Mercurial > octave-nkf
annotate scripts/statistics/distributions/nbinpdf.m @ 20642:9d2023d1a63c
binoinv.m: Implement binary search algorithm for 28X performance increase (bug #34363).
* binoinv.m: Call new functions scalar_binoinv or vector_binoinv to calculate
binoinv. If there are still uncalculated values then call bin_search_binoinv
to perform binary search for remaining values. Add more BIST tests.
* binoinv.m (scalar_binoinv): New subfunction to calculate binoinv for scalar x.
Stops when x > 1000.
* binoinv.m (vector_binoinv): New subfunction to calculate binoinv for scalar x.
Stops when x > 1000.
author | Lachlan Andrew <lachlanbis@gmail.com> |
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date | Sun, 11 Oct 2015 19:49:40 -0700 |
parents | d9341b422488 |
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1 ## Copyright (C) 2012 Rik Wehbring |
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2 ## Copyright (C) 1995-2015 Kurt Hornik |
6349 | 3 ## |
4 ## This file is part of Octave. | |
5 ## | |
6 ## Octave is free software; you can redistribute it and/or modify it | |
7 ## under the terms of the GNU General Public License as published by | |
7016 | 8 ## the Free Software Foundation; either version 3 of the License, or (at |
9 ## your option) any later version. | |
6349 | 10 ## |
11 ## Octave is distributed in the hope that it will be useful, but | |
12 ## WITHOUT ANY WARRANTY; without even the implied warranty of | |
13 ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU | |
14 ## General Public License for more details. | |
15 ## | |
16 ## You should have received a copy of the GNU General Public License | |
7016 | 17 ## along with Octave; see the file COPYING. If not, see |
18 ## <http://www.gnu.org/licenses/>. | |
6349 | 19 |
20 ## -*- texinfo -*- | |
21 ## @deftypefn {Function File} {} nbinpdf (@var{x}, @var{n}, @var{p}) | |
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22 ## For each element of @var{x}, compute the probability density function (PDF) |
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23 ## at @var{x} of the negative binomial distribution with parameters |
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24 ## @var{n} and @var{p}. |
6349 | 25 ## |
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26 ## When @var{n} is integer this is the Pascal distribution. |
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27 ## When @var{n} is extended to real numbers this is the Polya distribution. |
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28 ## |
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29 ## The number of failures in a Bernoulli experiment with success probability |
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30 ## @var{p} before the @var{n}-th success follows this distribution. |
6349 | 31 ## @end deftypefn |
32 | |
33 ## Author: KH <Kurt.Hornik@wu-wien.ac.at> | |
34 ## Description: PDF of the Pascal (negative binomial) distribution | |
35 | |
36 function pdf = nbinpdf (x, n, p) | |
37 | |
38 if (nargin != 3) | |
39 print_usage (); | |
40 endif | |
41 | |
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42 if (! isscalar (n) || ! isscalar (p)) |
6349 | 43 [retval, x, n, p] = common_size (x, n, p); |
44 if (retval > 0) | |
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45 error ("nbinpdf: X, N, and P must be of common size or scalars"); |
6349 | 46 endif |
47 endif | |
48 | |
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49 if (iscomplex (x) || iscomplex (n) || iscomplex (p)) |
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50 error ("nbinpdf: X, N, and P must not be complex"); |
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51 endif |
6349 | 52 |
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53 if (isa (x, "single") || isa (n, "single") || isa (p, "single")) |
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54 pdf = NaN (size (x), "single"); |
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55 else |
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56 pdf = NaN (size (x)); |
6349 | 57 endif |
58 | |
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59 ok = (x < Inf) & (x == fix (x)) & (n > 0) & (n < Inf) & (p >= 0) & (p <= 1); |
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60 |
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61 k = (x < 0) & ok; |
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62 pdf(k) = 0; |
6349 | 63 |
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64 k = (x >= 0) & ok; |
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65 if (isscalar (n) && isscalar (p)) |
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66 pdf(k) = bincoeff (-n, x(k)) .* (p ^ n) .* ((p - 1) .^ x(k)); |
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67 else |
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68 pdf(k) = bincoeff (-n(k), x(k)) .* (p(k) .^ n(k)) .* ((p(k) - 1) .^ x(k)); |
6349 | 69 endif |
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70 |
6349 | 71 |
72 endfunction | |
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73 |
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74 |
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75 %!shared x,y |
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76 %! x = [-1 0 1 2 Inf]; |
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77 %! y = [0 1/2 1/4 1/8 NaN]; |
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78 %!assert (nbinpdf (x, ones (1,5), 0.5*ones (1,5)), y) |
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79 %!assert (nbinpdf (x, 1, 0.5*ones (1,5)), y) |
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80 %!assert (nbinpdf (x, ones (1,5), 0.5), y) |
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81 %!assert (nbinpdf (x, [0 1 NaN 1.5 Inf], 0.5), [NaN 1/2 NaN 1.875*0.5^1.5/4 NaN], eps) |
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82 %!assert (nbinpdf (x, 1, 0.5*[-1 NaN 4 1 1]), [NaN NaN NaN y(4:5)]) |
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83 %!assert (nbinpdf ([x, NaN], 1, 0.5), [y, NaN]) |
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84 |
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85 ## Test class of input preserved |
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86 %!assert (nbinpdf (single ([x, NaN]), 1, 0.5), single ([y, NaN])) |
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87 %!assert (nbinpdf ([x, NaN], single (1), 0.5), single ([y, NaN])) |
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88 %!assert (nbinpdf ([x, NaN], 1, single (0.5)), single ([y, NaN])) |
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89 |
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90 ## Test input validation |
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91 %!error nbinpdf () |
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92 %!error nbinpdf (1) |
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93 %!error nbinpdf (1,2) |
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94 %!error nbinpdf (1,2,3,4) |
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95 %!error nbinpdf (ones (3), ones (2), ones (2)) |
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96 %!error nbinpdf (ones (2), ones (3), ones (2)) |
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97 %!error nbinpdf (ones (2), ones (2), ones (3)) |
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98 %!error nbinpdf (i, 2, 2) |
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99 %!error nbinpdf (2, i, 2) |
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100 %!error nbinpdf (2, 2, i) |
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101 |