-
Notifications
You must be signed in to change notification settings - Fork 0
/
fnchyppr.cpp
602 lines (538 loc) · 19.8 KB
/
fnchyppr.cpp
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
/*************************** fnchyppr.cpp **********************************
* Author: Agner Fog
* Date created: 2002-10-20
* Last modified: 2008-11-21
* Project: stocc.zip
* Source URL: www.agner.org/random
*
* Description:
* Calculation of univariate and multivariate Fisher's noncentral hypergeometric
* probability distribution.
*
* This file contains source code for the class CFishersNCHypergeometric
* and CMultiFishersNCHypergeometric defined in stocc.h.
*
* Documentation:
* ==============
* The file stocc.h contains class definitions.
* The file ran-instructions.pdf contains further documentation and
* instructions.
*
* Copyright 2002-2008 by Agner Fog.
* GNU General Public License http://www.gnu.org/licenses/gpl.html
*****************************************************************************/
#include <string.h> // memcpy function
#include "stocc.h" // class definition
/***********************************************************************
Methods for class CFishersNCHypergeometric
***********************************************************************/
CFishersNCHypergeometric::CFishersNCHypergeometric(int32_t n, int32_t m, int32_t N, double odds, double accuracy) {
// constructor
// set parameters
this->n = n; this->m = m; this->N = N;
this->odds = odds; this->accuracy = accuracy;
// check validity of parameters
if (n < 0 || m < 0 || N < 0 || odds < 0. || n > N || m > N) {
FatalError("Parameter out of range in class CFishersNCHypergeometric");
}
if (accuracy < 0) accuracy = 0;
if (accuracy > 1) accuracy = 1;
// initialize
logodds = log(odds); scale = rsum = 0.;
ParametersChanged = 1;
// calculate xmin and xmax
xmin = m + n - N; if (xmin < 0) xmin = 0;
xmax = n; if (xmax > m) xmax = m;
}
int32_t CFishersNCHypergeometric::mode(void) {
// Find mode (exact)
// Uses the method of Liao and Rosen, The American Statistician, vol 55,
// no 4, 2001, p. 366-369.
// Note that there is an error in Liao and Rosen's formula.
// Replace sgn(b) with -1 in Liao and Rosen's formula.
double A, B, C, D; // coefficients for quadratic equation
double x; // mode
int32_t L = m + n - N;
int32_t m1 = m+1, n1 = n+1;
if (odds == 1.) {
// simple hypergeometric
x = (m + 1.) * (n + 1.) / (N + 2.);
}
else {
// calculate analogously to Cornfield mean
A = 1. - odds;
B = (m1+n1)*odds - L;
C = -(double)m1*n1*odds;
D = B*B -4*A*C;
D = D > 0. ? sqrt(D) : 0.;
x = (D - B)/(A+A);
}
return (int32_t)x;
}
double CFishersNCHypergeometric::mean(void) {
// Find approximate mean
// Calculation analogous with mode
double a, b; // temporaries in calculation
double mean; // mean
if (odds == 1.) { // simple hypergeometric
return double(m)*n/N;
}
// calculate Cornfield mean
a = (m+n)*odds + (N-m-n);
b = a*a - 4.*odds*(odds-1.)*m*n;
b = b > 0. ? sqrt(b) : 0.;
mean = (a-b)/(2.*(odds-1.));
return mean;
}
double CFishersNCHypergeometric::variance(void) {
// find approximate variance (poor approximation)
double my = mean(); // approximate mean
// find approximate variance from Fisher's noncentral hypergeometric approximation
double r1 = my * (m-my); double r2 = (n-my)*(my+N-n-m);
double var = N*r1*r2/((N-1)*(m*r2+(N-m)*r1));
if (var < 0.) var = 0.;
return var;
}
double CFishersNCHypergeometric::moments(double * mean_, double * var_) {
// calculate exact mean and variance
// return value = sum of f(x), expected = 1.
double y, sy=0, sxy=0, sxxy=0, me1;
int32_t x, xm, x1;
const double accur = 0.1 * accuracy; // accuracy of calculation
xm = (int32_t)mean(); // approximation to mean
for (x=xm; x<=xmax; x++) {
y = probability(x);
x1 = x - xm; // subtract approximate mean to avoid loss of precision in sums
sy += y; sxy += x1 * y; sxxy += x1 * x1 * y;
if (y < accur) break;
}
for (x=xm-1; x>=xmin; x--) {
y = probability(x);
x1 = x - xm; // subtract approximate mean to avoid loss of precision in sums
sy += y; sxy += x1 * y; sxxy += x1 * x1 * y;
if (y < accur) break;
}
me1 = sxy / sy;
*mean_ = me1 + xm;
y = sxxy / sy - me1 * me1;
if (y < 0) y=0;
*var_ = y;
return sy;
}
double CFishersNCHypergeometric::probability(int32_t x) {
// calculate probability function
const double accur = accuracy * 0.1;// accuracy of calculation
if (x < xmin || x > xmax) return 0;
if (!rsum) {
// first time. calculate rsum = reciprocal of sum of proportional
// function over all probable x values
int32_t x1, x2; // x loop
double y; // value of proportional function
x1 = (int32_t)mean(); // start at mean
if (x1 < xmin) x1 = xmin;
x2 = x1 + 1;
scale = 0.; scale = lng(x1); // calculate scale to avoid overflow
rsum = 1.; // = exp(lng(x1)) with this scale
for (x1--; x1 >= xmin; x1--) {
rsum += y = exp(lng(x1)); // sum from x1 and down
if (y < accur) break; // until value becomes negligible
}
for (; x2 <= xmax; x2++) { // sum from x2 and up
rsum += y = exp(lng(x2));
if (y < accur) break; // until value becomes negligible
}
rsum = 1. / rsum; // save reciprocal sum
}
return exp(lng(x)) * rsum; // function value
}
double CFishersNCHypergeometric::probabilityRatio(int32_t x, int32_t x0) {
// Calculate probability ratio f(x)/f(x0)
// This is much faster than calculating a single probability because
// rsum is not needed
double a1, a2, a3, a4, f1, f2, f3, f4;
int32_t y, dx = x - x0;
int invert = 0;
if (x < xmin || x > xmax) return 0.;
if (x0 < xmin || x0 > xmax) {
FatalError("Infinity in CFishersNCHypergeometric::probabilityRatio");
}
if (dx == 0.) return 1.;
if (dx < 0.) {
invert = 1;
dx = -dx;
y = x; x = x0; x0 = y;
}
a1 = m - x0; a2 = n - x0; a3 = x; a4 = N - m - n + x;
if (dx <= 28 && x <= 100000) { // avoid overflow
// direct calculation
f1 = f2 = 1.;
// compute ratio of binomials
for (y = 0; y < dx; y++) {
f1 *= a1-- * a2--;
f2 *= a3-- * a4--;
}
// compute odds^dx
f3 = 1.; f4 = odds; y = dx;
while (y) {
if (f4 < 1.E-100) {
f3 = 0.; break; // avoid underflow
}
if (y & 1) f3 *= f4;
f4 *= f4;
y = (unsigned long)(y) >> 1;
}
f1 = f3 * f1 / f2;
if (invert) f1 = 1. / f1;
}
else {
// use logarithms
f1 = FallingFactorial(a1,dx) + FallingFactorial(a2,dx) -
FallingFactorial(a3,dx) - FallingFactorial(a4,dx) +
dx * log(odds);
if (invert) f1 = -f1;
f1 = exp(f1);
}
return f1;
}
double CFishersNCHypergeometric::MakeTable(double * table, int32_t MaxLength, int32_t * xfirst, int32_t * xlast, double cutoff) {
// Makes a table of Fisher's noncentral hypergeometric probabilities.
// Results are returned in the array table of size MaxLength.
// The values are scaled so that the highest value is 1. The return value
// is the sum, s, of all the values in the table. The normalized
// probabilities are obtained by multiplying all values in the table by
// 1/s.
// The tails are cut off where the values are < cutoff, so that
// *xfirst may be > xmin and *xlast may be < xmax.
// The value of cutoff will be 0.01 * accuracy if not specified.
// The first and last x value represented in the table are returned in
// *xfirst and *xlast. The resulting probability values are returned in the
// first (*xlast - *xfirst + 1) positions of table. If this would require
// more than MaxLength values then the table is filled with as many
// correct values as possible.
//
// The function will return the desired length of table when MaxLength = 0.
double f; // probability function value
double sum; // sum of table values
double a1, a2, b1, b2; // factors in recursive calculation of f(x)
int32_t x; // x value
int32_t x1, x2; // lowest and highest x
int32_t i, i0, i1, i2; // table index
int32_t mode = this->mode(); // mode
int32_t L = n + m - N; // parameter
int32_t DesiredLength; // desired length of table
// limits for x
x1 = (L > 0) ? L : 0; // xmin
x2 = (n < m) ? n : m; // xmax
if (MaxLength <= 0) {
// Return UseTable and LengthNeeded
DesiredLength = x2 - x1 + 1; // max length of table
if (DesiredLength > 200) {
double sd = sqrt(variance()); // calculate approximate standard deviation
// estimate number of standard deviations to include from normal distribution
i = (int32_t)(NumSD(accuracy) * sd + 0.5);
if (DesiredLength > i) DesiredLength = i;
}
if (xfirst) *xfirst = 1; // for analogy with CWalleniusNCHypergeometric::MakeTable
return DesiredLength;
}
// place mode in the table
if (mode - x1 <= MaxLength/2) {
// There is enough space for left tail
i0 = mode - x1;
}
else if (x2 - mode <= MaxLength/2) {
// There is enough space for right tail
i0 = MaxLength - x2 + mode - 1;
if (i0 < 0) i0 = 0;
}
else {
// There is not enough space for any of the tails. Place mode in middle of table
i0 = MaxLength/2;
}
// Table start index
i1 = i0 - mode + x1; if (i1 < 0) i1 = 0;
// Table end index
i2 = i0 + x2 - mode; if (i2 > MaxLength-1) i2 = MaxLength-1;
// make center
table[i0] = sum = f = 1.;
// make left tail
x = mode;
a1 = m + 1 - x; a2 = n + 1 - x;
b1 = x; b2 = x - L;
for (i = i0 - 1; i >= i1; i--) {
f *= b1 * b2 / (a1 * a2 * odds); // recursive formula
a1++; a2++; b1--; b2--;
sum += table[i] = f;
if (f < cutoff) {
i1 = i; break; // cut off tail if < accuracy
}
}
if (i1 > 0) {
// move table down for cut-off left tail
memcpy(table, table+i1, (i0-i1+1)*sizeof(*table));
// adjust indices
i0 -= i1; i2 -= i1; i1 = 0;
}
// make right tail
x = mode + 1;
a1 = m + 1 - x; a2 = n + 1 - x;
b1 = x; b2 = x - L;
f = 1.;
for (i = i0 + 1; i <= i2; i++) {
f *= a1 * a2 * odds / (b1 * b2); // recursive formula
a1--; a2--; b1++; b2++;
sum += table[i] = f;
if (f < cutoff) {
i2 = i; break; // cut off tail if < accuracy
}
}
// x limits
*xfirst = mode - (i0 - i1);
*xlast = mode + (i2 - i0);
return sum;
}
double CFishersNCHypergeometric::lng(int32_t x) {
// natural log of proportional function
// returns lambda = log(m!*x!/(m-x)!*m2!*x2!/(m2-x2)!*odds^x)
int32_t x2 = n - x, m2 = N - m;
if (ParametersChanged) {
mFac = LnFac(m) + LnFac(m2);
xLast = -99; ParametersChanged = 0;
}
if (m < FAK_LEN && m2 < FAK_LEN) goto DEFLT;
switch (x - xLast) {
case 0: // x unchanged
break;
case 1: // x incremented. calculate from previous value
xFac += log (double(x) * (m2-x2) / (double(x2+1)*(m-x+1)));
break;
case -1: // x decremented. calculate from previous value
xFac += log (double(x2) * (m-x) / (double(x+1)*(m2-x2+1)));
break;
default: DEFLT: // calculate all
xFac = LnFac(x) + LnFac(x2) + LnFac(m-x) + LnFac(m2-x2);
}
xLast = x;
return mFac - xFac + x * logodds - scale;
}
/***********************************************************************
calculation methods in class CMultiFishersNCHypergeometric
***********************************************************************/
CMultiFishersNCHypergeometric::CMultiFishersNCHypergeometric(int32_t n_, int32_t * m_, double * odds_, int colors_, double accuracy_) {
// constructor
int32_t N1;
int i;
// copy parameters
n = n_; m = m_; odds = odds_; colors = colors_; accuracy = accuracy_;
// check if parameters are valid
for (N = N1 = 0, i = 0; i < colors; i++) {
if (m[i] < 0 || odds[i] < 0) FatalError("Parameter negative in constructor for CMultiFishersNCHypergeometric");
N += m[i];
if (odds[i]) N1 += m[i];
}
if (N < n) FatalError("Not enough items in constructor for CMultiFishersNCHypergeometric");
if (N1< n) FatalError("Not enough items with nonzero weight in constructor for CMultiFishersNCHypergeometric");
// calculate mFac and logodds
for (i=0, mFac=0.; i < colors; i++) {
mFac += LnFac(m[i]);
logodds[i] = log(odds[i]);
}
// initialize
sn = 0;
}
void CMultiFishersNCHypergeometric::mean(double * mu) {
// calculates approximate mean of multivariate Fisher's noncentral
// hypergeometric distribution. Result is returned in mu[0..colors-1].
// The calculation is reasonably fast.
double r, r1; // iteration variable
double q; // mean of color i
double W; // total weight
int i; // color index
int iter = 0; // iteration counter
if (colors < 3) {
// simple cases
if (colors == 1) mu[0] = n;
if (colors == 2) {
mu[0] = CFishersNCHypergeometric(n,m[0],m[0]+m[1],odds[0]/odds[1]).mean();
mu[1] = n - mu[0];
}
return;
}
if (n == N) {
// Taking all balls
for (i = 0; i < colors; i++) mu[i] = m[i];
return;
}
// initial guess for r
for (i=0, W=0.; i < colors; i++) W += m[i] * odds[i];
r = (double)n * N / ((N-n)*W);
// iteration loop to find r
do {
r1 = r;
for (i=0, q=0.; i < colors; i++) {
q += m[i] * r * odds[i] / (r * odds[i] + 1.);
}
r *= n * (N-q) / (q * (N-n));
if (++iter > 100) FatalError("convergence problem in function CMultiFishersNCHypergeometric::mean");
}
while (fabs(r-r1) > 1E-5);
// store result
for (i=0; i < colors; i++) {
mu[i] = m[i] * r * odds[i] / (r * odds[i] + 1.);
}
}
void CMultiFishersNCHypergeometric::variance(double * var) {
// calculates approximate variance of multivariate Fisher's noncentral
// hypergeometric distribution (accuracy is not too good).
// Result is returned in variance[0..colors-1].
// The calculation is reasonably fast.
double r1, r2;
double mu[MAXCOLORS];
int i;
mean(mu);
for (i=0; i<colors; i++) {
r1 = mu[i] * (m[i]-mu[i]);
r2 = (n-mu[i])*(mu[i]+N-n-m[i]);
if (r1 <= 0. || r2 <= 0.) {
var[i] = 0.;
}
else {
var[i] = N*r1*r2/((N-1)*(m[i]*r2+(N-m[i])*r1));
}
}
}
double CMultiFishersNCHypergeometric::probability(int32_t * x) {
// Calculate probability function.
// Note: The first-time call takes very long time because it requires
// a calculation of all possible x combinations with probability >
// accuracy, which may be extreme.
// The calculation uses logarithms to avoid overflow.
// (Recursive calculation may be faster, but this has not been implemented)
int32_t xsum; int i, em;
for (xsum = i = 0; i < colors; i++) xsum += x[i];
if (xsum != n) {
FatalError("sum of x values not equal to n in function CMultiFishersNCHypergeometric::probability");
}
for (i = em = 0; i < colors; i++) {
if (x[i] > m[i] || x[i] > n - N + m[i]) return 0.;
if (odds[i] == 0. && x[i]) return 0.;
if (x[i] == m[i] || odds[i] == 0.) em++;
}
if (n == 0 || em == colors) return 1.;
if (sn == 0) SumOfAll(); // first time initialize
return exp(lng(x)) * rsum; // function value
}
double CMultiFishersNCHypergeometric::moments(double * mean, double * variance, int32_t * combinations) {
// calculates mean and variance of the Fisher's noncentral hypergeometric
// distribution by calculating all combinations of x-values with
// probability > accuracy.
// Return value = 1.
// Returns the mean in mean[0...colors-1]
// Returns the variance in variance[0...colors-1]
int i; // color index
if (sn == 0) {
// first time initialization includes calculation of mean and variance
SumOfAll();
}
// just copy results
for (i=0; i < colors; i++) {
mean[i] = sx[i];
variance[i] = sxx[i];
}
if (combinations) *combinations = sn;
return 1.;
}
void CMultiFishersNCHypergeometric::SumOfAll() {
// this function does the very time consuming job of calculating the sum
// of the proportional function g(x) over all possible combinations of
// the x[i] values with probability > accuracy. These combinations are
// generated by the recursive function loop().
// The mean and variance are generated as by-products.
int i; // color index
int32_t msum; // sum of m[i]
// get approximate mean
mean(sx);
// round mean to integers
for (i=0, msum=0; i < colors; i++) {
msum += xm[i] = (int32_t)(sx[i]+0.4999999);}
// adjust truncated x values to make the sum = n
msum -= n;
for (i = 0; msum < 0; i++) {
if (xm[i] < m[i]) {
xm[i]++; msum++;
}
}
for (i = 0; msum > 0; i++) {
if (xm[i] > 0) {
xm[i]--; msum--;
}
}
// adjust scale factor to g(mean) to avoid overflow
scale = 0.; scale = lng(xm);
// initialize for recursive loops
sn = 0;
for (i = colors-1, msum = 0; i >= 0; i--) {
remaining[i] = msum; msum += m[i];
}
for (i = 0; i < colors; i++) {
sx[i] = 0; sxx[i] = 0;
}
// recursive loops to calculate sums of g(x) over all x combinations
rsum = 1. / loop(n, 0);
// calculate mean and variance
for (i = 0; i < colors; i++) {
sxx[i] = sxx[i]*rsum - sx[i]*sx[i]*rsum*rsum;
sx[i] = sx[i]*rsum;
}
}
double CMultiFishersNCHypergeometric::loop(int32_t n, int c) {
// recursive function to loop through all combinations of x-values.
// used by SumOfAll
int32_t x, x0; // x of color c
int32_t xmin, xmax; // min and max of x[c]
double s1, s2, sum = 0.; // sum of g(x) values
int i; // loop counter
if (c < colors-1) {
// not the last color
// calculate min and max of x[c] for given x[0]..x[c-1]
xmin = n - remaining[c]; if (xmin < 0) xmin = 0;
xmax = m[c]; if (xmax > n) xmax = n;
x0 = xm[c]; if (x0 < xmin) x0 = xmin; if (x0 > xmax) x0 = xmax;
// loop for all x[c] from mean and up
for (x = x0, s2 = 0.; x <= xmax; x++) {
xi[c] = x;
sum += s1 = loop(n-x, c+1); // recursive loop for remaining colors
if (s1 < accuracy && s1 < s2) break; // stop when values become negligible
s2 = s1;
}
// loop for all x[c] from mean and down
for (x = x0-1; x >= xmin; x--) {
xi[c] = x;
sum += s1 = loop(n-x, c+1); // recursive loop for remaining colors
if (s1 < accuracy && s1 < s2) break; // stop when values become negligible
s2 = s1;
}
}
else {
// last color
xi[c] = n;
// sums and squaresums
s1 = exp(lng(xi)); // proportional function g(x)
for (i = 0; i < colors; i++) { // update sums
sx[i] += s1 * xi[i];
sxx[i] += s1 * xi[i] * xi[i];
}
sn++;
sum += s1;
}
return sum;
}
double CMultiFishersNCHypergeometric::lng(int32_t * x) {
// natural log of proportional function g(x)
double y = 0.;
int i;
for (i = 0; i < colors; i++) {
y += x[i]*logodds[i] - LnFac(x[i]) - LnFac(m[i]-x[i]);
}
return mFac + y - scale;
}