Cv Mat Variance

Output nx1 matrix with computed variance.

Cv mat variance.

Is defined as std x mean x and as such is a measure of the relative variability. In probability theory and statistics a covariance matrix also known as auto covariance matrix dispersion matrix variance matrix or variance covariance matrix is a square matrix giving the covariance between each pair of elements of a given random vector in the matrix diagonal there are variances i e the covariance of each element with itself. Input vector output cv. The size of this dimension becomes 1.

Output nx1 matrix with computed mean. The eigenvectors are unit vectors representing the direction of the largest variance of the data while the eigenvalues represent the magnitude of this variance in the corresponding directions. If a is a vector of observations the variance is a scalar. Vice versa variance is standard deviation squared.

An example using pca for dimensionality reduction while maintaining an amount of variance. If a is a multidimensional array then var a treats the values along the first array dimension whose size does not equal 1 as vectors. The function ignores nans. Cv vl where the covariance matrix can be represented as c vlv 1 which can be also obtained by singular value decomposition.

In our example the variance was 200 therefore standard deviation is 14 14. Github is home to over 50 million developers working together to host and review code manage projects and build software together. Cv getcv x compute the coefficient of variation c v of the input vector x. It computes in the same way as woud do reduce but with variance function.

To calculate standard deviation from variance only take the square root. Dismiss join github today. Coefficient of variation a scalar.