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 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.
Default: No error
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 of each column variable in the input sequence.
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.
Given m vectors of observed samples where the i^{th} column contains the variate x_{i}, the covariance matrix is defined as:
where ${\mu}_{i}$ is the mean of variate x_{i}.
Each element v_{ij} of covariance matrix v is the covariance between variates x_{i} and x_{j}. The diagonal of covariance matrix v contains the standard variances of each x_{i} variate.
mean vector returns the computed mean of each variate as shown by the following equation:
Where This Node Can Run:
Desktop OS: Windows
FPGA: This product does not support FPGA devices