# histc2

## PURPOSE

Multidimensional histogram count with weighted values.

## SYNOPSIS

function h = histc2( A, edges, wtMask )

## DESCRIPTION

     Multidimensional histogram count with weighted values.

     Creates a histogram h of the values in A [n x nd], with edges as
     specified. If nd==1, that is when A is a vector, the resulting histogram
     h is a vector of length nBins, where nBins=length(edges)-1. h(q) contains
     the weighted count of values v in A such that edges(q) <= v < edges(q+1).
     h(nBins) additionally contains the weighted count of values in A such
     that v==edges(nBins+1) -- which is different then how histc treates the
     boundary condition. Finally, h is normalized so that sum(h(:))==1.

     It usually makes sense to specify edges explicitly, especially if
     different histograms are going to be compared.  In general, edges must
     have monotonically non-decreasing values.  Also, if the exact bounds are
     unknown then it is convenient to set the first element in edges to -inf
     and the last to inf.  If h = histc2( A, nBins, ...), edges are
     automatically generated and have bins equally spaced between min(A) and
     max(A). That is the edges vector is generated by:
      edges = linspace( min(A)-eps, max(A)+eps, nBins+1 );

     If nd>1, that is when A is a 2d matrix instead of a vector, the created
     histogram is multi-dimensional with dimensions nBins^nd, where each bin
     h(q1,...,qnd) contains the the weighted count of vectors v in A such that
     edges{k}(qk) <= v(k) < edges{k}(qk+1), for k=1,...,nd.  Note that if nd>1
     edges may be a cell vector where each element is a vector of edges or a
     scalar nBins as before.

     Each value in A may optionally have an associated weight given by wtMask,
     which should have the same number of elements as A. If not specified, the
     default is wtMask=ones(n,1).

     USAGE
      h = histc2( A, edges, [wtMask] )

     INPUTS
      A           - [n x nd] 2D numeric array
      edges       - quantization bounds, see above
      wtMask      - [] length [n] vector of weights

     OUTPUTS
      h           - nd histogram [nBins^nd]

     EXAMPLE - 1D histograms
      A=filterGauss([1000 1000],[],[],0); A=A(:); n=length(A);
      h1 = histc2( A, 25 );              figure(1); bar(h1);
      h2 = histc2( A, 25, ones(n,1) );   figure(2); bar(h2);
      h3 = histc2( A, 25, A );           figure(3); bar(h3);

     EXAMPLE - 2D histograms
      A=filterGauss([1000 1000],[],[],0); A=A(:); n=length(A);
      h=histc2( [A A], 25 );    figure(1); im(h);  % decreasing along diag
      h=histc2( [A A], 25, A ); figure(2); im(h);  % constant along diag

     See also HISTC, ASSIGNTOBINS, BAR

     Piotr's Image&Video Toolbox      Version 2.0
     Copyright 2008 Piotr Dollar.  [pdollar-at-caltech.edu]
     Please email me if you find bugs, or have suggestions or questions!
     Licensed under the Lesser GPL [see external/lgpl.txt]

## CROSS-REFERENCE INFORMATION

This function calls:

This function is called by:

- histcImLoc Creates a series of locally position dependent histograms.