Module: Numo::GSL::Pdf

Defined in:
ext/numo/gsl/pdf/gsl_pdf.c

Class Method Summary collapse

Class Method Details

.bernoulli(k, p) ⇒ Numo::DFloat

This function computes the probability p(k) of obtaining k from a Bernoulli distribution with probability parameter p, using the formula given above.

Parameters:

  • k (Numo::UInt)
  • p (Float)

Returns:

  • (Numo::DFloat) —

    return



1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1555

static VALUE
pdf_s_bernoulli(VALUE mod,VALUE v0,VALUE v1)
{
    
    double c1;
    

    ndfunc_arg_in_t ain[1] = {{cUI,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_bernoulli,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt;
    c1 = NUM2DBL(v1); opt = &c1; //p
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.beta(x, a, b) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for a beta distribution with parameters a and b, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • a (Float)
  • b (Float)

Returns:

  • (Numo::DFloat) —

    return



1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1060

static VALUE
pdf_s_beta(VALUE mod,VALUE v0,VALUE v1,VALUE v2)
{
    
    double c1;
    double c2;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_beta,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt[2];
    
    c1 = NUM2DBL(v1); opt[0] = &c1; //a
    c2 = NUM2DBL(v2); opt[1] = &c2; //b
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.binomial(k, p, n) ⇒ Numo::DFloat

This function computes the probability p(k) of obtaining k from a binomial distribution with parameters p and n, using the formula given above.

Parameters:

  • k (Numo::UInt)
  • p (Float)
  • n (Integer)

Returns:

  • (Numo::DFloat) —

    return



1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1612

static VALUE
pdf_s_binomial(VALUE mod,VALUE v0,VALUE v1,VALUE v2)
{
    
    double c1;
    unsigned int c2;
    

    ndfunc_arg_in_t ain[1] = {{cUI,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_binomial,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt[2];
    
    c1 = NUM2DBL(v1); opt[0] = &c1; //p
    c2 = NUM2UINT(v2); opt[1] = &c2; //n
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.bivariate_gaussian(x, y, sigma_x, sigma_y, rho) ⇒ Numo::DFloat

This function computes the probability density p(x,y) at (x,y) for a bivariate Gaussian distribution with standard deviations sigma_x, sigma_y and correlation coefficient rho, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • y (Numo::DFloat)
  • sigma_x (Float)
  • sigma_y (Float)
  • rho (Float)

Returns:

  • (Numo::DFloat) —

    return



282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 282

static VALUE
pdf_s_bivariate_gaussian(VALUE mod,VALUE v0,VALUE v1,VALUE v2,VALUE v3,VALUE v4)
{
    
    double c2;
    double c3;
    double c4;
    

    ndfunc_arg_in_t ain[2] = {{cDF,0},{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_bivariate_gaussian,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    2,1,ain,aout};
    
    void *opt[3];
    
    c2 = NUM2DBL(v2); opt[0] = &c2; //sigma_x
    c3 = NUM2DBL(v3); opt[1] = &c3; //sigma_y
    c4 = NUM2DBL(v4); opt[2] = &c4; //rho
    
    
    return na_ndloop3(&ndf,opt,2,v0,v1); 
}

.cauchy(x, a) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for a Cauchy distribution with scale parameter a, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • a (Float)

Returns:

  • (Numo::DFloat) —

    return



503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 503

static VALUE
pdf_s_cauchy(VALUE mod,VALUE v0,VALUE v1)
{
    
    double c1;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_cauchy,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt;
    c1 = NUM2DBL(v1); opt = &c1; //a
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.chisq(x, nu) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for a chi-squared distribution with nu degrees of freedom, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • nu (Float)

Returns:

  • (Numo::DFloat) —

    return



891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 891

static VALUE
pdf_s_chisq(VALUE mod,VALUE v0,VALUE v1)
{
    
    double c1;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_chisq,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt;
    c1 = NUM2DBL(v1); opt = &c1; //nu
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.dirichlet(alpha[], theta[], axis: nil, keepdims: false) ⇒ Numo::DFloat

This function computes the probability density $p(\theta_1, \ldots , \theta_K)$ p(\theta_1, … , \theta_K) at theta[K] for a Dirichlet distribution with parameters alpha[K], using the formula given above.

Parameters:

  • alpha[] (Numo::DFloat)
  • theta[] (Numo::DFloat)
  • axis (Numeric, Array, Range) —

    (keyword) Axes along which the operation is performed.

  • keepdims (TrueClass) —

    (keyword) If true, the reduced axes are left in the result array as dimensions with size one.

Returns:

  • (Numo::DFloat) —

    return



1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1405

static VALUE
pdf_s_dirichlet(int argc, VALUE *argv, VALUE mod)
{
    VALUE reduce;
    ndfunc_arg_in_t ain[3] = {{cDF,0},{cDF,0},{sym_reduce,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = { iter_pdf_s_dirichlet, NDF_HAS_LOOP|NDF_FLAT_REDUCE|NDF_EXTRACT,
                     3, 1, ain, aout };

    if (argc<2) {
        rb_raise(rb_eArgError,"wrong number of argument (%d for >=2)",argc);
    }

    reduce = nary_reduce_dimension(argc-2, argv+2, 2, argv, &ndf, 0);
    return na_ndloop(&ndf, 3, argv[0], argv[1], reduce);
}

.dirichlet_ln(alpha[], theta[], axis: nil, keepdims: false) ⇒ Numo::DFloat

This function computes the logarithm of the probability density $p(\theta_1, \ldots , \theta_K)$ p(\theta_1, … , \theta_K) for a Dirichlet distribution with parameters alpha[K].

Parameters:

  • alpha[] (Numo::DFloat)
  • theta[] (Numo::DFloat)
  • axis (Numeric, Array, Range) —

    (keyword) Axes along which the operation is performed.

  • keepdims (TrueClass) —

    (keyword) If true, the reduced axes are left in the result array as dimensions with size one.

Returns:

  • (Numo::DFloat) —

    return



1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1453

static VALUE
pdf_s_dirichlet_ln(int argc, VALUE *argv, VALUE mod)
{
    VALUE reduce;
    ndfunc_arg_in_t ain[3] = {{cDF,0},{cDF,0},{sym_reduce,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = { iter_pdf_s_dirichlet_ln, NDF_HAS_LOOP|NDF_FLAT_REDUCE|NDF_EXTRACT,
                     3, 1, ain, aout };

    if (argc<2) {
        rb_raise(rb_eArgError,"wrong number of argument (%d for >=2)",argc);
    }

    reduce = nary_reduce_dimension(argc-2, argv+2, 2, argv, &ndf, 0);
    return na_ndloop(&ndf, 3, argv[0], argv[1], reduce);
}

.exponential(x, mu) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for an exponential distribution with mean mu, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • mu (Float)

Returns:

  • (Numo::DFloat) —

    return



339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 339

static VALUE
pdf_s_exponential(VALUE mod,VALUE v0,VALUE v1)
{
    
    double c1;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_exponential,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt;
    c1 = NUM2DBL(v1); opt = &c1; //mu
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.exppow(x, a, b) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for an exponential power distribution with scale parameter a and exponent b, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • a (Float)
  • b (Float)

Returns:

  • (Numo::DFloat) —

    return



448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 448

static VALUE
pdf_s_exppow(VALUE mod,VALUE v0,VALUE v1,VALUE v2)
{
    
    double c1;
    double c2;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_exppow,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt[2];
    
    c1 = NUM2DBL(v1); opt[0] = &c1; //a
    c2 = NUM2DBL(v2); opt[1] = &c2; //b
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.fdist(x, nu1, nu2) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for an F-distribution with nu1 and nu2 degrees of freedom, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • nu1 (Float)
  • nu2 (Float)

Returns:

  • (Numo::DFloat) —

    return



948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 948

static VALUE
pdf_s_fdist(VALUE mod,VALUE v0,VALUE v1,VALUE v2)
{
    
    double c1;
    double c2;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_fdist,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt[2];
    
    c1 = NUM2DBL(v1); opt[0] = &c1; //nu1
    c2 = NUM2DBL(v2); opt[1] = &c2; //nu2
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.flat(x, a, b) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for a uniform distribution from a to b, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • a (Float)
  • b (Float)

Returns:

  • (Numo::DFloat) —

    return



776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 776

static VALUE
pdf_s_flat(VALUE mod,VALUE v0,VALUE v1,VALUE v2)
{
    
    double c1;
    double c2;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_flat,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt[2];
    
    c1 = NUM2DBL(v1); opt[0] = &c1; //a
    c2 = NUM2DBL(v2); opt[1] = &c2; //b
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.gamma(x, a, b) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for a gamma distribution with parameters a and b, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • a (Float)
  • b (Float)

Returns:

  • (Numo::DFloat) —

    return



716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 716

static VALUE
pdf_s_gamma(VALUE mod,VALUE v0,VALUE v1,VALUE v2)
{
    
    double c1;
    double c2;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_gamma,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt[2];
    
    c1 = NUM2DBL(v1); opt[0] = &c1; //a
    c2 = NUM2DBL(v2); opt[1] = &c2; //b
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.gaussian(x, sigma) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for a Gaussian distribution with standard deviation sigma, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • sigma (Float)

Returns:

  • (Numo::DFloat) —

    return



62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 62

static VALUE
pdf_s_gaussian(VALUE mod,VALUE v0,VALUE v1)
{
    
    double c1;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_gaussian,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt;
    c1 = NUM2DBL(v1); opt = &c1; //sigma
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.gaussian_tail(x, a, sigma) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for a Gaussian tail distribution with standard deviation sigma and lower limit a, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • a (Float)
  • sigma (Float)

Returns:

  • (Numo::DFloat) —

    return



163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 163

static VALUE
pdf_s_gaussian_tail(VALUE mod,VALUE v0,VALUE v1,VALUE v2)
{
    
    double c1;
    double c2;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_gaussian_tail,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt[2];
    
    c1 = NUM2DBL(v1); opt[0] = &c1; //a
    c2 = NUM2DBL(v2); opt[1] = &c2; //sigma
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.geometric(k, p) ⇒ Numo::DFloat

This function computes the probability p(k) of obtaining k from a geometric distribution with probability parameter p, using the formula given above.

Parameters:

  • k (Numo::UInt)
  • p (Float)

Returns:

  • (Numo::DFloat) —

    return



1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1881

static VALUE
pdf_s_geometric(VALUE mod,VALUE v0,VALUE v1)
{
    
    double c1;
    

    ndfunc_arg_in_t ain[1] = {{cUI,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_geometric,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt;
    c1 = NUM2DBL(v1); opt = &c1; //p
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.gumbel1(x, a, b) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for a Type-1 Gumbel distribution with parameters a and b, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • a (Float)
  • b (Float)

Returns:

  • (Numo::DFloat) —

    return



1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1292

static VALUE
pdf_s_gumbel1(VALUE mod,VALUE v0,VALUE v1,VALUE v2)
{
    
    double c1;
    double c2;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_gumbel1,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt[2];
    
    c1 = NUM2DBL(v1); opt[0] = &c1; //a
    c2 = NUM2DBL(v2); opt[1] = &c2; //b
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.gumbel2(x, a, b) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for a Type-2 Gumbel distribution with parameters a and b, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • a (Float)
  • b (Float)

Returns:

  • (Numo::DFloat) —

    return



1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1352

static VALUE
pdf_s_gumbel2(VALUE mod,VALUE v0,VALUE v1,VALUE v2)
{
    
    double c1;
    double c2;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_gumbel2,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt[2];
    
    c1 = NUM2DBL(v1); opt[0] = &c1; //a
    c2 = NUM2DBL(v2); opt[1] = &c2; //b
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.hypergeometric(k, n1, n2, t) ⇒ Numo::DFloat

This function computes the probability p(k) of obtaining k from a hypergeometric distribution with parameters n1, n2, t, using the formula given above.

Parameters:

  • k (Numo::UInt)
  • n1 (Integer)
  • n2 (Integer)
  • t (Integer)

Returns:

  • (Numo::DFloat) —

    return



1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1941

static VALUE
pdf_s_hypergeometric(VALUE mod,VALUE v0,VALUE v1,VALUE v2,VALUE v3)
{
    
    unsigned int c1;
    unsigned int c2;
    unsigned int c3;
    

    ndfunc_arg_in_t ain[1] = {{cUI,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_hypergeometric,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt[3];
    
    c1 = NUM2UINT(v1); opt[0] = &c1; //n1
    c2 = NUM2UINT(v2); opt[1] = &c2; //n2
    c3 = NUM2UINT(v3); opt[2] = &c3; //t
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.landau(x) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for the Landau distribution using an approximation to the formula given above.

Parameters:

  • x (Numo::DFloat)

Returns:

  • (Numo::DFloat) —

    return



663
664
665
666
667
668
669
670
671
672
673
674
675
676
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 663

static VALUE
pdf_s_landau(VALUE mod,VALUE v0)
{
    
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_landau,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    
    return na_ndloop(&ndf,1,v0); 
}

.laplace(x, a) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for a Laplace distribution with width a, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • a (Float)

Returns:

  • (Numo::DFloat) —

    return



391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 391

static VALUE
pdf_s_laplace(VALUE mod,VALUE v0,VALUE v1)
{
    
    double c1;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_laplace,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt;
    c1 = NUM2DBL(v1); opt = &c1; //a
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.logarithmic(k, p) ⇒ Numo::DFloat

This function computes the probability p(k) of obtaining k from a logarithmic distribution with probability parameter p, using the formula given above.

Parameters:

  • k (Numo::UInt)
  • p (Float)

Returns:

  • (Numo::DFloat) —

    return



1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1998

static VALUE
pdf_s_logarithmic(VALUE mod,VALUE v0,VALUE v1)
{
    
    double c1;
    

    ndfunc_arg_in_t ain[1] = {{cUI,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_logarithmic,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt;
    c1 = NUM2DBL(v1); opt = &c1; //p
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.logistic(x, a) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for a logistic distribution with scale parameter a, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • a (Float)

Returns:

  • (Numo::DFloat) —

    return



1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1115

static VALUE
pdf_s_logistic(VALUE mod,VALUE v0,VALUE v1)
{
    
    double c1;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_logistic,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt;
    c1 = NUM2DBL(v1); opt = &c1; //a
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.lognormal(x, zeta, sigma) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for a lognormal distribution with parameters zeta and sigma, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • zeta (Float)
  • sigma (Float)

Returns:

  • (Numo::DFloat) —

    return



836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 836

static VALUE
pdf_s_lognormal(VALUE mod,VALUE v0,VALUE v1,VALUE v2)
{
    
    double c1;
    double c2;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_lognormal,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt[2];
    
    c1 = NUM2DBL(v1); opt[0] = &c1; //zeta
    c2 = NUM2DBL(v2); opt[1] = &c2; //sigma
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.multinomial(p[], n[], [axis0,axis1,..]) ⇒ Numo::DFloat

This function computes the probability $P(n_1, n_2, \ldots, n_K)$ P(n_1, n_2, …, n_K) of sampling n[K] from a multinomial distribution with parameters p[K], using the formula given above.

Parameters:

  • p[] (Numo::DFloat)
  • n[] (Numo::UInt)
  • axis (Numeric, Array, Range) —

    (keyword) Axes along which the operation is performed.

  • keepdims (TrueClass) —

    (keyword) If true, the reduced axes are left in the result array as dimensions with size one.

Returns:

  • (Numo::DFloat) —

    return



1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1665

static VALUE
pdf_s_multinomial(int argc, VALUE *argv, VALUE mod)
{
    VALUE reduce;
    ndfunc_arg_in_t ain[3] = {{cDF,0},{cUI,0},{sym_reduce,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = { iter_pdf_s_multinomial, NDF_HAS_LOOP|NDF_FLAT_REDUCE|NDF_EXTRACT,
                     3, 1, ain, aout };

    if (argc<2) {
        rb_raise(rb_eArgError,"wrong number of argument (%d for >=2)",argc);
    }

    reduce = nary_reduce_dimension(argc-2, argv+2, 2, argv, &ndf, 0);
    return na_ndloop(&ndf, 3, argv[0], argv[1], reduce);
}

.multinomial_ln(p[], n[], [axis0,axis1,..]) ⇒ Numo::DFloat

This function returns the logarithm of the probability for the multinomial distribution $P(n_1, n_2, \ldots, n_K)$ P(n_1, n_2, …, n_K) with parameters p[K].

Parameters:

  • p[] (Numo::DFloat)
  • n[] (Numo::UInt)
  • axis (Numeric, Array, Range) —

    (keyword) Axes along which the operation is performed.

  • keepdims (TrueClass) —

    (keyword) If true, the reduced axes are left in the result array as dimensions with size one.

Returns:

  • (Numo::DFloat) —

    return



1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1711

static VALUE
pdf_s_multinomial_ln(int argc, VALUE *argv, VALUE mod)
{
    VALUE reduce;
    ndfunc_arg_in_t ain[3] = {{cDF,0},{cUI,0},{sym_reduce,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = { iter_pdf_s_multinomial_ln, NDF_HAS_LOOP|NDF_FLAT_REDUCE|NDF_EXTRACT,
                     3, 1, ain, aout };

    if (argc<2) {
        rb_raise(rb_eArgError,"wrong number of argument (%d for >=2)",argc);
    }

    reduce = nary_reduce_dimension(argc-2, argv+2, 2, argv, &ndf, 0);
    return na_ndloop(&ndf, 3, argv[0], argv[1], reduce);
}

.negative_binomial(k, p, n) ⇒ Numo::DFloat

This function computes the probability p(k) of obtaining k from a negative binomial distribution with parameters p and n, using the formula given above.

Parameters:

  • k (Numo::UInt)
  • p (Float)
  • n (Float)

Returns:

  • (Numo::DFloat) —

    return



1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1766

static VALUE
pdf_s_negative_binomial(VALUE mod,VALUE v0,VALUE v1,VALUE v2)
{
    
    double c1;
    double c2;
    

    ndfunc_arg_in_t ain[1] = {{cUI,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_negative_binomial,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt[2];
    
    c1 = NUM2DBL(v1); opt[0] = &c1; //p
    c2 = NUM2DBL(v2); opt[1] = &c2; //n
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.pareto(x, a, b) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for a Pareto distribution with exponent a and scale b, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • a (Float)
  • b (Float)

Returns:

  • (Numo::DFloat) —

    return



1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1172

static VALUE
pdf_s_pareto(VALUE mod,VALUE v0,VALUE v1,VALUE v2)
{
    
    double c1;
    double c2;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_pareto,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt[2];
    
    c1 = NUM2DBL(v1); opt[0] = &c1; //a
    c2 = NUM2DBL(v2); opt[1] = &c2; //b
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.pascal(k, p, n) ⇒ Numo::DFloat

This function computes the probability p(k) of obtaining k from a Pascal distribution with parameters p and n, using the formula given above.

Parameters:

  • k (Numo::UInt)
  • p (Float)
  • n (Integer)

Returns:

  • (Numo::DFloat) —

    return



1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1826

static VALUE
pdf_s_pascal(VALUE mod,VALUE v0,VALUE v1,VALUE v2)
{
    
    double c1;
    unsigned int c2;
    

    ndfunc_arg_in_t ain[1] = {{cUI,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_pascal,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt[2];
    
    c1 = NUM2DBL(v1); opt[0] = &c1; //p
    c2 = NUM2UINT(v2); opt[1] = &c2; //n
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.poisson(k, mu) ⇒ Numo::DFloat

This function computes the probability p(k) of obtaining k from a Poisson distribution with mean mu, using the formula given above.

Parameters:

  • k (Numo::UInt)
  • mu (Float)

Returns:

  • (Numo::DFloat) —

    return



1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1503

static VALUE
pdf_s_poisson(VALUE mod,VALUE v0,VALUE v1)
{
    
    double c1;
    

    ndfunc_arg_in_t ain[1] = {{cUI,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_poisson,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt;
    c1 = NUM2DBL(v1); opt = &c1; //mu
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.rayleigh(x, sigma) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for a Rayleigh distribution with scale parameter sigma, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • sigma (Float)

Returns:

  • (Numo::DFloat) —

    return



555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 555

static VALUE
pdf_s_rayleigh(VALUE mod,VALUE v0,VALUE v1)
{
    
    double c1;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_rayleigh,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt;
    c1 = NUM2DBL(v1); opt = &c1; //sigma
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.rayleigh_tail(x, a, sigma) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for a Rayleigh tail distribution with scale parameter sigma and lower limit a, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • a (Float)
  • sigma (Float)

Returns:

  • (Numo::DFloat) —

    return



612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 612

static VALUE
pdf_s_rayleigh_tail(VALUE mod,VALUE v0,VALUE v1,VALUE v2)
{
    
    double c1;
    double c2;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_rayleigh_tail,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt[2];
    
    c1 = NUM2DBL(v1); opt[0] = &c1; //a
    c2 = NUM2DBL(v2); opt[1] = &c2; //sigma
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.tdist(x, nu) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for a t-distribution with nu degrees of freedom, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • nu (Float)

Returns:

  • (Numo::DFloat) —

    return



1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1003

static VALUE
pdf_s_tdist(VALUE mod,VALUE v0,VALUE v1)
{
    
    double c1;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_tdist,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt;
    c1 = NUM2DBL(v1); opt = &c1; //nu
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.ugaussian(x) ⇒ Numo::DFloat

These functions compute results for the unit Gaussian distribution. They are equivalent to the functions above with a standard deviation of one, sigma = 1.

Parameters:

  • x (Numo::DFloat)

Returns:

  • (Numo::DFloat) —

    return



110
111
112
113
114
115
116
117
118
119
120
121
122
123
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 110

static VALUE
pdf_s_ugaussian(VALUE mod,VALUE v0)
{
    
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_ugaussian,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    
    return na_ndloop(&ndf,1,v0); 
}

.ugaussian_tail(x, a) ⇒ Numo::DFloat

These functions compute results for the tail of a unit Gaussian distribution. They are equivalent to the functions above with a standard deviation of one, sigma = 1.

Parameters:

  • x (Numo::DFloat)
  • a (Float)

Returns:

  • (Numo::DFloat) —

    return



218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 218

static VALUE
pdf_s_ugaussian_tail(VALUE mod,VALUE v0,VALUE v1)
{
    
    double c1;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_ugaussian_tail,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt;
    c1 = NUM2DBL(v1); opt = &c1; //a
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}

.weibull(x, a, b) ⇒ Numo::DFloat

This function computes the probability density p(x) at x for a Weibull distribution with scale a and exponent b, using the formula given above.

Parameters:

  • x (Numo::DFloat)
  • a (Float)
  • b (Float)

Returns:

  • (Numo::DFloat) —

    return



1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
 
# File 'ext/numo/gsl/pdf/gsl_pdf.c', line 1232

static VALUE
pdf_s_weibull(VALUE mod,VALUE v0,VALUE v1,VALUE v2)
{
    
    double c1;
    double c2;
    

    ndfunc_arg_in_t ain[1] = {{cDF,0}};
    ndfunc_arg_out_t aout[1] = {{cDF,0}};
    ndfunc_t ndf = {iter_pdf_s_weibull,NO_LOOP|NDF_INPLACE|NDF_EXTRACT,
                    1,1,ain,aout};
    
    void *opt[2];
    
    c1 = NUM2DBL(v1); opt[0] = &c1; //a
    c2 = NUM2DBL(v2); opt[1] = &c2; //b
    
    
    return na_ndloop3(&ndf,opt,1,v0); 
}