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

Version:

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.

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.

## Algorithm for Computing the Covariance Matrix

Given m vectors of observed samples where the ith column contains the variate 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 variate xi.

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

mean vector returns the computed mean of each variate 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