uniquetol
Unique values within tolerance
Syntax
Description
returns
the unique elements in C
= uniquetol(A
,tol
)A
using tolerance tol
.
Two values, u
and v
, are within
tolerance if
abs(uv) <= tol*max(abs(A(:)))
That is, uniquetol
scales the tol
input
based on the magnitude of the data.
uniquetol
is similar to unique
.
Whereas unique
performs exact comparisons, uniquetol
performs
comparisons using a tolerance.
,
where C
= uniquetol(A
,tol
,occurrence
)occurrence
is 'highest'
, specifies
that when several values are within tolerance of each other, the highest value
should be selected as being unique. The default for
occurrence
is 'lowest'
, which selects
the lowest value as being unique.
[___] = uniquetol(___,
uses
additional options specified by one or more NameValue pair arguments
using any of the input or output argument combinations in previous
syntaxes. For example, Name,Value
)uniquetol(A,'ByRows',true)
determines
the unique rows in A
.
Examples
Unique Elements in Presence of Numerical Error
Create a vector x
. Obtain a second vector y
by transforming and untransforming x
. This transformation introduces roundoff differences in y
.
x = (1:6)'*pi; y = 10.^log10(x);
Verify that x
and y
are not identical by taking the difference.
xy
ans = 6×1
10^{14} ×
0.0444
0
0
0
0
0.3553
Use unique
to find the unique elements in the concatenated vector [x;y]
. The unique
function performs exact comparisons and determines that some values in x
are not exactly equal to values in y
. These are the same elements that have a nonzero difference in xy
. Thus, c
contains values that appear to be duplicates.
c = unique([x;y])
c = 8×1
3.1416
3.1416
6.2832
9.4248
12.5664
15.7080
18.8496
18.8496
Use uniquetol
to perform the comparison using a small tolerance. uniquetol
treats elements that are within tolerance as equal.
C = uniquetol([x;y])
C = 6×1
3.1416
6.2832
9.4248
12.5664
15.7080
18.8496
Determine Unique Rows
By default, uniquetol
looks for unique elements that are within tolerance, but it also can find unique rows of a matrix that are within tolerance.
Create a numeric matrix, A
. Obtain a second matrix, B
, by transforming and untransforming A
. This transformation introduces roundoff differences to B
.
A = [0.05 0.11 0.18; 0.18 0.21 0.29; 0.34 0.36 0.41; 0.46 0.52 0.76]; B = log10(10.^A);
Use unique
to find the unique rows in A
and B
. The unique
function performs exact comparisons and determines that all of the rows in the concatenated matrix [A;B]
are unique, even though some of the rows differ by only a small amount.
unique([A;B],'rows')
ans = 8×3
0.0500 0.1100 0.1800
0.0500 0.1100 0.1800
0.1800 0.2100 0.2900
0.1800 0.2100 0.2900
0.3400 0.3600 0.4100
0.3400 0.3600 0.4100
0.4600 0.5200 0.7600
0.4600 0.5200 0.7600
Use uniquetol
to find the unique rows. uniquetol
treats rows that are within tolerance as equal.
uniquetol([A;B],'ByRows',true)
ans = 4×3
0.0500 0.1100 0.1800
0.1800 0.2100 0.2900
0.3400 0.3600 0.4100
0.4600 0.5200 0.7600
Prepare Vectors for Exact Comparison
Create a vector, x
. Obtain a second vector, y
, by transforming and untransforming x
. This transformation introduces roundoff differences to some elements in y
.
x = (1:5)'*pi; y = 10.^log10(x);
Combine x
and y
into a single vector, A
. Use uniquetol
to reconstruct A
, treating the values that are within tolerance as equal.
A = [x;y]
A = 10×1
3.1416
6.2832
9.4248
12.5664
15.7080
3.1416
6.2832
9.4248
12.5664
15.7080
[C,IA,IC] = uniquetol(A); newA = C(IC)
newA = 10×1
3.1416
6.2832
9.4248
12.5664
15.7080
3.1416
6.2832
9.4248
12.5664
15.7080
You can use newA
with ==
or functions that use exact equality like isequal
or unique
in subsequent code.
D1 = unique(A)
D1 = 6×1
3.1416
3.1416
6.2832
9.4248
12.5664
15.7080
D2 = unique(newA)
D2 = 5×1
3.1416
6.2832
9.4248
12.5664
15.7080
Control Selection of Unique Values
Use the occurrence
option to control which elements uniquetol
selects as being unique.
Create a vector and find which elements are unique within a tolerance of 1e1
.
a = [1 1.1 1.11 1.12 1.13 2]; c = uniquetol(a,1e1)
c = 1×2
1 2
Since the first five elements in A
all have similar values with respect to the tolerance of 1e1
, only the lowest value among them is selected as being unique. This is because uniquetol
begins with the lowest value in a
and does not find a new element that is not within tolerance until the 2
at the end of the vector.
Use the 'highest'
option to specify that uniquetol
should begin with the highest value in a
. Now, the 1.13
element is selected as being unique since uniquetol
works down from the highest values.
d = uniquetol(a,1e1,'highest')
d = 1×2
1.1300 2.0000
Subset Data Using Large Tolerance
Create a cloud of 2D sample points constrained to be inside a circle of radius 0.5
centered at the point $$(\frac{1}{2},\frac{1}{2})$$.
x = rand(10000,2); insideCircle = sqrt((x(:,1).5).^2+(x(:,2).5).^2)<0.5; y = x(insideCircle,:);
Find a reduced set of points, such that each point of the original dataset is within tolerance of a point.
tol = 0.05;
C = uniquetol(y,tol,'ByRows',true);
Plot the reduced set of points as red dots on top of the original data set. The red dots are all members of the original data set. All the red dots are at least a distance tol
apart.
plot(y(:,1),y(:,2),'.') hold on axis equal plot(C(:,1), C(:,2), '.r', 'MarkerSize', 10)
Average Similar Values in Vector
Create a vector of random numbers and determine the unique elements using a tolerance. Specify OutputAllIndices
as true
to return all of the indices for the elements that are within tolerance of the unique values.
A = rand(100,1);
[C,IA] = uniquetol(A,1e2,'OutputAllIndices',true);
Find the average value of the elements that are within tolerance of the value C(2)
.
C(2)
ans = 0.0318
allA = A(IA{2})
allA = 3×1
0.0357
0.0318
0.0344
aveA = mean(allA)
aveA = 0.0340
Specify Absolute Tolerance
By default, uniquetol
uses a tolerance test of the form abs(uv) <= tol*DS
, where DS
automatically scales based on the magnitude of the input data. You can specify a different DS
value to use with the DataScale
option. However, absolute tolerances (where DS
is a scalar) do not scale based on the magnitude of the input data.
First, compare two small values that are a distance eps
apart. Specify tol
and DS
to make the within tolerance equation: abs(uv) <= 10^6
.
x = 0.1;
uniquetol([x, exp(log(x))], 10^6, 'DataScale', 1)
ans = 0.1000
Next, increase the magnitude of the values. The roundoff error in the calculation exp(log(x))
is proportional to the magnitude of the values, specifically to eps(x)
. Even though the two large values are a distance eps
from one another, eps(x)
is now much larger. Therefore, 10^6
is no longer a suitable tolerance.
x = 10^10;
uniquetol([x, exp(log(x))], 10^6, 'DataScale', 1)
ans = 1×2
10^{10} ×
1.0000 1.0000
Correct this issue by using the default (scaled) value of DS
.
format long
Y = [0.1 10^10];
uniquetol([Y, exp(log(Y))])
ans = 1×2
10^{10} ×
0.000000000010000 1.000000000000000
Specify DataScale by Column
Create a set of random 2D points, then use uniquetol
to group the points into vertical bands that have a similar (within tolerance) xcoordinate. Use these options with uniquetol
:
Specify
ByRows
astrue
since the point coordinates are in the rows ofA
.Specify
OutputAllIndices
astrue
to return the indices for all points that have an xcoordinate within tolerance of each other.Specify
DataScale
as[1 Inf]
to use an absolute tolerance for thex
coordinate while ignoring they
coordinate.
A = rand(1000,2); DS = [1 Inf]; [C,IA] = uniquetol(A, 0.1, 'ByRows', true, ... 'OutputAllIndices', true, 'DataScale', DS);
Plot the points and average value for each band.
hold on for k = 1:length(IA) plot(A(IA{k},1), A(IA{k},2), '.') meanAi = mean(A(IA{k},:)); plot(meanAi(1), meanAi(2), 'xr') end
Input Arguments
A
— Query array
scalar  vector  matrix  multidimensional array
Query array, specified as a scalar, vector, matrix, or multidimensional
array. A
must be full.
Data Types: single
 double
tol
— Comparison tolerance
positive, real scalar
Comparison tolerance, specified as a positive, real scalar. uniquetol
scales
the tol
input using the maximum absolute value
in input array A
. Then uniquetol
uses
the resulting scaled comparison tolerance to determine which elements
in A
are unique. If two elements in A
are
within tolerance of each other, then uniquetol
considers
them to be equal.
Two values, u
and v
, are
within tolerance if abs(uv) <= tol*max(abs(A))
.
To specify an absolute tolerance, specify both tol
and
the 'DataScale'
NameValue pair.
Example: tol = 0.05
Example: tol
= 1e8
Example: tol = eps
Data Types: single
 double
occurrence
— Occurrence of unique values
'lowest'
(default)  'highest'
Occurrence of unique values, specified as one of the options in this
table. The value of occurrence
determines which elements
uniquetol
selects as being unique.
Option  Description 

'lowest' 

'highest' 

Example: C = uniquetol(A,tol,'highest')
Example: C = uniquetol([1 2 3],2,'highest')
returns
3
, since uniquetol
starts with
the highest value in the input and all other values are within
tolerance.
Data Types: char
 string
NameValue Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Namevalue arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
Example: C = uniquetol(A,'ByRows',true)
OutputAllIndices
— Output index type
false
(default)  true
 0
 1
Output index type, specified as the commaseparated pair consisting
of 'OutputAllIndices'
and either false
(default), true
, 0
,
or 1
. uniquetol
interprets
numeric 0
as false
and numeric 1
as true
.
When OutputAllIndices
is true
,
the uniquetol
function returns the second output, IA
,
as a cell array. The cell array contains the indices for all elements
in A
that are within tolerance of a value in C
.
That is, each cell in IA
corresponds to a value
in C
, and the values in each cell correspond to
locations in A
.
Example: [C,IA] = uniquetol(A,tol,'OutputAllIndices',true)
ByRows
— Row comparison toggle
false
(default)  true
 0
 1
Row comparison toggle, specified as the commaseparated pair
consisting of 'ByRows'
and either false
(default), true
, 0
,
or 1
. uniquetol
interprets
numeric 0
as false
and numeric 1
as true
.
Use this option to find rows in A
that are unique,
within tolerance.
When ByRows
is true
:
A
must be a 2D array.uniquetol
compares the rows ofA
by considering each column separately. For two rows to be within tolerance of one another, each column has to be in tolerance.Each row in
A
is within tolerance of a row inC
. However, no two rows inC
are within tolerance of each other.
Two rows, u
and v
, are
within tolerance if all(abs(uv) <= tol*max(abs(A),[],1))
.
Example: C = uniquetol(A,tol,'ByRows',true)
DataScale
— Scale of data
scalar  vector
Scale of data, specified as the commaseparated pair consisting
of 'DataScale'
and either a scalar or vector.
Specify DataScale
as a numeric scalar, DS
,
to change the tolerance test to be abs(uv) <= tol*DS
.
When used together with the ByRows
option,
the DataScale
value also can be a vector. In this
case, each element of the vector specifies DS
for
a corresponding column in A
. If a value in the DataScale
vector
is Inf
, then uniquetol
ignores
the corresponding column in A
.
Example: C = uniquetol(A,'DataScale',1)
Example: [C,IA,IC] = uniquetol(A,'ByRows',true,'DataScale',[eps(1)
eps(10) eps(100)])
Data Types: single
 double
PreserveRange
— Range preservation toggle
false
(default)  true
 0
 1
Range preservation toggle, specified as the commaseparated pair
consisting of 'PreserveRange'
and either
false
(default), true
,
0
, or 1
.
uniquetol
interprets numeric
0
as false
and numeric
1
as true
. Use this option to
specify that the minimum and maximum values in the output
C
should be the same as those in
A
.
If the minimum and maximum values in A
are within
the tolerance tol
of each other, then
uniquetol
returns only one of the
values.
Example: C =
uniquetol(A,tol,'PreserveRange',true)
Output Arguments
C
— Unique elements in A
vector  matrix
Unique elements in A
(within tolerance),
returned as a vector or matrix. If A
is a row vector,
then C
is also a row vector. Otherwise, C
is
a column vector. The elements in C
are sorted in
ascending order. Each element in A
is within tolerance
of an element in C
, but no two elements in C
are
within tolerance of each other.
If the ByRows
option is true
,
then C
is a matrix containing the unique rows in A
.
In this case, the rows in C
are sorted in ascending
order by the first column. Each row in A
is within
tolerance of a row in C
, but no two rows in C
are
within tolerance of each other.
IA
— Index to A
column vector  cell array
Index to A
, returned as a column vector of
indices to the first occurrence of repeated elements, or as a cell
array. IA
generally satisfies C = A(IA)
,
with the following exceptions:
If the
ByRows
option istrue
, thenC = A(IA,:)
.If the
OutputAllIndices
option istrue
, thenIA
is a cell array andC(i)~A(IA{i})
where~
means the values are within tolerance of each other.
IC
— Index to C
column vector
Index to C
, returned as a column vector of
indices. IC
satisfies the following properties,
where ~
means the values are within tolerance of
each other.
If
A
is a vector, thenA~C(IC)
.If
A
is a matrix, thenA(:)~C(IC)
.If the
ByRows
option istrue
, thenA~C(IC,:)
.
Algorithms
uniquetol
sorts the input lexicographically, and then starts at
the lowest or highest value to find unique values within tolerance. As a result,
changing the sorting of the input could change the output. For example,
uniquetol(A)
might not give the same results as
uniquetol(A)
.
There can be multiple valid C
outputs that satisfy the condition,
no two elements in C
are within tolerance of each
other. The uniquetol
function can return several of
the valid outputs, depending on whether the value of occurrence
is
'highest'
or 'lowest'
and whether the
PreserveRange
option is specified.
Extended Capabilities
ThreadBased Environment
Run code in the background using MATLAB® backgroundPool
or accelerate code with Parallel Computing Toolbox™ ThreadPool
.
This function fully supports threadbased environments. For more information, see Run MATLAB Functions in ThreadBased Environment.
GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
Usage notes and limitations:
The
occurrence
argument for occurrence of unique values is not supported.The
'ByRows'
,'OutputAllIndices'
, and'PreserveRange'
options are not supported.64bit integers are not supported.
For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced in R2015aR2021b: Options to control element selection and preserve range of data
The occurrence
argument controls whether the algorithm begins
with the highest or lowest elements in the input data. The
'PreserveRange'
namevalue argument specifies whether the
range of the output data should be the same as the input data.
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