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annotate scripts/statistics/base/histc.m @ 20818:9d2023d1a63c

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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>
date Sun, 11 Oct 2015 19:49:40 -0700
parents d9341b422488
children
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rev   line source
19898
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1 ## Copyright (C) 2009-2015 Søren Hauberg
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2 ## Copyright (C) 2009 VZLU Prague
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3 ##
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4 ## This file is part of Octave.
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5 ##
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6 ## Octave is free software; you can redistribute it and/or modify it
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7 ## under the terms of the GNU General Public License as published by
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8 ## the Free Software Foundation; either version 3 of the License, or (at
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9 ## your option) any later version.
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10 ##
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11 ## Octave is distributed in the hope that it will be useful, but
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12 ## WITHOUT ANY WARRANTY; without even the implied warranty of
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13 ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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14 ## General Public License for more details.
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15 ##
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16 ## You should have received a copy of the GNU General Public License
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17 ## along with Octave; see the file COPYING. If not, see
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18 ## <http://www.gnu.org/licenses/>.
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19
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20 ## -*- texinfo -*-
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21 ## @deftypefn {Function File} {@var{n} =} histc (@var{x}, @var{edges})
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22 ## @deftypefnx {Function File} {@var{n} =} histc (@var{x}, @var{edges}, @var{dim})
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23 ## @deftypefnx {Function File} {[@var{n}, @var{idx}] =} histc (@dots{})
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24 ## Compute histogram counts.
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25 ##
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26 ## When @var{x} is a vector, the function counts the number of elements of
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27 ## @var{x} that fall in the histogram bins defined by @var{edges}. This must be
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28 ## a vector of monotonically increasing values that define the edges of the
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29 ## histogram bins. @code{@var{n}(k)} contains the number of elements in
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30 ## @var{x} for which @code{@var{edges}(k) <= @var{x} < @var{edges}(k+1)}.
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31 ## The final element of @var{n} contains the number of elements of @var{x}
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32 ## exactly equal to the last element of @var{edges}.
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33 ##
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34 ## When @var{x} is an @math{N}-dimensional array, the computation is carried
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35 ## out along dimension @var{dim}. If not specified @var{dim} defaults to the
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36 ## first non-singleton dimension.
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37 ##
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38 ## When a second output argument is requested an index matrix is also returned.
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39 ## The @var{idx} matrix has the same size as @var{x}. Each element of @var{idx}
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40 ## contains the index of the histogram bin in which the corresponding element
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41 ## of @var{x} was counted.
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42 ## @seealso{hist}
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43 ## @end deftypefn
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44
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45 function [n, idx] = histc (x, edges, dim)
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46
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47 if (nargin < 2 || nargin > 3)
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48 print_usage ();
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49 endif
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50
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51 if (! isreal (x))
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52 error ("histc: X argument must be real-valued, not complex");
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53 endif
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54
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55 num_edges = numel (edges);
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56 if (num_edges == 0)
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57 error ("histc: EDGES must not be empty");
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58 endif
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59
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60 if (! isreal (edges))
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61 error ("histc: EDGES must be real-valued, not complex");
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62 else
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63 ## Make sure 'edges' is sorted
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64 edges = edges(:);
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65 if (! issorted (edges) || edges(1) > edges(end))
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66 warning ("histc: edge values not sorted on input");
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67 edges = sort (edges);
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68 endif
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69 endif
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70
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71 nd = ndims (x);
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72 sz = size (x);
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73 if (nargin < 3)
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74 ## Find the first non-singleton dimension.
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75 (dim = find (sz > 1, 1)) || (dim = 1);
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76 else
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77 if (!(isscalar (dim) && dim == fix (dim))
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78 || !(1 <= dim && dim <= nd))
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79 error ("histc: DIM must be an integer and a valid dimension");
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80 endif
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81 endif
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82
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83 nsz = sz;
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84 nsz(dim) = num_edges;
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85
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86 ## the splitting point is 3 bins
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87
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88 if (num_edges <= 3)
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89
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90 ## This is the O(M*N) algorithm.
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91
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92 ## Allocate the histogram
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93 n = zeros (nsz);
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94
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95 ## Allocate 'idx'
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96 if (nargout > 1)
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97 idx = zeros (sz);
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98 endif
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99
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100 ## Prepare indices
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101 idx1 = cell (1, dim-1);
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102 for k = 1:length (idx1)
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103 idx1{k} = 1:sz(k);
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104 endfor
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105 idx2 = cell (length (sz) - dim);
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106 for k = 1:length (idx2)
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107 idx2{k} = 1:sz(k+dim);
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108 endfor
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109
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110 ## Compute the histograms
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111 for k = 1:num_edges-1
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112 b = (edges(k) <= x & x < edges(k+1));
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113 n(idx1{:}, k, idx2{:}) = sum (b, dim);
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114 if (nargout > 1)
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115 idx(b) = k;
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116 endif
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117 endfor
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118 b = (x == edges(end));
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119 n(idx1{:}, num_edges, idx2{:}) = sum (b, dim);
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120 if (nargout > 1)
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121 idx(b) = num_edges;
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122 endif
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123
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124 else
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125
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126 ## This is the O(M*log(N) + N) algorithm.
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127
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128 ## Look-up indices.
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129 idx = lookup (edges, x);
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130 ## Zero invalid ones (including NaNs). x < edges(1) are already zero.
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131 idx(! (x <= edges(end))) = 0;
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132
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133 iidx = idx;
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134
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135 ## In case of matrix input, we adjust the indices.
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136 if (! isvector (x))
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137 nl = prod (sz(1:dim-1));
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138 nn = sz(dim);
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139 nu = prod (sz(dim+1:end));
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140 if (nl != 1)
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141 iidx = (iidx-1) * nl;
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142 iidx += reshape (kron (ones (1, nn*nu), 1:nl), sz);
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143 endif
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144 if (nu != 1)
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145 ne =length (edges);
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146 iidx += reshape (kron (nl*ne*(0:nu-1), ones (1, nl*nn)), sz);
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147 endif
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148 endif
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149
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150 ## Select valid elements.
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151 iidx = iidx(idx != 0);
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152
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153 ## Call accumarray to sum the indexed elements.
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154 n = accumarray (iidx(:), 1, nsz);
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155
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156 endif
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157
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158 endfunction
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159
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160
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161 %!test
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162 %! x = linspace (0, 10, 1001);
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163 %! n = histc (x, 0:10);
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164 %! assert (n, [repmat(100, 1, 10), 1]);
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165
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166 %!test
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167 %! x = repmat (linspace (0, 10, 1001), [2, 1, 3]);
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168 %! n = histc (x, 0:10, 2);
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169 %! assert (n, repmat ([repmat(100, 1, 10), 1], [2, 1, 3]));
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170
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171 %!error histc ()
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172 %!error histc (1)
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173 %!error histc (1, 2, 3, 4)
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174 %!error histc ([1:10 1+i], 2)
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175 %!error histc (1:10, [])
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176 %!error histc (1, 1, 3)
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177