octave-nan 2.5.9-2 / usr / share / octave / packages / nan-2.5.9 / hist2res.m

This file is indexed.

/usr/share/octave/packages/nan-2.5.9/hist2res.m is in octave-nan 2.5.9-2.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below. 

  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
function [R]=hist2res(H,fun)
% Evaluates Histogram data
% [R]=hist2res(H)
%
% [y]=hist2res(H,fun)
%	estimates fun-statistic
%
% fun	'mean'	mean
%	'std'	standard deviation
%	'var'	variance
%	'sem'	standard error of the mean
%	'rms'	root mean square
%	'meansq' mean of squares
%	'sum'	sum
%	'sumsq'	sum of squares
%	'CM#'	central moment of order #
%	'skewness' skewness 
%	'kurtosis' excess coefficient (Fisher kurtosis)
%
% see also: NaN/statistic
%
% REFERENCES:
% [1] C.L. Nikias and A.P. Petropulu "Higher-Order Spectra Analysis" Prentice Hall, 1993.
% [2] C.E. Shannon and W. Weaver "The mathematical theory of communication" University of Illinois Press, Urbana 1949 (reprint 1963).
% [3] http://www.itl.nist.gov/
% [4] http://mathworld.wolfram.com/

% This program is free software; you can redistribute it and/or
% modify it under the terms of the GNU General Public License
% as published by the Free Software Foundation; either version 2
% of the License, or (at your option) any later version.
% 
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
% GNU General Public License for more details.
% 
% You should have received a copy of the GNU General Public License
% along with this program; if not, write to the Free Software
% Foundation, Inc., 51 Franklin Street - Fifth Floor, Boston, MA 02110-1301, USA.

%	$Id: hist2res.m 9387 2011-12-15 10:42:14Z schloegl $
%	Copyright (c) 1996-2002,2006 by Alois Schloegl <alois.schloegl@gmail.com>
%       This function is part of the NaN-toolbox
%       http://pub.ist.ac.at/~schloegl/matlab/NaN/


if strcmp(H.datatype,'HISTOGRAM'),

elseif strcmp(H.datatype,'qc:histo')
	HDR = H; 
	if isfield(H,'THRESHOLD'),
		TH  = H.THRESHOLD;
	else
		TH = repmat([-inf,inf],HDR.NS,1); 
	end;
	HIS = H.HIS; 

	% remove overflowing samples
	HIS.N = sumskipnan(HIS.H); 
	for k = 1:size(HIS.H,2);
		t = HIS.X(:,min(k,size(HIS.X,2))); 
		HIS.H(xor(t<=min(TH(k,:)), t>=max(TH(k,:))),k) = 0; 
	end; 
	Nnew = sumskipnan(HIS.H); 
	R.ratio_lost = 1-Nnew./HIS.N;
	HIS.N = Nnew; 
	  
	% scale into physical values
	if H.FLAG.UCAL,
		%t = HIS.X;
		%for k=1:length(HDR.InChanSelect),
		%	HIS.X(:,k) = t(:,min(size(t,2),k))*HDR.Calib(k+1,k)+HDR.Calib(1,k);
		%end;
		HIS.X = [ones(size(HIS.X,1),1),repmat(HIS.X,1,size(HIS.H,2)./size(HIS.X,2))]*H.Calib;
	end; 	
	H = HIS; 
else
        fprintf(2,'ERROR: arg1 is not a histogram\n');
        return;
end;
if nargin<2, fun=[]; end;

global FLAG_implicit_unbiased_estimation; 
%%% check whether FLAG was already defined 
if ~exist('FLAG_implicit_unbiased_estimation','var'),
	FLAG_implicit_unbiased_estimation=[];
end;
%%% set DEFAULT value of FLAG
if isempty(FLAG_implicit_unbiased_estimation),
	FLAG_implicit_unbiased_estimation=logical(1);
end;

sz 	= size(H.H)./size(H.X);
R.N 	= sumskipnan(H.H,1);
R.SUM 	= sumskipnan(H.H.*repmat(H.X,sz),1);
R.SSQ 	= sumskipnan(H.H.*repmat(H.X.*H.X,sz),1);
%R.S3P 	= sumskipnan(H.H.*repmat(H.X.^3,sz),1);	% sum of 3rd power
R.S4P 	= sumskipnan(H.H.*repmat(H.X.^4,sz),1);	% sum of 4th power
%R.S5P 	= sumskipnan(H.H.*repmat(H.X.^5,sz),1);	% sum of 5th power

R.MEAN	= R.SUM./R.N;
R.MSQ   = R.SSQ./R.N;
R.RMS	= sqrt(R.MSQ);
R.SSQ0  = R.SSQ-R.SUM.*R.MEAN;		% sum square of mean removed

if FLAG_implicit_unbiased_estimation,
    n1 	= max(R.N-1,0);			% in case of n=0 and n=1, the (biased) variance, STD and STE are INF
else
    n1	= R.N;
end;

R.VAR  	= R.SSQ0./n1;	     		% variance (unbiased) 
R.STD  	= sqrt(R.VAR);		     	% standard deviation
R.SEM  	= sqrt(R.SSQ0./(R.N.*n1)); 	% standard error of the mean
R.SEV	= sqrt(n1.*(n1.*R.S4P./R.N+(R.N.^2-2*R.N+3).*(R.SSQ./R.N).^2)./(R.N.^3)); % standard error of the variance
R.Coefficient_of_variation = R.STD./R.MEAN;

R.CM2	= R.SSQ0./n1;
x       = repmat(H.X,sz) - repmat(R.MEAN,size(H.X,1),1);
R.CM3 	= sumskipnan(H.H.*(x.^3),1)./n1;
R.CM4 	= sumskipnan(H.H.*(x.^4),1)./n1;
%R.CM5 	= sumskipnan(H.H.*(x.^5),1)./n1;

R.SKEWNESS = R.CM3./(R.STD.^3);
R.KURTOSIS = R.CM4./(R.VAR.^2)-3;
R.MAD = sumskipnan(H.H.*abs(x),1)./R.N; % mean absolute deviation

H.PDF = H.H./H.N(ones(size(H.H,1),1),:);
status=warning('off'); 
R.ENTROPY = -sumskipnan(H.PDF.*log2(H.PDF),1);
warning(status); 
R.QUANT = repmat(min(diff(H.X,[],1)),1,size(H.H,2)/size(H.X,2));
R.MAX = max(H.X); 
R.MIN = min(H.X); 
R.RANGE = R.MAX-R.MIN;

if ~isempty(fun),
        fun=upper(fun);
	if strncmp(fun,'CM',2) 
                oo = str2double(fun(3:length(fun)));
                R = sumskipnan(H.PDF.*(x.^oo),1);
    	else	            
		R = getfield(R,fun);
	end;
end;

APT Browse - Built by Thomas Orozco.