# Measures of Spread (Covariance Matrix) (G Dataflow)

Computes the covariance matrix of a sequence.

## x

The input sequence. Each column of x represents one vector of observed samples from one variable. Each row of x represents an observation from each variable.

## error in

Error conditions that occur before this node runs.

The node responds to this input according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error error in contains an error
If no error occurred before the node runs, the node begins execution normally.

If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Default: No error

## covariance matrix v

Covariance matrix of the input sequence.

If x is an n-by-m 2D array, then the covariance matrix is a square m-by-m matrix.

## mean vector

Mean of each column variable in the input sequence.

## error out

Error information.

The node produces this output according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error error in contains an error
If no error occurred before the node runs, the node begins execution normally.

If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

## Algorithm for Computing the Covariance Matrix

Given m vectors of observed samples where the ith column contains the random variable xi, the covariance matrix is defined as:

${v}_{ij}=\left({x}_{i}-{\mu }_{i}\right)\left({x}_{j}-{\mu }_{j}\right)$

where ${\mu }_{i}$ is the mean of random variable xi.

Each element vij of covariance matrix v is the covariance between random variables xi and xj. The diagonal of covariance matrix v contains the standard variances of each xi random variable.

mean vector returns the computed mean of each random variable as shown by the following equation:

${\mathrm{mean}\text{\hspace{0.17em}}\mathrm{vector}}_{i}={\mu }_{i}$

Where This Node Can Run:

Desktop OS: Windows

FPGA: Not supported

Web Server: Not supported in VIs that run in a web application