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Matrices Basics


In a (m x n) matrix, m is the number of rows, n is the number of columns.

Two matrices ( a x b ) and ( c x d ) can be multiplied only if b == c.

(m x n) multiply (n x p) will result into a (m x p) matrix

Only square matrices can be inverted. Inversion is a tedious numerical procedure. Guass jordan method is a popular method to matrix inversion.

A x A(inverse) = Ainverse) x A = I ( where I is an identity matrix, it is a special case of diagonal matrix where all diagonal elements are 1 ).

If a determinant has a nonzero value, its matrix is described as regular and that if a determinant has zero value, its matrix is described as singular.

Associated with any square matrix is a single number that represents a unique function of the numbers in the matrix. this scalar function of a square matrix is called the determinant. the determinant of a matrix a is denoted by |a|.

If the determinant of the (square) matrix is exactly zero, the matrix is said to be singular and it has no inverse.

Symmetric matrix means that the matrix and its transpose are identical (i.e., a = a’).

If a matrix m is orthogonal then M x M(transpose) = M(transpose) x M = I = 1

where I is the identity matrix. but, we know that M x M(inverse) = I = 1. consequently, M(transpose) = M(inverse) ( for orthogonal matrices ). A variance-covariance matrix is used for representing a multivariate vector of p elements. the determinant of of variance-covariance matrix, is sometimes called the generalized variance.

Is Junit Coverage Useful?


Looks like the whole Agile world is running towards the junit coverage for quality. Does higher Junit Tests Coverage ensures better code quality? No way. The only assurance of better code quality is low complexity.

Junit Tests should be used as a tool to assist in achieving low complexity. Only high code coverage never ensures a flexible maintainable code. I have explained all this in detail in “The Myths of Junit Code Coverage“. This will help you for sure.

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