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Measures of Spread (Covariance Matrix) (G Dataflow)

    Last Modified: January 9, 2017

    Computes the covariance matrix of a sequence.



    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 i j = ( x i μ i ) ( x j μ j )

    where μ 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:

    mean vector i = μ i

    Where This Node Can Run:

    Desktop OS: Windows

    FPGA: Not supported

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