Variance of input or sequence of inputs
DSP System Toolbox / Statistics
The Variance block computes the unbiased variance of each row or column of the input, or along vectors of a specified dimension of the input. It can also compute the variance of the entire input. You can specify the dimension using the Find the variance value over parameter. The Variance block can also track the variance in a sequence of inputs over a period of time. To track the variance in a sequence of inputs, select the Running variance parameter.
The Running mode in the Variance block will be removed in a future release. To compute the running variance in Simulink^{®}, use the Moving Variance block instead.
In
— Data inputThe block accepts realvalued or complexvalued multichannel and multidimensional inputs.
This port is unnamed until you select the Running
variance parameter and set the Reset
port parameter to any option other than
None
.
Data Types: single
 double
 int8
 int16
 int32
 uint8
 uint16
 uint32
 fixed point
Complex Number Support: Yes
Rst
— Reset portSpecify the event that causes the block to reset the running variance. The sample time of the Rst input must be a positive integer multiple of the input sample time.
To enable this port, select the Running
variance parameter and set the Reset
port parameter to any option other than
None
.
Data Types: single
 double
 int8
 int16
 int32
 uint8
 uint16
 uint32
 Boolean
Port_1
— Variance along the specified dimensionWhen you do not select the Running variance
parameter, the block computes the variance in each row or column of the
input, or along vectors of a specified dimension of the input. It can
also compute the variance of the entire input at each individual sample
time. Each element in the output array y
is the
variance of the corresponding column, row, or entire input. The output
array y
depends on the setting of the Find
the variance value over parameter.
Consider a threedimensional input signal of size MbyNbyP. When you set Find the variance value over to:
Entire input
— The output at
each sample time is a scalar that contains the variance of the
MbyNbyP
input matrix.
Each row
— The output at
each sample time consists of an
Mby1byP array,
where each element contains the variance of each vector over the
second dimension of the input. For an
MbyN matrix input,
the output at each sample time is an Mby1
column vector.
Each column
— The output at
each sample time consists of a
1byNbyP array,
where each element contains the variance of each vector over the
first dimension of the input. For an
MbyN matrix input,
the output at each sample time is a 1byN
row vector.
In this mode, the block treats lengthM unoriented vector inputs as Mby1 column vectors.
Specified dimension
— The
output at each sample time depends on the value of the
Dimension parameter. If you set the
Dimension to 1
, the
output is the same as when you select Each
column
. If you set the
Dimension to 2
, the
output is the same as when you select Each
row
. If you set the
Dimension to 3
, the
output at each sample time is an
MbyN matrix
containing the variance of each vector over the third dimension
of the input.
When you select Running variance, the block tracks the variance of each channel in a time sequence of inputs. In this mode, you must also specify a value for the Input processing parameter. When you set Input processing to:
Elements as channels (sample based)
— The block treats each element of the input as a
separate channel. For a threedimensional input signal of size
MbyNbyP,
the block outputs an
MbyNbyP
array. Each element
y_{ijk} of the output
contains the variance of the element
u_{ijk} for all
inputs since the last reset.
When a reset event occurs, the running variance y_{ijk} in the current frame is reset to the element u_{ijk}.
Columns as channels (frame based)
— The block treats each column of the input as a separate
channel. This option does not support input signals with more
than two dimensions. For a twodimensional input signal of size
MbyN, the block
outputs an MbyN matrix.
Each element y_{ij} of
the output contains the variance of the elements in the
jth column of all inputs since the last
reset, up to and including the element
u_{ij} of the
current input.
When a reset event occurs, the running variance for each channel becomes the variance of all the samples in the current input frame, up to and including the current input sample.
The data type of the output matches the data type of the input.
Data Types: single
 double
 int8
 int16
 int32
 uint8
 uint16
 uint32
 fixed point
Running variance
— Option to select running varianceWhen you select the Running variance parameter, the block tracks the variance value of each channel in a time sequence of inputs.
Find the variance value over
— Dimension over which variance is computedEach column
(default)  Entire input
 Each row
 Specified dimension
Each column
— The block
outputs the variance over each column.
Each row
— The block
outputs the variance over each row.
Entire input
— The block
outputs the variance over the entire input.
Specified dimension
—
The block outputs the variance over the dimension specified
in the Dimension parameter.
To enable this parameter, clear the Running variance parameter.
Dimension
— Custom dimension1
(default)  scalarSpecify the dimension (onebased value) of the input signal over which the variance is computed. The value of this parameter must be greater than 0 and less than or equal to the number of dimensions in the input signal.
To enable this parameter, set Find the variance value
over to Specified
dimension
.
Input processing
— Method to process the input in running modeColumns as channels (frame
based)
(default)  Elements as channels (sample
based)
Columns as channels (frame based)
— The block treats each column of the input as a separate
channel. This option does not support input signals with more
than two dimensions. For a twodimensional input signal of size
MbyN, the block
outputs an MbyN matrix.
Each element y_{ij} of
the output contains the variance of the elements in the
jth column of all inputs since the last
reset, up to and including the element
u_{ij} of the
current input.
When a reset event occurs, the running variance for each channel becomes the variance of all the samples in the current input frame, up to and including the current input sample.
Elements as channels (sample based)
— The block treats each element of the input as a
separate channel. For a threedimensional input signal of size
MbyNbyP,
the block outputs an
MbyNbyP
array. Each element
y_{ijk} of the output
contains the variance of the element
u_{ijk} for all
inputs since the last reset.
When a reset event occurs, the running variance y_{ijk} in the current frame is reset to the element u_{ijk}.
VariableSize Inputs
When your inputs are of variable size, and you select the Running variance parameter, then:
If you set the Input
processing parameter to
Elements as channels (sample
based)
, the state is reset.
If you set the Input
processing parameter to
Columns as channels (frame
based)
, then:
When the input size difference is in the number of channels (number of columns), the state is reset.
When the input size difference is in the length of channels (number of rows), the state is not reset, and the running operation is carried out as usual.
To enable this parameter, select the Running variance parameter.
Reset port
— Reset eventNone
(default)  Rising edge
 Falling edge
 Either edge
 Nonzero sample
The block resets the running variance whenever a reset event is detected at the optional Rst port. The reset sample time must be a positive integer multiple of the input sample time.
When a reset event occurs while the Input
processing parameter is set to Elements as
channels (sample based)
, the running variance for each
channel is initialized to the value in the corresponding channel of the
current input. Similarly, when the Input processing
parameter is set to Columns as channels (frame
based)
, the running variance for each channel becomes
the variance of all the samples in the current input frame, up to and
including the current input sample.
Use this parameter to specify the reset event.
None
— Disables the
Rst port.
Rising edge
— Triggers a
reset operation when the Rst input does one
of the following:
Rises from a negative value to either a positive value or zero.
Rises from zero to a positive value, where the rise is not a continuation of a rise from a negative value to zero.
Falling edge
— Triggers a
reset operation when the Rst input does one
of the following:
Falls from a positive value to a negative value or zero.
Falls from zero to a negative value, where the fall is not a continuation of a fall from a positive value to zero.
Either edge
— Triggers a
reset operation when the Rst input is a
Rising edge
or
Falling edge
.
Nonzero sample
— Triggers a
reset operation at each sample time, when the
Rst input is not zero.
When running simulations in the Simulink multitasking mode, reset signals have a onesample latency. Therefore, when the block detects a reset event, there is a onesample delay at the reset port rate before the block applies the reset. For more information on latency and the Simulink tasking modes, see Excess Algorithmic Delay (Tasking Latency) and TimeBased Scheduling and Code Generation (Simulink Coder).
To enable this parameter, select the Running variance parameter.
To use these parameters, the data input must be fixed point. For all other inputs, the parameters on the Data Types tab are ignored.
Rounding mode
— Method of rounding operationFloor
(default)  Ceiling
 Convergent
 Nearest
 Round
 Simplest
 Zero
Specify the rounding mode for fixedpoint operations. For more details, see rounding mode.
Saturate on integer overflow
— Method of overflow actionWhen you select this parameter, the block saturates the result of its
fixedpoint operation. When you clear this parameter, the block wraps
the result of its fixedpoint operation. For details on
saturate
and wrap
, see overflow
mode for fixedpoint operations.
Inputsquared product output
— Data type of the inputsquared termSame as input
(default)  Binary point scaling
The squares of the input elements are stored in the Inputsquared product output data type. If the input is complex, the squares of the real and imaginary parts of the input are stored in this data type. For more details, see Fixed Point.
You can set this parameter to:
Inherit: Same as input
— The
data type is same as the input data type.
Binary point scaling
— The
Inputsquared product output data type
uses binary point scaling. If you select this option, the block
displays the fields to specify the Word
length and Fraction length.
The Signedness is inherited from the
input.
Inputsumsquared product
— Data type of the inputsumsquared termSame as inputsquared
product
(default)  Binary point scaling
The squares of the sum of the input elements are stored in the Inputsumsquared product data type. If the input is complex, the squares of the sum of the real parts and the squares of the sum of the imaginary parts are stored in this data type. For more details, see Fixed Point.
You can set this parameter to:
Same as inputsquared product
— The data type is the same as the input squaredproduct
data type.
Binary point scaling
— The
Inputsumsquared product data type
uses binary point scaling. If you select this option, the block
displays the fields to specify the Word
length and Fraction length.
The Signedness is inherited from the
input.
Accumulator
— Accumulator data typeSame as inputsquared
product
(default)  Same as input
 Binary point scaling
Accumulator specifies the data type of the output of an accumulation operation in the Variance block. See Fixed Point for illustrations depicting the use of the accumulator data type in this block.
You can set this parameter to:
Same as inputsquared product
— The accumulator data type is the same as the
inputsquared product data type.
Same as input
— The
accumulator data type is the same as the input data type.
Binary point scaling
— The
Accumulator data type uses binary point
scaling. If you select this option, the block displays the
fields to specify the Word length and
Fraction length. The
Signedness is inherited from the
input.
Output
— Output data typeSame as inputsquared
product
(default)  Same as accumulator
 Same as input
 Binary point scaling
Output specifies the data type of the output of the Variance block. See Fixed Point for information about the use of the output data type in this block. You can set it to:
Same as inputsquared product
— The output data type is the same as the inputsquared
product data type.
Same as accumulator
— The
output data type is the same as the accumulator data
type.
Same as input
— The output
data type is the same as the input data type.
Binary point scaling
— The
Output data type uses binary point
scaling. If you select this option, the block displays the
fields to specify the Word length and
Fraction length. The
Signedness is inherited from the
input.
Lock data type settings against changes by the fixedpoint tools
— Prevent fixedpoint tools from overriding data typesSelect this parameter to prevent the fixedpoint tools from overriding the data types you specify on the block.
Data Types 

Direct Feedthrough 

Multidimensional Signals 

VariableSize Signals 

ZeroCrossing Detection 

The variance of a discretetime signal is the square of the standard deviation of the signal. Variance gives a measure of deviation of the signal from its mean value.
For purely real or imaginary input, u, of size MbyN, the variance is given by:
$$y={\sigma}^{2}=\frac{{\displaystyle \sum _{i=1}^{M}{\displaystyle \sum _{j=1}^{N}{\left{u}_{ij}\right}^{2}\frac{{\left{\displaystyle \sum _{i=1}^{M}{\displaystyle \sum _{j=1}^{N}{u}_{ij}}}\right}^{2}}{M*N}}}}{M*N1}.$$
where,
u_{ij} is the input data element at indices i, j.
M is the length of the jth column.
N is the number of columns.
For complex inputs, the variance is given by the following equation:
$${\sigma}^{2}={\sigma}_{\mathrm{Re}}{}^{2}+{\sigma}_{\mathrm{Im}}{}^{2}$$
where,
σ_{Re}^{2} is the variance of the real part of the complex input.
σ_{Im}^{2} is the variance of the imaginary part of the complex input.
When you clear the Running variance parameter in the block
and specify a dimension, the block produces results identical to the MATLAB^{®}
var
function when it is called as y =
var(u,0,D)
, where,
u
is the data input.
D
is the dimension.
y
is the variance along the specified
dimension.
When this block calculates the variance along the entire input, the result is
identical to calling the var
function as y =
var(u(:))
.
For a complex input signal, the variance is the sum of the variances of the real and imaginary parts.
$${\sigma}^{2}={\sigma}_{\mathrm{Re}}{}^{2}+{\sigma}_{\mathrm{Im}}{}^{2}$$
For purely real or imaginary input u of size MbyN, the variance is given by:
$$y={\sigma}^{2}=\frac{{\displaystyle \sum _{i=1}^{M}{\displaystyle \sum _{j=1}^{N}{\left{u}_{ij}\right}^{2}\frac{{\left{\displaystyle \sum _{i=1}^{M}{\displaystyle \sum _{j=1}^{N}{u}_{ij}}}\right}^{2}}{M*N}}}}{M*N1}.$$
The following diagram shows the data types used within the Variance block when the input is fixedpoint.
For complex inputs, the variance is given by the following equation:
$${\sigma}^{2}={\sigma}_{\mathrm{Re}}{}^{2}+{\sigma}_{\mathrm{Im}}{}^{2}$$
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