrix df0) {
125
126 int n = x.getRowDimension();
127
128 Matrix K = covFunction.compute(logtheta, x); // compute training set
129 // covariance matrix
130
131 CholeskyDecomposition cd = K.chol();
132 if (!cd.isSPD()) {
133 throw new RuntimeException(
134 "The covariance Matrix is not SDP, check your covariance function (maybe you mess the noise term..)");
135 } else {
136 L = cd.getL(); // cholesky factorization of the covariance
137
138 // alpha = L'\(L\y);
139 alpha = bSubstitutionWithTranspose(L, fSubstitution(L, y));
140
141 // double[][] yarr = y.getArray();
142 // double[][] alphaarr = alpha.getArray();
143 // double lml =0;
144 // for(int i=0; i<n; i++){
145 // lml+= yarr[i][0]*alphaarr[i][0];
146 // }
147 // lml*=0.5;
148 //
149
150 // compute the negative log marginal likelihood
151 double lml = (y.transpose().times(alpha).times(0.5)).get(0, 0);
152
153 for (int i = 0; i < L.getRowDimension(); i++)
154 lml += Math.log(L.get(i, i));
155 lml += 0.5 * n * Math.log(2 * Math.PI);
156
157 Matrix W = bSubstitutionWithTranspose(L,
158 (fSubstitution(L, Matrix.identity(n, n)))).minus(
159 alpha.times(alpha.transpose())); // precompute for
160 // convenience
161 for (int i = 0; i < df0.getRowDimension(); i++) {
162 df0.set(i, 0, sum(W.arrayTimes(covFunction.computeDerivatives(
163 logtheta, x, i))) / 2);
164 }
165
166 return lml;
167 }
168 }
169
170 /**
171 * Computes Gaussian predictions, whose mean and variance are returned. Note
172 * that in cases where the covariance function has noise contributions, the
173 * variance returned in S2 is for noisy test targets; if you want the
174 * variance of the noise-free latent function, you must subtract the noise
175 * variance.
176 *
177 * @param xstar
178 * test dataset
179 * @return [ystar Sstar] predicted mean and covariance
180 */
181
182 public Matrix[] predict(Matrix xstar) {
183
184 if (alpha == null || L == null) {
185 System.out.println("GP needs to be trained first..");
186 throw new IllegalStateException();
187 }
188 if (xstar.getColumnDimension() != X.getColumnDimension())
189 throw new IllegalArgumentException("Wrong size of the input "
190 + xstar.getColumnDimension() + " instead of "
191 + X.getColumnDimension());
192 Matrix[] star = covFunction.compute(logtheta, X, xstar);
193
194 Matrix Kstar = star[1];
195 Matrix Kss = star[0];
196
197 Matrix ystar = Kstar.transpose().times(alpha);
198
199 Matrix v = fSubstitution(L, Kstar);
200
201 v.arrayTimesEquals(v);
202
203 Matrix Sstar = Kss.minus(sumColumns(v).transpose());
204
205 // System.out.println("predict: "+ystar.get(0, 0));
206 return new Matrix[] { ystar, Sstar };
207 }
208
209 /**
210 * Computes Gaussian predictions, whose mean is returned. Note that in cases
211 * where the covariance function has noise contributions, the variance
212 * returned in S2 is for noisy test targets; if you want the variance of the
213 * noise-free latent function, you must substract the noise variance.
214 *
215 * @param xstar
216 * test dataset
217 * @return [ystar Sstar] predicted mean and covariance
218 */
219
220 public Matrix predictMean(Matrix xstar) {
221
222 if (alpha == null || L == null) {
223 System.out.println("GP needs to be trained first..");
224 throw new IllegalStateException();
225 }
226 if (xstar.getColumnDimension() != X.getColumnDimension())
227 throw new IllegalArgumentException("Wrong size of the input"
228 + xstar.getColumnDimension() + " instead of "
229 + X.getColumnDimension());
230
231 Matrix[] star = covFunction.compute(logtheta, X, xstar);
232
233 Matrix Kstar = star[1];
234
235 Matrix ystar = Kstar.transpose().times(alpha);
236
237 return ystar;
238 }
239
240 private static Matrix sumColumns(Matrix a) {
241 Matrix sum = new Matrix(1, a.getColumnDimension());
242 for (int i = 0; i < a.getRowDimension(); i++)
243 sum.plusEquals(a.getMatrix(i, i, 0, a.getColumnDimension() - 1));
244 return sum;
245 }
246
247 private static double sum(Matrix a) {
248 double sum = 0;
249 for (int i = 0; i < a.getRowDimension(); i++)
250 for (int j = 0; j < a.getColumnDimension(); j++)
251 sum += a.get(i, j);
252 return sum;
253 }
254
255 private static Matrix fSubstitution(Matrix L, Matrix B) {
256
257 final double[][] l = L.getArray();
258 final double[][] b = B.getArray();
259 final double[][] x = new double[B.getRowDimension()][B
260 .getColumnDimension()];
261
262 final int n = x.length;
263
264 for (int i = 0; i < B.getColumnDimension(); i++) {
265 for (int k = 0; k < n; k++) {
266 x[k][i] = b[k][i];
267 for (int j = 0; j < k; j++) {
268 x[k][i] -= l[k][j] * x[j][i];
269 }
270 x[k][i] /= l[k][k];
271 }
272 }
273 return new Matrix(x);
274 }
275
276 private static Matrix bSubstitution(Matrix L, Matrix B) {
277
278 final double[][] l = L.getArray();
279 final double[][] b = B.getArray();
280 final double[][] x = new double[B.getRowDimension()][B
281 .getColumnDimension()];
282
283 final int n = x.length - 1;
284
285 for (int i = 0; i < B.getColumnDimension(); i++) {
286 for (int k = n; k > -1; k--) {
287 x[k][i] = b[k][i];
288 for (int j = n; j > k; j--) {
289 x[k][i] -= l[k][j] * x[j][i];
290 }
291 x[k][i] /= l[k][k];
292 }
293 }
294 return new Matrix(x);
295
296 }
297
298 private static Matrix bSubstitutionWithTranspose(Matrix L, Matrix B) {
299
300 final double[][] l = L.getArray();
301 final double[][] b = B.getArray();
302 final double[][] x = new double[B.getRowDimension()][B
303 .getColumnDimension()];
304
305 final int n = x.length - 1;
306
307 for (int i = 0; i < B.getColumnDimension(); i++) {
308 for (int k = n; k > -1; k--) {
309 x[k][i] = b[k][i];
310 for (int j = n; j > k; j--) {
311 x[k][i] -= l[j][k] * x[j][i];
312 }
313 x[k][i] /= l[k][k];
314 }
315 }
316 return new Matrix(x);
317
318 }
319
320 private final static double INT = 0.1; // don't reevaluate within 0.1 of the
321 // limit of the current bracket
322
323 private final static double EXT = 3.0; // extrapolate maximum 3 times the
324 // current step-size
325
326 private final static int MAX = 20; // max 20 function evaluations per line
327 // search
328
329 private final static double RATIO = 10; // maximum allowed slope ratio
330
331 private final static double SIG = 0.1, RHO = SIG / 2; // SIG and RHO are the
332 // constants
333 // controlling the
334 // Wolfe-
335
336 // Powell conditions. SIG is the maximum allowed absolute ratio between
337 // previous and new slopes (derivatives in the search direction), thus
338 // setting
339 // SIG to low (positive) values forces higher precision in the
340 // line-searches.
341 // RHO is the minimum allowed fraction of the expected (from the slope at
342 // the
343 // initial point in the linesearch). Constants must satisfy 0 < RHO < SIG <
344 // 1.
345 // Tuning of SIG (depending on the nature of the function to be optimized)
346 // may
347 // speed up the minimization; it is probably not worth playing much with
348 // RHO.
349
350 private Matrix minimize(Matrix params, int length, Matrix in, Matrix out) {
351
352 double A, B;
353 double x1, x2, x3, x4;
354 double f0, f1, f2, f3, f4;
355 double d0, d1, d2, d3, d4;
356 Matrix df0, df3;
357 Matrix fX;
358
359 double red = 1.0;
360
361 int i = 0;
362 int ls_failed = 0;
363
364 int sizeX = params.getRowDimension();
365
366 df0 = new Matrix(sizeX, 1);
367 f0 = negativeLogLikelihood(params, in, out, df0);
368 // f0 = f.evaluate(params,cf, in, out, df0);
369
370 fX = new Matrix(new double[] { f0 }, 1);
371
372 i = (length < 0) ? i + 1 : i;
373
374 Matrix s = df0.times(-1);
375
376 // initial search direction (steepest) and slope
377 d0 = s.times(-1).transpose().times(s).get(0, 0);
378 x3 = red / (1 - d0); // initial step is red/(|s|+1)
379
380 final int nCycles = Math.abs(length);
381
382 int success;
383
384 double M;
385 while (i < nCycles) {
386 // System.out.println("-");
387 i = (length > 0) ? i + 1 : i; // count iterations?!
388
389 // make a copy of current values
390 double F0 = f0;
391 Matrix X0 = params.copy();
392 Matrix dF0 = df0.copy();
393
394 M = (length > 0) ? MAX : Math.min(MAX, -length - i);
395
396 while (true) { // keep extrapolating as long as necessary
397
398 x2 = 0;
399 f2 = f0;
400 d2 = d0;
401 f3 = f0;
402 df3 = df0.copy();
403
404 success = 0;
405
406 while (success == 0 && M > 0) {
407 // try
408 M = M - 1;
409 i = (length < 0) ? i + 1 : i; // count iterations?!
410
411 Matrix m1 = params.plus(s.times(x3));
412 // f3 = f.evaluate(m1,cf, in, out, df3);
413 f3 = negativeLogLikelihood(m1, in, out, df3);
414
415 if (Double.isNaN(f3) || Double.isInfinite(f3)
416 || hasInvalidNumbers(df3.getRowPackedCopy())) {
417 x3 = (x2 + x3) / 2; // catch any error which occured in
418 // f
419 } else {
420 success = 1;
421 }
422
423 }
424
425 if (f3 < F0) { // keep best values
426 X0 = s.times(x3).plus(params);
427 F0 = f3;
428 dF0 = df3;
429 }
430
431 d3 = df3.transpose().times(s).get(0, 0); // new slope
432
433 if (d3 > SIG * d0 || f3 > f0 + x3 * RHO * d0 || M == 0) { // are
434 // we
435 // done
436 // extrapolating?
437 break;
438 }
439
440 x1 = x2;
441 f1 = f2;
442 d1 = d2; // move point 2 to point 1
443 x2 = x3;
444 f2 = f3;
445 d2 = d3; // move point 3 to point 2
446
447 A = 6 * (f1 - f2) + 3 * (d2 + d1) * (x2 - x1); // make cubic
448 // extrapolation
449 B = 3 * (f2 - f1) - (2 * d1 + d2) * (x2 - x1);
450
451 x3 = x1 - d1 * (x2 - x1) * (x2 - x1)
452 / (B + Math.sqrt(B * B - A * d1 * (x2 - x1))); // num.
453 // error
454 // possible,
455 // ok!
456
457 if (Double.isNaN(x3) || Double.isInfinite(x3) || x3 < 0) // num
458 // prob
459 // |
460 // wrong
461 // sign?
462 x3 = x2 * EXT; // extrapolate maximum amount
463 else if (x3 > x2 * EXT) // new point beyond extrapolation limit?
464 x3 = x2 * EXT; // extrapolate maximum amount
465 else if (x3 < x2 + INT * (x2 - x1)) // new point too close to
466 // previous point?
467 x3 = x2 + INT * (x2 - x1);
468
469 }
470
471 f4 = 0;
472 x4 = 0;
473 d4 = 0;
474
475 while ((Math.abs(d3) > -SIG * d0 || f3 > f0 + x3 * RHO * d0)
476 && M > 0) { // keep interpolating
477
478 if (d3 > 0 || f3 > f0 + x3 * RHO * d0) { // choose subinterval
479 x4 = x3;
480 f4 = f3;
481 d4 = d3; // move point 3 to point 4
482 } else {
483 x2 = x3;
484 f2 = f3;
485 d2 = d3; // move point 3 to point 2
486 }
487
488 if (f4 > f0) {
489 x3 = x2 - (0.5 * d2 * (x4 - x2) * (x4 - x2))
490 / (f4 - f2 - d2 * (x4 - x2)); // quadratic
491 // interpolation
492 } else {
493 A = 6 * (f2 - f4) / (x4 - x2) + 3 * (d4 + d2); // cubic
494 // interpolation
495 B = 3 * (f4 - f2) - (2 * d2 + d4) * (x4 - x2);
496 x3 = x2
497 + (Math.sqrt(B * B - A * d2 * (x4 - x2) * (x4 - x2)) - B)
498 / A; // num. error possible, ok!
499 }
500
501 if (Double.isNaN(x3) || Double.isInfinite(x3)) {
502 x3 = (x2 + x4) / 2; // if we had a numerical problem then
503 // bisect
504 }
505
506 x3 = Math.max(Math.min(x3, x4 - INT * (x4 - x2)), x2 + INT
507 * (x4 - x2)); // don't accept too close
508
509 Matrix m1 = s.times(x3).plus(params);
510 // f3 = f.evaluate(m1,cf, in, out, df3);
511 f3 = negativeLogLikelihood(m1, in, out, df3);
512
513 if (f3 < F0) {
514 X0 = m1.copy();
515 F0 = f3;
516 dF0 = df3.copy(); // keep best values
517 }
518
519 M = M - 1;
520 i = (length < 0) ? i + 1 : i; // count iterations?!
521
522 d3 = df3.transpose().times(s).get(0, 0); // new slope
523
524 } // end interpolation
525
526 if (Math.abs(d3) < -SIG * d0 && f3 < f0 + x3 * RHO * d0) { // if
527 // line
528 // search
529 // succeeded
530 params = s.times(x3).plus(params);
531 f0 = f3;
532
533 double[] elem = fX.getColumnPackedCopy();
534 double[] newfX = new double[elem.length + 1];
535
536 System.arraycopy(elem, 0, newfX, 0, elem.length);
537 newfX[elem.length - 1] = f0;
538 fX = new Matrix(newfX, newfX.length); // update variables
539
540 // System.out.println("Function evaluation "+i+" Value "+f0);
541
542 double tmp1 = df3.transpose().times(df3)
543 .minus(df0.transpose().times(df3)).get(0, 0);
544 double tmp2 = df0.transpose().times(df0).get(0, 0);
545
546 s = s.times(tmp1 / tmp2).minus(df3);
547
548 df0 = df3; // swap derivatives
549 d3 = d0;
550 d0 = df0.transpose().times(s).get(0, 0);
551
552 if (d0 > 0) { // new slope must be negative
553 s = df0.times(-1); // otherwise use steepest direction
554 d0 = s.times(-1).transpose().times(s).get(0, 0);
555 }
556
557 x3 = x3 * Math.min(RATIO, d3 / (d0 - Double.MIN_VALUE)); // slope
558 // ratio
559 // but
560 // max
561 // RATIO
562 ls_failed = 0; // this line search did not fail
563
564 } else {
565
566 params = X0;
567 f0 = F0;
568 df0 = dF0; // restore best point so far
569
570 if (ls_failed == 1 || i > Math.abs(length)) { // line search
571 // failed twice
572 // in a row
573 break; // or we ran out of time, so we give up
574 }
575
576 s = df0.times(-1);
577 d0 = s.times(-1).transpose().times(s).get(0, 0); // try steepest
578 x3 = 1 / (1 - d0);
579 ls_failed = 1; // this line search failed
580
581 }
582
583 }
584
585 return params;
586 }
587
588 private static boolean hasInvalidNumbers(double[] array) {
589
590 for (double a : array) {
591 if (Double.isInfinite(a) || Double.isNaN(a)) {
592 return true;
593 }
594 }
595
596 return false;
597 }
598
599 /**
600 * A simple test
601 *
602 * @param args
603 */
604 public static void main(String[] args) {
605
606 CovarianceFunction covFunc = new CovSum(6, new CovLINone(),
607 new CovNoise());
608 GaussianProcess gp = new GaussianProcess(covFunc);
609
610 double[][] logtheta0 = new double[][] { { 0.1 }, { Math.log(0.1) } };
611 /*
612 * double[][] logtheta0 = new double[][]{ {0.1}, {0.2}, {0.3}, {0.4},
613 * {0.5}, {0.6}, {0.7}, {Math.log(0.1)}};
614 */
615
616 Matrix params0 = new Matrix(logtheta0);
617
618 Matrix[] data = CSVtoMatrix
619 .load("/Users/MaxLam/Desktop/Researches/ANAC2015/DragonAgent/DragonAgentBeta/src/armdata.csv",
620 6, 1);
621 Matrix X = data[0];
622 Matrix Y = data[1];
623
624 gp.train(X, Y, params0, -20);
625
626 // int size = 100;
627 // Matrix Xtrain = new Matrix(size, 1);
628 // Matrix Ytrain = new Matrix(size, 1);
629 //
630 // Matrix Xtest = new Matrix(size, 1);
631 // Matrix Ytest = new Matrix(size, 1);
632
633 // half of the sinusoid uses points very close to each other and the
634 // other half uses
635 // more sparse data
636
637 // double inc = 2 * Math.PI / 1000;
638 //
639 // double[][] data = new double[1000][2];
640 //
641 // Random random = new Random();
642 //
643 // for(int i=0; i<1000; i++){
644 // data[i][0] = i*inc;
645 // data[i][1] = Math.sin(i*+inc);
646 // }
647 //
648 //
649 // // NEED TO FILL Xtrain, Ytrain and Xtest, Ytest
650 //
651 //
652 // gp.train(Xtrain,Ytest,params0);
653 //
654 // // SimpleRealTimePlotter plot = new
655 // SimpleRealTimePlotter("","","",false,false,true,200);
656 //
657 // final Matrix[] out = gp.predict(Xtest);
658 //
659 // for(int i=0; i<Xtest.getRowDimension(); i++){
660 //
661 // final double mean = out[0].get(i,0);
662 // final double var = 3 * Math.sqrt(out[1].get(i,0));
663 //
664 // plot.addPoint(i, mean, 0, true);
665 // plot.addPoint(i, mean-var, 1, true);
666 // plot.addPoint(i, mean+var, 2, true);
667 // plot.addPoint(i, Ytest.get(i,0), 3, true);
668 //
669 // plot.fillPlot();
670 // }
671
672 Matrix[] datastar = CSVtoMatrix
673 .load("/Users/MaxLam/Desktop/Researches/ANAC2015/DragonAgent/DragonAgentBeta/src/armdatastar.csv",
674 6, 1);
675 Matrix Xstar = datastar[0];
676 Matrix Ystar = datastar[1];
677
678 Matrix[] res = gp.predict(Xstar);
679
680 res[0].print(res[0].getColumnDimension(), 16);
681 System.err.println("");
682 res[1].print(res[1].getColumnDimension(), 16);
683 }
684
685}
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