Histogram bin counts
[N,edges]
= histcounts(X)
[N,edges]
= histcounts(X,nbins)
[N,edges]
= histcounts(X,edges)
[N,edges,bin]
= histcounts(___)
N = histcounts(C)
N = histcounts(C,Categories)
[N,Categories]
= histcounts(___)
[___] = histcounts(___,Name,Value)
[
partitions the N
,edges
]
= histcounts(X
)X
values
into bins, and returns the count in each bin, as well as the bin edges.
The histcounts
function uses an automatic binning
algorithm that returns bins with a uniform width, chosen to cover
the range of elements in X
and reveal the underlying
shape of the distribution.
counts
only the elements in N
= histcounts(C
,Categories
)C
whose value is equal to
the subset of categories specified by Categories
.
[
also returns the categories
that correspond to each count in N
,Categories
]
= histcounts(___)N
using either
of the previous syntaxes for categorical arrays.
[___] = histcounts(___,
uses
additional options specified by one or more Name,Value
)Name,Value
pair
arguments using any of the input or output argument combinations in
previous syntaxes. For example, you can specify 'BinWidth'
and
a scalar to adjust the width of the bins for numeric data. For categorical
data, you can specify 'Normalization'
and either 'count'
, 'countdensity'
, 'probability'
, 'pdf'
, 'cumcount'
,
or 'cdf'
.
The behavior of histcounts
is
similar to that of the discretize
function. Use histcounts
to
find the number of elements in each bin. On the other hand, use discretize
to
find which bin each element belongs to (without counting).