BinarizeMatrix - Revision history

172.101.108.133: /* Function Source Code */

2017-07-12T23:19:46Z

Function Source Code

← Older revision		Revision as of 19:19, 12 July 2017
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	function [BIN, SCALED]=binarizeMatrix(Z, varargin)		function [BIN, SCALED]=binarizeMatrix(Z, varargin)
	%function [BIN, SCALED]=binarizeMatrix(Z, ~~thresh, clip~~)		%function [BIN, SCALED]=binarizeMatrix(Z, varargin)
	%binarizeMatrix takes a normalized matrix, Z, and scales and binarizes its		%binarizeMatrix takes a normalized matrix, Z, and scales and binarizes its
	%elements:		%elements:

172.101.108.133: /* Function Source Code */

2017-07-12T23:14:47Z

Function Source Code

← Older revision		Revision as of 19:14, 12 July 2017
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	== Function Source Code ==		== Function Source Code ==

	function [BIN, SCALED]=binarizeMatrix(Z, thresh, clip)		function [BIN, SCALED]=binarizeMatrix(Z, varargin)
	%binarizeMatrix takes a normalized matrix, Z, and scales and binarizes its		%function [BIN, SCALED]=binarizeMatrix(Z, thresh, clip)
			%binarizeMatrix takes a normalized matrix, Z, and scales and binarizes its
	%elements:		%elements:
	%positive values are set to 1 and negative values ar set to 0. If thresh is		%positive values are set to 1 and negative values ar set to 0. If thresh is
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	%are set to NaN. If a clipping value, clip, is provided, elements with an		%are set to NaN. If a clipping value, clip, is provided, elements with an
	%absolute value exceeding clip are first set to NaN before binarizing.		%absolute value exceeding clip are first set to NaN before binarizing.
	if(~~~exist~~('thresh', '~~var~~'))		%Required Parameters:
	~~thresh~~=1;		% Z: a normalized matrix or cell array
			%
			%Optional Parameters:
			% thresh: the z-score threshold above which the absolute values of the
			% normalized values in Z will be set to 1 (for positive values above the
			% threshold) or 0 (for negative values below the negative threshold)
			% clip: the z-score above which the absolute values will be set to NaN
			% precision: values are rounded to PRECISION decimal points
			%Sample usage:
			%thresh=1.96;
			%clip=3.0;
			%[BIN, SCALED]=binarizeMatrix(Z, 'thresh', thresh, 'clip', clip, 'precision', 2);
			%
			options = struct('thresh',1, ...
			'clip', inf, ...
			'precision', 1);

			% read the acceptable names
			optionNames = fieldnames(options);

			% count arguments
			nArgs = length(varargin);
			if (isstruct(varargin))
			options=varargin;
			else
			if round(nArgs/2)~=nArgs/2
			error('This function needs propertyName/propertyValue pairs')
			end

			for pair = reshape(varargin,2,[]) % pair is {propName;propValue}
			inpName = lower(pair{1}); % make case insensitive

			if any(strcmp(inpName,optionNames))
			% overwrite defaults
			options.(inpName) = pair{2};
			else
			error('%s is not a recognized parameter name',inpName)
			end
			end
	end		end
	~~if(~exist('clip', 'var'))~~
	~~clip=inf;~~		if(options.thresh==0)
	~~end~~		options.thresh=realmin; %workaround to allow a threshold of approx. zero
	if(thresh==0)
	thresh=realmin; %workaround to allow a threshold of approx. zero
	end		end
	if (~iscell(Z))		if (~iscell(Z))
	%Z is not a cell array, which is the easiest case		%Z is not a cell array, which is the easiest case
	SCALED=scaleMatrix(Z,clip, thresh);		SCALED=scaleMatrix(Z,options.clip, options.thresh, options.precision);
	BIN=SCALED;		BIN=SCALED;
	BIN(0<BIN & BIN<1)=nan;		BIN(0<BIN & BIN<1)=nan;
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	%M is a cell array, so we have to apply droprows and zscore to each		%M is a cell array, so we have to apply droprows and zscore to each
	%cell element		%cell element
	SCALED=cellfun(@(x) scaleMatrix(x, clip, thresh), Z, 'UniformOutput', false);		SCALED=cellfun(@(x) scaleMatrix(x, options.clip, options.thresh, options.precision), Z, 'UniformOutput', false);
	BIN=cellfun(@(x) nanBin(x), SCALED, 'UniformOutput', false);		BIN=cellfun(@(x) nanBin(x), SCALED, 'UniformOutput', false);
	end		end
	end		end

	%%Nested utility function scale matrix		%%Nested utility function scale matrix
	function SM=scaleMatrix(M, clip, thresh)		function SM=scaleMatrix(M, clip, thresh, precision)
	M(abs(M)>clip)=nan; %remove items to be clipped		M(abs(M)>clip)=nan; %remove items to be clipped
	M=M/thresh;%scale in terms of thresholds		M=M/thresh;%scale in terms of thresholds
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	%raise each value to the required exponent. 0s and 1s will be		%raise each value to the required exponent. 0s and 1s will be
	%unaffected		%unaffected
	SM=round(10*M.^centerexp)/10;		precis=power(10,precision);
			SM=round(precis*M.^centerexp)/precis;

	end		end

192.168.1.1 at 19:08, 9 February 2017

2017-02-09T19:08:34Z

← Older revision		Revision as of 15:08, 9 February 2017
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	binarizeMatrix is a Matlab function that takes as an input one or more normalized time series matrices, '''''Z''''', and returns up to two transformations of these matrices, '''''BIN''''' and '''''SCALED'''''. A Z-score threshold, '''''thresh''''' is applied to the normalized values: For all values greater than +thresh are set to 1; all values less than -thresh are set to 0. For the '''''SCALED''''' transformation, the intermediate values are scaled so that the columnar mean of the values between 0 and 1 is 0.5. For the '''''BIN''''' transformation, all fractional values are set to NaN. Additionally, a clipping value, '''''clip''''' can be supplied: all values with an absolute value exceeding the clip value are set to NaN. If a threshold is not supplied, 0 is used by default (i.e., all positive values are set to 1, and negative values are set to 0). If a clipping value is not used, no outlier values are filtered.		binarizeMatrix is a Matlab function that takes as an input one or more normalized time series matrices, '''''Z''''', and returns up to two transformations of these matrices, '''''BIN''''' and '''''SCALED'''''. A Z-score threshold, '''''thresh''''' is applied to the normalized values: For all values greater than +thresh are set to 1; all values less than -thresh are set to 0. For the '''''SCALED''''' transformation, the intermediate values are scaled so that the columnar mean of the values between 0 and 1 is 0.5. For the '''''BIN''''' transformation, all fractional values are set to NaN. Additionally, a clipping value, '''''clip''''' can be supplied: all values with an absolute value exceeding the clip value are set to NaN. If a threshold is not supplied, 0 is used by default (i.e., all positive values are set to 1, and negative values are set to 0). If a clipping value is not used, no outlier values are filtered.

			== Defensible Z threshold values ==
			{\|
			! AUC  !!  Z
			\|-
			\|.66 \|\| .44
			\|-
			\|.75 \|\| .68
			\|-
			\|.80 \|\| .84
			\|-
			\|.90 \|\| 1.28
			\|-
			\|.95 \|\| 1.65
			\|-
			\|.99 \|\| 2.33
			\|}

	== Function Source Code ==		== Function Source Code ==

Chris at 20:36, 29 September 2016

2016-09-29T20:36:48Z

← Older revision		Revision as of 16:36, 29 September 2016
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	end		end
	end		end

			[[Category: MATLAB functions]]

192.168.1.1: /* Function Source Code */

2016-09-16T15:56:01Z

Function Source Code

← Older revision		Revision as of 11:56, 16 September 2016
Line 3:		Line 3:
	== Function Source Code ==		== Function Source Code ==

	function [BIN, SCALED]=binarizeMatrix(Z, thresh, clip)		function [BIN, SCALED]=binarizeMatrix(Z, thresh, clip)
	%binarizeMatrix takes a normalized matrix, Z, and scales and binarizes its		%binarizeMatrix takes a normalized matrix, Z, and scales and binarizes its
	%elements:		%elements:
	%positive values are set to 1 and negative values ar set to 0. If thresh is		%positive values are set to 1 and negative values ar set to 0. If thresh is
	%set to a nonzero value, elements with an absolute value less than thresh		%set to a nonzero value, elements with an absolute value less than thresh
	%are set to NaN. If a clipping value, clip, is provided, elements with an		%are set to NaN. If a clipping value, clip, is provided, elements with an
	%absolute value exceeding clip are first set to NaN before binarizing.		%absolute value exceeding clip are first set to NaN before binarizing.
	if(~exist('thresh', 'var'))		if(~exist('thresh', 'var'))
	thresh=1;		thresh=1;
	end		end
	if(~exist('clip', 'var'))		if(~exist('clip', 'var'))
	clip=inf;		clip=inf;
	end		end
	if(thresh==0)		if(thresh==0)
	thresh=realmin; %workaround to allow a threshold of approx. zero		thresh=realmin; %workaround to allow a threshold of approx. zero
	end		end
	if (~iscell(Z))		if (~iscell(Z))
	%Z is not a cell array, which is the easiest case		%Z is not a cell array, which is the easiest case
	SCALED=scaleMatrix(Z,clip, thresh);		SCALED=scaleMatrix(Z,clip, thresh);
	BIN=SCALED;		BIN=SCALED;
	BIN(0<BIN & BIN<1)=nan;		BIN(0<BIN & BIN<1)=nan;
	else		else
	if(iscell(Z))		if(iscell(Z))
	%M is a cell array, so we have to apply droprows and zscore to each		%M is a cell array, so we have to apply droprows and zscore to each
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	BIN=cellfun(@(x) nanBin(x), SCALED, 'UniformOutput', false);		BIN=cellfun(@(x) nanBin(x), SCALED, 'UniformOutput', false);
	end		end
	end		end

	%%Nested utility function scale matrix		%%Nested utility function scale matrix
	function SM=scaleMatrix(M, clip, thresh)		function SM=scaleMatrix(M, clip, thresh)
	M(abs(M)>clip)=nan; %remove items to be clipped		M(abs(M)>clip)=nan; %remove items to be clipped
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	end		end
	%%Nested utility function sets all non 0-1 values to nan		%%Nested utility function sets all non 0-1 values to nan
	function BIN=nanBin(M)		function BIN=nanBin(M)
	BIN=M;		BIN=M;
	BIN(0<BIN & BIN<1)=nan;		BIN(0<BIN & BIN<1)=nan;
	end		end
	end		end

192.168.1.1: Created page with "binarizeMatrix is a Matlab function that takes as an input one or more normalized time series matrices, '''''Z''''', and returns up to two transformations of these matrices, '..."

2016-09-16T15:55:14Z

Created page with "binarizeMatrix is a Matlab function that takes as an input one or more normalized time series matrices, '''''Z''''', and returns up to two transformations of these matrices, '..."

New page

binarizeMatrix is a Matlab function that takes as an input one or more normalized time series matrices, '''''Z''''', and returns up to two transformations of these matrices, '''''BIN''''' and '''''SCALED'''''. A Z-score threshold, '''''thresh''''' is applied to the normalized values: For all values greater than +thresh are set to 1; all values less than -thresh are set to 0. For the '''''SCALED''''' transformation, the intermediate values are scaled so that the columnar mean of the values between 0 and 1 is 0.5. For the '''''BIN''''' transformation, all fractional values are set to NaN. Additionally, a clipping value, '''''clip''''' can be supplied: all values with an absolute value exceeding the clip value are set to NaN. If a threshold is not supplied, 0 is used by default (i.e., all positive values are set to 1, and negative values are set to 0). If a clipping value is not used, no outlier values are filtered.

== Function Source Code ==

function [BIN, SCALED]=binarizeMatrix(Z, thresh, clip)
%binarizeMatrix takes a normalized matrix, Z, and scales and binarizes its
%elements:
%positive values are set to 1 and negative values ar set to 0. If thresh is
%set to a nonzero value, elements with an absolute value less than thresh
%are set to NaN. If a clipping value, clip, is provided, elements with an
%absolute value exceeding clip are first set to NaN before binarizing.
if(~exist('thresh', 'var'))
thresh=1;
end
if(~exist('clip', 'var'))
clip=inf;
end
if(thresh==0)
thresh=realmin; %workaround to allow a threshold of approx. zero
end
if (~iscell(Z))
%Z is not a cell array, which is the easiest case
SCALED=scaleMatrix(Z,clip, thresh);
BIN=SCALED;
BIN(0<BIN & BIN<1)=nan;
else
if(iscell(Z))
%M is a cell array, so we have to apply droprows and zscore to each
%cell element
SCALED=cellfun(@(x) scaleMatrix(x, clip, thresh), Z, 'UniformOutput', false);
BIN=cellfun(@(x) nanBin(x), SCALED, 'UniformOutput', false);
end
end

%%Nested utility function scale matrix
function SM=scaleMatrix(M, clip, thresh)
M(abs(M)>clip)=nan; %remove items to be clipped
M=M/thresh;%scale in terms of thresholds
M(M>1)=1;%anything above +threshold=1
M(M<-1)=-1; %anything below -threshold=-1
M=M+1;%shift values from range -1:1 to 0:2
M=M/2;%now values range from 0:1
%subtly shift values so that mean value for each column is ~0.5
meansignal=nanmean(M);%what is the current mean of each column?
%use log to determine exponent required to shift each mean to 0.5
centerexp=repmat(power(log10(meansignal),-1).*log10(0.5),size(M,1),1);
%raise each value to the required exponent. 0s and 1s will be
%unaffected
SM=round(10*M.^centerexp)/10;

end
%%Nested utility function sets all non 0-1 values to nan
function BIN=nanBin(M)
BIN=M;
BIN(0<BIN & BIN<1)=nan;
end
end